• remote sensing;
  • meteorology;
  • climatology;
  • satellite


  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Satellite systems
  5. 3. Meteorological parameters
  6. 4. Conclusions
  7. Supporting information
  8. References
  9. Supporting Information

Spaceborne remote sensing provides valuable information about the state of the Earth-atmosphere system and its components in an area-wide and continuous manner. Over the past 50 years a range of satellite platforms carrying many different sensors has been constructed to monitor atmospheric parameters used in meteorological and climatological studies, and the information retrieved from satellite-based sensors has greatly enhanced our understanding of the processes and dynamics within the Earth-atmosphere system. The present paper gives an overview of existing satellites and sensors, together with the developed algorithms to retrieve meteorological and climatological parameters. Furthermore, it gives an outlook on new systems planned for the near future. Copyright © 2011 Royal Meteorological Society

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Satellite systems
  5. 3. Meteorological parameters
  6. 4. Conclusions
  7. Supporting information
  8. References
  9. Supporting Information

Satellite systems provide a unique opportunity to monitor Earth-atmosphere system processes and parameters continuously. In view of the great benefit provided by spaceborne Earth-atmosphere remote sensing, there were strong efforts to construct Earth observing satellite systems in the past. Satellite based observations of the Earth and the atmosphere started with the first meteorological satellite, the Television InfraRed Observation Satellite (TIROS-1), launched in 1960. During the following decades several satellite systems with different sensors provided data for a wide range of atmospheric parameters that enhanced our understanding of Earth-atmosphere processes and dynamics. Nowadays, operational satellite systems provide invaluable measurements of atmospheric parameters at regular intervals on a global scale. Smith et al. (1986) and Kidder and Vonder Haar (1995) give an overview of Earth-atmosphere observing satellite systems. More recently, Kidd et al. (2009) outlined the status of satellite based meteorological and climatological research.

The present paper gives an overview of existing satellite systems and the corresponding retrieval techniques to derive the desired meteorological and climatological parameters. It starts with a review of existing platforms and sensors. After the review of existing satellite systems and retrieval algorithms, satellite missions planned and approved for the near future will be introduced. This is followed by an overview of existing retrieval algorithms, which is structured by the different measurement parameters.

2. Satellite systems

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Satellite systems
  5. 3. Meteorological parameters
  6. 4. Conclusions
  7. Supporting information
  8. References
  9. Supporting Information

2.1. Past and current satellite systems

Satellite based Earth-atmosphere observations exploit geostationary (GEO) and low-Earth-orbiting (LEO) satellite systems, providing data in different spatial and temporal resolutions.

GEO satellite systems circulate the Earth at an altitude of about 36 000 km above the equator. Their orbital period is 24 h. Therefore, they appear to be stationary above a certain point above the equator, which enable observations with a high temporal resolution of 15 or 30 min. Operational geostationary platforms are the workhorses of meteorological nowcasting applications and are growing increasingly important for climate research due to the long time series of GEO data globally available.

Operational polar orbiting platforms (LEO) are complementing the GEO system in the global weather satellite system. They are frequently used to transfer sensors from experimental missions into operational use, which in a next step, if technically and financially possible, might become candidates for next generation GEO systems. Table S1 gives an overview of past and current Earth-atmosphere observing satellite systems.

2.2. Future perspectives of satellite meteorology

2.2.1. Geostationary platforms

Future GEO missions are primarily focusing on serving the NWP and the climate research communities by striving for improvements of the systems while at the same time warranting data and product continuity. Improvements are concerned for:

  • the spatial resolution, e.g. for local nowcasting and to capture subpixel atmospheric phenomena from the GEO orbit;

  • the spectral and radiometric (e.g. the signal to noise ratio) resolution to allow for new products and to boost the accuracy of hitherto developed products to the requirements of data assimilation in NWP models, and,

  • the temporal resolution to observe atmospheric phenomena with rapid life cycles.

Depending on the history of the specific satellite programmes, the operating agencies are currently developing second or third generation GEO missions which are listed in Table I. Generally, all programmes continue to focus on passive instruments and mainly two proven sensor families: (1) multispectral narrow and broad band imagers to retrieve data on radiation balance, clouds, aerosol, cloud and water vapour (WV) motion, winds, land and sea surface temperatures (LST/SST) as well as Earth's snow and ice cover, and, (2) atmospheric sounding capabilities to retrieve vertical profiles, mainly of temperature and humidity, but also water vapour winds and trace gas information. For the third generation imagers (Flexible Combined Imager (FCI) on Meteosat Third Generation (MTG), partly also for Advanced Baseline Imager (ABI) on GOES-R/Himawari-8), additional bands will be introduced as a heritage of LEO missions as e.g. MODIS: 0.444 and 0.510 µm (aerosol, ocean colour, phytoplankton), 0.910 µm (atmospheric water vapour), 1.375 µm (cirrus clouds) and 2.260 µm (aerosol). Rapid scan capabilities will be offered as well. Next generation GEO satellites will include sounding capabilities for the first time, or change from narrow-band channel IR radiometer solutions to more complex systems as spectrometer technology to improve accuracy and vertical resolution of the products develops. An IASI-like (Infrared Atmospheric Sounding Interferometer) on LEO MetOP (see Prunet et al., 1998; Blumstein et al., 2004) system, the Interferometric Infrared Sounder (IIS), is intended for launch on board the Chinese FY-4 GEO mission. A major improvement will be the 10 channel Michelson Fourier Transform spectrometer on MTG which will provide hyper-spectral sounding information in two bands, a long and a mid wave IR region with a high spatial resolution of 4 km every half hour (Stuhlmann et al., 2005). As a development of LEO instruments such as AIRS (Atmospheric Infra-red Sounder on Aqua), the main challenge for a transfer to a GEO system is to cope with the limitations due to diffraction which might in the worst case produce errors of up to 1 K in sensitive bands (Grandell and Stuhlmann, 2007). A new sensor type which shall be added to Meteosat and GOES third generation (MTG-LI (Lightning Imager)/GOES-R—GLM (Geostationary Lightning Mapper)) and to the Chinese second generation FY-4 satellites is devoted to the detection and nowcasting of lightning, also used as a proxy for severe convective weather and the production of NOx. All three future missions will rely on measurements in the ionized oxygen emission band at 777.4 nm (for the general technologies refer to Koshak et al., 2000). Another novel feature will be the GEO missions targeting atmospheric chemistry. The first spectrometer sensor (0.4 nm resolution) will be installed on board MTG as a development of EPS GOME and ENVISAT SCIAMACHY. It will be dedicated to air quality surveillance regarding emission plumes, aerosols and atmospheric gases (e.g. O3, NO2, SO2, H2CO). Gas retrieval is mainly conducted in the UV (290–400 nm) region while cloud and aerosol information is analysed in the oxygen A-Band region (755–775 nm). For further information on technology and physical background the reader may refer to Bovensmann et al. (1999). As not yet specified, the Korean Meteorological Agency (KMA), (a new player in the group of GEO mission operators), is planning a second GEO atmospheric chemistry mission with its Trace Gas Monitor (TBC) on COMS-FO B (Communication, Ocean and Meteorological Satellite), launch projected for 2018. To date, passive microwave (MW) instruments, which are a backbone for rainfall retrieval techniques, are restricted to LEO missions. This is due to the weak emitted MW signal of the Earth-atmosphere system which is a challenge to detect at GEO flight level (e.g. Joyce et al., 2004). The first MW instrument in Geostationary Orbit is planned to fly on China's FY-4 M mission with the Microwave Sounder (GEO-MWRI). Even though the instrument is not yet defined it can be expected that it resembles the spectral characteristics of the Micro-Wave Radiation Imager (MWRI) currently flown on the second generation LEO FY-3 mission (Zhang et al., 2006). The third generation GOES-R series will additionally provide instruments for observation of space weather with Space Environment In-Situ Suite (SEISS), EUV and X-Ray Irradiance Sensors (EXIS) and the Solar Ultraviolet Imager (SUVI), and for measurements of the Earth's geomagnetic field with Magnetometer (MAG) (Krimchansky et al., 2004).

Table I. Major approved and planned next generation GEO missions
Mission (agency), orbitSensorTechnologyLaunchaPriority productsImprovement
  • a

    Tentative launch data, x, nominal horizontal resolution.

FY-4 O (CMA/NSMC) China(1) Multi-channel Scan Imaging Radiometer (MCSI) (2) Interferometric Infrared Sounder (IIS) (3) Lightning Mapper (LM)(1) 12-channel VIS/IR imager (0.55–13.8 µm), x = 1–4 km (2) IR spectrometer/interferometer (4.44–6.06 and 8.85–14.6 µm); x = 8 (later 4) km (3) CCD camera (777.4 nm, O2); x = 10 km2015Cloud properties (1) Cloud and WV motion winds (1) Radiation components (1) Rainfall rate (1) Temperature/humidity profiles (2) Profiles of WV motion winds (2)Three-axis stabilization More powerful imager and lightning mapper Sounding capability
FY-4 M (CMA/NSMC) China(1) MicroWave Radiation Imager (GEO-MWRI)(1) To be defined; current MWRI on LEO FY-3: 6-frequency, 12-channel MW radiometer, (10.7–150 GHz, V/H)2015Precipitation Temperature/humidity sounding Cloud liquid and ice waterFirst MW imager on GEO
GOES-R (NOAA/NASA) USA(1) Advanced Baseline Imager (ABI) (2) Space Environmental In-Situ Suite (SEISS) (3) Solar Ultra Violet Imager (SUVI) (4) Extreme Ultra Violet/X-Ray Irradiance Sensors (EXIS) (5) Geostationary Lightning Mapper (GLM) (6) Magnetometer (MAG)(1) 16-channel multispectral VIS-IR imager (0.45–13.6 µm); x = 0.5–2 km (2) 11-channel instrument with magnetospheric (MPS), energetic heavy ion (EHIS), solar/galactic proton sensor (SGPS) (30 eV to 4 MeV; 30 eV to 500 MeV; > 500 MeV) (3) 6-channel imager, (0.9–32 nm) (4) irradiance Sensor (5–127 nm) (5) 0.7774 µm oxygen emission channel; x = 10 km (6) ± 1000 nT with 0.016 nT2015Aerosol optical depth (1) Cloud properties (1) Radiation components (1) Rainfall rate (1) Columnar water vapour (1) Temperature and moisture profiles (1) Motion winds (1) LST/SST (skin) (1) Snow cover (1) Energetic heavy ions (2) Magnetic electrons and protons (2) Solar and galactic protons (2) Solar imagery: X-Ray (3) Solar flux: EUV, Flux: X-ray (4) Lightning events (5) Geomagnetic field (6)Improved resolution (4X), faster coverage (5 X), more bands (3 X), better imagers VIS inflight calibration Continuous coverage of total lightning flash rate over land and water Additional solar/space monitoring Improved heavy ion detection, adds low energy electrons and protons
INSAT-3D (IMA/ISRO) India(1) Imager (INSAT) (2) Sounder (INSAT)(1) 6-channel VIS/IR radiometer (0.65–12 µm), x = 1–8 km (2) 19-channel IR radiometer + 1 VIS (3.7–14.7 µm), x = 10 km2011Cloud properties (1) Cloud and WV motion winds (1) Radiation components (1) Rainfall rate (1) Temperature/humidity profiles (2) 
Meteosat Third Generation (MTG) (ESA/EUMETSAT) Europe(1) Flexible Combined Imager (FCI) (2) Lightning Imager (LI) (3) InfraRed Sounder (IRS) (4) Ultra Violet and Near Infrared Sounder (UVN)(1) 18-channel VIS/IR radiometer, x = 0.5–2 km (2) Narrow band (1.4 nm) imager (777.4 nm); x = 10 km (3) 10-channel Michelson Fourier Transform Spectrometer; x = 3–6 km; z = 1–2 km (4) High-resolution spectrometer (UV:290–400 nm; VIS:400–500 nm; NIR:755–775 nm); x = 10 km2017Loud properties (1) Full disc lightning discharges (2) WV 3-D wind vectors (3) 3-D temperature/humidity fields (3) Pollution cloud and plume detection (O3, NO2, SO2 und H2CO) (4) Aerosol profile (4) UV radiation (4)Improved MSG- SEVIRI/HRV Very-short-term nowcasting of cloud development Continuously full disc lightning discharges Higher resolution profiles Clear-sky wind fields First time air quality surveillance on GEO
2.2.2. Low Earth orbit platforms

As stressed for GEO systems, next generation LEO-missions also pursue the improvement of the spatial, spectral and radiometric resolution and thus an improvement of operational products for nowcasting and climate research, but also the implementation of new operational products. Most LEO programmes have been recently updated, such as, e.g., the FY-3 mission of CMA (Zhang et al., 2006) and the Russian Meteor-M-N1 (; accessed 24 March 2011).

The next updates of operational LEO systems to next generation status are expected for the European and the US-American programmes (see Table II). For the latter, the second generation LEO mission Joint Polar Satellite System (JPSS) will follow the NOAA series after 19 satellites are launched. The payload will take advantage of current experimental in-orbit LEO missions such as EOS-Terra and Aqua. The multi-purpose imager represents a subset of the successful MODIS sensor onboard Terra/Aqua going into operational uses (Schueler and Barnes, 1998). It has the high potential to observe cloud and retrieve their properties with higher accuracy (Hutchison et al., 2005; Wong et al., 2007). This is also the case for land surface parameters related to climate, as vegetation index, snow and fires (Townshend and Justice, 2002) as well as land (LST) and sea (SST) surface temperatures (Yu et al., 2005). The sensor is also designed to trace aerosols and smoke plumes where a novel feature will be the analysis of clouds and land surfaces under very low light conditions which will help to improve cloud products, for example, (Lee et al., 2006). The key instrument to construct vertical profiles of atmospheric temperature and moisture is an interferometric sounding sensor, the Cross-track Infrared Sounder (CrIS) (Bloom, 2001) in combination with the Advanced Technology Microwave Sounder (ATMS) (Muth et al., 2004). Kleespies (2007) stressed that ATMS can significantly improve temperature and moisture retrievals in comparison to current in-orbit sounder technology only if footprint matching is employed to use oversampled ATMS observations. Advanced retrieval of ozone will be warranted by the novel ozone mapping and profiler suite (OMPS, Flynn et al., 2004) in conjunction with the development of new algorithms particularly addressing the greater spectral coverage and better height resolution of the suite (Flynn et al., 2009).

Table II. Major approved and planned next generation LEO missions
Mission (agency), orbitSensorTechnologyLaunchaPriority productsImprovement
  • a

    Tentative lauch data, x, nominal horizontal resolution.

Joint Polar Satellite System (JPSS) (NOAA/NASA) USA(1) Visible/Infrared Imager/Radiometer Suite (VIIRS) (2) Cross-track Infrared Sounder (CrIS) (3) Advanced Technology Microwave Sounder (ATMS) (4) Ozone Mapping and Profiler Suite (OMPS) (5) Clouds and the Earth's Radiant Energy System (CERES)(1) 22-channel VIS-IR radiometer (0.3–14 µm), x = 0.4 km (2) Scanning Fourier Transform IR Spectrometer (3.9–15.4 µm); x = 14 km (3) 22-channel MW radiometer (23–183 GHz), x = 16–75 km (4) Nadir total column spectrometer (300–380 nm) and nadir profile spectrometer (250–310 nm, x = 50–250 km; z = 5 km (up to 60 km) (5) 3-channel broadband imager (0.3 > 50 µm), x = 10–20 km2014Cloud properties (1–3) SST/LST (1, 2) Precipitation type/rate (1–3) Atmospheric temperature, moisture and pressure profiles and columnar values (2, 3) Soil moisture (3) Vertical/horizontal ozone distribution (4) Components radiation balance (5)Continuation and improvement of LEO missions on e.g. TERRA, AQUA, DMSP etc. Day/night band for low levels of VIS-NIR radiance Higher (spatial, temporal, and spectral) resolution Higher radiometric calibration and accuracy More accurate soundings NWP assimilation quality
Post-EPS (ESA/EUMETSAT) (Feasibility study pending) Europe(1) Visible/Infrared Imaging (VII) (2) Low Light Imaging (LLI) (3) Infrared Sounding (IRS) (4) Microwave Sounding (MWS) (5) Radio Occultation (RO) (6) Nadir viewing UV/VIS/NIR - SWIR Sounding (UVNS) (7) Multi-viewing, -channel, -polarization Imaging (3MI) (8) Scatterometry (SCA) (9) Microwave Imaging (MWI) (10) Radiant Energy Radiometry (RER)(1) 16-channel multispectral imager (0.44–13.4 µm) (2) OLS-like; radiometer(0.47–0.95 µm photo-multiplier tube) (3) IASI-heritage (4) ATMS-like [RIGHTWARDS ARROW] JPSS (5) Limb scanning, z = 0.5 km (6) GOME-2, SCIAMACHY, OMI heritage [RIGHTWARDS ARROW] to be specified (7) Polder-like, nine-wavelength radiometer with three polarizations at four wavelengths (0.44–0.9 µm; 20 nm width); x = 4 km (8) ASCAT-like, side looking C-band radar scatterometer (5.255 GHz); x = 25 km (9) MW radiometer AMSR-E heritage (18.7–664 GHz) (10) CERES-like [RIGHTWARDS ARROW] JPSS2020Cloud properties (1, 7,8) Aerosol properties (1, 2, 6, 7) LST/SST, surface albedo (1, 3, 7) Snow cover (1, 2) Land surface (Vegetation, fire) (1) Ocean colour (1,7) Night-time cloud imagery (2) Temperature/humidity profile and humidity columns (3, 4, 5, 8) Trace gases (3, 6) Cloud liquid water total column (4) Bending angles profiles (5) Ionospheric electron content (5) Ozone profile and column (6) Ocean surface wind vectors (8) Soil moisture (8) Snow equivalent water (8) Sea-ice properties (8, 9) Precipitation (9) Radiation balance componentsHigh vertical resolution sounders High spatial resolution Temperature/humidity profile also under cloud contamination More than 4000 occultation per day by tracking GPS and Galileo Focus on air quality monitoring High frequency MW for cloud products

The follow-on mission of the successful European Meteorological Operational (MetOp) platform is still in the planning stage which means that most instruments are not yet specified. The Post-EPS feasibility study starts in 2011 and concentrates on several missions which are mostly developments of successful missions currently onboard experimental LEO missions. The core instrument will be a MODIS-like high-performance multi-spectral imaging radiometer, the Visible-Infrared Imager (VII) (Schmülling et al., 2010). Low light imaging is also an important topic where a sensor will be most likely adapted from NOAA OLS (Operational Linescan System on DMSP; e.g. Elvidge et al., 1998; Cinzano et al., 2000). A higher accuracy and vertical resolution will be achieved with Radio Occultation (RO) by improving the GRAS sensor currently flying on MetOp (Loiselet et al., 2000). One major focus of Post-EPS is to bring hitherto experimental atmospheric chemistry missions onto an operational LEO platform. The Nadir viewing UV/VIS/NIR—SWIR Sounding (UVNS) unit will be a development of successful SCIAMACHY and OMI instruments and is mainly dedicated to trace gas cartography of the atmosphere (Schlüssel et al., 2009). This is also supported by the Multi-viewing, channel and polarization imaging (3MI sensor), an instrument similar to the POLDER instrument (Polarization and Directionality of the Earth Reflectances) (Deschamps et al., 1994; King et al., 1999). A further instrument transferred to operational application will be a device derived from the active C-band scatterometer ASCAT, mainly for the retrieval of Ocean surface wind vectors (Figa-Saldaña et al., 2002). To improve quantitative precipitation (Nielsen and Long, 2009) and soil moisture (Njoku et al., 2003) retrievals, as well as cloud and snow liquid water estimates, a microwave radiometer mission is proposed which will be based on the AMSR-E instrument on board EOS-Aqua (Parkinson, 2003).

2.2.3. Approved near future experimental missions

While the future operational GEO and LEO systems mostly rely on passive instruments, experimental missions will be used particularly to develop active RADAR and LIDAR sensors (Table III). The major aims of the upcoming experimental LEO missions are to improve atmospheric sounding of temperature and humidity, but major attention is given to novel technologies to retrieve rainfall and cloud properties and their vertical distribution. Some of the missions only relying on passive instruments are partly compatible to the next generation operational LEO missions. The Japanese Space Agency plans to launch two polar platforms of the Global Change Observation Mission (GCOM), where the first satellite GCOM-C will carry a second generation imager while the second (GCOM-W) will be equipped with an Advanced Scanning Microwave Radiometer (Sasaki and Nakagawa, 2009). The MODIS-like Second-Generation Global Imager (SGLI) which offers 19 spectral channels has a special feature, the three polarization angle (0, 60 and 120°) polarimeter. Polarization observations in the range of 670 and 865 nm with tilting function will particularly be used to improve the retrieval of aerosol properties over land surfaces (Okamura et al., 2008; Tanaka et al., 2009). The Advanced Microwave Scanning Radiometer-2 (AMSR2) will be installed on GCOM-W. A major advantage of AMSR2 is its large revolving space antenna (2 m) and the ability to detect very weak MW signals at an increased temporal resolution which will improve the retrieval quality of precipitation and atmospheric humidity. An additional 7.3 GHz channels will mitigate radio-frequency interferences (Kachi et al., 2008). The stratospheric wind interferometer for transport studies (SWIFT) will not only measure stratospheric winds but also ozone densities by means of the wind-induced phase shifts of interferograms from atmospheric limb radiance spectra in the vicinity of the vibration–rotation ozone line at 8.82 µm (Shepherd et al., 2001; Rahnama et al., 2006). A new satellite of the ESA's Earth Explorer core missions, ADM-Aeolus (Atmospheric Dynamics Mission) will measure vertical wind profiles from space. The mission employs a high-performance Doppler wind lidar based on direct-detection interferometric techniques (refer to Stoffelen et al., 2005). Tan and Andersson (2005) showed that, based on simulations, the gained accuracy for both, the boundary layer and the free troposphere is sufficient for global data-assimilation systems. As a development of recent satellites and instruments such as Cryosat, MERIS and the improved AATSR (both on Envisat), the European Sentinel-3 mission will observe a wide range of land, ocean and atmospheric parameter in the frame of the Global Monitoring for Environment and Security (GMES) program of ESA. The core instrument of the topography mission is the SRAL radar altimeter (Le Roy et al., 2010). The two-channel microwave radiometer (MWR) aims at the analysis of ice and snow (Tran et al., 2008). The Ocean Land Color Instrument (OLCI) is planned as an improved successor to MERIS while the Sea and Land Surface Temperature Radiometer (SLSTR) provides data continuity from the previous AATSR and ATSR-1/2 instruments. Two more SWIR channels will support better clouds and aerosols screening while two further channels are added for global-scale fire monitoring (Coppo et al., 2010). Three future missions are especially devoted to rainfall, cloud and aerosol remote sensing. The first French/Indian mission, Megha-Tropiques, relies on passive instruments and is restricted to the tropics (from 23°N to 23°S) due to its inclined orbit which, however, provides high temporal resolution, inevitably to study tropical convection with short life cycles (Karouche and Raju, 2010). The core instrument for rainfall retrievals, the MADRAS MW imager, will be a development of the TRMM-TMI sensor. Compared to TMI, higher MADRAS frequency channels at 89 and 157 GHz might help to improve the retrieval of ice phase hydrometeors (Balaji et al., 2009). The humidity sounder SAPHIR is designed to provide the humidity profile (Eymard et al., 2001). This satellite will also carry an ERB (Earth Radiation Budget) instrument called the Scanner for Radiation Budget (ScaRaB) which has been proven to deliver somewhat lower data quality than the ERB CERES (JPSS) mission (Viollier et al., 2009). The second future mission dedicated to rainfall retrieval is the core satellite of the Global Precipitation Measurement (GPM) mission (for GPM refer to Smith et al., 2007). The satellite is a direct development of the TRMM mission with its first precipitation radar in space. Consequently, the GPM core harbours a passive microwave imager (GMI) and dual-frequency precipitation radar (DPR) (Flaming, 2005). GMI is similar to TRMM's TMI but will provide significantly improved spatial resolution thanks to a bigger 1.2 m diameter antenna (Bidwell, 2005). The dual-frequency of the precipitation radar (DPR) is a great step forward in comparison to the single-frequency PR on TRMM. This shall allow the retrieval of more accurate rain rates in areas of cold clouds which are frequently characterized by solid precipitation with deviating hydrometeor shapes (Nakamura et al., 2005). The mission devoted to aerosol, clouds and rainfall is one of ESA's future Earth Explorer core missions EarthCare with its four instruments: an atmospheric backscatter lidar, a cloud profiling radar, a multi-spectral imager and a broad band radiometer (Bezy et al., 2005). A general improvement compared to the Cloudsat mission's radar is the Doppler capability of EarthCare's CPR. However, Schutgens (2008) showed with simulations that the large forward motion of the Doppler cloud profiling radar might cause biases in the observed Doppler speeds which have to be corrected by respective algorithms. The synergy between the lidar and the cloud radar, especially, is suggested to improve the retrieval of radiative and microphysical properties of clouds (Tinel et al., 2005).

Table III. Approved experimental missions
Mission (agency), orbitSensorTechnologyLaunchaPriority productsImprovement
  • a

    Tentative year of launch; x, nominal horizontal resolution, z, nominal vertical resolution; ESA, European; CSA, Canadian; JAXA, Japanese; NASA, American; CNES, French; and ISRO, Indian Space Agencies.

  • LEO, Low Earth Orbit

  • GEO, Geostationary Earth Orbit.

ADM-Aeolus (ESA) LEO(1) ALADIN (Atmospheric laser Doppler instrument)(1) Doppler Wind Lidar (355 nm), x = 50 km, z = 0.5–2.5 km2013Vertical wind profiles (0–30 km) Vertical profiles of cloud properties Aerosol optical depth and distributionImproved analysis of circulation systems Provides for the first time global wind profiles Wind parameters meet NWP assimilation accuracy requirements
Chinook (CSA) LEO(1) SWIFT (Stratospheric Wind Interferometer for Transport Studies) (2) ARGO (Atmospheric Research with GPS Occultation)(1) Doppler IR radiometer (at 9 µm), x = 15–55 km, z = 3–5 km (2) IGOR (Integrated GPS Occultation Receiver); x = 8–160 km; z = 0.5 km2011Ozone concentration (1) Stratospheric wind (1) Bending angle (temperature, humidity) (2)Improved accuracy First time global observation of stratospheric winds Assimilation to NWP
EarthCARE (ESA/JAXA) LEO(1) HSR (High Spectral resolution lidar) (2) CPR (Cloud Profiling Radar) (3) MSI (Multi-Spectral Imager) (4) BBR (Broadband Radiometer)(1) Lidar (355 nm), z = 100–300 m, x = 0.1–10 km (2) Doppler millimeter wave radar (94 GHz), z = 500 m, x = 1–10 km (3) Multi-spectral imager (seven bands, 0.6.12 µm), x = 1 km (4) Two band (broadband SW and LW), x = 10 km2015Vertical profiles aerosol extinction Aerosol type Vertical profiles clouds properties Mass flux in clouds Drizzle and precipitation rates Vertical radiative flux gradientsFirst space-borne radar with a Doppler capability Higher accuracy Parameters meet NWP assimilation accuracy requirements
Global Change Observation Mission (GCOM), GCOM-C (Climate) (JAXA) LEO(1) Second-generation Global Imager (SGLI)(1) 19-channel radiometer (0.38–12 µm), x = 0.25–1 km2014Cloud properties Aerosol properties Ocean colour Vegetation indexImproves MODIS Polarimetry function Forward/backward function in red/NIR
Global Change Observation Mission (GCOM), GCOM-W (Water) (JAXA) LEO(1) Advanced Microwave Scanning Radiometer-2 (AMSR2)(1) MW radiometer with six frequencies/12 channels (V/H) (6.9–89 GHz); x = 5–10 km2012Precipitation Atmospheric moisture Soil moisture, snow depth SST Sea surface wind speedLargest revolving space antenna Detects weak MW signals Higher accuracy High temporal resolution due to revolving antenna
Global Precipitation Measurement (GPM) mission core satellite (NASA/JAXA) LEO, non-sun synchronous orbit(1) GPM Microwave Imager (GMI) (2) Dual–frequency Precipitation Radar (DPR)(1) 13-channels (10–183 GHz), x = 4.4–32.2 km (2) Ku-band (13.6 GHz) and Ka-band (35.6 GHz) precipitation radar; x = 5 km20133-D cloud structure 3-D rainfall and rain ratesImproves TRMM-TMI by four high frequency channels about 166 and 183 GHz Improvement of TRMM-PR by adding Ka-band radar High temporal resolution; 3 h global rain maps Higher sensitivity for light rain and solid precipitation detection
Megha-Tropiques (CNES, ISRO) LEO, 20° inclination above the equator(1) Microwave Analysis and Detection of Rain and Atmospheric Structures (MADRAS) (2) Humidity Sounder (SAPHIR) (3) Scanner for Radiation Budget Measurement (SCARAB)(1) Self-calibrating microwave imager, 5-channels (18.7–157 GHz), H + V polarization; x = 6–40 km (2) 6-channel passive microwave humidity sounder (183.31 ± 12 GHz); x = 10 km (3) Multi-spectral scanning radiometer, 4-channel (0.5–12.5 µm); x = 40 km2011Cloud condensed water content Cloud ice content Convective-stratiform cloud discrimination Rain rate Latent heat release Integrated water vapour content Radiative fluxes at the top of the atmosphere Sea surface windHigh temporal sampling of a MW system Can follow the life cycle of tropical mesoscale convective systems
National Polar-orbiting Operational Environmental Satellite System (NPOESS) (NOAA/NASA) LEO(1) Visible/Infrared Imager/Radiometer Suite (VIIRS) (2) Cross-track Infrared Sounder (CrIS) (3) Advanced Technology Microwave Sounder (ATMS) (4) Microwave Imager/Sounder (MIS) (5) Ozone Mapping and Profiler Suite (OMPS) (6) Clouds and the Earth's Radiant Energy System (CERES) (7) Space Environment Monitor (SEM-N)(1) 22-channel VIS-IR radiometer (0.3–14 µm), x = 0.4 km (2) Scanning Fourier Transform IR Spectrometer (3.9–15.4 µm); x = 14 km (3) 22-channel MW radiometer (23–183 GHz), x = 16–75 km (4) 10-channel instrument (6–183 GHz; 4 VH polarized; 3 polarimetric), x = 10-40 km (5) Nadir total column spectrometer (300–380 nm) and nadir profile spectrometer (250–310 nm, x = 50–250 km; z = 5 km (up to 60 km) (6) 3-channel broadband imager (0.3–50 µm), x = 10–20 km (7) Special Sensor J5 (SSJ5) for low-energy, Energetic Particle Spectrometer (EPS) for medium-energy, and omnidirectional detectors for high-energy particles2013Cloud properties (1–4) SST/LST (1,2) Precipitation type/rate (1–4) Atmospheric temperature, moisture and pressure profiles and columnar values (2, 3, 4) Soil moisture (3, 4) Vertical/horizontal ozone distribution (5) Components radiation balance (6) Space weather (7)Continuation and improvement of LEO missions on e.g. TERRA, AQUA, DMSP etc. Day/night band for low levels of VIS-NIR radiance Higher (spatial, temporal, and spectral) resolution Higher radiometric calibration and accuracy More accurate soundings NWP assimilation quality
Sentinel-3 (EUMETSAT) LEO(1) Synthetic Aperture Radar Altimeter (SRAL) (2) Microwave radiometer (MWR) (3) Ocean Land Color Instrument (OLCI) (4) Land Surface Temperature Radiometer (SLSTR)(1) Dual-band Ku- (13.575 GHz) and C-band (5.41 GHz), x = 0.3 km (2) Two-channel microwave radiometer (23.8, 36.5 GHz), x = 20 km (3) Medium Resolution Imaging Spectrometer, 21-channels UV to NIR; x = 0.3–1.2 km (4) Dual viewing technique, 9-channels (0.555–12 µm), x = 0.5–1 km2013Ocean, ice, land and inland water surface topography (1) Integrated atmospheric water vapour column (2) Cloud liquid water content (2) Ocean colour (3) Land surface biophysical properties (3) LST/SST (4)Improvement Cryosat With atmospheric correction Improved MERIS Reduced sunglint effect Improved AATSR

3. Meteorological parameters

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Satellite systems
  5. 3. Meteorological parameters
  6. 4. Conclusions
  7. Supporting information
  8. References
  9. Supporting Information

After the overview of past, current and future satellite systems, the following section focuses on the respective meteorological parameters most relevant for a better understanding of the Earth-atmosphere system (see also Table S2).

3.1. Radiation

Radiation energy and its spatio-temporal distribution is the driver for atmospheric dynamics. To understand weather and climate, measurements of the radiation that enters and leaves the Earth-atmosphere system are necessary. To provide these measurements, a succession of satellite instruments have been developed (Table S2). Satellite-based methods for estimating the long wave radiation balance are reviewed in Schmetz (1989). More recently, Loeb et al. (2007) summarized observed radiation retrievals from different sensors.

The Earth's radiation budget (ERB) operational product at NOAA/NESDIS has a history since 1974. The ERB consists of two components: the emitted long wave radiation at the top of the atmosphere (TOA) (OLR: outgoing long wave radiation), and the absorbed solar radiation, (ASR: absorbed solar radiation or net solar radiation). The algorithm was developed for the NOAA AVHRR series. Its development and continuous improvements are documented in Wydick et al. (1987), Ruff and Gruber (1988) and Taylor (1990).

The ERBE instruments provided radiation flux information at the top of the atmosphere (TOA) since 1984 (Barkstrom and Smith, 1986; Barkstrom et al., 1989). Ellingson et al. (1989) developed a multispectral regression technique to estimate OLR using the HIRS radiance observations. This algorithm was further adapted for the Geostationary Operational Environmental Sounder (GOES) (Ba et al., 2003), as well as for the GOES Imager (Lee et al., 2004). ERBE data provided insights in the great importance of clouds in regulating the radiation budget (Ramanathan et al., 1989; Harrison et al., 1990). As a result, the Clouds and Earth Radiant Energy System (CERES) sensor was designed to provide radiation fluxes at the surface and at levels throughout the atmosphere and to investigate the cloud-radiation feedback on the Earth's climate system (Wielicki et al., 1996; Loeb et al., 2001; Geier et al., 2003). Huang et al. (2008) and Sun et al. (2010) used collocated AIRS hyperspectral radiance measurements and CERES outgoing long wave fluxes to estimate TOA OLR from AIRS radiance measurements.

3.2. Surface temperature

Retrievals of the sea surface temperature (SST) and land surface temperature (LST) from space provide information for interactions between ocean/land and atmosphere such as evaporation processes and boundary layer dynamics.

Satellite measurements of SST have been provided by the NOAA/AVHRR with multiple infrared channels since 1981 (McClain et al., 1983, Table S2). SST and LST retrievals using IR channels are based on the fundamental theory, that differential spectral absorption in multiple infrared channels is used to obtain absolute SST estimates (McMillin and Crosby, 1984; McClain et al., 1985). This so-called ‘split-window’ method is being used successfully to retrieve the sea surface temperature from satellite radiances where the emissivity is assumed equal to unity (Njoku et al., 1985). It relies on the fact that the atmospheric attenuation is greater in the 12.0 µm channel than in the 11.0 µm channel. As the attenuation increases, primarily as a result of increasing atmospheric water vapour, the difference in the radiance measured in the two bands increases. Since the surface source of the radiance does not change between the bands, the differential shift in sensor measured radiance originates from atmospheric attenuation (Ouaidrari et al., 2002).

The split window method has been modified to retrieve land surface temperature and several split-window equations have been developed for the land surface (e.g., McClain et al., 1985; Prata, 1994a, 1994b; Sobrino et al., 1996). Modified versions of the split window algorithms have been successfully applied to the LST retrieval from the data observed by MODIS and SEVIRI (Sobrino and Romaguera, 2004; Wan et al., 2004; Trigo et al., 2008).

Infrared measurements of SST can only be obtained in cloud free conditions. Furthermore, IR estimates of SST are also contaminated by high atmospheric aerosol loading (Diaz et al., 2001). Because clouds and aerosols are essentially transparent to microwave radiation at frequencies below about 12 GHz, microwave remote sensing has the potential to eliminate the atmospheric contamination. Microwave estimates of SST are possible because the surface radiance is proportional to SST at frequencies between about 4 and 12 GHz. However, SST retrievals at these frequencies must consider the effects of wind on the emissivity of the sea surface (Chelton and Wentz, 2005). High-quality microwave SST data first became available from measurements at 10.7 GHz by the TRMM TMI (Wentz et al., 2000). With its cloud-penetrating ability, AMSR-E provides a unique dataset of global all-weather SST measurements (Chelton and Wentz, 2005). AMSR-E retrievals of SST are based on measurements of brightness temperature at 6.9 GHz, which is more sensitive to SST than are the 10.7-GHz measurements used in TMI retrievals. The empirical retrieval algorithm for SSM/I matches the SSM/I brightness temperature with buoy and/or radiosonde measurements by means of a neural network algorithm (Stogryn et al., 1994; Krasnopolsky et al., 1995; Liu et al., 2001).

LST exhibits a strong diurnal variability that cannot be captured from polar orbiting satellites that sample each location approximately twice a day. Geostationary satellites provide diurnal coverage, and allow derivation of the LST diurnal cycle.

3.3. Wind

Wind fields derived from satellites provide continuous area-wide information about atmospheric dynamics in a high spatial and temporal resolution. Such information is of great benefit as an input parameter for numerical weather prediction. Thus, atmospheric motion vectors, derived by tracking atmospheric features (e.g. clouds or water vapour) with satellites were one of the first satellite data products assimilated in global numerical weather prediction (Hayden et al., 1993; Eyre, 1997; Tomassini et al., 1999; Menzel, 2001).

Satellite-based winds are derived from a variety of active and passive techniques (see Table S2). A comprehensive review is given in Isaacs et al. (1986) and Kidder and Vonder Haar (1995).

Winds at higher atmospheric levels are deduced by tracking the motion of features in satellite imagery. The tracking technique relies on passive optical satellite data, preferably from geostationary systems (Fujita, 1968; Hubert and Whitney, 1971). This technique estimates the horizontal wind by determining the vector difference of the location of a feature in successive images. The method is applicable in any spectral region in which distinctive features may be identified. Most popular are the visible and infrared channels where clouds act as tracers to be tracked (Allison et al., 1972; Fischer et al., 1981; Le Marshall et al., 1985; Stewart et al., 1985). The retrieved winds are called cloud motion vectors (CMV). Other channels are the 6.7 µm water vapour band (Laurent, 1993; Velden et al., 1997) or the 15 µm CO2 band (Menzel et al., 1983), for which the retrieved winds are called atmospheric motion vectors (AMV).

Nowadays, satellite-based winds are retrieved from all operational geostationary satellites. For polar latitudes, Key et al. (2002) retrieved CMV/AMV from MODIS data, which provide a temporal resolution of about 1 h in the Polar Regions. Horvath and Davies (2001a, 2001b) and Moroney et al. (2002) presented a retrieval of CMV for the Multiangle Imaging SpectroRadiometer (MISR) on the Terra satellite. These are derived by matching cloud reflectivity patterns from three different view angles. The across-track and along-track disparities are considered to determine height of cloud tops and the motion effects separately. The results are the cloud motion components parallel to and orthogonal to the satellite's direction.

Wind fields near the surface can be determined from the observed microwave emissivity of the ocean surface (e.g., Wilheit and Chang, 1980; Meissner et al., 2001). In constructing algorithms to retrieve ocean wind speed from passive microwave radiometers, higher frequencies (such as 36 GHz) rather than lower frequencies are commonly used to retrieve low to moderate winds up to 25 m s−1 in no-rain areas, since the sensitivity to ocean wind at higher frequencies is better than that at lower frequencies (Wentz, 1983). Kidder et al. (1978, 1980) developed a statistical method for the 55.45 GHz channel of the Scanning Microwave Spectrometer on Nimbus-6 to study for the first time surface wind speed in tropical cyclones. This was followed by algorithms developed by Velden and Smith (1983), Velden (1989) and Velden et al. (1991) who expanded the use of high-resolution data from the Microwave Sounding Unit (MSU) on National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites. Brueske and Velden (2003) extended the work of Kidder et al. (1978) to estimate wind speed in tropical cyclones from AMSU data, a successor of the MSU.

WindSat is the first passive microwave polarimetric radiometer. The objective of WindSat is to demonstrate the capability of polarimetric, microwave radiometers to measure near-surface ocean wind speed and direction in all-weather conditions (Connor et al., 2004; Gaiser et al., 2004). The WindSat wind retrieval algorithm uses a variational technique in which the atmospheric state vector is found, which minimizes the difference between the satellite radiance measurements and the forward-model equivalents (Bettenhausen et al., 2006). Kim and Lyzenga (2008) proposed a method for estimating the atmospheric transmittance and wind speed over the ocean from WindSat data. They used a simplified model for the ocean surface reflectivity to calculate both the surface emissivity and the reflection of downwelling atmospheric radiation.

Near-surface winds over oceans can also be derived from microwave radar backscatter from the ocean (e.g., Cardone et al., 1983; Chelton and Freilich, 2005). The backscattering co-efficient is mainly influenced by the wind speed and the angle formed by the wind vector and the satellite's antenna direction. Wind speed and direction can be deduced from a set of near-simultaneous measurements of backscatter co-efficient at a single location for different viewing angles. Therefore, scatterometer sensors have several antennae, or a rotating antenna and two radar beams in the case of the SeaWinds sensor (Fichaux et al., 2005). The use of radar scatterometers to measure the ocean surface wind vectors is well established (Moore and Jones, 2004). Scatterometers such as the Active Microwave Instrument (AMI) on the European Remote Sensing (ERS) 1 and 2 satellites have observed global ocean winds continuously since the early 1990s (Fichaux et al., 2005; Adams et al., 2006). The utility of QuikSCAT winds in the analysis and forecasting of extra tropical cyclones and marine weather in the mid- and high latitudes is documented by Atlas et al. (2001), Chelton et al. (2006) and Von Ahn et al. (2006).

SeaWinds scatterometer data have been used for tropical cyclone forecasting (Jones et al., 1999; Weissman et al., 2003). The Advanced SCATterometer (ASCAT) aboard MetOp was launched by EUMETSAT to measure surface wind (Figa-Saldaña et al., 2002).

Synthetic Aperture Radar (SAR) combines the measurement of a backscattering coefficient with the azimuth Doppler analysis of the measured signal. This enables measurement of backscattering coefficients with a high spatial resolution (Fichaux et al., 2005).

3.4. Water vapour

Water vapour is the principal greenhouse gas in the atmosphere and a key compound of the global climate (Gedzelman et al., 2003). It is important for many atmospheric processes, such as radiative transfer, circulation dynamics (Hanisco et al., 2007; Strong et al., 2007), cloud formation (Schmidt et al., 2005), precipitation (Bowen and Revenaugh, 2003) and the greenhouse effect (Schneider et al., 2006; Hartmann, 2002). Information about the distribution and variability of atmospheric water vapour is critical for understanding these processes controlling the Earth radiative budget and the hydrological cycle.

Several efforts have been made to use satellite datasets to measure the spatio temporal distribution and variability of atmospheric water vapour (see Table S2). A good overview and summary can be found in Kley and Russel (2001). Passive methods for retrieving water vapour (or precipitable water vapour, the vertical integral of the water vapour mixing ratio) exploit water vapour absorption bands in three distinct spectral domains. Methods based on solar reflectance channels rely on absorption between about 0.9 and 1.0 µm (Gao and Kaufman, 2003; Albert et al., 2005). Methods based on thermal-infrared channels rely on absorption and emission between about 6.5, 8.7 and 12 µm (Seemann et al., 2003). Microwave techniques exploit water vapour absorption lines at either 22.2 or 183.3 GHz (Engelen and Stephens, 1999; Sohn and Smith, 2003).

Several studies showed that measurements in the visible spectral region can be used to derive water vapour total columns (e.g. Noel et al., 1999; Casadio et al., 2000; Maurellis et al., 2000; Lang et al., 2003; Wagner et al., 2003; Buchwitz et al., 2004). One of these retrieval methods is the so-called Air Mass Corrected Differential Optical Absorption Spectroscopy approach (e.g. Noel et al., 1999, 2004, 2005, 2008) which also relies on water vapour and molecular oxygen absorption between 688 and 700 nm to derive total column water vapour.

MODIS provides five near-infrared bands located within and around the 0.94 µm water vapour band, that are used to retrieve column water vapour amounts (King et al., 2003). The retrieval relies on observations of water vapour absorption of near-infrared solar radiation reflected by the bottom surface and uses ratios of water vapour absorbing bands (within the 0.94 µm water vapour band) with atmospheric window bands at 0.86 and 1.24 µm to derive atmospheric water vapour transmittances (King et al., 1992). Bennartz and Fischer (2001) describe an algorithm to derive columnar water vapour from backscattered solar radiation in the MERIS near-infrared channels using radiative transfer simulations for the MERIS 900 and 885 nm channels.

The water vapour absorption band at 6–7 µm of NOAA/TOVS and its successor NOAA/ATOVS is used to retrieve water vapour in the atmosphere (Chaboureau et al., 1998 and references therein). Soden and Bretherton (1994) and Jackson and Bates (2001) used the GOES 6.7 µm channel to estimate atmospheric water vapour. The NESDIS/CIMSS three-layer precipitable water product integrates GOES sounding retrievals to provide a measurement of the layer and total precipitable water in clear and partly cloudy conditions (Schmit et al., 2002).

Several techniques for estimating precipitable water vapour use data from two adjacent channels in the infrared split-window region near 10–12 µm (GOES, Birkenheuer and Gutman, 2005).

Milz et al. (2005) present water vapour profiles obtained from infrared limb emission measurements recorded by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on ENVISAT. The retrieval is based on constrained non-linear least squares fitting.

Typical sensors for water vapour retrieval methods based on microwave wavelength radiances are MSU/AMSU (Staelin et al., 1976), SSM/I or the SSM Temperature and Water vapour Profiler (SSM/T) (Schlüssel and Emery, 1990; Bauer and Schluessel, 1993). Grody et al. (1998) used radiative transfer simulations to derive total precipitable water vapour from AMSU dual-frequency microwave channels. The optimal channel combination and coefficients were obtained by performing regression analysis on the simulated AMSU measurements and standard sounding as well as surface data. Houshangpour et al. (2005) developed a regression method to retrieve upper tropospheric water vapour from AMSU radiances. Singh and Bhatia (2008) propose a neural-network-algorithm using simulated brightness temperatures at four frequencies, 23.4, 31.4, 50.3 and 89.0 GHz for the retrieval of precipitable water vapour from AMSU data. The method is based on surface observations of the skin temperature and ocean surface, wind speed and direction. Deeter (2007) presents a method for retrieving precipitable water vapour using observations from AMSR-E. The method relies on a simple but accurate parameterization which relates AMSR-E polarization-difference signals at 18.7 and 23.8 GHz to precipitable water vapour, liquid water path and the surface emissivity polarization difference.

3.5. Gases

As a response on the increasing human impact on the evolution of the global climate and on the stratospheric ozone layer much effort has been made to understand the underlying chemical and physical processes and the role of anthropogenic gas emissions. To fulfill this objective there is a clear need for global observation of gas emissions and concentrations in the Earth atmosphere system (Nett et al., 2001). Satellite measurements are a powerful tool for monitoring gas emissions, since the whole globe is observed with a single instrument over long periods (Beirle et al., 2010). Therefore, in response to the identified need for global observations, several satellite based instruments dedicated to the monitoring of the lower and middle atmosphere have been embarked in the past (see Table S2). Satellite measurements of atmospheric chemical constituents have enhanced our understanding of how human activities affect climate in the Earth system (Warner et al., 2010).

This section gives an overview of satellite based measurements of gas emissions and concentrations in the Earth atmosphere system.

3.5.1. Carbon dioxide (CO2)

To predict the future carbon dioxide (CO2) concentration in the atmosphere, and the resulting radiative forcing of climate change, knowledge of current CO2 sources and sinks, their spatial distribution and variability is essential (Crevoisier et al., 2009b). Satellite measurements of the distribution of global atmospheric CO2 concentration can contribute to such knowledge. Information on CO2 atmospheric distribution can be retrieved from thermal infrared sounders (Chédin et al., 2002, 2003; Crevoisier et al., 2004; Engelen et al., 2004; Chahine et al., 2006; Maddy et al., 2008; Strow and Hannon, 2008) and from near infrared remote sensing (Buchwitz et al., 2005a; Barkley et al., 2007; Schneising et al., 2008). The Atmospheric Infrared Sounder (AIRS) together with the Advanced Microwave Sounding Unit (AMSU) on board the NASA/Aqua satellite has brought new insights to CO2 monitoring from space (Chahine et al., 2006). A set of 43 AIRS channels, located in the CO2 absorption bands, near 15 and 4.3 µm are considered in the Optimum Sensitivity Profile method (Crevoisier et al., 2003). These channels are characterized by a strong sensitivity to CO2 variations and a low sensitivity to other atmospheric components and surface characteristics.

3.5.2. Methane (CH4)

Methane (CH4) is second only to carbon dioxide (CO2) as an anthropogenic greenhouse gas (IPCC, 2007). However, the exact location, intensity and nature of methane sources and sinks are still not fully elucidated. For these reasons it is essential to have more knowledge of current CH4 sources and sinks, their spatio-temporal distribution and variability to make projections of future CH4 concentrations in the atmosphere (Crevoisier et al., 2009a). To fulfill this task, several remote sensing instruments have been embarked on satellite platforms to gain information on the distribution of methane in various parts of the atmosphere. For example, total columns of methane have been retrieved from SCIAMACHY data (Buchwitz et al., 2006; Carmo et al., 2006; Frankenberg et al., 2006, 2008). The SCIAMACHY methane distributions present high concentrations in the tropical region, which are possibly related to methane emission from terrestrial plants (Keppler et al., 2006). The CH4 retrieval algorithm for AIRS is presented in Susskind et al. (2003). The atmospheric temperature profile, water profile, surface temperature and surface emissivity required as inputs are derived from appropriate AIRS channels. For the CH4 retrieval, these data plus a first-guess profile of CH4 are used as inputs to the forward model (Strow et al., 2003) to compute the upwelling radiance (Susskind et al., 2003, 2006; Xiong et al., 2008). Ravazi et al. (2009) describe a method to retrieve methane concentrations from IASI. The inversion model is based on the Optimal Estimation Method (OEM) (Rodgers, 2000). Space-borne instruments working in a limb viewing geometry (e.g. MIPAS) add information on the vertical distribution of methane but are only sensitive from the upper troposphere to higher altitudes (Park et al., 2004; Raspollini et al., 2006; De Maziere et al., 2008).

3.5.3. Carbon monoxide (CO)

In recent years, extensive CO observations from a number of satellite platforms have yielded a global view of the CO distribution (George et al., 2009). Limb-sounders such as MIPAS/ENVISAT (Funke et al., 2007) or MLS/AURA (Pumphrey et al., 2007; Livesey et al., 2008) provide vertically resolved profiles for the mid/high-troposphere.

The MOPITT CO retrieval algorithm employs a nonlinear optimal estimation method to solve iteratively for the CO profile, producing a result which is statistically most consistent with both the satellite-measured radiances and a priori information (Pan et al., 1998; Deeter et al., 2003). The current AIRS CO physical retrieval algorithm uses radiances in the 4.58–4.50 µm region and seeks to minimize the weighted difference between the clear column radiance observations and the radiance computed using the five AIRS forward model (SARTA) (Strow et al., 2003) by varying the geophysical state (Susskind et al., 2003).

3.5.4. Ozone (O3)

The first spaceborne instruments for ozone measurements were the Solar Backscatter Ultraviolet (SBUV) and Total Ozone Mapping Spectrometer (TOMS) on NASA's Nimbus-7 satellite (Heath et al., 1975). The Solar Backscattered Ultraviolet model 2 instrument (SBUV/2) on the National Oceanic and Atmospheric Administration's NOAA-11 satellite has been providing global measurements of the total column ozone and the ozone profile since 1988 (Planet et al., 1994; Hilsenrath et al., 1995).

Takahashi et al. (1992) present a method for the derivation of the horizontal distribution of total ozone amounts from the brightness temperature data obtained by the HIRS/2 sensor on board the NOAA satellites. Chance et al. (1991, 1997) extended the ozone profile information to lower altitudes, including the troposphere, by using high spectral resolution hyperspectral data from GOME. Since then, several physically based retrieval algorithms were developed to retrieve ozone profiles from GOME radiances (Munro et al., 1998; Hoogen et al., 1999; Hasekamp and Landgraf, 2001; van der A et al., 2002; Liu et al., 2005). The operational algorithm for the retrieval of total ozone column from the GOME-2/MetOp is the GOME-2 Data Processor. In GDP the ozone slant columns are derived with a standard Differential Optical Absorption Spectroscopy retrieval (Loyola et al., 1997; Burrows et al., 1999; Spurr et al., 2005; van Roozendael et al., 2006; Valks and Loyola, 2008).

3.5.5. Nitrogen dioxide (NO2)

Satellite observation of tropospheric NO2 columns started in 1995 with the Global Ozone Monitoring Experiment (GOME-1) (Burrows et al., 1999). By using NO2 data retrieved from GOME the influence of biomass burning, high lightning activity or soil emissions on the global distribution of tropospheric NO2 is detectable (e.g. Leue et al., 2001; Richter and Burrows, 2002; Beirle et al., 2004; Jaegle et al., 2004). These observations are continued with GOME-2 (Callies et al., 2000), the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) (Bovensmann et al., 1999) and the Ozone Monitoring Instrument (OMI) (Levelt et al., 2006).

For OMI the standard NO2 retrieval algorithm uses the Differential Optical Absorption Spectroscopy technique (Platt, 1994) to determine the slant column densities by nonlinear least squares fitting in the 415–465 nm window (Boersma et al., 2002; Bucsela et al., 2006; Celarier et al., 2008). Boersma et al. (2007) present a new near-real time technique for OMI to retrieve tropospheric NO2 columns. The technique is based on a combined retrieval-assimilation-modelling approach for tropospheric NO2 from the GOME and SCIAMACHY satellite instruments (Boersma et al., 2004).

3.5.6. Nitrogen monoxide (NO)

Data from GOME have also been used to retrieve global NO column amounts to study the behaviour of stratospheric NO (Wenig et al., 2004). Burrows et al. (1999) presented early results from GOME, which indicate enhanced NO over the populated areas of the Eastern United States and Europe. A new generation of satellite instruments now provides measurements of NO at spatial resolutions that exceed GOME resolutions by factors of seven or more (e.g. SCIAMACHY; Bovensmann et al., 1999).

3.5.7. Nitric acid (HNO3)

Global distributions of HNO3 in the stratosphere have been obtained from a series of limb-sounders operating in the infrared or millimetre spectral range. The first global observations of HNO3 were based on the Limb Infrared Monitor of the Stratosphere (LIMS) launched on the Nimbus 7 spacecraft (Gille et al., 1980; Gille and Russell, 1984; Gille et al., 1984). The longest and the most complete observations of HNO3 were measured by the Microwave Limb Sounder (MLS) on board the Upper Atmosphere Research Satellite (UARS) between 1991 and 1998 (Santee et al., 1995, 1999; Waters et al., 1999). These observations are complemented by MLS on the Aura satellite since 2004 (Santee et al., 2004; Urban et al., 2009), as well as by MIPAS (Stiller et al., 2003; Wang et al., 2007).

Two additional global HNO3 observing instruments have recently been launched. The first is the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) (Bernath et al., 2005) and the second is a follow-on version of the UARS MLS instrument that was also launched on the EOS Aura satellite (Waters et al., 2006; Santee et al., 2005, 2007).

3.5.8. Sulphur dioxide (SO2)

Satellite-based SO2 observations were first performed using the Total Ozone Mapping Spectrometer (TOMS) (Krueger, 1983). This was followed by other satellite sensors as GOME, SCIAMACHY and OMI (e.g. Krueger et al., 1995; Eisinger and Burrows, 1998; Carn et al., 2004; Richter et al., 2006; Yang et al., 2007; Krotkov et al., 2008).

TOMS, GOME and OMI algorithms use reflected ultraviolet sunlight to determine column SO2 amounts (Prata et al., 2007). The Differential Optical Absorption Spectroscopy technique (Platt, 1994) has been successfully employed for SO2 measurements on global and regional scales (e.g. Chance, 1998; Martin et al., 2002; Palmer et al., 2003; Wittrock et al., 2006; Lee et al., 2008).

Sulphur dioxide has a strong spectral absorption region centred near 7.4 µm, and a second, weaker, absorption region located near 8.7 µm (Ackerman et al., 2008). Spectral signature methods using measurements at 7.3, 8.5, 11, and 12 µm from the High-Resolution Infrared Radiation Sounder (HIRS) have been used successfully in detecting volcanic plumes (e.g. Baran et al., 1993; Ackerman and Strabala, 1994; Ackerman, 1997; Yu and Rose, 2000; Prata et al., 2003). High spectral resolution observations from AIRS are also used to analyse SO2 plumes from volcanoes (e.g. Carn et al., 2005; Prata and Bernardo, 2007). Prata et al. (2007) used AIRS measurements to infer volcanic SO2 concentrations in the upper troposphere. The algorithm relies on the strong SO2 absorption feature near 7.3 µm and takes into account interference from water vapour across the band.

3.5.9. Sulphur monoxide (SO)

The first quantitative SO concentration data for a major volcanic eruption was obtained from the TOMS for the El Chichon eruption in 1982 (Krueger, 1983). All significant eruptions since 1978 have now been measured by the series of TOMS instruments (Bluth et al., 1992, 1993; Krueger et al., 1995, 2000; Schnetzler et al., 1997; Carn et al., 2003). Greatly improved sensitivity concerning volcanic and anthropogenic SO detection is provided by GOME and SCIAMACHY full spectrum UV data (Eisinger and Burrows, 1998; Bovensmann et al., 1999; Burrows et al., 1999). Infrared detection of volcanic SO has also been demonstrated with AIRS data (Carn et al., 2005), but IR sensors have low sensitivity to tropospheric and boundary layer SO emissions.

3.6. Profiles

Temperature sounding instruments on board polarorbiting satellites provide high spatio-temporal resolution and enable a more reliable analysis of long term atmospheric temperature trends. Since 1978 atmospheric temperatures for the lower troposphere to the stratosphere have been retrieved through combined measurements in the 15 µm carbon dioxide (CO2) absorption band from the High-Resolution Infrared Radiation Sounder (HIRS) and in the oxygen (O2) absorption band around 60 GHz from the Microwave Sounding Unit (MSU) on board National Oceanic and Atmospheric Administration (NOAA) operational polar-orbiting satellites (Auman et al., 2003; Chung et al., 2010). In 1998 the MSU was replaced by the Advanced Microwave Sounding Unit (AMSU). The combination of HIRS/3, AMSU-A and AMSU-B constitutes the current operational sounding system of the National Oceanic and Atmospheric Administration (NOAA) (Auman et al., 2003). With channels in the oxygen absorption band, AMSU-A is designed to retrieve the atmospheric temperature profiles. AMSU-B module makes measurements in the vicinity of the strong water vapour absorption line at 183 GHz and is used for atmospheric water vapour sounding. Several retrieval techniques have been developed for temperature and/or humidity sounding with AMSU-A/B and other microwave radiometers measurements (see Table S2). Radiation measurements from layers as high as the lower stratosphere (Channel 1: 14.7 µm) down to the surface (Channel 8: 11.0 µm) from GOES Sounder enable to derive vertical variations of temperature, moisture, and total column ozone (Smith, 1983; Hayden, 1988; Ma et al., 1999; Li et al., 2001). The operational MODIS atmospheric profile algorithm uses 12 infrared bands with wavelengths between 4.47 and 14.24 µm that are related to MODIS infrared band radiances calculated from more than 8400 global radiosonde profiles of temperature, moisture and ozone (King et al., 2003).

Weisz et al. (2007) present an AIRS alone single field of view retrieval algorithm to simultaneously retrieve temperature, humidity and ozone profiles under all weather conditions. A fast cloudy radiative transfer model accounting for clouds of various phases, cloud particle sizes and optical thicknesses was used to simulate cloudy radiances representing the regression training set.

The HIRDLS algorithm to retrieve atmospheric temperature and humidity profiles is based on the optimal estimation solution technique for inverse problems (Rodgers, 2000) that relates forward radiative transfer model calculated radiances with those observed (Khosravi et al., 2009).

Amato et al. (2009) propose a retrieval algorithm that uses a statistical strategy based on dimension reduction for the IASI instrument to retrieve atmospheric profiles of temperature, water vapour and ozone.

3.7. Aerosols

Aerosols in the troposphere are a major climate forcing parameter, due to the direct and indirect aerosol effect (Twomey et al., 1984; Kaufman and Nakajima, 1993; Ramaswamy, 2001). Despite this importance there are still significant uncertainties concerning the physical and optical properties of tropospheric aerosols and their interaction with global climate (IPCC, 2007). This is mainly due to the inadequate quantitative knowledge of global aerosol characteristics and their temporal variability (Bates et al., 2006; Penner et al., 2006; Li et al., 2009). To evaluate the aerosol radiative effects together with the magnitude and the potential variability of the aerosol climate forcing it is therefore essential to monitor aerosols on the global scale (Kiehl and Briegleb, 1993; Taylor and Penner, 1994). In this context, passive and active satellite sensors have been used to retrieve global distributions of aerosol properties (see Table S2). Overviews of existing retrieval algorithms can be found in King et al. (1999), Yu et al. (2006), Mishchenko et al. (2007), Fishman et al. (2008), and Tanré (2010).

The most relevant parameters to characterize aerosol properties and distribution and that are accessible from satellite data are the aerosol optical depth (AOD), a measure of the integrated aerosol load through the atmosphere, and the Angstrom Exponent (AE), which is related the spectral dependence of the AOD and is a measure for the column integrated aerosol size distribution (Tanré, 2010).

Satellite-based AOD retrieval has been successfully applied over oceans by using the low reflectance of oceans at solar wavelengths in the red and near-infrared spectral regions (Griggs, 1983; Stowe et al., 1997; Tanré et al., 1997).

Aerosol retrieval over land is more complicated due the higher and more variable surface reflectance. Since the measured signal consists of sunlight reflected by both the Earth's surface and the aerosol layer, some assumptions about the surface reflectance properties and aerosol properties have to be made to separate the aerosol contribution to the signal from that of the surface (Kaufman et al., 2002). Algorithms for the successful AOD retrieval over land were developed by e.g. Kaufman and Sendra (1988), Fraser et al. (1992), and Kaufman et al. (1997).

The first satellite based aerosol studies used data from geostationary satellites (GOES, METEOSAT), or from polar orbiting platforms (NOAA/AVHRR series) (e.g. Fraser et al., 1984; Husar et al., 1997). The near-daily global coverage from the MODIS AOD retrieval provides a high temporal resolution and uses multiple MODIS channels and separate algorithms for ocean (Tanré et al., 1997) and land (Kaufman et al., 1997; Remer et al., 2005). The algorithms are continuously validated and updated (Hsu et al., 2004; Levy et al., 2005, 2007; Li et al., 2007; Remer et al., 2007).

The additional angular information from the MISR AOD retrieval (Diner et al., 2005; Martonchik et al., 2009) allows reduction of algorithmic assumptions and retrieval bias (Kahn et al., 2007). The MISR product consists of the AOT, Angstrom exponent and aerosol type retrieved over both land and the oceans (Mishchenko et al., 2007).

The ATSR-2 and AATSR sensors on ENVISAT uses two view directions in a wide spectral range (0.55–1.65 µm) to measure aerosol properties and their distribution (Veefkind et al., 1998, North et al., 1999; Grey et al., 2006). The retrieval of aerosol properties over land from POLDER is based on polarized reflectance measurements (Deuze et al., 1999, 2000). Scattering by aerosol particles generates highly polarized light (Deuze et al., 1999) which makes the polarized satellite radiances more sensitive to the presence of aerosols. The new generation of satellite based high spectral resolution infrared sounders AIRS and IASI are well suited to retrieve aerosol properties such as optical depth, altitude and mean particle size (Pierangelo et al., 2005; Peyridieu et al., 2010). The algorithms are based on the fact that long wave channels (8–12 µm) are sensitive to both the AOD and the altitude of the dust layer while short wave channels (around 4 µm) are essentially sensitive to the dust optical depth. The aerosol algorithm for OMI (Torres et al., 2007) makes use of the full UV-to-visible spectral coverage to derive spectral aerosol extinction optical depth. It uses forward calculations for a number of microphysical aerosol models defined by the size distribution and the complex refractive index as well as the AOD and the aerosol layer height.

Algorithms to retrieve aerosol vertical structure by using CALIPSO CALIOP has been successfully implemented by Winker et al. (2007), Kim et al. (2008), and Vaughan et al. (2009). Chand et al. (2008) examine two techniques for CALIPSO to deduce AOD and the Angstrom exponent directly from aerosol effects on light transmissions. Josset et al. (2008) retrieve AOD over oceans by combining CALIOP and Cloudsat measurements (Stephens et al., 2002). The combination of spectral measurements from MODIS with active lidar measurements provided by CALIPSO CALIOP allows the derivation of information on the aerosol size distribution along the vertical atmospheric path (Kaufman et al., 2003; Leon et al., 2003).

3.8. Clouds

3.8.1. Cloud classification

Identifying clouds in satellite imagery is an important first step in the retrieval of both surface and atmospheric properties (Berendes et al., 1999). In the past, various cloud classification techniques have been developed for the different satellite systems and for a variety of purposes (see Table S2). Pankiewicz (1995) and Bankert et al. (2009) give an extensive review of satellite based cloud classification research.

Most commonly, spectral and/or textural features are used for cloud classification. Spectral features are generally more important. They make use of the information on the cloud radiance in different spectral bands. Some of the most commonly used methods in this category include threshold based schemes (e.g. Bendix et al., 2004), multi-spectral approaches and histogram schemes. Famous threshold-based algorithms developed for NOAA-AVHRR data are the APOLLO scheme (Saunders, 1986; Saunders and Kriebel, 1988; Kriebel et al., 2003) or the multispectral cloud analysis scheme SCANDIA (Karlsson, 1989, 1996). Approaches using one-dimensional and/or multidimensional histogram techniques are presented by Desbois et al. (1982), Simmer et al. (1982), Phulpin et al. (1983), and Kärner (1997).

Because of their physical importance, spectral features have proven to be effective and simple. However, they also encounter some problems because of the spectral similarities of certain objects such as ice cloud and snow. Other factors, such as moisture in the atmosphere, may also alter the multi-spectral characteristics and thus affect the classification result (Kaur and Ganju, 2008).

Textural features distinguish different cloud types by the spatial distribution characteristics of gray levels corresponding to a region in one specific channel. While the spectral characteristics of clouds may change, their textural properties are often distinct and tend to be less sensitive to the effects of atmosphere (Kaur and Ganju, 2008). For example, Coakley and Bretherton (1982) applied tests on spatial coherence to detect cloud-filled pixels and classify the cloud type (low, medium, high cloud, thin cirrus).

Fuzzy logic and neural network approaches were explored for cloud classification e.g. by Baum et al. (1997), Lewis et al. (1997), Miller and Emery (1997), Tian et al. (1999), McIntire and Simpson (2002), and Ghosh et al. (2006).

The National Oceanic and Atmospheric Administration Clouds from AVHRR algorithm incorporates multispectral information, channel differences, and spatial differences and applies a series of sequential decision tree tests (Stowe et al., 1991, 1999) to identify cloud-free, mixed and cloudy regions in the image. The MODIS cloud mask algorithm uses a series of sequential cloud detection tests to indicate a level of confidence that MODIS is observing a clear-sky scene (Ackerman et al., 1998; King et al., 2003; Platnick et al., 2003; Frey et al., 2008).

Breon and Colzy (2000) describe a cloud screening algorithm for the POLDER instrument over land surfaces. Four tests are applied to the measurements. First a threshold on the 0.44 µm reflectance after atmospheric correction is applied. Second a similar but smaller threshold is applied only over targets with significant spectral variation. Third the surface pressure is compared to an estimate derived from two POLDER channels centred on an oxygen absorption band. The final test makes use of POLDER polarization capabilities and seeks the presence of a rainbow generated by water clouds.

There are some algorithms to identify multilayer clouds with passive imagers. Pavolonis and Heidinger (2004) developed a pixel-level algorithm applicable to AVHRR and MODIS that uses ratios and differences of reflectances and brightness temperatures in various bands. The algorithm introduced by Baum et al. (2000) and Nasiri and Baum (2004) is a statistically based algorithm for MODIS that retrieves a multilayer cloud probability for a box area of user defined size. The MODIS operational multilayer cloud detection algorithm considers the difference between above-cloud precipitable water obtained from using the 0.94 µm band and above-cloud precipitable water retrieved from the CO2 slicing–derived cloud-top altitude to determine whether the cloud is multilayered (Wind et al., 2010).

Specific algorithms have been developed for the detection of fog/low stratus (NOAA: Bendix, 2002; MODIS: Bendix et al., 2006; MSG: Cermak and Bendix, 2007, 2008) and ground fog (Bendix et al., 2005; Cermak and Bendix, 2011) on different LEO and GEO sensors, using VIS to MIR spectral information.

3.8.2. Cloud properties

Clouds play in important role in the atmospheric radiation budget and in the global hydrological cycle and, since they act as a key modifier of the global climate, the IPCC emphasized the need for more global measurements on cloud properties to investigate the corresponding radiative fluxes and forcing (IPCC, 2007).

Typical cloud parameters that can be derived from satellite data and that are useful for such investigations comprise cloud-top height, cloud optical thickness, cloud effective particle radius, cloud liquid water path and cloud phase (Reuter et al., 2009).

Several techniques for the retrieval of cloud-top pressure/height (CTP/CTH) have been developed (see Table S2). The most common technique is to obtain a satellite-based measure of the thermal cloud-top brightness temperature. These temperature measurements are obtained in the infrared window wavelength of 10–12 µm and are compared to corresponding temperature profile data to determine the cloud-top height (Fritz and Winston, 1962; Smith and Platt, 1978). Another approach, known as the CO2 slicing technique, considers the cloud's emission within the carbon dioxide (CO2) absorption channels around 14 µm to derive the cloud-top temperature (Wielicki and Coakley, 1981; Menzel et al., 1983, 2002). The cloud-top height is inferred by comparing the retrieved cloud-top temperature with corresponding atmospheric temperature and humidity profiles (Frey et al., 1999). The CO2 slicing technique has a long tradition. It was first applied to the High Resolution Infrared Radiometer Sounder (HIRS; Wylie and Menzel, 1999) and the Geostationary Operational Environmental Satellite (GOES) sounder (Menzel et al., 1992; Menzel and Purdom, 1994).

A further method, which is applied to Medium-Resolution Imaging Spectrometer (MERIS) data, is based on measurements of the cloud-reflected solar radiation within the oxygen A band around 761 nm (Fischer and Grassl, 1991).

The MISR CTH retrieval is based on cloud detection by cameras at two different angles and positions. The height of the cloud relative to the surface is calculated from the apparent change in position (Diner et al., 1999; Moroney et al., 2002; Muller et al., 2002; Chae and Sherwood, 2010).

The CALIPSO cloud data product provide the number of vertical cloud layers and the cloud top and cloud base height for each of these layers (Stubenrauch et al., 2010). The cloud profiling radar (CPR) of the CloudSat mission (Stephens et al., 2002; Mace et al., 2007) also provides information on vertical cloud layer structure. For a complete picture of the vertical cloud structure, the Cloudsat Geometrical Profiling Product (Mace et al., 2007; Marchand et al., 2008) and the CALIPSO Vertical Feature Mask (Vaughan et al., 2004) have been merged into a combined Radar-Lidar Geometrical Profile Product (Mace et al., 2009).

Cloud microphysical retrievals using visible and near-infrared channels rely on the fact that the reflection function at the non-absorbing wavelength is mainly a function of the cloud optical thickness, and the reflection at an absorbing wavelength is largely determined by the effective radius. The retrieval techniques are based on the 1-D radiation concept where a cloud is regarded as a plane-parallel, vertically and horizontally homogeneous layer which completely covers a remotely sensed picture element. The general principles of inversion theory in terms of cloud microphysics retrievals are presented and discussed by Twomey and Seton (1980). The retrieval of cloud optical properties from multispectral satellite data include studies by Arking and Childs (1985), Twomey and Cocks (1989), Nakajima et al. (1991) and Minnis et al. (1992a, 1992b). Satellite-based algorithms to retrieve cloud properties generally use some kind of look-up table (LUT) approach (e.g. Nakajima and King, 1990; Han et al., 1994; Nakajima and Nakajima, 1995). Pre-calculated radiative transfer results are iteratively matched with actual measured values in the visible and near-infrared channels.

Various algorithms have been developed to retrieve cloud optical thickness and cloud effective particle radius. Most of the retrieval techniques have been developed for optical sensors aboard polar-orbiting satellites (e.g. King et al., 1997; Platnick et al., 2003). Techniques are also available for geostationary satellite systems (e.g. Han et al., 1994; Feijt et al., 2004; Roebeling et al., 2006, 2008).

Chang and Li (2002, 2003) proposed a technique to estimate the vertical profile of droplet effective radius for water clouds using multispectral near-infrared measurements from MODIS. The underlying principle of the retrieval technique is that radiance measurements at different near infrared wavelengths possess different penetration depths inside the cloud, providing information on the vertical profile of the effective radius. Kokhanovsky et al. (2005) present a cloud retrieval algorithm, which is based on the two-wavelength semi-analytical cloud retrieval algorithm for the liquid water path and the cloud particle size determination proposed by Kokhanovsky et al. (2003). Kokhanovsky et al. (2006) developed a new technique to identify mixed-phase clouds and clouds with supercooled water droplets. The technique uses AATSR and SCIAMACHY data and is based on measurements of the backscattered solar light at wavelengths 1.55 and 1.67 m in combination with cloud brightness temperature measurements at 12 µm. Greenwald et al. (1997) derived the first estimates of the cloud liquid water path and the droplet effective radius for marine stratocumulus clouds using the GOES-8 imager.

For cirrus clouds there are also algorithms depending only on the infrared channels (Ackerman et al., 1990; Strabala et al., 1994). Yang et al. (2007) use a bispectral technique based on pre-calculated lookup tables of ice cloud radiances to infer the optical thickness and effective particle size of an ice cloud from the MODIS measurements during daytime.

The polarization characteristics in the visible and near-infrared spectral region from POLDER can be used to retrieve cloud properties (Chepfer et al., 1998, 1999; Breon and Colzy, 2000; Masuda et al., 2002). Liou and Takano (2002) demonstrated that information on ice crystal shape and crystal orientation can be inferred from the reflected polarization patterns.

Night time algorithms for cloud property retrievals are also available (Baum et al., 1994, 2003; Minnis et al., 1998; Perez et al., 2000; Wong et al., 2007; Merk et al., 2010). Generally, these algorithms are based on the concept of effective emissivity (Platt and Stephens, 1980; Liou et al., 1990). Because of the exponential relationship between optical depth and emissivity the maximum retrievable optical thickness is about 6–8 (Liou et al., 1990; Minnis et al., 1998). Han et al. (2009) developed a new IR technique based on direct radiative transfer calculations, which extends the range of the retrievable optical depth and enables the use of instantaneous atmospheric profiles for improving retrieval accuracy. Compared to other passive remote sensing instruments, the high spectral resolution of IR vertical sounders such as the AIRS leads to especially reliable properties of cirrus with optical depth as low as 0.1, day and night (e.g. Stubenrauch et al., 1999, 2006; Kahn et al., 2007).

Retrievals of cloud liquid water path (LWP) using visible and near infrared channels depend on the assumed droplet size distribution and its vertical variation. These techniques are prone to non-plane parallel and solar/viewing geometry effects in retrieving microphysical cloud properties (Varnai and Marshak, 2002; Marshak et al., 2006; Kato and Marshak, 2009). Retrieval techniques relying on passive microwave observations are prone to uncertainties in cloud temperature, surface emissivity, atmospheric absorption and the assumed temperature/humidity structure of the lower atmosphere, as well as to the effects of sub-field-of-view clouds (Greenwald et al., 2007; Stephens and Kummerow, 2007; Horvath and Gentemann, 2007; Zuidema and Joyce, 2008).

A great variety of LWP retrieval algorithms have been introduced since the first SSM/I was launched (e.g., Petty, 1990; Bauer and Schluessel, 1993; Greenwald et al., 1993; Liu and Curry, 1993; Lin and Rossow, 1994; Weng and Grody, 1994; Jung et al., 1998).

Wentz and Meissner (2000) use the liquid-sensitive 37 GHz channel measurements of AMSR-E to retrieve LWP products. Zhao and Weng (2002) developed an algorithm to derive cloud ice water path and ice particle effective diameters from AMSU measurements. Both parameters are related to the ice particle scattering parameters, which are determined from the AMSU 89 and 150 GHz measurements. The ratio of the scattering parameters measured at two frequencies provides a direct estimate of the ice particle effective diameter.

Hu et al. (2009) introduced an improved cloud phase determination algorithm using the active system CALIPSO. Based on theoretical and modelling studies, the algorithm differentiates cloud phases by using the spatial correlation of layer-integrated attenuated backscatter and layer-integrated particulate depolarization ratio. Yoshida et al. (2010) introduce a method for discriminating cloud particle types for CALIOP on CALIPSO. The authors theoretically estimated the relationship between the depolarization ratio and cloud extinction on the basis of the backward Monte Carlo method.

3.9. Precipitation

Precipitation is a key factor of the global water cycle and affects all aspects of human life. Because of its great importance and its high spatial and temporal variability, the correct spatio-temporal detection and quantification of precipitation has been one of the main goals of meteorological satellite missions. Precipitation retrieval from satellite data can provide area-wide information in regions for which data from rain gauge or radar networks are sparse or unavailable (Kuligowski, 2002). During the last decades several satellite-based rainfall retrieval algorithms have been developed (see Table S2). A comprehensive overview of existing satellite-based rainfall retrieval methods can be found in Kidder and Vonder Haar (1995), Kidd (2001), Levizzani et al. (2001), Levizzani (2003), Scofield and Kuligowski (2003), Anagnostou (2004), Stephens and Kummerow (2007), and Kidd and Levizzani (2011). An overview of existing retrieval techniques based on passive microwave sensors can be found in Wilheit et al. (1994), Petty (1995), Kummerow et al. (2001), Levizzani et al. (2002), Weng et al. (2003), and Joyce et al. (2004). Regarding explanations of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR), the reader is referred to Iguchi et al. (2000) and Ferreira et al. (2001). The following overview is arranged by the complexity of the algorithms according to Barrett and Martin (1981).

Cloud index methods use thresholds for IR cloud-top temperature to detect rain areas, to which a rainfall rate is assigned. The most popular cloud index method is the Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI; Arkin and Meisner, 1987), which uses a cloud-top temperature of 235 K as a threshold to delineate precipitating clouds. A constant rainfall rate is assigned to these raining pixels.

Feature-based methods rely on the assumption that the relationship between the satellite cloud-top brightness temperature and surface rainfall rate is not unique for most pixel-based rainfall estimation algorithms. Feature-based classification schemes use IR cloud-top temperature to classify different cloud types. The relationship between cloud top temperature and rainfall rate is retrieved for respective classified cloud types (e.g. Wu et al., 1985; Hsu et al., 2002; Bellerby, 2004). The Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Cloud Classification System algorithm by Hong et al. (2004) first separates 10.7 µm cloud images into distinctive cloud patches and then extracts different cloud features. The cloud patches are clustered into subgroups, which the rainfall rate is assigned to as a function of cloud-top IR temperature.

Multispectral methods are based on the assumption that precipitating clouds have a high VIS reflectivity and a cold IR cloud-top temperature, which is ideally valid for deep convective clouds. A prominent example is the ‘RAINSAT’ algorithm developed by Lovejoy and Austin (1979) and Bellon et al. (1980). Lensky and Rosenfeld (1997) and Rosenfeld and Lensky (1998) used the 3.7 µm reflectance and the 11 µm brightness temperature to detect rain areas and estimate rainfall rates. Capacci and Porcu (2009) present a daytime surface rain-rate classifier for MSG SEVIRI, based on artificial neural networks trained with data from ground based radar networks. The GOES multispectral rainfall algorithm (Ba and Gruber, 2001) combines information from five channels for the detection of precipitating cloud areas. The rainfall rate is assigned by the product of rainfall probability and mean rainfall rate, calculated as a function of the 11 µm temperatures.

Life cycle methods consider the temporal variability of convective systems and the involved precipitation processes. Griffith et al. (1978) used the cloud-top temperature difference between two consecutive scenes as a measure for the activity of convective clouds. Negri et al. (1984) classified different life cycles of convective clouds based on a single scene and attained comparable results to Griffith et al. (1978).

Cloud model techniques try explicitly to consider the physical processes that clouds undergo. The assigned rainfall rates are based on numerically simulated cloud-top temperatures and the corresponding rainfall rate (Gruber, 1973; Wylie, 1979). Based on studies of Adler and Mack (1984), Adler and Negri (1988) developed the Convective Stratiform Technique (CST) for subtropical convective systems which has been successfully applied also to tropical rain systems (Bendix, 1997). The CST has become a widely used and intensively validated technique.

Despite the variety of existing satellite-based rainfall retrieval techniques, most retrieval schemes developed for Geostationary Earth Orbit (GEO) systems rely on a relationship between IR cloud-top temperature, rainfall probability and rainfall rate. Such IR retrievals are appropriate for convective clouds that can easily be identified by their cold cloud-top temperature in the IR channel (e.g. Levizzani et al., 2001; Levizzani, 2003), but show considerable drawbacks concerning the detection and quantification of rain from stratiform clouds in connection with extratropical cyclones (e.g. Ebert et al., 2007; Früh et al., 2007). Such precipitating clouds are characterized by relatively warm and spatially homogeneous cloud-top temperatures that differ insignificantly from raining to non-raining regions. Therefore, retrieval techniques based solely on IR cloud-top temperature lead to an underestimation of the detected rain area and to uncertainties concerning the assigned rainfall rate (e.g. Ebert et al., 2007).

To overcome these drawbacks, several authors suggest using optical and microphysical cloud parameters derived from multispectral data of new generation satellite systems to improve rainfall retrievals (e.g. Ba and Gruber, 2001; Lensky and Rosenfeld, 2003a, 2003b; Nauss and Kokhanovsky, 2006, 2007; Thies et al., 2008a, 2008b, 2008c; Roebeling and Holleman, 2009; Küehnlein et al., 2010). They could show that cloud areas with a high optical thickness and a large effective particle radius possess a high amount of cloud water and are characterized by a higher rainfall probability than cloud areas with a low optical thickness and a small effective particle radius.

Microwave radiances are related more directly to precipitation rates. Rainfall retrievals using data from satellite microwave radiometers are more physically based on the relationship between the observed microwave brightness temperatures and the available liquid water within the cloud (Levizzani et al., 2007). Different microwave sensors have been used in the past for satellite-based rainfall retrieval. Among them there are the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the Special Sensor Microwave Imager (SSM/I), and the Advanced Microwave Scanning Radiometer (AMSR). Since the early Special Sensor Microwave Imager (SSM/I) retrievals a number of algorithms have been developed. Prominent SSM/I based rainfall retrieval techniques are the NOAA/NESDIS algorithm (Grody, 1991; Ferraro and Marks, 1995; Ferraro, 1997; McCollum and Ferraro, 2003; Dinku and Anagnostou, 2005) and the Goddard scattering algorithm (Adler et al., 1994).

The most recent rainfall retrieval algorithms rely on the 98 and 150 GHz window channels of AMSU data (e.g. Weng and Grody, 2000; Bennartz et al., 2002; Zhao and Weng, 2002; Chen and Staelin, 2003; Ferraro et al., 2005). Di Tomaso et al. (2009) propose a technique based entirely on the observations made by AMSU/B. The method incorporates the measurements at 89 and 150 GHz (window channels) and the signal received in the 183 GHz water vapour band (opaque channels). The differences between the measurements are analysed through radiative transfer simulations for the estimation of precipitation over both land and water surfaces and are related to rain rate values in different atmospheric scenarios. The Goddard Profiling algorithm (GPROF; Kummerow et al., 2001) is one of the most successful techniques in this category. The GPROF algorithm is an inversion-type algorithm providing estimates of instantaneous rainfall rates, the vertical structure of precipitation and the associated latent heating. The algorithm is based on large databases of cloud model derived profiles which are used for radiative transfer calculations at cloud model resolution (Kummerow and Giglio, 1994; Kummerow et al., 2001; Masunaga and Kummerow, 2005).

Mitrescu et al. (2010) developed a technique to retrieve light precipitation from CloudSat's millimetre-wavelength Cloud Profiling Radar (CPR) measurements. The radar model relies on the description of clouds and rain particles in terms of a drop size distribution function. Berg et al. (2010) use a combination of rainfall estimates from the 13.8-GHz TRMM PR (Iguchi et al., 2000) and the 94 GHz CloudSat Cloud Profiling Radar (Haynes et al., 2009) to assess the distribution of rainfall intensity over tropical and subtropical oceans. The PR provides the total rain volume because of its ability to estimate the intensity of all but the lightest rain rates, while the higher sensitivity of the CloudSat radar provides estimates of drizzle and light rain.

While satellite-based rain rate estimates are reliable and operational (Olson et al., 1996, 2006; Ferraro et al., 2005), the measurement of snowfall rates from space is a relatively new field (Chen and Staelin, 2003; Kongoli et al., 2003; Liu, 2004; Skofronick-Jackson et al., 2004; Noh et al., 2006). One major challenge associated with retrieving snowfall rates are the complex macrophysical and microphysical features of snow clouds (Kim et al., 2008). Kim et al. (2008) present a physical model to retrieve snowfall rate over land using brightness temperature observations from AMSU-B at 89, 150, 183.3, 183.3 and 183.3 GHz.

Many algorithms show encouraging results by combining data from IR and PMW sensors (e.g. Adler et al., 1993; Kummerow and Giglio, 1995; Miller et al., 2001b; Todd et al., 2001; Turk et al., 2003; Huffman et al., 2007; Bellerby et al., 2009; Hsu et al., 2009). The self-calibrating multivariate precipitation retrieval (Kuligowski, 2002) is another multisensor approach, which adds the dimension of being calibrated in real time against rain rates from PMW sensors. Other retrieval techniques developed in the past several years, are the Climate Prediction Center (CFC) morphing algorithm (CMORPH; Joyce et al., 2004), the Naval Research Laboratory Global Blended-Statistical Precipitation Analysis (NRLgeo; Turk and Miller, 2005), the Passive Microwave-Calibrated Infrared algorithm (PMIR; Kidd et al., 2003), and the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN; Sorooshian et al., 2000). Adler et al. (2000, 2003) describe a technique to use TRMM combined PR-TMI measurements to adjust geostationary IR data (the TRMM Adjusted Geostationary Operational Environmental Satellite Precipitation Index (AGPI)). Layberry et al. (2006) introduce a high-resolution multiplatform multisensor satellite rainfall product for southern Africa. The microwave infrared rainfall algorithm (MIRA) combines high spatial and temporal resolution Meteosat IR data with infrequent SSM/I overpasses. A transfer function relating Meteosat thermal infrared cloud brightness temperatures to SSM/I rainfall estimates is derived using collocated data from the two instruments and is then applied to the full coverage of the Meteosat data. The TMPA 3B42RT product (Huffman et al., 2007) is a near-real-time precipitation rate product at time and space scales. This product makes use of TRMM PR/TMI observations, along with high quality passive microwave-based rain estimates from three to seven polar-orbiting satellites (e.g., AMSR-E, SSMI/DMSP, AMSU-A), and all the geostationary IR sensors (e.g., Meteosat, GOES, GMS). The combined quasi-global rain map at 3 h resolution is produced by using TRMM to calibrate the estimates from all the other satellites and then combining all the estimates into the TMPA final product.

4. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Satellite systems
  5. 3. Meteorological parameters
  6. 4. Conclusions
  7. Supporting information
  8. References
  9. Supporting Information

Operational satellite systems provide valuable information on atmospheric parameters at regular intervals on a global scale. This satellite-based information about the Earth-atmosphere system and its components greatly enhance our knowledge and understanding of the processes and dynamics within the Earth-atmosphere system.

The current paper presents the development of meteorological satellites and techniques to retrieve information for weather forecast and climate research, ranging from the beginning of meteorological remote sensing to the near future of approved next-generation satellite systems. Successful instruments from experimental LEO systems have been improved and transferred to the next generation of operational LEO systems, while proven instruments from operational LEO systems have made it on board follow-up GEO systems.

Future satellite systems must sustain the observational capabilities of key global climate parameters. On the one hand this involves the improvement and further development of operational missions, critical for routine weather observation and numerical weather forecast. On the other hand this also implies the continuation of existing and established systems to assure the provision of long-term data sets for climate monitoring.

Beside the numerical weather prediction and climate monitoring, another important field of application is the global hydrological cycle. In this context improved observation capabilities concerning the water cycle and the interactions of its respective components are needed. A further important topic is the investigation of CO2 sources and sinks as well as the analysis of other greenhouse gases. In this context observations on regional scales with high accuracy are still of major priority.

Supporting information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Satellite systems
  5. 3. Meteorological parameters
  6. 4. Conclusions
  7. Supporting information
  8. References
  9. Supporting Information

The following supporting information is available as part of the online article:

Table S1. Existing LEO and GEO satellite systems and meteorological parameters.

Table S2. Overview of meteorological parameters retrieved from satellite based sensors.


  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Satellite systems
  5. 3. Meteorological parameters
  6. 4. Conclusions
  7. Supporting information
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Satellite systems
  5. 3. Meteorological parameters
  6. 4. Conclusions
  7. Supporting information
  8. References
  9. Supporting Information
met_288_sm_supportinginformationtS2.pdf252KSupporting Information
met_288_sm_supportinginformationtS1.pdf145KSupporting Information

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