Evaluation of satellite-based upper troposphere cloud top height retrievals in multilayer cloud conditions during TC4



[1] Upper troposphere cloud top heights (CTHs), restricted to cloud top pressures (CTPs) < 500 hPa, inferred using four satellite retrieval methods applied to Twelfth Geostationary Operational Environmental Satellite (GOES-12) data are evaluated using measurements during the July–August 2007 Tropical Composition, Cloud and Climate Coupling Experiment (TC4). The four methods are the single-layer CO2-absorption technique (SCO2AT), a modified CO2-absorption technique (MCO2AT) developed for improving both single-layered and multilayered cloud retrievals, a standard version of the Visible Infrared Solar-infrared Split-window Technique (old VISST), and a new version of VISST (new VISST) recently developed to improve cloud property retrievals. They are evaluated by comparing with ER-2 aircraft-based Cloud Physics Lidar (CPL) data taken during 9 days having extensive upper troposphere cirrus, anvil, and convective clouds. Compared to the 89% coverage by upper tropospheric clouds detected by the CPL, the SCO2AT, MCO2AT, old VISST, and new VISST retrieved CTPs < 500 hPa in 76, 76, 69, and 74% of the matched pixels, respectively. Most of the differences are due to subvisible and optically thin cirrus clouds occurring near the tropopause that were detected only by the CPL. The mean upper tropospheric CTHs for the 9 days are 14.2 (±2.1) km from the CPL and 10.7 (±2.1), 12.1 (±1.6), 9.7 (±2.9), and 11.4 (±2.8) km from the SCO2AT, MCO2AT, old VISST, and new VISST, respectively. Compared to the CPL, the MCO2AT CTHs had the smallest mean biases for semitransparent high clouds in both single-layered and multilayered situations whereas the new VISST CTHs had the smallest mean biases when upper clouds were opaque and optically thick. The biases for all techniques increased with increasing numbers of cloud layers. The transparency of the upper layer clouds tends to increase with the numbers of cloud layers.

1. Introduction

[2] Passive satellite instruments have long been used for monitoring large-scale cloud systems in time and space. Yet, the retrieved cloud properties are still subject to large uncertainties. Retrievals of cloud top height (CTH), a fundamental cloud property, are often biased by 1.5 km or more, even for single-layered cloud systems [e.g., Smith et al., 2008; Menzel et al., 2008]. On average, those errors can exceed 3 km for thin upper tropospheric cirrus clouds that are semitransparent in the infrared wavelengths [e.g., Holz et al., 2008; Chang et al., 2010]. In the presence of multilayer clouds, errors in the retrieved CTHs are often greater due to the assumption of a single-layered cloud employed in operational satellite retrieval techniques [Chang and Li, 2005, 2010; Naud et al., 2005]. That is, the retrieval methods interpret the spectral radiances from a given scene as being the result of interactions among the radiances leaving the surface and scattering, absorption, and emission by the atmosphere and a cloud at one particular altitude. When a thin, high cloud overlaps a low cloud, the retrieved CTH is typically found somewhere between the two clouds, its value depending mainly on the high-cloud optical depth and the separation of the two cloud layers. To provide more accurate cloud observations for climate monitoring and the development and validation of cloud process models in weather forecasting, it is necessary to employ a different approach to determine CTH. Active sensors, i.e., cloud lidars and radars, at the surface [e.g., Clothiaux et al., 2000], on aircraft [e.g., McGill et al., 2004], and on satellites [Winker et al., 2007; Stephens and Kummerow, 2007] are ideal for accurately determining the vertical layering of clouds, but are quite limited temporally or spatially. Until the challenges of actively sensing clouds on large spatial and relatively high-resolution temporal scales are overcome, it is necessary to develop and test new techniques for unscrambling the passively sensed radiances to retrieve more accurate cloud properties for both single- and multilayer clouds.

[3] Chang et al. [2010] recently developed a modified CO2-absorption technique (MCO2AT) that uses two spectral channels, centered near 11 and 13.3 μm, to infer the CTH for the highest cloud whether for single- or multilayered conditions. It differs from the traditional CO2-slicing methods [e.g., Smith and Platt, 1978; Wielicki and Coakley, 1981; Wylie and Menzel, 1999; Holz et al., 2008; Menzel et al., 2008] in that it solves for the cloud top radiating temperature using estimates for the effective background radiances, instead of using the clear-sky background radiances for the solution. Because the new approach utilizes the 11- and 13.3-μm channels on several newer operational geostationary satellites, such as the Twelfth Geostationary Operational Environmental Satellite Imager (GOES-12) [Schmit et al., 2001], it has the potential for improving the inference of the upper troposphere transmissive cloud properties in both single-layer and multilayer situations at relatively high temporal and spatial resolutions.

[4] Cloud top properties such as cloud effective radiating temperature, optical depth, and effective ice-crystal diameter are retrieved from geostationary satellite imager data in near-real time [Minnis et al., 2008a] based on the Visible Infrared Solar-infrared Split-window Technique (hereafter referred to as the old VISST) (P. Minnis et al., CERES Edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data, Part I: Algorithms, submitted to IEEE Transactions on Geoscience and Remote Sensing, 2010a). The VISST is an operational algorithm developed at NASA Langley Research Center (LaRC) for retrieving satellite cloud optical and microphysical properties for the Cloud and the Earth's Radiant Energy System (CERES) and operational geostationary satellite projects. Since then, the old VISST has been modified to develop a new VISST algorithm (P. Minnis et al., Cloud properties determined from GOES and MODIS data during TC4, submitted to Journal of Geophysical Research, 2010b) for improving the GOES-12 retrievals of upper tropospheric cloud optical and microphysical properties using model reflectances based on rough ice crystals [Yang et al., 2008a, 2008b], improved ozone and Rayleigh scattering corrections, and a new thick-cloud top height correction. In essence, the VISST, uses the cloud optical depth retrieved using the visible-wavelength reflectance to estimate the cloud emissivity based on the single-layer cloud assumption. Cloud top effective temperature Teff is then retrieved from the 10.8-μm radiance data by correcting for transmission through the cloud. An effective cloud height Zeff corresponding to Teff is inferred, which is usually located somewhere within the cloud, lower than the physical cloud top. Empirical methods are used to estimate the cloud physical top height CTH from Teff. While the new VISST differs from the old version in many respects, the differences between the old and new VISST retrievals have not yet been evaluated.

[5] Data taken during the NASA-sponsored Tropical Composition, Cloud, and Climate Coupling (TC4) Experiment conducted from Costa Rica during July and August 2007 [Toon et al., 2010] are ideal for evaluating passive CTH retrievals from geostationary satellite data. The Cloud Physics Lidar (CPL) on the NASA ER-2 high-altitude aircraft made highly accurate CTH measurements during all of the TC4 flight hours. The flights were conducted during daylight and sampled the clouds at several local times, thus providing data at most solar zenith angles and at different points in the diurnal cycle of convection.

[6] To date, the MCO2AT has only been tested against active sensor retrievals over limited midlatitude regions. Chang et al. [2010] found that the MCO2AT-inferred CTHs are significantly improved over the CTHs inferred by the single-layered CO2-absorption technique (SCO2AT). Much additional testing of the MCO2AT and SCO2AT is needed to ensure that it works well in all conditions, including the high-altitude deep convective conditions in the tropics. The improvements in the VISST have not been quantified for any conditions. Since both the old and new VISSTs were used to analyze the same GOES-12 data during TC4 (Minnis et al., submitted manuscript, 2010b), it is possible to determine how accurately the new VISST retrieves ice cloud top heights compared to the old VISST and the CO2-absorption techniques (CO2ATs) using independent measurements from that experiment.

[7] The primary objective of this paper is to evaluate the upper troposphere CTHs (<500 hPa) inferred by the MCO2AT and by the new VISST relative to the SCO2AT and old VISST, respectively. The TC4 CPL CTH data serve as the ground truth for all of the retrievals. This study focuses on the upper troposphere clouds comprised of convective towers, optically thick, optically thin anvils and cirrus, as well as many multilayered clouds.

[8] The paper is organized as follows. Section 2 describes the GOES-12 imager and the ER-2 CPL data used in this study. Section 3 describes the different methodologies of the SCO2AT, MCO2AT and the old and new VISST. Section 4 compares the GOES-12 CTH retrievals from the four techniques, which are evaluated by comparing with the aircraft CPL CTH data obtained during TC4. Analyses and discussions are also provided for optically thin, optically thick, and multilayer cloud scenarios. The final section gives the summary and conclusions.

2. Data

2.1. GOES-12 Data

[9] The GOES-12 imager at 0°N, 75°W was used to aid mission planning during TC4 and provide high temporal-resolution cloud products for the entire TC4 experimental area [Toon et al., 2010; Minnis et al., submitted manuscript, 2010b]. The GOES-12 imager 10.8- and 13.3-μm channels are used in the SCO2AT and MCO2AT for retrieving upper troposphere cloud top pressure (CTP) as presented in Section 3.1. The 0.65-, 3.9-, 10.8- and 13.3-μm channel data are used by both the old and new VISST (Minnis et al., submitted manuscript, 2010b) for retrieving the cloud effective temperature and cloud top temperature (CTT) for clouds located at all altitudes as described in Section 3.2. The CTPs from the SCO2AT and MCO2AT and the CTTs from the two VISST algorithms are converted to CTHs using profiles of atmospheric pressure, temperature and height obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) data set [Kalnay et al., 1990; Kanamitsu et al., 1991].

[10] Half-hourly observations taken by GOES-12 approximately 15 and 45 min after the UTC hours were analyzed during TC4. The half-hourly GOES-12 imagery and the old and new VISST cloud products were taken from the NASA Langley TC4 imagery and cloud product archives (Minnis et al., submitted manuscript, 2010a) (see http://www-angler.larc.nasa.gov/). Those data have a nominal 4 km × 3.2 km spatial resolution at nadir. The original scanning resolution is about 4 km × 2.3 km (north–south direction × east–west direction) for the 10.8-μm channel and about 8 km × 2.3 km (north–south × east–west) for the 13.3-μm channel.

2.2. CPL Data

[11] The NASA ER-2 flew at an altitude of 20 km, well above the highest cloud tops. The CPL is an active lidar used on high-altitude aircraft to measure attenuated backscatter lidar signals at 355-, 532- and 1064-nm wavelengths and is highly sensitive to optically thin cirrus and sub-visible clouds [McGill et al., 2002]. Cloud and aerosol backscatter and optical properties are retrieved from the CPL data at 1 s (∼200 m along track) horizontal resolution and 30-m vertical resolution. The CPL retrievals provide the top and bottom heights of all layers detected by the lidar up to a maximum of 10 layers with cumulative optical depths up to ∼3. To determine whether the CPL-detected upper tropospheric cloud is above the 500-hPa pressure level, the CPL uppermost CTHs are also converted to corresponding CTPs using the NCEP GFS profiles.

3. Techniques

3.1. CO2ATs

[12] Two CO2ATs, i.e., SCO2AT and MCO2AT, are applied by following the methods described in Chang et al. [2010]. Note that methods of CO2ATs differ from the method of traditional CO2-slicing techniques. The CO2-slicing technique often uses several CO2-absorption channels located in 13.3–14.3-μm wavelength range and requires at least two CO2-absorption channels having different central wavelengths that are close enough so that the two spectral cloud emissivities can be assumed to be equal. The CO2ATs use only the radiance pair from the 10.8-μm window and the 13.3-μm CO2-absorption channel to infer upper troposphere CTH. The CO2ATs are based on the well-mixed nature of CO2 gas in the upper troposphere. The difference between the 10.8- and 13.3-μm upwelling radiances due to the presence of an upper troposphere cloud is thus used to infer the cloud top pressure (CTP). However, CO2ATs are only useful for retrieving upper troposphere clouds because the 13.3-μm radiance loses its sensitivity to low clouds, owing to an increased CO2-absorption path length between the top of atmosphere (TOA) and the low cloud top. In this study, evaluations of the CO2ATs are conservatively restricted to only the CTHs retrieved above the 500-hPa level (∼5.7 km in altitude) to maximize the signal-to-noise ratio and to avoid the effects of variable CO2-gas concentrations in the lower troposphere.

[13] The SCO2AT is briefly described first, followed by details of the MCO2AT. The SCO2AT applied to the GOES-12 imager data is similar to the radiance ratio methods described earlier by McCleese and Wilson [1976], Smith and Platt [1978] and Wielicki and Coakley [1981]. For simplicity, let us use the superscript 11 for the 10.8-μm channel and superscript 13 for the 13.3-μm channel.

[14] By assuming cloud reflectance to be negligible at both the 10.8- and 13.3-μm channels, the satellite-observed radiances Robs11 and Robs13 for the two channels can thus be written as

equation image
equation image

where ɛc = ecAc denotes an effective cloud emissivity with eic being the cloud emissivity and Ac being the cloud cover fraction of the imager pixel, Rovc denotes the overcast radiance as ɛc = 1, and Rclr denotes the clear-sky radiance as ɛc = 0.

[15] The clear-sky radiances Rclr11 and Rclr13 for specified surface temperature Tg and surface pressure Pg are given by

equation image
equation image

where B11 and B13 denote the Planck functions and ξ11(P) and ξ13(P) denote the transmittances between the TOA (P = 0) and pressure-level P for the two associated channels. Similarly, the overcast radiances Rovc11 and Rovc13 for specific cloud top temperature Tc and cloud top pressure Pc are give by

equation image
equation image

The computations in equations (3)(6) use the atmospheric profile data obtained from the NCEP GFS data set [Kalnay et al., 1990; Kanamitsu et al., 1991] and the MODTRAN4 radiative transfer code [Berk et al., 1999].

[16] To solve for TcPc with specified TgPg, the ratios of (1) and (2) are manipulated to yield

equation image

The solution of TcPc can thus be inferred by searching for the solutions of Rovc11 and Rovc13 that best satisfy (7) for the satellite-observed pair, Robs11 and Robs13. The SCO2AT-inferred CTH is then derived by comparing the inferred TcPc to the atmosphere temperature/pressure and height profile data. Note that previous studies often assumed ɛc11 ≅ ɛc13 in equation (7). Here the relation between ɛc11 and ɛc13 is determined based on radiative transfer calculations [Chang et al., 2010].

[17] The MCO2AT is a modified version of the SCO2AT. As the SCO2AT assumes clouds are single-layered with a clear-sky background, the MCO2AT determines the effective background radiances Rebg11 and Rebg13 and their corresponding effective background temperature Tebg and pressure Pebg for the lower cloud in a multilayer cloud situation or for the clear-sky background for single-layer clouds. As such, equation (7) is modified in the MCO2AT by

equation image


equation image
equation image

To solve for TcPc using equation (8), the MCO2AT needs to determine TebgPebg using an iterative algorithm as illustrated in Figure 1. In the iterative algorithm, the solution of a SCO2AT-retrieved TcPc is first obtained using equation (7). If the SCO2AT Pc < 500 hPa, it proceeds to the MCO2AT iterative algorithm to estimate new TebgPebg and infer new TcPc using equation (8). Note that the inferred effective background radiance Rebg11 is bound between the clear-sky radiance Rclr11 and the midway radiance (Rclr11 + Robs11)/2 whereas the inferred TcPc is bound by the tropopause [Chang et al., 2010].

Figure 1.

Schematic diagram for illustrating the SCO2AT and MCO2AT algorithms.

3.2. VISSTs

[18] The VISST algorithm matches theoretically computed radiances with the GOES-12 imager-observed radiances at the 0.65-, 3.9-, 10.8-, and 13.3-μm channels to retrieve cloud parameters such as optical depth (OD), effective particle size, water phase, emissivity, effective cloud temperature, pressure and height, etc. The effective cloud temperature Teff and OD are primarily retrieved from the 10.8- and 0.65-μm data. For retrieving properties of ice and water clouds, the hexagonal ice crystal and spherical water droplet models are assumed, respectively [Minnis et al., 1998]. For semitransparent clouds, OD is small making the satellite-observed 10.8-μm brightness temperature larger than the physical temperature of cloud top. The value of Teff is then corrected to account for the gray body emission and transmission from below the cloud, using the cloud emissivity and transmissivity estimated from the retrieved OD [Minnis et al., 1998, also submitted manuscript, 2010a]. The retrieved Teff is then converted to an effective cloud height Zeff using the NCEP GFS atmospheric data.

[19] The old VISST (Minnis et al., submitted manuscript, 2010a) assumes that CTH is equivalent to Zeff for clouds with OD > 6. For clouds with OD equation image 6, empirical formulae are then applied to determine the CTHs for thin clouds. In the new VISST, several changes are made to improve the retrievals of OD and CTH. First, a cloud reflectance model based on the single-scattering properties of ice crystals having surface roughness [Yang et al., 2008a, 2008b] replaces the old ice-crystal model that was based on smooth-faced hexagonal columns. It was found that using the new rough-surfaced ice-crystal model often reduces the retrieved ice-cloud OD, but it can cause either an increase or decrease in OD, depending on the viewing and illumination angles. Second, a new ozone correction is applied to the visible channel retrieval because correction of the ozone absorption in the old version of VISST was too large. As detailed by Minnis et al. (submitted manuscript, 2010a), the visible-channel ozone transmittance in the new VISST is reduced by ∼12%. Additionally, the Rayleigh scattering optical depth was too large for GOES retrievals in the old VISST, so it was reduced in new VISST. Thus, the rough-surfaced ice-crystal model and the new ozone absorption and Rayleigh scattering corrections generally result in smaller retrieved ODs than their counterparts in the old VISST. For semitransparent clouds, the smaller ODs would result in the higher Zeff in new VISST. The third correction for Zeff derived in the new VISST is based on the study of Minnis et al. [2008b] to account for the differences between Zeff and CTH. This correction is only applied for ice clouds with retrieved OD > 6, using an empirical model to adjust Zeff toward CTH. Thus, most of the corrections should result in higher CTHs from the new VISST algorithm.

4. Results

4.1. Comparisons of Upper Troposphere CTHs

[20] The CPL and GOES-12 matched data are analyzed from 9 selected ER-2 flight days during the July–August 2007 TC4 experiment. Data from four other days (July 14, 25, 29 and August 9) are not included here because they were taken during transit flights or flights dedicated to measuring boundary layer clouds and/or aerosols. The CPL uppermost CTHs were averaged every 10 s. The averaging time of 10 s implies a ground track of ∼2 km since the ER-2 traveled at a speed of ∼200 m/s. Each 10-s averaged CPL CTH was matched with collocated GOES-12 pixel data from the two closest imagery scan times, one scanned before and another scanned after the CPL time. Since the GOES-12 imager scans at 30-min intervals, the collocated GOES-12-retrieved CTHs from the two images scanned before and after were then linearly interpolated in time to match the CPL CTH observation. However, when only one image pixel had retrieved CTH, that pixel CTH was treated as a match to the CPL data if the observing time difference between the image pixel and CPL data was less than 3 min. It is noted that the different times and horizontal resolutions of the GOES-12 and CPL cloud data make the comparisons of CTHs from the two measurements somewhat problematic, for example, a cloud could appear or disappear between the 30-min intervals or it may only occur in part of the pixel. To reduce the impacts of cloud breaks and inhomogeneous CTHs, the comparisons between matched CPL and GOES-12 CTH data are restricted by two conditions: the 10-s CPL data detect 100% cloud coverage and the 10-s CPL averaged uppermost CTH is above the 500-hPa level.

[21] Table 1 shows the numbers of matched data points obtained for the CPL and GOES-12 and those with CTHs above the 500-hPa level retrieved by the CPL, CO2ATs, and VISSTs from the nine flights. In each flight, the total numbers of matched data (Nmatch) are divided into three categories (under NCPL), denoted by h, m and l, according to the statistics of each 10-s CP CTH data. The h category denotes those that satisfy the aforementioned two conditions, i.e., the 10-s CPL had 100% cloud coverage and the 10-s mean uppermost CTH is above the 500-hPa level. The m category denotes those having a few CPL CTHs above 500 hPa, but their 10-s mean CTH is below the 500-hPa level. The l category then denotes the remaining matched data points which had either lower CTHs or no cloud retrieved by the CPL. In the three categories, the number of matched data having a valid CTP < 500 hPa inferred by the CO2ATs, the old and new VISST are denoted in Table 1 by NCO2AT, NVISST-old and NVISST-new. Only those matched CPL and GOES-12 data in the h category are compared in this study. Numbers in the m and l categories may be less reliable and could indicate data mismatches or overestimations by individual satellite techniques.

Table 1. ER-2 Flight Dates, Time Periods, Numbers of Matched CPL and GOES-12 Data, and the Numbers of Data Having Retrieved CTP < 500 hPa From the CPL, CO2ATs, Old VISST, and New VISSTa
  • a

    Nmatch, total number of matched CPL and GOES-12 data; NCPL, NCO2AT, NVISST-old and NVISST-new, numbers of the data having retrieved CTP < 500 hPa from the CPL, CO2ATs, old VISST, and new VISST, respectively; h category, numbers of CPL data having uniform CTP < 500 hPa; m category, numbers of CPL data having partial CTP < 500 hPa; l category, numbers of CPL data having no CTP < 500 hPa.

Jul. 1712:59:25–16:44:0913481262 h963806890
(39) m646
(47) l111
Jul. 1912:55:21–17:51:4117771053 h513450528
(71) m111
(653) l000
Jul. 2212:29:23–17:15:4517171628 h147512591417
(52) m27613
(37) l445
Jul. 2412:11:31–18:14:4221791745 h129212251312
(61) m655
(373) l1089
Jul. 3113:15:56–17:19:4014621462 h143513791396
(0) m000
(0) l000
Aug. 313:49:16–17:51:1714521452 h134911131213
(0) m000
(0) l000
Aug. 513:21:29–16:58:1112981298 h124411431218
(0) m000
(0) l000
Aug. 612:40:47–18:14:0319991694 h230191242
(84) m131
(221) l000
Aug. 812:40:45–17:40:1617961793 h172415681667
(2) m101
(1) l000

[22] In general, from comparisons of Nmatch and NCPL, the CPL detected large percentages of CTP < 500 hPa (four days had ∼100%). Based on NCO2AT, the CO2ATs retrieved large percentages (75–98%) of those upper troposphere clouds (CTP < 500 hPa), except for July 19 (∼49%) and August 6 (∼14%). The two versions of VISST also retrieved consistently large percentages of CTP < 500 hPa. The new VISST showed good agreement with the CO2ATs while the old VISST retrieved about 10% fewer than those from the new VISST. More than 2% of the matched data had some scattered CPL CTPs < 500 hPa within 10-s average CTPs that are greater than 500 hPa (the m category). Because of the inhomogeneous cloud top fluctuations and/or broken cloud fields for this category, large discrepancies between the CTP and GOES-12 retrieved CTPs are expected and are therefore excluded from the comparisons. About 9% of the matched data had no CPL < 500 hPa retrieved by CPL. Less than 0.2% of the pixels have no CTP < 500 hPa from the CPL, while CTPs < 500 hPa were retrieved by the CO2ATs and VISSTs (the l category).

[23] Figure 2 illustrates the matched CTHs inferred by the new VISST (blue), old VISST (green), MCO2AT (red) and SCO2AT (purple) overlaid on the ER-2 CPL vertical cloud mask data for 4 flight days. Each figure shows a 3-h period of matched data obtained during the ER-2 flights on August 8 (Figure 2a), July 31 (Figure 2b), July 17 (Figure 2c) and July 19 (Figure 2d), which were selected to demonstrate different cloud scenarios. An example shown in Figure 3 illustrates the GOES-12 imagery 0.65- (Figure 3a) and 10.8-μm (Figure 3b) data and the MCO2AT (Figure 3c) and new-VISST (Figure 3d) inferred CTPs for the data obtained at 14:45 UTC, August 8, 2007 for the TC4 region. The ER-2 aircraft trajectories (flying at 20-km altitude) for the 3-h time period shown in Figure 2a are also plotted in Figure 3. Note that the aircraft trajectory is for the flight time between 12:40:45 and 15:40:45 (UTC) whereas the GOES-12 images resemble the snapshot at 14:45 (UTC).

Figure 2.

Comparisons of the different CTHs inferred from the GOES-12 imager data using the new-VISST (blue), old-VISST (green), SCO2AT (purple), and MCO2AT (red). The CPL cloud vertical mask is shown in gray. (a) August 8 between 12:40:45–15:40:45 UTC. (b) July 31 between 13:15:56–16:15:56 UTC. (c) July 17 between 12:59:25–15:59:25 UTC. (d) July 19 between 12:55:21–15:55:21 UTC.

Figure 3.

GOES-12 (a) 0.65-μm and (b) 10.8-μm images and associated (c) MCO2AT and (d) new-VISST derived CTHs for 14:45 UTC 8 August 2007 over the TC4 area with overlaid ER-2 flight tracks between 12:40:45 and 13:40:45 (cyan), 13:40:45 and 14:40:45 (blue), and 14:40:45 and 15:40:45 (yellow).

[24] During August 8 (Figure 2a), the ER-2 flew over several convective cores and anvils. Comparing the data from this flight (12:40:45–17:40:16) when the CO2ATs had valid CTH retrievals (CTP < 500 hPa), the CPL measured a mean (±standard deviations) CTH of 13.9 ± 1.4 km whereas the MCO2AT, SCO2AT, new VISST, and old VISST inferred 12.3 ± 1.1, 10.7 ± 1.8, 11.4 ± 2.5, and 9.7 ± 2.4 km, respectively. Generally, good agreement among the CPL, MCO2AT, and new-VISST CTHs was found near the convective cores, but away from the cores their CTH differences increased as the anvil cloud optical depths decreased. The MCO2AT CTHs were sometimes a few kilometers lower and the new-VISST CTHs were sometimes much lower than the CPL heights. On average, when compared with the MCO2AT, the new-VISST CTHs were lower by 0.9 km, the old-VISST CTHs were lower by 2.6 km, and the SCO2AT CTHs were lower by 1.6 km.

[25] On July 31 (Figure 2b), the ER-2 flew over some geometrically thick anvils formed by a large mesoscale complex in the Pacific just off the coast of Costa Rica. The data from this flight (13:15:56–17:19:40) show that when the CO2ATs had valid CTH retrievals, the CPL measured a mean CTH of 16.3 ± 0.3 km whereas the MCO2AT, SCO2AT, new VISST and old VISST inferred mean CTHs of 12.8 ± 1.7, 12.2 ± 2.0, 13.0 ± 2.7, and 11.7 ± 2.5 km, respectively. While all four techniques underestimated the optically thin anvil CTHs by more than 3 km, differences between their mean CTHs were generally quite small (within 1.3 km) with the new VISST being the highest and the old VISST being the lowest. It was also found that the new VISST had better agreement with the CPL for optically thicker anvils (see Figure 2b) and convective cores (see Figure 2a). This day also had the highest percentages of CTP < 500 hPa retrieved by all four techniques (CO2ATs ∼98%, new VISST ∼95% and old VISST ∼94%).

[26] On July 17 (Figure 2c), the ER-2 flew over a large mesoscale complex off the Pacific coast of Costa Rica. Many optically thin cirrus clouds were missed by the four techniques at the beginning of this flight. The CPL-measured CTHs showed large fluctuations over the mesoscale complex causing problems in collocating the CPL and GOES-12 imager data. The CPL detected CTP < 500 hPa ∼94% of the time, compared to about 71, 66, and 60% for the CO2ATs, the new VISST and the old VISST, respectively. For the period 12:59:25–16:44:09 UTC, when CO2ATs retrieved CTP < 500 hPa, the associated mean CTHs were 12.8 ± 1.8 km (CPL), 12.0 ± 1.5 km (MCO2AT), 10.3 ± 2.2 km (SCO2AT), 10.3 ± 3.1 km (new VISST), and 8.8 ± 3.0 km (old VISST).

[27] On July 19 (Figure 2d), the ER-2 flew over the cores of several convective systems in the Pacific and then over the Caribbean to measure Sahara dust and low-lying clouds. There were high-altitude sub-visible thin-cirrus clouds above the convective systems during the first couple of flight hours. The sub-visible, thin cirrus clouds were generally not well retrieved by the four satellite techniques, but the new VISST showed significant improvement in the CTH retrievals relative to the old VISST. Comparing the data when CO2ATs had valid CTP < 500 hPa, the mean CTHs inferred on this day were 14.5 ± 1.3 km (CPL), 12.2 ± 1.2 km (MCO2AT), 10.5 ± 1.9 km (SCO2AT), 11.7 ± 2.4 km (new VISST) and 9.2 ± 3.0 km (old VISST). The later periods of this flight were mainly over low-lying stratocumulus clouds [Toon et al., 2010]. Overall, the CPL detected ∼59% of CTP < 500 hPa during the flight as compared to only ∼29%, ∼30% and ∼25%, detected by the CO2ATs, new VISST, and old VISST, respectively.

[28] On August 6 (Table 1), the CPL detected an extensive, thin layer of sub-visible high-altitude (∼15 km) cirrus clouds that occurred high above a deck of low-altitude (∼1 km) boundary layer clouds [Toon et al., 2010]. The sub-visible cirrus clouds were generally missed by the four satellite techniques, leading to the largest differences in Table 1 between NCPL (1694), NCO2AT (230), NVISST-old (191) and NVISST-new (242). The sub-visible cirrus clouds on this day are responsible for most of the undetected upper troposphere clouds in the passive retrieval results.

[29] Overall, there were a total of 15,028 matched data points as shown in Table 1. Out of these, ∼89% or 13,387 pixels (NCPL) had CPL-retrieved CTHs above 500 hPa. There were ∼68% (NCO2AT) having CO2AT-retrieved CTHs above 500 hPa (i.e., CTP < 500 hPa). The CO2ATs retrieved CTHs above 500 hPa only 0.5% of the time when the CPL did not retrieve a valid CTP < 500 hPa. The new VISST (NVISST-new) retrieved CTPs < 500 hPa for ∼66% of the pixels in contrast to 61% for the old VISST (NVISST-old). The rates of overestimation by both new and old VISSTs are smaller than the 0.5% by the CO2ATs. Relatively speaking, when the CPL retrieved upper tropospheric clouds (CTP < 500 hPa), the CO2ATs retrieved ∼76%, the new VISST retrieved ∼74% and the old VISST retrieved ∼69% of such upper tropospheric clouds. The findings that large percentages (24–31%) of upper tropospheric clouds were not retrieved by the satellite techniques are reasonable considering the large fractions of optically very thin cirrus clouds that occurred during the TC4 experiment [Toon et al., 2010]. The lidar system is much more sensitive to optically thin clouds than the passive sensors on the GOES-12 imager, which results in more detection of high clouds by the CPL.

[30] Figure 4 shows scatterplots comparing the CTHs retrieved from the four satellite techniques to those from the CPL for all 9 flight days when the CO2ATs retrieved CTPs < 500 hPa. The mean CTHs are 14.2 ± 2.1, 10.7 ± 2.1, 12.1 ± 1.6, 9.7 ± 2.9, and 11.4 ± 2.8 km for the CPL, SCO2AT (Figure 4a), MCO2AT (Figure 4b), the old VISST (Figure 4c), and the new VISST (Figure 4d), respectively. The corresponding overall mean biases relative to the CPL are −3.5, −2.1, −4.5 km, and −2.8 km. The MCO2AT reduced the mean biases of the SCO2AT by 1.4 km whereas the new VISST reduced the mean biases of the old VISST by 1.7 km. Note that much better agreement between the new VISST and CPL are found for CTH > 14 km. Unlike the new VISST, all of the SCO2AT (Figure 4a), MCO2AT (Figure 4b) and old VISST (Figure 4c) have generally underestimated the CTHs between 14 and 16.5 km.

Figure 4.

Comparisons of CTHs inferred from the GOES-12 imager and the CPL data. (a) SCO2AT versus CPL. (b) MCO2AT versus CPL. (c) Old VISST versus CPL. (d) New VISST versus CPL.

4.2. Cloud Emissivities and Multilayer Clouds

[31] Figure 5 shows the CTH differences (dzc) between the CPL and the four passive methods as a function of the MCO2AT-inferred cloud 10.8-mm effective emissivity (ɛc11). Results in the figure were obtained from the 9-day data shown in Figure 4. Overall mean biases, assuming that CPL CTHs were the truth, are −3.5 ± 2.3 (SCO2AT), −2.1 ± 2.0 (MCO2AT), −4.5 ± 2.9 (old VISST), −2.8 ± 2.8 (new VISST) km, as given in each sub-panel. For more opaque and likely optically thick clouds with ɛc11 > 0.95, the mean dzc were found to be −1.9, −1.4, −2.4, and −0.2 km for the SCO2AT (Figure 5a), MCO2AT (Figure 5b), old VISST (Figure 5c), and new VISST (Figure 5d), respectively. The underestimation of CTH by 1.4–2.4 km for those nearly opaque clouds (except for the new VISST case) are consistent with earlier results found by Sherwood et al., [2004] , who showed that the satellite infrared-derived CTHs were 1–2 km below the physical cloud tops detected by lidar instruments. This underestimation appeared to have been largely corrected for optically thick clouds, using the method of Minnis et al. [2008b] in the new-VISST algorithm (Figure 5d).

Figure 5.

CTH difference dzc as a function of the 10.8-μm cloud effective emissivity ɛc11. (a) SCO2AT minus CPL. (b) MCO2AT minus CPL. (c) Old VISST minus CPL. (d) New VISST minus CPL. Thick gray lines represent the running means.

[32] For less opaque clouds with ɛc11 < 0.95, the absolute differences increase progressively with decreasing ɛc11. For instance, for semitransparent clouds at ɛc11 ∼ 0.3, the mean dzc were found to be −5.1 km (SCO2AT − CPL), −2.8 km (MCO2AT − CPL), −5.7 km (old VISST − CPL) and −3.9 km (new VISST − CPL). Note that the MCO2AT appeared to have more overestimated CTHs for less opaque clouds (ɛc11 < 0.8) and have the overall smallest mean dzc compared to the SCO2AT (Figure 5a) and two VISSTs (Figures 5c and 5d).

[33] To examine the impact of multilayer clouds on the retrievals, Figure 6 shows the CTH differences from Figure 5 plotted as a function of the 10-s averaged number of cloud layers (Nlayer) retrieved by the CPL. In general, the absolute mean dzc of all four techniques increase with increasing Nlayer, except that the MCO2AT shows the smallest mean biases for all single- and multilayered clouds and it systematically reduces the SCO2AT mean biases by ∼40%. The increased dzc with increasing Nlayer as revealed in Figures 6a, 6c and 6d may be attributed to the single-layer cloud assumption used by the SCO2AT and the old and new VISSTs in multilayered cases. It is also possible that the CPL retrieved more cloud layers when the uppermost or upper cloud layers were optically thinner in those cases. This may imply that the increase in Nlayer is related to the decrease in ɛc11.

Figure 6.

CTH difference dzc as a function of the number of cloud layers Nlayer. (a) SCO2AT minus CPL. (b) MCO2AT minus CPL. (c) Old VISST minus CPL. (d) New VISST minus CPL. Thick gray lines represent the running means.

[34] Figures 79 present dzc as a function of ɛc11 by separating the single-layered (Figure 7), two-layered (Figure 8), and multilayered (Figure 9) clouds. For the single-layered cases, the mean dzc is fairly constant within each technique until ɛc11 falls below 0.5. For 0.5 < ɛc11 < 0.95, the MCO2AT has the smallest mean dzc (−0.5 to −1.0 km) and it reduces the absolute mean biases of the SCO2AT by ∼1 km from −1.88 (Figure 7a) to −0.92 km (Figure 7b). The new VISST also reduces the absolute mean biases of the old VISST significantly toward larger ɛc11.

Figure 7.

Same as in Figure 5 but for the single-layered (Nlayer = 1) clouds.

Figure 8.

Same as in Figure 5 but for the two-layered (1 < Nlayerequation image 2) clouds.

Figure 9.

Same as in Figure 5 but for the multilayered (Nlayer > 2) clouds.

[35] For the two-layered (Figure 8) and multilayered (Figure 9) cases, their mean differences behaved like those discussed in Figure 5 because the majority of TC4 clouds mainly consist of more than one cloud layer. Nonetheless, for two-layered and multilayered clouds, the absolute mean biases in all four techniques and in every bin of ɛc11 are generally twice as large as those from the single-layered conditions. The respective mean biases for SCO2AT, MCO2AT, old VISST, and new VISST are −1.88, −0.92, −2.52 and −0.84 km for single-layered cases (Figure 7), −3.27, −1.93, −4.12 and −2.47 km for two-layered (Figure 8), and −4.88, −3.03, −6.24 and −4.64 km for multilayered cases (Figure 9). Among the four techniques, the MCO2AT has the smallest absolute mean biases when ɛc11 < 0.9. Even though the MCO2AT was developed to account for multilayer cloud conditions, the mean biases of MCO2AT also increased significantly from −0.92 for single-layered to −3.03 for multilayered cases. This increase is likely caused by multiple transmissive upper level layers. In those instances, the MCO2AT infers an average height for the multiple transmissive layers. Additionally, many of the upper layer clouds in these cases are clouds that cannot be detected by the CO2AT even in single-layer conditions. Hence, there is not enough change in the radiances for the MCO2AT to account for the small optical depth of the uppermost cloud. Finally, it is worth noting that about 21% (89% – 68%) of the matched data had the CPL-retrieved CTP < 500 hPa, but had no CO2ATs CTP retrieval. Among these data, nearly half of them had VISST-retrieved CTHs and these are plotted in Figure 10a (old VISST) and Figure 10b (new VISST) as compared with the CPL CTHs. Since such cases were very optically thin clouds, it is not surprising to see that most of the VISST CTHs are much too low, especially since there were no MCO2AT/SCO2AT retrievals available. The mean CTHs in Figure 10 are 3.3 km for the old VISST and improved to 3.9 km for the new VISST as versus 13.2 km for the CPL. The ODs of these clouds were on the order of equation image ∼0.1. The accuracy of their retrieved CTHs is, thus, limited by both the sensitivity and horizontal resolution of the passive satellite instruments like GOES-12.

Figure 10.

Comparisons of the (a) old-VISST and (b) new-VISST CTHs with the CPL CTH when there was no SCO2AT/MCO2AT retrieval.

5. Summary and Conclusions

[36] Nine days of daytime upper troposphere cloud top height (CTH) measurements obtained from GOES-12 imager data and the ER-2 CPL data during the July–August 2007 TC4 were compared to evaluate four satellite retrieval techniques for processing enhanced satellite single-layer and multilayer cloud property retrieval products at NASA Langley Research Center (LaRC) [Minnis et al., 2008, also submitted manuscripts, 2010a, 2010b]. The comparisons focused on upper tropospheric clouds retrieved with CTP < 500 hPa using a standard single-layered CO2-absorption technique (SCO2AT), a modified CO2-absorption technique (MCO2AT), an earlier version of a Visible-Infrared-Shortwave-Split-window Technique (old VISST), and a recently improved version of the VISST (new VISST).

[37] Among the four techniques, the MCO2AT generally produces better agreement with the CPL for optically thin clouds when CTPs < 500 hPa were retrieved. The MCO2AT also has the best performance for all upper transmissive clouds that are in single- and multilayered conditions. It yields mean CTHs that exceed the mean SCO2AT CTH by ∼1 km and, thus, is 40% less biased than the SCO2AT. The new VISST produces more accurate CTHs for the tropical upper tropospheric clouds compared to the old VISST. The new correction for adjusting cloud effective height Zeff in the old VISST to CTH employed in the new VISST algorithm produced the best agreement with the CPL for optically thick clouds. The new ozone correction and new ice crystal models, also employed in the new VISST, increased the detection of upper tropospheric transmissive clouds. Overall, the new VISST algorithm enhanced the cirrus cloud detection by more than 5% compared to the old VISST algorithm. The overall correction in the new VISST CTHs yielded a nearly unbiased result for optically thick clouds.

[38] The evaluations of the four satellite techniques are important because the old VISST and MCO2AT algorithms are currently operating together to provide satellite-retrieved cloud property products at LaRC for single- and multilayered clouds. The new VISST algorithm is expected to improve those cloud products. In comparisons with the CPL CTHs, the mean CTH biases with the MCO2AT are smaller by a factor of ∼1.7 than those with the SCO2AT whereas the mean biases for the new VISST are smaller by a factor of ∼1.6 than those for the old VISST. Overall, the CPL retrieved ∼89% of the data with CTPs < 500 hPa whereas the SCO2AT, MCO2AT, old VISST, and new VISST retrieved 76, 76, 69, and 74% of those, respectively. When both the CPL and CO2ATs retrieved CTPs < 500 hPa, the mean CTHs from the CPL, SCO2AT and MCO2AT are 14.2 ± 2.1, 10.7 ± 2.1, and 12.1 ± 1.6 km and their associated mean CTHs from the old and new VISSTs are 9.7 ± 2.9 and 11.4 ± 2.8 km, respectively. These results are encouraging when one considers the large percentages of semi-transparent upper tropospheric clouds found during TC4. Although the MCO2AT CTHs are generally in better agreement with the CPL data, a mean bias of −2.1 km in MCO2AT CTHs found here for the TC4 tropical clouds is twice as large as the mean bias of about −1 km shown by Chang et al. [2010] who evaluated the MCO2AT-inferred CTHs for midlatitude clouds between 20°N–55°N. The larger mean bias found here is likely owing to the high occurrences of very optically thin cirrus clouds during TC4. However, both studies show that the MCO2AT-inferred CTHs are on average ∼1.4 km higher than the SCO2AT-inferred CTHs.

[39] As demonstrated in this study, the main cause of the CTH biases in all four satellite techniques applied to the GOES-12 imager data is associated with the semi-transparencies of tropical upper tropospheric clouds. Their retrieval biases increased progressively as the cloud effective emissivity decreased below about 0.5. Further analysis on multilayered clouds also showed that the mean CTH biases increased from single-layered cases to multilayered cases in all four techniques. However, larger uncertainties were still associated mainly with upper transmissive clouds having emissivities less than ∼0.5. It was found that the mean biases increased with increasing number of cloud layers because the multilayered clouds were associated with more upper transmissive cloud layers.

[40] From the perspective that the MCO2AT uses only the infrared data at 10.8- and 13.3-μm channels, the technique can be applied equally for daytime and nighttime observations and is applicable to the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on Meteosat-8 and -9, the Moderate-resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua, and the upcoming GOES-R imager series [Schmit et al., 2005]. The new VISST algorithm can be further improved using the MCO2AT. Another application of the MCO2AT is for multilayer cloud retrieval as shown by Chang and Li [2005]. The MCO2AT in conjunction with the new VISST has recently been developed for an integrated multilayer cloud retrieval algorithm as illustrated by Minnis et al. (submitted manuscript, 2010a). Future work requires more validation studies for more assessment of the MCO2AT, the new VISST, and the multilayer retrieval technique.


[41] This research was supported by the NASA Radiation Science Program TC4 project, the NASA Applied Sciences Program, the Department of Energy Atmospheric Radiation Measurement Program through interagency agreement DE-AI02-07ER64546, and the NOAA Center for Satellite Applications and Research GOES-R program.