3.1. MM5-NOAH land surface model configuration
The Fifth-Generation NCAR/Penn State Mesoscale Model MM5 is the latest in a series developed from a mesoscale model used at Penn State in the early 1970s that was later documented by Anthes and Warner (1978). Particular attention has been given to the non-hydrostatic dynamics, multi nesting capability and four dimensional data assimilation. Although mesoscale model users are being encouraged to move on to the next generation mesoscale model, the Weather Research and Forecasting system (WRF) (Skamarock et al., 2005), the MM5 system is still a popular choice due in part to its performance, availability and stability (see for instance Liguori et al., 2009, 2011). Mesoscale models in general and MM5 in particular are often used for three key applications: (1) regional climate simulations, (2) numerical weather prediction, and, (3) air quality prediction. However, the MM5 can be coupled with a Land surface model, and one of the main prognostic parameters is the volumetric soil moisture vsm. The MM5 is a grid based model, using the finite differencing method to resolve the model dynamics at different pressure levels. Detailed information of the MM5 dynamics and its integration can be found in the MM5 online tutorial by Dudhia et al. (2005).
The original LSM was developed at the Oregon State University (OSU) by Pan and Mahrt (1987). It is based on the coupling of the diurnally dependent Penman potential evaporation approach of Mahrt and Ek (1984), the multilayer soil model of Mahrt and Pan (1984), and the primitive canopy model of Pan and Mahrt (1987). It has been modified by Chen et al. (1996) to include an explicit canopy resistance formulation used by Jacquemin and Noilhan (1990) and a surface runoff scheme of Schaake et al. (1996). The NOAH LSM has benefited from a series of improvements particularly in increasing the soil layers from two to four. Hence, it is widely adopted by the National Centres for Environmental Prediction (NCEP) and showed an adequate performance in the NCEP coupled Eta Model. This is one of the reasons why the NOAH LSM was selected to be implemented in the MM5 model besides its moderate complexity (Chen and Dudhia, 2001). The coupled MM5-NOAH LSM model has a vertical soil profile with a total depth of 2 m below the surface and it is partitioned into four soil layers with lower boundaries at 10, 40, 100 and 200 cm below the surface (Figure 2). The root zone is fixed at 100 cm (i.e. including the top three soil layers). Thus, the lower 100 cm of soil layer acts as a reservoir with gravity drainage at the bottom. The MM5-NOAH LSM has one canopy layer and one snow layer and has the following prognostic variables: soil moisture and soil temperature in the soil layers, canopy moisture, snow height, and surface and ground runoff accumulation. Evapotranspiration is handled by using soil and vegetation types. The vegetation characteristics of each grid of the model are represented by the dominant vegetation type of that grid because the model horizontal grid resolution is larger than 1 km × 1 km.
The soil thermal properties depend on the soil type. The soil water movement and flow between the soil layers is governed by the mass conservation law and the diffusivity form of Richards' equation (Chen and Dudhia, 2001) as follows:
where θ is the volumetric soil water content, D is the soil water diffusivity (m2 s−1) and K is the hydraulic conductivity (m s−1) and both are functions of θ; t is time (s) and z is the soil layer depth (m); and Fθ is represent sources and sinks for soil water (i.e., precipitation, evaporation and runoff). D and K are highly nonlinearly dependent on the soil moisture (Chen and Dudhia, 2001) and in particular when the soil is dry, they can change several orders of magnitude even for a small variation in soil moisture. By expanding and integrating Equation ((1)) (Chen and Dudhia, 2001) over four soil layers, the following layers are produced:
where, is the soil layer thickness (for layers 1–4 respectively); Pd is the precipitation not intercepted by the canopy; is the canopy transpiration taken by the canopy root within the root zone layers (the root zone is up to three layers in the coupled MM5-LSM); and Edir is the direct evaporation from the top surface soil layer. A conceptual parameterization for the sub-grid treatment of precipitation and soil moisture is governed by the infiltration. The heat transfer through the soil vertical profile is governed by the thermal diffusion equation. A single linearized surface energy budget equation is applied to determine the surface temperature to reflect a linearly combined ground-vegetation surface (Chen and Dudhia, 2001; Chen et al., 2010). The surface runoff is calculated using the Simple Water Balance (SWB) technique. A detailed description of the MM5-NOAH LSM can be found in (Chen and Dudhia, 2001).
For the purposes of this study, the MM5 model was set up to have three domains (D1, D2 and D3) with grid resolutions of 108 km for the outside domain, 36 km for the middle domain and 12 km for the inner domain. The innermost domain D3 is able to capture the local scale features of the study area. The MM5 domains are nested with two-way interaction, in which the boundary conditions for the finer grid are generated from the coarse grid model results while the fine grid model results update the variables on the coarse grid (Dudhia et al., 2005/MM5 tutorial). In addition, the standard nesting ratio used by MM5 in every time step is 3:1, in which each domain takes information from the mother domain. For each mother domain time step, the domain runs three time steps before feeding back information to the mother domain. Hence, nested domains feeding back to each other can lead to improved model behaviour at the boundaries. In order to mitigate the spatial distortion associated with the map projection applied, the domains are positioned in such a way that the Brue catchment is located at the centre of all three domains (see Figure 3).
Figure 3. Configuration of the MM5-NOAH LSM domains D2 and D3 to produce a 12 km resolution (D3) soil moisture over the Brue
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The model is configured to have 23 vertical levels with thinner layers near the ground surface (the model top is at 100 hPa) and a dimensionless quantity σ is used to define the model's vertical levels as it is shown in the following equation:
where p0 is the reference-state pressure, pt is a specified constant top pressure, and ps0 is the reference-state surface pressure.
Four Dimensional Data Assimilation (FDDA) is used in which a full-physics of the model is run while incorporating observations/re-analysis. In this way a dynamical consistency would be assured while the observations keep the model close to the true conditions (Stauffer and Seaman, 1990). Newtonian-relaxation or nudging is the technique used by the MM5 model in such data assimilation scheme. In this technique, an artificial tendency term based on the difference between the model state and the observed state is added to the prognostic equations for a particular variable such as wind, temperature and water vapour in order to relax the model state towards the observed state or a given analysis. For this study, the grid nudging is implemented through feeding the model in its standard input format with the given re-analysis on the model grid over the data assimilation period. Thus, the model relaxes its solution towards the re-analysis data. The Nudging Factor Gα (where α represent a particular variable) determines the relative magnitude of the term that is added into the variable prognostic equation. In this study, three dimensional analysis nudging is implemented for the wind and temperatures fields and their nudging factor values are selected according to the study done by Stauffer and Seaman (1990).
The cumulus parameterization scheme used in this study for the MM5 model was the Kain–Fritsch (KF), which is based on relaxation to a profile and predicts both updraft and downdraft properties and also detrains cloud and precipitation. This cumulus parameterization scheme uses a sophisticated cloud-mixing scheme in order to determine entrainment/detrainment. More details can be found in Kain and Fritsch (1993). For the Planetary Boundary Layer PBL, the MRF or Hong-Pan scheme was the selected option due to its suitability for high resolution in PBL. This scheme was implemented in the NCEP MRF model (see Hong and Pan, 1996 for more details). Mixed-Phase was the selected scheme that dealt with the microphysics of the model. This scheme adds supercooled water to cloud and rain water field that predicted explicitly the microphysical processes. The NOAH LSM was used to retrieve the surface parameters and in particular soil moisture (see Chen and Dudhia, 2001).
3.2. MM5-NOAH LSM soil moisture estimation
Numerical experiments were conducted in this study for several events in 2004–2006 to predict a continuous time series of the soil moisture for three soil layers. The ECMWF/Era-interim/reanalysis data with a spatial resolution of 1.5° × 1.5° and a temporal resolution of 6 h was used in this study as initial and lateral boundary conditions. The boundary conditions are fed on to the coarsest domain which has a comparable resolution (108 km) and it is then dynamically downscaled all the way down, from 108 km for domain 1–36 km and 12 km for domains 2 and 3 respectively. The smallest resolution of 12 km is consistent with the area covered by the Brue catchment area. The classification of vegetation by the U.S Geological Survey (USGS) is adopted in the MM5-NOAH LSM to define the vegetation types that cover the study area, whereas the soil types are defined by the Food and Agriculture Organisation (FAO) database. Default values of the model parameters such as the soil and vegetation parameters were selected in this study. All simulations are conducted from 0000 UTC 1 January 2004 to 0000 UTC 31 December 2004 for the first simulation and similarly for the follow-on simulations using 2005 and 2006 data. The model output was retrieved at hourly intervals. As a result, 3 year hourly time series of three soil layers (with layer thicknesses of 10, 30, 60 cm) of soil moisture values are estimated from the three MM5-NOAH LSM domains. The innermost domain (domain 3) soil moisture was adopted in this study (see Figure 4(c)), as domain 3 has the highest and required spatial resolution of 12 km, which has a similar area to the Brue catchment.
Figure 4. Brue catchment (2004–2005): (a) evapotranspiration (ET), (b) precipitation (c) volumetric soil moisture (VSM) from the MM5 LSM for three single layers with soil depths: surface layer (0–10 cm), second layer (10–40 cm) and third layer (40–100 cm); and (d) VSM from the MM5 LSM combining several layers; (e) AMSRE satellite soil moisture time series
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A three-level soil layer configuration was adopted in the coupled MM5-NOAH LSM in this study in order to capture the daily, weekly and seasonal evolution of soil moisture and mitigating the possible truncation error in discretization (Sridhar et al., 2002). The combination of soil moisture over several layers was adopted here to take into account the entire depth of the soil column when using the water balance changing in the storage to assess the estimated soil moisture from the NOAH LSM. It is assumed that the soil is homogenous and has no significant variations in its characteristics. Hence, two cases of layer combinations were produced. Firstly, the soil moisture was combined over the first two layers (first and second) of the NOAH LSM where the soil column depth would be 40 cm. Secondly, the soil moisture over the three first layers (first, second and third) was combined with a total soil column depth of 100 cm. The soil moisture in the combined layers is computed by:
where θ is the original soil moisture estimation from the LSM, z is the soil layer depth, and the subscripts (1, 2 or 3) indicate the soil layer indices.
For comparison purposes of the MM5-NOAH LSM soil moisture assessment against the retrieved soil moisture from satellite, a third set of numerical experiments was conducted for some selected events in 2006. The same simulation settings were used for the 2004, 2005 and 2006 simulation experiments. A statistical t-test was conducted to examine the significance of the linear correlation between the changing in the water storage and the changes in the MM5-NOAH LSM soil moisture. In such a test, the p-value, which measures the strength of evidence, is computed. A value of 0.05 was adopted as the significance level. If the computed p-value is below the significance level then there is a significant linear relationship between the change in the storage and change in the estimated soil moisture from the MM5-NOAH LSM, otherwise there is no statistical significance. The t-test results are presented later in this paper.
3.3. AMSR-E soil moisture estimation
The explanation of estimation of near surface soil moisture from the AMSR-E satellite is summarized in this section. It is important to mention that a detailed description of the developed methodology to estimate soil moisture from satellite can be found in Al-Shrafany et al. (2012). The AMSR-E instrument is a passive microwave radiometer that makes measurements of thermal radiation from the land surface in the centimetre wave band at ascending (1300) and descending (0130) passes (Owe et al., 2001; Njoku et al., 2003). The AMSR-E measurements from the descending pass were considered in this study. This is due to the reasonable uniform temperature and moisture profiles at night times, while in the daytime soil moisture estimates may reflect the effects of diurnal surface layer drying. The techniques adopted for soil moisture retrieval provided spatially averaged soil moisture data, which is ideal for environmental and hydrological modelling and monitoring. Such spatially averaged area data sets are logistically and economically difficult to obtain through traditional in situ measurement techniques. The technique only uses the horizontal and vertical polarization brightness temperatures Tb at one frequency (6.9 GHz) observed by the AMSR-E in a descending mode. The use of lower frequencies (e.g. 6.9 GHz) allows a greater penetration depth and the measurements are less affected by the vegetation (Schmugge et al., 2002).
The AMSR-E satellite has a footprint size of 25 km at which all retrieval calculations are based on. With respect to the average soil and vegetation biophysical characteristics, a uniform footprint is assumed. Therefore, the surface soil moisture and vegetation optical depth are subsequently extracted as averaged footprint values. The soil and vegetation temperatures are assumed to be approximately equal in the use of the AMSR-E descending measurements as the temperature and soil profiles are reasonably uniform. Moreover, the effects of the atmospheric moisture and the multiple scattering in the vegetation layer are negligible due to the AMSR-E low frequency measurements (up to X-band, i.e. ∼10 GHz). The radiative transfer equation explains the relationship between surface parameters such as surface soil moisture, vegetation water content, surface temperature, and microwave brightness temperature (Tb) (Jackson et al., 1982; Njoku et al., 2003). It includes contributions from the soil, vegetation and atmosphere in the upwelling radiation from the land surface as observed by the instrument. The brightness temperatures at H and V polarizations are given by:
where the subscripts H and V refer to the horizontal and vertical polarizations respectively; Ts is the single surface temperature; es is the soil emissivity at H (esH) and V (esV) polarizations; ω is the vegetation single scattering albedo and Γ is the transmissivity. The contributions from soil and vegetation represented by the surface roughness and density of vegetation canopy respectively have significant effects on the soil reflectivity. Surface roughness reduces the sensitivity of emissivity to soil moisture variations, and thus reduces the range in measurable emissivity from dry to wet soil conditions (Wang, 1983). The statistical parameters that characterize the scale of roughness of a randomly rough surface are known as the h and Q parameters. The h-Q model developed by Wang and Choudhury (1980) was considered in Al-Shrafany et al. (2012) to account for the roughness effects when the soil moisture was retrieved from the AMSR-E.
The vegetation will absorb or scatter the radiation emanating from the soil, and it will also emit its own radiation. Generally speaking, the integral contribution of the surface roughness and vegetation canopy is more difficult to separate unless one of them is known a priori. An analytical approach developed by Meesters et al. (2005) is considered in Al-Shrafany et al. (2012) for calculating vegetation optical depth from the Microwave Polarization Difference Index (MPDI) and the dielectric constant of the soil. The MPDI effectively normalizes out the effects of the surface temperature, resulting in a quantity that is highly dependent on the soil moisture and vegetation. The MPDI is defined as:
Hence, the brightness temperatures are converted to volumetric soil moisture values with the Land Parameter Retrieval Model LPRM (Owe et al., 2008; Wang et al., 2009). The volumetric soil moisture vsm is retrieved from Equation ((11)) (after substituting in Equations ((9)) and ((10)) and taking into account of the roughness effect) as:
where esH and esV is the soil emissivity at H and V polarization respectively, τ is the vegetation optical depth. The h and Q parameters are empirically calibrated since the lowest frequency of the AMSR-E instrument is at 6.9 GHz, and its footprint scale is large which results in no data available to quantify the regional variability of the those parameters. Therefore, in order to estimate the optimal (h and Q) values for a particular catchment area, a new approach is proposed in Al-Shrafany et al. (2012) for this purpose. That approach used the event-based water balance approach in the context of catchment storage calculation. Hence, for a range of h and Q values, the volumetric soil moisture was retrieved from the AMSR-E using Equation ((12)) and taking into account the best correlation between changes in the water storage Δs (from the water balance using rainfall, runoff and evapotranspiration) and changes in the satellite soil moisture Δθ. The optimal values for the h and Q parameters were obtained when the best correlation between Δs and Δθ was achieved. Al-Shrafany et al. (2012) also showed that changes in the volumetric soil moisture were very sensitive to the selection of the h parameter, but less sensitive to the selection of the Q parameter. This method was considered as a potential technique to assess the retrieved soil moisture from the AMSR-E satellite for hydrological applications. In this current study, the same approach is also adopted to assess the estimated soil moisture from the three layers of the MM5-NOAH LSM. Therefore, a brief summary of the event-based water balance approach is presented in the next section.
3.4. Water balance as a proposed scheme
Water balance is a modelling framework for simplifying, describing and quantifying the hydrological water budget. It can be applied to a catchment area within a time interval (annual, monthly, weekly). Water balance is hydrologically driven by the variation in precipitation and temperature, besides some other local factors such as vegetation, soil and land use. A water balance can be used to help manage water supply and predict where there may be water shortages. It is also used in irrigation, runoff assessment (e.g. through the Rainfall-Runoff model), flood control and pollution control. In this study, the water balance is the basis of the proposed scheme and offers a preliminary validation tool in the hydrological and meteorological community for the retrieved soil moisture from the MM5-NOAH LSM. This scheme was developed due to the lack of ground in situ measurements of soil moisture in the UK and most other places around the world. The scheme can be used to calibrate and validate soil moisture estimations in large areas due to the abundance of hydrological data (rainfall and flow) such as the Brue catchment. The performance of this scheme is based on the correlation between the change in catchment water storage Δs and the corresponding change in soil moisture Δθ retrieved from the MM5-NOAH LSM approach calculated on an event basis.
The water balance equation is given by:
where P is the precipitation in mm, R is the runoff volume in mm, ET0 is the evapotranspiration in mm and Δs is the change in soil water storage in mm. The water balance takes into account the main hydrological processes taking place within the catchment. The evapotranspiration (ET0) is calculated with a method called the Penman Monteith equation recommended by the Food and Agriculture Organisation (FAO) (Allen et al., 1998) and it is used in this study to incorporate the impacts of the evapotranspiration in the water storage calculations. The FAO Penman–Monteith method requires the following meteorological data: solar radiation, air temperature, air humidity and wind speed, to derive the parameters for calculating ET0. The observed meteorological data for the Brue catchment were obtained from the British Atmospheric Data Centre BADC. The mathematical formulation of the Penman-Monteith equation and all the related calculation procedures can be found in the FAO report published by Allen et al. (1998).
The water balance application in this study is an event-based approach. The rainfall-runoff events have been chosen over a 3 year period (2004–2006) and for each selected event, the total runoff volume (both direct runoff and base flow) can be calculated using the measured flows. Several selected rainfall-flow events were used to assess soil moisture estimation from the MM5-NOAH LSM (see Tables 1 and 2). The change in the soil moisture Δθ is given by:
where Δθ is the change in the vsm, θ1 is the vsm before the event and θ2 is the vsm after the event. Assuming a sufficient number of rainfall-runoff events, the correlation between Δs and Δθ can be calculated. The following section explains in detail the result of this analysis.
Table 1. Results summary of Δs (change in the storage) and Δθ (changes in the MM5-NOAH LSM soil moisture estimation) over three soil layer depths for the Brue catchment in (2004 and 2005)
|Flow event||Duration||Total rain (mm)||Run off volume (mm)||Total ET (mm)||ΔS (mm)||Δθ (%) at single LSM layers||Δθ (%) at combined LSM layers|
|2004||Start date, time||End date, time|| ||(0–10) cm||(10–40) cm||(40–100) cm||(0–40) cm||(0–100) cm|
|January_1||6 January 0200||11 January 0000||27.8||16.6||4.2||7.0||0.75||0.75||1.50||1.30||1.65|
|January_2||11 January 0100||13 January 0100||28.4||16.1||4.2||8.1||1.00||1.00||0.75||0.67||0.93|
|February_1||1 February 0200||3 February 0100||14.7||11.2||3.5||0.03||0.00||0.00||0.00||0.13||0.13|
|February_2||6 February 0200||8 February 0200||31.9||20.5||4.4||7.0||0.50||0.75||1.75||0.57||0.98|
|March_1||11 March 0100||18 March 0200||22.2||4.5||4.2||13.5||1.25||2.00||1.75||1.65||1.60|
|March_2||18 March 0300||23 March 0200||14.6||4.9||7.4||2.3||0.75||1.25||1.75||0.96||1.15|
|April||21 April 0200||24 August 0200||13.8||5.2||7.4||1.2||0.25||0.25||0.75||0.21||0.60|
|May||4 May 0100||8 May 0200||34.8||11.8||10.3||12.7||1.50||1.25||0.50||1.01||0.85|
|June||22 June 0200||26 June 0000||33.4||1.4||15.7||16.3||1.50||2.00||0.75||1.52||1.38|
|July||7 July 0100||13 July 0200||37.4||3.7||19.4||14.3||2.25||2.00||2.25||1.72||2.48|
|August||2 August 0100||6 August 0000||25.5||0.9||14.8||9.8||0.25||0.50||0.00||0.62||0.27|
|September||17 September 2300||21 September 0000||4.8||0.8||10.1||− 6.1||0.00||0.00||0.75||− 0.45||0.82|
|October_1||2 October 0200||6 October 2300||31.2||4.4||8.1||18.7||1.00||2.00||1.25||1.68||1.52|
|October_2||13 October 0100||16 October 0000||11.5||4.9||4.7||1.9||0.50||0.75||1.25||0.61||1.02|
|November.||20 November 0200||23 November 2300||12.5||7.2||3.5||1.8||0.25||0.25||1.50||0.21||0.80|
|December||18 December 0300||21 December 0000||36.1||18.9||2.8||14.4||1.20||1.00||1.70||0.69||0.48|
|2005|| || || || || || || || || || || |
|January_1||10 January 0100||13 January 2300||11.0||7.2||3.5||0.3||0.75||0.50||0.75||0.48||0.85|
|January_2||22 January 0000||25 January 0200||10.0||6.7||2.7||0.6||0.25||0.25||0.00||0.22||0.10|
|February_1||5 February 0100||8 February 2300||22.8||8.9||3.7||10.2||0.75||0.75||0.25||0.80||0.10|
|February_2||10 February 0200||14 February 0100||24.4||10.7||4.8||8.9||1.00||1.25||1.75||1.22||1.60|
|March||29 March 0100||1 April 0100||33.5||8.3||4.7||20.5||1.75||2.50||2.75||2.23||2.65|
|April_1||17 April 0200||20 April 0200||23.4||6.3||6.7||10.4||1.50||1.50||1.50||1.27||1.50|
|April_2||26 April 0000||30 April 2300||14.5||7.3||7.7||− 0.5||0.25||− 0.25||− 1.00||− 0.45||− 0.70|
|May||19 May 2300||23 May 0000||36.5||4.8||9.7||22||2.00||2.75||1.50||1.89||1.78|
|June_1||5 June 0100||8 June 0100||14.1||4.2||7.6||2.3||0.75||0.75||0.50||0.64||0.60|
|June_2||24 June 0200||27 June 0200||19.3||5.5||11.3||2.5||0.25||− 0.50||0.00||− 0.18||− 0.35|
|July||24 July 0100||26 June 0200||19.2||0.9||11.9||6.4||0.75||1.75||0.50||1.12||0.93|
|August||13 August 0000||15 August 2300||15.8||0.6||10.5||4.7||0.50||0.25||0.00||0.26||0.15|
|September||10 September 0300||13 September 0000||8.8||0.4||9.6||− 1.2||0.50||0.25||0.25||0.62||0.43|
|October_1||12 October 0200||15 October 0100||19.0||1.6||5.7||11.7||1.25||1.50||0.75||1.25||0.73|
|October_2||29 October 0000||1 November 2300||15.6||5.0||7.9||2.7||0.75||1.00||0.50||0.66||0.63|
|November||6 November 0100||8 November 2300||24.9||15.6||4.6||4.7||1.00||0.50||1.25||0.47||1.00|
|December||1 December 0100||6 December 0200||65.6||42.0||4.5||19.1||1.25||1.25||2.00||1.53||1.70|
Table 2. Results summary of Δs (change in the storage) and Δθ (changes in the MM5-NOAH LSM soil moisture estimation) over three soil layer depths for the Brue catchment in (2006)
|Flow event||Duration||Total rain (mm)||Run off volume (mm)||Total ET (mm)||ΔS (mm)||Δθ (%) at single LSM layers||Δθ (%) at combined LSM layers|
|2006||Start date, time||End date, time|| ||(0–10) cm||(10–40) cm||(40–100) cm||(0–40) cm||(0–100) cm|
|January||30 December 2005 0000||5 January 2006 0200||33.6||16.8||3.8||13.0||0.38||0.41||0.56||0.39||0.49|
|February||14 February 0100||19 February 0200||26.2||11.8||3.4||11.0||1.00||0.31||1.34||0.48||0.99|
|March_1||7 March 0000||13 March 0200||30.8||14.4||4.6||11.8||0.85||0.76||− 1.47||0.78||− 0.57|
|March_2||26 March 0100||28 March 2300||8.4||4.1||4.0||0.3||− 0.50||− 0.15||0.71||− 0.24||0.33|
|April||29 March 2300||4 April 0200||21.4||7.7||6.9||6.8||− 0.25||0.04||− 0.03||− 0.03||− 0.03|
|May||21 May 0000||31 May 0200||65.8||21.4||10.2||34.2||1.25||1.58||2.11||1.49||1.86|
|June||25 June 0200||29 June 0100||19.2||1.7||12.1||5.4||− 0.50||− 0.41||− 0.29||− 0.43||− 0.35|
|July||5 July 0100||10 July 0000||39.0||1.8||14.6||22.6||0.35||0.88||− 1.42||0.75||− 0.55|
|August||28 August 0100||31 August 2300||20.6||1.3||13.3||6.0||− 1.00||− 0.33||− 0.08||− 0.49||− 0.25|
|September||28 September 2300||5 October 0200||27.6||2.5||9.9||15.2||1.00||1.80||1.60||1.60||1.60|
|October||19 October 0000||28 October 0200||77.8||28.1||6.4||43.3||3.25||4.34||6.57||4.07||5.57|
|November||23 October 0000||29 October 2300||51.0||33.7||4.2||13.1||1.25||0.99||3.87||1.05||2.74|
|December_1||3 December 0100||6 December 0000||15.2||9.3||3.3||2.6||0.75||0.47||0.89||0.54||0.75|
|December_2||10 December 0200||15 December 0200||18.3||14.3||2.9||1.1||0.00||0.51||0.00||0.38||0.15|