EVALUATION METHOD FOR SATELLITE RETRIEVED LIQUID WATER CLOUD PROPERTIES USING SENSOR SYNERGY

Transcription

1 EVALUATION METHOD FOR SATELLITE RETRIEVED LIQUID WATER CLOUD PROPERTIES USING SENSOR SYNERGY S. Placidi 1, C.L. Brandau 1, D.P. Donovan 2, H.W.J. Russchenberg 1, R.A. Roebeling 2 1 Delft University of Technology, TUD, Delft, The Netherlands 2 Royal Netherlands Meteorological Institute, KNMI, De Bilt, The Netherlands S.Placidi@tudelft.nl Abstract In this work, we describe a new approach for the evaluation of satellite cloud property retrievals making use of ground-based measurements and simulated satellite reflectances. The synergy of groundbased measurements obtained by the cloud radar, the ceilometer and the microwave radiometer, is used to retrieve cloud microphysical properties of the observed cloud. These cloud properties are used to generated the cloud field that is input into the simulator for generating the synthetic satellite observations for different visible and near-infrared channels. The simulator is also used to simulate the global irradiances at the surface in order to obtain a radiation closure with the Pyranometer measurements. Moreover, a radar check is also carried on in order to see if the simulated radar reflectivity of the synthetic realistic cloud correspond to the measured one. The radar check and the radiation closure will define the correct retrievals of the cloud properties from the ground-based observations. The comparison of the satellite retrieved cloud properties with the original ground-based indipendently retrieved cloud properties is used to assess the accuracy of the satellite retrievals. The measured radar reflectivity has a mean value of dbz and standard deviation of 4.13 dbz while the same figures for the simulated radar reflectivity are dbz and 3.79 dbz. The radiation closure, as well, shows a very good correlation of 0.91 between the radiative transfer model surface irradiances output and the pyranometer measured ones. The positive results from the radiation closure and the radar reflectivity comparison show that this method can be used to indipendently validate the satellite cloud property retrievals with a good consistency.the main advantage of this evaluation method is the removal of the error sources related to the two different observation techniques appearing when comparing low spatial resolution satellite measurements with ground-based profiling sensor observations. INTRODUCTION The microphysical and optical properties of liquid water clouds are essential parameters for the quantification of the surface radiation budget since they have strong impact on the absorption and scattering processes within the clouds. Modern meteorological and climate models treat clouds in a too simple way due to the large variability in the relevant cloud properties at small and large scales. Groundbased remote sensing enables continuous and long-term detailed observations of the cloud-radiation interactions on a regional scale. Conversely, satellite observations provides observations of the interactions of clouds with radiation on a larger global scale. Accurate observations of cloud properties and their spatial and temporal variations are hence required to overcome these uncertainties. Satellite observations can provide long-term and large scale observations of the cloud properties, however, they need to be integrated with ground-based observations in order to provide detailed and accurate information on the cloud properties. In fact, the cloud properties are retrieved from the satellite measurements of radiances of the clouds by means of a cloud model with several assumptions and accordingly to the Nakajima and King s method that uses the visible and near-infrared channels to retrieve information about the optical thickness and the effective radius of the clouds (Nakajima and King, 1990). 1

2 Figure 1: Schematic of the methodology used in this work. These cloud properties can be validated by comparisons with in-situ measurements or with groundbased measurements. These validations of satellite cloud products are contaminated by errors related to the retrieved quantities themselves as well as to the validation method. When comparing groundbased cloud properties with the ones retrieved from satellite observations, there are two types of issue: scale differences and geometry effect (Greuell and Roebeling, 2009). The differences in the observation scale arise because the ground-based cloud radar, the microwave radiometer (MWR) and the lidar ceilometer vertically profile the atmosphere with a very narrow beam around nadir. The fields of view of the profiling ground-based instruments then is tipically much smaller than the satellite pixel. The geometry effect, called parallax, describes the displacement of a cloud seen by the satellite with an oblique angle with respect to the ground-based instruments that have the cloud right above them. Different techniques have been introduced in order to cope with these issues, such as time-integration of the ground-based measurements in order to represent the satellite pixel and a correction of the groundbased instrument location on the satellite image. However, these two error sources can still lead to errors due to the fact that other factors play a role in the comparison of the ground-based with satellite measurements, for example the wind direction and speed moving the cloud and if the ground-based sensors are not located to the centre of the pixel but more on the sides. Another relevant difference is related to the horizontal homogeneity of the cloud being observed, if the cloud is smaller than the satellite pixel the satellite will pick up also reflectances from the ground, while the ground-based instruments only measure the cloud above them. To overcome these issues and remove them from the algorithm validation, we propose a method to evaluate the satellite retrievals using ground-based measurements and the European Space Agency EarthCARE mission SIMulator (ECSIM). The use of the EarthCARE SIMulator makes possible to compare the exact retrievals of the cloud properties without being biased by the different observation scales and the parallax effect. In the following chapters, first, the methodology followed in this work along with an overview of the measured data and of the EarthCARE SIMulator are described. Secondly, the radar and the radiation closure along with the comparison results are shown. The concluding chapter gives a summary and describes the work to be performed in the future. METHODOLOGY This chapter describes the methodology followed in the work. A schematic of the procedure can be seen in figure 1. This method uses ground-based measurements from cloud radar, lidar ceilometer and microwave radiometer along with a cloud model to infer the microphysical and optical properties of the cloud being observed. The retrieved cloud properties, liquid water content LW C and effective radius R eff, are then used to create an equivalent synthetic realistic cloud to use in the EarthCARE Simulator for the radar and radiation transfer calculations. The output of the radiative transfer calcula- 2

3 tions for the broadband shortwave channel is then compared to the broadband shortwave Pyranometer measurements at the surface in order to obtain a radiation closure experiment. Furthermore, the radiative transfer calculations compute also the narrowband visible, near-infrared and thermal infrared used for the satellite retrievals of cloud properties. The satellite retrievals can then be compared to the cloud properties obtained from the ground-based measurements to evaluate the satellite retrieval algorithm. Moreover, as a further qualitative check on the retrieved cloud properties, also the radar forward model is run on the cloud scene and the output reflectivity is compared to the measured reflectivity. The radiation closure and the radar reflectivity comparison indicate that the retrieved parameters are well describing the measured cloud. The retrieved cloud properties, such as the effective radius R eff, the extinction ext, the liquid water content LW C, the cloud boundaries H, the droplet number concentration N and the optical thickness COT, are obtained from the measurements of the cloud radar reflectivity and the microwave radiometer liquid water path. A full description of the technique used in this work to obtain the cloud properties from a synergy of ground-based instruments can be found in Brandau et al., 2010 and Brandau et al., submitted to Jour. of Atmospheric Measurement Techniques. In order to show the capability and the potentiality of this method we use a real study case observed by the ARM Mobile Facility (AMF) during the COPS campaign in 2007 in the Black Forest in Germany. The study case refers to October 24th, 2007 from to UTC. For this case the Sun zenith angle varied from 60 to 75 degrees, while the Sun azimuth angle from 185 to 235 degrees. The atmosphere status during the case resembled the Mid-Latitude Winter atmosphere with a slightly higher amount of water vapour of 1.2 cm. The surface used for the radiative transfer calculations is assumed flat with a wavelenght-dependent albedo referring to low-moderate vegetation. Figure 2: Atmospheric status during the selected study case The Ground-based Observations Figure 2 shows the status of the atmosphere measured by the ground-based instruments. It can be seen an horizontally homogeneous stratocumulus cloud field with Liquid Water Path LW P bounded between 50 and 150 g/m 2 and optical depth between 15 and 45. The solar irradiance at surface mostly follows the Sun position and starts at about 70 W/m 2 and ends at about 0 W/m 2, with a pick at about 120 W/m 2. Because of the shadowing effect of the mountains around the remote sensing site, the data 3

4 for the radiation closure and for the satellite retrieved cloud properties comparisons have been used only until 14:50 UTC. The bottom plot of figure 2 depicts the retrieved effective radius during the entire duration of the observation. The Simulations The EarthCARE Simulator - ECSIM The EarthCARE simulator - ECSIM is a computational tool which can simulate the complete Earth- CARE mission (Voors et al, 2007). This simulator uses several forward and retrieval models, utility programs and plotting tools to simulate and visualize what the EarthCARE measurements and retrievals would be (Figure 3). ECSIM can simulate all the 4 instruments aboard the EarthCARE satellite, such as the 94-GHz cloud profiling radar, the high spectral resolution lidar at 353 nm, the multispectral imager and the broad-band radiometer. Its use is rather straitghforward: given a cloud scene as input and chosen the instruments to simulate, ECSIM runs the selected models and simulations and gives netcdf file outputs for every chosen instrument for further data analysis. The cloud scenes, as input for the simulations, can be created using the embedded ECSIM cloud generator or they can be converted from Cloud Resolving Models, from Large Eddy Simulation or from real ground-based observations to ECSIM standard input cloud scene. The main idea behind the simulator is to be able to reproduce the properties of the input atmosphere making a comparison between the synthetic observations and the retrievals with the original properties. Figure 3: Schematic of the EarthCARE SIMulator The satellite simulations and retrievals ECSIM, by mean of two radiative transfer models, can simulate the seven narrowbad shortwave and longwave channels of the Multi Spectral Imager (0.660, 0.865, 1.61, 2.20, 8.8, 10.8, 12.0 µm) as well as the two broadband channel of the BroadBand Radiometer (0.2-4, µm). The original code of the simulator has been modified in order to produce also the surface irradiances and the Top-of- Atmosphere (TOA) upward fluxes for narrow- and broad-bands. The shortwave narrow- and broad-band calculations are computed using the shortwave model which allows to work with two different radiative transfer models, mono-dimensional and tridimensional. The radiative transfer models used for the work described in this paper is the mono-dimensional Indipendent Column Approximation radiative transfer model with the DISORT solver. After the simulations, the narrowbands BRDFs, together with the Sun-satellite geometry and other auxiliary data from the simulations, are used as input for the Cloud Physical Properties (CPP) retrieval algorithm (Roebeling et al., 2006). The CPP retrieval algorithm has been developed at the Royal Nether- 4

5 (a) ECSIM LWC (b) ECSIM Reff Figure 4: LWC and R eff of the reconstructed ECSIM scene lands Meteorological Institute (KNMI) as part of the EUMETSAT Climate Monitoring Satellite Application Facility. This algorithm retrieves COT and R eff in an iterative manner, by comparing satellite observed or simulated reflectances at visible (0.6µm) and near-infrared (1.6µm) to the reflectances simulated with the Doubling Adding KNMI - DAK radiative transfer model. The DAK radiative transfer model (De Haan et al. 1987; Stammes 2001) is used to simulate reflectances for plane-parallel clouds embedded in a midlatitude summer atmosphere. The underlying surface is assumed to be Lambertian, for which the reflectances were obtained from MODIS white-sky albedo data. The vertical distribution of the assumed spherical cloud droplets is parameterized in terms of the effective radius, using a modified gamma distribution with an effective variance of The cloud reflectances for the Look-Up Tables are simulated at 0.6, 0.8, 1.6, 2.1, 3.8 µm, for optical thicknesses between 0 and 256 and droplet effective radii between 1 and 24 µm. The LWP, assuming a fixed vertical profile for liquid water content, is computed from the COT and R eff using the following formula: LWP = 2 3 COT R eff ρ (1) Furthermore, the cloud geometrical thickness H and the cloud droplet number concentration N are retrieved according to the quasi-adiabatic cloud model described in Boers et al., This model parameterizes the vertical variation of the cloud microphysical and optical properties. The essential point of the cloud model is that COT and R eff are explicit functions of N and H. The algorithm contains implicit assumptions about the nature of the basic thermodynamic and microphysical points that introduce uncertainties in the retrieved properties. However, referring to previous studies (Boers et al., 2006), the sub-adiabatic character of the cloud is the only variable parameter that gives the largest uncertainties. In this work, the sub-adiabatic factor used in the satellite retrievals correspond to the one retrieved from the ground-based measurements. Figures 4(a) and 4(b) show the profiles of the synthetic LWC and R eff of the recreated scene in ECSIM and used for the radar and radiation simulations. THE RADAR COMPARISON AND THE RADIATION CLOSURE The radiation closure along with the radar comparison will determine the accuracy with which the cloud microphysics are retrieved from the ground-based observations. Figure 5(a) shows the radiation closure results. The upper plot shows the scatter plot for the surface irradiance versus the ECSIM simulated one. The middle plot describes the histogram of the differences between ECSIM minus the measured irradiances at surface. The lower plot show the trends in time of the two surface irradiances. From all the three plots, it can be clearly seen that the radiation closure shows a very good correlation of 0.92, even if a bias of about 15 W/m 2 is present. The trend of the radiation reaching the surface can be accurately represented in the radiative transfer calculations. 5

6 (a) Radiation Closure with Pyranometer measurements (b) Radar closure with ground-based 94GHz radar observations Figure 5: Radiation closure and radar comparison The comparison of the radar measurements with the simulated radar shows that also the radar reflectivity can be simulated correctly and with high accuracy. In figure 5(b) the measured radar image on the bottom plot can be reproduced by the ECSIM radar simulations on the top two plots. The averaged measured radar reflectivity is about dbz with a standard deviation of 4.13 dbz, while for the simulated radar reflectivity the averaged on is dbz with 3.79 dbz of standard deviation. COMPARISON OF THE RETRIEVED CLOUD PROPERTIES The radiative transfer model in ECSIM computes the Top-of-Atmosphere narrowband BRDF s to be used for the satellite cloud property retrievals. The visible channel at 0.6 µm and the near-infrared at 1.61 µm are used to retrieve the cloud properties with the KNMI-CPP algorithm. These satellite retrieved cloud properties are then compared to the ground-based retrieved ones. Figure 6 shows the five scatter plots relative to the retrieved cloud optical and microphysical properties. The overall behaviour of the retrievals show a good correlation with the presence of some bias for all the cloud properties. This bias can be explained referring to the about 15 W/m 2 bias seen in the radiation closure. From the CPP retrieved optical thickness, it can be concluded that the cloud is optically thick and this is consistent with the low simulated surface irradiances in the radiation closure. Consequently, higher optical thickness with correct effective radius generates also higher liquid water path retrieved using formula 1, as seen in the lower plot of figure 6. The plot for the comparison of the effective radius show that the averaged effective radius has a better correlation with the satellite retrieved ones, instead of the maximum effective radius in the profile. The geometrical thickness and the cloud droplet number concentration are also well retrieved. The geometrical thickness from the ground is retrieved with discrete values resulting in the mid-left plot. Overall, there is very good correlation between the ground-based and the satellite retrievals, assessing that the CPP algorithm works correctly. SUMMARY AND FUTURE WORKS This work shows a novel method to evaluate satellite cloud property retrieval algorithms avoiding the problematic issues of the different observation scales and geometry effects caused by the completely diverse observation methods. We show the potentiality of this method to evaluate the cloud properties retrieved from narrowband channels using a synergy of indipendent ground-based sensors 6

7 Figure 6: Comparison between the ground-based retrieved and simulated cloud properties and the ESA EarthCARE Simulator. The comparison between the ground-based and the satellite retrievals highlights the robustness of this method as well as the capacity of the KNMI-CPP in retrieving the properties of the original clouds. According to the comparison results, the cloud droplet number concentration and the geometrical thickness retrievals are also working fine as long as the cloud subadiabatic fraction is correctly set for the cloud cases. Future work will focus on removing the bias from the radiation closure comparison in order to be consistent with the observations. The bias removal will be investigated using the Monte Carlo 3-D radiative transfer model in order to be as close as possible to the real observations of the radiation at surface. Future works will also focus on the differences in the TOA and surface fluxes generated when using an effective radius profile with one vertically homogeneous averaged value or with the real effective radius profile changing in height. The use of different effective radius profiles will be studied with respect to the satellite cloud property retrievals to see if the assumption of having one single effective radius averaged in height is consistent with the ground-based observations. ACKNOWLEDGEMENTS The data were obtained from the Atmospheric Radiation Measurement (ARM) program sponsored by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Climate and Environmental Sciences Divisions. The cloud boundaries and reflectivty data from the ground-based measurements are WACR_ARSCL (W-band ARM Cloud Radar-Active Remote Sensing of Clouds) value added products from Kollias et al., REFERENCES Boers, R., J. R. Acarreta, and J. L. Gras (2006), Satellite Monitoring of the First Indirect Aerosol Effect: Retrieval of Cloud Droplet Concentration, J. Geophys. Res, 111 7

8 Brandau, C.L., H.W.J. Russchenberg, W.H. Knap, (2010): Evaluation of ground based remotely sensed liquid water cloud properties using shortwave radiation measurements, Atmospheric Research, 96, Brandau, C.L., P.Wang, W.H. Knap, H.W.J. Russchenberg, ( ), The impact of horizontal and vertical cloud inhomogeneities on a shortwave radiation closure experiment of a water cloud case study, submitted to Jour. of Atmospheric Measurement Techniques De Haan, J. F., P. Bosma, and J. W. Hovenier (1987), The adding method for multiple scattering calculations of polarized light,j. Astron.Astrophys., 183, Donovan D.P., Voors, R.H., van Zadelhoff, G.-J. and Acarreta, J.R., 2008: ECSIM Models and Algorithms Document Greuell, W. and Roebeling, R.A., (2009), Toward a standard procedure for validation of satellitederived cloud liquid water path: a study with SEVIRI data, J. Appl. Meteorol. Clim., 48, Kollias, P., Clothiaux, E. E., Miller, M. A., Luke, E. P., Johnso,n K. L., Moran, K. P., Widener, K. B., and Albrecht B.A., (2007): The Atmospheric Radiation Measurement Program Cloud Profiling Radars: Second-Generation Sampling Strategies, Processing, and Cloud Data Products. J. Atmos. Oceanic Technol., 24 Nakajima, T. and M.D.King (1990), Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements.part I.Theory, J.Atmos.Sci.,47, Roebeling, R.A., A.J. Feijt, and P. Stammes (2006), Cloud property retrievals for climate monitoring: implications of differences between SEVIRI on METEOSAT-8 and AVHRR on NOAA-17, J. Geophys. Res., 11 Roebeling, R.A., H.M. Deneke and A.J. Feijt, (2008) Validation of cloud liquid water path retrievals from SEVIRI using one year of CLOUDNET observations, J. Appl. Meteor., 47, Roebeling, R.A., S. Placidi, D.P.Donovan, H.W.J.Russchenberg and A.J.Feijt, (2008), Validation of liquid property retrievals from SEVIRI using ground-based observations, Geophys. Res. Lett., 35 Schüller, L., J. L. Brenguier, and H. Pawlowska (2003), Retrieval of microphysical, geometrical, and radiative properties of marine stratocumulus from remote sensing, J. Geophys. Res., 108, 8631 Szczodrak, M., P.H. Austin, and P. Krummel (2001), Variability of optical depth and effective radius in marine stratocumulus clouds. J. Atmos. Sci., 58, Stammes, P. (2001), Spectral radiance modeling in the UV-Visible range.irs 2000: Current problems in Atmospheric Radiation, edited by W.L.Smith and Y.M. Timofeyev, , A. Deepak Publ., Hampton, Va. Voors, R., Donovan D.P., Acarreta J.R., Eisinger M., Franco R., Lajas D., Moyano R., Pirondini F., Ramos J., and Wehr T., 2007: ECSIM: the simulator framework for EarthCARE, Proc. SPIE