Jules R. Dim

Validation of Two MODIS Aerosols Algorithms with SKYNET and Prospects for Future Climate Satellites Such as the GCOM-C/SGLI

Potential improvements of aerosols algorithms for future climate-oriented satellites such as the coming Global Change Observation Mission Climate/Second generation Global Imager (GCOM-C/SGLI) are discussed based on a validation study of three years’ (2008–2010) daily aerosols properties, that is, the aerosol optical thickness (AOT) and the Angstrom exponent (AE) retrieved from two MODIS algorithms. The ground-truth data used for this validation study are aerosols measurements from 3 SKYNET ground sites. The results obtained show a good agreement between the ground-truth data AOT and that of one of the satellites’ algorithms, then a systematic overestimation (around 0.2) by the other satellites’ algorithm. The examination of the AE shows a clear underestimation (by around 0.2–0.3) by both satellites’ algorithms. The uncertainties explaining these ground-satellites’ algorithms discrepancies are examined: the cloud contamination affects differently the aerosols properties (AOT and AE) of both satellites’ algorithms due to the retrieval scale differences between these algorithms. The deviation of the real part of the refractive index values assumed by the satellites’ algorithms from that of the ground tends to decrease the accuracy of the AOT of both satellites’ algorithms. The asymmetry factor (AF) of the ground tends to increase the AE ground-satellites discrepancies as well.

Alternative Approach for Satellite Cloud Classification: Edge Gradient Application

Global atmospheric heat exchanges are highly dependent on the variation of cloud types and amounts. For a better understanding of these exchanges, an appropriate cloud type classification method is necessary. The present study proposes an alternative approach to the often used cloud optical and thermodynamic properties based classifications. This approach relies on the application of edge detection techniques on cloud top temperature (CTT) derived from global satellite maps. The gradient map obtained through these techniques is then used to distinguish various types of clouds. The edge detection techniques used are based on the idea that a pixel’s neighborhood contains information about its intensity. The variation of this intensity (gradient) offers the possibility to decompose the image into different cloud morphological features. High gradient areas would correspond to cumulus-like clouds, while low gradient areas would be associated with stratus-like clouds. Following the application of these principles, the results of the cloud classification obtained are evaluated against a common cloud classification method based on cloud optical properties’ variations. Relatively good matches between the two approaches are obtained. The best results are observed with high gradient clouds and the worst with low gradient clouds.

Performance of the GCOM-C/SGLI satellite prelaunch phase cloud properties’ algorithm

Abstract The performance of the cloud properties algorithm of the future Global Change Observation Mission-Climate/Second-Generation Global Imager (GCOM-C/SGLI) satellite is compared with that of a spectrally compatible sensor, the moderate resolution image spectroradiometer (MODIS). The results obtained are evaluated against the target accuracy of the GCOM-C/SGLI satellite mission. Three direct cloud parameters: the cloud optical thickness (COT), the cloud particle effective radius (CLER), and the cloud top temperature (CTT), and an indirect parameter: the cloud liquid water path (CLWP), are the cloud properties that are evaluated. The satellite–satellite comparison shows a good alignment between the retrievals of the GCOM-C/SGLI algorithm and those of MODIS in most of the areas and agreement with the accuracy targets of the new satellite mission. However, the COT comparison shows an increasing dispersion with the increase of the cloud thickness along the GCOM-C/SGLI-MODIS 1 ∶ 1 line. The CTT is systematically overestimated by the GCOM-C/SGLI (against MODIS), particularly in mid-thermal clouds. This is found to be due to an insufficient cloud emissivity correction of the thermal radiances by the GCOM-C/SGLI algorithm. The lowest COT, CLER, and CLWP accuracies, noticed in forest areas, are found to be related to the cloud detection uncertainty and the nonabsorption channel sensitivity differences.

Comparison between Satellite Water Vapour Observations and Atmospheric Models’ Predictions of the Upper Tropospheric Thermal Radiation

Atmospheric profiles (temperature, pressure, and humidity) are commonly used parameters for aerosols and cloud properties retrievals. In preparation of the launch of the Global Change Observation Mission-Climate/Second-Generation GLobal Imager (GCOM-C/SGLI) satellite, an evaluation study on the sensitivity of atmospheric models to variations of atmospheric conditions is conducted. In this evaluation, clear sky and above low clouds water vapour radiances of the upper troposphere obtained from satellite observations and those simulated by atmospheric models are compared. The models studied are the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) and the National Center for Environmental Protection/Department Of Energy (NCEP/DOE). The satellite observations are from the Terra/Moderate Resolution Imaging Spectroradiometer (Terra/MODIS) satellite. The simulations performed are obtained through a forward radiative transfer calculation procedure. The resulting radiances are transformed into the upper tropospheric brightness temperature (UTBT) and relative humidity (UTRH). The discrepancies between the simulated data and the observations are analyzed. These analyses show that both the NICAM and the NCEP/DOE simulated UTBT and UTRH have comparable distribution patterns. However the simulations’ differences with the observations are generally lower with the NCEP/DOE than with the NICAM. The NCEP/DOE model outputs very often overestimate the UTBT and therefore present a drier upper troposphere. The impact of the lower troposphere instability (dry convection) on the upper tropospheric moisture and the consequences on the models’ results are evaluated through a thunderstorm and moisture predictor (the K-stability index). The results obtained show a positive relation between the instability and the root mean square error (RMSE: observation versus models). The study of the impact of convective clouds shows that the area covered by these clouds increases with the humidity of the upper troposphere in clear sky and above low clouds, and at the same time, the error between the observations and the models also increases. The impact of the above low clouds heat distribution on the models is studied through the relation between the low clouds cover and their effective emissivity. The models’ error appears to be high at midrange effective emissivity clouds.

Vegetation canopy optical and structural variability based on radiometric and laser analysis

For a comprehensive vegetation monitoring and/or management, a good understanding of the distribution of the solar radiation energy among components of this vegetation is needed. The energy received by the vegetation is measured by spectroradiometers either at satellite elevations or near the ground (in situ measurements). In this study, in situ, radiometric data and laser scanning techniques are combined, in order to evaluate the contribution of the vegetation structure to the variability of canopy reflectance. Advanced processing laser techniques are not only an efficient tool for the generation of physical models but also give information about the vertical structure of canopies (height, shape, density) and their horizontal extension. To conduct this study, airborne multispectral radiation data and, laser pulse returns are recorded from a low flying helicopter above the vegetation of a boreal forest. These measurements are used to derive canopy optical and structural variables. The impact of the canopy 2-dimensional structural variability on the distribution of the solar radiation reflected by plants of this area is discussed. The results obtained show that the laser technology can be used for the selection of the most appropriate configuration of radiation measurements, and optimization of canopy physical characteristics, in future airborne missions.