Browsing by Subject "Moderate Resolution Imaging Spectroradiometer (MODIS)"
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Item A critical examination of the residual cloud contamination and diurnal sampling effects on MODIS estimates of aerosol over ocean(IEEE, 2005-11-21) Kaufman, Y. J.; Remer, L. A.; Tanre, D.; Li, Rong-Rong; Kleidman, R.; Mattoo, S.; Levy, R. C.; Eck, Thomas; Holben, B. N.; Ichoku, C.; Martins, J. V.; Koren, IllanObservations of the aerosol optical thickness (AOT) by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard Terra and Aqua satellites are being used extensively for applications to climate and air quality studies. Data quality is essential for these studies. Here we investigate the effects of unresolved clouds on the MODIS measurements of the AOT. The main cloud effect is from residual cirrus that increases the AOT by 0.015/spl plusmn/0.003 at 0.55 /spl mu/m. In addition, lower level clouds can add contamination. We examine the effect of lower clouds using the difference between simultaneously measured MODIS and AERONET AOT. The difference is positively correlated with the cloud fraction. However, interpretation of this difference is sensitive to the definition of cloud contamination versus aerosol growth. If we consider this consistent difference between MODIS and AERONET to be entirely due to cloud contamination we get a total cloud contamination of 0.025/spl plusmn/0.005, though a more likely estimate is closer to 0.020 after accounting for aerosol growth. This reduces the difference between MODIS-observed global aerosol optical thickness over the oceans and model simulations by half, from 0.04 to 0.02. However it is insignificant for studies of aerosol cloud interaction. We also examined how representative are the MODIS data of the diurnal average aerosol. Comparison to monthly averaged sunphotometer data confirms that either the Terra or Aqua estimate of global AOT is a valid representation of the daily average. Though in the vicinity of aerosol sources such as fires, we do not expect this to be true.Item The importance of considering sub-grid cloud variability when using satellite observations to evaluate the cloud and precipitation simulations in climate models(Copernicus Publications, 2018-08-03) Song, Hua; Zhang, Zhibo; Ma, Po-Lun; Ghan, Steven; Wang, MinghuaiSatellite cloud observations have become an indispensable tool for evaluating general circulation models (GCMs). To facilitate the satellite and GCM comparisons, the CFMIP (Cloud Feedback Model Inter-comparison Project) Observation Simulator Package (COSP) has been developed and is now increasingly used in GCM evaluations. Real-world clouds and precipitation can have significant sub-grid variations, which, however, are often ignored or oversimplified in the COSP simulation. In this study, we use COSP cloud simulations from the Super-Parameterized Community Atmosphere Model (SPCAM5) and satellite observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and CloudSat to demonstrate the importance of considering the sub-grid variability of cloud and precipitation when using the COSP to evaluate GCM simulations. We carry out two sensitivity tests: SPCAM5 COSP and SPCAM5-Homogeneous COSP. In the SPCAM5 COSP run, the sub-grid cloud and precipitation properties from the embedded cloud-resolving model (CRM) of SPCAM5 are used to drive the COSP simulation, while in the SPCAM5-Homogeneous COSP run only grid-mean cloud and precipitation properties (i.e., no sub-grid variations) are given to the COSP. We find that the warm rain signatures in the SPCAM5 COSP run agree with the MODIS and CloudSat observations quite well. In contrast, the SPCAM5-Homogeneous COSP run which ignores the sub-grid cloud variations substantially overestimates the radar reflectivity and probability of precipitation compared to the satellite observations, as well as the results from the SPCAM5 COSP run. The significant differences between the two COSP runs demonstrate that it is important to take into account the sub-grid variations of cloud and precipitation when using COSP to evaluate the GCM to avoid confusing and misleading results.Item Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach(Copernicus Publications on behalf of the European Geosciences Union, 2019-03-18) Noia, Antonio Di; Hasekamp, Otto P.; Diedenhoven, Bastiaan van; Zhang, ZhiboThis paper describes a neural network algorithm for the estimation of liquid water cloud optical properties from the Polarization and Directionality of Earth’s Reflectances-3 (POLDER-3) instrument aboard the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The algorithm has been trained on synthetic multi-angle, multi-wavelength measurements of reflectance and polarization and has been applied to the processing of 1 year of POLDER-3 data. Comparisons of the retrieved cloud properties with Moderate Resolution Imaging Spectroradiometer (MODIS) products show that the neural network algorithm has a low bias of around 2 in cloud optical thickness (COT) and between 1 and 2 µm in the cloud effective radius. Comparisons with existing POLDER-3 datasets suggest that the proposed scheme may have enhanced capabilities for cloud effective radius retrieval, at least over land. An additional feature of the presented algorithm is that it provides COT and effective radius retrievals at the native POLDER-3 Level 1B pixel level.