Abstract
The Clear Sky Radiance (CSR) product has been widely used instead of Level 1 (L1) geostationary imager data in data assimilation for numerical weather prediction due to its many advantages concerning superobservation methodology. In this study, CSR was produced in two water vapor channels (channels 9 and channel 10, with wavelengths at 5.8–6.7 μm and 6.9–7.3 μm) of the Advanced Geostationary Radiation Imager aboard Fengyun 4A. The root mean square error (RMSE) between CSR observations and backgrounds was used as a quality flag and was predicted by cloud cover, standard deviation (STD), surface type, and elevation of a CSR field of view (FOV). Then, a centesimal scoring system based on the predicted RMSE was set to a CSR FOV that indicates its percentile point in the quality distribution of the whole FOV. Validations of the scoring system demonstrated that the biases of the predicted RMSE were small for all FOVs and that the score was consistent with the predicted RMSE, especially for FOVs with high scores. We suggest using this score for quality control (QC) to replace the QC of cloud cover, STD, and elevation of CSR, and we propose 40 points as the QC threshold for the two channels, above which the predicted RMSE of a CSR is superior to the RMSE of averaged clear-sky L1 data.
Highlights
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites (LRCVES), FengYun Meteorological Satellite Innovation Center (FY-MSIC), National Satellite Meteorological Center, Citation: Yu, T.; Ma, G.; Lu, F.; Abstract: The Clear Sky Radiance (CSR) product has been widely used instead of Level 1 (L1)
The Clear Sky Radiance (CSR) product for geostationary satellites has been developed for data assimilation in the numerical weather prediction (NWP) model and introduced into operation in ECMWF [11,12]
We identified the surface type using the following rule: a CSR field of view (FOV) is over land only if all the Advanced Geostationary Radiation Imager (AGRI) pixels inside it are over land, and it is over the coast when both terrestrial and marine pixels are found in it
Summary
Weather analysis and numerical weather prediction (NWP) models have benefitted from image data and retrieved motion vectors by water vapor (WV) channels at 6.7 μm, which are usually aboard geostationary satellites [1,2]. Neutral-to-positive impacts on forecast skill have been found in both global and regional models when clear-sky WV radiance or brightness temperature (BT) is assimilated. The Clear Sky Radiance (CSR) product for geostationary satellites has been developed for data assimilation in the NWP model and introduced into operation in ECMWF [11,12]. As the resolution of geostationary satellite imagers generally exceeds that of global models, the representation errors will be reduced to gain consistency in the resolutions between the NWP model and satellite observations [17] when CSR is assimilated in NWP compared with L1 data. For the CSR product, the representativeness of averaged clear-sky pixels in superobservation segment should be considered for a large number of FOVs that are partly contaminated, and the QC scheme should be designed .
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