Launched in 2013, and 2017 respectively, Chinese Fengyun-3C and 3D meteorological satellites are equipped with two microwave sounders, Microwave Temperature Sounder (MWTS) and Microwave Humidity Sounder -2 (MWHS-2), whose observations play an important role in numerical weather prediction by data assimilation. Data quality control should be carried out before assimilation to filter out bad data, such as cloud- or rain-polluted data and questionable data. This work can’t be accomplished purely depending on MWTS or MWHS-2 themselves. MWHS-2 is taken as an example to do quality control in the paper, and the method is suitable for MWTS too. Multi-source information from other instruments onboard FY-3 is extracted to assist in the work. Cloud mask product from VIRR (Visible and InfraRed Radiometer), oceanic cloud liquid water content product from MWRI (Microwave Radiation Imager), and global rain rate product from MWRI are mapped to MWHS-2 for quality control in combination with oceanic rain detection product from MWHS-2 itself. 6 kinds of cloud and rain detection schemes are then designed to get the best choice by analyzing the characteristics of background departure. RTTOV v10 is adopted to simulate brightness temperature of MWHS-2 at all channels. The results suggested that scheme RI RC (MWRI cloud and rain information ingested) and scheme RC (all information ingested) are the two best choices for numerical assimilation application, and scheme RI RC can retain more samples. Questionable data can also be found in the way to help monitor the operational status of instruments.
Cloudiness and precipitation are important output parameters in numerical weather prediction (NWP) models, both for their own sake and because they strongly affect other parameters, e.g., surface temperature. The spin-up problem is an important reason resulting in low accuracy of forecasts during the early prediction stage (0–6 h), so it’s necessary to introduce cloud-related information to eliminate or weaken this problem. For typhoon prediction, microwave satellite data is crucial. Profiles of cloud microphysical parameters can be retrieved from the microwave imager using certain inversion technology. The microwave imager onboard Fengyun-3B (FY-3B MWRI), the microwave imager onboard Tropical Rainfall Measuring Mission (TRMM TMI), and the Advanced Microwave Scanning Radiometer (AMSR-E) onboard AQUA are selected to do this research. Experiments of initialization of the cloud microphysical information in Global and Regional Assimilation and Prediction System (GRAPES) during typhoon MA-ON activity are carried out to investigate their impact on forecasts. The results indicate that prediction of hydrometer parameters and surface rain rate can be faster through initialization of cloud information derived from satellite microwave observations in GRAPES model and there is positive contribution during the first 6 hours of the model integration. Retrievals from MWRI, TMI and AMSR-E have a good consistency, and fusion data with three kinds of retrievals shows a more positive impact.
China’s Fengyun-3D satellite (FY-3D), which can obtain global cloud amount data, was launched in November 2017. In this study, we qualitatively and quantitatively compare the cloud amount products from FY-3D and the moderate resolution imaging spectroradiometer (MODIS) AQUA in July and October,2018. The results show that the global spatial distribution characteristics of the FY-3D cloud amount products is consistent with that of MODIS. The distributions of the cloud amount of the two products are more similar in October, but theFY-3D cloud amount data are generally higher than the MODIS cloud amount data. After strict space-time matching, the matched samples are used for quantitative accuracy evaluation. In July 2018, the absolute error of the FY-3D cloud amount relative to MODIS is 4.5, the relative error is 0.11, the deviation is 1.68, the error standard deviation is 12.11, and the correlation coefficient reaches 84.8%. In October 2018, the absolute error of the FY-3D cloud amount relative to MODIS is 3.20, the relative error is 0.05, the deviation is 1.68, the error standard deviation is 6.38, and the correlation coefficient is 93.4%. The global error distribution shows that at mid-low latitudes, the quality of the two products is similar and the error ranges between -10 and 10%, while at high latitudes the error is relatively large. FY-3D cloud amount products can be used in studies of the global climate and climate change.
KEYWORDS: Satellites, Remote sensing, Vegetation, Meteorology, Statistical analysis, Data modeling, Meteorological satellites, Temperature metrology, Data archive systems, Data processing
In this paper, 30 years conventional data of China are processed, the anomaly of precipitation, land
surface temperature and air temperature are calculated and their relations are analyzed by using
regressive statistics analysis and Singular Value Decomposition (SVD). The result shows that
precipitation anomaly has a good negative correlation to both surface temperature anomaly and air
temperature anomaly. Moreover, 20 years satellite brightness temperature anomaly and the same period
precipitation anomaly are also calculated and analyzed; the similar result is obtained. It indicates that
brightness temperature anomaly is an important factor for drought monitoring by using remote sensing
data. Moreover, compared with historic data, the change of Normalized Difference Vegetation Index (NDVI)
is another factor for drought monitoring. Drought index is formed by these two factors normalization and
mean in weight. This remote sensing method on drought was used to some experiments and the results
show that the drought distribution on space is very similar, compared with conventional drought index.
Now this method is being used in operational system on drought monitoring in National Satellite
Meteorological Center (NSMC), China meteorology administration (CMA).
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