Direct assimilation of Chinese FY-3C Microwave Temperature Sounder-2 radiances in the global GRAPES system

Abstract

Abstract. FengYun-3C (FY-3C) is an operational polarorbiting satellite carrying the new-generation microwave sounding instruments in China. This paper describes the assimilation of the FY-3C Microwave Temperature Sounder-2 (MWTS-2) radiances in the Global and Regional Assimilation and PrEdiction System (GRAPES) of China Meteorological Administration. A quality control (QC) procedure for the assimilation of MWTS-2 radiance is proposed. Extensive monitoring before assimilation shows that MWTS-2 observations exhibit a clear striping pattern. A technique combining principal component analysis (PCA) and ensemble empirical mode decomposition (EEMD) is applied to the observations to remove the striping noise. Cloudy field-of-views (FOVs) are identified by applying the Visible and InfrarRed Radiometer (VIRR) cloud fraction threshold of 76 %. Other QC steps are conducted in the follow order: (i) coastal FOVs are removed, (ii) eight outmost FOVs are not used, (iii) channel 5 data over sea ice and land are not used, (iv) channel 6 observations are not used if the terrain altitudes are higher than 500 m, and (v) outliers with large differences between observations and model simulations are removed. Approximately 83, 75, 40, and 40 % of the observations are removed by the proposed QC for channels 5-8, respectively. After QC, the global biases and standard deviations are reduced significantly. The assimilation of the MWTS-2 radiances shows a positive impact when the control experiment assimilates only conventional observations. The experiments also show that the analysis and forecast errors are slightly reduced when the striping noise is removed from the observations. The quality control scheme of extracting the striping noise may contribute to the analysis and forecast accuracy.

Mitigating and comparing the striping noise in FengYun‐3B/C/D microwave radiation imager brightness temperature observations

“…This phenomenon was also found in observations from other sensors such as the Advanced Technology Microwave Sounder (ATMS: Bormann et al, 2013), the Advanced Microwave Sounding Unit-B and the Microwave Humidity Sounder (Qin et al, 2013), the Microwave Temperature Sounder 2 (Li and Liu, 2016), and the Tropical Rainfall Measuring Mission Microwave Imager and Global Precipitation Measurement Microwave Imager 2018). This striping noise may result in striping patterns in retrieved environmental parameters (Ma and Zou, 2015;Dong, 2018) and may also have a negative effect during assimilation of these data (Bormann et al, 2013;Li and Liu, 2016). Qin et al (2013) proposed a method for reducing the striping noise in ATMS observations.…”

Dynamic Channel Selection of Microwave Temperature Sounding Channels under Cloudy Conditions

“…Similar channels are found in both the Advanced Technology Microwave Sounder (ATMS) and the Microwave Temperature Sounder (MWTS)−2, which have comparable observation accuracy [14]. Observations from all three of these sounders have a positive impact on the forecast accuracy of numerical models through data assimilation [15][16][17].…”

“…Through a number of tests, a reasonable threshold can be derived for precipitation detection, as an alternative to a conventional precipitation detection. This method has also been applied to data from the Fengyun-3C/D (FY-3C/D) MWTS-2 in the Global and Regional Assimilation and PrEdiction System (GRAPES) of the China Meteorological Administration (CMA) [16]. Similarly, the high-resolution cloud products of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imager Radiometer Suite (VIIRS) were used for precipitation detection of AMSU-A and ATMS, respectively, by Han et al [30].…”

Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

“…Satellite data is an important source of information in NWP DA for accurately describing structures of atmospheric temperature, moisture, wind fields, surface pressure, and clouds. In particular, PMW radiances sensitive to atmospheric temperature and moisture have been demonstrated to be important observation types for global (Li and Liu, 2016;Geer et al, 2017;Lawrence et al, 2018;Carminati et al, 2020;Jiang et al, 2020), as well as for regional NWP (Storto and Randriamampianina, 2010;Schwartz et al, 2012;Xu et al, 2016;Zou et al, 2017). The PMW radiances demonstrated to be useful in global NWP reside from a wide range of instruments, including AMSU-A and MHS (Geer et al, 2017;Lawrence et al, 2018) as well as MWHS-2 (Li and Liu, 2016;Lawrence et al, 2018;Car-minati et al, 2020;Jiang et al, 2020).…”

“…Geer et al (2017) have reported on a growing impact from humidity sensitive PMW radiances. For regional models the importance of PMW radiances has been demonstrated using various approaches, including an energy-norm based method (Storto and Randriamampianina, 2010), case studies (Li and Liu, 2016;Jiang et al, 2020), and a data denial procedure (Schwartz et al, 2012).…”