Review of Pre-processing Techniques for Meteorological Satellite Data Assimilation in Numerical Prediction

Ma Gang; Huang Jing; Gong Xinya; Xi Shuang; Xue Lei; Li Juan; Zhang Peng; Gong Jiandong

doi:10.11898/1001-7313.20240202

Vol.35, NO.2, 2024

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Article Navigation > Journal of Applied Meteorological Science > 2024 > 35(2): 142-155

Ma Gang, Huang Jing, Gong Xinya, et al. Review of pre-processing techniques for meteorological satellite data assimilation in numerical prediction. J Appl Meteor Sci, 2024, 35(2): 142-155. DOI: 10.11898/1001-7313.20240202.

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Ma Gang, Huang Jing, Gong Xinya, et al. Review of pre-processing techniques for meteorological satellite data assimilation in numerical prediction. J Appl Meteor Sci, 2024, 35(2): 142-155. DOI: 10.11898/1001-7313.20240202.

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Review of Pre-processing Techniques for Meteorological Satellite Data Assimilation in Numerical Prediction

DOI: 10.11898/1001-7313.20240202

Ma Gang^{1, 2, 3
,
,},
Huang Jing^{1, 2, 3},
Gong Xinya⁴,
Xi Shuang^{1, 2, 3},
Xue Lei⁵,
Li Juan^{1, 2, 3},
Zhang Peng⁴,
Gong Jiandong^{1, 2, 3}

1.
CMA Earth System Modeling and Prediction Center, Beijing 100081
2.
Key Laboratory of Earth System Modeling and Prediction, CMA, Beijing 100081
3.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
4.
National Satellite Meteorological Center, Beijing 100081
5.
National Meteorological Information Center, Beijing 100081

Received Date: 2023-09-15
Rev Recd Date: 2024-01-03
Publish Date: 2024-03-31

Abstract

Abstract

Satellite data assimilation pre-processing plays a crucial role in bridging satellite data pre-processing and numerical weather prediction model assimilation. It involves integrating pre-processed satellite measurements, orbital splicing and sparsification of satellite data, the fusion of boundary condition parameters under satellite pixels, and the assimilation pre-processing quality control of satellite data by using a unified file format and in accordance with the requirements of the data assimilation system. In the variational assimilation of numerical forecasts, assimilation pre-processing filters effective information from satellite pre-processed measurements, supports the positive contribution of satellite data assimilation to numerical forecast operations, and is an important link in determining the efficiency, quality and effectiveness of assimilation of large quantities of satellite data. In response to the complex format of satellite data from multiple channels, CMA has developed a standard format of high time-sensitive satellite data splicing technology in the pre-processing of satellite data assimilation, which effectively reduces the negative impact of the time lag of whole-orbit satellite data on operational numerical prediction. In the assimilation pre-processing of Fengyun satellite data, the assimilation prequality control of multi-spectral data fusion is achieved by front-loading cloud and precipitation detection, data analysis, analysis and other processing into the assimilation pre-processing, which ensures the positive assimilation contribution of Fengyun microwave temperature sounding data and infrared hyperspectral data. In the development of satellite data assimilation pre-processing technology, reprocessing of pre-processed satellite data by using a unified data format, expanding the processing for satellite imagery and active detection data, and front-loading a part of satellite data assimilation quality control function into data assimilation pre-processing are important trends in the development of future assimilation pre-processing technology for Fengyun satellite data.
- numerical weather prediction;
- satellite measurements;
- pre-processing techniques for satellite data assimilation

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References(74)

Figures and Tables

Fig. 1 Measurements to Channel 1 of NOAA-19 AMSUA supposed to be assimilated(a) and multi-satellites measurements actually assimilated(b) within 6-hour assimilation time window at 1200 UTC 14 Dec 2023

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Fig. 2 Relay stitched mosaic brightness temperature measurements to Channel 1 of SNPP ATMS from HRPT stations of Jiamusi, Guangzhou and Urumqi within 6-hour assimilation time window at 0600 UTC 17 Dec 2023

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Fig. 3 Relay stitched mosaic brightness temperature measurements to Channel 1 of AMSUA of NOAA-18, NOAA-19,METOP-A, METOP-C and ATMS of NOAA-20 global multi-satellite orbital measurements within 6-hour assimilation time window at 0600 UTC 17 Dec 2023

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Table 1 ATOVS orbital and HRPT measurements obtained in CMA in 2009

来源	覆盖范围	滞后时效/h	卫星	搭载仪器	资料类型	起始获取年份
NESDIS(GTS)	全球	3~9	NOAA15-19、AQUA	AMSU、MHS、AIRS	L1B	2004
EUMETSAT(GTS)	全球	3~7	NOAA15-17	AMSU、MHS、HIRS	L1B	2007
EUMETSAT(GTS)	全球	3~7	NOAA15-21、SNPP、METOP2-A-C	AMSU、MHS、HIRS、CrIS、ATMS、IASI	L1C	2009
EUMETCAST (卫星转发)	全球	3~7	NOAA15-21、SNPP、METOP A-C、AQUA	AMSU、MHS、IASI、CrIS、ATMS、AIRS	L1C	2009
EUMETCAST (卫星转发)	北半球	1~2	NOAA15-21、SNPP、METOP A/B/C、AQUA	AMSU、MHS、IASI、CrIS、ATMS、AIRS	L1C	2007
RARS (GTS的HRPT站)	全球大部分地区	1.5	NOAA15-21、SNPP、METOP A-C、AQUA	AMSU、MHS、IASI、CrIS、ATMS、AIRS	L1C	2005
DBnet(GTS的50多个HRPT站)	全球	1~2	NOAA15-21、SNPP、METOP A-C、AQUA	AMSU、MHS、IASI、CrIS、ATMS、AIRS	L1C	2016
NSMC	东亚	0.5	NOAA15-19、SNPP、AQUA	AMSU、MHS、ATMS	源包	1999

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