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: 2024-03-27

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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References

[1]	English S J, Renshaw R J, Dibben P C, et al.A comparison of the impact of TOVS arid ATOVS satellite sounding data on the accuracy of numerical weather forecasts.Q J R Meteor Soc, 2000, 126(569):2911-2931.
[2]	Eyre J R, Bell W, Cotton J, et al. Assimilation of satellite data in numerical weather prediction. Part Ⅱ: Recent years. Q J R Meteor Soc, 2022, 148(743): 521-556. doi: 10.1002/qj.4228
[3]	Kaplan L D. Inference of atmospheric structure from remote radiation measurements. J Opt Soc Amer, 1959, 49(10): 1004-1007. doi: 10.1364/JOSA.49.001004
[4]	McMillin L M, Dean C. Evaluation of a new operational technique for producing clear radiances. J Appl Meteor, 1982, 21(7): 1005-1014. doi: 10.1175/1520-0450(1982)021<1005:EOANOT>2.0.CO;2
[5]	Stogryn A. Estimates of brightness temperatures from scanning radiometer data. IEEE Trans Anntenas Propag, 1978, 26(5): 720-726. doi: 10.1109/TAP.1978.1141919
[6]	Andersson E, Pailleux J, Thepaut J N, et al. Use of cloud-cleared radiances in three/four-dimensional variational data assimilation. Q J R Meteor Soc, 1994, 120(517): 627-653.
[7]	McNally A P, Vesperini M. Variational analysis of humidity information from TOVS radiances. Q J R Meteor Soc, 1996, 122(535): 1521-1544. doi: 10.1002/qj.49712253504
[8]	Zhou X S, Guo Q Y, Xia Y C, et al. Inspection of FY-3D satellite temperature data based on horizontal drift round-trip sounding data. J Appl Meteor Sci, 2023, 34(1): 52-64. doi: 10.11898/1001-7313.20230105
[9]	Ohring G. Impact of satellite temperature sounding data on wea- ther forecasts. Bull Amer Meteor Soc, 1979, 60(10): 1142-1147. doi: 10.1175/1520-0477(1979)060<1142:IOSTSD>2.0.CO;2
[10]	Lorenc A C. Analysis methods for numerical weather prediction. Q J R Meteor Soc, 1986, 112(474): 1177-1194. doi: 10.1002/qj.49711247414
[11]	Talagrand O, Courtier P. Variational assimilation of meteorological observations with the adjoint vorticity equation. Ⅰ: Theory. Q J R Meteor Soc, 1987, 113(478): 1311-1328. doi: 10.1002/qj.49711347812
[12]	Eyre J. A Fast Radiative Transfer Model for Satellite Sounding Systems. ECMWF Technical Memorandum, 176, 1991.
[13]	McMillin L M, Crone L J, Goldberg M D, et al. Atmospheric transmittance of an absorbing gas. 4. OPTRAN: A computationally fast and accurate transmittance model for absorbing gases with fixed and with variable mixing ratios at variable viewing angles. Appl Opt, 1995, 34(27): 6269-6274. doi: 10.1364/AO.34.006269
[14]	Eyre J R, Kelly G A, McNally A P, et al. Assimilation of TOVS radiance information through one-dimensional variational analysis. Q J R Meteor Soc, 1993, 119(514): 1427-1463.
[15]	Derber J C, Wu W S. The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon Wea Rev, 1998, 126(8): 2287-2299. doi: 10.1175/1520-0493(1998)126<2287:TUOTCC>2.0.CO;2
[16]	McNally A, Andersson E, Kelly G, et al. The Use of Raw TOVS/ATOVS Radiances in the ECMWF 4D-var Assimilation System//Technical Proceedings of the 10th International TOVS Study Conference, 1999: 377-384.
[17]	Wang J C, Lu H J, Han W, et al. Improvements and performances of the operational GRAPES_GFS 3Dvar system. J Appl Meteor Sci, 2017, 28(1): 11-24. doi: 10.11898/1001-7313.20170102
[18]	Zhang L, Liu Y Z, Liu Y, et al. The operational global four-dimensional variational data assimilation system at the China Meteorological Administration. Q J R Meteor Soc, 2019, 145(722): 1882-1896. doi: 10.1002/qj.3533
[19]	Lorenc A C. A global three-dimensional multivariate statistical interpolation scheme. Mon Wea Rev, 1981, 109(4): 701-721. doi: 10.1175/1520-0493(1981)109<0701:AGTDMS>2.0.CO;2
[20]	Smith W L, Woolf H M. The use of eigenvectors of statistical covariance matrices for interpreting satellite sounding radiometer observations. J Atmos Sci, 1976, 33(7): 1127-1140. doi: 10.1175/1520-0469(1976)033<1127:TUOEOS>2.0.CO;2
[21]	Saunders R, Matricardi M, Brunel P. An improved fast radiative transfer model for assimilation of satellite radiance observations. Q J R Meteor Soc, 1999, 125(556): 1407-1425. doi: 10.1002/qj.1999.49712555615
[22]	McNally A P, Derber J C, Wu W, et al. The use of TOVS level-1b radiances in the NCEP SSI analysis system. Q J R Meteor Soc, 2000, 126(563): 689-724.
[23]	Li Z, Chen J, Ma Z S, et al. Deviation distribution features of CMA-GFS cloud prediction. J Appl Meteor Sci, 2022, 33(5): 527-540. doi: 10.11898/1001-7313.20220502
[24]	English S, Renshaw R, Dibben P, et al. The AAPP Module for Identifying Precipitation, Ice Cloud, Liquid Water and Surface Types on the AMSU-A grid//Technical Proceedings of the 9th International TOVS Study Conference, 1997: 119-130.
[25]	Saunders R, Andersson E, Kelly G, et al. Developments in Assimilating Global TOVS Data at the UK Met Office//Technical Proceedings of the 9th International TOVS Study Conference, 1997: 417-428.
[26]	English S J, Eyre J R, Smith J A. A cloud-detection scheme for use with satellite sounding radiances in the context of data assimilation for numerical weather prediction. Q J R Meteor Soc, 1999, 125(559): 2359-2378.
[27]	Dong C H, Zhang F Y, Zheng B, et al. A regional satellite atmosphere sounding data operational processing system. J Appl Meteor Sci, 1991, 2(1): 22-31. http://qikan.camscma.cn/article/id/19910103
[28]	Qian J M, Zheng X D. The satellite data archive system of National Satellite Meteorological Center. J Appl Meteor Sci, 2003, 14(6): 756-762. doi: 10.3969/j.issn.1001-7313.2003.06.015
[29]	Geer A J, Baordo F, Bormann N, et al. The growing impact of satellite observations sensitive to humidity, cloud and precipitation. Q J R Meteor Soc, 2017, 143(709): 3189-3206. doi: 10.1002/qj.3172
[30]	Menzel W P, Schmit T J, Zhang P, et al. Satellite-based atmospheric infrared sounder development and applications. Bull Amer Meteor Soc, 2018, 99(3): 583-603. doi: 10.1175/BAMS-D-16-0293.1
[31]	Noh Y, Sohn B, Kim Y, et al. A new infrared atmospheric sounding interferometer channel selection and assessment of its impact on Met Office NWP Forecasts. Adv Atmos Sci, 2017, 34: 1265-1281. doi: 10.1007/s00376-017-6299-8
[32]	Rodgers C D. Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation. Rev Geophys, 1976, 14(4): 609-624. doi: 10.1029/RG014i004p00609
[33]	Goldberg M D, Qu Y, McMillin L M, et al. AIRS near-real-time products and algorithms in support of operational numerical weather prediction. IEEE Trans Geosci Remote Sens, 2003, 41(2): 379-389. doi: 10.1109/TGRS.2002.808307
[34]	Zhang Q. Impacts on initial condition modification from hyperspectral infrared sounding data assimilation: Comparisons between full-spectrum and channel-selection scheme based on two-month experiments using CrIS and IASI observation. Inter J Geosci, 2021, 12(9): 763-783. doi: 10.4236/ijg.2021.129043
[35]	Chen Y, Han Y. Evaluation of Different Calibration Approaches for S-NPP CRIS Full Spectral Resolution SDR Processing//IEEE International Geoscience and Remote Sensing Symposium(IGARSS), 2015: 2127-2130.
[36]	Collard A D. Selection of IASI channels for use in numerical weather prediction. Q J R Meteor Soc, 2007, 133(629): 1977-1991. doi: 10.1002/qj.178
[37]	Smith J, Gambacorta A, Barnet C, et al. The NPP and J1 CrIS Operational High-Resolution Channel Selection for the NUCAPS algorithm: A Demonstration of Global Applicability to Meet Users Needs//AGU Fall Meeting, 2016.
[38]	Ma G, Guo Y, Su J, et al. An overview of fast calculation to clear atmospheric absorption transmittance of satellite channel. Chinese J Geophys, 2023, 66(6): 2275-2291. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX202306005.htm
[39]	Pavelin E G, Candy B. Assimilation of surface-sensitive infrared radiances over land: Estimation of land surface temperature and emissivity. Q J R Meteor Soc, 2014, 140(681): 1198-1208. doi: 10.1002/qj.2218
[40]	Duan S B, Han X J, Huang C, et al. Land surface temperature retrieval from passive microwave satellite observations: State-of-the-art and future directions. Remote Sens, 2020, 12(16). DOI: 10.3390/rs12162573.
[41]	Prigent C, Aires F, Wang D, et al. Sea-surface emissivity parametrization from microwaves to millimetre waves. Q J R Meteor Soc, 2017, 143(702): 596-605. doi: 10.1002/qj.2953
[42]	Karbou F, Prigent C, Eymard L, et al. Microwave land emissivity calculations using AMSU measurements. IEEE Trans Geosci Remote Sens, 2005, 43(5): 948-959. doi: 10.1109/TGRS.2004.837503
[43]	Xiao H Y, Li J, Liu G Q, et al. Assimilation of AMSU-A surface-sensitive channels in CMA-GFS 4D-var system over land. Wea Forecasting, 2023, 38(9): 1777-1790. doi: 10.1175/WAF-D-23-0032.1
[44]	Noh Y C, Lim A H N, Huang H L, et al. Global forecast impact of low data latency infrared and microwave sounders observations from polar orbiting satellites. Remote Sens, 2020, 12(14). DOI: 10.3390/rs12142193.
[45]	Diaz S W, CIMAS, Miami F L, et al. Impact of Satellite Data Latency on Global Weather Forecasts. 100th American Meteorological Society, 2020.
[46]	Lin H D, Weygandt S S, Benjamin S G, et al. Satellite radiance data assimilation within the hourly updated rapid refresh. Wea Forecasting, 2017, 32(4): 1273-1287. doi: 10.1175/WAF-D-16-0215.1
[47]	Liou K N. An Introduction to Atmospheric Radiation. New York, 2002.
[48]	Shen X S, Su Y, Hu J L, et al. Development and operation transformation of GRAPES global middle-range forecast system. J Appl Meteor Sci, 2017, 28(1): 1-10. doi: 10.11898/1001-7313.20170101
[49]	Liu Z Q, Jiang L P, Shi C X, et al. CRA-40/Atmosphere-The first-generation Chinese atmospheric reanalysis(1979-2018): System description and performance evaluation. J Meteor Res, 2023, 37(1): 1-19. doi: 10.1007/s13351-023-2086-x
[50]	Zhang J, Sun J, Shen X S, et al. Key model technologies of CMA-GFS V4.0 and application to operational forecast. J Appl Meteor Sci, 2023, 34(5): 513-526. doi: 10.11898/1001-7313.20230501
[51]	Zhang P, Lu Q F, Hu X Q, et al. Latest progress of the Chinese meteorological satellite program and core data processing technologies. Adv Atmos Sci, 2019, 36(9): 1027-1045. doi: 10.1007/s00376-019-8215-x
[52]	Dong P M, Wang H J, Han W, et al. The effect of water content on the simulation of satellite microwave observation in cloudy and rainy area. J Appl Meteor Sci, 2009, 20(6): 682-691. doi: 10.3969/j.issn.1001-7313.2009.06.005
[53]	Gu S Y, Wang Z Z, Ma G. Meteorological Satellite Microwave Atmospheric Remote Sensing. Beijing: Science Press, 2021.
[54]	English S, McNally A, Bormann N, et al. Impact of satellite data. ECMWF Technical Memorandum 804, 2013.
[55]	Wang L K, Tremblay D, Zhang B, et al. Fast and accurate collocation of the visible infrared imaging radiometer suite measurements with cross-track infrared sounder. Remote Sens, 2016, 8(1). DOI: 10.3390/rs8010076.
[56]	Li J, Liu G Q. Direct assimilation of Chinese FY-3C microwave temperature sounder-2 radiances in the global GRAPES system. Atmos Meas Tech, 2016, 9(7): 3095-3113. doi: 10.5194/amt-9-3095-2016
[57]	Qin L Y, Chen Y D, Yu T L, et al. Dynamic channel selection of microwave temperature sounding channels under cloudy conditions. Remote Sens, 2020, 12(3). DOI: 10.3390/rs12030403.
[58]	Qin L Y, Chen Y D, Ma G, et al. Assimilation of FY-3D MWTS-Ⅱ radiance with 3D precipitation detection and the impacts on typhoon forecasts. Adv Atmos Sci, 2023, 40(5): 900-919. doi: 10.1007/s00376-022-1252-x
[59]	Kan W L, Dong P M, Weng F Z, et al. Impact of Fengyun-3E microwave temperature and humidity sounder data on CMA global medium range weather forecasts. Remote Sens, 2022, 14(19). DOI: 10.3390/rs14195014.
[60]	Ventress L, Dudhia A. Improving the selection of IASI channels for use in numerical weather prediction. Q J R Meteor Soc, 2014, 140(684): 2111-2118. doi: 10.1002/qj.2280
[61]	Yin R Y, Han W, Gao Z Q, et al. The evaluation of FY4A's Geostationary Interferometric Infrared Sounder(GIIRS) long-wave temperature sounding channels using the GRAPES global 4D-Var. Q J R Meteor Soc, 2020, 146(728): 1459-1476. doi: 10.1002/qj.3746
[62]	Yan X S, Chen Y D, Ma G, et al. A 3-D cloud detection method for FY-4A GIIRS and its application in operational numerical weather prediction system. IEEE Trans Geosci Remote Sens, 2023, 61: 1-13.
[63]	Wang S J, Cui P, Zhang P, et al. FY-3C/VIRR sea surface temperature products and quality validation. J Appl Meteor Sci, 2020, 31(6): 729-739. doi: 10.11898/1001-7313.20200608
[64]	Cui P, Wang S J, Lu F, et al. FY-4A/AGRI sea surface temperature product and quality validation. J Appl Meteor Sci, 2023, 34(3): 257-269. doi: 10.11898/1001-7313.20230301
[65]	Xiao H Y, Han W, Wang H, et al. Impact of FY-3D MWRI radiance assimilation in GRAPES 4DVar on forecasts of Typhoon Shanshan. J Meteor Res, 2020, 34(4): 836-850. doi: 10.1007/s13351-020-9122-x
[66]	Guo Y, Lu Q F, Lu N M, et al. FY-3C MWHTS observed brightness temperature quality score based on the multi-source telemetry parameter quality control. National Remote Sens Bull, 2022, 26(11): 2147-2161. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB202211003.htm
[67]	Qi C L, Wu C Q, Hu X Q, et al. High spectral infrared atmospheric sounder(HIRAS): System overview and on-orbit performance assessment. IEEE Trans Geosci Remote Sens, 2020, 58(6): 4335-4352. doi: 10.1109/TGRS.2019.2963085
[68]	McNally C. Assimilation of Radiance Products from Geostationary Satellites: 1-year Report. EUMET/ECMWF Fellowship Programme Research Reports 21, 2011.
[69]	Burrows C. Assimilation of Radiance Observations from Geostationary Satellites: Third Year Report. EUMET/ECMWF Fellowship Programme Research Reports 52, 2020.
[70]	Li J, Geer A J, Okamoto K, et al. Satellite all-sky infrared radiance assimilation: Recent progress and future perspectives. Adv Atmos Sci, 2022, 39(1): 9-21. doi: 10.1007/s00376-021-1088-9
[71]	Lu Y H, Zhang F Q. A novel channel-synthesizing method for reducing uncertainties in satellite radiative transfer modeling. Geophys Res Lett, 2018, 45(10): 5115-5125. doi: 10.1029/2018GL077342
[72]	Yu T L, Ma G, Lu F, et al. Quality scoring of the Fengyun 4A clear sky radiance product. Remote Sens, 2021, 13(18). DOI: 10.3390/rs13183658.
[73]	Wang C F, Li X, Chen Y T, et al. Design of CMA's broadcast system for meteorological data-CMACast. J Appl Meteor Sci, 2012, 23(1): 113-120. doi: 10.3969/j.issn.1001-7313.2012.01.013
[74]	Huang L P, Deng L T, Wang R C, et al. Key technologies of CMA-MESO and application to operational forecast. J Appl Meteor Sci, 2022, 33(6): 641-654. doi: 10.11898/1001-7313.20220601