Impact of assimilating multi-source observations on meteorological and PM

Data assimilation (DA) is a promising approach to improve meteorological and PM2.5 forecasts, but to what extent and by what process the DA of meteorological fields helps improve PM2.5 forecast still call for more discussion. By utilizing WRFDA and WRF-Chem models, we have assimilated AMSU-A, MHS ra...
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Autor*in:	Liu, Jian [verfasserIn] Hong, Jia [verfasserIn] Mao, Feiyue [verfasserIn] Gong, Wei [verfasserIn] Shen, Longjiao [verfasserIn] Liang, Shengwen [verfasserIn] Chen, Jiangping [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Data assimilation WRFDA WRF-Chem AMSU-A MHS Proprietary in situ observations

Übergeordnetes Werk:	Enthalten in: Atmospheric research - Amsterdam [u.a.] : Elsevier, 1986, 241
Übergeordnetes Werk:	volume:241

DOI / URN:	10.1016/j.atmosres.2020.104945

Katalog-ID:	ELV004028201

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520			\|a Data assimilation (DA) is a promising approach to improve meteorological and PM2.5 forecasts, but to what extent and by what process the DA of meteorological fields helps improve PM2.5 forecast still call for more discussion. By utilizing WRFDA and WRF-Chem models, we have assimilated AMSU-A, MHS radiances and conventional observations, and studied the influences of the meteorological DA on meteorological and PM2.5 forecasts over Central China through a series of experiments. The results show that multi-source meteorological DA helps improve temperature and relative humidity forecasts in the lower atmosphere, and the improved meteorological fields further improve PM2.5 forecast with a reduction of bias and RMSE by 7.4% and 4.1% over the study area, especially during PM2.5 episode. This study also helps understand how DA improve the PM2.5 forecasts over Central China.
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700	1		\|a Liang, Shengwen \|e verfasserin \|4 aut
700	1		\|a Chen, Jiangping \|e verfasserin \|4 aut
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author_variant	j l jl j h jh f m fm w g wg l s ls s l sl j c jc
matchkey_str	article:01698095:2020----::matfsiiaigutsucosrainomt
hierarchy_sort_str	2020
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publishDate	2020
allfields	10.1016/j.atmosres.2020.104945 doi (DE-627)ELV004028201 (ELSEVIER)S0169-8095(19)31191-3 DE-627 ger DE-627 rda eng 550 530 DE-600 38.81 bkl Liu, Jian verfasserin aut Impact of assimilating multi-source observations on meteorological and PM 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data assimilation (DA) is a promising approach to improve meteorological and PM2.5 forecasts, but to what extent and by what process the DA of meteorological fields helps improve PM2.5 forecast still call for more discussion. By utilizing WRFDA and WRF-Chem models, we have assimilated AMSU-A, MHS radiances and conventional observations, and studied the influences of the meteorological DA on meteorological and PM2.5 forecasts over Central China through a series of experiments. The results show that multi-source meteorological DA helps improve temperature and relative humidity forecasts in the lower atmosphere, and the improved meteorological fields further improve PM2.5 forecast with a reduction of bias and RMSE by 7.4% and 4.1% over the study area, especially during PM2.5 episode. This study also helps understand how DA improve the PM2.5 forecasts over Central China. Data assimilation WRFDA WRF-Chem AMSU-A MHS Proprietary in situ observations Hong, Jia verfasserin aut Mao, Feiyue verfasserin aut Gong, Wei verfasserin aut Shen, Longjiao verfasserin aut Liang, Shengwen verfasserin aut Chen, Jiangping verfasserin aut Enthalten in Atmospheric research Amsterdam [u.a.] : Elsevier, 1986 241 Online-Ressource (DE-627)320502430 (DE-600)2012396-6 (DE-576)258584130 0169-8095 nnns volume:241 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.81 Atmosphäre AR 241
spelling	10.1016/j.atmosres.2020.104945 doi (DE-627)ELV004028201 (ELSEVIER)S0169-8095(19)31191-3 DE-627 ger DE-627 rda eng 550 530 DE-600 38.81 bkl Liu, Jian verfasserin aut Impact of assimilating multi-source observations on meteorological and PM 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data assimilation (DA) is a promising approach to improve meteorological and PM2.5 forecasts, but to what extent and by what process the DA of meteorological fields helps improve PM2.5 forecast still call for more discussion. By utilizing WRFDA and WRF-Chem models, we have assimilated AMSU-A, MHS radiances and conventional observations, and studied the influences of the meteorological DA on meteorological and PM2.5 forecasts over Central China through a series of experiments. The results show that multi-source meteorological DA helps improve temperature and relative humidity forecasts in the lower atmosphere, and the improved meteorological fields further improve PM2.5 forecast with a reduction of bias and RMSE by 7.4% and 4.1% over the study area, especially during PM2.5 episode. This study also helps understand how DA improve the PM2.5 forecasts over Central China. Data assimilation WRFDA WRF-Chem AMSU-A MHS Proprietary in situ observations Hong, Jia verfasserin aut Mao, Feiyue verfasserin aut Gong, Wei verfasserin aut Shen, Longjiao verfasserin aut Liang, Shengwen verfasserin aut Chen, Jiangping verfasserin aut Enthalten in Atmospheric research Amsterdam [u.a.] : Elsevier, 1986 241 Online-Ressource (DE-627)320502430 (DE-600)2012396-6 (DE-576)258584130 0169-8095 nnns volume:241 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.81 Atmosphäre AR 241
allfields_unstemmed	10.1016/j.atmosres.2020.104945 doi (DE-627)ELV004028201 (ELSEVIER)S0169-8095(19)31191-3 DE-627 ger DE-627 rda eng 550 530 DE-600 38.81 bkl Liu, Jian verfasserin aut Impact of assimilating multi-source observations on meteorological and PM 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data assimilation (DA) is a promising approach to improve meteorological and PM2.5 forecasts, but to what extent and by what process the DA of meteorological fields helps improve PM2.5 forecast still call for more discussion. By utilizing WRFDA and WRF-Chem models, we have assimilated AMSU-A, MHS radiances and conventional observations, and studied the influences of the meteorological DA on meteorological and PM2.5 forecasts over Central China through a series of experiments. The results show that multi-source meteorological DA helps improve temperature and relative humidity forecasts in the lower atmosphere, and the improved meteorological fields further improve PM2.5 forecast with a reduction of bias and RMSE by 7.4% and 4.1% over the study area, especially during PM2.5 episode. This study also helps understand how DA improve the PM2.5 forecasts over Central China. Data assimilation WRFDA WRF-Chem AMSU-A MHS Proprietary in situ observations Hong, Jia verfasserin aut Mao, Feiyue verfasserin aut Gong, Wei verfasserin aut Shen, Longjiao verfasserin aut Liang, Shengwen verfasserin aut Chen, Jiangping verfasserin aut Enthalten in Atmospheric research Amsterdam [u.a.] : Elsevier, 1986 241 Online-Ressource (DE-627)320502430 (DE-600)2012396-6 (DE-576)258584130 0169-8095 nnns volume:241 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.81 Atmosphäre AR 241
allfieldsGer	10.1016/j.atmosres.2020.104945 doi (DE-627)ELV004028201 (ELSEVIER)S0169-8095(19)31191-3 DE-627 ger DE-627 rda eng 550 530 DE-600 38.81 bkl Liu, Jian verfasserin aut Impact of assimilating multi-source observations on meteorological and PM 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data assimilation (DA) is a promising approach to improve meteorological and PM2.5 forecasts, but to what extent and by what process the DA of meteorological fields helps improve PM2.5 forecast still call for more discussion. By utilizing WRFDA and WRF-Chem models, we have assimilated AMSU-A, MHS radiances and conventional observations, and studied the influences of the meteorological DA on meteorological and PM2.5 forecasts over Central China through a series of experiments. The results show that multi-source meteorological DA helps improve temperature and relative humidity forecasts in the lower atmosphere, and the improved meteorological fields further improve PM2.5 forecast with a reduction of bias and RMSE by 7.4% and 4.1% over the study area, especially during PM2.5 episode. This study also helps understand how DA improve the PM2.5 forecasts over Central China. Data assimilation WRFDA WRF-Chem AMSU-A MHS Proprietary in situ observations Hong, Jia verfasserin aut Mao, Feiyue verfasserin aut Gong, Wei verfasserin aut Shen, Longjiao verfasserin aut Liang, Shengwen verfasserin aut Chen, Jiangping verfasserin aut Enthalten in Atmospheric research Amsterdam [u.a.] : Elsevier, 1986 241 Online-Ressource (DE-627)320502430 (DE-600)2012396-6 (DE-576)258584130 0169-8095 nnns volume:241 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.81 Atmosphäre AR 241
allfieldsSound	10.1016/j.atmosres.2020.104945 doi (DE-627)ELV004028201 (ELSEVIER)S0169-8095(19)31191-3 DE-627 ger DE-627 rda eng 550 530 DE-600 38.81 bkl Liu, Jian verfasserin aut Impact of assimilating multi-source observations on meteorological and PM 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data assimilation (DA) is a promising approach to improve meteorological and PM2.5 forecasts, but to what extent and by what process the DA of meteorological fields helps improve PM2.5 forecast still call for more discussion. By utilizing WRFDA and WRF-Chem models, we have assimilated AMSU-A, MHS radiances and conventional observations, and studied the influences of the meteorological DA on meteorological and PM2.5 forecasts over Central China through a series of experiments. The results show that multi-source meteorological DA helps improve temperature and relative humidity forecasts in the lower atmosphere, and the improved meteorological fields further improve PM2.5 forecast with a reduction of bias and RMSE by 7.4% and 4.1% over the study area, especially during PM2.5 episode. This study also helps understand how DA improve the PM2.5 forecasts over Central China. Data assimilation WRFDA WRF-Chem AMSU-A MHS Proprietary in situ observations Hong, Jia verfasserin aut Mao, Feiyue verfasserin aut Gong, Wei verfasserin aut Shen, Longjiao verfasserin aut Liang, Shengwen verfasserin aut Chen, Jiangping verfasserin aut Enthalten in Atmospheric research Amsterdam [u.a.] : Elsevier, 1986 241 Online-Ressource (DE-627)320502430 (DE-600)2012396-6 (DE-576)258584130 0169-8095 nnns volume:241 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.81 Atmosphäre AR 241
language	English
source	Enthalten in Atmospheric research 241 volume:241
sourceStr	Enthalten in Atmospheric research 241 volume:241
format_phy_str_mv	Article
bklname	Atmosphäre
institution	findex.gbv.de
topic_facet	Data assimilation WRFDA WRF-Chem AMSU-A MHS Proprietary in situ observations
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authorswithroles_txt_mv	Liu, Jian @@aut@@ Hong, Jia @@aut@@ Mao, Feiyue @@aut@@ Gong, Wei @@aut@@ Shen, Longjiao @@aut@@ Liang, Shengwen @@aut@@ Chen, Jiangping @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
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author	Liu, Jian
spellingShingle	Liu, Jian ddc 550 bkl 38.81 misc Data assimilation misc WRFDA misc WRF-Chem misc AMSU-A misc MHS misc Proprietary in situ observations Impact of assimilating multi-source observations on meteorological and PM
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title	Impact of assimilating multi-source observations on meteorological and PM
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title_full	Impact of assimilating multi-source observations on meteorological and PM
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author_browse	Liu, Jian Hong, Jia Mao, Feiyue Gong, Wei Shen, Longjiao Liang, Shengwen Chen, Jiangping
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title_sort	impact of assimilating multi-source observations on meteorological and pm
title_auth	Impact of assimilating multi-source observations on meteorological and PM
abstract	Data assimilation (DA) is a promising approach to improve meteorological and PM2.5 forecasts, but to what extent and by what process the DA of meteorological fields helps improve PM2.5 forecast still call for more discussion. By utilizing WRFDA and WRF-Chem models, we have assimilated AMSU-A, MHS radiances and conventional observations, and studied the influences of the meteorological DA on meteorological and PM2.5 forecasts over Central China through a series of experiments. The results show that multi-source meteorological DA helps improve temperature and relative humidity forecasts in the lower atmosphere, and the improved meteorological fields further improve PM2.5 forecast with a reduction of bias and RMSE by 7.4% and 4.1% over the study area, especially during PM2.5 episode. This study also helps understand how DA improve the PM2.5 forecasts over Central China.
abstractGer	Data assimilation (DA) is a promising approach to improve meteorological and PM2.5 forecasts, but to what extent and by what process the DA of meteorological fields helps improve PM2.5 forecast still call for more discussion. By utilizing WRFDA and WRF-Chem models, we have assimilated AMSU-A, MHS radiances and conventional observations, and studied the influences of the meteorological DA on meteorological and PM2.5 forecasts over Central China through a series of experiments. The results show that multi-source meteorological DA helps improve temperature and relative humidity forecasts in the lower atmosphere, and the improved meteorological fields further improve PM2.5 forecast with a reduction of bias and RMSE by 7.4% and 4.1% over the study area, especially during PM2.5 episode. This study also helps understand how DA improve the PM2.5 forecasts over Central China.
abstract_unstemmed	Data assimilation (DA) is a promising approach to improve meteorological and PM2.5 forecasts, but to what extent and by what process the DA of meteorological fields helps improve PM2.5 forecast still call for more discussion. By utilizing WRFDA and WRF-Chem models, we have assimilated AMSU-A, MHS radiances and conventional observations, and studied the influences of the meteorological DA on meteorological and PM2.5 forecasts over Central China through a series of experiments. The results show that multi-source meteorological DA helps improve temperature and relative humidity forecasts in the lower atmosphere, and the improved meteorological fields further improve PM2.5 forecast with a reduction of bias and RMSE by 7.4% and 4.1% over the study area, especially during PM2.5 episode. This study also helps understand how DA improve the PM2.5 forecasts over Central China.
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title_short	Impact of assimilating multi-source observations on meteorological and PM
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doi_str	10.1016/j.atmosres.2020.104945
up_date	2024-07-06T21:36:21.190Z
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Impact of assimilating multi-source observations on meteorological and PM

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