A revised method with a temperature constraint for assimilating satellite-derived humidity in forecasting sea fog over the Yellow Sea

Numerical forecast of sea fog is very challenging work because of its high sensitivity to model initial conditions. For better depicting the humidity structure of the marine atmospheric boundary layer (MABL), Wang et al. (2014) assimilated satellite-derived humidity from sea fog at its initial stage...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xiaoyu Gao [verfasserIn] Shanhong Gao [verfasserIn] Ziru Li [verfasserIn] Yongming Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	sea fog Yellow Sea marine atmospheric boundary layer (MABL) WRF model data assimilation satellite-derived humidity

Übergeordnetes Werk:	In: Frontiers in Earth Science - Frontiers Media S.A., 2014, 10(2023)
Übergeordnetes Werk:	volume:10 ; year:2023

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/feart.2022.992246

Katalog-ID:	DOAJ082729522

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520			\|a Numerical forecast of sea fog is very challenging work because of its high sensitivity to model initial conditions. For better depicting the humidity structure of the marine atmospheric boundary layer (MABL), Wang et al. (2014) assimilated satellite-derived humidity from sea fog at its initial stage over the Yellow Sea (W14 method), using an extended three-dimensional variational data assimilation (3DVAR) with the Weather Research and Forecasting model (WRF). This article proposes a revised version of the W14 method. The major ingredient of the revision is the inclusion of a temperature constraint into the satellite-derived humidity, not only for the missed fog area that the W14 method primarily considers, but also for the false fog area that is not handled in the W14 method. The numerical experiment results of 10 sea fog cases over the Yellow Sea show that the revised method can effectively alleviate the wet bias occasionally occurring in the W14 method, resulting in an improvement by about 15% for an equitable threat score of the simulated fog area. In addition, a detailed case study is conducted to illustrate the working mechanism of the revised method, including sensitivity experiments focusing on the roles of two kinds of background error covariances (CV5 and CV6) in the assimilation by the WRF-3DVAR. The results suggest that CV6 with multivariate cross-correlation is probably more beneficial to the revised method’s performance.
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author	Xiaoyu Gao
spellingShingle	Xiaoyu Gao misc sea fog misc Yellow Sea misc marine atmospheric boundary layer (MABL) misc WRF model misc data assimilation misc satellite-derived humidity misc Science misc Q A revised method with a temperature constraint for assimilating satellite-derived humidity in forecasting sea fog over the Yellow Sea
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topic_title	A revised method with a temperature constraint for assimilating satellite-derived humidity in forecasting sea fog over the Yellow Sea sea fog Yellow Sea marine atmospheric boundary layer (MABL) WRF model data assimilation satellite-derived humidity
topic	misc sea fog misc Yellow Sea misc marine atmospheric boundary layer (MABL) misc WRF model misc data assimilation misc satellite-derived humidity misc Science misc Q
topic_unstemmed	misc sea fog misc Yellow Sea misc marine atmospheric boundary layer (MABL) misc WRF model misc data assimilation misc satellite-derived humidity misc Science misc Q
topic_browse	misc sea fog misc Yellow Sea misc marine atmospheric boundary layer (MABL) misc WRF model misc data assimilation misc satellite-derived humidity misc Science misc Q
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title	A revised method with a temperature constraint for assimilating satellite-derived humidity in forecasting sea fog over the Yellow Sea
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title_full	A revised method with a temperature constraint for assimilating satellite-derived humidity in forecasting sea fog over the Yellow Sea
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title_sort	revised method with a temperature constraint for assimilating satellite-derived humidity in forecasting sea fog over the yellow sea
title_auth	A revised method with a temperature constraint for assimilating satellite-derived humidity in forecasting sea fog over the Yellow Sea
abstract	Numerical forecast of sea fog is very challenging work because of its high sensitivity to model initial conditions. For better depicting the humidity structure of the marine atmospheric boundary layer (MABL), Wang et al. (2014) assimilated satellite-derived humidity from sea fog at its initial stage over the Yellow Sea (W14 method), using an extended three-dimensional variational data assimilation (3DVAR) with the Weather Research and Forecasting model (WRF). This article proposes a revised version of the W14 method. The major ingredient of the revision is the inclusion of a temperature constraint into the satellite-derived humidity, not only for the missed fog area that the W14 method primarily considers, but also for the false fog area that is not handled in the W14 method. The numerical experiment results of 10 sea fog cases over the Yellow Sea show that the revised method can effectively alleviate the wet bias occasionally occurring in the W14 method, resulting in an improvement by about 15% for an equitable threat score of the simulated fog area. In addition, a detailed case study is conducted to illustrate the working mechanism of the revised method, including sensitivity experiments focusing on the roles of two kinds of background error covariances (CV5 and CV6) in the assimilation by the WRF-3DVAR. The results suggest that CV6 with multivariate cross-correlation is probably more beneficial to the revised method’s performance.
abstractGer	Numerical forecast of sea fog is very challenging work because of its high sensitivity to model initial conditions. For better depicting the humidity structure of the marine atmospheric boundary layer (MABL), Wang et al. (2014) assimilated satellite-derived humidity from sea fog at its initial stage over the Yellow Sea (W14 method), using an extended three-dimensional variational data assimilation (3DVAR) with the Weather Research and Forecasting model (WRF). This article proposes a revised version of the W14 method. The major ingredient of the revision is the inclusion of a temperature constraint into the satellite-derived humidity, not only for the missed fog area that the W14 method primarily considers, but also for the false fog area that is not handled in the W14 method. The numerical experiment results of 10 sea fog cases over the Yellow Sea show that the revised method can effectively alleviate the wet bias occasionally occurring in the W14 method, resulting in an improvement by about 15% for an equitable threat score of the simulated fog area. In addition, a detailed case study is conducted to illustrate the working mechanism of the revised method, including sensitivity experiments focusing on the roles of two kinds of background error covariances (CV5 and CV6) in the assimilation by the WRF-3DVAR. The results suggest that CV6 with multivariate cross-correlation is probably more beneficial to the revised method’s performance.
abstract_unstemmed	Numerical forecast of sea fog is very challenging work because of its high sensitivity to model initial conditions. For better depicting the humidity structure of the marine atmospheric boundary layer (MABL), Wang et al. (2014) assimilated satellite-derived humidity from sea fog at its initial stage over the Yellow Sea (W14 method), using an extended three-dimensional variational data assimilation (3DVAR) with the Weather Research and Forecasting model (WRF). This article proposes a revised version of the W14 method. The major ingredient of the revision is the inclusion of a temperature constraint into the satellite-derived humidity, not only for the missed fog area that the W14 method primarily considers, but also for the false fog area that is not handled in the W14 method. The numerical experiment results of 10 sea fog cases over the Yellow Sea show that the revised method can effectively alleviate the wet bias occasionally occurring in the W14 method, resulting in an improvement by about 15% for an equitable threat score of the simulated fog area. In addition, a detailed case study is conducted to illustrate the working mechanism of the revised method, including sensitivity experiments focusing on the roles of two kinds of background error covariances (CV5 and CV6) in the assimilation by the WRF-3DVAR. The results suggest that CV6 with multivariate cross-correlation is probably more beneficial to the revised method’s performance.
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title_short	A revised method with a temperature constraint for assimilating satellite-derived humidity in forecasting sea fog over the Yellow Sea
url	https://doi.org/10.3389/feart.2022.992246 https://doaj.org/article/c20910a74b01422a9fd4b487ff35d597 https://www.frontiersin.org/articles/10.3389/feart.2022.992246/full https://doaj.org/toc/2296-6463
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A revised method with a temperature constraint for assimilating satellite-derived humidity in forecasting sea fog over the Yellow Sea

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