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기상청 GloSea의 위성관측 기반 토양수분(SMAP) 동화: 예비 실험 분석

Assimilation of Satellite-Based Soil Moisture (SMAP) in KMA GloSea6: The Results of the First Preliminary Experiment

류영|부경온|황승언|현유경|이조한|지희숙
한국기상학회 (Korean Meteorological Society)|2022.12|대기 (Atmosphere)|제32권 제4호|pp. 395-409|DOI 10.14191/Atmos.2022.32.4.395

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참고문헌

키워드

Soil moistureData assimilationSoil Moisture Active Passive (SMAP)Local Ensemble Transform Kalman Filter (LETKF)
주제분류대기과학

초록

A new soil moisture initialization scheme is applied to the Korea MeteorologicalAdministration (KMA) Global Seasonal forecasting system version 6 (GloSea6). It is designedto ingest the microwave soil moisture retrievals from Soil Moisture Active Passive (SMAP) radiometerusing the Local Ensemble Transform Kalman Filter (LETKF). In this technical note,we describe the procedure of the newly-adopted initialization scheme, the change of soil moisturestates by assimilation, and the forecast skill differences for the surface temperature and precipitationby GloSea6 simulation from two preliminary experiments. Based on a 4-year analysisexperiment, the soil moisture from the land-surface model of current operational GloSea6 isfound to be drier generally comparing to SMAP observation. LETKF data assimilation showsa tendency toward being wet globally, especially in arid area such as deserts and TibetanPlateau. Also, it increases soil moisture analysis increments in most soil levels of wetness inland than current operation. The other experiment of GloSea6 forecast with application of thenew initialization system for the heat wave case in 2020 summer shows that the memory ofsoil moisture anomalies obtained by the new initialization system is persistent throughout theentire forecast period of three months. However, averaged forecast improvements are not substantialand mixed over Eurasia during the period of forecast: forecast skill for the precipitationimproved slightly but for the surface air temperature rather degraded. Our preliminary resultssuggest that additional elaborate developments in the soil moisture initialization are still requiredto improve overall forecast skills.

Abstract

A new soil moisture initialization scheme is applied to the Korea MeteorologicalAdministration (KMA) Global Seasonal forecasting system version 6 (GloSea6). It is designedto ingest the microwave soil moisture retrievals from Soil Moisture Active Passive (SMAP) radiometerusing the Local Ensemble Transform Kalman Filter (LETKF). In this technical note,we describe the procedure of the newly-adopted initialization scheme, the change of soil moisturestates by assimilation, and the forecast skill differences for the surface temperature and precipitationby GloSea6 simulation from two preliminary experiments. Based on a 4-year analysisexperiment, the soil moisture from the land-surface model of current operational GloSea6 isfound to be drier generally comparing to SMAP observation. LETKF data assimilation showsa tendency toward being wet globally, especially in arid area such as deserts and TibetanPlateau. Also, it increases soil moisture analysis increments in most soil levels of wetness inland than current operation. The other experiment of GloSea6 forecast with application of thenew initialization system for the heat wave case in 2020 summer shows that the memory ofsoil moisture anomalies obtained by the new initialization system is persistent throughout theentire forecast period of three months. However, averaged forecast improvements are not substantialand mixed over Eurasia during the period of forecast: forecast skill for the precipitationimproved slightly but for the surface air temperature rather degraded. Our preliminary resultssuggest that additional elaborate developments in the soil moisture initialization are still requiredto improve overall forecast skills.

참고문헌 (50)

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