气候变化领域集成服务门户(CCPortal): Improvement of the soil-atmosphere interactions and subsequent heavy precipitation modelling by enhanced initialization using remotely sensed 1 km soil moisture information

CCPortal

DOI	10.1016/j.rse.2020.111812
	Improvement of the soil-atmosphere interactions and subsequent heavy precipitation modelling by enhanced initialization using remotely sensed 1 km soil moisture information
	Helgert S.; Khodayar S.
发表日期	2020
ISSN	00344257
卷号	246
英文摘要	This study assesses the impact of an improved soil moisture (SM) initialization using direct insertion methodology for convection-resolving modelling of heavy precipitation events (HPEs). State-of-the-art 1 km SM data from the Soil Moisture and Ocean Salinity (SMOS) mission, SMOS-BEC L4 version 3 are used for this purpose. A strategy is developed to prepare the SMOS-L4 surface soil moisture (SSM) product for the COnsortium for Small-scale MOdelling (COSMO) model initialization by applying a cumulative density function (CDF)-matching bias-correction and the exponential filter method to calculate corresponding SM profiles (L4-Expo). The processed satellite-derived product is validated with 38 observing sites from three in-situ SM networks REMEDHUS (19), SMOSMANIA (11) and VAS (8). All networks measure at a soil depth of 5 cm, only at the SMOSMANIA network additional measurements at 10, 20 and 30 cm are available. Four HPEs are selected to evaluate the impact of the high-resolution realistic initialization. The results show a high agreement index (AI = 0.91) and a low root-mean-square deviation (0.03 m3/m3) of the high-resolution, bias-corrected SMOS-L4 SSM product compared to in-situ observations. Moreover, the derived L4-Expo SM profile field agrees with ground-based observations and successfully removes the wet bias of the original COSMO simulated SM profile. Enhanced SM initialization with the SMOS-L4 SM derived profiles improves precipitation modelling in all selected HPEs as a consequence of improved near-surface 2 m-temperature and induced changes in the pressure field (−0.5 hPa), atmospheric humidity distribution (dqs = 5–15%) as well as wind circulations (dw700hPa = 25%), thus convergence/divergence fields. The sensitivity study, applying the same methodology for SM initialization for a SMOS-L3 (~25 km), shows a weaker improvement of the precipitation forecast of an analysed HPE than the 1 km SMOS-L4 product. Our results highlight the benefit of high-resolution SSM remote sensing satellite data for scientific disciplines like meteorology in overcoming present limitations such as the uncertainty associated to SM initialization in models. © 2020 Elsevier Inc.
英文关键词	convection-permitting simulations; heavy precipitation; HyMeX; realistic SM initialization; SMOS-L4
语种	英语
scopus关键词	Atmospheric humidity; Exhibitions; Remote sensing; Soil moisture; Weather forecasting; Convergence/divergence; Cumulative density functions; Ground-based observations; Humidity distribution; Precipitation forecast; Remote sensing satellites; Root mean square deviations; Soil moisture and ocean salinity missions; Soil surveys; convection; divergence; humidity; meteorology; precipitation (climatology); pressure field; remote sensing; satellite data; SMOS; soil depth; soil moisture
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179277
作者单位	Institute of Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology, Karlsruhe, Germany; Mediterranean Centre for Environmental Studies (CEAM), Valencia, Spain
推荐引用方式 GB/T 7714	Helgert S.,Khodayar S.. Improvement of the soil-atmosphere interactions and subsequent heavy precipitation modelling by enhanced initialization using remotely sensed 1 km soil moisture information[J],2020,246.
APA	Helgert S.,&Khodayar S..(2020).Improvement of the soil-atmosphere interactions and subsequent heavy precipitation modelling by enhanced initialization using remotely sensed 1 km soil moisture information.Remote Sensing of Environment,246.
MLA	Helgert S.,et al."Improvement of the soil-atmosphere interactions and subsequent heavy precipitation modelling by enhanced initialization using remotely sensed 1 km soil moisture information".Remote Sensing of Environment 246(2020).