A SNOW DEPTH ANALYSIS FOR THE NEXT GENERATION OF GLOBAL PREDICTION SYSTEMS

被引：0

作者：

Kongoli, Cezar ^{[1
,2
]}

Romanov, Peter ^{[2
,4
]}

Helfrich, Sean ^{[2
]}

Dong, Jiarui ^{[3
]}

Ek, Michael ^{[3
]}

Smith, Tomas ^{[2
]}

机构：

[1] Univ Maryland, ESSIC, College Pk, MD 20742 USA

[2] NOAA, NESDIS, College Pk, MD 20740 USA

[3] NOAA, EMC, College Pk, MD USA

[4] CUNY, NOAA, CREST, New York, NY 10021 USA

来源：

INTERNATIONAL JOURNAL OF ECOSYSTEMS AND ECOLOGY SCIENCE-IJEES | 2018年 / 8卷 / 02期

关键词：

snow depth analysis; generation; global prediction systems;

D O I：

暂无

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Information on snow depth is a primary input to NOAA's operational numerical weather prediction (NWP) models. Current NOAA's National Centers for Environmental Prediction (NCEP) operational NWP models rely on snow depth observational data for their land surface model initializations. A new snow depth analysis system based on optimal interpolation method is being developed for NCEP NWP models with improved spatial resolution and utilization of multiple sources of observational data. The analysis blends bias-corrected satellite snow depth from the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on board the Global Change Observation Mission 1st - Water (GCOM-W1) with an extended network of in-situ stations from the Global Historical Climatology Network (GHCN) to generate snow depth globally at 12 km resolution. A simplified snow accumulation and melt model driven by Global Forecast System (GFS)'s precipitation and temperature has been developed to estimate first guess snow depth fields. Details of the main components of the algorithm and evaluation results are presented.

引用

页码：189 / 192

页数：4

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