气候变化领域集成服务门户(CCPortal): Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems

CCPortal

DOI	10.5194/tc-9-1735-2015
	Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems
	Posey P.G.; Metzger E.J.; Wallcraft A.J.; Hebert D.A.; Allard R.A.; Smedstad O.M.; Phelps M.W.; Fetterer F.; Stewart J.S.; Meier W.N.; Helfrich S.R.
发表日期	2015
ISSN	19940416
卷号	9 期号:4
英文摘要	This study presents the improvement in ice edge error within the US Navy's operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration products. Since the late 1980's, the ice forecast systems have assimilated near real-time sea ice concentration derived from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI and then SSMIS). The resolution of the satellite-derived product was approximately the same as the previous operational ice forecast system (25 km). As the sea ice forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, a new Navy sea ice forecast system (Arctic Cap Nowcast/Forecast System - ACNFS) went into operations with a horizontal resolution of ∼ 3.5 km at the North Pole. A method of blending ice concentration observations from the Advanced Microwave Scanning Radiometer (AMSR2) along with a sea ice mask produced by the National Ice Center (NIC) has been developed, resulting in an ice concentration product with very high spatial resolution. In this study, ACNFS was initialized with this newly developed high resolution blended ice concentration product. The daily ice edge locations from model hindcast simulations were compared against independent observed ice edge locations. ACNFS initialized using the high resolution blended ice concentration data product decreased predicted ice edge location error compared to the operational system that only assimilated SSMIS data. A second evaluation assimilating the new blended sea ice concentration product into the pre-operational Navy Global Ocean Forecast System 3.1 also showed a substantial improvement in ice edge location over a system using the SSMIS sea ice concentration product alone. This paper describes the technique used to create the blended sea ice concentration product and the significant improvements in ice edge forecasting in both of the Navy's sea ice forecasting systems. © Author(s) 2015.
学科领域	AMSR-E; concentration (composition); data assimilation; data set; forecasting method; global ocean; microwave imagery; sea ice; spatial resolution; Arctic Ocean
语种	英语
scopus关键词	AMSR-E; concentration (composition); data assimilation; data set; forecasting method; global ocean; microwave imagery; sea ice; spatial resolution; Arctic Ocean
来源期刊	Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/119825
作者单位	Naval Research Laboratory, Stennis Space CenterMS, United States; Vencore Services and Solutions, Inc., Stennis Space CenterMS, United States; Jacobs Technology Inc., Stennis Space CenterMS, United States; National Snow and Ice Data Center, Boulder, CO, United States; J. Scott Stewart of Exploratory Thinking, Longmont, CO, United States; NASA Goddard Space Flight Center, Greenbelt, MD, United States; US National Ice Center, Suitland, MD, United States
推荐引用方式 GB/T 7714	Posey P.G.,Metzger E.J.,Wallcraft A.J.,et al. Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems[J],2015,9(4).
APA	Posey P.G..,Metzger E.J..,Wallcraft A.J..,Hebert D.A..,Allard R.A..,...&Helfrich S.R..(2015).Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems.Cryosphere,9(4).
MLA	Posey P.G.,et al."Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems".Cryosphere 9.4(2015).