Taking into account atmospheric uncertainty improves sequential assimilation of SMOS sea ice thickness data in an ice-ocean model

Journal of Atmospheric and Oceanic Technology

Volumn 33, Issue 3, 2016, Pages 397-407

  Yang, Qinghua ^a,b   Losch, Martin ^b   Losa, Svetlana N ^b   Jung, Thomas ^b,c   Nerger, Lars ^b  

^a Key Laboratory of Research on Marine Hazards Forecasting   (China)

^b ALFRED WEGENER INSTITUTE FOR POLAR AND MARINE RESEARCH   (Germany)

^c UNIVERSITY OF BREMEN   (Germany)

Author keywords

Data assimilation; Forecasting; Models and modeling; Numerical weather prediction forecasting; Probability forecasts models distribution

Indexed keywords

FORECASTING; MICROWAVE SENSORS; NUMERICAL MODELS; RAIN; SEA ICE; SOIL MOISTURE; UNCERTAINTY ANALYSIS; WEATHER FORECASTING;

DATA ASSIMILATION; ENSEMBLE BASED DATA ASSIMILATION; ENSEMBLE PREDICTION SYSTEMS; MASSACHUSETTS INSTITUTE OF TECHNOLOGY; NUMERICAL WEATHER PREDICTION/FORECASTING; PROBABILITY FORECASTS/MODELS/DISTRIBUTION; SOIL MOISTURE OCEAN SALINITIES; SPECIAL SENSOR MICROWAVE IMAGERS;

ICE;

CLIMATE PREDICTION; DATA ASSIMILATION; ENSEMBLE FORECASTING; ICE THICKNESS; KALMAN FILTER; PROBABILITY; SATELLITE DATA; SEA ICE; SMOS; WEATHER FORECASTING;

ARCTIC OCEAN;

EID: 84962905566     PISSN: 07390572     EISSN: 15200426     Source Type: Journal    
DOI: 10.1175/JTECH-D-15-0176.1     Document Type: Article

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