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Improvement of Soil Moisture Initialization for a Global Seasonal Forecast System
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  • Journal title : Atmosphere
  • Volume 26, Issue 1,  2016, pp.35-45
  • Publisher : Korean Meteorological Society
  • DOI : 10.14191/Atmos.2016.26.1.035
 Title & Authors
Improvement of Soil Moisture Initialization for a Global Seasonal Forecast System
Seo, Eunkyo; Lee, Myong-In; Jeong, Jee-Hoon; Kang, Hyun-Suk; Won, Duk-Jin;
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 Abstract
Initialization of the global seasonal forecast system is as much important as the quality of the embedded climate model for the climate prediction in sub-seasonal time scale. Recent studies have emphasized the important role of soil moisture initialization, suggesting a significant increase in the prediction skill particularly in the mid-latitude land area where the influence of sea surface temperature in the tropics is less crucial and the potential predictability is supplemented by land-atmosphere interaction. This study developed a new soil moisture initialization method applicable to the KMA operational seasonal forecasting system. The method includes first the long-term integration of the offline land surface model driven by observed atmospheric forcing and precipitation. This soil moisture reanalysis is given for the initial state in the ensemble seasonal forecasts through a simple anomaly initialization technique to avoid the simulation drift caused by the systematic model bias. To evaluate the impact of the soil moisture initialization, two sets of long-term, 10-member ensemble experiment runs have been conducted for 1996~2009. As a result, the soil moisture initialization improves the prediction skill of surface air temperature significantly at the zero to one month forecast lead (up to ~60 days forecast lead), although the skill increase in precipitation is less significant. This study suggests that improvements of the prediction in the sub-seasonal timescale require the improvement in the quality of initial data as well as the adequate treatment of the model systematic bias.
 Keywords
Seasonal forecast system;soil moisture initialization;land-atmosphere interaction;anomaly initialization;
 Language
Korean
 Cited by
1.
GloSea5 모델의 자료처리 시스템 구축 및 시·공간적 재현성평가,문수진;한수희;최광순;송정현;

한국수자원학회논문집, 2016. vol.49. 9, pp.761-771 crossref(new window)
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