연구성과
더 나은 예측을 향한 연구의 결실
수치예보의 발전을 위해 쌓아 온 KIAPS의 연구성과를 소개합니다.
논문부터 기술과 소프트웨어까지, 더 정확한 예측을 위한 지식과
경험을 나눕니다.
[논문] Improving weather forecast skill of the Korean Integrated Model by assimilating Soil Moisture Active Passive soil moisture anomalies
- 작성자
- 마스터관리자
- 작성일
- 조회수
- 57
Abstract:
This study investigates the potential benefit of assimilating soil moisture (SM) data retrieved from Soil Moisture Active Passive (SMAP) in improving global SM estimates and enhancing the weather forecast skill of the Korean Integrated Model (KIM). The 36-km SMAP L2 SM retrievals are assimilated into the Noah land surface model (LSM) using the ensemble Kalman filter scheme through the National Aeronautics and Space Administration Land Information System (LIS) that is weakly coupled to KIM. A suite of cycling experiments of the KIM–LIS system that includes land and atmospheric data assimilation (DA) and five-day weather forecasts are conducted over the global domain from March to July 2022. In the SMAP SM DA, two different SM bias correction methods, namely the cumulative distribution function matching and anomaly-based bias correction, are applied to correct systematic biases between SMAP and Noah-LSM before assimilation. The global triple collocation analysis reveals that compared to the control case (without SM DA), significant improvements in the global SM estimates are achieved by assimilating the SMAP data, especially by employing the anomaly-based bias correction. Notably, the improved SM initial conditions lead to an improved screen-level specific humidity and air temperature analysis and forecasts when the results are compared against the European Centre for Medium Range Weather Forecasts-Integrated Forecasting System analysis. The beneficial impacts of the SMAP DA on the atmospheric variables extend up to an atmospheric level of 700 hPa. Prominent improvements in the KIM forecast skill by the SMAP DA applying the anomaly-based bias correction are observed in the northern part of Africa and West and Central Asia with stronger impacts for longer forecast lead time. This paper demonstrates the feasibility of assimilating the SMAP data within KIM–LIS to enhance the KIM weather forecasts in the lower atmosphere when a proper SM bias correction method is applied.
Citation:
Kwon, Y., Jun, S., Kim,E., Seol, K.H., Hong, S., Kwon, I.H. et al. (2024)Improving weather forecast skill of the KoreanIntegrated Model by assimilating Soil MoistureActive Passive soil moisture anomalies. QuarterlyJournal of the Royal Meteorological Society, 150(765),5305–5336.
This study investigates the potential benefit of assimilating soil moisture (SM) data retrieved from Soil Moisture Active Passive (SMAP) in improving global SM estimates and enhancing the weather forecast skill of the Korean Integrated Model (KIM). The 36-km SMAP L2 SM retrievals are assimilated into the Noah land surface model (LSM) using the ensemble Kalman filter scheme through the National Aeronautics and Space Administration Land Information System (LIS) that is weakly coupled to KIM. A suite of cycling experiments of the KIM–LIS system that includes land and atmospheric data assimilation (DA) and five-day weather forecasts are conducted over the global domain from March to July 2022. In the SMAP SM DA, two different SM bias correction methods, namely the cumulative distribution function matching and anomaly-based bias correction, are applied to correct systematic biases between SMAP and Noah-LSM before assimilation. The global triple collocation analysis reveals that compared to the control case (without SM DA), significant improvements in the global SM estimates are achieved by assimilating the SMAP data, especially by employing the anomaly-based bias correction. Notably, the improved SM initial conditions lead to an improved screen-level specific humidity and air temperature analysis and forecasts when the results are compared against the European Centre for Medium Range Weather Forecasts-Integrated Forecasting System analysis. The beneficial impacts of the SMAP DA on the atmospheric variables extend up to an atmospheric level of 700 hPa. Prominent improvements in the KIM forecast skill by the SMAP DA applying the anomaly-based bias correction are observed in the northern part of Africa and West and Central Asia with stronger impacts for longer forecast lead time. This paper demonstrates the feasibility of assimilating the SMAP data within KIM–LIS to enhance the KIM weather forecasts in the lower atmosphere when a proper SM bias correction method is applied.
Citation:
Kwon, Y., Jun, S., Kim,E., Seol, K.H., Hong, S., Kwon, I.H. et al. (2024)Improving weather forecast skill of the KoreanIntegrated Model by assimilating Soil MoistureActive Passive soil moisture anomalies. QuarterlyJournal of the Royal Meteorological Society, 150(765),5305–5336.