인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
ABSTRACT Soil moisture (SM) is one of the crucial variables of the earth system that needs to be accurately initialized in a numerical weather model for accurate weather predictions. As the availability of in situ SM observations is sparse in space and time, satellite‐derived SM estimates are widely used to create model surface boundary conditions through assimilation techniques. SM retrievals from the soil moisture active passive (SMAP) satellite have been assimilated into the high‐resolution NCUM‐R operational forecasting system over the Indian region for the first time in this study. The simplified extended Kalman filter (sEKF) based Land Data Assimilation System (LDAS) creates land surface SM initial conditions for NCUM‐R by assimilating SMAP‐derived SM and screen‐level observations. Two numerical experiments, namely CTL (incorporating only screen level observations in LDAS) and SMP (assimilating both SMAP SM and screen level observations in LDAS), are carried out to assess the model's forecast skill improvement by assimilating SM. The validation analysis with the SM in situ observations network indicates skill improvement of 0.013 and 0.002 for anomaly correlation and unbiased RMSE in the accuracy of SM estimates with assimilation. The skill improvement is found to be higher in the wetter SM regions. Furthermore, the positive impact of SM assimilation on the forecast of surface air temperature is also noted. Finally, we demonstrated that the SMAP assimilation has led to a more realistic representation of SM than in the control simulation for various precipitation events, suggesting its usage for drought/flood monitoring in the long term.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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