Strongly Coupled Land-Atmosphere Data Assimilation and Its Impact on Near-surface Weather Forecasting

Abstract

Near-surface weather forecasts are critical for protecting life and human activities. However, they remain a challenging problem in modern numerical weather prediction (NWP) due to difficulties in representing complicated land–atmosphere interactions in numerical models. This talk will outline recent advancements made by the first author's research team in understanding and developing coupled land-atmosphere data assimilation methods that improve near-surface weather forecasts. The association between near-surface variables and soil moisture was evaluated with observations, coupled land–atmosphere model, and data assimilation. Results indicated a strong coupling between soil moisture and the low-level atmosphere, especially the atmospheric boundary layer. Then, the weakly and strongly coupled land–atmosphere data assimilation methods were compared regarding their impact on the prediction of near-surface atmospheric conditions. Results showed that strongly coupled land–atmosphere data assimilation, with simultaneous corrections to the land and atmospheric conditions, outperformed weakly coupled data assimilation.  More importantly, the recent efforts in developing strongly coupled land–atmosphere data assimilation with NOAA Unified Forecast System (UFS) model and Joint Efforts for Data Assimilation Integration (JEDI) system, as well as the progress with NASA Unified Weather Research and Forecasting (NU-WRF) model and GSI data assimilation system will be reported. The impacts of coupled land-atmosphere data assimilation systems on short- and medium-range weather forecasting will also be discussed.