Coupling data assimilation and machine learning to improve land surface conditions and near-surface temperature and humidity forecasts

Abstract

Enhancing the representation of land surface conditions and improving the accuracy of near-surface weather forecasts remain critical challenges for numerical weather prediction (NWP). This study coupled a hybrid data assimilation-machine learning framework (DL) with the Weather Research and Forecasting (WRF) model to quantify the impacts of incorporating soil moisture (SM) and vegetation data on land surface initialization and near-surface weather forecast accuracy. This was achieved by integrating satellite-based leaf area index (LAI) and multi-source SM data into the WRF model in the Southern Great Plains (SGP) of the United States. The results indicate that optimizing LAI and SM significantly improves the simulation of water table depth, evapotranspiration (ET), air temperature and humidity in the WRF model. In addition to SM, LAI optimization provides additional benefits to the WRF model in dry years. A series of comparison experiments were conducted across both dry and wet years to evaluate the accuracy of air temperature and humidity forecasts. The optimized vegetation and SM conditions from the DL method were used as initial conditions for the early days of the forecast period. The results confirm that the DL method effectively refines the land surface initial conditions at the beginning of the forecast period. This effect improves the estimation of near-surface atmospheric conditions (e.g., air temperature and humidity) and alters precipitation patterns during the forecast period. In addition, the integration of LAI and SM is more effective in improving forecasts in wet/normal years than dry years. Analysis of the forecast results illustrates that the DL method can optimize initial conditions and improve near-surface weather forecasts over the next month.