Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter

摘要

Abstract The next‐generation geostationary satellite Himawari‐8 has a much higher observation frequency of the aerosol field than polar‐orbiting satellites. Aerosol analyses with a geostationary satellite can advance our understanding of the rapid spatiotemporal evolution of aerosols, which is especially critical for studies of air pollution and its mechanisms. We present a one‐monthlong hourly aerosol analysis using an aerosol data assimilation based on the local ensemble Kalman filter (LETKF), Himawari‐8‐retrieved hourly aerosol optical thicknesses (AOTs), and a global model named Non‐hydrostatic Icosahedral Atmospheric Model coupled with an aerosol model named Spectral Radiation Transport Model for Aerosol Species (NICAM‐SPRINTARS). To assimilate asynchronous observations and avoid frequent switching between the assimilation and ensemble aerosol forecasts, the LETKF is also extended to the four‐dimensional LETKF (4D‐LETKF). The hourly aerosol analyses are evaluated with both the assimilated Himawari‐8 AOTs and independent Moderate Resolution Imaging Spectroradiometer (MODIS)‐ and AErosol RObotic NETwork (AERONET)‐retrieved AOTs. All evaluations show that the assimilations positively affect the model performances and produce simulated AOTs that are closer to the observations. The analyses correctly reduce the significantly positive biases and root‐mean‐square errors of the control experiment, especially over East China and Australia. Our results also show that hourly aerosol analyses with more frequent Himawari‐8 observations are superior to those using the polar satellite MODIS observations. The performances among the LETKF and 4D‐LETKF experiments are generally not so different, but the computational load of the 4D‐LETKF is much lighter than that of the LETKF.