Improvement of Surface PM_2.5 Diurnal Variation Simulations in East Africa for the MAIA Satellite Mission

Abstract

The Multi-Angle Imager for Aerosols (MAIA), supported by NASA and the Italian Space Agency, is planned for launch into space in 2025. As part of its mission goal, outputs from a chemical transport model, the Unified Inputs for Weather Research and Forecasting Model coupled with Chemistry (UI-WRF-Chem), will be used together with satellite data and surface observations for estimating surface PM 2.5 . Here, we develop a method to improve UI-WRF-Chem with surface observations at the U.S. embassy in Ethiopia, one of MAIA's primary target areas in east Africa. The method inversely models the diurnal profile and amount of anthropogenic aerosol and trace gas emissions. Lowcost PurpleAir sensor data are used for validation after applying calibration functions obtained from the collocated data at the embassy. With the emission updates in UI-WRF-Chem, independent validation for February 2022 at several different PurpleAir sites shows an increase in the linear correlation coefficients from 0.1−0.7 to 0.6−0.9 between observations and simulations of the diurnal variation of surface PM 2.5 . Furthermore, even by using the emissions optimized for February 2021, the UI-WRF-Chem forecast for March 2022 is also improved. Annual update of monthly emissions via inverse modeling has the potential and is needed to improve MAIA's estimate of surface PM 2.5 .

Large contribution of soil emissions to the atmospheric nitrogen budget and their impacts on air quality and temperature rise in North China

“…The 0.25° × 0.25° Global Land Data Assimilation System (GLDAS) data provides the initial and boundary conditions of soil properties, i.e., soil moisture and temperature (Rodell, 2004). Details of Unified Inputs of meteorological and chemical position data for UI-WRF-Chem, can be found in recent publications (Li et al, 2024;Wang et al, 2023c (Guenther et al, 2012).…”