DOI: 10.48577/jpl.x52mvo
title: Development of UI-WRF-Chem (v1.0) for the MAIA satellite mission: case demonstration

description: The Multi-Angle Imager for Aerosols (MAIA) satellite mission, to be jointly implemented by 23 NASA and the Italian Space Agency with an expected 2026 launch, aims to study how different 24 types of particulate matter (PM) pollution affect human health. The investigation will primarily 25 focus on a discrete set of globally distributed Primary Target Areas (PTAs) containing major 26 metropolitan cities, and will integrate satellite observations, ground observations, and chemical 27 transport model (CTM) outputs (meteorology variables and PM concentrations) to generate maps 28 of near-surface total and speciated PM within the PTAs. In addition, the MAIA investigation will 29 provide satellite measurements of aerosols over a set of Secondary Target Areas (STAs), which 30 are useful for studying air quality more broadly. For the CTM, we have developed a Unified Inputs 31 (of initial and boundary conditions) for WRF-Chem (UI-WRF-Chem) modeling framework to 32 support the MAIA satellite mission, building upon the standard WRF-Chem model. The 33 framework includes newly developed modules and major enhancements that aim to improve model 34 simulated meteorology variables, total and speciated PM concentrations as well as AOD. These 35 developments include: (1) application of NASA GEOS FP and MERRA-2 data to provide both 36 meteorological and chemical initial and boundary conditions for performing WRF-Chem 37 simulations at a fine spatial resolution for both forecast and reanalysis modes; (2) application of 38 GLDAS and NLDAS data to constrain surface soil properties such as soil moisture; (3) application 39 of recent available MODIS land data to improve land surface properties such as land cover type; 40 (4) development of a new soil NOx emission scheme – the Berkeley Dalhousie Iowa Soil NO 41 Parameterization (BDISNP); (5) development of a stand-alone emission preprocessor that ingests 42 both global and regional anthropogenic emission inventories as well as fire emissions. 2 43 44 Here, we illustrate the model improvements enabled by these developments over four target areas: 45 Beijing in China, CHN-Beijing (STA); Rome in Italy, ITA-Rome (PTA); Los Angeles in the U.S., 46 USA-Angeles (PTA), and Atlanta in the U.S., USA-Atlanta (PTA). UI-WRF-Chem is configured 47 as 2 nested domains using an outer domain (D1) and inner domain (D2) with 12 km and 4 km 48 spatial resolution, respectively. For each target area, we first run a suite of simulations to test the 49 model sensitivity to different physics schemes and then select the optimal combination based on 50 evaluation of model simulated meteorology with ground observations. For the inner domain (D2), 51 we have chosen to turn off the traditional Grell 3D ensemble (G3D) cumulus scheme. We 52 conducted a case study over USA-Atlanta for June 2022 to demonstrate the impacts of the cumulus 53 scheme on precipitation and subsequent total and speciated PM2.5 concentrations. Our results show 54 that keeping the G3D cumulus scheme turned on results in higher precipitation and lower total and 55 speciated PM2.5 than the simulation with the G3D cumulus scheme turned off. Compared with 56 surface observations of precipitation and PM2.5 concentration, the simulation with the G3D scheme 57 off shows better performance. We focus on two dust intrusion events over CHN-Beijing and ITA58 Rome, which occurred in March 2018 and June 2023, respectively. We carried out a suite of 59 sensitivity simulations using UI-WRF-Chem by excluding chemical boundary conditions or 60 including MERRA-2 chemical boundary conditions. Our results show that using MERRA-2 data 61 to provide chemical boundary conditions can help improve model simulation of surface PM 62 concentrations and AOD. Some of the target areas have also experienced significant changes in 63 land cover and land use over the past decade. Our case study over CHN-Beijing in July 2018 64 investigates the impacts of improved land surface properties with recent available MODIS land 65 data for capturing the urban heat island phenomenon. Model-simulated surface skin temperature 66 shows better agreement with MODIS observed land surface temperature. The updated soil NOx 67 emission scheme in July 2018 also leads to higher NO2 vertical column density (VCD) in rural 68 areas within the CHN-Beijing target area, which matches better with TROPOMI observed NO2 69 VCD. This in turn affects the simulation of surface nitrate concentration. Lastly, we conducted a 70 case study over USA-LosAngeles to tune dust emissions. These examples illustrate the fine-tuning 71 work conducted over each target area for the purpose of evaluating and improving model 72 performance.