Development of UI-WRF-Chem for the MAIA Satellite Mission: Case Demonstration over Different Target Areas

The Multi-Angle Imager for Aerosols (MAIA) satellite mission, to be jointly implemented by NASA and the Italian Space Agency with an expected 2026 launch, aims to study how different types of particulate matter (PM) pollution affect human health. The investigation will primarily focus on a discrete set of globally distributed Primary Target Areas (PTAs) containing major metropolitan cities, and will integrate satellite observations, ground observations, and chemical transport model (CTM) outputs to generate maps of near-surface total and speciated PM within the PTAs. In addition, the investigation will provide satellite measurements of aerosols, which are useful for studying air quality more broadly, over a set of scientifically interesting Secondary Target Areas (STAs). For the CTM, we have developed a unified inputs (initial and boundary condition) framework for WRF-Chem (UI-WRF-Chem). These developments include:

(a) application of NASA GEOS FP and MERRA-2 data to provide both meteorological and chemical initial and boundary conditions for performing WRF-Chem forecasts of air quality at a fine spatial resolution,

(b) a stand-alone emission preprocessor that ingests both global and regional anthropogenic emission inventories (e.g., EDGAR-HTAP, NEI) as well as fire emissions,

(c) application of MODIS land data to improve surface properties such as land cover type,

(d) application of GLDAS and NLDAS data to constrain surface soil conditions,

(e) a new soil NOx emission scheme – the Berkeley Dalhousie Iowa Soil NO Parameterization (BDISNP).

Here, we will illustrate the model improvements as the results of these developments as well as model performance over 5 of the target areas (https://maia.jpl.nasa.gov/mission/#target_areas). Four of them are PTAs: Los Angeles in the U.S., Atlanta in the U.S., Tel-Aviv in Israel and Rome in Italy and one is a STA: Beijing in China. Beijing was a PTA and has recently been changed to a STA. However, the case study over Beijing can still serve as a good example of the model capability. These 5 target areas together have a good representation of the range of aerosol air pollution levels across the target areas (Los Angeles and Atlanta on the lower end, Tel-Aviv and Rome in the middle and Beijing on the higher end). Some of these target areas are also impacted by long-range transport events (e.g. dust intrusion). Overall, it can provide a good picture of model performance for different applications. We mainly focus on the evaluation of model simulated meteorology variables such as wind speed, surface aerosol concentration (total PM_2.5 and PM₁₀ concentration) and optical properties (AOD). For each target area, we first run a suite of sensitivity simulations to test the model sensitivity to different options of the physics scheme and then select the optimal combination of physics scheme based on evaluation of model simulated meteorology.

We focus on two dust intrusion events over Beijing and Rome, which occurred in March 2018 and June 2023, respectively. We carry out a suite of sensitivity simulations using UI-WRF-Chem by excluding chemical boundary conditions or including MERRA-2 chemical boundary conditions. Our results show that using MERRA-2 data to provide chemical boundary conditions can help improve model simulation of surface PM_2.5 and AOD. Some of the target areas have also experienced significant changes in land cover and land use over the past decade. Our case study over Beijing in July 2018 investigates the impacts of improved land surface properties on capturing the urban heat island phenomena. Model-simulated surface skin temperature shows better agreement with MODIS observed land surface temperature using the improved land surface properties with MODIS data over Beijing. The updated soil NOx emission scheme leads to higher NO₂ vertical column density (VCD) in rural areas over Beijing target area, which matches better with TROPOMI observed NO₂ VCD. This in turn improves the simulation of nitrate concentration.