Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland

Abstract

Based on the Weather Research and Forecasting model with Chemistry (WRF-Chem) model and Gridpoint Statistical Interpolation (GSI) assimilation tool, a forecasting system was used for two selected episodes (winter and summer) over Eastern Europe. During the winter episode, very high particular matter (PM2.5, diameter less than 2.5 µm) concentrations, related to low air temperatures and increased emission from residential heating, were measured at many stations in Poland. During the summer episode, elevated aerosol optical depth (AOD), likely related to the transport of pollution from biomass fires, was observed in Southern Poland. Our aim is to verify if there is a relevant positive impact of surface and satellite data assimilation (DA) on modeled PM2.5 concentrations, and to assess whether there are significant differences in the DA’s impact on concentrations between the two seasons. The results show a significant difference in the impact of surface and satellite DA on the model results between the summer and winter episode, which to a large degree is related to the availability of the satellite data. For example, the application of satellite DA raises the factor of two statistic from 0.18 to 0.78 for the summer episode, whereas this statistic remains unchanged (0.71) for the winter. The study suggests that severe winter air pollution episodes in Poland and Eastern Europe in general, often related to the dense cover of low clouds, will benefit from the assimilation of surface observations rather than satellite data, which can be very sparse in such meteorological situations. In contrast, the assimilation of satellite data can have a greater positive impact on the model results during summer than the assimilation of surface data for the same period.

Evaluation of Two Low-Cost Optical Particle Counters for the Measurement of Ambient Aerosol Scattering Coefficient and Ångström Exponent

“…However, in this case, the spatial and temporal variability of aerosol columnar optical properties (which are used to estimate aerosol radiative forcing) is lower than the variability of PM mass concentration [14]. Gaps in the spatial coverage of monitoring stations are reduced by satellite observations [15][16][17]. Unfortunately, only aerosol optical depth (AOD) has acceptable spatial coverage and is of a relatively good quality [18].…”

“…Unfortunately, only aerosol optical depth (AOD) has acceptable spatial coverage and is of a relatively good quality [18]. In the case of the PM mass concentration, data quality and representativeness are much worse and thus more problematic for data assimilation in aerosol transport models [16,19].…”

Introducing the MISR level 2 near real-time aerosol product

“…MISR employs two processing pathways for aerosol retrievals, one for observations over land (Martonchik et al, 2009) and another for dark water (DW) (Kalashnikova et al, 2013), which applies over deep oceans, seas, and lakes. Previous versions of the MISR aerosol product were extensively validated over the years (e.g., Kahn et al, 2010;Kahn and Gaitley, 2015;Kalashnikova et al, 2013;Shi et al, 2014;Witek et al, 2013), showing high retrieval quality over land and ocean.…”

Extension of WRF-Chem for birch pollen modelling—a case study for Poland

“…Initial and boundary conditions for meteorological fields were obtained from the NCEP FNL Operational Global Analysis data with a horizontal resolution of 1°× 1°, 27 (32 since 11 May 2016) vertical levels, and temporal resolution of 6 h. The data were interpolated to the model grid using the WRF pre-processing system (WPS). Model configurations regarding physical parameters are the same as in Werner et al (2019a).…”