Ponente
Descripción
Poor air quality (AQ) is a most pressing international environmental problem. A soon-to-be-operational network of geostationary AQ satellites (GEMS, TEMPO, and Sentinel 5) that will effectively cover the Northern Hemisphere with hourly AQ observations at ~5 km resolution will revolutionize AQ forecasting/data assimilation. I will present preliminary results from two multi-constituent ensemble AQ forecast/assimilation experiments with WRF-Chem/DART: (i) FRAPPE – a 15 km grid space domain covering the western continental US (CONUS); and (ii) COLORADO – a 4 km grid space domain covering Colorado. These experiments will highlight some of the expected benefits from the geostationary array of AQ satellites.
WRF-Chem/DART incorporates the Weather Research and Forecast (WRF) model with on-line chemistry (WRF-Chem) into the ensemble Kalman filter assimilation system known as the Data Assimilation Research Testbed (DART). We assimilate the ground-based AQS CO, O3, NO2, SO2, PM10, and PM2.5 observations together with MOPITT CO, IASI CO, MODIS AOD, OMI NO2, TROPOMI CO, NO2, and synthetic TEMPO NO2 total/partial column and/or profiles retrievals. We use: (i) the state augmentation method for emissions adjustment, and (ii) state-space localization to enable cross-species adjustments by controlling which observations update which state variables.
I will discuss three basis experiments: (i) CONTROL – assimilates only meteorology; (ii) ALLCHEM – same as CONTROL but also assimilates AQS, MOPITT, IASI, MODIS, OMI, TROPOMI and/or synthetic TEMPO; and (iii) EMISADJ – same as ALLCHEM but also includes emissions adjustment. Our results show that: (i) assimilating chemical observations increases AQ forecast skill; (ii) including emissions adjustment increases forecast skill/predictability time; (iii) including emissions adjustment reduces the magnitude and areal extent of the analysis increments; and (iv) assimilation of high-resolution retrievals from TROPOMI and TEMPO increases the magnitude of the increments. Based on the earlier work of my collaborators, we expect that assimilation of high-resolution retrievals will also increase predictability.
