Accurate air quality forecasts are critical to protecting human health and the environment. This paper shows how ensemble assimilation of MOPITT CO compact phase space retrieval (CPSR) profiles in WRF-Chem/DART provides significant improvements in the air quality forecasts over the CONUS when compared to independent remote (IASI CO retrieval profiles) and in situ (IAGOS/MOZAIC) observations. It also extends the CPSR algorithm to assimilation of truncated retrieval profiles.

This study presents, to our knowledge, the first data assimilation experiments in the Sea of Marmara. We propose a FerryBox network for monitoring the state of the sea and show that assimilation of the temperature and salinity improves the forecasts in the basin. The flow of the Bosphorus helps to propagate the error reduction. The study can be taken as a step towards a marine forecasting system in the Sea of Marmara that will help to improve the forecasts in the adjacent Black and Aegean seas.

We describe a chemical ensemble data assimilation system with high spatial and temporal resolution that simultaneously adjusts meteorological and chemical variables and NO_x emissions. We investigate the sensitivity of emission inversions to the accuracy and uncertainty of the wind analyses and the emission update scheme. The results provide insight into optimal uses of the observations from future geostationary satellite missions that will observe atmospheric composition.

This work tests the use of an ensemble prediction system for aerosol forecasting, including an ensemble adjustment Kalman filter for MODIS AOT assimilation. Key findings include (1) meteorology and source-perturbed ensembles are needed to capture long-range transport and near-source aerosol events, (2) adaptive covariance inflation is recommended for assimilating spatially heterogeneous observations and (3) the ensemble system captures sharp gradients relative to a deterministic/variational system.

This paper introduces (i) WRF-Chem/DART – a state-of-the-art chemical transport/data assimilation system, and (ii) compact phase space retrievals (CPSRs). WRF-Chem/DART is NCAR's regional chemical weather forecasting prototype. Such systems require assimilation of chemical composition observations, such as trace gas retrievals. Retrievals are expensive to assimilate. CPSRs reduce those assimilation costs (~ 35 % for MOPITT CO) without loss in forecast skill by removing redundant information.