Ensemble versus Variational Data Assimilation Methods for Incorporating Sparse Observational Data Streams into Aerosol Forecasting Systems

Aerosol forecasting capabilities have been implemented at many of the world's numerical weather prediction (NWP) centers. Daily aerosol forecasts, initialized with analysis fields from a data assimilation (DA) system, are produced at the centers with the employed DA systems ranging from 2-dimensional variational (2DVar), 3-dimensional variational (3DVar), and 4-dimensional variational (4DVar) to ensemble systems. One consistency across all of these systems is the assimilation of derivatives of aerosol optical thickness (AOT) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) with its daily global spatial coverage. However, it is necessary to consider how other aerosol-related data streams can be used to move aerosol forecasting forward. In particular, there are many sparse observational data streams that could be beneficial for aerosol forecasting, including narrow swath satellite products from MISR and CALIPSO and ground-based measurements from AERONET and MPLNET. In this work, we evaluate the impact of assimilating sparse observational datasets on the Navy's aerosol forecasting system and its dependence on the data assimilation methodology. A range of aerosol-related observational datasets are tested within the Navy Aerosol Analysis Prediction System (NAAPS) framework, using variational and ensemble data assimilation methods. Navy aerosol forecasting is currently comprised of a single, deterministic NAAPS simulation coupled to the Navy Variational Data Assimilation System for Aerosol Optical Depth (NAVDAS-AOD), a 2-dimensional variational data assimilation system. Recently, an ensemble version of NAAPS system (ENAAPS) coupled to an Ensemble Adjustment Kalman Filter (EAKF) from NCAR's Data Assimilation Research Testbed (DART) for assimilation of MODIS AOT was developed. The NAAPS/ENAAPS framework is used to evaluate the impact of ground-based measurements, including AERONET AOT and fine/course aerosol partitioning as well as lidar observations. Case studies from the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surverys (SEAC4RS) will be used to evaluate the impact of data assimilation.