![]() Verification statistics are broadly homogeneous in space and time with regional differences Spectral de-convolution algorithm on a 3-hourly basis). That the reanalysis data set is highly accurate (mean bias = −0.05, RMSE = 0.12 and r = 0.81 when compared to retrievals from the Independent evaluation using Aerosol Robotic Network (AERONET) dust-filtered optical depth retrievals indicates Which proves the consistency of the data assimilation method. The analysis is statistically closer to the assimilated retrievals than the first guess, ![]() The spatial and temporal distribution of the dust fields follows well-known dust cycle features controlledīy seasonal changes in meteorology and vegetation cover. A set of ensemble statistics is archived for each output variable, namely the ensemble mean, The variables that are diagnosed from the state vector. Both analysis and first-guess (analysis-initialized simulation) fields are available for Ranges from 0.2 to 20 µm in particle diameter. Some dust variables, such as concentrations and wet and dry deposition, are expressed for a binned size distribution that The reanalysis data set consists of upper-air variables (dust massĬoncentrations and the extinction coefficient), surface variables (dust deposition and solar irradiance fields among them) and total column variables (e.g. dust opticalĭepth and load). The assimilated data are coarse-mode dust optical depth retrievedįrom the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue Level 2 products. Reanalysis was produced using local ensemble transform Kalman filter (LETKF) data assimilation in the Multiscale Online Nonhydrostatic AtmospheReĬHemistry model (MONARCH) developed at the Barcelona Supercomputing Center (BSC). Horizontal resolution is 0.1 ∘ latitude × 0.1 ∘ longitude in a rotated grid, and the temporal resolution is 3 h. Middle East and Europe along with the Mediterranean Sea and parts of central Asia and the Atlantic and Indian oceans between 20. Here, we present a high-resolution regional reanalysis data set of desert dust aerosols that covers Northern Africa, the Observationally constrained continuous 3D dust fields are needed to assess dust variability, climate effects and impacts upon a variety of Satellites typically provide column-integrated aerosol measurements, but Measurements, particularly in the areas most affected by dust storms. One of the challenges in studying desert dust aerosol along with its numerous interactions and impacts is the paucity of direct in situ a now at: NASA Goddard Institute for Space Studies (GISS), New York, New York, USAĬorrespondence: Enza Di Tomaso Hide author detailsĬorrespondence: Enza Di Tomaso Received: – Discussion started: – Revised: – Accepted: – Published:.13 ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, Spain.12 Agencia Estatal de Meteorología (AEMET), Barcelona, Spain.11 Weather and Climate Change Impact Research, Finnish Meteorological Institute (FMI), Helsinki, Finland. ![]()
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