@article{gudoshava_semazzi_2019, title={Customization and Validation of a Regional Climate Model Using Satellite Data Over East Africa}, volume={10}, ISSN={["2073-4433"]}, DOI={10.3390/atmos10060317}, abstractNote={This study focused on the customization of the fourth generation International Center for Theoretical Physics Regional Climate Model version 4.4 and its ability to reproduce the mean climate and most dominant modes of variability over East Africa. The simulations were performed at a spatial resolution of 25 km for the period 1998–2013. The model was driven by ERA-Interim reanalysis. The customization focus was on cumulus and microphysics schemes during the Short Rains for the year 2000. The best physics combinations were then utilized for the validation studies. The East Africa region and Lake Victoria Basin region are adapted to carry out empirical orthogonal function analysis, during the Short and Long Rains. Tropical Rainfall Measuring Mission data was utilized in the validation of the model. The first mode of variability from the model and observational data during the Short Rains was associated with the warming of the Pacific Ocean and the sea surface temperature gradients over the Indian Ocean. During the Long rains, the inter-annual rainfall variability over the Lake Victoria region was associated with the sea surface temperature anomaly over the Indian Ocean and for the East Africa region the associations were weak. The drivers during the Long Rains over East Africa region were then further investigated by splitting the season to the March–April and May periods. The March–April period was positively correlated to the West Pacific and Indian Ocean dipole index, while May was associated with the Quasi-Biennial Oscillation. In conclusion, although the model can reproduce the dominant modes of variability as in the observational data sets during the Short Rains, skill was lower during the Long Rains.}, number={6}, journal={ATMOSPHERE}, author={Gudoshava, Masilin and Semazzi, Fredrick H. M.}, year={2019}, month={Jun} }
 @article{yahya_wang_gudoshava_glotfelty_zhang_2015, title={Application of WRF/Chem over North America under the AQMEII Phase 2: Part I. Comprehensive evaluation of 2006 simulation}, volume={115}, ISSN={["1873-2844"]}, DOI={10.1016/j.atmosenv.2014.08.063}, abstractNote={The Weather Research and Forecasting model with Chemistry (WRF/Chem) version 3.4.1 has been modified to include the Carbon Bond 2005 (CB05) gas-phase mechanism, the Modal for Aerosol Dynamics for Europe (MADE) and the Volatility Basis Set (VBS) approach for secondary organic aerosol (hereafter WRF/Chem-CB05-MADE/VBS), and aerosol-cloud-radiation feedbacks to improve predictions of secondary organic aerosols (SOA) and to study meteorology-chemistry feedbacks. In this Part I paper, a comprehensive evaluation is performed for WRF/Chem-CB05-MADE/VBS to simulate air quality over a large area in North America for the full year of 2006. Operational, diagnostic, and mechanistic evaluations have been carried out for major meteorological variables, gas and aerosol species, as well as aerosol-cloud-radiation variables against surface measurements, sounding data, and satellite data on a seasonal and annual basis. The model performs well for most meteorological variables with moderate to relatively high correlation and low mean biases (MBs), but with a cold bias of 0.8–0.9 °C in temperature, a moderate overprediction with normalized mean biases (NMBs) of 17–22% in wind speed, and large underpredictions with NMBs of −65% to −62% in cloud optical depths and cloud condensation nuclei over the ocean. Those biases are attributed to uncertainty in physical parameterizations, incomplete treatments of hydrometeors, and inaccurate aerosol predictions. The model shows moderate underpredictions in the mixing ratios of O3 with an annual NMB of −12.8% over rural and national park sites, which may be caused by biases in temperature and wind speed, underestimate in wildfire emissions, and underestimate in biogenic organic emissions (reflected by an NMB of −79.1% in simulated isoprene mixing ratio). The model performs well for PM2.5 concentrations with annual NMBs within ±10%; but with possible bias compensation for PM2.5 species concentrations. The model simulates well the domainwide organic carbon and SOA concentrations at two sites in the southeastern U.S. but it overpredicts SOA concentrations at two sites and underpredicts OC at one site in the same area. Those biases in site-specific SOA and OC predictions are attributed to underestimates in observed SOA, uncertainties in VOC emissions, inaccurate meteorology, and the inadequacies in the VBS treatment. Larger biases exist in predictions of dry and wet deposition fluxes of gas and PM species due mainly to overpredictions in their concentrations and precipitation, uncertainties in model treatments of deposition processes, and uncertainties in the CASTNET dry deposition data. Comparison of WRF and WRF/Chem simulations shows that the inclusion of chemical feedbacks to meteorology, clouds, and radiation results in improved predictions in most meteorological variables. Aerosol optical depth correlates strongly with aerosol concentration and cloud optical depth. The relationships between the aerosol and cloud variables are complex as the cloud variables are not only influenced by aerosol concentrations but by larger-scale dynamical processes.}, journal={ATMOSPHERIC ENVIRONMENT}, author={Yahya, Khairunnisa and Wang, Kai and Gudoshava, Masi Lin and Glotfelty, Timothy and Zhang, Yang}, year={2015}, month={Aug}, pages={733–755} }