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In this article, four continually processed sea surface temperature (SST) datasets, including the Reynolds SST (RYD), the global final analysis of skin temperature at oceans (FNL), and two Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua SSTs retrieved from thermal infrared imagery (TIR) and midinfrared imagery (MIR), were compared. The results show variations from each other. In comparison with the RYD SST, the FNL data have -0.5° ∼ 0.5°C perturbations, while the TIR and MIR SSTs possess larger deviations of -2° ∼ 1°C, mainly due to algorithm and/or sensor differences in these SST datasets. A regional model, the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5), was used to investigate whether model atmospheric predictions, especially those concerning precipitation during the North American monsoon season, are sensitive to these SST variations. A comparison of rainfall, atmospheric height, temperature, and wind fields produced by model results, reanalysis data, and observations indicates that, at monthly scale, the model shows changes in the simulations for three consecutive years; in particular, rainfall amounts, timing, and even patterns vary at some specific regions. Forced by the MODIS Aqua midinfrared SST (MIR), which includes large regions with SST values lower than the conventional Reynolds SST, the MM5 rain field predictions show reduced errors over land and oceans compared to when the model is forced by other SST data. Specifically, rainfall estimates are improved over the offshore of southern Mexico, the Gulf of Mexico, the coastal regions of southern and eastern Mexico, and the southwestern U.S. monsoon active region, but only slightly improved over the monsoon core and the high-elevated Great Plains. Using MIR SST data, one is also capable of improving geopotential height and temperature fields in comparison with the reanalysis data. [ABSTRACT FROM AUTHOR]

Accurate summertime weather forecasts, particularly the quantitative precipitation forecast (QPF), over the semiarid southwest United States pose a difficult challenge for numerical models. Two case studies, one with typical weather on 6 July 1999 and another with unusual flooding on 8 July 1999, using the Regional Atmospheric Modeling System (RAMS) nested inside the regional Eta Model, were conducted to test numerical weather prediction capabilities over the lower Colorado River basin. The results indicate that the rapid changes in synoptic patterns during these two cases strongly affect the weather and rainfall situation in the basin. The model illustrates that the midlevel sinking over the low elevation of the southwest area of the basin “capped” the development of deep convection in case 1; meanwhile, in case 2, a shear line and convergence over the Las Vegas area valley stimulated intense convective storms in the region. In both cases, the low-level jet (LLJ) stream from the Gulf of California was the major source of atmospheric moisture for the basin. Local topography and thermodynamics also play a significant role in the formation of the weather features. The “thermal low” over the Sonoran Desert is responsible for the LLJ stream, which led to the valley of the Colorado River becoming the warmest and moistest area in the basin. By nesting fine-resolution grids over the Las Vegas area, the representation of local topography in the region was improved in the RAMS model, compared with that in the relatively coarse resolution Eta Model. This appears to be the major reason that the RAMS model could predict intense convective storms over Las Vegas, while the operational Eta forecast could not. [ABSTRACT FROM AUTHOR]