Sensitivity of North American Monsoon Rainfall to Multisource Sea Surface Temperatures in MM5

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