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Warm-season precipitation in the Lower Mississippi River Alluvial Valley (LMRAV) is heavily dominated by the rates of evapotranspiration and surface heat fluxes and is a primary water resource for agriculture. However, the stochastic nature of LMRAV warm-season thunderstorms makes precipitation forecasts challenging. The Weather Research and Forecasting Hydrologic (WRF-Hydro) model, coupled with the multi-parameter Noah land surface (Noah-MP) model, has improved estimates of important warm-season precipitation process. Given the widespread agriculture and dominance of crop and forested landscapes over the region, proper assessment of land use / land cover (LULC) is critical in predicting warm-season precipitation patterns. The objective of this study is to quantify simulated latent heat flux sensitivity (important for warm-season precipitation) to temporally updated LULC datasets. Both the model default and annually updated LULC conditions were used to initialize a 16-year WRF-Hydro simulation from which warm-season latent heat flux estimates were obtained. Annual root mean square difference was computed at each gridpoint. Cluster analysis preprocessed with kernel principal component analysis was used to identify spatial RMSD structures that quantified sensitivity to updated LULC conditions. Results showed the largest impacts occurred directly in the LMRAV and for points slightly east and revealed a meteorological link between these regions. [ABSTRACT FROM AUTHOR]

Heatwaves are predicted to increase in frequency, duration, and severity in the future, including over Central Europe where populations are sensitive to extreme temperature. This paper studies six recent major heatwave events over Poland from 2006 through 2015 using regional-scale simulations (10-km grid spacing, hourly frequency) from the Weather Research and Forecast (WRF) model to define local-scale 2-m temperature patterns. For this purpose, a heatwave is defined as at least three consecutive days with maximum 2-m air temperature exceeding 30 °C. The WRF simulations were validated using maximum daily 2-m temperature observations from 12 meteorological stations in select Polish cities, which were selected to have even spatial coverage across the study area. Synoptic analysis of the six study events shows that the inflow of tropical air masses from the south is the primary driver of heatwave onset and maintenance, the highest temperatures (and most vulnerable areas) occur over arable land and artificial surfaces in central and western Poland, while coastal areas in the north, mountain areas in the south, and forested and mosaic areas of smaller fields and pastures of the northwest, northeast, and southeast are less affected by prolonged periods of elevated temperatures. In general, regional differences in 2-m temperature between the hottest and coolest areas is about 2-4 °C. Large urban areas like Warsaw, or the large complex of artificial areas in the conurbation of Silesian cities, are also generally warmer than surrounding areas by roughly 2-4 °C, and even up to 6 °C, especially during the night. Additionally, hot air from the south of Poland flows through a low-lying area between two mountain ranges (Sudetes and Carpathian Mountains)—the so-called Moravian Gate—hitting densely populated urban areas (Silesian cities) and Cracow. These patterns occur only during high-pressure synoptic conditions with low cloudiness and wind and without any active fronts or mesoscale convective disturbances. [ABSTRACT FROM AUTHOR]

Abstract: Dynamic numerical weather prediction models have been designed to deal with large-scale, highly predictable midlatitude atmospheric patterns. However, the capability of these models to simulate thermodynamically driven warm-season rainfall events, such as afternoon airmass thunderstorm formation in subtropical summers, is highly limited. Current methods of addressing this issue have included ensemble numerical weather prediction simulations, where an ensemble mean of multiple simulations with varied model physics is used as an improved prediction over any individual ensemble member. These approaches still yield only modest skill primarily due to inherent biases in each ensemble member. As such, the current research will utilize machine learning to combine logically ensemble members into a single prediction of warm-season rainfall. In particular, a support vector machine classification scheme that employs members of a 30 member ensemble as predictors and observed rainfall patterns as a predictand will be formulated on multiple warm-season rainfall days in an effort to develop an improved prognosis of warm-season rainfall that can be implemented in operational meteorology forecasts. The primary goal of the work is to obtain a statistically significant improvement of predictive skill over currently utilized ensemble member approaches. [Copyright &y& Elsevier]

Lake Okeechobee, located in southern Florida, is an important component in the regional hydrologic system. Currently, the Southeast River Forecast Center (SERFC) is setting up a forecasting scheme for Lake Okeechobee and its major inflows. An important aspect in calibrating the system is estimating the depth of direct precipitation over the water surface. Within this project, National Weather Service (NWS) and South Florida Water Management District (SFWMD) surface gauges, along with stage III multisensor precipitation estimates, are used to create time series of mean areal precipitation (MAP). The computed MAP values are compared in order to find the relative differences between them, and to determine the utility of using each data source for calibration and in future operations. It was found that the SFWMD gauge-based MAP was the most useful data source, because it had a suitable period of record and the SFWMD gauges had a better spatial sampling of precipitation over the lake surface. The radar-based stage III estimates were not found to be a useful source of data, despite the superior spatial sampling resolution, because they had too short a period of record and a number of changes in the processing algorithms made the associated MAP nonhomogeneous and inappropriate for model calibration. [ABSTRACT FROM AUTHOR]

Abstract: This paper provides a meteorological overview of the 2015 mega‐heatwave (MHW) over Poland and compares the event with other MHWs in the region since World War II (up until 2015). A mega‐heatwave is defined as an event with at least 6 consecutive days with a maximum air temperature in excess of 30°C. These events are analysed here using observational meteorological data from ten major cities in Poland and neighbouring countries, along with 0.25° × 0.25° analysis fields from the Global Forecast System (GFS). Although the various events had different regional characteristics, in general they were caused by the inflow of a hot tropical air mass from the south, and were perpetuated by a high‐pressure system to the east of Poland and in some cases a low‐pressure system to the west. The 2015 MHW was the most extreme of the studied events, lasting from 9 days in Bialystok to 14 days in Warsaw and Poznan. [ABSTRACT FROM AUTHOR]