Dağlık alanlarda uzaktan algılama yolu ile elde edilen yağış tahminlerinin geliştirilmesi.

In support of the National Oceanic and Atmospheric Administration (NOAA) National Weather Service’s (NWS) flash flood warning and heavy precipitation forecast efforts, the NOAA National Environmental Satellite Data and Information Service (NESDIS) Center for Satellite Applications and Research (STAR) has been providing satellite based precipitation estimates operationally since 1978. Two of the satellite based rainfall algorithms are the Hydro-Estimator (HE) and the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR). Satellite based rainfall algorithms need to be adjusted for the orographic events and atmospheric variables for the continued improvement of the estimates. However, unlike the HE algorithm, the SCaMPR does not currently make any adjustments for the effects of complex topography on rainfall estimate. Bias structure of the SCaMPR algorithm suggests that the rainfall algorithm underestimates precipitation in case of upward atmospheric movements and high temperature levels. Also SCaMPR algorithm overestimates rainfall in case of downward atmospheric movements and low temperature levels. A regionally dependent empirical elevation-based bias correction technique and also a temperature based bias correction technique may help to improve the quality of satellite-derived precipitation products. In this study, an orographic correction method and a temperature correction method that will enhance precipitation distribution, improve accuracy and remove topography and temperature dependent bias is developed for the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) rainfall algorithm to be used in operational forecasting for meteorological and hydrological applications.M.S. - Master of Scienc

Genetic diversity of food originated Salmonella isolates

Currently, Salmonella enterica is the most common bacterial foodborne pathogen, causing serious extraintestinal disease. Typing methods play an important role on pathogens’ source tracking, knowing the source(s) of bacteria in pharmaceutical sciences, preventing and controlling the diarrhea and food-poisoning outbreaks. The purpose of this study is to use different moleculer typing methods to determine the genetic variability of 38 foodborne Salmonella isolates that were previously identified by biochemical tests. The methods were evaluated by four molecular techniques including 16S rRNA sequencing, PFGE, PCR-RFLP and invA-spvC PCR. 16S rRNA sequencing results showed that four of the 38 isolates were Escherichia coli, Proteus mirabilis and Citrobacter murliniae, and the others were Salmonella enterica. Thirty-eight strains were subtyped by XbaI-PFGE into 20 profiles with different clusters, while they were subtyped by 16S rRNA-RFLP into 9 profiles with a single cluster. Out of two Salmonella isolates, the invasion gene (invA) was detected in all other Salmonella isolates (94%) and the virulence gene (spvC) was detected in 11% of Salmonella isolates. Our results suggested that the PFGE subtyping is the prominent method for the evaluation and benchmarking of molecular subtyping