Rainfall Analysis and Trends for the State of Texas in 201

This study investigates the extreme rainfall patterns over the state of Texas during 2015 when the state experienced tornadoes, flooding and El Nino, which contributed to massive rainfall around the state. Rainfall distribution pattern is one of the most important factors for hydrological modeling and agricultural purposes. Texas is at the crossroads of large-scale weather patterns and is affected by what is happening in the Pacific and Atlantic Oceans, particularly the El Nino and La Nina cycle. Texas suffered of damaging floods and record rainfall in May 2015 after years of severe drought, which started in 2010. According to the National Weather Service, in 2015 Texas suffered from severe rain and flash floods that caused an estimated 35 trillion gallons of water to fall on Texas, which was enough to cover the entire state in eight inches of water. In addition, this amount is enough to supply the world's drinking water for 27 years.;In this study, an analysis was conducted through a network of approximately 1011 rain gauges maintained by the National Climatic Data Center NCDC and the United States Geological Survey covering the state of Texas. These stations provide data that are typically be recorded at 15- to 60-minute intervals from automated or manual readings. The stations provided the basis for 12 months of precipitation dataset in 2015. In addition, precipitation measurements were collected by 13 WSR-88D Weather Surveillance Radar-1988 Doppler Next Generation Weather Radar NEXRAD with Multi-sensor Precipitation Estimator (MPE) Stage III that cover the state of Texas and are maintained by the National Weather Service NWS. Radar data are available for 152366 grids a 4 x 4 km covering the entire state of Texas.;In general, numerous stations recorded rainfall that indicated that 2015 was the wettest year in the state records. The results of rain gauges and weather radar show that May and October were the wettest months in 2015 respectively, while the months with the least rainfall were April, November and June respectively. In addition, a correlation of radar estimates of rainfall to actual rain gauge dataset has been examined on monthly bases for 2015 and shows a deviation of 17% between the two products. Further studies are required to create model that performs reasonably well in generating rainfall dataset with less deviation and that takes into account errors from all possible factors affecting the accuracy of rainfall estimations.