Discipline: Ecology Environmental and Earth Sciences
Subcategory: Geosciences and Earth Sciences
Edgar Gomez - CUNY New York City College of Technology
Co-Author(s): Andre Rodriguez, CUNY New York City College of Technology, Brooklyn, NY
In order to improve hydrologic predictions, accurate precipitation estimates at fine scales (hourly time scale and few kilometers spatial scale) are required. Geostationary Satellites (GOES) Infrared (IR) based precipitation estimates are available at such scale. However, these satellite precipitation estimates (SPEs) are prone to spatial error and can be observed when compared with ground-based measurements. Detecting and applying correction of location error in IR based SPEs have been minimal. The research objective is to observe and assess location error within NOAA / NESDIS / STAR?s satellite precipitation data fields, Hydro-Estimator (HE) and Self Calibrating Multivariate Precipitation Retrieval (SCaMPR), and NOAA / NWS / NCEP / Climate Prediction Center?s QMORPH against Stage-IV ground-based radar-gauge precipitation estimate (ST-IV). It is hypothesized that due to spatial inaccuracies within SPEs it is possible to calculate location error and indicate which SPE is the most accurate. With this in mind, it is highly probable that HE will give the most reliable readings due to its development in 2002 before any other SPE, giving more time for improvement on algorithms. A series of MATLAB functions were created to measure SPEs? shifts in latitude, shifts in longitude, and combinations of them. The satellite data was then shifted in forty-nine different ways over ST-IV to calculate the minimum root-mean-square-error between SPEs and ST-IV estimates. Forty-nine frames were created by shifting the satellite data over calculated longitudinal and latitudinal directions ranging from -3 to +3 grid spaces. The study specifically focuses on tornado cases for the months of May 2010 and 2015. The geographic location of the study area consists of the states of Oklahoma, Kansas, and Nebraska within the region called Tornado Alley. After correcting satellite estimations for spatial bias, significant improvements were found in correlation coefficients between satellite and ground radar. In some cases, up to 30% improvement was found and HE in 2015 as was predicted, was the most reliable SPE reading with a grid shift of (2,0) indicating a 8 kilometer shift compared to SC and QM which both had a (2,-2). However, in 2010 SCaMPR had the most reliable data with a grid shift of (1, 0) indicating only a 4 kilometer shift compared to HE which had a (1,-1) grid shift. Overall, all SPE?s from both years had a correlation increase after spatial correction. This gives NOAA / NESDIS / STAR the opportunity to improve SPEs and provide more reliable predictions to agencies like the National Weather Service (NWS) who utilize this data. Further studies will be conducted for non-tornadic events.
Funder Acknowledgement(s): This project is supported by the National Science Foundation Research Experiences for Undergraduates (Grant # 1560050), under the direction of Dr. Reginald A. Blake, Dr. Janet Liou-Mark, and Ms. Laura Yuen-Lau. The authors are grateful for the support from The National Oceanic and Atmospheric Administration - Cooperative Science Center for Earth System Sciences and Remote Sensing Technologies Summer Bridge program (Grant # NA16SEC4810008) under the direction of Dr. Reza Khanbilvardi and Dr. Shakila Merchant. Special thanks to Dr. Robert Kuligowski from NOAA NESDIS STAR for providing us with the satellite information.
Faculty Advisor: Dr. Kibrewossen Tesfagiorgis, email@example.com
Role: I contributed in creating scripts and improving the MATLAB program that was used. Using knowledge of functions in programming I was able to make the program run faster by getting rid of redundant tasks. With my partner, Andre Rodriguez, I filtered the Excel spreadsheet that contained tornado events which define our study area and time period for this project. With this data a script in MATLAB was created to filter files of satellite precipitation estimates (SPEs). I also created an arrow diagram that shows the increase in correlation coefficient of our three different SPEs.