Ronald Adomako

Ronald Adomako
M.S, Data Science Engineering, Graduate, 06/04/2021

Cohort Level: Cohort - IV

Career Goal: Further advancement in Machine Learning Engineering

Expected Graduation Date: May 27, 2021

Degree: M.S Data Science Engineering

Research Title: Distribution of GOES Radiance Anomalies

Research Synopsis: The geostationary advanced baseline imager on GOES-17 (west) is susceptible to the Loop-Heat-Pipe (LHP) issue. As a result, the images in some GOES-17 ABI channels are degraded at certain times of day, and certain times of the year when the cooling system isn’t able to radiate enough of the sun’s energy properly. NOAA has compared the mean value in a region of interest over the equator between the two satellites (GOES-16 and GOES-17). We have been able to reproduce these results. This research extends the analysis by evaluating histogram matching as a means to quantify the change in the brightness temperature histogram due to the LHP heating. The focus is to develop a reliable transform from GOES-17 quantile to a GOES-16 quantile to map temperature values. In addition we will evaluate the application of an empirical mapping function as a means of compensating for errors in the apparent brightness temperature.