Comparing PERSIANN and African rainfall climatology satellite products with the ground observation

Abstract

Satellites are gaining global attention for use in operations and research. This is because of the scarcity of ground weather and climate data. However, to use these datasets, extensive validation is required. In this study, two widely available satellite precipitation products:- African Rainfall Climatology version 2 (ARC2) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) were validated and intercompared on dekadal timescales with ground observation data for the period 1990–2020, using 10 weather stations over Uganda. The statistical methods used were correlation coefficient (CC), mean error (ME), and Root mean square error (RMSE). Contingency table statistics were used to get an insight into the skill of the products in estimating rainfall amounts at monthly scale at different stations in the country. From the results, it was observed that the two products generally overestimated rainfall for the majority of the stations. PERSIANN over estimated rainfall at Jinja, Kampala, and Kasese while ARC2 over estimated rainfall at Arua, Kampala, and Kasese. The time series for the two rainfall estimates analysis exhibit the same temporal variation patterns with similar amplitudes over all of the ten stations for the period from July 1999 to December 2020. In a comparison of the two products, ARC2 has a better representation of the observed rainfall. Results show that PERSIANN presented largely high POD over most stations, greater than 97%. ARC2 presented moderately high POD all over the stations with percentages greater than 60%. Jinja presented the highest POD with the PERSIANN satellite data compared to the rest of the stations with Kasese having the lowest. However, for the ARC2, Mbarara presented a higher percentage of POD of about 71% while Kampala had the lowest POD of about 62%. The two products had at least 30% of the identified rainfall days as false alarms, and the products rarely correctly identified more than 80% of the observed rainfall days. The study concluded that ARC2 has a better representation of observation data compared to PERSSIANN.