A new article in the International Journal of Applied Earth Observation and Geoinformation evaluated the performance of five satellite precipitation products (SPPs) from the Precipitation Remotely Sensed Information using Artificial Neural Networks (PERSIANN) family for depicting precipitation changes in Taiwan over multiple timescales. Precipitation gauge data provided by Taiwan’s Central Weather Bureau were used as a reference for evaluation of during wet seasons (May-October) from 2003–2019.
In general, the two relatively new products (PDIR-Now and PERSIANN-CCS-CDR) showed better root mean square error performance than the other SPPs on different timescales. PERSIANN-CCS-CDR was the best product for quantitatively estimating precipitation on diurnal and daily timescales; PDIR-Now was best for interannual, annual, and seasonal timescales. The findings also highlighted that the performance of the PERSIANN-family products in Taiwan did not depend primarily on their spatiotemporal resolution but perhaps to the “cloud patch” approach and the inclusion of weather station information in producing PDIR-Now and PERSIANN-CCS-CDR.
The paper is freely available at https://doi.org/10.1016/j.jag.2021.102521
Reference: Wan-Ru Huang, Pin-Yi Liu and Jie Hsu. 2021. Multiple timescale assessment of wet season precipitation estimation over Taiwan using the PERSIANN family products. International Journal of Applied Earth Observation and Geoinformation, Volume 103, 1 December 2021, 10252.