TY - JOUR
T1 - Can near-real-time satellite precipitation products capture rainstorms and guide flood warning for the 2016 summer in South China?
AU - Tang, Guoqiang
AU - Zeng, Ziyue
AU - Ma, Meihong
AU - Liu, Ronghua
AU - Wen, Yixin
AU - Hong, Yang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8
Y1 - 2017/8
N2 - In the summer of 2016, severe storms caused serious casualties and destruction of facilities and properties over South China. Near-real-time (NRT) satellite precipitation products are attractive to rainstorm monitoring and flood warning guidance owing to its combination of timeliness, high spatiotemporal resolution, and broad coverage. We evaluate the performance of four NRT satellite products, i.e., Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, 3B42RT, Global Satellite Mapping of Precipitation (GSMaP) NRT, and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) Late run using a high-quality merged product in the rainy June over South China. In addition, a method based on an empirical flash flood guidance and the Flash Flood Potential Index is proposed to examine the applicability of satellite products in guiding flood warning. The IMERG Late run and GSMaP NRT perform the closest-to-ground observations. 3B42RT detects the most flood warning events due to its notable overestimation of actual precipitation. We recommend that the IMERG Late run is the best NRT satellite product in capturing flood hazard events according to the Pareto Efficiency of jointly optimizing higher hit ratio and lower false alarms.
AB - In the summer of 2016, severe storms caused serious casualties and destruction of facilities and properties over South China. Near-real-time (NRT) satellite precipitation products are attractive to rainstorm monitoring and flood warning guidance owing to its combination of timeliness, high spatiotemporal resolution, and broad coverage. We evaluate the performance of four NRT satellite products, i.e., Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, 3B42RT, Global Satellite Mapping of Precipitation (GSMaP) NRT, and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) Late run using a high-quality merged product in the rainy June over South China. In addition, a method based on an empirical flash flood guidance and the Flash Flood Potential Index is proposed to examine the applicability of satellite products in guiding flood warning. The IMERG Late run and GSMaP NRT perform the closest-to-ground observations. 3B42RT detects the most flood warning events due to its notable overestimation of actual precipitation. We recommend that the IMERG Late run is the best NRT satellite product in capturing flood hazard events according to the Pareto Efficiency of jointly optimizing higher hit ratio and lower false alarms.
KW - Flash flood guidance (FFG)
KW - Flood warning
KW - Rainstorms
KW - Satellite precipitation
KW - South China
UR - https://www.scopus.com/pages/publications/85020375852
U2 - 10.1109/LGRS.2017.2702137
DO - 10.1109/LGRS.2017.2702137
M3 - Article
AN - SCOPUS:85020375852
SN - 1545-598X
VL - 14
SP - 1208
EP - 1212
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
IS - 8
M1 - 7942042
ER -