TY - JOUR
T1 - Improving weather radar precipitation maps
T2 - A fuzzy logic approach
AU - Silver, Micha
AU - Svoray, Tal
AU - Karnieli, Arnon
AU - Fredj, Erick
N1 - Funding Information:
The authors thank the Israeli Meteorological Service for their effort in offering public access to their climate data. We also thank Dr. Nir Sapir from the University of Haifa, Department of Evolutionary and Environmental Biology, and his research staff for their help in obtaining the radar data files. This research was partially funded by DFG/IMAP grant GZ: KU2090/7-2, AOBJ: 633213.
Funding Information:
The authors thank the Israeli Meteorological Service for their effort in offering public access to their climate data. We also thank Dr. Nir Sapir from the University of Haifa, Department of Evolutionary and Environmental Biology, and his research staff for their help in obtaining the radar data files. This research was partially funded by DFG/IMAP grant GZ: KU2090/7-2 , AOBJ : 633213 .
Publisher Copyright:
© 2019
PY - 2020/4/1
Y1 - 2020/4/1
N2 - Weather radar can provide spatially explicit precipitation grids. However interference, ground clutter and various causes of attenuation introduce uncertainty into the result. Typically, rain gauge observations, recognized as a precise measure of precipitation at point locations, are used to adjust weather radar grids to obtain more accurate precipitation maps. This adjustment involves one or more of various geostatistic techniques. Yet, since gauges are sparsely located, a geostatistic approach is sometimes limited or even not applicable. This work adopts an alternative to radar adjustment by merging location-based variables with rain grids from weather radar. Recognizing that location-based variables: elevation, slope, aspect and distance from the coast all affect precipitation, these are applied to the original weather radar grid to produce an altered precipitation distribution. The merging procedure presented here uses fuzzy logic, whereby all variables, as well as the original radar are assigned probabilities known as membership functions (MF), then a joint membership function (JMF) combines all MFs in the fuzzy set, each multiplied by its weight, to create a precipitation probability grid. This JMF probability grid is validated with gauge observation data. We show up to 30% higher correlation coefficients between gauges and the JMF grid than between gauges and the original radar. The improved correlation results from the flexibility of fuzzy logic in transforming location-based variables to probabilities.
AB - Weather radar can provide spatially explicit precipitation grids. However interference, ground clutter and various causes of attenuation introduce uncertainty into the result. Typically, rain gauge observations, recognized as a precise measure of precipitation at point locations, are used to adjust weather radar grids to obtain more accurate precipitation maps. This adjustment involves one or more of various geostatistic techniques. Yet, since gauges are sparsely located, a geostatistic approach is sometimes limited or even not applicable. This work adopts an alternative to radar adjustment by merging location-based variables with rain grids from weather radar. Recognizing that location-based variables: elevation, slope, aspect and distance from the coast all affect precipitation, these are applied to the original weather radar grid to produce an altered precipitation distribution. The merging procedure presented here uses fuzzy logic, whereby all variables, as well as the original radar are assigned probabilities known as membership functions (MF), then a joint membership function (JMF) combines all MFs in the fuzzy set, each multiplied by its weight, to create a precipitation probability grid. This JMF probability grid is validated with gauge observation data. We show up to 30% higher correlation coefficients between gauges and the JMF grid than between gauges and the original radar. The improved correlation results from the flexibility of fuzzy logic in transforming location-based variables to probabilities.
KW - Fuzzy logic
KW - Gauges
KW - Location-based
KW - Precipitation
KW - Weather radar
UR - http://www.scopus.com/inward/record.url?scp=85074447674&partnerID=8YFLogxK
U2 - 10.1016/j.atmosres.2019.104710
DO - 10.1016/j.atmosres.2019.104710
M3 - Article
AN - SCOPUS:85074447674
SN - 0169-8095
VL - 234
JO - Atmospheric Research
JF - Atmospheric Research
M1 - 104710
ER -