TY - JOUR
T1 - Evaluating remotely sensed rainfall estimates using nonlinear mixed models and geographically weighted regression
AU - Kamarianakis, Y.
AU - Feidas, H.
AU - Kokolatos, G.
AU - Chrysoulakis, N.
AU - Karatzias, V.
N1 - Funding Information:
This work was conducted in the frame of the SATERM (A Satellite Technique for Estimating Rainfall over the Mediterranean basin) project funded by the “Competitiveness”, Action 4.3.6.1d “Cooperation with R&T institutions in non-European countries” of the General Secretary of Research and Technology, Ministry of Development of Greece.
PY - 2008/12
Y1 - 2008/12
N2 - This article evaluates an infrared-based satellite algorithm for rainfall estimation, the Convective Stratiform technique, over Mediterranean. Unlike a large number of works that evaluate remotely sensed estimates concentrating on global measures of accuracy, this work examines the relationship between ground truth and satellit0e derived data in a local scale. Hence, we examine the fit of ground truth and remotely sensed data on a widely adopted probability distribution for rainfall totals - the mixed lognormal distribution - per measurement location. Moreover, we test for spatial nonstationarity in the relationship between in situ observed and satellite-estimated rainfall totals. The former investigation takes place via using recent algorithms that estimate nonlinear mixed models whereas the latter uses geographically weighted regression.
AB - This article evaluates an infrared-based satellite algorithm for rainfall estimation, the Convective Stratiform technique, over Mediterranean. Unlike a large number of works that evaluate remotely sensed estimates concentrating on global measures of accuracy, this work examines the relationship between ground truth and satellit0e derived data in a local scale. Hence, we examine the fit of ground truth and remotely sensed data on a widely adopted probability distribution for rainfall totals - the mixed lognormal distribution - per measurement location. Moreover, we test for spatial nonstationarity in the relationship between in situ observed and satellite-estimated rainfall totals. The former investigation takes place via using recent algorithms that estimate nonlinear mixed models whereas the latter uses geographically weighted regression.
KW - Geographically weighted regression
KW - Nonlinear mixed models
KW - Rainfall estimation
KW - Remotely sensed estimations
KW - Zero inflated lognormal distribution
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U2 - 10.1016/j.envsoft.2008.04.007
DO - 10.1016/j.envsoft.2008.04.007
M3 - Article
AN - SCOPUS:47849088725
VL - 23
SP - 1438
EP - 1447
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
SN - 1364-8152
IS - 12
ER -