Evaluating remotely sensed rainfall estimates using nonlinear mixed models and geographically weighted regression

Y. Kamarianakis, H. Feidas, G. Kokolatos, N. Chrysoulakis, V. Karatzias

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1438-1447
Number of pages10
JournalEnvironmental Modelling and Software
Volume23
Issue number12
DOIs
StatePublished - Dec 1 2008

Keywords

  • Geographically weighted regression
  • Nonlinear mixed models
  • Rainfall estimation
  • Remotely sensed estimations
  • Zero inflated lognormal distribution

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modeling

Fingerprint Dive into the research topics of 'Evaluating remotely sensed rainfall estimates using nonlinear mixed models and geographically weighted regression'. Together they form a unique fingerprint.

Cite this