Natural three-dimensional predictor domains for statistical precipitation downscaling

Journal of Climate

Volumn 24, Issue 23, 2011, Pages 6132-6145

  Sauter, Tobias ^a   Venema, Victor ^b  

^a RWTH AACHEN UNIVERSITY   (Germany)

^b UNIVERSITY OF BONN   (Germany)

Author keywords

Algorithms; Climate classification regimes; Local effects; Precipitation; Statistical techniques

Indexed keywords

AIR MASS; ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC PROCESS; CLIMATE CLASSIFICATION/REGIMES; CLUSTERING METHODS; DOWN-SCALING; DOWNSCALING METHODS; GLOBAL SENSITIVITY ANALYSIS; GRID POINTS; HIGH IMPACT; LOCAL EFFECTS; MODEL FREE; MODELING PROCESS; NON-PARAMETRIC; NONLINEAR OPTIMIZATION METHODS; POTENTIAL REGION; PRECIPITATION RATES; SOURCES OF UNCERTAINTY; SPATIAL STRUCTURE; STATISTICAL DOWNSCALING; STATISTICAL PRECIPITATION; STATISTICAL TECHNIQUES;

ALGORITHMS; CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; CONFORMAL MAPPING; PRECIPITATION (CHEMICAL); SELF ORGANIZING MAPS; SENSITIVITY ANALYSIS; THREE DIMENSIONAL; TIME SERIES; UNCERTAINTY ANALYSIS;

SIMULATED ANNEALING;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; CLUSTER ANALYSIS; DOWNSCALING; NUMERICAL MODEL; OPTIMIZATION; PRECIPITATION (CLIMATOLOGY); SELF ORGANIZATION; SENSITIVITY ANALYSIS; SIMULATED ANNEALING; STATISTICAL ANALYSIS; TIME SERIES ANALYSIS; UNCERTAINTY ANALYSIS;

EID: 80052483773     PISSN: 08948755     EISSN: None     Source Type: Journal    
DOI: 10.1175/2011JCLI4155.1     Document Type: Article

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