Comparative analysis of spatial and seasonal variability: Austrian precipitation during the 20th century

被引:37
作者
Matulla, C
Penlap, EK
Haas, P
Formayer, H
机构
[1] GKSS Forschungszentrum Geesthacht GmbH, Inst Coastal Res, Geesthacht, Germany
[2] Univ Nat Resources & Appl Life Sci, Inst Meteorol & Phys, Vienna, Austria
[3] Univ Yaounde, Fac Sci, Dept Phys, Atmospher Sci Lab, Yaounde, Cameroon
关键词
homogeneous regions; REOFs; cluster analysis; ANNs; Austria's precipitation climate;
D O I
10.1002/joc.960
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
The purpose of this investigation is to demonstrate the usability of objective methods to study the variability of precipitation and hence to contribute to a better understanding of spatial and seasonal variability of Austria's precipitation climate during the 20th century. This will be achieved by regionalizing the intra-annual variability of seasonal precipitation distributions during three non-overlapping 33 year samples (1901-33, 1934-66, 1967-99). Monthly precipitation totals were extracted at 31 Austrian stations from a homogenized long-term climate dataset provided by the Austrian weather service. Three statistical techniques, namely cluster analysis (CLA), rotated empirical orthogonal functions (REOFs) and an unsupervised learning procedure of artificial neural networks (ANNs), were utilized to find homogeneous precipitation regions. The results of summer (June, July, August (JJA)) and winter (December, January, February (DJF)) seasons are presented. The resulting homogeneous precipitation regions depend on season, period and method in this order. Hence, differences introduced by using different methods are small compared with those inferred by investigating different episodes and especially with those related to the seasons. During winter, three homogeneous precipitation regions are found, independent from the period considered. These regions can be assigned to different airflows dominating Austria's climate and triggering precipitation events during the cold season. The situation during summer is more complicated. Thus, at least four clusters are necessary to record the circumstances, which are caused by spatially inhomogeneous convective events such as thunderstorms. Copyright (C) 2003 Royal Meteorological Society.
引用
收藏
页码:1577 / 1588
页数:12
相关论文
共 23 条
[1]  
Auer I, 2001, OSTERREICHISCHE BEIT, V25
[2]  
Auer I., 1993, OSTERREICHISCHE BEIT, V7
[3]   Regional temperature variability in the European Alps:: 1760-1998 from homogenized instrumental time series [J].
Böhm, R ;
Auer, I ;
Brunetti, M ;
Maugeri, M ;
Nanni, T ;
Schöner, W .
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2001, 21 (14) :1779-1801
[4]   A REGIONALIZATION OF AUSTRIA PRECIPITATION CLIMATE USING PRINCIPAL COMPONENT ANALYSIS [J].
EHRENDORFER, M .
JOURNAL OF CLIMATOLOGY, 1987, 7 (01) :71-89
[5]   Applications of the self-organising feature map neural network in community data analysis [J].
Foody, GM .
ECOLOGICAL MODELLING, 1999, 120 (2-3) :97-107
[6]  
Haykin S., 1994, NEURAL NETWORKS COMP
[7]   Self-organizing maps: applications to synoptic climatology [J].
Hewitson, BC ;
Crane, RG .
CLIMATE RESEARCH, 2002, 22 (01) :13-26
[8]   TOWARDS A CLASSIFICATION OF TROPICAL RAINFALL STATIONS [J].
JACKSON, IJ ;
WEINAND, H .
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 1994, 14 (03) :263-286
[9]   The sensitivity of Austrian forests to scenarios of climatic change:: a large-scale risk assessment based on a modified gap model and forest inventory data [J].
Lexer, MJ ;
Hönninger, K ;
Scheifinger, H ;
Matulla, C ;
Groll, N ;
Kromp-Kolb, H ;
Schadauer, K ;
Starlinger, F ;
Englisch, M .
FOREST ECOLOGY AND MANAGEMENT, 2002, 162 (01) :53-72
[10]   Classification of rainfall variability by using artificial neural networks [J].
Michaelides, S ;
Pattichis, CS ;
Kleovoulou, G .
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2001, 21 (11) :1401-1414