High resolution space-time modelling of rainfall: the "String of Beads" model

被引:70
|
作者
Pegram, GGS [1 ]
Clothier, AN [1 ]
机构
[1] Univ Natal, Dept Civil Engn, ZA-4001 Durban, South Africa
关键词
stochastic processes; modelling; precipitation; remote sensing;
D O I
10.1016/S0022-1694(00)00373-5
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
A stochastic space-time model of rainfall is proposed, which is defined by a small number of parameters and models rainfall intensity images measured by radar. It is a phenomenological (rather than a physically based) model which provides sequences of realistic stochastically generated rainfields over an area covered by an S-band weather radar sited near Bethlehem, South Africa. The weather types studied vary from predominantly scattered convective thunder-shower type rainfall through to widespread, general, stratiform rainfields with high Wetted Area Ratios. The methods of analysis and modelling of rainfall events as measured by radar are described. A three-dimensional (two space and one time) simulated rainfall event designed to mimic a real sequence is generated, analysed and compared to the observed rainfall event. Not only does it reproduce the statistics without obvious bias, it has been validated using the Generalized structure function. Variograms (specialisations of the Generalized structure function) fitted to the data measured by radar from a variety of rainfield types suggest that the correlation distance in rainfields is limited to between 12 and 25 km. The implication is that although the rainfields appear to be locally nonstationary, they are stationary over areas typically measured by radar. This is a post-justification for using methods based on the assumption that rainfields are stationary random fields and allows the use of straightforward statistical and spectral techniques which are the heart of the String of Beads model. This paper concentrates on modelling the rainfall process during a wet period, deferring the treatment of the inter-arrival of wet and dry periods to a follow-on paper. (C) 2001 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:26 / 41
页数:16
相关论文
共 50 条
  • [31] Estimation of daily space-time precipitation series for macroscale hydrological modelling
    Haberlandt, U
    Kite, GW
    HYDROLOGICAL PROCESSES, 1998, 12 (09) : 1419 - 1432
  • [32] Revisiting model complexity: Space-time correction of high dimensional variable sets in climate model simulations
    Kusumastuti, Cilcia
    Mehrotra, Rajeshwar
    Sharma, Ashish
    JOURNAL OF HYDROLOGY X, 2024, 25
  • [33] Space-time variability of rainfall and hydrological trends in the Alto Sao Francisco River basin
    da Silva, Richarde Marques
    Guimaraes Santos, Celso Augusto
    Aires Macedo, Monica Larissa
    Silva, Leonardo Pereira E.
    De Macedo Machado Freire, Paula Karenina
    CLIMATE AND LAND SURFACE CHANGES IN HYDROLOGY, 2013, 359 : 177 - 182
  • [34] Joint treatment of point measurement, sampling and neighborhood uncertainty in space-time rainfall mapping
    Ehlers, L. B.
    Sonnenborg, T. O.
    Heuvelink, G. B. M.
    He, X.
    Refsgaard, J. C.
    JOURNAL OF HYDROLOGY, 2019, 574 : 148 - 159
  • [35] On modelling internet transactions as a time-dependent random walk: An application of the retail aggregate space-time trip (RASTT) model
    Baker R.G.V.
    GeoJournal, 2001, 53 (4) : 407 - 418
  • [36] STORAGE (STOchastic RAinfall GEnerator): A User-Friendly Software for Generating Long and High-Resolution Rainfall Time Series
    De Luca, Davide Luciano
    Petroselli, Andrea
    HYDROLOGY, 2021, 8 (02)
  • [37] A Novel Space-Time Adaptive Method for Rainfall Estimation by means ofWeather Radar and Rain Gauges
    Biondi, Alessio
    Cuccoli, Fabrizio
    Facheris, Luca
    Argenti, Fabrizio
    Baldini, Luca
    2024 4TH URSI ATLANTIC RADIO SCIENCE MEETING, AT-RASC 2024, 2024,
  • [38] Space-Time Variability of the Rainfall over Sahel: Observation of a Latitudinal Sharp Transition of the Statistical Properties
    Sy, Abdoulaye
    Duroure, Christophe
    Baray, Jean-Luc
    Gour, Yahya
    Van Baelen, Joel
    Diop, Bouya
    ATMOSPHERE, 2018, 9 (12):
  • [39] Stochastic Space-Time Downscaling of Rainfall Using Event-Based Multiplicative Cascade Simulations
    Raut, Bhupendra A.
    Reeder, Michael J.
    Jakob, Christian
    Seed, Alanw.
    JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2019, 124 (07) : 3889 - 3902
  • [40] Space-time Prediction of High Resolution Raster Data: An Approach based on Spatio-temporal Bayesian Network (STBN)
    Das, Monidipa
    Ghosh, Soumya K.
    PROCEEDINGS OF THE 6TH ACM IKDD CODS AND 24TH COMAD, 2019, : 129 - 135