Local climatic guidance for probabilistic quantitative precipitation forecasting

被引:0
|
作者
Krzysztofowicz, R [1 ]
Sigrest, AA [1 ]
机构
[1] UNIV VIRGINIA,DIV STAT,CHARLOTTESVILLE,VA 22903
关键词
D O I
10.1175/1520-0493(1997)125<0305:LCGFPQ>2.0.CO;2
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
The predictand of a probabilistic quantitative precipitation forecast (PQPF) for a river basin has two parts: (i) the basin average precipitation amount accumulated during a fixed period and (ii) the temporal disaggregation of the total amount into subperiods. To assist field forecasters in the preparation of well-calibrated (reliable) and informative PQPFs, local climatic guidance (LCG) was developed. LCG provides climatic statistics of the predictand for a particular river basin, month, and period (e.g., 24-h period beginning at 1200 UTC and divided into four 6-h subperiods). These statistics can be conditioned on information entered by the forecaster such as the probability of precipitation occurrence and various hypotheses regarding the precipitation amount and timing. This article describes two probability models of the predictand, details guidance products, and illustrates them for the Lower Monongahela River basin in Pennsylvania. The first model provides marginal climatic statistics of the predictand on an ''average'' day of the month. The second model conditions the statistics on the timing of precipitation within the diurnal cycle. The resultant characterization of the precipitation process allows the forecaster to decompose the complex assessment of a multivariate PQPF into a sequence of feasible judgmental tasks.
引用
收藏
页码:305 / 316
页数:12
相关论文
共 50 条
  • [41] Bayesian Model Averaging with Stratified Sampling for Probabilistic Quantitative Precipitation Forecasting in Northern China during Summer 2010
    Zhu, Jiangshan
    Kong, Fanyou
    Ran, Lingkun
    Lei, Hengchi
    MONTHLY WEATHER REVIEW, 2015, 143 (09) : 3628 - 3641
  • [42] Quantifying the uncertainty of precipitation forecasting using probabilistic deep learning
    Xu, Lei
    Chen, Nengcheng
    Yang, Chao
    Yu, Hongchu
    Chen, Zeqiang
    HYDROLOGY AND EARTH SYSTEM SCIENCES, 2022, 26 (11) : 2923 - 2938
  • [43] DISTRIBUTIONAL REGRESSION FORESTS FOR PROBABILISTIC PRECIPITATION FORECASTING IN COMPLEX TERRAIN
    Schlosser, Lisa
    Hothorn, Torsten
    Stauffer, Reto
    Zeileis, Achim
    ANNALS OF APPLIED STATISTICS, 2019, 13 (03): : 1564 - 1589
  • [44] Integration of quantitative precipitation forecasts with real-time hydrology and hydraulics modeling towards probabilistic forecasting of urban flooding
    Brendel, Conrad E.
    Dymond, Randel L.
    Aguilar, Marcus F.
    ENVIRONMENTAL MODELLING & SOFTWARE, 2020, 134
  • [45] Probabilistic quantitative precipitation forecast for flood prediction: An application
    Reggiani, P.
    Weerts, A. H.
    JOURNAL OF HYDROMETEOROLOGY, 2008, 9 (01) : 76 - 95
  • [46] Probabilistic Weather Forecasting with Deterministic Guidance-Based Diffusion Model
    Yoon, Donggeun
    Seo, Minseok
    Kim, Doyi
    Choi, Yeji
    Cho, Donghyeon
    COMPUTER VISION - ECCV 2024, PT XXX, 2025, 15088 : 108 - 124
  • [47] On the uncertainties of flash flood guidance: Toward probabilistic forecasting of flash floods
    Ntelekos, Alexandros A.
    Georgakakos, Konstantine P.
    Krajewski, Witold F.
    JOURNAL OF HYDROMETEOROLOGY, 2006, 7 (05) : 896 - 915
  • [48] Demonstrating a Probabilistic Quantitative Precipitation Estimate for Evaluating Precipitation Forecasts in Complex Terrain
    Bytheway, Janice L.
    Hughes, Mimi
    Cifelli, Rob
    Mahoney, Kelly
    English, Jason M.
    WEATHER AND FORECASTING, 2022, 37 (01) : 45 - 64
  • [49] Simulation of precipitation fields in space and time from probabilistic quantitative precipitation forecast
    Seo, DJ
    Finnerty, B
    14TH CONFERENCE ON PROBABILITY AND STATISTICS IN THE ATMOSPHERIC SCIENCES, 1998, : 140 - 141
  • [50] APPLICATION OF MODEL OUTPUT STATISTICS TO FORECASTING QUANTITATIVE PRECIPITATION
    BERMOWITZ, RJ
    MONTHLY WEATHER REVIEW, 1975, 103 (02) : 149 - 153