Postprocessing of GEFS Precipitation Ensemble Reforecasts over the US Mid-Atlantic Region

被引:18
作者
Yang, Xingchen [1 ]
Sharma, Sanjib [1 ]
Siddique, Ridwan [1 ]
Greybush, Steven J. [2 ]
Mejia, Alfonso [1 ]
机构
[1] Penn State Univ, Dept Civil & Environm Engn, University Pk, PA 16802 USA
[2] Penn State Univ, Dept Meteorol, University Pk, PA 16802 USA
关键词
EXTENDED LOGISTIC-REGRESSION; PREDICTION SYSTEMS; MODEL OUTPUT; MOS METHODS; FORECASTS; ECMWF;
D O I
10.1175/MWR-D-16-0251.1
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
The potential of Bayesian model averaging (BMA) and heteroscedastic censored logistic regression (HCLR) to postprocess precipitation ensembles is investigated. For this, outputs from the National Oceanic and Atmospheric Administration's (NOAA's) National Centers for Environmental Prediction (NCEP) 11-member Global Ensemble Forecast System Reforecast, version 2 (GEFSRv2), dataset are used. As part of the experimental setting, 24-h precipitation accumulations and forecast lead times of 24 to 120 h are used, over the mid-Atlantic region (MAR) of the United States. In contrast with previous postprocessing studies, a wider range of forecasting conditions is considered here when evaluating BMA and HCLR. Additionally, BMA and HCLR have not yet been compared against each other under a common and consistent experimental setting. To compare and verify the postprocessors, different metrics are used (e.g., skills scores and reliability diagrams) conditioned upon the forecast lead time, precipitation threshold, and season. Overall, HCLR tends to slightly outperform BMA but the differences among the postprocessors are not as significant. In the future, an alternative approach could be to combine HCLR with BMA to take advantage of their relative strengths.
引用
收藏
页码:1641 / 1658
页数:18
相关论文
共 54 条
  • [1] Bremnes JB, 2004, MON WEATHER REV, V132, P338, DOI 10.1175/1520-0493(2004)132<0338:PFOPIT>2.0.CO
  • [2] 2
  • [3] From ensemble forecasts to predictive distribution functions
    Broecker, Jochen
    Smith, Leonard A.
    [J]. TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY, 2008, 60 (04) : 663 - 678
  • [4] The Ensemble Verification System (EVS): A software tool for verifying ensemble forecasts of hydrometeorological and hydrologic variables at discrete locations
    Brown, James D.
    Demargne, Julie
    Seo, Dong-Jun
    Liu, Yuqiong
    [J]. ENVIRONMENTAL MODELLING & SOFTWARE, 2010, 25 (07) : 854 - 872
  • [5] A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems
    Buizza, R
    Houtekamer, PL
    Toth, Z
    Pellerin, G
    Wei, MZ
    Zhu, YJ
    [J]. MONTHLY WEATHER REVIEW, 2005, 133 (05) : 1076 - 1097
  • [6] Clark MP, 2004, J HYDROMETEOROL, V5, P15, DOI 10.1175/1525-7541(2004)005<0015:UOMNWP>2.0.CO
  • [7] 2
  • [8] MAXIMUM LIKELIHOOD FROM INCOMPLETE DATA VIA EM ALGORITHM
    DEMPSTER, AP
    LAIRD, NM
    RUBIN, DB
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL, 1977, 39 (01): : 1 - 38
  • [9] Impact of Bias-Correction Type and Conditional Training on Bayesian Model Averaging over the Northeast United States
    Erickson, Michael J.
    Colle, Brian A.
    Charney, Joseph J.
    [J]. WEATHER AND FORECASTING, 2012, 27 (06) : 1449 - 1469
  • [10] Calibrating Multimodel Forecast Ensembles with Exchangeable and Missing Members Using Bayesian Model Averaging
    Fraley, Chris
    Raftery, Adrian E.
    Gneiting, Tilmann
    [J]. MONTHLY WEATHER REVIEW, 2010, 138 (01) : 190 - 202