Probabilistic Weather Prediction with an Analog Ensemble

Probabilistic Weather Prediction with an Analog Ensemble

Abstract This study explores an analog-based method to generate an ensemble [analog ensemble (AnEn)] in which the probability distribution of the future state of the atmosphere is estimated with a set of past observations that correspond to the best analogs of a deterministic numerical weather predi...

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Bibliographic Details
Published in:Monthly weather review 2013-10, Vol.141 (10), p.3498-3516
Main Authors: DELLE MONACHE, Luca, ECKEL, F. Anthony, RIFE, Daran L, NAGARAJAN, Badrinath, SEARIGHT, Keith
Format: Article
Language:eng
Subjects:
Advantages
Analogs
Calibration
Datasets
Decision making
Earth, ocean, space
Exact sciences and technology
External geophysics
Lead time
Mathematical models
Meteorology
Methods
Modelling
Numerical prediction
Numerical weather forecasting
Precipitation
Predictions
Probability distribution
Probability theory
Rain
Regression analysis
Statistical analysis
Statistical methods
Studies
Weather
Weather forecasting
Wind speed
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Summary:Abstract This study explores an analog-based method to generate an ensemble [analog ensemble (AnEn)] in which the probability distribution of the future state of the atmosphere is estimated with a set of past observations that correspond to the best analogs of a deterministic numerical weather prediction (NWP). An analog for a given location and forecast lead time is defined as a past prediction, from the same model, that has similar values for selected features of the current model forecast. The AnEn is evaluated for 0–48-h probabilistic predictions of 10-m wind speed and 2-m temperature over the contiguous United States and against observations provided by 550 surface stations, over the 23 April–31 July 2011 period. The AnEn is generated from the Environment Canada (EC) deterministic Global Environmental Multiscale (GEM) model and a 12–15-month-long training period of forecasts and observations. The skill and value of AnEn predictions are compared with forecasts from a state-of-the-science NWP ensemble system, the 21-member Regional Ensemble Prediction System (REPS). The AnEn exhibits high statistical consistency and reliability and the ability to capture the flow-dependent behavior of errors, and it has equal or superior skill and value compared to forecasts generated via logistic regression (LR) applied to both the deterministic GEM (as in AnEn) and REPS [ensemble model output statistics (EMOS)]. The real-time computational cost of AnEn and LR is lower than EMOS.
ISSN:0027-0644
1520-0493