Application and Verification of Probabilistic Forecast of Precipitation During Super Long Meiyu Season in 2020
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Abstract:
Based on the ensemble forecast data derived from European Centre for Medium-range Weather Forecasts (ECMWF) ensemble forecast system and observation data derived from automatic observation stations in Zhejiang region, the Bayesian Model Averaging (BMA) method is used to calibrate the probabilistic forecasts of precipitation during the super long Meiyu season in 2020. In this paper, we verify the raw ensemble probabilistic forecast and BMA calibrated probabilistic forecast from 1 June to 15 July, 2020, by Mean Absolute Error (MAE), Continuous Ranked Probability Score (CRPS), Brier Score (BS), Talagrand, Probability Integral Transform (PIT) histogram, and attribute diagram. The verification results before and after calibration are compared. The analysis results are listed as follows. (1) In 8 different training periods (10 days to 80 days), 50 days correspond to smaller MAE and CRPS score values. So we set 50 days as the optimal BMA training period for ECMWF ensemble forecast calibration in the Meiyu season in Zhejiang Province. After BMA calibration in the optimal training period, the spread of ensemble forecast increases and the forecast error decreases. Analysing from the quantitative verification indicators, BMA can effectively calibrate the overall precipitation in the test stage, but it cannot calibrate the daily precipitation in the test stage. (2) For forecasting of different threshold precipitation, BMA has different calibration performance. For the thresholds of 0.1 mm, 10.0 mm, and 25.0 mm, BMA has a significant calibration effect. After BMA calibration, the CRPS of precipitation probabilistic forecast for these three thresholds (0.1 mm, 10.0 mm, and 25.0 mm) decreases by 25.92%, 19.29%, and 4.76%, respectively. However, the calibration effect of BMA weakens with the increase of precipitation threshold. For the events with total precipitation exceeding 50.0 mm, the BMA calibration effect is not as significant as that of the smaller threshold. In addition, BMA can effectively improve the forecast skills of 0.1 mm, 10.0 mm and 25.0 mm threshold precipitation and make the forecast probability more closely match the observation. (3) In the case of heavy rain, the high probability range of the raw ensemble probabilistic forecast is always wider than that of the observation. BMA has the ability to slightly calibrate the raw ensemble forecast probability. After BMA calibration, the high probability range of precipitation forecast at each threshold effectively reduces the deviation. The empty message information and the probability of empty message events also reduce after calibration. So BMA can make the calibrated high probability range of precipitation forecast more consistent with the observed range. But unfortunately, BMA cannot adjust the spatial distribution of precipitation forecast probability.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.