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2020年超長(cháng)梅汛期降水概率預報應用與檢驗
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浙江省氣象局青年項目(2021QN11)、浙江省氣象局重點(diǎn)項目(2022ZD01)資助


Application and Verification of Probabilistic Forecast of Precipitation During Super Long Meiyu Season in 2020
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    摘要:

    基于歐洲中期天氣預報中心(European Center for Medium-range Weather Forecasts,ECMWF)集合預報資料及浙江全省自動(dòng)站降水觀(guān)測資料,采用貝葉斯模型平均(Bayesian Model Average, BMA)方法對2020年浙江超長(cháng)梅汛期開(kāi)展降水概率預報訂正試驗。采用平均絕對誤差、連續等級概率評分、布萊爾評分BS、Talagrand、概率積分變換(Probability Integral Transform, PIT)直方圖及屬性圖檢驗方法對本次過(guò)程BMA訂正前后的概率預報進(jìn)行對比分析,結果表明:①50 d為適用于浙江梅汛期ECMWF集合預報訂正的BMA最優(yōu)訓練期,經(jīng)最優(yōu)訓練期的BMA訂正后,預報離散度有所增加,預報誤差有所下降;②BMA對0.1 mm、10.0 mm和25.0 mm閾值降水的訂正效果顯著(zhù),經(jīng)BMA訂正后3個(gè)閾值的降水預報BS下降率分別為25.92%、19.29%、4.76%,但對超過(guò)50.0 mm的降水訂正效果不明顯,且隨著(zhù)降水閾值增加,BMA的訂正效果減弱;③在強降水個(gè)例中,BMA能有效減少各閾值降水預報概率大值落區偏差,使訂正后的降水預報概率大值區與觀(guān)測落區更一致。

    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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姚夢(mèng)穎,婁小芬,劉雪晴,邱金晶.2020年超長(cháng)梅汛期降水概率預報應用與檢驗[J].氣象科技,2024,52(3):367~379

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  • 收稿日期:2023-05-26
  • 定稿日期:2024-01-11
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  • 在線(xiàn)發(fā)布日期: 2024-06-25
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