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基于TIGGE多模式集合的24小时气温BMA概率预报

刘建国 谢正辉 赵琳娜 贾炳浩

大气科学2013,Vol.37Issue(1):43-53,11.
大气科学2013,Vol.37Issue(1):43-53,11.DOI:10.3878/j.issn.1006-9895.2012.11232

基于TIGGE多模式集合的24小时气温BMA概率预报

BMA Probabilistic Forecasting for the 24-h TIGGE Multi-model Ensemble Forecasts of Surface Air Temperature

刘建国 1谢正辉 2赵琳娜 1贾炳浩3

作者信息

  • 1. 中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室,北京100029
  • 2. 中国科学院大学,北京100049
  • 3. 中国气象局公共气象服务中心,北京100081
  • 折叠

摘要

Abstract

Bayesian model averaging (BMA) probability forecast models were established through calibration of their parameters using 24-h ensemble forecasts of average daily surface air temperature provided by single-center ensemble prediction systems (EPSs) from the following agencies: the European Centre for Medium-Range Weather Forecasts (ECMWF), the United Kingdom Meteorological Office (UKMO), the China Meteorological Administration (CMA), and the United States National Center for Environmental Prediction (NCEP) and its multi-center model grand-ensemble (GE) EPSs in the THORPEX Interactive Grand Global Ensemble (TIGGE), and observations in the Huaihe basin. The BMA probability forecasts of average daily surface air temperature for different EPSs were assessed by comparison with observations in the Huaihe basin. The results suggest that performance was better in the BMA predictive models than that in raw ensemble forecasts. The BMA predictive models for the four single-center EPSs all had good forecast skills; among them, the ECMWF EPS had the best. The BMA predictive models for the GE EPS performed better than any of the four single-center EPSs; those for the GE EPS with exchangeable members (EGE) quickened the computation rate and had the best forecast skill in BMA models for all EPSs. The mean absolute error (MAE) and continuous ranked probability score (CRPS) skills of the BMA models for EGE improved approximately 7% and 10%, respectively, compared with those of raw ensemble forecasts. On the basis of percentile forecasts from the BMA predictive models for EGE, an extreme scorching weather warning scheme was proposed in the study area, which is of significant importance for precautionary measures against such weather conditions.

关键词

贝叶斯模型平均/TIGGE/地面日均气温/集合预报/概率预报

Key words

Bayesian model averaging/ TIGGE/ Average daily surface air temperature/ Ensemble forecasts/ Probabilistic forecasts

分类

天文与地球科学

引用本文复制引用

刘建国,谢正辉,赵琳娜,贾炳浩..基于TIGGE多模式集合的24小时气温BMA概率预报[J].大气科学,2013,37(1):43-53,11.

基金项目

公益性行业(气象)科研专项GYHY201006037,国家自然科学基金资助项目41075062、91125016,国家重点基础研究发展规划项目2010CB951001、2010CB428403 (气象)

大气科学

OA北大核心CSCDCSTPCD

1006-9895

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