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A Note on Some Methods Suitable for Verifying and Correcting the Prediction of Climatic Anomaly


doi: 10.1007/BF02666540

  • The weighted correlation coefficient of the predicted and observed anomalies and the ratio of the weighted norm of predicted anomaly to the observed one, both together, are suggested to be suitable for the estimating of the correctness of climate prediction. The former shows the similarity of the two patterns, and the later indicates the correctness of the predicted intensity of the anomaly. The weighting function can be different for different emphasis, for example, a constant weight means that the correlation coefficient is the conventional one, but same non-uniform weight leads to the ratio of correct sign of the anomaly, the stepwise weight leads lo the formulation of correctness of prediction represented by grades of the anomaly, and so on.Three methods for making correction to the prediction are given in this paper. After subtracting lie mean error of the prediction, one method is developed for maximizing the similarity between the predicted and observed patterns, based on the transformation of the spatial coordinates. Another method is to minimize the mean difference between the two fields. This method can also be simplified as similar to the “optimum interpolation” in the objective analysis of weather chart. The third method is based on the expansion of the anomaly into series or EOF, where the coefficients are the predicted but the EOFs are taken as the “observed” calculated from historical samples.
  • [1] SU Qin, LU Riyu, LI Chaofan, 2014: Large-scale Circulation Anomalies Associated with Interannual Variation in Monthly Rainfall over South China from May to August, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 273-282.  doi: 10.1007/s00376-013-3051-x
    [2] LI Xiaofan, SHEN Xinyong, LIU Jia, 2014: Effects of Doubled Carbon Dioxide on Rainfall Responses to Large-Scale Forcing: A Two-Dimensional Cloud-Resolving Modeling Study, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 525-531.  doi: 10.1007/s00376-013-3030-2
    [3] DING Yihui, LIU Yiming, SHI Xueli, LI Qingquan, LI Qiaoping, LIU Yan, 2006: Multi-Year Simulations and Experimental Seasonal Predictions for Rainy Seasons inChina byUsing a Nested Regional ClimateModel (RegCM NCC) Part II: The Experimental Seasonal Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 487-503.  doi: 10.1007/s00376-006-0323-8
    [4] Lingyun LOUSchool, of Earth, Zhejiang University, Xiaofan LISchool, 2016: Radiative Effects on Torrential Rainfall during the Landfall of Typhoon Fitow (2013), ADVANCES IN ATMOSPHERIC SCIENCES, 33, 101-109.  doi: 10.1007/s00376-015-5139-y
    [5] YUE Caijun, GAO Shouting, LIU Lu, LI Xiaofan, 2015: A Diagnostic Study of the Asymmetric Distribution of Rainfall during the Landfall of Typhoon Haitang (2005), ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1419-1430.  doi: 10.1007/s00376-015-4246-0
    [6] MA Leiming, DUAN Yihong, ZHU Yongti, 2004: The Structure and Rainfall Features of Tropical Cyclone Rammasun (2002), ADVANCES IN ATMOSPHERIC SCIENCES, 21, 951-963.  doi: 10.1007/BF02915597
    [7] ZHAI Guoqing, LI Xiaofan, ZHU Peijun, SHEN Hangfeng, ZHANG Yuanzhi, 2014: Surface Rainfall and Cloud Budgets Associated with Mei-yu Torrential Rainfall over Eastern China during June 2011, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 1435-1444.  doi: 10.1007/s00376-014-3256-7
    [8] John ABBOT, Jennifer MAROHASY, 2012: Application of Artificial Neural Networks to Rainfall Forecasting in Queensland, Australia, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 717-730.  doi: 10.1007/s00376-012-1259-9
    [9] Yu DU, Yian SHEN, Guixing CHEN, 2022: Influence of Coastal Marine Boundary Layer Jets on Rainfall in South China, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 782-801.  doi: 10.1007/s00376-021-1195-7
    [10] REN Baohua, LU Riyu, XIAO Ziniu, 2004: A Possible Linkage in the Interdecadal Variability of Rainfall over North China and the Sahel, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 699-707.  doi: 10.1007/BF02916367
    [11] Riyu LU, Saadia HINA, Xiaowei HONG, 2020: Upper- and Lower-tropospheric Circulation Anomalies Associated with Interannual Variation of Pakistan Rainfall during Summer, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1179-1190.  doi: 10.1007/s00376-020-0137-0
    [12] Meiying DONG, Chunxiao JI, Feng CHEN, Yuqing WANG, 2019: Numerical Study of Boundary Layer Structure and Rainfall after Landfall of Typhoon Fitow (2013): Sensitivity to Planetary Boundary Layer Parameterization, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 431-450.  doi: 10.1007/s00376-018-7281-9
    [13] Chen SHENG, Bian HE, Guoxiong WU, Yimin LIU, Shaoyu ZHANG, 2022: Interannual Influences of the Surface Potential Vorticity Forcing over the Tibetan Plateau on East Asian Summer Rainfall, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1050-1061.  doi: 10.1007/s00376-021-1218-4
    [14] HAN Jinping, ZHANG Renhe, 2009: The Dipole Mode of the Summer Rainfall over East China during 1958--2001, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 727-735.  doi: 10.1007/s00376-009-9014-6
    [15] Song YANG, Eric A.SMITH, 2005: Resolving SSM/I-Ship Radar Rainfall Discrepancies from AIP-3, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 903-914.  doi: 10.1007/BF02918689
    [16] ZHOU Lian-Tong, Chi-Yung TAM, ZHOU Wen, Johnny C. L. CHAN, 2010: Influence of South China Sea SST and the ENSO on Winter Rainfall over South China, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 832-844.  doi: 10.1007/s00376--009-9102-7
    [17] Min-Hee LEE, Chang-Hoi HO, Joo-Hong KIM, 2010: Influence of Tropical Cyclone Landfalls on Spatiotemporal Variations in Typhoon Season Rainfall over South China, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 443-454.  doi: 10.1007/s00376-009-9106-3
    [18] Jian YUE, Zhiyong MENG, Cheng-Ku YU, Lin-Wen CHENG, 2017: Impact of Coastal Radar Observability on the Forecast of the Track and Rainfall of Typhoon Morakot (2009) Using WRF-based Ensemble Kalman Filter Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 66-78.  doi: 10.1007/s00376-016-6028-8
    [19] Xinyu LI, Riyu LU, 2021: Decadal Change in the Influence of the Western North Pacific Subtropical High on Summer Rainfall over the Yangtze River Basin in the Late 1970s, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1823-1834.  doi: 10.1007/s00376-021-1051-9
    [20] Fei WANG, Lifang SHENG, Xiadong AN, Haixia ZHOU, Yingying ZHANG, Xiaodong LI, Yigeng DING, Jing YANG, 2022: The Impact of an Abnormal Zonal Vertical Circulation in Autumn of Super El Niño Years on Non-tropical-cyclone Heavy Rainfall over Hainan Island, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1914-1924.  doi: 10.1007/s00376-022-1388-8

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Manuscript History

Manuscript received: 10 April 1994
Manuscript revised: 10 April 1994
通讯作者: 陈斌, bchen63@163.com
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A Note on Some Methods Suitable for Verifying and Correcting the Prediction of Climatic Anomaly

  • 1. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100080,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100080,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100080,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100080,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100080,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100080,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100080

Abstract: The weighted correlation coefficient of the predicted and observed anomalies and the ratio of the weighted norm of predicted anomaly to the observed one, both together, are suggested to be suitable for the estimating of the correctness of climate prediction. The former shows the similarity of the two patterns, and the later indicates the correctness of the predicted intensity of the anomaly. The weighting function can be different for different emphasis, for example, a constant weight means that the correlation coefficient is the conventional one, but same non-uniform weight leads to the ratio of correct sign of the anomaly, the stepwise weight leads lo the formulation of correctness of prediction represented by grades of the anomaly, and so on.Three methods for making correction to the prediction are given in this paper. After subtracting lie mean error of the prediction, one method is developed for maximizing the similarity between the predicted and observed patterns, based on the transformation of the spatial coordinates. Another method is to minimize the mean difference between the two fields. This method can also be simplified as similar to the “optimum interpolation” in the objective analysis of weather chart. The third method is based on the expansion of the anomaly into series or EOF, where the coefficients are the predicted but the EOFs are taken as the “observed” calculated from historical samples.

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