Framework for statistical downscaling of the global climate model seasonal geopotential thickness fields to seasonal maximum temperature in Southern Africa to aid climate change adaptation

Maximum temperature and rainfall observed data files were downloaded from the IRI Data Library as well as the model predicted 850-to-500 geopotential thickness fields (used to predict maximum temperature over southern Africa) and 850 circulation data fields (predictor for rainfall). Model Output statistics in CPT - climate predictability tool, was set up using CCA - canonical correlation analysis to produce retroactive forecasts. MATLAB was further utilized to post-process / fine-tune the output from CPT and to produce other results. The researcher used the output from the global climate model to develop a statistical model for maximum temperature seasonal forecasts for Southern Africa.

本研究数据集包括从IRI数据库下载的最高气温和降雨量观测数据文件，以及用于预测南部非洲最高气温的850至500位势高度场模型预测数据（用于预测降雨的850环流场数据）。利用典范相关分析（CCA）建立模型输出统计（CPT - 气候可预测性工具）以生成回溯性预测。进一步采用MATLAB对CPT的输出进行后处理/微调，并生成其他结果。研究者利用全球气候模型的输出，开发了一套针对南部非洲最高气温季节性预测的统计模型。