African Handbook of Climate Change Adaptation
African Handbook of Climate Change Adaptation
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非洲气候变化适应手册
DOI：
10.1007/978-3-030-45106-6_97
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Akeh U
中科院分区：
文献类型：
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作者：
Akeh U
非洲和亚洲大部分地区的农民仍然实行自给农业，农业生产主要依赖季节性降雨。及时准确地预测降雨的开始、停止、预计降雨量及其季节内变化，很有可能减少极端天气的损失和风险，并最大限度地提高农业产量，确保粮食安全。进行了一项研究，以评估欧洲中期天气预报中心（ECMWF）数值天气预报模式及其亚季节到季节（S2 S）降水预报，以确定其作为尼日利亚气候变化适应工具的有用性。使用了1995-2015年期间CHIRPS再分析降水量和ECMWF亚季节周降水预报数据。分析了5月至9月的预报和观测降水量，同时使用海洋厄尔尼诺指数确定了厄尔尼诺和拉尼娜年。预测技巧是由标准度量确定的：偏差，均方根误差（RMSE），和异常相关系数（ACC）。偏差，均方根误差，和ACC分数表明，ECMWF模型能够预测尼日利亚南部的降水，最好的技能在一周的提前时间和最差的技能在4周的提前时间。结果还表明，在厄尔尼诺年，该模式是更可靠的比拉尼娜。然而，ECMWF对该模型的一些改进可以提供更好的结果，并使该工具成为一个更可靠的工具，用于备灾风险，减少和预防雨季极端降雨可能造成的损害和损失，从而加强气候变化适应。
Farmers in most parts of Africa and Asia still practice subsistence farming which relies minly on seasonal rainfall for Agricultural production. A timely and accurate prediction of the rainfall onset, cessation, expected rainfall amount, and its intra-seasonal variability is very likely to reduce losses and risk of extreme weather as well as maximize agricultural output to ensure food security.Based on this, a study was carried out to evaluate the performance of the European Centre for Medium-range Weather Forecast (ECMWF) numerical Weather Prediction Model and its Subseasonal to Seasonal (S2S) precipitation forecast to ascertain its usefulness as a climate change adaptation tool over Nigeria. Observed daily and monthly CHIRPS reanalysis precipitation amount and the ECMWF subseasonal weekly precipitation forecast data for the period 1995–2015 was used. The forecast and observed precipitation were analyzed from May to September while El Nino and La Nina years were identified using the Oceanic Nino Index. Skill of the forecast was determined from standard metrics: Bias, Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC).The Bias, RMSE, and ACC scores reveal that the ECMWF model is capable of predicting precipitation over Southern Nigeria, with the best skill at one week lead time and poorest skills at lead time of 4 weeks. Results also show that the model is more reliable during El Nino years than La-Nina. However, some improvement in the model by ECMWF can give better results and make this tool a more dependable tool for disaster risk preparedness, reduction and prevention of possible damages and losses from extreme rainfall during the wet season, thus enhancing climate change adaptation.