Seasonal drought prediction for semiarid northeastern Brazil: verification of six hydro-meteorological forecast products
Seasonal drought prediction for semiarid northeastern Brazil: verification of six hydro-meteorological forecast products
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DOI：
10.5194/hess-22-5041-2018
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发表时间：
2018-09-28
影响因子：
6.3
通讯作者：
Francke, Till
中科院分区：
文献类型：
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作者：
Delgado, Jose Miguel;Voss, Sebastian;Francke, Till
对巴西东北部半干旱地区的Jaguaribe河的一套季节性干旱预报模型进行了评估和验证。气象季节预报由欧洲气象研究基金会（FUNCEME）和欧洲中期天气预报中心（ECMWF）使用的预报系统提供。对1981 ~ 2014年的三种降尺度方法（经验分位数映射、扩展降尺度和天气模式分类）进行了检验，并与后验模式模型相结合。预报发布时间为1月，预报期为1 - 6月。水文干旱指数通过对观测数据进行多元线性回归拟合得到。简而言之，可以获得(a)月降水量、(b)气象干旱指数和(c)水文干旱指数的预报。采用均方根误差（RMSE）、Brier技能分数（BSS）和相对操作特征技能分数（ROCSS）对预测系统的技能进行评价。测试的预测产品在分析的指标中表现出相似的性能。考虑RMSE的月降水预报几乎没有技巧，而考虑BSS的月降水预报基本没有技巧。在预测气象干旱指数时也出现了类似的情况：在RMSE和BSS方面的技能较低，而在区分ROCSS给出的命中率和虚警率方面的技能显著（例如，预测SPEI1的干旱事件，ROCSS约为0.5）。各气象指标预测能力的时间变化特征为，4月份与雨季其他月份相比变化最大，水库容积预测能力随提前时间的延长而降低。这项工作表明，多模式集合可以熟练地预测巴西东北部与水管理人员相关的时间尺度的干旱事件。但在月度降水或较低尺度的干旱指数（如SPI1）的预测中没有或很少发现技巧。这项工作和这里重新审视的工作都表明，在预测巴西东北部的雨季方面需要迈出重要的一步。
A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceara's research foundation for meteorology) and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation, (b) meteorological drought indices, and (c) hydrological drought indices.The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time.This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.