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Incorporating Reanalysis-Based Short-Term Forecasts from a Regional Climate Model in an Irrigation Schedulinq Optimization Problem

机译:在灌溉Schedulinq优化问题中纳入基于区域分析模型的基于重新分析的短期预报

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摘要

A coupled simulation-optimization with reanalysis-based short-term weather forecasts from a regional climate model (RCM) is proposed to optimize an irrigation scheduling problem. Using different physical configurations of the climate extension of a weather research and forecasting model (CWRF) that is driven by national atmospheric model project reanalysis data, five ensemble outlooks of 15 consecutive daily forecasts have been generated during five different crop-growing seasons. Six daily climatic variables are forecasted, namely, rainfall, minimum temperature, maximum temperature, humidity, wind speed, and solar radiation. To correct the forecasts for any inherent bias, the quantile mapping method is applied to all six daily climatic variables. After bias correction, a skill assessment of the reanalysis-based RCM forecasts indicate that only the first three climatic variables are predicted with reliable accuracy; thus, average climatic means are used to replace the remaining three variables (humidity, wind speed, and solar radiation). The framework is applied to the Havana Lowlands region, Illinois, as a case study, and the value of forecasts is assessed against two baseline scenarios: no-rain forecast (a pessimistic case) and average climatology (a normal case). Using reanalysis-based RCM forecasts to guide farmers' irrigation decisions could yield about 1-3% in expected profit gain and 4-6% in water reduction when compared to the no-rain forecast scenario, and 1-6% in expected profit gain when compared to the average climatology scenario. This study is a first preliminary attempt to use an ensemble of weather simulations in the optimization of irrigation scheduling, and the developed framework can be used to incorporate operational forecasting once the reanalysis boundary is replaced by global weather forecasts.
机译:提出了一种基于区域气候模型(RCM)的基于重新分析的短期天气预报的模拟优化,以优化灌溉调度问题。使用由国家大气模型项目再分析数据驱动的天气研究和预报模型(CWRF)的气候扩展的不同物理配置,在五个不同的作物生长季节中,已经生成了15个连续的每日预报的五个集合前景。预测了六个每日气候变量,即降雨量,最低温度,最高温度,湿度,风速和太阳辐射。为了更正任何固有偏差的预测,将分位数映射方法应用于所有六个每日气候变量。经过偏差校正后,对基于再分析的RCM预测进行的技能评估表明,只有前三个气候变量的预测具有可靠的准确性;因此,使用平均气候平均值来代替其余三个变量(湿度,风速和太阳辐射)。该框架以案例研究的形式应用于伊利诺伊州哈瓦那低地地区,并根据两种基准情景评估了预报的价值:无雨预报(悲观案例)和平均气候(正常案例)。与无雨预报相比,使用基于重新分析的RCM预测来指导农民的灌溉决策可能会带来约1-3%的预期利润增长和4-6%的节水量,而预期收益则为1-6%与平均气候情景相比。这项研究是在优化灌溉计划中使用天气模拟整体的首次初步尝试,一旦再分析边界被全球天气预报所取代,则可以使用已开发的框架来合并操作预报。

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  • 作者单位

    Joint Global Change Research Institute, Univ. of Maryland,Pacific Northwest National Laboratory, 5825 University Research Court,Suite 3500, College Park, MD 20740 Dept.of Civil and Environmental Engineering, Univ. of Illinois, Urbana,IL 61801;

    Dept. of Civil and Environmental Engineering, Univ. of Illinois, 2535c Hydrosystems Laboratory, 301 N. Mathews Ave., Urbana,IL 61801;

    Dept. or Civil and Environmental Engineering, Princeton Univ., E318 Engineering Quad, Princeton, NJ 08544 formerly, Postdoctoral Research Associate, Illinois State Water Survey, Dept. of Natural Resources, Univ. of Illinois, Urbana, IL;

    Dept. of Atmospheric and Oceanic Science, Univ. of Maryland, 5825 University Research Court, Suite 4001, College Park,MD 20740 Dept. of Atmospheric Sciences, Univ. of Illinois, Urbana, IL, and Illinois State Water Survey, Dept. of Natural Resources, Univ. of Illinois, Urbana, IL;

    Dept. of Civil and Environmental Engineering, Univ. of Illinois, 2527B Hydrosystems Laboratory, 301 N. Mathews Ave., Urbana,IL 61801;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Regional climate model; Dynamic downscaling; Bias correction; Irrigation scheduling; Weather forecast; Optimization;

    机译:区域气候模式;动态降级;偏差校正;灌溉计划;天气预报;优化;

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