首页> 外文期刊>International Journal of Climatology: A Journal of the Royal Meteorological Society >Assessing reference evapotranspiration estimation from reanalysis weather products. An application to the Iberian Peninsula
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Assessing reference evapotranspiration estimation from reanalysis weather products. An application to the Iberian Peninsula

机译:评估再分析天气产物的参考蒸散估计值。适用于伊比利亚半岛

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摘要

Computing crop reference evapotranspiration (ETo) with the FAO Penman-Monteith method (PM-ETo) requires maximum and minimum air temperature, shortwave radiation, relative air humidity and wind speed. These data are often not available, thus requiring alternative computation procedures. Although some proposed approximations may provide ETo values with small estimation errors, the physics of the ET processes may then not be well described. The use of reanalysis data, which is common in climate studies, represents an alternative to observation data for the weather variables referred above, when these are not available. This study focuses on the use of the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) blended reanalysis products with gridded data sets for the computation of PM-ETo in the Iberian Peninsula. A monthly time step was adopted. The PM-ETo time series computed with the blended reanalysis data sets were compared with those obtained using observations for 130 weather stations in the Iberian Peninsula. Results show that the PM-ETo computed with blended reanalysis compares well with the series computed from observation data (average root mean square error, RMSE = 0.49 mm day(-1)). The weather variables derived from reanalysis were also compared with observation data. Results supported the quality of ETo computations because, overall, there was a good match between solar radiation (average RMSE = 1.76 MJm(-2) day(-1)) and maximum temperature (average RMSE = 1.48 degrees C) derived from reanalysis and in situ observations. By contrast, the wind speed from reanalysis highly overestimated observations and this is likely a reason for the slight overestimation of ETo computed from reanalysis (percentage bias, PBIAS>20 in 89 of cases). In addition, the reanalysis products are apparently influenced by modelled warming, which contributes to overestimation of the minimum temperature and, to a lesser extent, of the relative humidity. The spatial pattern of accuracy indicators reveals that poorer results correspond to the southern and south-eastern coastal areas of Iberia, where climate is semi-arid. The compatibility of the PM-ETo computed with monthly inputs and of the daily ETo cumulated to the month using the PM-ETo equation was confirmed, thus allowing to extend conclusions of this study to daily computations. Alternative reanalysis products were also assessed. Tests for ERA-Interim reanalysis products revealed overestimation of ETo and those for NCEP/NCAR Reanalysis II have shown large underestimation. Results suggest that the blended reanalysis products are suitable for the estimation of ETo in Iberia since they integrate an appropriate correction of radiation and temperature, which proved essential for the good estimation results obtained.
机译:使用粮农组织Penman-Monteith方法(PM-ETo)计算作物参考蒸散量(ETo)需要最高和最低气温、短波辐射、相对空气湿度和风速。这些数据通常不可用,因此需要替代计算程序。尽管一些提出的近似值可能提供具有较小估计误差的ETo值,但ET过程的物理特性可能无法得到很好的描述。在气候研究中很常见的再分析数据的使用,是上述天气变量观测数据的替代方法,如果这些数据不可用。本研究的重点是使用国家环境预测中心/国家大气研究中心 (NCEP/NCAR) 混合再分析产品与网格数据集来计算伊比利亚半岛的 PM-ETo。会议采用了每月一次的时间步长。将使用混合再分析数据集计算的PM-ETo时间序列与使用伊比利亚半岛130个气象站的观测获得的时间序列进行比较。结果表明,通过混合再分析计算的PM-ETo与根据观测数据计算的序列(平均均方根误差,RMSE = 0.49 mm day(-1))进行了很好的比较。再分析得出的天气变量也与观测数据进行了比较。结果支持ETo计算的质量,因为总体而言,太阳辐射之间有很好的匹配(平均RMSE = 1。76 MJm(-2)、天(-1))和最高温度(平均RMSE = 1.48°C)来自再分析和原位观测。相比之下,再分析的风速高估了观测值,这可能是再分析计算出的ETo略微高估的原因(百分比偏差,PBIAS>89%的情况下为20%)。此外,再分析产物显然受到模拟变暖的影响,这导致了对最低温度的高估,并在较小程度上高估了相对湿度。精度指标的空间模式表明,较差的结果对应于伊比利亚南部和东南部沿海地区,那里的气候是半干旱的。证实了使用月输入计算的 PM-ETo 和使用 PM-ETo 方程累积到月的每日 ETo 的兼容性,从而可以将本研究的结论扩展到每日计算。还评估了替代再分析产品。对ERA-Interim 再分析产品的测试显示对 ETo 的高估,而对 NCEP/NCAR 再分析 II 的测试显示严重低估。结果表明,混合再分析产品适用于伊比利亚的ETo估计,因为它们集成了适当的辐射和温度校正,这对于获得良好的估计结果至关重要。

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