Generating surfaces of daily meteorological variables over large regions of complex terrain
Generating surfaces of daily meteorological variables over large regions of complex terrain
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DOI：
10.1016/s0022-1694(96)03128-9
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发表时间：
1997-03-15
影响因子：
6.4
通讯作者：
White, MA
中科院分区：
文献类型：
--
作者：
Thornton, PE;Running, SW;White, MA
提出了一种在复杂地形的大区域内生成温度、降水、湿度和辐射日面的方法。所需的输入包括数字高程数据以及地面气象站的最高温度、最低温度和降水量观测值。我们的方法是基于截断高斯加权滤波器的空间卷积的一组站的位置。对复杂地形中典型的非均匀台站分布的敏感性是通过迭代台站密度算法来实现的。对气温和降水与海拔高度的关系进行了空间和时间上明确的经验分析，并探讨了这些关系的特征空间和时间尺度。介绍了一种日降水量发生率算法，作为日降水量预测的先导。湿度表面（蒸汽压赤字）是作为预测的日最低温度和预测的日平均日光温度的函数生成的。每日表面的入射salar辐射产生的太阳斜率的几何形状和内插昼夜温度范围的函数。这些方法的应用在美国西北部约400 000 km（2）的区域进行了为期1年的演示，包括参数化过程的详细说明。进行了交叉验证分析，比较了预测值和观测到的日平均值和年平均值。预测的年平均最高和最低温度的平均绝对误差（MAE）分别为0.7摄氏度和1.2摄氏度，偏差分别为+0.1摄氏度和-0.1摄氏度。预测的年总降水量的MAE为13.4 cm，或表示为观测的年总降水量的百分比，19.3%。日降水预报成功率为83.3%。特别注意的是，预测和观察到的降水频率和强度之间的关系，他们被证明是相似的。我们测试了这些方法预测网格点间距的敏感性，发现网格间距从500米到32公里的区域平均值不变。我们测试了结果对时间步长的依赖性，发现温度预测算法在这方面具有很好的扩展性。降水预测的时间尺度是复杂的日常发生的预测，但非常接近相同的预测，每天和每年的时间步长。(C)1997年Elsevier Science B.V.
A method for generating daily surfaces of temperature, precipitation, humidity, and radiation over large regions of complex terrain is presented. Required inputs include digital elevation data and observations of maximum temperature, minimum temperature and precipitation from ground-based meteorological stations. Our method is based on the spatial convolution of a truncated Gaussian weighting filter with the set of station locations. Sensitivity to the typical heterogeneous distribution of stations in complex terrain is accomplished with an iterative station density algorithm. Spatially and temporally explicit empirical analyses of the relationships of temperature and precipitation to elevation were performed, and the characteristic spatial and temporal scales of these relationships were explored. A daily precipitation occurrence algorithm is introduced, as a precursor to the prediction of daily precipitation amount. Surfaces of humidity (vapor pressure deficit) are generated as a function of the predicted daily minimum temperature and the predicted daily average daylight temperature. Daily surfaces of incident salar radiation are generated as a function of Sun-slope geometry and interpolated diurnal temperature range. The application of these methods is demonstrated over an area of approximately 400 000 km(2) in the northwestern USA, for 1 year, including a detailed illustration of the parameterization process. A cross-validation analysis was performed, comparing predicted and observed daily and annual average values. Mean absolute errors (MAE) for predicted annual average maximum and minimum temperature were 0.7 degrees C and 1.2 degrees C, with biases of +0.1 degrees C and -0.1 degrees C, respectively. MAE for predicted annual total precipitation was 13.4 cm, or, expressed as a percentage of the observed annual totals, 19.3%. The success rate for predictions of daily precipitation occurrence was 83.3%. Particular attention was given to the predicted and observed relationships between precipitation frequency and intensity, and they were shown to be similar. We tested the sensitivity of these methods to prediction grid-point spacing, and found that areal averages were unchanged for grids ranging in spacing from 500 m to 32 km. We tested the dependence of the results on timestep, and found that the temperature prediction algorithms scale perfectly in this respect. Temporal scaling of precipitation predictions was complicated by the daily occurrence predictions, but very nearly the same predictions were obtained at daily and annual timesteps. (C) 1997 Elsevier Science B.V.