WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas
WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas
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DOI：
10.1002/joc.5086
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发表时间：
2017-10-01
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影响因子：
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通讯作者：
Hijmans, Robert J.
中科院分区：
文献类型：
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作者：
Fick, Stephen E.;Hijmans, Robert J.
我们以非常高的空间分辨率(约1公里(2))为全球陆地区域创建了一个新的空间内插月度气候数据集。我们包括了月温度(最低、最高和平均)、降水量、太阳辐射、水汽压和风速，汇总了1970年至2000年目标时间范围内的数据，使用了9,000到60000个气象站的数据。气象站数据使用薄板样条法进行内插，协变量包括海拔、到海岸的距离和利用MODIS卫星平台获得的三个卫星衍生协变量：最高和最低地表温度以及云量。根据站点密度，对23个不同大小的区域进行了内插。卫星数据将温度变量的预测精度提高了5-15%(0.07-0.17摄氏度)，特别是对站点密度较低的地区，尽管这些地区对所有气候变量的预测误差仍然很高。卫星协变量对其他变量的贡献几乎可以忽略不计，尽管它们的重要性因区域而异。与为整个世界使用单一模型公式的常见方法不同，我们通过为每个地区和变量选择表现最好的模型来构建最终产品。全球温度和湿度的交叉验证相关系数为0.99，降水量为0.86，风速为0.76。通过使用卫星协变量，我们的大多数气候表面估计仅略有改善，这一事实突显了拥有密集、高质量的气候站数据网络的重要性。
We created a new dataset of spatially interpolated monthly climate data for global land areas at a very high spatial resolution (approximately 1km(2)). We included monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 1970-2000, using data from between 9000 and 60000 weather stations. Weather station data were interpolated using thin-plate splines with covariates including elevation, distance to the coast and three satellite-derived covariates: maximum and minimum land surface temperature as well as cloud cover, obtained with the MODIS satellite platform. Interpolation was done for 23 regions of varying size depending on station density. Satellite data improved prediction accuracy for temperature variables 5-15% (0.07-0.17 degrees C), particularly for areas with a low station density, although prediction error remained high in such regions for all climate variables. Contributions of satellite covariates were mostly negligible for the other variables, although their importance varied by region. In contrast to the common approach to use a single model formulation for the entire world, we constructed the final product by selecting the best performing model for each region and variable. Global cross-validation correlations were0.99 for temperature and humidity, 0.86 for precipitation and 0.76 for wind speed. The fact that most of our climate surface estimates were only marginally improved by use of satellite covariates highlights the importance having a dense, high-quality network of climate station data.