High-Resolution Gridded Daily Rainfall and Temperature for the Hawaiian Islands (1990-2014)
High-Resolution Gridded Daily Rainfall and Temperature for the Hawaiian Islands (1990-2014)
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DOI：
10.1175/jhm-d-18-0112.1
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发表时间：
2019-03-01
影响因子：
3.8
通讯作者：
Clark, Martyn P.
中科院分区：
文献类型：
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作者：
Longman, Ryan J.;Frazier, Abby G.;Clark, Martyn P.
空间连续数据产品对于气候和水文建模、天气预报和水资源管理等许多应用至关重要。在这项工作中，描述和评估了用于绘制夏威夷每日降雨量和温度的距离加权插值方法。为 1990-2014 年期间的每日降雨量以及每日最高 (T-max) 和最低 (T-min) 近地表气温绘制了新的高分辨率 (250 m) 地图。地图是使用气候辅助插值法制作的，其中使用优化的反距离加权方法对台站异常进行插值，然后与长期方法相结合以产生每日网格估计。进行留一法交叉验证来评估最终每日网格的质量。降雨量绝对预测误差中位数为 0.1 毫米，总降雨量小于 1 毫米的日子平均高估 (+0.6 毫米)。在总降雨量大于 1 毫米的日子里，绝对预测误差中位数为 2 毫米，降雨量通常低于 10 毫米阈值。对于每日温度，T-max 和 T-min 的中位绝对预测误差分别为 3.1 摄氏度和 2.8 摄氏度。平均而言，该方法高估了 T-max（+1.1 摄氏度）和 T-min（+1.5 摄氏度），并且各站点之间的误差差异很大。所有变量的误差都表现出显着的季节性变化。然而，年度误差范围很小。这里介绍的方法提供了一种有效的方法来绘制地形多样化地区的日常天气场图，并在空间分辨率、覆盖时间段和数据使用方面改进了以前的产品。
Spatially continuous data products are essential for a number of applications including climate and hydrologic modeling, weather prediction, and water resource management. In this work, a distance-weighted interpolation method used to map daily rainfall and temperature in Hawaii is described and assessed. New high-resolution (250 m) maps were developed for daily rainfall and daily maximum (T-max) and minimum (T-min) near-surface air temperature for the period 1990-2014. Maps were produced using climatologically aided interpolation, in which station anomalies were interpolated using an optimized inverse distance weighting approach and then combined with long-term means to produce daily gridded estimates. Leave-one-out cross validation was performed to assess the quality of the final daily grids. The median absolute prediction error for rainfall was 0.1 mm with an average overprediction (+0.6 mm) on days when total rainfall was less than 1 mm. On days with total rainfall greater than 1 mm, median absolute prediction errors were 2 mm and rainfall was typically underpredicted above the 10-mm threshold. For daily temperature, median absolute prediction errors were 3.1 degrees and 2.8 degrees C for T-max and T-min, respectively. On average, this method overpredicted T-max (+1.1 degrees C) and T-min (+1.5 degrees C), and errors varied considerably among stations. Errors for all variables exhibited significant seasonal variations. However, the annual range of errors was small. The methods presented here provide an effective approach for mapping daily weather fields in a topographically diverse region and improve on previous products in their spatial resolution, time period of coverage, and use of data.