Meteorological data are essential components of decision-making systems for agriculture. Fine-resolution meteorological datasets are important where crops are grown in hilly areas with variable microclimates. A method for compiling daily surface air temperature (SAT) with 50 m resolution was previously developed. The compiled daily air temperature forecasts are available to farmers who produce Ujicha, tea via a web application. However, the complex terrain affects the accuracy of these forecasts. Our goal was to develop a practical system to provide hourly SAT data with 50 m resolution for a day-to-day decision-making system for agriculture. We applied two methods: a method for developing forecast models to forecast potential temperature differences between each grid and an observation site to compile 50 m resolution grid data; and a method for correcting SAT differences between the global spectral model output and observations (referred to as GSM_error). In both these methods, SAT was continuously forecast using the radiative cooling scale (RCS) computed near a public observation site. The RCS is a meteorological factor defined as the difference in potential temperature between an upper air pressure level and that at ground level. Forecast models for GSM_error were developed from observation data using a stepwise multiple regression (SMR), with geographic factors as independent variables in each group classified by the RCS. Statistical techniques were used for observation data to develop forecast models using machine learning, including lasso, ridge, random forest, support vector and deep neural network regression techniques. The SMR produced better forecast models for GSM_error than the other techniques. We found that SMR was better because it selected features relevant to GSM_error compared with machine learning, which fitted all features. Our grid data were compiled for hourly SAT with 50 m resolution and had a root mean square error of 1.67 °C.

中文翻译：

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使用辐射冷却尺度和数值天气预报模型输出，以 50 米分辨率编译每小时网格化的地表气温数据集

气象数据是农业决策系统的重要组成部分。对于小气候多变的丘陵地区种植农作物的情况，高分辨率气象数据集非常重要。先前开发了一种以 50 m 分辨率编制每日表面气温 (SAT) 的方法。生产宇治茶的农民可以通过网络应用程序获取汇总的每日气温预测。然而，复杂的地形影响了这些预测的准确性。我们的目标是开发一个实用的系统，为农业日常决策系统提供每小时 50 m 分辨率的 SAT 数据。我们采用了两种方法：一种是建立预测模型来预测每个网格和观测点之间的潜在温差，以编制50 m分辨率的网格数据；以及校正全局光谱模型输出和观测值之间的 SAT 差异的方法（称为 GSM_error）。在这两种方法中，SAT 都是使用公共观测点附近计算的辐射冷却尺度 (RCS) 进行连续预报的。 RCS 是一个气象因素，定义为高空气压水平与地面气压水平之间的潜在温度差。 GSM_error 的预测模型是使用逐步多元回归 (SMR) 根据观测数据开发的，其中地理因素作为 RCS 分类的每个组中的自变量。统计技术用于观测数据，以利用机器学习开发预测模型，包括套索、岭、随机森林、支持向量和深度神经网络回归技术。 SMR 为 GSM_error 生成了比其他技术更好的预测模型。我们发现 SMR 更好，因为与拟合所有特征的机器学习相比，它选择了与 GSM_error 相关的特征。我们的网格数据是针对每小时 SAT 编制的，分辨率为 50 m，均方根误差为 1.67 °C。