Modeling hourly air temperature based on internationally agreed times and the daily minimum temperature

ABSTRACT There are several fields that require knowledge of air temperature variation throughout the day, such as disease prediction or calculation of chill-hours. However, automatic meteorological stations are not always located in the vicinity to accurately monitor this variable. In this sense, models that describe the daily temporal variation of air temperature can be used to meet this demand, and transform the climatic data series of conventional meteorological stations into an estimated hourly series. The aim of this study was to adjust and validate models for the hourly air temperature variation through data obtained at internationally agreed times (0, 12 and 18 h Universal Time Coordinated: UTC) and the daily minimum air temperature. The hourly database of the automatic station was used for model adjustment and validation. Functions were adjusted based on values measured at internationally agreed times and the daily minimum air temperature for certain daily variation patterns. The air temperature estimation was performed on an hourly basis using sinusoidal and linear models. The model that presented the lowest root mean square error (RMSE) was used for the estimation. The accuracy of the air temperature estimates varied according to the time, presenting RMSE from 0.7 to 1.6 °C, with maximum mean deviation of 0.4 °C. The results of this study showcase the necessity of knowledge of the daily air temperature variation, as well as a series of data from conventional meteorological stations, which can be estimated using hourly models.摘要：诸多领域需要掌握单日逐时段气温变化规律，例如疾病预测或冷时数（chill-hours）计算。但自动气象站（automatic meteorological stations）并非总能布设至目标区域以精准监测该气象变量。在此背景下，可借助描述日气温时间变化的模型满足此类需求，将常规气象站（conventional meteorological stations）的气候序列数据转换为估算得到的逐小时气温序列。本研究旨在基于国际约定时刻（协调世界时（Universal Time Coordinated, UTC）的0、12及18时）与日最低气温获取的数据，调整并验证逐小时气温变化模型。研究采用自动气象站的逐小时数据库开展模型的调整与验证工作。针对特定的日气温变化模式，基于国际约定时刻的实测值与日最低气温拟合得到相关函数。本研究分别采用正弦模型与线性模型开展逐小时气温估算，并选取均方根误差（root mean square error, RMSE）最低的模型用于最终估算。气温估算精度随时刻存在差异，均方根误差介于0.7至1.6℃之间，平均最大偏差为0.4℃。本研究结果证实，掌握日气温变化规律以及常规气象站的序列数据具有重要意义，而后者可通过逐小时模型实现估算。