Evaluating the Accuracy of a Gridded Near-Surface Temperature Dataset over Mainland China

Meijuan Qiu,Yuan Liu,Yueying Zhang,Shuai Han,Buchun Liu

doi:10.3390/atmos10050250

Abstract

High-resolution meteorological data products are crucial for agrometeorological studies. Here, we study the accuracy of an important gridded dataset, the near-surface temperature dataset from the 5 km × 5 km resolution China dataset of meteorological forcing for land surface modeling (published by the Beijing Normal University). Using both the gridded dataset and the observed temperature data from 590 meteorological stations, we calculate nine universal meteorological indices (mean, maximum, and minimum temperatures of daily, monthly, and annual data) and five agricultural thermal indices (first frost day, last frost day, frost-free period, and ≥0 °C and ≥10 °C active accumulated temperature, i.e., AAT0 and AAT10) of the 11 temperature zones over mainland China. Then, for each meteorological index, we calculate the root mean square errors (RMSEs), correlation coefficient and climate trend rates of the two datasets. The results show that the RMSEs of these indices are usually lower in the north subtropical, mid-subtropical, south subtropical, marginal tropical and mid-tropical zones than in the plateau subfrigid, plateau temperate, and plateau subtropical mountains zones. Over mainland China, the AAT0, AAT10, and mean and maximum temperatures calculated from the gridded data show the same climate trends with those derived from the observed data, while the minimum temperature and its derivations (first frost day, last frost day, and frost-free period) show the opposite trends in many areas. Thus, the mean and maximum temperature data derived from the gridded dataset are applicable for studies in most parts of China, but caution should be taken when using the minimum temperature data.

Highlights

To a large extent, temperature determines optimal regional crop types, cropping systems, as well as farming activities, having considerable effects on agricultural output [1,2,3,4]
The first frost day, last frost day, frost-free period and annual temperature are of great significance in agricultural production, as well as the ≥0 ◦ C active accumulated temperature (AAT0) and ≥10 ◦ C active accumulated temperature (AAT10)
The accuracy of the high-resolution gridded data offered by the Beijing Normal University was re-evaluated by using root mean square error, correlation test and climate trend analysis

Summary

Introduction

Temperature determines optimal regional crop types, cropping systems, as well as farming activities, having considerable effects on agricultural output [1,2,3,4]. Related indices, such as agricultural critical temperature, accumulated temperature, and frost-free period, are usually calculated from the available temperature dataset to estimate regional thermal resources and to provide instructions for agricultural production [5,6,7,8,9]. Over mainland China, observed data from only about 200 meteorological stations are provided for international exchange and constructing these datasets. These datasets are too coarse (spatial resolution from 1.25◦ × 1.25◦ to 5◦ × 5◦ ) to be used at local or regional scales

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Conclusion