Yue Caiya, Hu Liya, Yan Yueguan
School of Geography and Environment, Liaocheng University, Liaocheng, 252059, China.
Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, Beijing, 100830, China.
Heliyon. 2024 May 25;10(11):e31964. doi: 10.1016/j.heliyon.2024.e31964. eCollection 2024 Jun 15.
Since much of the current researches have focused on daily, monthly or annual near-surface (2 m) temperature lapse rate (NSTLR), there is little guidance on best estimation practices and analyses of time-varying characteristics for the hourly NSTLR. To estimate hourly NSTLR and identify its time-varying characteristics accurately and objectively, this study proposed a robust estimation strategy based on IGGIII equivalent weight using multiple linear regression models. The accuracy and reliability of the proposed method was verified. The results show that the robust estimation strategy can further improve the hourly NSTLR solution accuracy relative to the least square (LSQ) method, especially in the time period of relatively high temperature. The hourly NSTLR was positively correlated with temperature, with a 24-h average maximum of 0.604 °C/100 m at universal time coordinated (UTC) 7.2 h and minimum of 0.284 °C/100 m at UTC 20.5 h, respectively. Throughout the year, the NSTLR was the largest from June to August, with an average median of around 0.492 °C/100 m. However, from November to the following January, the NSTLR value was the smallest, with a mean median of about 0.323 °C/100 m. In addition, the hourly NSTLR values were essentially less than the constant value of 0.65 °C/100 m. When the hourly NSTLR estimated based on the proposed method was applied to the temperature interpolation, the interpolation accuracies at the highest altitude (1545 m) and other meteorological stations (below 310 m) can increase by 22.4 % and 8.1 %, respectively, relative to the hourly NSTLR calculated by the LSQ method, and increased by 55.6 % and 13.0 %, respectively, relative to the no-NSTLR correction. The results are important for the fine establishment of high spatiotemporal resolution temperature fields and for the study of climatic phenomena characterized with rapid spatiotemporal variation.
由于目前的许多研究都集中在日、月或年近地表（2米）气温直减率（NSTLR）上，对于每小时NSTLR的最佳估计方法和时变特征分析几乎没有指导。为了准确客观地估计每小时NSTLR并识别其随时间变化的特征，本研究提出了一种基于IGGIII等效权重的稳健估计策略，使用多元线性回归模型。验证了所提方法的准确性和可靠性。结果表明，相对于最小二乘法（LSQ），稳健估计策略可以进一步提高每小时NSTLR解算精度，尤其是在温度相对较高的时间段。每小时NSTLR与温度呈正相关，协调世界时（UTC）7.2小时的24小时平均最大值为0.604℃/100米，UTC 20.5小时的最小值为0.284℃/100米。全年中，NSTLR在6月至8月最大，平均中位数约为0.492℃/100米。然而，从11月至次年1月，NSTLR值最小，平均中位数约为0.323℃/100米。此外，每小时NSTLR值基本上小于0.65℃/100米的常数。当将基于所提方法估计的每小时NSTLR应用于温度插值时，相对于通过LSQ方法计算的每小时NSTLR，最高海拔（1545米）和其他气象站（低于310米）的插值精度分别可提高22.4%和8.1%，相对于无NSTLR校正分别提高55.6%和13.0%。这些结果对于精细建立高时空分辨率温度场以及研究具有快速时空变化特征的气候现象具有重要意义。
