| 香港地区顾及高度改正的加权平均温度模型 |
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| 引用本文： | 崔进业,马下平,刘晓鹏.香港地区顾及高度改正的加权平均温度模型[J].大地测量与地球动力学,2020,40(10):1022-1026. |
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| 作者姓名： | 崔进业 马下平 刘晓鹏 |
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| 摘 要： | 选用2012~2017年Kings Park 站探空资料，基于迭代最小二乘方法构建2种香港地区顾及高度改正的加权平均温度模型--Tm_hk1和Tm_hk2，并利用2018年探空资料对模型在香港地区的精度和适用性进行评估。结果表明，在香港地区，依赖测站温度的Tm_hk1模型具有较高的精度，年均偏差优于0.3 K，均方根误差优于1.8 K，与Bevis公式和GPT2w模型相比，Tm_hk1模型的精度分别提升35.4%和29.7%；而不依赖气象参数的Tm_hk2模型与GPT2w模型的精度相当，年均方根误差均优于2.5 K，Bevis公式的精度最差(RMS为2.7 K)，且具有较大负偏差(bias为－1.8 K)。从季节性分析可知，Bevis公式、Tm_hk2 和GPT2w模型精度具有明显的季节性变化，总体为夏季精度较高(RMSE为1.3~2.2 K)，冬季精度较低(RMSE为3.0~4.4 K)；Tm_hk1模型在各季节均具有最高精度（RMSE为1.4~2.4 K）和适用性。
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| 关 键 词： | 加权平均温度 建模 香港地区 精度分析 大气遥感 |
Weighted Mean Temperature Models with VerticalAdjustment for Hong Kong Region |
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| CUI Jinye,MA Xiaping,LIU Xiaopeng.Weighted Mean Temperature Models with VerticalAdjustment for Hong Kong Region[J].Journal of Geodesy and Geodynamics,2020,40(10):1022-1026. |
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| Authors: | CUI Jinye MA Xiaping LIU Xiaopeng |
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| Abstract: | Based on the iterative least square method, we develop two empirical Tm models for Hong Kong region: Tm_hk1 andTm_hk2, with vertical adjustment using the radiosonde data of Kings Park station during the 2012-2017 period. The precision and reliability of the developed models, Bevis and global pressure temperature 2 wet(GPT2w), over Hong Kong region are evaluated using the sounding profiles throughout 2018. Results show thatTm_hk1, which requires the surface temperature at the station, can achieve a high precision with annual mean bias better than 0.3 K and the root mean square error(RMSE) within 1.8 K. Compared with the Bevis formula and GPT2w model, the accuracy ofTm_hk1 model increased by 35.4% and 29.7%, respectively. TheTm_hk2 model without the requirement of the meteorological parameter can achieve the same accuracy as the GPT2w model, and annual mean RMS error of both models are better than 2.5 K. Bevis formula has the worst accuracy(RMSE=2.7 K) and a large negative bias of -1.8 K. From the analysis, it can be found that the precision of Bevis,Tm_hk2, and GPT2w models show an obvious seasonal variation. The overall precision of the models during summer is higher(RMSE=1.3-2.2 K) than that during winter(RMSE=3.0-4.4 K). Furthermore,Tm_hk1 model performs the highest precision and applicability during all seasons, with the RMSE ranging from 1.4-2.4 K. |
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| Keywords: | weighted mean temperature modeling Hong Kong region precision analysis atmospheric remote sensing |
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