Study on Sub-Regional Weighted Mean Atmospheric Temperature Modeling and Its Application in China
With the daily average surface temperature data from 1980 to 2010 provided by the China Meteorology Data Service Center and 64,131 observations in 2015 from 90 sounding stations,we firstly use the diffusion interpolation with barriers for interpolation and then divide the results into eight grades a...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Pearl River
2022-01-01
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| Series: | Renmin Zhujiang |
| Subjects: | |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2022.01.016 |
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| Summary: | With the daily average surface temperature data from 1980 to 2010 provided by the China Meteorology Data Service Center and 64,131 observations in 2015 from 90 sounding stations,we firstly use the diffusion interpolation with barriers for interpolation and then divide the results into eight grades and thirteen districts at a temperature interval of 3.4 K.By experimental analysis,it is found that an approximately exponential function relationship between factors,i.e.,the number of sounding observation layers and the ceiling height,and the accuracy of weighted mean atmospheric temperature (T<sub>m</sub>).Considering only one factor,the root-mean-square (RMS) between the calculated T<sub>m</sub> using resampled and the measured one is about 1 K when the ceiling height is beyond 9 km,or the number of observation layers is beyond thirteen.Then,given the distribution of the actual ceiling height and the number of layers of samples,radiosonde observations with the ceiling height of over 9 km and the number of observation layers greater than eight are applied to build a national and a sub-regional regression model,respectively.In this way,the impact of inconsistent applicability of the national model in different regions is efficiently reduced,and the RMS of fitting residue declines by 17.8%on average.Finally,we apply the sub-regional model to retrieve the time series of precipitable water vapor (PWV) and compare them with contemporaneous PWV data observed by radiosondes. |
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| ISSN: | 1001-9235 |