Paper
9 December 2015 Analysis of applicability to build K-value model in southwest China
Lilong Liu, Pituan Wu, Jun Chen, Junyu Li, Haiyang Feng, Feida Li
Author Affiliations +
Proceedings Volume 9808, International Conference on Intelligent Earth Observing and Applications 2015; 98082Y (2015) https://doi.org/10.1117/12.2207904
Event: International Conference on Intelligent Earth Observing and Applications, 2015, Guilin, China
Abstract
Using the eleven Radiosonde Stations’ data in southwestern of China from 2010 to 2013 to calculate the conversion coefficient K which is a reference value of Precipitable Water Vapor (PWV). Then build the EMARDSON model and the EMARDSON K model which introduced with elevation parameter and altitude. And to analysis the accuracy of the two models in the southwest China by radiosonde data in 2014. The results show: 1) The K value calculated by EMARDSON model has good adaptability in southwest region. 2) The method of spatial interpolation prediction by choosing 7 Radiosonde Stations’ K value uniformity is more adaptable than using 11 Radiosonde Stations’ K value to build basic model in the case of predicting 11 Radiosonde Stations’ K value, and it has a certain accuracy when predicting by using spatial interpolation in some areas where lacking data. 3) The accuracy by using the A-EMARDSON model to predict K value was improved obviously. at the same time, when predicting K value by the method of spatial interpolation, both the precision of inner and the precision of outer are better than EMARDSON model. So it can be concluded that the altitude factor is an important factor to influence the K value prediction.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lilong Liu, Pituan Wu, Jun Chen, Junyu Li, Haiyang Feng, and Feida Li "Analysis of applicability to build K-value model in southwest China", Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98082Y (9 December 2015); https://doi.org/10.1117/12.2207904
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KEYWORDS
Data modeling

Atmospheric modeling

Data conversion

Climatology

Geomatics

Humidity

Lithium

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