scholarly journals Spatial interpolation of daily humidity using natural neighbours over mountain areas in south eastern Australia

2008 ◽  
Vol 61 ◽  
pp. 292-295 ◽  
Author(s):  
K.S. Kim ◽  
R.M. Beresford ◽  
W.R. Henshall

Natural neighbour interpolation was investigated to estimate daily humidity at specific sites in a mountain area The Global Summary of Day (GSOD) dataset was used to obtain weather data in mountain areas in south eastern Australia Eighteen weather stations were selected as validation sites Dew point temperature was estimated from January to December 2007 When the inverse distance weight method was used without adjusting the elevation difference between stations accuracy of virtual dew point temperature was poor with a mean absolute error (MAE) of 36C When natural neighbour interpolation was used the MAE for dew point temperature was 21C with altitude adjustment Furthermore application of wet adiabatic lapse rate (0004C/m) for altitude adjustment reduced the MAE to 13C These results will be used to improve the accuracy of weather estimates in areas with complex terrain in order to implement crop disease predictions using risk models

2019 ◽  
Vol 8 (3) ◽  
pp. 28
Author(s):  
Davidson Odafe Akpootu ◽  
Mukhtar Isah Iliyasu ◽  
Wahidat Mustapha ◽  
Simeon Imaben Salifu ◽  
Hassan Taiwo Sulu ◽  
...  

2013 ◽  
Vol 45 (2) ◽  
pp. 165-181 ◽  
Author(s):  
Jalal Shiri ◽  
Sungwon Kim ◽  
Ozgur Kisi

The present study investigates the ability of two different artificial neural network (ANN) models and gene expression programming (GEP) technique for estimating daily dew point temperature by using recorded weather data. The weather data used consist of 8 years of daily records of air temperature, wind speed, relative humidity, atmospheric pressure, incoming solar radiation and dew point temperature from two weather stations (Seoul and Incheon, in the Republic of Korea). Two different data management scenarios are applied in this paper. In the first scenario, weather data obtained from each station are used to estimate Tdew at the same station (at-station approach). In the second scenario, the ANN and GEP models are used for estimating dew point temperature of each station by using the data of the other station (cross-station application), through the optimal input combinations of the first scenario. Comparison of the results reveals that the GEP model surpasses ANN in estimating daily dew point temperature values.


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