scholarly journals Reputation system for ensuring data trustworthiness of crowdsourced weather stations

Author(s):  
Alexander B. Chen ◽  
Madhur Behl ◽  
Jonathan L. Goodall
2020 ◽  
Vol 82 ◽  
pp. 149-160
Author(s):  
N Kargapolova

Numerical models of the heat index time series and spatio-temporal fields can be used for a variety of purposes, from the study of the dynamics of heat waves to projections of the influence of future climate on humans. To conduct these studies one must have efficient numerical models that successfully reproduce key features of the real weather processes. In this study, 2 numerical stochastic models of the spatio-temporal non-Gaussian field of the average daily heat index (ADHI) are considered. The field is simulated on an irregular grid determined by the location of weather stations. The first model is based on the method of the inverse distribution function. The second model is constructed using the normalization method. Real data collected at weather stations located in southern Russia are used to both determine the input parameters and to verify the proposed models. It is shown that the first model reproduces the properties of the real field of the ADHI more precisely compared to the second one, but the numerical implementation of the first model is significantly more time consuming. In the future, it is intended to transform the models presented to a numerical model of the conditional spatio-temporal field of the ADHI defined on a dense spatio-temporal grid and to use the model constructed for the stochastic forecasting of the heat index.


Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.


2021 ◽  
Vol 295 ◽  
pp. 117056
Author(s):  
Tonghe Wang ◽  
Jian Guo ◽  
Songpu Ai ◽  
Junwei Cao

2016 ◽  
Vol 9 (18) ◽  
pp. 6335-6350
Author(s):  
Fu-Hau Hsu ◽  
Yu-Liang Hsu ◽  
Yan-Ling Hwang ◽  
Li-Han Chen ◽  
Chuan-Sheng Wang ◽  
...  

Computer ◽  
2021 ◽  
Vol 54 (2) ◽  
pp. 39-49
Author(s):  
Goncalo Sousa Mendes ◽  
Daniel Chen ◽  
Bruno M. C. Silva ◽  
Carlos Serrao ◽  
Joao Casal
Keyword(s):  

2021 ◽  
Vol 254 ◽  
pp. 105511
Author(s):  
Mousumi Ghosh ◽  
Jitendra Singh ◽  
Sheeba Sekharan ◽  
Subimal Ghosh ◽  
P.E. Zope ◽  
...  

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