scholarly journals Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and temperature

2003 ◽  
Vol 24 ◽  
pp. 161-180 ◽  
Author(s):  
SM Vicente-Serrano ◽  
MA Saz-Sánchez ◽  
JM Cuadrat
Keyword(s):  
Comparative Analysis ◽  
Annual Precipitation ◽  
Interpolation Methods ◽  
Ebro Valley ◽  
Precipitation And Temperature

scholarly journals Comparison of spatial interpolation methods for estimating the precipitation distribution in Portugal

2021 ◽  
Author(s):  
Alexandru Antal ◽  
Pedro M. P. Guerreiro ◽  
Sorin Cheval
Keyword(s):  
Spatial Distribution ◽  
Spatial Interpolation ◽  
Polynomial Interpolation ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Precipitation Distribution ◽  
Interpolation Methods ◽  
Mainland Portugal ◽  
The Mean ◽  
Spatial Interpolation Methods

Abstract Precipitation has a strong and constant impact on different economic sectors, environment, and social activities all over the world. An increasing interest for monitoring and estimating the precipitation characteristics can be claimed in the last decades. However, in some areas the ground-based network is still sparse and the spatial data coverage insufficiently addresses the needs. In the last decades, different interpolation methods provide an efficient response for describing the spatial distribution of precipitation. In this study, we compare the performance of seven interpolation methods used for retrieving the mean annual precipitation over the mainland Portugal, as follows: local polynomial interpolation (LPI), global polynomial interpolation (GPI), radial basis function (RBF), inverse distance weighted (IDW), ordinary cokriging (OCK), universal cokriging (UCK) and empirical Bayesian kriging regression (EBKR). We generate the mean annual precipitation distribution using data from 128 rain gauge stations covering the period 1991 to 2000. The interpolation results were evaluated using cross-validation techniques and the performance of each method was evaluated using mean error (ME), mean absolute error (MAE), root mean square error (RMSE), Pearson’s correlation coefficient (R) and Taylor diagram. The results indicate that EBKR performs the best spatial distribution. In order to determine the accuracy of spatial distribution generated by the spatial interpolation methods, we calculate the prediction standard error (PSE). The PSE result of EBKR prediction over mainland Portugal increases form south to north.

Algorithm for interpolation of magnetic field variations

2021 ◽  
Author(s):  
Dmitrii Vishniakov ◽  
Ivan Lygin ◽  
David Arutyunyan
Keyword(s):  
Magnetic Field ◽  
Comparative Analysis ◽  
Central Europe ◽  
Optimal Algorithm ◽  
Physical Parameters ◽  
Interpolation Algorithm ◽  
Earth’S Magnetic Field ◽  
Interpolation Methods ◽  
Weighting Coefficients ◽  
Magnetic Field Variations

<p>To solve many geological and geophysical problems, it is very important to study variations of the Earth's magnetic field. The observed variations are usually obtained from data from observatories or temporary variation stations. However, while performing various regional magnetic prospecting works, the network of observatories is not complete enough to account for the variation field correctly.</p><p>In this regard, it is becoming necessary to interpolate the data on variations from the points of irregular network. At the same time, obtaining the optimal algorithm is an ambiguous task, its solution requires taking a whole list of factors into account that determine regularity of distribution of physical parameters over the area.</p><p>This project represents an interpolation algorithm using method of complex weighting coefficients. The technique was tested on data from the Intermagnet observatories for central Europe, and the obtained accuracy was ± 2 nT. Comparative analysis with known interpolation methods by interpolation methods was carried out.</p>

Sign in / Sign up

Export Citation Format

Share Document