A global correlation of the step-wise consolidated crust-stripped gravity field quantities with the topography, bathymetry, and the CRUST 2.0 Moho boundary
We investigate globally the correlation of the step-wise consolidated cruststripped gravity field quantities with the topography, bathymetry, and the Moho boundary. Global correlations are quantified in terms of Pearson's correlation coefficient. The elevation and bathymetry data from the ETOPO5 are used to estimate the correlation of the gravity field quantities with the topography and bathymetry. The 2×2 arc-deg discrete data of the Moho depth from the global crustal model CRUST 2.0 are used to estimate the correlation of the gravity field quantities with the Moho boundary. The results reveal that the topographically corrected gravity field quantities have the highest absolute correlation with the topography. The negative correlation of the topographically corrected gravity disturbances with the topography over the continents reaches -0.97. The ocean, ice and sediment density contrasts stripped and topographically corrected gravity field quantities have the highest correlation with the bathymetry (ocean bottom relief). The correlation of the ocean, ice and sediment density contrasts stripped and topographically corrected gravity disturbances over the oceans reaches 0.93. The consolidated crust-stripped gravity field quantities have the highest absolute correlation with the Moho boundary. In particular, the global correlation of the consolidated crust-stripped gravity disturbances with the Moho boundary is found to be -0.92. Among all the investigated gravity field quantities, the consolidated crust-stripped gravity disturbances are thus the best suited for a refinement of the Moho density interface by means of the gravimetric modeling or inversion.
Volodymyr Hlotov, Alla Hunina, Mariana Yurkiv and Zbigniew Siejka
is important to investigate all possible factors affecting the stability of a UAV flight. However, for ultralight UAVs, there are still many unsolved problems, especially when performing aerial photography. The aerial photography requirements are fairly rigid; first of all, the inclination angles should not exceed 3–5 degrees, since this will affect the accuracy of determining the coordinates of the points of objects. Therefore, this publication presents the results of the study of correlation between angular elements of exterior orientation with each other, in
Advantages and disadvantages of least squares collocation (LSC) and kriging have recently been discussed, especially as interdisciplinary research becomes popular. These statistical methods, based on a least squares rule, have infinite number of applications, also in the domains different than Earth sciences. The paper investigates covariance parameters estimation for spatial LSC interpolation, via a kind of cross-validation, called hold-out (HO) validation. Two covariance models are applied in order to reveal also those differences that come solely from the covariance model.
Typical covariance models have a few variable parameters, the selection of which requires analysis of the actual data distribution. Properly chosen covariance parameters result in accurate and reliable predictions. The correlation length (CL), also known as the correlation distance in the Gauss-Markov covariance functions, the variance (C0) and a priori noise parameter (N) are analyzed in this paper, using local terrain elevations. The covariance matrix is used in LSC, as analogy to the correlation matrix often present in the kriging-related investigations. Therefore the covariance parameter N has the same scale as the data and can be analyzed in relation to the data errors, spatial data resolution and prediction errors.
The vector of the optimal three covariance parameters is sometimes determined approximately for the purposes of modeling with limited accuracy requirements. This is done e.g. by the fitting of analytical model to the empirical covariance values. The more demanding predictions need precise estimation of the covariance parameters vector and the researchers solve this problem via least squares methods or maximum likelihood (ML) inference. Nevertheless, both least squares and ML produce an error of the parameters and it is often large. The reliability of LSC or kriging using parameters with an error of e.g. a quarter of the parameter value is usually not discussed. This paper involves a kind of cross-validation, performed to observe possible influence of the parameters error on the prediction accuracy. This kind of validation serves for a basis of considerations on the accuracy of covariance parameters estimation with other different techniques.
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Mojmír Kohu, Jaroslav Rožnovský and Grazyna Knozová
Information about water evaporation is essential for the calculation of water balance. Evaporation, however, is a very complex physical process and it is therefore difficult to quantify. Evaporation measurements from the weather station network of the Czech Hydrometeorological Institute between 1968 and 2011 were performed using the evaporimeter GGI-3000. Evaporation was calculated using modified standard method based on FAO. The aim of the article was to compare the measured values and calculations. It has been found that the evaporation values from water surface calculated using the empirical equation are usually higher than the measured values by on average 0.8 mm, in extreme cases even 6.9 mm. The measured data shows higher variability than the calculated values, which means that correlations between series are not strong, the correlation coefficient being 0.7. Nevertheless the findings can be used for homogenization of series measured by the GGI-3000 evaporimeter.
Viliam Šimor, Kamila Hlavčová, Silvia Kohnová and Ján Szolgay
This article presents an application of Artificial Neural Networks (ANNs) and multiple regression models for estimating mean annual maximum discharge (index flood) at ungauged sites. Both approaches were tested for 145 small basins in Slovakia in areas ranging from 20 to 300 km2. Using the objective clustering method, the catchments were divided into ten homogeneous pooling groups; for each pooling group, mutually independent predictors (catchment characteristics) were selected for both models. The neural network was applied as a simple multilayer perceptron with one hidden layer and with a back propagation learning algorithm. Hyperbolic tangents were used as an activation function in the hidden layer. Estimating index floods by the multiple regression models were based on deriving relationships between the index floods and catchment predictors. The efficiencies of both approaches were tested by the Nash-Sutcliffe and a correlation coefficients. The results showed the comparative applicability of both models with slightly better results for the index floods achieved using the ANNs methodology.
Miloš Revallo, Fridrich Valach, Pavel Hejda and Josef Bochníček
A model of geomagnetic storms based on the method of artificial neural networks (ANN) combined with an analytical approach is presented in the paper. Two classes of geomagnetic storms, caused by coronal mass ejections (CMEs) and those caused by corotating interaction regions (CIRs), of medium and week intensity are subject to study. As the model input, the hourly solar wind parameters measured by the ACE satellite at the libration point L1 are used. The time series of the Dst index is obtained as the model output. The simulated Dst index series is compared with the corresponding observatory data. The model reliabilty is assessed using the skill scores, namely the correlation coefficient CC and the prediction efficiency PE. The results show that the model performance is better for the CME driven storms than for the CIR driven storms. At the same time, it appears that in the case of medium and weak storms the model performance is worse than in the case of intense storms