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Comparison and combination of interpolation methods for daily precipitation in Poland: evaluation using the correlation coefficient and correspondence ratio

Konca-Kędzierska Krystyna, Wibig Joanna, Gruszczyńska Marta

Meteorology Hydrology and Water Management. Research and Operational Applications

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2023

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Vol. 11, Iss. 2

1--27

EN

Interpolation of precipitation data is a common practice for generating continuous, spatially-distributed fields that can be used for a range of applications, including climate modeling, water resource management, and agricultural planning. To obtain the reference field, daily observation data from the measurement network of the Institute of Meteorology and Water Management – National Research Institute was used. In this study, we compared and combined six different interpolation methods for daily precipitation in Poland, including bilinear and bicubic interpolation, inverse distance weighting, distance-weighted average, nearest neighbor remapping, and thin plate spline regression. Implementations of these methods available in the R programming language (e.g., from packages akima, gstat, fields) and the Climate Data Operators (CDO) were applied. The performance of each method was evaluated using multiple metrics, including the Pearson correlation coefficient (RO) and the correspondence ratio (CR), but there was no clear optimal method. As an interpolated resulting field, a field consisting of the best interpolations for individual days was proposed. The assessment of daily fields was based on the CR and RO parameters. Our results showed that the combined approach outperformed individual methods with higher accuracy and reliability and allowed for generating more accurate and reliable precipitation fields. On a group of selected stations (data quality and no missing data), the precipitation result fields were compared with the fields obtained in other projects-CPLFD-GDPT5 (Berezowski et al. 2016) and G2DC-PLC (Piniewski et al. 2021). The variance inflation factor (VIF) was bigger for the resulting fields (~5), while for the compared fields, it was below 3. However, for the mean absolute error (MAE), the relationship was reversed - the MAE was approximately half as low for the fields obtained in this work.

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A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy

Alemam Mustafa K., Yong Bin, Sani-Mohammed Abubakar

Artificial Satellites : Journal of Planetary Geodesy

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2023

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Vol. 58, No. 3

122--170

EN

The digital elevation model (DEM) is one of the most critical sources of terrain elevations, which are essential in various geoscience applications. Most of these applications need precise elevations, which are available at a high cost. Thus, sources like the Shuttle Radar Topography Mission (SRTM) DEM are frequently accessible to all users but with low accuracy. Consequently, many studies have tried to improve the accuracy of DEMs acquired from these free sources. Importantly, using the SRTM DEM is not recommended for an area that partly contains high-accuracy data. Thus, there is a need for a merging technique to produce a merged DEM of the whole area with improved accuracy. In recent years, advancements in geographic information systems (GIS) have improved data analysis by providing tools for applying merging techniques (like the minimum, maximum, last, first, mean, and blend (conventional methods)) to improve DEMs. In this article, DEM merging methods based on artificial neural network (ANN) and interpolation techniques are proposed. The methods are compared with other existing methods in commercial GIS software. The kriging, inverse distance weighted (IDW), and spline interpolation methods were considered for this investigation. The essential step for achieving the merging stage is the correction surface generation, which is used for modifying the SRTM DEM. Moreover, two cases were taken into consideration, i.e., the zeros border and the H border. The findings show that the proposed DEM merging methods (PDMMs) improved the accuracy of the SRTM DEM more than the conventional methods (CDMMs). The findings further show that the PDMMs of the H border achieved higher accuracy than the PDMMs of the zeros border, while kriging outperformed the other interpolation methods in both cases. The ANN outperformed all methods with the highest accuracy. Its improvements in the zeros and H border respectively reached 22.38% and 75.73% in elevation, 34.67% and 54.83% in the slope, and 40.28% and 52.22% in the aspect. Therefore, this approach would be cost-effective, especially in critical engineering projects.

3

A Comparison of Interpolation Methods for Estimation of Air Temperature

Kuzevicova Z., Palkova J., Kuzevic S., Gergel'ova M.

Inżynieria Mineralna

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2016

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R. 17, nr 1

163--170

EN

The present article deals with the estimation of air temperature with selected spatial interpolation methods. There are a number of methods of spatial interpolation, which allows to estimate the values if they are not available measurement data. One of the important indicators for many practical analyses is the air temperature. It is well known fact that the air temperature is dependent on the altitude. However, not all of the interpolation methods in their algorithms allow to take into account this important factor that can significantly affect the final estimate. For the estimation of air temperature were chosen method IDS (Inverse Distance Squared) and its modification GIDS (gradient plus inverse distance squared) and GIDS-a. The first method was implemented directly in the software. For the method GIDS and - a GIDS was created application through VBA.

PL

Prezentowany artykuł omawia zagadnienie oceny temperatury powietrza za pomocą wybranych przestrzennych metod interpolacyjnych. Istnieje wiele metod przestrzennej interpolacji, które pozwalają na ocenę wartości, jeśli dane pomiarowe nie są dostępne. Jednym z ważnych wskaźników do wielu praktycznych analiz jest temperatura powietrza. Jest wiadomym faktem, że jest ona zależna od wysokości. Jednakże, nie wszystkie metody interpolacyjne w swych algorytmach pozwalają na wzięcie pod uwagę tej istotnej zmiennej, która może znacząco wpłynąć na końcową wartość estymowanego wskaźnika. Do oceny temperatury powietrza wybrano metodę IDS (metoda odwrotnych kwadratów odległości) oraz jej modyfikację GIDS (gradientowa metoda odwrotnych kwadratów odległości) oraz GIDS-a. Pierwsza metoda została zastosowana bezpośrednio w programie komputerowym. Dla metod GIDS i GIDS-a utworzono aplikację za pośrednictwem VBA.

4

Porównanie wybranych metod interpolacji ruchu

Smołka J., Skublewska-Paszkowska M.

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2013

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nr 3

14--17

PL

Interpolacja jest jednym z kluczowych elementów wykorzystywanych w animacji komputerowej. Dobór odpowiedniej metody interpolacji wpływa na ruch animowanej postaci. Artykuł przedstawia wybrane metody interpolacji i porównuje je ze względu na czas wykonywania obliczeń oraz dokładność uzyskanych wyników. Algorytmy, które przeanalizowano to: metoda Catmula-Roma, zmodyfikowana metoda Catmulla-Roma oraz krzywe przejściowe między parabolami (blended parabolas). Eksperymenty numeryczne przeprowadzono za pomocą programu komputerowego napisanego w języku C++.

EN

Interpolation is one of key aspects of computer animation. The selection of the proper interpolation method influences motion of animated objects. The paper presents selected interpolation methods and compares them with respect to computation time and accuracy. The three algorithms that were analyzed are: Catmull-Rom Spline, modified Catmull-Rom Spline and Blended Parabolas method. Numerical experiments were performed using a program written in C++ language.

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Rola zmiennych objaśniajacych w interpretacji przestrzennej wybranych elementów klimatu

Łupikasza E., Ustrnul Z., Czekierda D.

Roczniki Geomatyki

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2007

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T. 5, z. 5

55-64

EN

In the paper, the problem of application of explanatory variables in the spatialisation methods is presented. The analysis was performed for the territory of Poland with respect to 3 main climate parameters: air temperature, precipitation totals and general cloudiness. Elaboration of meteorological/ climatological maps is a complicated task. It requires careful and detailed analysis of respective element fields and thorough knowledge of physical processes connected with the complexity of geographical environment. Simultaneously, the application of additional explanatory variables (such as altitude, aspect, land use, relative height, etc.) is highly recommended. There is no one universal spatialisation method and one explanatory variable relevant for different climatological problems and for different spatial and temporal scales. Each element and resolution requires individual approach. It was found that residual kriging is the best solution for monthly and seasonal means of air temperature and precipitation totals. Spatialisation of the precipitation totals is particularly difficult due to its highly temporal and spatial differentiation. However, for both elements and also for cloudiness the application of altitude usually improves spatialisation results. It is especially recommended for seasonal values and for larger areas. The application of circulation types . as the main predictor - usually improves spatialisation of the daily values for most climatic elements. However, the use of some non.advective types does not improve results. Maybe the application of some additional explanatory parameters should be considered (e.g. humidity, vertical profiles, air masses types, etc).

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Metody analiz przestrzennych w badaniu zmienności opadów w Europie

Łupikasza E.

Roczniki Geomatyki

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2007

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T. 5, z. 1

71-80

EN

The goal of this study is a selection of the best spatialisation method of precipitation fields for a large territory of Europe. The main dataset contains mean monthly sums of precipitation for normal period 1961.1990 from 816 meteorological stations located in Europe and neighbouring areas. Four precipitation indices differing in the range of variability and the pattern of spatial distribution were examination. The precipitation indices were interpolated by deterministic methods as well as geostatistic ones provided by Geostatistical Analyst Tools for ArcMap. It was stated on the base of the statistical characteristics of prediction error, that ordinary kriging seems to be the most suitable method for interpolation of annual, summer and winter sums of precipitation on the large scale of Europe. The precipitation concentration index shows considerably smaller range of the spatial variability than precipitation sums. This difference is significant feature from interpolation methods. point of view. As it turned out the spatial analysis of the concentration index using simple kriging gives the smallest value of root mean square error (RMS).