scholarly journals Evaluation of Satellite and Reanalysis Precipitation Products Using GIS for All Basins in Turkey

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Ahmet Irvem ◽  
Mustafa Ozbuldu
Keyword(s):  
Rain Gauge ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
The West ◽  
Data Set ◽  
Precipitation Product ◽  
Distance Weighting ◽  
Areal Precipitation ◽  
Average Annual Precipitation ◽  
Inverse Distance

Use of the satellite and reanalysis precipitation products, as supplementary data sources, are steadily rising for hydrometeorological applications, especially in data-sparse areas. However, the accuracy of these data sets is often lacking, especially in Turkey. This study evaluates the accuracy of satellite precipitation product (TRMM 3B42V7) and reanalysis precipitation product (NCEP-CFSR) against rain gauge observations for the 1998–2010 periods. Average annual precipitation for the 25 basins in Turkey was calculated using rain gauge precipitation data from 225 stations. The inverse distance weighting (IDW) method was used to calculate areal precipitation for each basin using GIS. According to the results of statistical analysis, the coefficient of determination for the TRMM product gave satisfactory results (R2 > 0.88). However, R2 for the CFSR data set ranges from 0.35 for the Eastern Black Sea basin to 0.93 for the West Mediterranean basin. RMSE was calculated to be 95.679 mm and 128.097 mm for the TRMM and CFSR data, respectively. The NSE results of TRMM data showed very good performance for 6 basins, while the PBias value showed very good performance for 7 basins. The NSE results of CFSR data showed very good performance for 3 basins, while the PBias value showed very good performance for 6 basins.

scholarly journals A Multimethod Analysis for Average Annual Precipitation Mapping in the Khorasan Razavi Province (Northeastern Iran)

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 592
Author(s):  
Mehdi Aalijahan ◽  
Azra Khosravichenar
Keyword(s):  
Regional Planning ◽  
Interpolation Method ◽  
Absolute Error ◽  
Rain Gauge ◽  
Annual Precipitation ◽  
Coefficient Of Determination ◽  
Distance Weighting ◽  
Average Annual Precipitation ◽  
Inverse Distance

The spatial distribution of precipitation is one of the most important climatic variables used in geographic and environmental studies. However, when there is a lack of full coverage of meteorological stations, precipitation estimations are necessary to interpolate precipitation for larger areas. The purpose of this research was to find the best interpolation method for precipitation mapping in the partly densely populated Khorasan Razavi province of northeastern Iran. To achieve this, we compared five methods by applying average precipitation data from 97 rain gauge stations in that province for a period of 20 years (1994–2014): Inverse Distance Weighting, Radial Basis Functions (Completely Regularized Spline, Spline with Tension, Multiquadric, Inverse Multiquadric, Thin Plate Spline), Kriging (Simple, Ordinary, Universal), Co-Kriging (Simple, Ordinary, Universal) with an auxiliary elevation parameter, and non-linear Regression. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2) were used to determine the best-performing method of precipitation interpolation. Our study shows that Ordinary Co-Kriging with an auxiliary elevation parameter was the best method for determining the distribution of annual precipitation for this region, showing the highest coefficient of determination of 0.46% between estimated and observed values. Therefore, the application of this method of precipitation mapping would form a mandatory base for regional planning and policy making in the arid to semi-arid Khorasan Razavi province during the future.

Application of the "genRE" approach to spatial interpolation of precipitation gauge data for the Suir River Basin

2021 ◽  
Author(s):  
Georgios Boumis ◽  
Bart van Osnabrugge ◽  
Jan Verkade
Keyword(s):  
Real Time ◽  
River Basin ◽  
Spatial Interpolation ◽  
Cross Validation ◽  
Inverse Distance Weighting ◽  
Data Sets ◽  
Precipitation Data ◽  
Data Set ◽  
Distance Weighting ◽  
Inverse Distance

<p>Operational near real-time flood forecasting relies heavily on adequate spatial interpolation of precipitation forcing which bears a huge impact on the accuracy of hydrologic forecasts. In this study, the generalized REGNIE (genRE) interpolation technique is examined. The genRE approach was shown to enhance the traditional Inverse Distance Weighting (IDW) method with information from existing observed climatological precipitation data sets (Van Osnabrugge, 2017). The successful application of the genRE method with a re-analysis precipitation data set, expands the applicability of the method as detailed re-analysis data sets become more prevalent while high density observation networks remain scarce.</p><p>Here, the approach is extended to use climatological precipitation data from the Met Éireann’s Re-Analysis (MÉRA). Investigations are carried out using hourly precipitation accumulations for two major flood events induced by Atlantic storms in the Suir River Basin, Ireland. Alongside genRE, the following techniques are comparatively explored: Inverse Distance Weighting (IDW), Ordinary Kriging (OK) and Regression Kriging (RK). Cross-validation is applied in order to compare the different interpolation methods, while spatial maps and correlation coefficients are utilized for assessing the skill of the interpolators to emulate the climatology of MÉRA. In the process, a preliminary intercomparison between the observed precipitation and MÉRA precipitation for the two events is also made.</p><p>In a statistical sense, cross-validation results verify that genRE performs slightly better than all three interpolation techniques for both events studied. Overall, OK is found to be the most inadequate approach, specifically in terms of preserving the original variance in observed precipitation. MÉRA manages to reproduce the temporal variations of observations in a good manner for both events, whereas it displays less skill when considering spatial variations especially where topography has a major influence. Finally, genRE outperforms all other interpolators in mimicking the climatological conditions of MÉRA for both events.</p><p> </p><p>Van Osnabrugge, B., Weerts, A.H. and Uijlenhoet, R., 2017. genRE: A method to extend gridded precipitation climatology data sets in near real-time for hydrological forecasting purposes. Water Resources Research, 53(11), pp.9284-9303.</p>

scholarly journals Modeling Insulin-Glucose Dynamics during Insulin Induced Hypoglycemia. Evaluation of Glucose Counterregulation

1999 ◽  
Vol 1 (4) ◽  
pp. 313-323 ◽  
Author(s):  
Boris P. Kovatchev ◽  
Leon S. Farhy ◽  
Daniel J. Cox ◽  
Martin Straume ◽  
Vladimir I. Yankov ◽  
...  
Keyword(s):  
Temporal Pattern ◽  
Type I Diabetes ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
Type I ◽  
Euglycemic Hyperinsulinemic Clamp ◽  
Data Set ◽  
Utilization Coefficient ◽  
History Of ◽  
Glucose Dynamics

A dynamical network model of insulin-glucose interactions in subjects with Type I Diabetes was developed and applied to data sets for 40 subjects. Each data set contained the amount of dextrose + insulin infused and blood glucose (BG) determinations, sampled every 5 minutes during a one-hour standardized euglycemic hyperinsulinemic clamp and a subsequent one-hour BG reduction to moderate hypoglycemic levels. The model approximated the temporal pattern of BG and on that basis predicted the counterregulatory response of each subject. The nonlinear fits explained more than 95% of the variance of subjects' BG fluctuations, with a median coefficient of determination 97.7%. For all subjects the model-predicted counterregulatory responses correlated with measured plasma epinephrine concentrations. The observed nadirs of BG during the tests correlated negatively with the model-predicted insulin utilization coefficient (r = -0.51,p< 0.001) and counterregulation rates (r= -0.63,p< 0.001). Subjects with a history of multiple severe hypoglycemic episodes demonstrated slower onset of counterregulation compared to subjects with no such history (p< 0.03).

scholarly journals Development and evaluation of an extended inverse distance weighting method for streamflow estimation at an ungauged site

2015 ◽  
Vol 47 (2) ◽  
pp. 333-343 ◽  
Author(s):  
Muhammad Waseem ◽  
Muhammad Ajmal ◽  
Ungtae Kim ◽  
Tae-Woong Kim
Keyword(s):  
Weighted Average ◽  
Inverse Distance Weighting ◽  
General Idea ◽  
Coefficient Of Determination ◽  
Weighting Method ◽  
Ungauged Sites ◽  
Distance Weighting ◽  
Leave One Out ◽  
Mean Square Errors ◽  
Inverse Distance

In spatial interpolation, one of the most widely used deterministic methods is the inverse distance weighting (IDW) technique. The general idea of IDW is primarily based on the hypothesis that the attribute value of an ungauged site is the weighted average of the known attribute values within the neighborhood, and the ‘weights’ are merely associated with the horizontal distances between the gauged and ungauged sites. However, here we propose an extended version of IDW (hereafter, called the EIDW method) to provide ‘alternative weights’ based on the blended geographical and physiographical spaces for estimation of streamflow percentiles at ungauged sites. Based on the leave-one-out cross-validation procedure, the coefficient of determination had a value of 0.77 and 0.82 for the proposed EIDW models, M1 and M2, respectively, with low root mean square errors. Moreover, after investigating the relationship between the prediction efficiency and the distance decay parameter (C), the better performance of the M1 and M2 resulted at C = 2. Furthermore, the results of this study show that the EIDW could be considered as a constructive way forward to provide more accurate and consistent results in comparison to the traditional IDW or the dimension reduction technique-based IDW.

scholarly journals Doppler radar rainfall prediction and gauge data

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Jesse W. Lansford ◽  
Tyson H. Walsh ◽  
T. V. Hromadka ◽  
P. Rao
Keyword(s):  
Doppler Radar ◽  
Weather Forecasting ◽  
Rain Gauge ◽  
Data Sets ◽  
Multiple Sources ◽  
Data Set ◽  
Radar Rainfall ◽  
Rainfall Prediction ◽  
Doppler Data ◽  
Precipitation Estimates

Abstract Objective The data herein represents multiple gauge sets and multiple radar sites of like-type Doppler data sets combined to produce populations of ordered pairs. Publications spanning decades yet specific to Doppler radar sites contain graphs of data pairs of Doppler radar precipitation estimates versus rain gauge precipitation readings. Data description Taken from multiple sources, the data set represents several radar sites and rain gauge sites combined for 8830 data points. The data is relevant in various applications of hydrometeorology and engineering as well as weather forecasting. Further, the importance of accuracy in radar and precipitation estimates continues to increase, necessitating the incorporation of as much data as possible.

scholarly journals Geostatistical Merging of a Single-Polarized X-Band Weather Radar and a Sparse Rain Gauge Network over an Urban Catchment

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 496 ◽  
Author(s):  
Ibrahim Seck ◽  
Joël Van Baelen
Keyword(s):  
Mean Field ◽  
Radar Data ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
X Band ◽  
Distance Weighting ◽  
Field Bias ◽  
The Mean ◽  
Inverse Distance

Optimal Quantitative Precipitation Estimation (QPE) of rainfall is crucial to the accuracy of hydrological models, especially over urban catchments. Small-to-medium size towns are often equipped with sparse rain gauge networks that struggle to capture the variability in rainfall over high spatiotemporal resolutions. X-band Local Area Weather Radars (LAWRs) provide a cost-effective solution to meet this challenge. The Clermont Auvergne metropolis monitors precipitation through a network of 13 rain gauges with a temporal resolution of 5 min. 5 additional rain gauges with a 6-minute temporal resolution are available in the region, and are operated by the national weather service Météo-France. The LaMP (Laboratoire de Météorologie Physique) laboratory’s X-band single-polarized weather radar monitors precipitation as well in the region. In this study, three geostatistical interpolation techniques—Ordinary kriging (OK), which was applied to rain gauge data with a variogram inferred from radar data, conditional merging (CM), and kriging with an external drift (KED)—are evaluated and compared through cross-validation. The performance of the inverse distance weighting interpolation technique (IDW), which was applied to rain gauge data only, was investigated as well, in order to evaluate the effect of incorporating radar data on the QPE’s quality. The dataset is comprised of rainfall events that occurred during the seasons of summer 2013 and winter 2015, and is exploited at three temporal resolutions: 5, 30, and 60 min. The investigation of the interpolation techniques performances is carried out for both seasons and for the three temporal resolutions using raw radar data, radar data corrected from attenuation, and the mean field bias, successively. The superiority of the geostatistical techniques compared to the inverse distance weighting method was verified with an average relative improvement of 54% and 31% in terms of bias reduction for kriging with an external drift and conditional merging, respectively (cross-validation). KED and OK performed similarly well, while CM lagged behind in terms of point measurement QPE accuracy, but was the best method in terms of preserving the observations’ variance. The correction schemes had mixed effects on the multivariate geostatistical methods. Indeed, while the attenuation correction improved KED across the board, the mean field bias correction effects were marginal. Both radar data correction schemes resulted in a decrease of the ability of CM to preserve the observations variance, while slightly improving its point measurement QPE accuracy.

scholarly journals Robust Multivariate Correlation Techniques: A Confirmation Analysis using Covid-19 Data Set

2021 ◽  
Vol 29 (2) ◽  
Author(s):  
Friday Zinzendoff Okwonu ◽  
Nor Aishah Ahad ◽  
Joshua Sarduana Apanapudor ◽  
Festus Irismisose Arunaye
Keyword(s):  
Conventional Method ◽  
Strong Association ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
Data Set ◽  
Multivariate Correlation ◽  
Correlation Techniques ◽  
Conventional Methods ◽  
Death Cases ◽  
Confirmation Analysis

Robust multivariate correlation techniques are proposed to determine the strength of the association between two or more variables of interest since the existing multivariate correlation techniques are susceptible to outliers when the data set contains random outliers. The performances of the proposed techniques were compared with the conventional multivariate correlation techniques. All techniques under study are applied on COVID-19 data sets for Malaysia and Nigeria to determine the level of association between study variables which are confirmed, discharged, and death cases. These techniques’ performances are evaluated based on the multivariate correlation (R), multivariate coefficient of determination (R^2), and Adjusted R^2. The proposed techniques showed R=0.99 and the conventional methods showed that R ranges from 0.44 to 0.73. The R^2 and the Adjusted R^2 for proposed methods are 0.98 and 0.97 while the conventional methods showed that R equals 0.53, 0.44, and 0.19 whereas Adjusted R^2 equals 0.52, 0.43, and 0.18, respectively. The proposed techniques strongly affirmed that for any patient to be discharged or die of the Covid-19, the patient must be confirmed Covid-19 positive, whereas the conventional method showed moderate to very weak affirmation. Based on the results, the proposed techniques are robust and show a very strong association between the variables of interest than the conventional techniques.

Experiment, evaluate methods of interpolation of terain surface for different types of terrain

2020 ◽  
Vol 61 (2) ◽  
pp. 116-125
Author(s):  
Yen Quoc Phan ◽  
Nga Thu Thi Nguyen ◽  
Keyword(s):  
Nearest Neighbor ◽  
Polynomial Interpolation ◽  
Spline Functions ◽  
Mountainous Terrain ◽  
Mean Square ◽  
Data Set ◽  
Natural Neighbor ◽  
Distance Weighting ◽  
Different Types ◽  
Inverse Distance

Surface modeling is done by many classic and modern algorithms such as Polynomial Interpolation, Delaunay Triangulation, Nearest Neighbor, Natural Neighbor, Kriging, Inverse Distance Weighting (IDW), Spline Functions, etc. The important issue is to experiment, evaluate and select algorithms suitable to the reality of the data and the study area. The paper used three algorithms IDW, Kriging and Natural Neighbor to model the terrain on two map sheets representing different types of terrain. From there, compare the results and evaluate the accuracy of the methods based on random test data from the data set which is extracted from the original map. In addition, checking the contour determined from the algorithm compared to the original contour were also carried out on the entire map sheet. Results show that: Natural Neighbor algorithm gives better results on both experimental areas, then IDW and Kriging algorithms, the root mean Square Error of 15.2922, 16.4754 and 17.9949 m respectively for average high terrain and 13.9728, 15.2466, 15.7613 meters with high mountainous terrain

scholarly journals Distance in spatial interpolation of daily rain gauge data

2006 ◽  
Vol 10 (2) ◽  
pp. 197-208 ◽  
Author(s):  
B. Ahrens
Keyword(s):  
Spatial Interpolation ◽  
Rain Gauge ◽  
Inverse Distance Weighting ◽  
Mountainous Terrain ◽  
Geographical Distance ◽  
Rain Gauge Data ◽  
Distance Weighting ◽  
Gauge Data ◽  
Statistical Distance ◽  
Inverse Distance

Abstract. Spatial interpolation of rain gauge data is important in forcing of hydrological simulations or evaluation of weather predictions, for example. This paper investigates the application of statistical distance, like one minus common variance of observation time series, between data sites instead of geographical distance in interpolation. Here, as a typical representative of interpolation methods the inverse distance weighting interpolation is applied and the test data is daily precipitation observed in Austria. Choosing statistical distance instead of geographical distance in interpolation of available coarse network observations to sites of a denser network, which is not reporting for the interpolation date, yields more robust interpolation results. The most distinct performance enhancement is in or close to mountainous terrain. Therefore, application of statistical distance in the inverse distance weighting interpolation or in similar methods can parsimoniously densify the currently available observation network. Additionally, the success further motivates search for conceptual rain-orography interaction models as components of spatial rain interpolation algorithms in mountainous terrain.

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