scholarly journals Sensitivity Analysis of Quality Assurance Using the Spatial Regression Approach—A Case Study of the Maximum/Minimum Air Temperature

2005 ◽  
Vol 22 (10) ◽  
pp. 1520-1530 ◽  
Author(s):  
Kenneth G. Hubbard ◽  
Jinsheng You
Keyword(s):  
Air Temperature ◽  
Time Window ◽  
Spatial Regression ◽  
Optimal Number ◽  
Quality Data ◽  
Distance Weighting ◽  
Time Offset ◽  
Inverse Distance ◽  
Minimum Air Temperature

Abstract Both the spatial regression test (SRT) and inverse distance weighting (IDW) methods have been applied to provide estimates for the maximum air temperature (Tmax) and the minimum air temperature (Tmin) in the Applied Climate Information System (ACIS). This is critical to the processes of estimating missing data and identifying suspect data and is undertaken here to ensure quality data in ACIS. The SRT method was previously found to be superior to the IDW method; however, the sensitivity of the performance of both methods to input parameters has not been evaluated. A set of analyses is presented for both methods whereby the sensitivity to the radius of inclusion, the regression time window, the regression time offset, and the number of stations used to make the estimates are examined. Comparisons were also conducted between the SRT and the IDW methods. The performance of the SRT method stabilized when 10 or more stations were applied in the estimates. The optimal number of stations for the IDW method varies from only a few to 30. The results indicate that the best estimates obtained using the IDW method are still inferior to the worst estimates obtained using the SRT method.

scholarly journals Interpolation of Small Datasets in the Sandstone Hydrocarbon Reservoirs, Case Study of the Sava Depression, Croatia

Geosciences ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 201 ◽  
Author(s):  
Tomislav Malvić ◽  
Josip Ivšinović ◽  
Josipa Velić ◽  
Rajna Rajić
Keyword(s):  
Inverse Distance Weighting ◽  
Challenging Problem ◽  
Interpolation Methods ◽  
Distance Weighting ◽  
Hydrocarbon Fields ◽  
Northern Croatia ◽  
First Time ◽  
Inverse Distance ◽  
Selection Of

The interpolation of small datasets is challenging problem regarding the selection of interpolation methods and type of datasets. Here, for such analysis, the analysed data was taken in two hydrocarbon fields (“A” and “B”), located in the western part of the Sava Depression (in Northern Croatia). The selected reservoirs “L” (in the “A” Field) and “K” (“B”) are of Lower Pontian (Upper Miocene) age and belong to the Kloštar-Ivanić Formation. Due to strong tectonics, there are numerous tectonic blocks, each sampled with only a few wells. We selected two variables for interpolation—reservoirs permeabilities and injected volumes of field water. The following interpolation methods are described, compared and applied: Nearest Neighbourhood, Natural Neighbour (for the first time in the Sava Depression) and Inverse Distance Weighting. The last one has been recommended as the most appropriate in this study. Also, the presented research can be repeated in similar clastic environments at the same level hydrocarbon of exploration.

scholarly journals Analysis of Mapping Techniques for Mountain Precipitation: A Case Study of Alpine Region, Austria

2018 ◽  
Vol 8 (4) ◽  
pp. 3213-3217
Author(s):  
A. N. Laghari ◽  
G. D. Walasai ◽  
D. K. Bangwar ◽  
A. H. Memon ◽  
A. H. Shaikh
Keyword(s):  
Cross Validation ◽  
Geographical Information ◽  
Missing Information ◽  
Local Data ◽  
Consistency Test ◽  
Distance Weighting ◽  
Map Generation ◽  
Mapping Techniques ◽  
Inverse Distance

Truly representative precipitation map generation of mountain regions is a difficult task. Due to poor gauge representativity, complex topography and uneven density factors make the generation of representative precipitation maps a very difficult task. To generate representative precipitation maps, this study focused on analyzing four different mapping techniques: ordinary kriging, spline technique (SP), inverse distance weighting (IDW) and regression kriging (RK). The generated maps are assessed through cross-validation statistics, spatial cross-consistency test and by water balance approach. The largest prediction error is produced by techniques missing information on co-variables. The ME and RMSE values show that IDW and SP are the most biased techniques. The RK technique produced the best model results with 1.38mm and 72.36mm ME and RMSE values respectively. The comparative analysis proves that RK model can produce reasonably accurate values at poorly gauged areas, where geographical information compensated the poor availability of local data.

scholarly journals Sediment production in flysch badlands: A case study from Slovenian Istria

2020 ◽  
Vol 60 (1) ◽  
Author(s):  
Gregor Kovačič
Keyword(s):  
Air Temperature ◽  
Precipitation Amount ◽  
Meteorological Station ◽  
The Other ◽  
Average Wind Speed ◽  
Sediment Production ◽  
Average Wind ◽  
Erosion Pins ◽  
Minimum Air Temperature

This article deals with the results of seven years of measurements of sediment release from the flysch badlands in the Rokava River headwaters. Measurements of sediment production were carried out in erosion plots, and measurements of cliff (or rockwall) retreat using erosion pins. Selected meteorological time series from the Portorož Airport meteorological station were included in the analysis. The calculation showed that from 2008 to 2015 (149 measurements) sediment production was 36 kg/m² per year and the flysch cliff retreated by 146 mm or 21 mm per year. The amount of sediment produced is moderately positively correlated with the number of days between successive measurements (r = 0.51), with a recorded daily transition of air temperature over/below 0 °C (r = 0.56) and slightly more weakly correlated with the precipitation amount (r = 0.45). On the other hand, the amount of sediment produced has a low negative correlation with average air temperature (r = −0.29) and average minimum air temperature (r = −0.30). However, no statistically significant correlation was calculated between the amount of sediment produced and average wind speed.

scholarly journals Application of the Modified Shepard’s Method (MSM): A Case Study with the Interpolation of Neogene Reservoir Variables in Northern Croatia

Stats ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 68-83 ◽  
Author(s):  
Tomislav Malvić ◽  
Josip Ivšinović ◽  
Josipa Velić ◽  
Jasenka Sremac ◽  
Uroš Barudžija
Keyword(s):  
Cross Validation ◽  
Statistical Properties ◽  
Inverse Distance Weighting ◽  
Geological Mapping ◽  
Interpolation Methods ◽  
Geometrical Features ◽  
Distance Weighting ◽  
Northern Croatia ◽  
Inverse Distance

Interpolation is a procedure that depends on the spatial and/or statistical properties of the analysed variable(s). It is a particularly challenging task for small datasets, such as in those with less than 20 points of data. This problem is common in subsurface geological mapping, i.e., in cases where the data is taken solely from wells. Successful solutions of such mapping problems depend on interpolation methods designed primarily for small datasets and the datasets themselves. Here, we compare two methods, Inverse Distance Weighting and the Modified Shepard’s Method, and apply them to three variables (porosity, permeability, and thickness) measured in the Neogene sandstone hydrocarbon reservoirs (northern Croatia). The results show that cross-validation itself will not provide appropriate map selection, but, in combination with geometrical features, it can help experts eliminate the solutions with low-probable structures/shapes. The Golden Software licensed program Surfer 15 was used for the interpolations in this study.

scholarly journals Application of Modified Shepard's Method (MSM) case study with interpolation of Neogene reservoirs variables in the Northern Croatia

2019 ◽  
Author(s):  
Tomislav Malvić ◽  
Josip Ivšinović ◽  
Josipa Velić ◽  
Jasenka Sremac ◽  
Uroš Barudžija
Keyword(s):  
Cross Validation ◽  
Statistical Properties ◽  
Inverse Distance Weighting ◽  
Geological Mapping ◽  
Interpolation Methods ◽  
Geometrical Features ◽  
Distance Weighting ◽  
Northern Croatia ◽  
Inverse Distance

Interpolation is procedure that depends on spatial and/or statistical properties of analysed variable(s). It is special challenging task for data that included low number of samples, like dataset with less than 20 data. This problem is especially emphasized in the subsurface geological mapping, i.e. in the cases where data are taken solely from wells. Successful solutions of such mapping problems ask for knowledge about interpolation methods designed primarily for small datasets and dataset itself. Here are compared two methods, namely Inverse Distance Weighting and Modified Shepard’s Method, applied for three variables (porosity, permeability, thickness) measured in the Neogene sandstone hydrocarbon reservoirs (Northern Croatia). The results showed that pure cross-validation is not enough condition for appropriate map selection, but also geometrical features need to be considered, for datasets with less than 20 points.

scholarly journals Assessing the Immediate Effect of Covid-19 Lockdown on Air Quality: A Case Study of Delhi, India

2020 ◽  
Vol 13 (3-4) ◽  
pp. 27-33
Author(s):  
Ankit Sikarwar ◽  
Ritu Rani
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Spatial Interpolation ◽  
World Health ◽  
Guideline Values ◽  
Distance Weighting ◽  
Health Organization ◽  
The City ◽  
Inverse Distance

Abstract In India, a nationwide lockdown due to COVID-19 has been implemented on 25 March 2020. The lockdown restrictions on more than 1.3 billion people have brought exceptional changes in the air quality all over the country. This study aims to analyze the levels of three major pollutants: particulate matter sized 2.5 μm (PM2.5) and 10 μm (PM10), and nitrogen dioxide (NO2) before and during the lockdown in Delhi, one of the world’s most polluted cities. The data for PM2.5, PM10, and NO2 concentrations are derived from 38 ground stations dispersed within the city. The spatial interpolation maps of pollutants for two times are generated using Inverse Distance Weighting (IDW) model. The results indicate decreasing levels of PM2.5, PM10, and NO2 concentrations in the city by 93%, 83%, and 70% from 25 February 2020 to 21 April 2020 respectively. It is found that one month before the lockdown the levels of air pollution in Delhi were critical and much higher than the guideline values set by the World Health Organization. The levels of air pollution became historically low after the lockdown. Considering the critically degraded air quality for decades and higher morbidity and mortality rate due to unhealthy air in Delhi, the improvement in air quality due to lockdown may result as a boon for the better health of the city’s population.

scholarly journals Spatial Accuracy of Climate Networks: A Case Study in Nebraska

2014 ◽  
Vol 53 (8) ◽  
pp. 1932-1942 ◽  
Author(s):  
Andrea J. Coop ◽  
Kenneth G. Hubbard ◽  
Martha D. Shulski ◽  
Jinsheng You ◽  
David B. Marx
Keyword(s):  
Air Temperature ◽  
Weather Data ◽  
Reference Network ◽  
Daily Maximum ◽  
Spatial Accuracy ◽  
Climate Data ◽  
Historical Climatology ◽  
Mean Square Errors ◽  
Minimum Air Temperature

AbstractClimate data are increasingly scrutinized for accuracy because of the need for reliable input for climate-related decision making and assessments of climate change. Over the last 30 years, vast improvements to U.S. instrumentation, data collection, and station siting have created more accurate data. This study explores the spatial accuracy of daily maximum and minimum air temperature data in Nebraska networks, including the U.S. Historical Climatology Network (HCN), the Automated Weather Data Network (AWDN), and the more recent U.S. Climate Reference Network (CRN). The spatial structure of temperature variations at the earth’s surface is compared for timeframes 2005–09 for CRN and AWDN and 1985–2005 for AWDN and HCN. Individual root-mean-square errors between candidate station and surrounding stations were calculated and used to determine the spatial accuracy of the networks. This study demonstrated that in the 5-yr analysis CRN and AWDN were of high spatial accuracy. For the 21-yr analysis the AWDN proved to have higher spatial accuracy (smaller errors) than the HCN for both maximum and minimum air temperature and for all months. In addition, accuracy was generally higher in summer months and the subhumid area had higher accuracy than did the semiarid area. The findings of this study can be used for Nebraska as an estimate of the uncertainty associated with using a weather station’s data at a decision point some distance from the station.

scholarly journals An enhanced dual IDW method for high-quality geospatial interpolation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhanglin Li
Keyword(s):  
Ordinary Kriging ◽  
Interpolation Method ◽  
The Public ◽  
Data Process ◽  
Distance Weighting ◽  
The Face ◽  
Spatial Interpolation Method ◽  
Stability And Accuracy ◽  
Inverse Distance

AbstractMany geoscience problems involve predicting attributes of interest at un-sampled locations. Inverse distance weighting (IDW) is a standard solution to such problems. However, IDW is generally not able to produce favorable results in the presence of clustered data, which is commonly used in the geospatial data process. To address this concern, this paper presents a novel interpolation approach (DIDW) that integrates data-to-data correlation with the conventional IDW and reformulates it within the geostatistical framework considering locally varying exponents. Traditional IDW, DIDW, and ordinary kriging are employed to evaluate the interpolation performance of the proposed method. This evaluation is based on a case study using the public Walker Lake dataset, and the associated interpolations are performed in various contexts, such as different sample data sizes and variogram parameters. The results demonstrate that DIDW with locally varying exponents stably produces more accurate and reliable estimates than the conventional IDW and DIDW. Besides, it yields more robust estimates than ordinary kriging in the face of varying variogram parameters. Thus, the proposed method can be applied as a preferred spatial interpolation method for most applications regarding its stability and accuracy.

scholarly journals Assessing the immediate effect of COVID-19 lockdown on air quality: A case study of Delhi, India

2020 ◽  
Author(s):  
Ankit Sikarwar ◽  
Ritu Rani
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Spatial Interpolation ◽  
World Health ◽  
Guideline Values ◽  
Distance Weighting ◽  
Health Organization ◽  
The City ◽  
Inverse Distance

Abstract In India, the nationwide lockdown due to COVID-19 has been implemented on 25 March 2020. The lockdown restrictions on more than 1.3 billion people have brought exceptional changes in the air quality all over the country. This study aims to analyze the levels of three major pollutants (PM2.5, PM10, and NO2) before and during the lockdown in Delhi, one of the world’s most polluted cities. The data for PM2.5, PM10, and NO2 concentrations are derived from 38 ground stations dispersed within the city. The spatial interpolation maps of pollutants for two times are generated using Inverse Distance Weighting (IDW) model. The results indicate the lowering of PM2.5, PM10, and NO2 concentrations in the city by 93%, 83%, and 70% from 25 February 2020 to 21 April 2020 respectively. It is found that before one month of the lockdown the levels of air pollution in Delhi were critically high and far beyond the guideline values set by the World Health Organization. The levels of air pollution are historically low after the lockdown. Considering the critically degraded air quality for decades and higher morbidity and mortality rate due to unhealthy air in Delhi, the improvement in air quality due to lockdown may result as a boon for the better health of the city’s population.

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