Using Data From Personal Weather Stations to Improive Precipitation Estimation and Interpolation

2020 ◽  
Author(s):  
Jochen Seidel ◽  
Abbas El Hachem ◽  
András Bárdossy
Keyword(s):  
Spatial Interpolation ◽  
Cross Validation ◽  
Temporal Order ◽  
Distribution Functions ◽  
Secondary Network ◽  
Precipitation Estimation ◽  
Weather Stations ◽  
High Precipitation ◽  
Using Data ◽  
Service Data

<p>The number of private meteorological stations with data available online through the internet is increasing gradually in many parts of the world. The purpose of this study is to investigate the applicability of these data for the spatial interpolation of precipitation for high intensity events of different durations. Due to unknown biases of the observations, rainfall amounts of the secondary network are not considered directly. Instead, only their temporal order is assumed to be correct. The crucial step is to find the stations with informative measurements. This is done in two steps, first by selecting the locations using time series of indicators of high precipitation amounts. The remaining stations are checked whether they fit into the spatial pattern of the other stations. Thus it is assumed that the percentiles at the secondary network accurate. These percentiles are then translated to precipitation amounts using the distribution functions which were interpolated using the weather service data only. The suggested procedure was tested for the State of Baden-Württemberg in Germany. A detailed cross validation of the interpolation was carried out for aggregated precipitation amounts of 1, 3, 6, 12 and 24 hours. For each aggregations, nearly 200 intense events were evaluated. The results show that filtering the secondary observations is necessary, the interpolation error after filtering and data transformation decreases significantly. The biggest improvement is achieved for the shortest time aggregations.</p>

scholarly journals The use of personal weather station observation for improving precipitation estimation and interpolation

2020 ◽  
Author(s):  
András Bárdossy ◽  
Jochen Seidel ◽  
Abbas El Hachem
Keyword(s):  
Spatial Interpolation ◽  
Cross Validation ◽  
Temporal Order ◽  
Distribution Functions ◽  
The Internet ◽  
Crucial Step ◽  
The World ◽  
Precipitation Estimation ◽  
Weather Stations ◽  
High Precipitation

Abstract. The number of personal weather stations (PWS) with data available online through the internet is increasing gradually in many parts of the world. The purpose of this study is to investigate the applicability of these data for the spatial interpolation of precipitation for high intensity events of different durations. Due to unknown errors and biases of the observations rainfall amounts of the PWS network are not considered directly. Instead, only their temporal order is assumed to be correct. The crucial step is to find the stations with informative measurements. This is done in two steps, first by selecting the locations using time series of indicators of high precipitation amounts. The remaining stations are checked whether they fit into the spatial pattern of the other stations. Thus, it is assumed that the percentiles of the PWS network are accurate. These percentiles are then translated to precipitation amounts using the distribution functions which were interpolated using the information from German National Weather Service (DWD) data only. The suggested procedure was tested for the State of Baden-Württemberg in Germany. A detailed cross validation of the interpolation was carried out for aggregated precipitation amounts of 1, 3, 6, 12 and 24 hours. For each aggregation nearly 200 intense events were evaluated. The results show that filtering the secondary observations is necessary as the interpolation error after filtering and data transformation decreases significantly. The biggest improvement is achieved for the shortest time aggregations.

scholarly journals The use of personal weather station observations to improve precipitation estimation and interpolation

2021 ◽  
Vol 25 (2) ◽  
pp. 583-601
Author(s):  
András Bárdossy ◽  
Jochen Seidel ◽  
Abbas El Hachem
Keyword(s):  
Spatial Interpolation ◽  
Empirical Distribution ◽  
Precipitation Amount ◽  
Distribution Functions ◽  
Quantile Mapping ◽  
Novel Approach ◽  
Precipitation Estimation ◽  
Weather Stations ◽  
High Precipitation ◽  
Empirical Distribution Functions

Abstract. The number of personal weather stations (PWSs) with data available through the internet is increasing gradually in many parts of the world. The purpose of this study is to investigate the applicability of these data for the spatial interpolation of precipitation using a novel approach based on indicator correlations and rank statistics. Due to unknown errors and biases of the observations, rainfall amounts from the PWS network are not considered directly. Instead, it is assumed that the temporal order of the ranking of these data is correct. The crucial step is to find the stations which fulfil this condition. This is done in two steps – first, by selecting the locations using the time series of indicators of high precipitation amounts. Then, the remaining stations are then checked for whether they fit into the spatial pattern of the other stations. Thus, it is assumed that the quantiles of the empirical distribution functions are accurate. These quantiles are then transformed to precipitation amounts by a quantile mapping using the distribution functions which were interpolated from the information from the German National Weather Service (Deutscher Wetterdienst – DWD) data only. The suggested procedure was tested for the state of Baden-Württemberg in Germany. A detailed cross validation of the interpolation was carried out for aggregated precipitation amount of 1, 3, 6, 12 and 24 h. For each of these temporal aggregations, nearly 200 intense events were evaluated, and the improvement of the interpolation was quantified. The results show that the filtering of observations from PWSs is necessary as the interpolation error after the filtering and data transformation decreases significantly. The biggest improvement is achieved for the shortest temporal aggregations.

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

scholarly journals A Prototype Quantitative Precipitation Estimation Algorithm for Operational S-Band Polarimetric Radar Utilizing Specific Attenuation and Specific Differential Phase. Part II: Performance Verification and Case Study Analysis

2019 ◽  
Vol 20 (5) ◽  
pp. 999-1014 ◽  
Author(s):  
Stephen B. Cocks ◽  
Lin Tang ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
Brian Kaney ◽  
...  
Keyword(s):  
Real Time ◽  
Heavy Precipitation ◽  
Estimation Algorithm ◽  
Differential Phase ◽  
Specific Attenuation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Bias Ratio ◽  
Using Data ◽  
Precipitation Events

Abstract The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.

scholarly journals Comparative Analysis of Different Spatial Interpolation Methods Applied to Monthly Rainfall as Support for Landscape Management

2021 ◽  
Vol 11 (20) ◽  
pp. 9566
Author(s):  
Tommaso Caloiero ◽  
Gaetano Pellicone ◽  
Giuseppe Modica ◽  
Ilaria Guagliardi
Keyword(s):  
New Zealand ◽  
Spatial Interpolation ◽  
Cross Validation ◽  
Landscape Management ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
The Other ◽  
Geostatistical Methods ◽  
The Cross ◽  
Almost All

Landscape management requires spatially interpolated data, whose outcomes are strictly related to models and geostatistical parameters adopted. This paper aimed to implement and compare different spatial interpolation algorithms, both geostatistical and deterministic, of rainfall data in New Zealand. The spatial interpolation techniques used to produce finer-scale monthly rainfall maps were inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), and ordinary cokriging (COK). Their performance was assessed by the cross-validation and visual examination of the produced maps. The results of the cross-validation clearly evidenced the usefulness of kriging in the spatial interpolation of rainfall data, with geostatistical methods outperforming IDW. Results from the application of different algorithms provided some insights in terms of strengths and weaknesses and the applicability of the deterministic and geostatistical methods to monthly rainfall. Based on the RMSE values, the KED showed the highest values only in April, whereas COK was the most accurate interpolator for the other 11 months. By contrast, considering the MAE, the KED showed the highest values in April, May, June and July, while the highest values have been detected for the COK in the other months. According to these results, COK has been identified as the best method for interpolating rainfall distribution in New Zealand for almost all months. Moreover, the cross-validation highlights how the COK was the interpolator with the best least bias and scatter in the cross-validation test, with the smallest errors.

Comparison of Annual Maximum and Peaks Over Threshold Methods with Automated Threshold Selection in Flood Frequency Analysis: A Case Study for Australia

2021 ◽  
Author(s):  
Xiao Pan ◽  
Ataur Rahman
Keyword(s):  
Frequency Analysis ◽  
Flood Frequency ◽  
Distribution Functions ◽  
Flood Frequency Analysis ◽  
Annual Maximum ◽  
Threshold Selection ◽  
Peaks Over Threshold ◽  
Design Flood ◽  
Threshold Detection ◽  
Using Data

Abstract Flood frequency analysis (FFA) enables fitting of distribution functions to observed flow data for estimation of flood quantiles. Two main approaches, Annual Maximum (AM) and peaks-over-threshold (POT) are adopted for FFA. POT approach is under-employed due to its complexity and uncertainty associated with the threshold selection and independence criteria for selecting peak flows. This study evaluates the POT and AM approaches using data from 188 gauged stations in south-east Australia. POT approach adopted in this study applies a different average numbers of events per year fitted with Generalised Pareto (GP) distribution with an automated threshold detection method. The POT model extends its parametric approach to Maximum Likelihood Estimator (MLE) and Point Moment Weighted Unbiased (PMWU) method. Generalised Extreme Value (GEV) distribution using L-moment estimator is used for AM approach. It has been found that there is a large difference in design flood estimates between the AM and POT approaches for smaller average recurrence intervals (ARI), with a median difference of 25% for 1.01 year ARI and 5% for 50 and 100 years ARIs.

Pediatric Equestrian Injuries

PEDIATRICS ◽  
1992 ◽  
Vol 89 (6) ◽  
pp. 1173-1176
Author(s):  
Doris M. Bixby-Hammett
Keyword(s):  
United States ◽  
Surveillance System ◽  
National Park ◽  
National Park Service ◽  
Injury Surveillance System ◽  
Common Area ◽  
Park Service ◽  
System Data ◽  
Using Data ◽  
Service Data

Using data from four sources, horse-related injuries are summarized for persons younger than 25 years of age. Head injury caused 57% of deaths. The upper extremity was the most common area injured, with the next most frequent areas the lower extremity (National Park Service data) and the head (United States Pony Clubs [USPC] data). Injured females outnumbered injured males and had a greater percentage of participants injured (USPC data). Injuries occurred at home in 41% (National Electronic Injury Surveillance System data). USPC figures suggest that greater knowledge may reduce the severity of horse-related injuries. Previous horse-related injury had occurred in 1 of 4 of those injured (USPC data). One third of accidents occurred during lessons (USPC data). Riding instructors should be certified by a recognized organization, and parents should evaluate an instructor's personal riding and their safety records with students. The pediatrician's role should be in counseling parents with children who ride and in offering recommendations for safety to governing boards of youth horse activities.

scholarly journals Determination of αs in NNLO QCD using H1 jet cross section measurements

2019 ◽  
Vol 206 ◽  
pp. 01002
Author(s):  
Vladimir Chekelian
Keyword(s):  
Strong Coupling ◽  
Cross Section ◽  
Cross Sections ◽  
Neutral Current ◽  
Distribution Functions ◽  
Boson Mass ◽  
Parton Distribution Functions ◽  
Inclusive Jet ◽  
Using Data ◽  
Jet Cross Sections

Measurements of jet cross sections in neutral current deep-inelastic scattering (NC DIS) using data taken with the H1 detector at HERA are accomplished by the precision measurement of double-differential inclusive jet, dijet and trijet cross sections at low photon virtualities 5.5 < Q2 < 80 GeV2, and by extending previous inclusive jet measurements in the range 150 < Q2 < 15000 GeV2 to low transverse jet momenta 5 < PT < 7 GeV. The strong coupling constant at the Z-boson mass, αs(mZ), is determined in next-to-next-to-leading order (NNLO) QCD using H1 inclusive jet and dijet cross section measurements. Complementary, αs(mZ) is determined together with parton distribution functions of the proton (PDFs) from jet and inclusive DIS data measured by the H1 experiment. The running of the strong coupling is tested at different values of the renormalisation scale and the results are found to be in agreement with the QCD expectations.

The Wind Flow Characteristics Measuring Simulations of Soundproof Tunnels on Expressways

2018 ◽  
Vol 878 ◽  
pp. 70-75 ◽  
Author(s):  
Jang Youl You ◽  
Sun Young Paek ◽  
Doo Kie Kim ◽  
Ki Pyo You
Keyword(s):  
Wind Velocity ◽  
Flow Characteristics ◽  
Wind Flow ◽  
Cfd Analysis ◽  
Analysis Method ◽  
Residential Areas ◽  
Heat Islands ◽  
Computational Fluid Dynamics Cfd ◽  
Weather Stations ◽  
Using Data

Soundproof tunnels and soundproof walls constructed on expressways are designed to prevent noise for the nearby surrounding residential areas. These soundproof walls and tunnels feature excellent noise prevention for residential areas nearby, but they hamper the dispersion of air pollutants generated, thus promoting the creation of heat islands during summer and cold islands during winter.The computational fluid dynamics (CFD) analysis method was used to investigate the wind flow around soundproof tunnels. The wind angle and the size of the wind velocity were determined using data from weather stations near soundproof tunnels. The CFD analysis results of the soundproof tunnels on expressways revealed that the wind velocity decreased by 30–60% following the installation of soundproof tunnels.

scholarly journals Error Analysis of Satellite Precipitation Products in Mountainous Basins

2014 ◽  
Vol 15 (5) ◽  
pp. 1778-1793 ◽  
Author(s):  
Yiwen Mei ◽  
Emmanouil N. Anagnostou ◽  
Efthymios I. Nikolopoulos ◽  
Marco Borga
Keyword(s):  
Error Analysis ◽  
Warm Season ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Precipitation ◽  
Rain Gauge ◽  
Satellite Precipitation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Regimes ◽  
Precipitation Estimation ◽  
High Precipitation

Abstract Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events (HPEs). In situ observations over mountainous areas are limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for hydrological applications. In this study, four widely used satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, version 7 (3B42-V7), and in near–real time (3B42-RT); Climate Prediction Center (CPC) morphing technique (CMORPH); and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)] are evaluated with respect to their performance in capturing the properties of HPEs over different basin scales. Evaluation is carried out over the upper Adige River basin (eastern Italian Alps) for an 8-yr period (2003–10). Basin-averaged rainfall derived from a dense rain gauge network in the region is used as a reference. Satellite precipitation error analysis is performed for warm (May–August) and cold (September–December) season months as well as for different quantile ranges of basin-averaged precipitation accumulations. Three error metrics and a score system are introduced to quantify the performances of the various satellite products. Overall, no single precipitation product can be considered ideal for detecting and quantifying HPE. Results show better consistency between gauges and the two 3B42 products, particularly during warm season months that are associated with high-intensity convective events. All satellite products are shown to have a magnitude-dependent error ranging from overestimation at low precipitation regimes to underestimation at high precipitation accumulations; this effect is more pronounced in the CMORPH and PERSIANN products.

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