scholarly journals Alternative methodology to gap filling for generation of monthly rainfall series with GIS approach

RBRH ◽  
2018 ◽  
Vol 23 (0) ◽  
Author(s):  
Claudio Bielenki Junior ◽  
Franciane Mendonça dos Santos ◽  
Silvia Cláudia Semensato Povinelli ◽  
Frederico Fábio Mauad

ABSTRACT As an alternative to Gap filling in monthly average rainfall series, we attempted to present a methodology for the generation of series only with the observed data available in the rainfall stations present in the study area and its surroundings. For this, a computational tool was developed with a GIS approach, using scripts in the Python language, to automate the study steps. Two calculation alternatives for the mean precipitation, variable Thiessen polygons or variable inverse distance weights (IDW), were considered. Random gaps were imposed from a series of data without gaps allowing us to evaluate the presented methodology. The results of the series calculated according to this methodology were compared to two methods of Gap filling. The behavior of the series was evaluated through the analysis of position and dispersion measurements as well as the temporal behavior by the evaluation of the correlograms and periodograms. The results are found to be satisfactory, which demonstrates the equivalence of the proposal with results found with the gap filling methods under the tested conditions. The differences found between the series were small, which was reflected in the Nash-Sutcliffe Indexes. There were no significant differences between the calculation alternatives by Thiessen polygons or IDW weights.

2017 ◽  
Vol 56 (9) ◽  
pp. 2561-2575 ◽  
Author(s):  
Aitor Atencia ◽  
Isztar Zawadzki ◽  
Marc Berenguer

AbstractThe most widely used technique for nowcasting of quantitative precipitation in operational and research centers is the Lagrangian extrapolation of the latest radar observations. However, this technique has a limited forecast skill because of the assumption made on its formulation, such as the fact that the motion vectors do not change and, even more important for convective events, neglect any growth or decay in the precipitation field. In this work, the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) errors have been computed for 10 yr of radar composite data over the continental United States. The study of these errors shows systematic bias depending on the time of day. This effect is related to the solar cycle, whose heating energy results in an increase in the average rainfall in the afternoon. This external forcing interacts with the atmospheric system, creating local initiation and dissipation of convection depending on orography, land use, cloud coverage, etc. The signal of the diurnal cycle in MAPLE precipitation forecast has been studied in different locations and spatial scales as a function of lead time in order to recognize where, when, and for which spatial scales the signal is significant. This information has been used in the development of a scaling correction scheme where the mean errors due to the diurnal cycle are adjusted. The results show that the developed methodology improves the forecast for the spatial scales and locations where the diurnal cycle signal is significant.


2021 ◽  
Vol 29 ◽  
pp. 157-168
Author(s):  
Janaina Cassiano dos Santos ◽  
Gustavo Bastos Lyra ◽  
Marcel Carvalho Abreu ◽  
Daniel Carlos de Menezes

The aim of this work was to propose a method for the consistency of climatic series of monthly rainfall using a supervised and unsupervised approach. The methodology was applied for the series (1961-2010) of rainfall from weather stations located in the State of Rio de Janeiro (RJ) and in the borders with the states of São Paulo, Minas Gerais and Espírito Santo with the State of Rio de Janeiro. The data were submitted to quality analysis (physical and climatic limit and, space-time tendency) and gap filling, based on simple linear regression analysis, associated with the prediction band (p < 0.05 or 0.01), in addition to the Z-score (3, 4 or 5). Next, homogeneity analysis was applied to the continuous series, using the method of cumulative residuals. The coefficients of determination (r²) between the assessed series and the reference series were greater than 0.70 for gap filling both for the supervised and unsupervised approaches. In the analysis of data homogeneity, supervised and unsupervised approaches were effective in selecting homogeneous series, in which five out of the nine final stations were homogeneous (p > 0.9). In the other series, the homogeneity break points were identified and the simple linear regression method was applied for their homogenization. The proposed method was effective to consist of the rainfall series and allows the use of these data in climate studies.


2007 ◽  
Vol 135 (2) ◽  
pp. 598-617 ◽  
Author(s):  
Mateusda Silva Teixeira ◽  
Prakki Satyamurty

Abstract The dynamical and synoptic characteristics that distinguish heavy rainfall episodes from nonheavy rainfall episodes in southern Brazil are discussed. A heavy rainfall episode is defined here as one in which the 50 mm day−1 isohyet encloses an area of not less than 10 000 km2 in the domain of southern Brazil. One hundred and seventy such events are identified in the 11-yr period of 1991–2001. The mean flow patterns in the period of 1–3 days preceding the episodes show some striking synoptic-scale features that may be considered forerunners of these episodes: (i) a deepening midtropospheric trough in the eastern South Pacific approaches the continent 3 days before, (ii) a surface low pressure center forms in northern Argentina 1 day before, (iii) a northerly low-level jet develops over Paraguay 2 days before, and (iv) a strong moisture flux convergence over southern Brazil becomes prominent 1 day before the episode. A parameter called rainfall quantity, defined as the product of the area enclosed by the 50 mm day−1 isohyet and the average rainfall intensity, is correlated with fields of atmospheric variables such as 500-hPa geopotential and 850-hPa meridional winds. Significant lag correlations show that the anomalies of some atmospheric variables could be viewed as precursors of heavy rainfall in southern Brazil that can be explored for use in improving the forecasts.


2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Beata Calka ◽  
Elzbieta Bielecka ◽  
Mariusz Figurski

AbstractThe article presents the spatial pattern analysis of the ASG-EUPOS permanent GNSS stations in Poland. Using different methods and tools (nearest neighbour, Riplay’s K-function, morphology of Thiessen polygons) we proved that the station distribution model changes within scales. At short distances up to 65 km, which are typical lengths in the network, stations are irregularly dispersed. Increasing this distance to 130 km and over could result in a clustered pattern.The Thiessen polygon area in 72% depends on the level of urbanization, especially coverage of forested and built-up areas as well as the density of the transportation network. The smallest density of the ASG-EUPOS sites is one station over 10,000 sq. km, which is two times more than is stated in the national regulations. The mean distance from ASG-EUPOS location to the nearest station is about 41.5 km.


2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Octavio R. Hinojosa de la Garza ◽  
Luz H. Sanín ◽  
María Elena Montero Cabrera ◽  
Korina Ivette Serrano Ramirez ◽  
Enrique Martínez Meyer ◽  
...  

This study correlated lung cancer (LC) mortality with statistical data obtained from government public databases. In order to asses a relationship between LC deaths and radon accumulation in dwellings, indoor radon concentrations were measured with passive detectors randomly distributed in Chihuahua City. Kriging (K) and Inverse-Distance Weighting (IDW) spatial interpolations were carried out. Deaths were georeferenced and Moran’sIcorrelation coefficients were calculated. The mean values (overn=171) of the interpolation of radon concentrations of deceased’s dwellings were 247.8 and 217.1 Bq/m3, for K and IDW, respectively. Through the Moran’sIvalues obtained, correspondingly equal to 0.56 and 0.61, it was evident that LC mortality was directly associated with locations with high levels of radon, considering a stable population for more than 25 years, suggesting spatial clustering of LC deaths due to indoor radon concentrations.


HortScience ◽  
2000 ◽  
Vol 35 (4) ◽  
pp. 558D-558c
Author(s):  
J. Logan ◽  
M.A. Mueller

Tennessee is located in an area of diverse topography, ranging in elevation from <100 m to ≈2000 m, with numerous hills and valleys. The physiography makes it very difficult to spatially interpolate weather data related to vegetable production, such as spring and fall freeze dates and growing degree days (GDD). In addition, there is a poor distribution of cooperative weather stations, especially those with 30 years or more of data. There are climate maps available for Tennessee, but they are of such a general format as to be useless for operational applications. This project is designed to use a geographic information system (GIS) and geospatial techniques to spatially interpolate freeze (0 °C) dates and GDD for different base temperatures and make the data available as Internet-based maps. The goal is to develop reasonable climate values for vegetable growing areas <1000 m in elevation at a 100 square km resolution. The geostatistics that we are evaluating include Thiessen polygons, triangulated irregular network (TIN), inverse distance weighting (IDW), spline, kriging, and cokriging. Data from 140 locations in and around Tennessee are used in the analysis. Incomplete data from 100 other locations are used to validate the models. GDD, which have much less year-to-year variability than freeze dates, can be successfully interpolated using inverse distance weighting (IDW) or spline techniques. Even a simple method like Thiessen produces fairly accurate maps. Freeze dates, however, are better off analyzed on an annual basis because the patterns can vary significantly from year to year. The annual maps can then be superimposed to give a better estimate of average spring and fall freeze dates.


2018 ◽  
Vol 30 (1) ◽  
Author(s):  
Muhammad Noor ◽  
Tarmizi Ismail

Downscaling Global Circulation Model (GCM) output is important in order tounderstand the present climate as well as future climate changes at local scale. In this study,Random Forest (RF) was used to downscale the mean daily rainfall at Kota Bahru meteorologicalstation located in Kelantan Malaysia. The RF model was used to downscale daily rainfall fromGCM of Coupled Model Intercomparison Project Phase 5 (CMIP5), BCC-CSM1.1. The potentialpredictors were selected using stepwise regression at grid points located around the study area.Quantile mapping was used to remove the bias in the prediction. The results showed that the RFmodel was able to establish a good relation between observed and downscaled rainfall. TheQuantile mapping was found to perform well to correct errors in prediction. The statisticalmeasures of performance of downscaling and bias correction approaches show that they are ableto replicate daily observed rainfall with Nash-Schutclif efficiency greater than 0.75 for all themonths. It can be concluded that RF and Quantile mapping are reliable and effective methods fordownscaling rainfall.


Irriga ◽  
2018 ◽  
Vol 22 (1) ◽  
pp. 177-193 ◽  
Author(s):  
Iug Lopes ◽  
Juliana Maria M De Melo ◽  
Brauliro Gonçalves Leal

ESPACIALIZAÇÃO DA TEMPERATURA DO AR PARA A REGIÃO DO SUBMÉDIO SÃO FRANCISCO  IUG LOPES¹; JULIANA MARIA MEDRADO DE MELO² E BRAULIRO GONÇALVES LEAL³ ¹ Departamento de Engenharia Agrícola, Universidade Federal Rural de Pernambuco, Rua Dom Manoel de Medeiros, Dois Irmão, CEP: 52171-900 – Recife, PE. [email protected]² Departamento de Agronomia, Universidade do Estado da Bahia, Rua Edgar Chastinet, s/n - São Geraldo, BA, 48905-680 – Juazeiro, BA. [email protected]³ Colegiado de Engenharia da Computação, Universidade Federal do Vale do São Francisco – Campus Juazeiro, Av. Antonio Carlos Magalhães, 510 Country Club, CEP: 48.902-300 – Juazeiro, BA. [email protected]  1 RESUMO  Dentre as variáveis meteorológicas requeridas para o cálculo do balanço hídrico destacam-se as temperaturas mínimas, médias e máximas do ar, que apresentam uma continuidade no quantitativo de distância e assim permitem de uma maneira mais simples a criação de campos contínuos utilizando métodos de interpolação espacial. O objetivo deste trabalho foi avaliar potências para o método de interpolação do Inverso da Potência da Distância (IPD) na espacialização de valores diários da temperatura no Submédio São Francisco, para os períodos de um ano, das estações do ano (inverno, primavera, verão e outono). Foram obtidos os parâmetros de potência do interpolador Inverso da Potência da Distância das temperaturas mínimas, médias e máximas a partir dos dados medidos em 14 estações meteorológicas automáticas do INMET em operação no Pólo de Desenvolvimento Petrolina-Juazeiro. Foram realizadas interpolações para as épocas: anual, inverno, primavera, verão e outono. A variação diária do erro relativo médio obtida, para a época ano, calculado utilizando os dados de temperatura mínima, média e máxima utilizando o valor da potência do interpolador foram iguais a 3,3; 3,4; e 3,4, respectivamente. Os valores de erro médio foram pequenos quando comparados com o erro instrumental. Palavras-chave: interpolação, validação cruzada, estação meteorológica  LOPES, I; MELO, J. M. M.; LEAL, B. G. SPATIALIZATION OF AIR TEMPERATURE TO THE REGION OF SUBMEDIO SÃO FRANCISCO  2 ABSTRACT Among the meteorological variables required for the calculation of the water balance are the temperatures, which present a continuity in the quantitative distance and thus allow in a simpler way the creation of continuous fields using spatial interpolation methods. The objective of this work was to evaluate the power of the Inverse Distance Power (IPD) in the spatialization of daily values of temperature in the Submedia of São Francisco, for the one-year periods of the seasons (winter, spring, summer it's fall). The power parameters of the Inverse Distance Power Interpolator were obtained from the minimum, average and maximum temperatures from the data measured in 14 INMET automatic meteorological stations operating at the Petrolina-Juazeiro Development Pole. Interpolations were performed for annual, winter, spring, summer and fall seasons. The daily variation of the average relative error obtained for the year time, calculated using the data of minimum, average and maximum temperature using the value of the power of the interpolator were equal to 3.3; 3.4; and 3.4, respectively. The mean error values were small when compared to instrumental error. Keywords: interpolation, validation cross, meteorological station


Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 496 ◽  
Author(s):  
Ibrahim Seck ◽  
Joël Van Baelen

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.


Author(s):  
Xiaojun Yang

Spatial interpolation is a core component of data processing and analysis in geoinformatics. The purpose of this chapter is to discuss the concept and techniques of spatial interpolation. It begins with an overview of the concept and brief history of spatial interpolation. Then, the chapter reviews some commonly used interpolations that are specifically designed for working with point data, including inverse distance weighting, kriging, triangulation, Thiessen polygons, radial basis functions, minimum curvature, and trend surface. This is followed by a discussion on some criteria that are proposed to help select an appropriate interpolator; these criteria include global accuracy, local accuracy, visual pleasantness and faithfulness, sensitivity, and computational intensity. Finally, future research needs and new, emerging applications are presented.


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