Uncertainty in merged radar - rain gauge rainfall products
|
|
- Alison Scott
- 6 years ago
- Views:
Transcription
1 Uncertainty in merged radar - rain gauge rainfall products Francesca Cecinati University of Bristol francesca.cecinati@bristol.ac.uk Supervisor: Miguel A. Rico-Ramirez This project has received funding from the European Union s Seventh Framework Programme for research, technological development and demonstration under grant agreement no
2 Who I am Environmental Engineer (M.Sc. Univ. Genova / M.Eng. MIT) Young Graduate Trainee ESA PhD University of Bristol Started November 2014 (2 year) Marie Curie Early Stage Researcher Project: QUICS (Quantifying Uncertainty in Integrated Catchment Studies) Seconded to TU Delft 4 months (2015) Seconded to EAWAG 3 months (summer 2016) Seconded to W+B 2 months (soon)
3 Quantifying Uncertainty in Integrated Catchment Studies Rainfall Water Framework Directive (2000/60/EC) Image credits: Melbourne Water
4 PhD topic: rainfall uncertainty Radar Uncertainty Radar Uncertainty Propagation Rain Gauge uncertainty Merged product uncertainty Radar + Rain Gauge merged products Merged Product uncertainty propagation
5 Merging Radar Rain Gauge rainfall products Trying to keep the advantages of both: areal estimates at high spatio-temporal resolution high accuracy Many methods, two main families: Bias corrections Kriging based
6 How to estimate uncertainty Radar: Comparison with rain gauges Modelling source by source Rain gauges: Test studies Theoretical models Merged radar rain gauge : It depends on the method!
7 Kriging with External Drift KED is one of the best and most efficients merging methods The estimate is based on the kriging interpolation of rain gauges The mean of the process is modelled as a linear function of the radar (external drift) The process is assumed to be Gaussian It also estimates the uncertainty associated with the prediction (kriging variance)
8 KED uncertainty modelling 1) Interpolation uncertainty Kriging variance 2) Rain gauge errors Kriging for uncertain data (KUD) 3) Radar residual biases Variation of KED including external drift for SD too 4) Gaussian approximation TransGaussian Kriging, etc
9 KED Z x = m x + Y x m x = a R x + b n Z x 0 = λ i i=1 G x i n i=1 λ i = 1 n j=1 λ i γ ij + μ 1 + μ 2 R x i = γ i0 i = 1,2,, n n λ i i=1 R x i = R x 0
10 1) Kriging variance Z x 0 σ 2 x 0 = W T G = c W T D C = W = C 1 D The kriging variance mainly describes the interpolation uncertainty C 10 C 11 C 1n 1 R 1 C n1 C nn 1 R n R 1 R n 0 0 D = C n0 1 R 0 W = λ 1 λ n μ 1 μ 2
11 KED uncertainty modelling 1) Interpolation uncertainty Kriging variance 2) Rain gauge errors Kriging for uncertain data (KUD) 3) Radar residual biases Variation of KED including external drift for SD too 4) Gaussian approximation TransGaussian Kriging, etc
12 2) Kriging for Uncertain Data (KUD) The nugget effect is a jump in the covariance function that describes the variance at infinitesimal distance C d = c + c 0 d = 0 c exp 3d r d > 0 If the measurement errors are different for the different rain gauges we need a specific c 0i for the i th rain gauge: C = c + c 01 C 1n 1 R 1 C n1 c + c 0n 1 R n R 1 R n 0 0
13 mm/h Findings Predictions 1. Applying KUD to KED usually improves the estimates Variance 2. It is not always true for ordinary kriging mm 2 /h 2 3. KUD has a smoothing effect on the less reliable rain gauges 4. The improvement (or not) is a function of many factors, including the geometry and the characteristics of the rainfall event
14 KED uncertainty modelling 1) Interpolation uncertainty Kriging variance 2) Rain gauge errors Kriging for uncertain data (KUD) 3) Radar residual biases Variation of KED including external drift for SD too 4) Gaussian approximation TransGaussian Kriging, etc
15 3) Radar residual biases The absolute value of radar estimates does not affect the predictions of KED, because a linear function of it is used as trend. If radar bias was spatially homogeneous, it wouldn t affect the KED prediction. Nevertheless, radar uncertainty is not uniform in space, and this needs to be modelled
16 Modification of KED: Regular KED: Z x = m x + Y(x) p m x = α i f i (x) i=1 Radar estimates Modified KED: Z x = m x + σ x ε(x) p m x = α i f i (x) i=1 p σ x = β j g j (x) j=1 Y x zero-mean, second order stationary process ε x zero-mean, unit variance, second order stationary process Elevation Clutter map Bright-band area
17 KED uncertainty modelling 1) Interpolation uncertainty Kriging variance 2) Rain gauge errors Kriging for uncertain data (KUD) 3) Radar residual biases Variation of KED including external drift for SD too 4) Gaussian approximation TransGaussian Kriging, etc
18 4) Gaussian approximations Kriging methods are based on the assumption of secondorder stationary Gaussian processes. Rainfall distribution is not Gaussian, it is skewed, only positive, with a discontinuity in zero. Partial solutions: TransGaussian Kriging Indicator Kriging Disjunctive Kriging Singularity analysis. Controversial Not easily adaptable to KED
19 TransGaussian kriging Rain Gauge data Radar data transformation transformation KED Back transformation (Box-Cox, normal quantiles, etc ) KED merged rainfall estimate Box-Cox Transformations: y = λ = 0.5 Square root λ = 0.25 Square root Square root λ = 0 Logarithm log x if λ = 0 x λ 1 λ if λ 0 Normal quantiles (Normal Scores): empirical transformation
20 Singularity analysis Born in fractal theories, adapted to Bayesian merging by Wang et al. 2015, Imperial College Local Singularities: structures in which the areal average varies with the considered area with a power function They are not fully captured by second-order tools, like the variogram used in kriging, and therefore cannot be maintained in kriging merging, due to the Gaussianity assumption. Radar Rain Gauges Remove singularities KED Recover Singularities Result
21 Evaluation Rain Gauge data Radar data transformation transformation 1 How effective are the transformation methods? How gaussian are the transformed variables? KED Back transformation How well are the results backtransformed? Is the original PDF reproduced? 2 KED merged rainfall estimate What is the quality and the reliability of the final rainfall product? 3 1) Kurtosis 2) Skewness 3) Approx. Negentropy R 2 in QQ-plot regression RG validation: 1) MRTE 2) BIAS 3) Hanssen Kuipers d.
22 1) Gaussianity Test
23 2) QQ-plots
24 3) Validation with Rain Gauges Box-Cox with λ = 0.1 introduces a high bias Singularity analysis not suitable for KED Merging usually improves the results Transformations are helpful, but a simple square root does already the job
25 Event on 2009/06/17 20:00 (event 2) Black-border dots: rain gauges used for merging Green-border dots: rain gauges used for validation Notice the radar artefact attenuation
26 Some work done Representing radar rainfall uncertainty with ensembles based on a time-variant geostatistical error modelling approach Submitted to the Journal of Hydrology, F. Cecinati, M. A. Rico-Ramirez, D. Han, and G. B. M. Heuvelink a ) b ) c ) Integration of rain gauge measurement errors with the overall rainfall uncertainty estimation using kriging methods Presented at EGU 2016 To be submitted soon to a journal Evaluation and correction of uncertainty due to Gaussian approximation in radar rain gauge merging using kriging with external drift Abstract submitted for AGU 2016 To be submitted soon to a journal
27 To conclude Understanding and modelling uncertainty in merged radar-rain gauge products is complex Rain gauge uncertainty: you need to know your gauges. Can you help me with the UK? Radar uncertainty: what spatially variant covariates can affect NIMROD radar uncertainty magnitude? Transformations are good, but advanced methods only introduce instability/approximations What do you think affects KED uncertainty? Thank you!!! This project has received funding from the European Union s Seventh Framework Programme for research, technological development and demonstration under grant agreement no
28 Some references Berndt, C., Rabiei, E. & Haberlandt, U., Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios. Journal of Hydrology, 508, pp Clark, I., Statistics or geostatistics? Sampling error or nugget effect? Journal of the Southern African Institute of Mining and Metallurgy, 110(6), pp Mazzetti, C. & Todini, E., Combining Weather Radar and Raingauge Data for Hydrologic Applications. In Flood Risk Management: Research and Practice. London: Taylor & Francis Group Erdin, R., Frei, C. & Künsch, H.R., Data Transformation and Uncertainty in Geostatistical Combination of Radar and Rain Gauges. Journal of Hydrometeorology, 13(1987), pp Wang, L.-P. et al., Singularity-sensitive gauge-based radar rainfall adjustment methods for urban hydrological applications. Hydrology and Earth System Sciences, 19(9), pp
Evaluation and correction of uncertainty due to Gaussian approximation in radar rain gauge merging using kriging with external drift
Evaluation and correction of uncertainty due to Gaussian approximation in radar rain gauge merging using kriging with external drift F. Cecinati* 1, O. Wani 2,3, M. A. Rico-Ramirez 1 1 University of Bristol,
More informationRepresenting radar rainfall uncertainty with ensembles based on a time-variant geostatistical error modelling approach
Representing radar rainfall uncertainty with ensembles based on a time-variant geostatistical error modelling approach Bristol, 4 th March 2016 Francesca Cecinati Background M.Eng. Environmental and Water
More informationRadars, Hydrology and Uncertainty
Radars, Hydrology and Uncertainty Francesca Cecinati University of Bristol, Department of Civil Engineering francesca.cecinati@bristol.ac.uk Supervisor: Miguel A. Rico-Ramirez Research objectives Study
More informationComparing two different methods to describe radar precipitation uncertainty
Comparing two different methods to describe radar precipitation uncertainty Francesca Cecinati, Miguel A. Rico-Ramirez, Dawei Han, University of Bristol, Department of Civil Engineering Corresponding author:
More informationRadar-raingauge data combination techniques: A revision and analysis of their suitability for urban hydrology
9th International Conference on Urban Drainage Modelling Belgrade 2012 Radar-raingauge data combination techniques: A revision and analysis of their suitability for urban hydrology L. Wang, S. Ochoa, N.
More informationEstimation of high spatial resolution precipitation fields using merged rain gauge - radar data. Antonio Manuel Moreno Ródenas
Estimation of high spatial resolution precipitation fields using merged rain gauge - radar data. 0- Index 1. 2. Methods 3. Case study 4. Results and discussion 5. Conclusions. 1.- 1.- Fuentes Methods de
More informationImproved rainfall estimates and forecasts for urban hydrological applications
Improved rainfall estimates and forecasts for urban hydrological applications Innovyze User Days - Drainage and Flooding User Group Wallingford, 20 th June 2013 Contents 1. Context 2. Radar rainfall processing
More informationInfluence of parameter estimation uncertainty in Kriging: Part 2 Test and case study applications
Hydrology and Earth System Influence Sciences, of 5(), parameter 5 3 estimation (1) uncertainty EGS in Kriging: Part Test and case study applications Influence of parameter estimation uncertainty in Kriging:
More informationGridding of precipitation and air temperature observations in Belgium. Michel Journée Royal Meteorological Institute of Belgium (RMI)
Gridding of precipitation and air temperature observations in Belgium Michel Journée Royal Meteorological Institute of Belgium (RMI) Gridding of meteorological data A variety of hydrologic, ecological,
More informationThe development of a Kriging based Gauge and Radar merged product for real-time rainfall accumulation estimation
The development of a Kriging based Gauge and Radar merged product for real-time rainfall accumulation estimation Sharon Jewell and Katie Norman Met Office, FitzRoy Road, Exeter, UK (Dated: 16th July 2014)
More informationRainfall-runoff modelling using merged rainfall from radar and raingauge measurements
Rainfall-runoff modelling using merged rainfall from radar and raingauge measurements Nergui Nanding, Miguel Angel Rico-Ramirez and Dawei Han Department of Civil Engineering, University of Bristol Queens
More informationFlood Forecasting with Radar
Flood Forecasting with Radar Miguel Angel Rico-Ramirez m.a.rico-ramirez@bristol.ac.uk Encuentro Internacional de Manejo del Riesgo por Inundaciones, UNAM, 22 th Jan 2013 Talk Outline Rainfall estimation
More informationImproving the applicability of radar rainfall estimates for urban pluvial flood modelling and forecasting
Improving the applicability of radar rainfall estimates for urban pluvial flood modelling and forecasting Paper 19 Session 6: Operational Monitoring & Control Susana Ochoa-Rodriguez 1 *, Li-Pen Wang 1,2,
More informationSub-kilometer-scale space-time stochastic rainfall simulation
Picture: Huw Alexander Ogilvie Sub-kilometer-scale space-time stochastic rainfall simulation Lionel Benoit (University of Lausanne) Gregoire Mariethoz (University of Lausanne) Denis Allard (INRA Avignon)
More informationHourly Precipitation Estimation through Rain-Gauge and Radar: CombiPrecip
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Documentation of MeteoSwiss Grid-Data Products Hourly Precipitation Estimation through Rain-Gauge and Radar:
More informationUniversity of Bristol - Explore Bristol Research
Fadhel, S., Rico-Ramirez, M., & Han, D. (2017). Exploration of an adaptive merging scheme for optimal precipitation estimation over ungauged urban catchment. Journal of Hydroinformatics, 19(2), 225-237.
More informationInvestigation of Monthly Pan Evaporation in Turkey with Geostatistical Technique
Investigation of Monthly Pan Evaporation in Turkey with Geostatistical Technique Hatice Çitakoğlu 1, Murat Çobaner 1, Tefaruk Haktanir 1, 1 Department of Civil Engineering, Erciyes University, Kayseri,
More informationON THE PROPAGATION OF RAINFALL BIAS AND SPATIAL VARIABILITY THROUGH URBAN PLUVIAL FLOOD MODELLING
ON THE PROPAGATION OF RAINFALL BIAS AND SPATIAL VARIABILITY THROGH RBAN PLVIAL FLOOD MODELLING by L. Wang (1), C. Onof (2), S. Ochoa (3), N. Simões (4) and Č. Maksimović (5) (1) Department of Civil and
More informationConcepts and Applications of Kriging. Eric Krause
Concepts and Applications of Kriging Eric Krause Sessions of note Tuesday ArcGIS Geostatistical Analyst - An Introduction 8:30-9:45 Room 14 A Concepts and Applications of Kriging 10:15-11:30 Room 15 A
More informationReport. Combination of radar and raingauge data for precipitation estimation
- Unrestricted Report Combination of radar and raingauge data for precipitation estimation Report for the NFR Energy Norway funded project "Utilisation of weather radar data in atmospheric and hydrological
More informationBayesian Transgaussian Kriging
1 Bayesian Transgaussian Kriging Hannes Müller Institut für Statistik University of Klagenfurt 9020 Austria Keywords: Kriging, Bayesian statistics AMS: 62H11,60G90 Abstract In geostatistics a widely used
More informationImprovement of quantitative precipitation estimates in Belgium
Improvement of quantitative precipitation estimates in Belgium L. Delobbe, E. Goudenhoofdt, and B. Mohymont Royal Meteorological Institute of Belgium 1. Introduction In this paper we describe the recent
More informationEffect of spatiotemporal variation of rainfall on dissolved oxygen depletion in integrated catchment studies
8 th International Conference on Sewer Processes and Networks August 31 September 2, 2016, Rotterdam, The Netherlands Effect of spatiotemporal variation of rainfall on dissolved oxygen depletion in integrated
More informationWeather radar rainfall for hydrological hazard risk management
Weather radar rainfall for hydrological hazard risk management Dawei Han 韩大卫 Department of Civil Engineering University of Bristol, UK Hydrological hazards related to rainfall Floods Droughts Debris flows
More informationA Framework for Daily Spatio-Temporal Stochastic Weather Simulation
A Framework for Daily Spatio-Temporal Stochastic Weather Simulation, Rick Katz, Balaji Rajagopalan Geophysical Statistics Project Institute for Mathematics Applied to Geosciences National Center for Atmospheric
More informationCombining radar and rain gauges rainfall estimates using conditional merging: a case study
Combining radar and rain gauges rainfall estimates using conditional merging: a case study Alberto Pettazzi, Santiago Salsón MeteoGalicia, Galician Weather Service, Xunta de Galicia, C/ Roma 6, 15707 Santiago
More informationMultivariate Geostatistics
Hans Wackernagel Multivariate Geostatistics An Introduction with Applications Third, completely revised edition with 117 Figures and 7 Tables Springer Contents 1 Introduction A From Statistics to Geostatistics
More informationMerging Rain-Gauge and Radar Data
Merging Rain-Gauge and Radar Data Dr Sharon Jewell, Obserations R&D, Met Office, FitzRoy Road, Exeter sharon.jewell@metoffice.gov.uk Outline 1. Introduction The Gauge and radar network Interpolation techniques
More informationWP2: Fine-scale rainfall data acquisition and prediction:
WP1 WP2: Fine-scale rainfall data acquisition and prediction: Objective: develop and implement a system for estimation and forecasting of fine-scale (100m, minutes) rainfall Rainfall estimation: combining
More informationConcepts and Applications of Kriging. Eric Krause Konstantin Krivoruchko
Concepts and Applications of Kriging Eric Krause Konstantin Krivoruchko Outline Introduction to interpolation Exploratory spatial data analysis (ESDA) Using the Geostatistical Wizard Validating interpolation
More information118 RECONSTRUCTION OF RADAR RFLECTIVITY IN CLUTTER AREAS
8 RECONSTRUCTION OF RADAR RFLECTIVITY IN CLUTTER AREAS Shinju Park, Marc Berenguer Centre de Recerca Aplicada en Hidrometeorologia, Universitat Politècnica de Catalunya (BarcelonaTech), Barcelona, Spain..
More informationA Comparison of Rainfall Estimation Techniques
A Comparison of Rainfall Estimation Techniques Barry F. W. Croke 1,2, Juliet K. Gilmour 2 and Lachlan T. H. Newham 2 SUMMARY: This study compares two techniques that have been developed for rainfall and
More informationBruno Sansó. Department of Applied Mathematics and Statistics University of California Santa Cruz bruno
Bruno Sansó Department of Applied Mathematics and Statistics University of California Santa Cruz http://www.ams.ucsc.edu/ bruno Climate Models Climate Models use the equations of motion to simulate changes
More informationAreal rainfall estimation using moving cars computer experiments including hydrological modeling
Hydrol. Earth Syst. Sci. Discuss., doi:.194/hess-16-17, 16 Areal rainfall estimation using moving cars computer experiments including hydrological modeling Ehsan Rabiei 1, Uwe Haberlandt 1, Monika Sester
More informationProgress in Operational Quantitative Precipitation Estimation in the Czech Republic
Progress in Operational Quantitative Precipitation Estimation in the Czech Republic Petr Novák 1 and Hana Kyznarová 1 1 Czech Hydrometeorological Institute,Na Sabatce 17, 143 06 Praha, Czech Republic (Dated:
More informationSurface Hydrology Research Group Università degli Studi di Cagliari
Surface Hydrology Research Group Università degli Studi di Cagliari Evaluation of Input Uncertainty in Nested Flood Forecasts: Coupling a Multifractal Precipitation Downscaling Model and a Fully-Distributed
More informationGeostatistics for Seismic Data Integration in Earth Models
2003 Distinguished Instructor Short Course Distinguished Instructor Series, No. 6 sponsored by the Society of Exploration Geophysicists European Association of Geoscientists & Engineers SUB Gottingen 7
More informationComparison of rainfall distribution method
Team 6 Comparison of rainfall distribution method In this section different methods of rainfall distribution are compared. METEO-France is the French meteorological agency, a public administrative institution
More informationSensor networks and urban pluvial flood modelling in an urban catchment
Environmental virtual observatories: managing catchments with wellies, sensors and smartphones Sensor networks and urban pluvial flood modelling in an urban catchment 28 th February 2013 Contents 1. Context
More informationCombining Satellite And Gauge Precipitation Data With Co-Kriging Method For Jakarta Region
City University of New York (CUNY) CUNY Academic Works International Conference on Hydroinformatics 8-1-214 Combining Satellite And Gauge Precipitation Data With Co-Kriging Method For Jakarta Region Chi
More informationSatellite and gauge rainfall merging using geographically weighted regression
132 Remote Sensing and GIS for Hydrology and Water Resources (IAHS Publ. 368, 2015) (Proceedings RSHS14 and ICGRHWE14, Guangzhou, China, August 2014). Satellite and gauge rainfall merging using geographically
More informationProbabilistic forecasting for urban water management: A case study
9th International Conference on Urban Drainage Modelling Belgrade 212 Probabilistic forecasting for urban water management: A case study Jeanne-Rose Rene' 1, Henrik Madsen 2, Ole Mark 3 1 DHI, Denmark,
More informationStochastic disaggregation of spatial-temporal rainfall with limited data
XXVI General Assembly of the European Geophysical Society Nice, France, 25-3 March 2 Session NP5.3/ Scaling, multifractals and nonlinearity in geophysics: Stochastic rainfall modelling: scaling and other
More informationHeavier summer downpours with climate change revealed by weather forecast resolution model
SUPPLEMENTARY INFORMATION DOI: 10.1038/NCLIMATE2258 Heavier summer downpours with climate change revealed by weather forecast resolution model Number of files = 1 File #1 filename: kendon14supp.pdf File
More informationImproved radar QPE with temporal interpolation using an advection scheme
Improved radar QPE with temporal interpolation using an advection scheme Alrun Jasper-Tönnies 1 and Markus Jessen 1 1 hydro & meteo GmbH & Co, KG, Breite Str. 6-8, 23552 Lübeck, Germany (Dated: 18 July
More informationMultivariate autoregressive modelling and conditional simulation of precipitation time series for urban water models
European Water 57: 299-306, 2017. 2017 E.W. Publications Multivariate autoregressive modelling and conditional simulation of precipitation time series for urban water models J.A. Torres-Matallana 1,3*,
More informationLecture 9: Introduction to Kriging
Lecture 9: Introduction to Kriging Math 586 Beginning remarks Kriging is a commonly used method of interpolation (prediction) for spatial data. The data are a set of observations of some variable(s) of
More informationREQUIREMENTS FOR WEATHER RADAR DATA. Review of the current and likely future hydrological requirements for Weather Radar data
WORLD METEOROLOGICAL ORGANIZATION COMMISSION FOR BASIC SYSTEMS OPEN PROGRAMME AREA GROUP ON INTEGRATED OBSERVING SYSTEMS WORKSHOP ON RADAR DATA EXCHANGE EXETER, UK, 24-26 APRIL 2013 CBS/OPAG-IOS/WxR_EXCHANGE/2.3
More informationReal-time radar rain-gauge merging using spatio-temporal co-kriging with external drift in the alpine terrain of Switzerland
Quarterly Journalof the Royal Meteorological Society Q. J. R. Meteorol. Soc. 14: 197 1111, April 214 DOI:1.12/qj.2188 Real-time radar rain-gauge merging using spatio-temporal co-kriging with external drift
More informationKriging Luc Anselin, All Rights Reserved
Kriging Luc Anselin Spatial Analysis Laboratory Dept. Agricultural and Consumer Economics University of Illinois, Urbana-Champaign http://sal.agecon.uiuc.edu Outline Principles Kriging Models Spatial Interpolation
More informationAUTOMATIC ESTIMATION OF RAINFALL FIELDS FOR HYDROLOGICAL APPLICATIONS: BLENDING RADAR AND RAIN GAUGE DATA IN REAL TIME
P13R.12 AUTOMATIC ESTIMATION OF RAINFALL FIELDS FOR HYDROLOGICAL APPLICATIONS: BLENDING RADAR AND RAIN GAUGE DATA IN REAL TIME Carlos Velasco-Forero 1,*, Daniel Sempere-Torres 1, Rafael Sánchez-Diezma
More informationApplication of Radar QPE. Jack McKee December 3, 2014
Application of Radar QPE Jack McKee December 3, 2014 Topics Context Precipitation Estimation Techniques Study Methodology Preliminary Results Future Work Questions Introduction Accurate precipitation data
More informationAreal rainfall estimation using moving cars computer experiments including hydrological modeling
doi:10.5194/hess-20-3907-2016 Author(s) 2016. CC Attribution 3.0 License. Areal rainfall estimation using moving cars computer experiments including hydrological modeling Ehsan Rabiei 1, Uwe Haberlandt
More informationSTATISTICAL MODELS FOR QUANTIFYING THE SPATIAL DISTRIBUTION OF SEASONALLY DERIVED OZONE STANDARDS
STATISTICAL MODELS FOR QUANTIFYING THE SPATIAL DISTRIBUTION OF SEASONALLY DERIVED OZONE STANDARDS Eric Gilleland Douglas Nychka Geophysical Statistics Project National Center for Atmospheric Research Supported
More informationA STUDY OF MEAN AREAL PRECIPITATION AND SPATIAL STRUCTURE OF RAINFALL DISTRIBUTION IN THE TSEN-WEN RIVER BASIN
Journal of the Chinese Institute of Engineers, Vol. 24, No. 5, pp. 649-658 (2001) 649 A STUDY OF MEAN AREAL PRECIPITATION AND SPATIAL STRUCTURE OF RAINFALL DISTRIBUTION IN THE TSEN-WEN RIVER BASIN Jet-Chau
More information7 Geostatistics. Figure 7.1 Focus of geostatistics
7 Geostatistics 7.1 Introduction Geostatistics is the part of statistics that is concerned with geo-referenced data, i.e. data that are linked to spatial coordinates. To describe the spatial variation
More informationState-space calibration of radar rainfall and stochastic flow forecasting for use in real-time Control of urban drainage systems
State-space calibration of radar rainfall and stochastic flow forecasting for use in real-time Control of urban drainage systems 9th Int. Conf. On Urban Drainage Modeling Roland Löwe *, Peter Steen Mikkelsen,
More informationHEPS. #HEPEX Quebec 2016 UPGRADED METEOROLOGICAL FORCING FOR OPERATIONAL HYDROLOGICAL ENSEMBLE PREDICTIONS: CHALLENGES, RISKS AND CHANCES
Zappa M, Andres N, Bogner K, Liechti K #HEPEX Quebec 2016 UPGRADED METEOROLOGICAL FORCING FOR OPERATIONAL HYDROLOGICAL ENSEMBLE PREDICTIONS: CHALLENGES, RISKS AND CHANCES HEPS Contact: massimiliano.zappa@wsl.ch
More informationCIE4491 Lecture. How to quantify stormwater flow urban rainfall data and resolution Marie-claire ten Veldhuis
CIE4491 Lecture. How to quantify stormwater flow urban rainfall data and resolution Marie-claire ten Veldhuis 18-9-2013 Delft University of Technology Challenge the future Coping with heavy rainfall -
More informationModeling of Atmospheric Effects on InSAR Measurements With the Method of Stochastic Simulation
Modeling of Atmospheric Effects on InSAR Measurements With the Method of Stochastic Simulation Z. W. LI, X. L. DING Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hung
More informationQuantification Of Rainfall Forecast Uncertainty And Its Impact On Flood Forecasting
City University of New York (CUNY) CUNY Academic Works International Conference on Hydroinformatics 8-1-2014 Quantification Of Rainfall Forecast Uncertainty And Its Impact On Flood Forecasting Niels Van
More informationSpatio-temporal precipitation modeling based on time-varying regressions
Spatio-temporal precipitation modeling based on time-varying regressions Oleg Makhnin Department of Mathematics New Mexico Tech Socorro, NM 87801 January 19, 2007 1 Abstract: A time-varying regression
More informationA RADAR-BASED CLIMATOLOGY OF HIGH PRECIPITATION EVENTS IN THE EUROPEAN ALPS:
2.6 A RADAR-BASED CLIMATOLOGY OF HIGH PRECIPITATION EVENTS IN THE EUROPEAN ALPS: 2000-2007 James V. Rudolph*, K. Friedrich, Department of Atmospheric and Oceanic Sciences, University of Colorado at Boulder,
More informationBayesian SAE using Complex Survey Data Lecture 4A: Hierarchical Spatial Bayes Modeling
Bayesian SAE using Complex Survey Data Lecture 4A: Hierarchical Spatial Bayes Modeling Jon Wakefield Departments of Statistics and Biostatistics University of Washington 1 / 37 Lecture Content Motivation
More informationIntroduction. Semivariogram Cloud
Introduction Data: set of n attribute measurements {z(s i ), i = 1,, n}, available at n sample locations {s i, i = 1,, n} Objectives: Slide 1 quantify spatial auto-correlation, or attribute dissimilarity
More informationThe Australian Operational Daily Rain Gauge Analysis
The Australian Operational Daily Rain Gauge Analysis Beth Ebert and Gary Weymouth Bureau of Meteorology Research Centre, Melbourne, Australia e.ebert@bom.gov.au Daily rainfall data and analysis procedure
More informationGaussian Process Approximations of Stochastic Differential Equations
Gaussian Process Approximations of Stochastic Differential Equations Cédric Archambeau Centre for Computational Statistics and Machine Learning University College London c.archambeau@cs.ucl.ac.uk CSML
More informationEstimating the long-term health impact of air pollution using spatial ecological studies. Duncan Lee
Estimating the long-term health impact of air pollution using spatial ecological studies Duncan Lee EPSRC and RSS workshop 12th September 2014 Acknowledgements This is joint work with Alastair Rushworth
More informationANALYSIS OF DEPTH-AREA-DURATION CURVES OF RAINFALL IN SEMIARID AND ARID REGIONS USING GEOSTATISTICAL METHODS: SIRJAN KAFEH NAMAK WATERSHED, IRAN
JOURNAL OF ENVIRONMENTAL HYDROLOGY The Electronic Journal of the International Association for Environmental Hydrology On the World Wide Web at http://www.hydroweb.com VOLUME 14 2006 ANALYSIS OF DEPTH-AREA-DURATION
More informationConcepts and Applications of Kriging
Esri International User Conference San Diego, California Technical Workshops July 24, 2012 Concepts and Applications of Kriging Konstantin Krivoruchko Eric Krause Outline Intro to interpolation Exploratory
More informationProbabilistic temperature post-processing using a skewed response distribution
Probabilistic temperature post-processing using a skewed response distribution Manuel Gebetsberger 1, Georg J. Mayr 1, Reto Stauffer 2, Achim Zeileis 2 1 Institute of Atmospheric and Cryospheric Sciences,
More informationStatistícal Methods for Spatial Data Analysis
Texts in Statistícal Science Statistícal Methods for Spatial Data Analysis V- Oliver Schabenberger Carol A. Gotway PCT CHAPMAN & K Contents Preface xv 1 Introduction 1 1.1 The Need for Spatial Analysis
More informationPropagation of Errors in Spatial Analysis
Stephen F. Austin State University SFA ScholarWorks Faculty Presentations Spatial Science 2001 Propagation of Errors in Spatial Analysis Peter P. Siska I-Kuai Hung Arthur Temple College of Forestry and
More informationGridded monthly temperature fields for Croatia for the period
Gridded monthly temperature fields for Croatia for the 1981 2010 period comparison with the similar global and European products Melita Perčec Tadid melita.percec.tadic@cirus.dhz.hr Meteorological and
More informationIs there still room for new developments in geostatistics?
Is there still room for new developments in geostatistics? Jean-Paul Chilès MINES ParisTech, Fontainebleau, France, IAMG 34th IGC, Brisbane, 8 August 2012 Matheron: books and monographs 1962-1963: Treatise
More informationAcceptable Ergodic Fluctuations and Simulation of Skewed Distributions
Acceptable Ergodic Fluctuations and Simulation of Skewed Distributions Oy Leuangthong, Jason McLennan and Clayton V. Deutsch Centre for Computational Geostatistics Department of Civil & Environmental Engineering
More informationResults of Intensity-Duration- Frequency Analysis for Precipitation and Runoff under Changing Climate
Results of Intensity-Duration- Frequency Analysis for Precipitation and Runoff under Changing Climate Supporting Casco Bay Region Climate Change Adaptation RRAP Eugene Yan, Alissa Jared, Julia Pierce,
More informationStorm rainfall. Lecture content. 1 Analysis of storm rainfall 2 Predictive model of storm rainfall for a given
Storm rainfall Lecture content 1 Analysis of storm rainfall 2 Predictive model of storm rainfall for a given max rainfall depth 1 rainfall duration and return period à Depth-Duration-Frequency curves 2
More informationQPE and QPF in the Bureau of Meteorology
QPE and QPF in the Bureau of Meteorology Current and future real-time rainfall products Carlos Velasco (BoM) Alan Seed (BoM) and Luigi Renzullo (CSIRO) OzEWEX 2016, 14-15 December 2016, Canberra Why do
More informationRadar precipitation measurement in the Alps big improvements triggered by MAP
Radar precipitation measurement in the Alps big improvements triggered by MAP Urs Germann, Gianmario Galli, Marco Boscacci MeteoSwiss, Locarno-Monti MeteoSwiss radar Monte Lema, 1625m Can we measure precipitation
More informationChapter 4 - Fundamentals of spatial processes Lecture notes
TK4150 - Intro 1 Chapter 4 - Fundamentals of spatial processes Lecture notes Odd Kolbjørnsen and Geir Storvik January 30, 2017 STK4150 - Intro 2 Spatial processes Typically correlation between nearby sites
More information4th HR-HU and 15th HU geomathematical congress Geomathematics as Geoscience Reliability enhancement of groundwater estimations
Reliability enhancement of groundwater estimations Zoltán Zsolt Fehér 1,2, János Rakonczai 1, 1 Institute of Geoscience, University of Szeged, H-6722 Szeged, Hungary, 2 e-mail: zzfeher@geo.u-szeged.hu
More information3. Estimating Dry-Day Probability for Areal Rainfall
Chapter 3 3. Estimating Dry-Day Probability for Areal Rainfall Contents 3.1. Introduction... 52 3.2. Study Regions and Station Data... 54 3.3. Development of Methodology... 60 3.3.1. Selection of Wet-Day/Dry-Day
More informationGeostatistics in Hydrology: Kriging interpolation
Chapter Geostatistics in Hydrology: Kriging interpolation Hydrologic properties, such as rainfall, aquifer characteristics (porosity, hydraulic conductivity, transmissivity, storage coefficient, etc.),
More informationConcepts and Applications of Kriging
2013 Esri International User Conference July 8 12, 2013 San Diego, California Technical Workshop Concepts and Applications of Kriging Eric Krause Konstantin Krivoruchko Outline Intro to interpolation Exploratory
More informationEnabling Climate Information Services for Europe
Enabling Climate Information Services for Europe Report DELIVERABLE 6.5 Report on past and future stream flow estimates coupled to dam flow evaluation and hydropower production potential Activity: Activity
More informationBasics in Geostatistics 2 Geostatistical interpolation/estimation: Kriging methods. Hans Wackernagel. MINES ParisTech.
Basics in Geostatistics 2 Geostatistical interpolation/estimation: Kriging methods Hans Wackernagel MINES ParisTech NERSC April 2013 http://hans.wackernagel.free.fr Basic concepts Geostatistics Hans Wackernagel
More informationKriging method for estimation of groundwater resources in a basin with scarce monitoring data
36 New Approaches to Hydrological Prediction in Data-sparse Regions (Proc. of Symposium HS. at the Joint IAHS & IAH Convention, Hyderabad, India, September 9). IAHS Publ. 333, 9. Kriging method for estimation
More informationUsing linear and non-linear kriging interpolators to produce probability maps
To be presented at 2001 Annual Conference of the International Association for Mathematical Geology, Cancun, Mexico, September, IAMG2001. Using linear and non-linear kriging interpolators to produce probability
More informationEvaluation of radar-gauge merging methods for quantitative precipitation estimates
Hydrol. Earth Syst. Sci., 3, 95 3, 9 www.hydrol-earth-syst-sci.net/3/95/9/ Author(s) 9. This work is distributed under the Creative Commons Attribution 3. License. Hydrology and Earth System Sciences Evaluation
More informationComparison of Interpolation Methods for Precipitation Data in a mountainous Region (Upper Indus Basin-UIB)
Comparison of Interpolation Methods for Precipitation Data in a mountainous Region (Upper Indus Basin-UIB) AsimJahangir Khan, Doctoral Candidate, Department of Geohydraulicsand Engineering Hydrology, University
More informationIndex. Geostatistics for Environmental Scientists, 2nd Edition R. Webster and M. A. Oliver 2007 John Wiley & Sons, Ltd. ISBN:
Index Akaike information criterion (AIC) 105, 290 analysis of variance 35, 44, 127 132 angular transformation 22 anisotropy 59, 99 affine or geometric 59, 100 101 anisotropy ratio 101 exploring and displaying
More informationCombining Rain Gauge and Radar Measurements of a Heavy Precipitation Event over Switzerland
Veröffentlichung MeteoSchweiz Nr. 81 Combining Rain Gauge and Radar Measurements of a Heavy Precipitation Event over Switzerland Comparison of Geostatistical Methods and Investigation of Important Influencing
More informationArcGIS for Geostatistical Analyst: An Introduction. Steve Lynch and Eric Krause Redlands, CA.
ArcGIS for Geostatistical Analyst: An Introduction Steve Lynch and Eric Krause Redlands, CA. Outline - What is geostatistics? - What is Geostatistical Analyst? - Spatial autocorrelation - Geostatistical
More informationInfluence of rainfall space-time variability over the Ouémé basin in Benin
102 Remote Sensing and GIS for Hydrology and Water Resources (IAHS Publ. 368, 2015) (Proceedings RSHS14 and ICGRHWE14, Guangzhou, China, August 2014). Influence of rainfall space-time variability over
More informationPRODUCING PROBABILITY MAPS TO ASSESS RISK OF EXCEEDING CRITICAL THRESHOLD VALUE OF SOIL EC USING GEOSTATISTICAL APPROACH
PRODUCING PROBABILITY MAPS TO ASSESS RISK OF EXCEEDING CRITICAL THRESHOLD VALUE OF SOIL EC USING GEOSTATISTICAL APPROACH SURESH TRIPATHI Geostatistical Society of India Assumptions and Geostatistical Variogram
More informationEmpirical Bayesian Kriging
Empirical Bayesian Kriging Implemented in ArcGIS Geostatistical Analyst By Konstantin Krivoruchko, Senior Research Associate, Software Development Team, Esri Obtaining reliable environmental measurements
More informationInflow Forecasting for Hydropower Operations: Bayesian Model Averaging for Postprocessing Hydrological Ensembles
Inflow Forecasting for Hydropower Operations: Bayesian Model Averaging for Postprocessing Hydrological Ensembles Andreas Kleiven, Ingelin Steinsland Norwegian University of Science & Technology Dept. of
More informationSpatial statistics, addition to Part I. Parameter estimation and kriging for Gaussian random fields
Spatial statistics, addition to Part I. Parameter estimation and kriging for Gaussian random fields 1 Introduction Jo Eidsvik Department of Mathematical Sciences, NTNU, Norway. (joeid@math.ntnu.no) February
More informationGeostatistics: Kriging
Geostatistics: Kriging 8.10.2015 Konetekniikka 1, Otakaari 4, 150 10-12 Rangsima Sunila, D.Sc. Background What is Geostatitics Concepts Variogram: experimental, theoretical Anisotropy, Isotropy Lag, Sill,
More information