Spatial rainfall distribution in flood modelling Adam Baylis Research Scientist 21 September 2015.

Slides:



Advertisements
Similar presentations
Be a weather presenter Activity 4. Extreme weather images Cyclone Yasi – 3 February 2011 Category 5 cyclone Source: BOM.
Advertisements

Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, september 2008.
Space …. are big. Really big. You just won't believe how vastly, hugely, mindbogglingly big they are. Massive data streams Douglas Adams – Hitchhiker’s.
AVHRR Data for Monitoring Drought, Environment and Socioeconomic Activities Felix Kogan NOAA/NESDIS Office of Satellite Research and Applications.
Accelerating Spatially Varying Gaussian Filters Jongmin Baek and David E. Jacobs Stanford University.
Very-Short-Range Forecast of Precipitation in Japan World Weather Research Program Symposium on Nowcasting and Very Short Range Forecasting Toulouse France,
Influence of raingauge network characteristics on hydrological response at catchment scale 4 th International Workshop on Hydrological Extremes AMHY-FRIEND.
Grant: EP/FP202511/1 Advances in Flood Risk Management Science - Improved short term rainfall and urban flood prediction.
Monika Sester Institute of Cartography and Geoinformatics Leibniz Universität Hannover Germany Collaborative data acquisition and processing.
I) Methods of interpolation II) Cloud tracking III) Future Work : radar images & Flood modelling Maureen Coat ENSTA Paristech.
Floodplain Mapping using HEC-RAS and ArcView GIS Eric Tate Francisco Olivera David Maidment
HyDis: Hydrologic Data and Information System Prof. Sudha Ram Dongwon Lee Yousab Hwang Vijay Khatri Department of Management Information Systems Sponsor:
Modeling spatially-correlated sensor network data Apoorva Jindal, Konstantinos Psounis Department of Electrical Engineering-Systems University of Southern.
Differences in Model Transport of CO2. Cloud Contamination ✦ Radar indicates precipitation along fronts ✦ Coincidentally, this is where much of interesting.
Ensemble Post-Processing and it’s Potential Benefits for the Operational Forecaster Michael Erickson and Brian A. Colle School of Marine and Atmospheric.
Detectors point count detectors linear position-sensitive detectors area detectors energy dispersive detectors point count detectors linear position-sensitive.
Data mining in large spatiotemporal data sets Dr Amy McGovern Associate Professor, School of Computer Science Adjunct Associate Professor,
WORKSHOP ON PRECIPITATION MEASUREMENTS MADRID SEPTEMBER 2002 AN INTERCOMPARISON OF GAUGE, RADAR AND SATELLITE RAINFALL IN THE TROPICS A. J. Pereira.
Flood Risk Modeling in Thailand … and more … Alexander Lotsch Commodity Risk Management Group Agriculture and Rural Development The World Bank Group.
CARPE DIEM Centre for Water Resources Research NUID-UCD Contribution to Area-3 Dusseldorf meeting 26th to 28th May 2003.
Ethiopia Improving Availability, Access and Use of Climate Information in Ethiopia Tufa Dinku 1, Kinfe Hilemariam 2, David Grimes 3, and Stephen Connor.
Weather: Lesson 26 Precipitation Expectations: 1. Listen carefully 2. Work hard 3. Be responsible.
Integration of Multiple Precipitation Estimates for Flash Flood Forecasting Reggina Cabrera NOAA/National Weather Service.
The Hydrometeorology Testbed Network. 2 An AR-focused long-term observing network is being installed in CA as part of a MOA between CA-DWR, NOAA and Scripps.
Wayne Faas Chief, NOAA National Climatic Data Center Data Operations Division December 3, 2003.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss High-resolution data assimilation in COSMO: Status and.
CARPE-DIEM 13/6/02, slide 1German Aerospace Center Microwaves and Radar Institute CARPE-DIEM Besprechung Helsinki, June 2004 Ewan.
Disadvantages of solar energy
Recent Advances in Climate Extremes Science AVOID 2 FCO-Roshydromet workshop, Moscow, 19 th March 2015 Simon Brown, Met Office Hadley Centre.
hydro-meteorological network
The NOAA Hydrology Program and its requirements for GOES-R Pedro J. Restrepo Senior Scientist Office of Hydrologic Development NOAA’s National Weather.
Alvarado radar loop as received from Mexico. This is the radar reflectivity animation covering the period September 16, Z to September 17, 2010.
REVIEW OF SPATIAL STOCHASTIC MODELS FOR RAINFALL Andrew Metcalfe School of Mathematical Sciences University of Adelaide.
Introduction. Spatial sampling. Spatial interpolation. Spatial autocorrelation Measure.
Page 1© Crown copyright 2004 Meteorological Inputs Groundwater Workshop, Birmingham Murray Dale, 4/11/04.
LMRFC March, 2009 Calibration at Finer Time and Space Scales.
Page 1© Crown copyright 2004 The use of an intensity-scale technique for assessing operational mesoscale precipitation forecasts Marion Mittermaier and.
Barcelona Toward an error model for radar quantitative precipitation estimation in the Cévennes- Vivarais region, France Pierre-Emmanuel Kirstetter, Guy.
Cost Modeling of Spatial Query Operators Using Nonparametric Regression Songtao Jiang Department of Computer Science University of Vermont October 10,
JMA Japan Meteorological Agency QPE/QPF of JMA Application of Radar Data Masashi KUNITSUGU Head, National Typhoon Center Japan Meteorological Agency TYPHOON.
Sarah Callaghan British Atmospheric Data Centre, UK, The effects of climate change on rain The consensus in the climate change.
2 Challenge reach its 20% energy reduction goal by to help a mid-sized city in the United States Increase energy efficiency of 150 public buildings.
WEEK 1 E. FACHE, A. GANGOTRA, K. MAHFOUD, A. MARTYSZUNIS, I. MIRALLES, G. ROSAT, S. SCHROERS, A. TILLOY 19. February 2016.
Team  Spatially distributed deterministic models  Many hydrological phenomena vary spatially and temporally in accordance with the conservation.
Kvalobs QC2d3: spatial control (at the observation time resolution) 1.
Climate Smart Agricultural Development in the Goulburn Broken A regional climate change adaptation partnership project.
HydroNET and Real Time Radar Rainfall Data
Climate Smart Agricultural Development in the Goulburn Broken
Database management system Data analytics system:
Volume: The measurement of the amount of space a object occupies.
Prof. Čedo Maksimović, Dr. Christian Onof, Li-Pen Wang
Thirty-year Time Series of Merged Raingauge-Satellite Rainfall Data over Ethiopia Tufa Dinku1, Stephen Connor1, David Grimes2, Kinfe Hailemariam3, Ross.
Meng Lu and Edzer Pebesma
BACK SOLUTION:
Future Year Scheduling
Challenge: High resolution models need high resolution observations
Climate Smart Agricultural Development in the Goulburn Broken
Predicting the Weather
QQ Plot Quantile to Quantile Plot Quantile: QQ Plot:
Summary: Lateral Resolution
Modeling Hydrological Processes
Hydrologically Relevant Error Metrics for PEHRPP
788.11J Presentation “Atmospheric Observatory”
POWER CHALLENGES Several Ways To Solve 7 CHALLENGES.
ECE 299 Holography and Coherent Imaging
C.2.10 Sample Questions.
C.2.8 Sample Questions.
Predicting the Weather
C.2.8 Sample Questions.
Predicting the Weather
Presentation transcript:

Spatial rainfall distribution in flood modelling Adam Baylis Research Scientist 21 September 2015

11 Our challenge Benefits of a more complicated approach Minimal increase in modelling costs

12 Possible solutions Improved screening tools Automate parts of the process Important samples / re-sampling

13 Bonus challenge synthetic spatial rainfall Weather radar and raingauges available Varying quality Varying temporal resolution Varying length of record Irregularly spaced

Feedback: Privacy Policy Feedback
Do Not Sell My Personal Information
About project: SlidePlayer Terms of Service

© 2025 SlidePlayer.com. Inc. All rights reserved.