Variational Doppler Radar Assimilation System (VDRAS) for Ramp Prediction
|
|
|
- Thomasine Perry
- 7 years ago
- Views:
Transcription
1 Variational Doppler Radar Assimilation System (VDRAS) for Ramp Prediction Juanzhen (Jenny) Sun MMM/RAL, NCAR May 12, 2010
2 Outline Challenge of ramp forecast VDRAS - background and technique Real-time low-level frequent wind analysis 0-6 hour convective forecast initialized by high-resolution data Summary May 12, 2010
3 Challenge of ramp prediction Sudden wind change associated with thunderstorms Current operational NWP has difficulty in forecasting the pattern and timing of storms Require high resolution data (radar, lidar, mesonet, ) and advanced data assimilation techniques May 12, 2010
4 Current Skill in Rainfall Prediction Critical Success Index (CSI) Accuracy of Rainfall Nowcasts NWP >1 mm/h GRID MESH 20 km Jun-Oct 2002 Courtesy of Shingo Yamada JMA Forecast Length, hours
5 General description of VDRAS VDRAS is an advanced data assimilation system for high-resolution (1-3 km) and rapid updated (12 min) wind analysis The main sources of data are radar radial velocity, reflectivity, and highfrequency surface obs. The core is a 4-dimensional data assimilation scheme based on a cloudscale model Nowcasting can be produced by the cloud model or providing initial conditions for WRF VDRAS has been installed at more than 20 sites for various applications May 12, 2010
6 History of VDRAS Development milestones 1991: First version of VDRAS developed and successfully applied to simulated radar data (Sun et al 1991) 1997: Extended to a full troposphere cloud model (Sun and Crook 1997,1998) 2001: Applied to lidar data for convective boundary layer analysis (VLAS) 2005: Added the capability to cover multiple radars (Sun and Ying 2007) 2007: Coupling with mesoscale models (mm5 or WRF) 2008: Began to explore how to use VDRAS analysis to initialize WRF May 12, 2010
7 History of VDRAS cont Real-time installations 1998: Implemented at Sterling, NWS 2000: Installed at Sydney, Australia for the Olympics : Field Demonstration for FAA aviation weather program Beijing 2008 Olympics 2003-now: Run at various mission agencies ATEC Dugway Utah : Real-time demonstration for Beijing Olympics 2008 Currently: NWS at Melbourne, Florida NWS at Dallas, Texas ATEC at Dugway, Utah Beijing, China Taipei, Taiwan May 12, 2010
8 Major components of VDRAS Data Ingest Rawinsondes Mesoscale model Profilers Mesonet Doppler radars/lidars Data Preprocessing Quality control Interpolation Background analysis First Guess Output &Display (CIDD) Plots and images Animations Diagnostics and statistics 4DVAR Assimilation Cloud-scale model Adjoint model Cost function Weighting specification Minimization May 12, 2010
9 How VDRAS analysis is produced with time 0 min 4DVar 12 min 18 min 6-min Forward Integration 4DVar 30 min time KVNX KDDC KICT KTLX Cold start Mesoscale analysis as first guess 6-min Forecast as first guess; Mesoscale analysis Output of u,v,w,t,qv,qc,qr Output of u,v,w,t,qv,qc,qr Model data Sounding VAD profile Surface obs. Model data Sounding VAD profile Surface obs.
10 VDRAS analysis by assimilating 8 NEXRADs Radar radial velocities over IHOP domain Analyzed temperature Red contour: 25 dbz ref.
11 High-resolution data assimilation reveals how low-level wind evolves with cold pools UTC UTC; every 24 min Pert. Temp. (color) Wind vector at km (black arrow) Red contour (25 dbz observed reflectivity) QuickTime and a BMP decompressor are needed to see this picture. 4DVar analyses with radar data assimilation via VDRAS
12 VDRAS wind analysis over complex terrain in Taiwan Red contours: observed 25 & 35 dbz reflectivity black contour: 100 meter terrain line QuickTime and a BMP decompressor are needed to see this picture. SPol
13 VDRAS Verification from previous studies Dual-Doppler verification ACARS (Sun and Crook 2000) Cpol Dual-Doppler (Crook and Sun 2004) Kurn ell Research aircraft (Sun and Crook 1998) Profiler, AWS (Sun et al. 2010) rms(u dual u vdras ) = 1.4 m/s rms(v dual v vdras ) = 0.8 m/s May 12, 2010
14 VDRAS verification for Olympics 2008 FDP VDRAS cold pool compared with AWS
15 0-6 hour forecast is very sensitive to initial conditions No radar Physics Initial conditions With radar
16 2-hour forecast of a gust front using VDRAS cloud model Advantage: Balanced initial Conditions QuickTime and a Cinepak decompressor are needed to see this picture. Disadvantage: The model is Relatively simple
17 Inserting VDRAS analysis into WRF inner domain Interpolated fields of VDRAS analysis (MESO) to RTFDDA_d02 U-wind at the 1 st level Without (left) and with (right) weighting on RTFDDA 19 UTC 15 June 2002 VDRAS RTFDDA_d02
18 2-hour WRF forecast No VDRAS OBS With VDRAS
19 5-hour WRF forecast No VDRAS OBS With VDRAS
20 WRF Radar data assimilation Front Range Radar data assimilation testbed through NCAR s Short Term Explicit Prediction (STEP) program WRFDA 3DVAR + DDFI WRFDA 3DVAR + RTFDDA WRFDA EnKF HRRR with second-pass DDFI Obs. No radar With radar
21 Summary Accurate nowcasting of thunderstorms and the associated sudden wind change is critical for ramp prediction VDRAS produces robust and accurate high temporal and spatial resolution analysis of wind by combining radar, surface, and mesoscale model data and has a good potential for application of ramp prediction High-resolution data assimilation systems that aim at the improvement of 0-6 wind and precipitation forecasts are being actively developed and tested at NCAR May 12, 2010
IMPACT OF SAINT LOUIS UNIVERSITY-AMERENUE QUANTUM WEATHER PROJECT MESONET DATA ON WRF-ARW FORECASTS
IMPACT OF SAINT LOUIS UNIVERSITY-AMERENUE QUANTUM WEATHER PROJECT MESONET DATA ON WRF-ARW FORECASTS M. J. Mueller, R. W. Pasken, W. Dannevik, T. P. Eichler Saint Louis University Department of Earth and
Developing Continuous SCM/CRM Forcing Using NWP Products Constrained by ARM Observations
Developing Continuous SCM/CRM Forcing Using NWP Products Constrained by ARM Observations S. C. Xie, R. T. Cederwall, and J. J. Yio Lawrence Livermore National Laboratory Livermore, California M. H. Zhang
Weather Radar Basics
Weather Radar Basics RADAR: Radio Detection And Ranging Developed during World War II as a method to detect the presence of ships and aircraft (the military considered weather targets as noise) Since WW
Weather Help - NEXRAD Radar Maps. Base Reflectivity
Weather Help - NEXRAD Radar Maps Base Reflectivity Base Reflectivity Severe Thunderstorm/Torna do Watch Areas 16 levels depicted with colors from dark green (very light) to red (extreme) that indicate
Project Title: Quantifying Uncertainties of High-Resolution WRF Modeling on Downslope Wind Forecasts in the Las Vegas Valley
University: Florida Institute of Technology Name of University Researcher Preparing Report: Sen Chiao NWS Office: Las Vegas Name of NWS Researcher Preparing Report: Stanley Czyzyk Type of Project (Partners
IBM Big Green Innovations Environmental R&D and Services
IBM Big Green Innovations Environmental R&D and Services Smart Weather Modelling Local Area Precision Forecasting for Weather-Sensitive Business Operations (e.g. Smart Grids) Lloyd A. Treinish Project
Center for Analysis and Prediction of Storms, and School of Meteorology, University of Oklahoma, Norman, Oklahoma KEITH BREWSTER
FEBRUARY 2006 H U E T A L. 675 3DVAR and Cloud Analysis with WSR-88D Level-II Data for the Prediction of the Fort Worth, Texas, Tornadic Thunderstorms. Part I: Cloud Analysis and Its Impact MING HU AND
Developing sub-domain verification methods based on Geographic Information System (GIS) tools
APPROVED FOR PUBLIC RELEASE: DISTRIBUTION UNLIMITED U.S. Army Research, Development and Engineering Command Developing sub-domain verification methods based on Geographic Information System (GIS) tools
A Real Case Study of Using Cloud Analysis in Grid-point Statistical Interpolation Analysis and Advanced Research WRF Forecast System
A Real Case Study of Using Cloud Analysis in Grid-point Statistical Interpolation Analysis and Advanced Research WRF Forecast System Ming Hu 1 and Ming Xue 1, 1 Center for Analysis and Prediction of Storms,
Mode-S Enhanced Surveillance derived observations from multiple Air Traffic Control Radars and the impact in hourly HIRLAM
Mode-S Enhanced Surveillance derived observations from multiple Air Traffic Control Radars and the impact in hourly HIRLAM 1 Introduction Upper air wind is one of the most important parameters to obtain
3.5 THREE-DIMENSIONAL HIGH-RESOLUTION NATIONAL RADAR MOSAIC
3.5 THREE-DIMENSIONAL HIGH-RESOLUTION NATIONAL RADAR MOSAIC Jian Zhang 1, Kenneth Howard 2, Wenwu Xia 1, Carrie Langston 1, Shunxin Wang 1, and Yuxin Qin 1 1 Cooperative Institute for Mesoscale Meteorological
Comparative Evaluation of High Resolution Numerical Weather Prediction Models COSMO-WRF
3 Working Group on Verification and Case Studies 56 Comparative Evaluation of High Resolution Numerical Weather Prediction Models COSMO-WRF Bogdan Alexandru MACO, Mihaela BOGDAN, Amalia IRIZA, Cosmin Dănuţ
RAPIDS Operational Blending of Nowcasting and NWP QPF
RAPIDS Operational Blending of Nowcasting and NWP QPF Wai-kin Wong and Edwin ST Lai Hong Kong Observatory The Second International Symposium on Quantitative Precipitation Forecasting and Hydrology 5-8
Stan Benjamin Eric James Haidao Lin Steve Weygandt Susan Sahm Bill Moninger NOAA Earth System Research Laboratory, Boulder, CO
Impact of upper-air and near-surface observations on short-range forecasts from NOAA hourly assimilation cycles (RUC and Rapid Refresh) aircraft profiler VAD winds rawinsonde GPS precipitable water METARs
Nowcasting: analysis and up to 6 hours forecast
Nowcasting: analysis and up to 6 hours forecast Very high resoultion in time and space Better than NWP Rapid update Application oriented NWP problems for 0 6 forecast: Incomplete physics Resolution space
Estimation of satellite observations bias correction for limited area model
Estimation of satellite observations bias correction for limited area model Roger Randriamampianina Hungarian Meteorological Service, Budapest, Hungary roger@met.hu Abstract Assimilation of satellite radiances
Nowcasting of significant convection by application of cloud tracking algorithm to satellite and radar images
Nowcasting of significant convection by application of cloud tracking algorithm to satellite and radar images Ng Ka Ho, Hong Kong Observatory, Hong Kong Abstract Automated forecast of significant convection
ICAO Developments (Hazardous Weather Information)
ICAO Developments (Hazardous Weather Information) USER NEEDS AND EXPECTATION IATA s viewpoint Harmonized information about hazardous meteorological conditions that may constrain operations along intended
Parameterization of Cumulus Convective Cloud Systems in Mesoscale Forecast Models
DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Parameterization of Cumulus Convective Cloud Systems in Mesoscale Forecast Models Yefim L. Kogan Cooperative Institute
Titelmasterformat durch Klicken. bearbeiten
Evaluation of a Fully Coupled Atmospheric Hydrological Modeling System for the Sissili Watershed in the West African Sudanian Savannah Titelmasterformat durch Klicken June, 11, 2014 1 st European Fully
Application of Numerical Weather Prediction Models for Drought Monitoring. Gregor Gregorič Jožef Roškar Environmental Agency of Slovenia
Application of Numerical Weather Prediction Models for Drought Monitoring Gregor Gregorič Jožef Roškar Environmental Agency of Slovenia Contents 1. Introduction 2. Numerical Weather Prediction Models -
Triple eyewall experiment of the 2012 typhoon Bolaven using cloud resolving ensemble forecast
The 3 rd Research meeting of Ultrahigh Precision Meso Scale Weather Prediction Thu. 21 Mar. 2013 Triple eyewall experiment of the 2012 typhoon Bolaven using cloud resolving ensemble forecast Seiji ORIGUCHI,
The impact of window size on AMV
The impact of window size on AMV E. H. Sohn 1 and R. Borde 2 KMA 1 and EUMETSAT 2 Abstract Target size determination is subjective not only for tracking the vector but also AMV results. Smaller target
Can latent heat release have a negative effect on polar low intensity?
Can latent heat release have a negative effect on polar low intensity? Ivan Føre, Jon Egill Kristjansson, Erik W. Kolstad, Thomas J. Bracegirdle and Øyvind Sætra Polar lows: are intense mesoscale cyclones
Proposals of Summer Placement Programme 2015
Proposals of Summer Placement Programme 2015 Division Project Title Job description Subject and year of study required A2 Impact of dual-polarization Doppler radar data on Mathematics or short-term related
Coupling micro-scale CFD simulations to meso-scale models
Coupling micro-scale CFD simulations to meso-scale models IB Fischer CFD+engineering GmbH Fabien Farella Michael Ehlen Achim Fischer Vortex Factoria de Càlculs SL Gil Lizcano Outline Introduction O.F.Wind
Wind Field Observations with a Monostatic and Bistatic C-band Doppler Radar Network
Wind Field Observations with a Monostatic and Bistatic C-band Doppler Radar Network Martin Hagen, DLR Oberpfaffenhofen Wind field and Doppler radar Doppler radar (and lidar) can only measure one component
The Value That Wind Profilers Bring To Weather Forecasting. George Frederick March 2005
The Value That Wind Profilers Bring To Weather Forecasting George Frederick March 2005 What Value Do Wind Profilers Bring to Weather Forecasting? Wind profilers are an important source of meteorological
NOWCASTING OF PRECIPITATION Isztar Zawadzki* McGill University, Montreal, Canada
NOWCASTING OF PRECIPITATION Isztar Zawadzki* McGill University, Montreal, Canada 1. INTRODUCTION Short-term methods of precipitation nowcasting range from the simple use of regional numerical forecasts
Development of a. Solar Generation Forecast System
ALBANY BARCELONA BANGALORE 16 December 2011 Development of a Multiple Look ahead Time Scale Solar Generation Forecast System John Zack Glenn Van Knowe Marie Schnitzer Jeff Freedman AWS Truepower, LLC Albany,
A Microwave Retrieval Algorithm of Above-Cloud Electric Fields
A Microwave Retrieval Algorithm of Above-Cloud Electric Fields Michael J. Peterson The University of Utah Chuntao Liu Texas A & M University Corpus Christi Douglas Mach Global Hydrology and Climate Center
Evalua&ng Downdra/ Parameteriza&ons with High Resolu&on CRM Data
Evalua&ng Downdra/ Parameteriza&ons with High Resolu&on CRM Data Kate Thayer-Calder and Dave Randall Colorado State University October 24, 2012 NOAA's 37th Climate Diagnostics and Prediction Workshop Convective
Cloud Model Verification at the Air Force Weather Agency
2d Weather Group Cloud Model Verification at the Air Force Weather Agency Matthew Sittel UCAR Visiting Scientist Air Force Weather Agency Offutt AFB, NE Template: 28 Feb 06 Overview Cloud Models Ground
H45-TAMU Page 1 of 17 Report 2. Report 2 Data Assimilation/Ensemble Techniques
H45-TAMU Page 1 of 17 Report 2 Report 2 Data Assimilation/Ensemble Techniques TERC Project H-45-S-B-2004-T2 Modeling Strategy in Support of TexAQS-II and 8 Hour Ozone Assessment John Nielsen-Gammon and
WxFUSION. A. Tafferner. Folie 1. iport Meeting 13.10.2010 @ DLR OP
WxFUSION A. Tafferner iport Meeting 13.10.2010 @ DLR OP Folie 1 Upper Danube Catchment MUC DLR Andechs Munich Vienna Folie 2 Local and propagating thunderstorms in the Upper Danube Catchment B. Barternschlager,
The Beijin2008 FDP/RDP project
1 st meeting WWRP WG MWF, 10 October 2007, Dubrovnik The Beijin2008 FDP/RDP project Kazuo Saito Meteorological Research Institute/Japan Meteorological Agency ksaito@mri jma.go.jp Presentation thanks to:
118358 SUPERENSEMBLE FORECASTS WITH A SUITE OF MESOSCALE MODELS OVER THE CONTINENTAL UNITED STATES
118358 SUPERENSEMBLE FORECASTS WITH A SUITE OF MESOSCALE MODELS OVER THE CONTINENTAL UNITED STATES Donald F. Van Dyke III * Florida State University, Tallahassee, Florida T. N. Krishnamurti Florida State
NowCastMIX. A fuzzy logic based tool for providing automatic integrated short-term warnings from continuously monitored nowcasting systems
NowCastIX A fuzzy logic based tool for providing automatic integrated short-term warnings from continuously monitored nowcasting systems Paul James, Deutscher Wetterdienst ES eeting, Berlin, 12.09.2011
Learning Module 4 - Thermal Fluid Analysis Note: LM4 is still in progress. This version contains only 3 tutorials.
Learning Module 4 - Thermal Fluid Analysis Note: LM4 is still in progress. This version contains only 3 tutorials. Attachment C1. SolidWorks-Specific FEM Tutorial 1... 2 Attachment C2. SolidWorks-Specific
Recent activities on Big Data Assimilation in Japan
August 17, 2014, WWOSC, Montreal, Canada Recent activities on Big Data Assimilation in Japan M. Kunii, J. Ruiz, K. Kondo, and Takemasa Miyoshi* RIKEN Advanced Institute for Computational Science *PI and
Roelof Bruintjes, Sarah Tessendorf, Jim Wilson, Rita Roberts, Courtney Weeks and Duncan Axisa WMA Annual meeting 26 April 2012
Aerosol affects on the microphysics of precipitation development in tropical and sub-tropical convective clouds using dual-polarization radar and airborne measurements. Roelof Bruintjes, Sarah Tessendorf,
WSR - Weather Surveillance Radar
1 of 7 Radar by Paul Sirvatka College of DuPage Meteorology WSR - Weather Surveillance Radar It was learned during World War II that electromagnetic radiation could be sent out, bounced off an object and
Urban heat islands and summertime convective thunderstorms in Atlanta: three case studies
Atmospheric Environment 34 (2000) 507}516 Urban heat islands and summertime convective thunderstorms in Atlanta: three case studies Robert Bornstein*, Qinglu Lin Department of Meteorology, San Jose State
COSMO Data Assimilation. Applications for Romanian Territory
1 Working Group on Data Assimilation 19 COSMO Data Assimilation. Applications for Romanian Territory Amalia IRIZA 1,2, Rodica Claudia DUMITRACHE 1, Cosmin Dănuţ BARBU 1, Aurelia Lupaşcu 1, Bogdan Alexandru
Partnership to Improve Solar Power Forecasting
Partnership to Improve Solar Power Forecasting Venue: EUPVSEC, Paris France Presenter: Dr. Manajit Sengupta Date: October 1 st 2013 NREL is a national laboratory of the U.S. Department of Energy, Office
6.4 THE SIERRA ROTORS PROJECT, OBSERVATIONS OF MOUNTAIN WAVES. William O. J. Brown 1 *, Stephen A. Cohn 1, Vanda Grubiši 2, and Brian Billings 2
6.4 THE SIERRA ROTORS PROJECT, OBSERVATIONS OF MOUNTAIN WAVES William O. J. Brown 1 *, Stephen A. Cohn 1, Vanda Grubiši 2, and Brian Billings 2 1 National Center for Atmospheric Research, Boulder, Colorado.
ANALYSIS OF THUNDERSTORM CLIMATOLOGY AND CONVECTIVE SYSTEMS, PERIODS WITH LARGE PRECIPITATION IN HUNGARY. Theses of the PhD dissertation
ANALYSIS OF THUNDERSTORM CLIMATOLOGY AND CONVECTIVE SYSTEMS, PERIODS WITH LARGE PRECIPITATION IN HUNGARY Theses of the PhD dissertation ANDRÁS TAMÁS SERES EÖTVÖS LORÁND UNIVERSITY FACULTY OF SCIENCE PhD
Towards an NWP-testbed
Towards an NWP-testbed Ewan O Connor and Robin Hogan University of Reading, UK Overview Cloud schemes in NWP models are basically the same as in climate models, but easier to evaluate using ARM because:
Mesoscale re-analysis of historical meteorological data over Europe Anna Jansson and Christer Persson, SMHI ERAMESAN A first attempt at SMHI for re-analyses of temperature, precipitation and wind over
MM5/COSMO-DE Model Inter-Comparison and Model Validation
MM5/COSMO-DE Model Inter-Comparison and Model Validation Klaus Dengler and Christian Keil DLR, Institute of Atmospheric Physics Assessment of forecast quality using observations of the FRA airport campaign
Preliminary summary of the 2015 NEWS- e realtime forecast experiment
Preliminary summary of the 2015 NEWS- e realtime forecast experiment December 15, 2015 1. Introduction The NOAA Warn- on- Forecast (WoF) research project is tasked with developing a regional 1- km storm-
Guy Carpenter Asia-Pacific Climate Impact Centre, School of energy and Environment, City University of Hong Kong
Diurnal and Semi-diurnal Variations of Rainfall in Southeast China Judy Huang and Johnny Chan Guy Carpenter Asia-Pacific Climate Impact Centre School of Energy and Environment City University of Hong Kong
An Operational Local Data Integration System (LDIS) at WFO Melbourne
1. Introduction An Operational Local Data Integration System (LDIS) at WFO Melbourne Peter F. Blottman, Scott M. Spratt, David W. Sharp, and Anthony J. Cristaldi III WFO Melbourne, FL Jonathan L. Case
Hybrid Data Assimilation in the GSI
Hybrid Data Assimilation in the GSI Rahul Mahajan NOAA / NWS / NCEP / EMC IMSG GSI Hybrid DA Team: Daryl Kleist (UMD), Jeff Whitaker (NOAA/ESRL), John Derber (EMC), Dave Parrish (EMC), Xuguang Wang (OU)
Wind resources map of Spain at mesoscale. Methodology and validation
Wind resources map of Spain at mesoscale. Methodology and validation Martín Gastón Edurne Pascal Laura Frías Ignacio Martí Uxue Irigoyen Elena Cantero Sergio Lozano Yolanda Loureiro e-mail:mgaston@cener.com
Assimilation of cloudy infrared satellite observations: The Met Office perspective
Assimilation of cloudy infrared satellite observations: The Met Office perspective Ed Pavelin, Met Office International Symposium on Data Assimilation 2014, Munich Contents This presentation covers the
IMPROVING AEROSOL DISTRIBUTIONS
IMPROVING AEROSOL DISTRIBUTIONS BY ASSIMILATING SATELLITE- RETRIEVED CLOUD DROPLET NUMBER AND AEROSOL OPTICAL DEPTH Pablo Saide 1, G. Carmichael 1, S. Spak 1, P. Minnis 2, K. Ayers 2, Z. Liu 3, H.C. Lin
Presented by Stella Melo Environment Canada, Science and Technology, Cloud Physics and Severe Weather Research Section
Validation/Verification of the microphysical and dynamic characteristics of Arctic Cloud systems: what infrastructure is required to meet the challenge? Presented by Stella Melo Environment Canada, Science
Hong Kong Observatory Summer Placement Programme 2015
Annex I Hong Kong Observatory Summer Placement Programme 2015 Training Programme : An Observatory mentor with relevant expertise will supervise the students. Training Period : 8 weeks, starting from 8
MountainZebra: Real-Time Archival and 4D Visualization of Radar Volumes Over Complex Terrain
MountainZebra: Real-Time Archival and D Visualization of Radar Volumes Over Complex Terrain Curtis James, Stacy Brodzik, Harry Edmon, Robert Houze, and Sandra Yuter Department of Atmospheric Sciences,
The ARM-GCSS Intercomparison Study of Single-Column Models and Cloud System Models
The ARM-GCSS Intercomparison Study of Single-Column Models and Cloud System Models R. T. Cederwall and D. J. Rodriguez Atmospheric Science Division Lawrence Livermore National Laboratory Livermore, California
Cloud/Hydrometeor Initialization in the 20-km RUC Using GOES Data
WORLD METEOROLOGICAL ORGANIZATION COMMISSION FOR BASIC SYSTEMS OPEN PROGRAMMME AREA GROUP ON INTEGRATED OBSERVING SYSTEMS EXPERT TEAM ON OBSERVATIONAL DATA REQUIREMENTS AND REDESIGN OF THE GLOBAL OBSERVING
USING SIMULATED WIND DATA FROM A MESOSCALE MODEL IN MCP. M. Taylor J. Freedman K. Waight M. Brower
USING SIMULATED WIND DATA FROM A MESOSCALE MODEL IN MCP M. Taylor J. Freedman K. Waight M. Brower Page 2 ABSTRACT Since field measurement campaigns for proposed wind projects typically last no more than
MOGREPS status and activities
MOGREPS status and activities by Warren Tennant with contributions from Rob Neal, Sarah Beare, Neill Bowler & Richard Swinbank Crown copyright Met Office 32 nd EWGLAM and 17 th SRNWP meetings 1 Contents
CLOUD BASED N-DIMENSIONAL WEATHER FORECAST VISUALIZATION TOOL WITH IMAGE ANALYSIS CAPABILITIES
CLOUD BASED N-DIMENSIONAL WEATHER FORECAST VISUALIZATION TOOL WITH IMAGE ANALYSIS CAPABILITIES M. Laka-Iñurrategi a, I. Alberdi a, K. Alonso b, M. Quartulli a a Vicomteh-IK4, Mikeletegi pasealekua 57,
Visualization Process. Alark Joshi
Visualization Process Alark Joshi Task-specific Visualization Design LA Treinish, Task-specific visualization design, IEEE Computer Graphics and Applications, 1999 Generalized visualization systems are
Towards assimilating IASI satellite observations over cold surfaces - the cloud detection aspect
Towards assimilating IASI satellite observations over cold surfaces - the cloud detection aspect Tuuli Perttula, FMI + Thanks to: Nadia Fourrié, Lydie Lavanant, Florence Rabier and Vincent Guidard, Météo
Cloud Grid Information Objective Dvorak Analysis (CLOUD) at the RSMC Tokyo - Typhoon Center
Cloud Grid Information Objective Dvorak Analysis (CLOUD) at the RSMC Tokyo - Typhoon Center Kenji Kishimoto, Masaru Sasaki and Masashi Kunitsugu Forecast Division, Forecast Department Japan Meteorological
A system of direct radiation forecasting based on numerical weather predictions, satellite image and machine learning.
A system of direct radiation forecasting based on numerical weather predictions, satellite image and machine learning. 31st Annual International Symposium on Forecasting Lourdes Ramírez Santigosa Martín
6.9 A NEW APPROACH TO FIRE WEATHER FORECASTING AT THE TULSA WFO. Sarah J. Taylor* and Eric D. Howieson NOAA/National Weather Service Tulsa, Oklahoma
6.9 A NEW APPROACH TO FIRE WEATHER FORECASTING AT THE TULSA WFO Sarah J. Taylor* and Eric D. Howieson NOAA/National Weather Service Tulsa, Oklahoma 1. INTRODUCTION The modernization of the National Weather
FORENSIC WEATHER CONSULTANTS, LLC
SAMPLE, CONDENSED REPORT DATES AND LOCATIONS HAVE BEEN CHANGED FORENSIC WEATHER CONSULTANTS, LLC Howard Altschule Certified Consulting Meteorologist 1971 Western Avenue, #200 Albany, New York 12203 518-862-1800
Frequency distribution by mont
Windshear and Turbulence Alerting Services Hong Kong Observatory December 2010 Frequency distribution by Significant windshear: 1 in 500 flights Significant turbulence: month 2nd peak 1 in 2000 flights
4.4 GRAPHICAL AND ANALYTICAL SOFTWARE VISUALIZATION TOOLS FOR EVALUATING METEOROLOGICAL AND AIR QUALITY MODEL PERFORMANCE
4.4 GRAPHICAL AND ANALYTICAL SOFTWARE VISUALIZATION TOOLS FOR EVALUATING METEOROLOGICAL AND AIR QUALITY MODEL PERFORMANCE Irene Lee * Exponent Inc., Natick, Massachusetts 1. INTRODUCTION A critical part
Development of an Integrated Data Product for Hawaii Climate
Development of an Integrated Data Product for Hawaii Climate Jan Hafner, Shang-Ping Xie (PI)(IPRC/SOEST U. of Hawaii) Yi-Leng Chen (Co-I) (Meteorology Dept. Univ. of Hawaii) contribution Georgette Holmes
A verification score for high resolution NWP: Idealized and preoperational tests
Technical Report No. 69, December 2012 A verification score for high resolution NWP: Idealized and preoperational tests Bent H. Sass and Xiaohua Yang HIRLAM - B Programme, c/o J. Onvlee, KNMI, P.O. Box
OBSERVATIONS FROM THE APRIL 13 2004 WAKE LOW DAMAGING WIND EVENT IN SOUTH FLORIDA. Robert R. Handel and Pablo Santos NOAA/NWS, Miami, Florida ABSTRACT
OBSERVATIONS FROM THE APRIL 13 2004 WAKE LOW DAMAGING WIND EVENT IN SOUTH FLORIDA Robert R. Handel and Pablo Santos NOAA/NWS, Miami, Florida ABSTRACT On Tuesday, April 13, 2004, a high wind event swept
Design and Deployment of Specialized Visualizations for Weather-Sensitive Electric Distribution Operations
Fourth Symposium on Policy and Socio-Economic Research 4.1 Design and Deployment of Specialized Visualizations for Weather-Sensitive Electric Distribution Operations Lloyd A. Treinish IBM, Yorktown Heights,
Tropical Cloud Population
Tropical Cloud Population Before Satellites Visual Observation View from and aircraft flying over the South China Sea Radiosonde Data Hot tower hypothesis Riehl & Malkus 1958 Satellite Observations Post
All-sky assimilation of microwave imager observations sensitive to water vapour, cloud and rain
All-sky assimilation of microwave imager observations sensitive to water vapour, cloud and rain A.J. Geer, P. Bauer, P. Lopez and D. Salmond European Centre for Medium-Range Weather Forecasts, Reading,
The Wind Integration National Dataset (WIND) toolkit
The Wind Integration National Dataset (WIND) toolkit EWEA Wind Power Forecasting Workshop, Rotterdam December 3, 2013 Caroline Draxl NREL/PR-5000-60977 NREL is a national laboratory of the U.S. Department
Ongoing Development and Testing of Generalized Cloud Analysis Package within GSI for Initializing Rapid Refresh
Preprints, 13 th Conf. on Aviation, Range and Aerospace Meteorology. January 2008, New Orleans, LA, Amer. Meteor. Soc. 7.4 Ongoing Development and Testing of Generalized Cloud Analysis Package within GSI
Testing and Evaluation of GSI-Hybrid Data Assimilation and Its Applications for HWRF at the Developmental Testbed Center
214 Tropical Cyclone Research Forum (TCRF)/68th IHC, March 3-6, 214, College Park, MD Testing and Evaluation of GSI-Hybrid Data Assimilation and Its Applications for HWRF at the Developmental Testbed Center
Suman Goyal Sc E /In-charge SYNOPTIC Application Unit Satellite Meteorology Davison
Suman Goyal Sc E /In-charge SYNOPTIC Application Unit Satellite Meteorology Davison Introduction What is ForTraCC? It is an algorithm for tracking and forecasting radiative and morphological characteristics
Regional Forecast Center Timişoara 15. Gh. Adam St., Timişoara, Romania, e-mail: cristi_nichita2004@yahoo.com
Analele UniversităŃii din Oradea Seria Geografie Tom XX, no. 2/2010 (December), pp 197-203 ISSN 1221-1273, E-ISSN 2065-3409 Article no. 202106-492 SOME DOPPLER RADAR FEATURES OF SEVERE WEATHER IN SUPERCELLS
Satellite Remote Sensing of Volcanic Ash
Marco Fulle www.stromboli.net Satellite Remote Sensing of Volcanic Ash Michael Pavolonis NOAA/NESDIS/STAR SCOPE Nowcasting 1 Meeting November 19 22, 2013 1 Outline Getty Images Volcanic ash satellite remote
1. Introduction. 2. AP Clutter Mitigation Scheme 14.13
14.13 itigating Ground Clutter Contamination in the WSR-88D Scott Ellis 1, Cathy Kessinger 1, Timothy D. O Bannon 2 and Joseph VanAndel 1 1. National Center for Atmospheric Research, Boulder, CO. 2. National
WEATHER RADAR VELOCITY FIELD CONFIGURATIONS ASSOCIATED WITH SEVERE WEATHER SITUATIONS THAT OCCUR IN SOUTH-EASTERN ROMANIA
Romanian Reports in Physics, Vol. 65, No. 4, P. 1454 1468, 2013 ATMOSPHERE PHYSICS WEATHER RADAR VELOCITY FIELD CONFIGURATIONS ASSOCIATED WITH SEVERE WEATHER SITUATIONS THAT OCCUR IN SOUTH-EASTERN ROMANIA
Nowcasting applications. putsay.m@met.hu Hungarian Meteorological Service
Nowcasting applications Putsay Mária, M putsay.m@met.hu Hungarian Meteorological Service Principles of Satellite Meteorology, Online Course, 20 September 2011 Outlines Satellite images Derived products
Large Eddy Simulation (LES) & Cloud Resolving Model (CRM) Françoise Guichard and Fleur Couvreux
Large Eddy Simulation (LES) & Cloud Resolving Model (CRM) Françoise Guichard and Fleur Couvreux Cloud-resolving modelling : perspectives Improvement of models, new ways of using them, renewed views And
OPERATIONAL USE OF TOTAL LIGHTNING INFORMATION FOR WEATHER AND AVIATION AT DALLAS-FORT WORTH
7.4 OPERATIONAL USE OF TOTAL LIGHTNING INFORMATION FOR WEATHER AND AVIATION AT DALLAS-FORT WORTH Martin J. Murphy*, Ronald L. Holle, and Nicholas W.S. Demetriades Vaisala Inc., Tucson, Arizona 1. INTRODUCTION
Science Goals for the ARM Recovery Act Radars
DOE/SC-ARM-12-010 Science Goals for the ARM Recovery Act Radars JH Mather May 2012 DISCLAIMER This report was prepared as an account of work sponsored by the U.S. Government. Neither the United States
AROME Weather Model - A Search for the Future
Status of AROME news since Bratislava '05 based on contributions from CNRM GMAP & GMME teams,lab. Aérologie, aladinists 1. System & workforce 2. Model & software 3. Assimilation 4. The Future 1 1. Multiscale
2D Modeling of Urban Flood Vulnerable Areas
2D Modeling of Urban Flood Vulnerable Areas Sameer Dhalla, P.Eng. Dilnesaw Chekol, Ph.D. A.D. Latornell Conservation Symposium November 22, 2013 Outline 1. Toronto and Region 2. Evolution of Flood Management
A model to observation approach to evaluating cloud microphysical parameterisations using polarimetric radar
A model to observation approach to evaluating cloud microphysical parameterisations using polarimetric radar Monika Pfeifer G. Craig, M. Hagen, C. Keil Polarisation Doppler Radar POLDIRAD Rain Graupel
Assimilation of radar derived rain rates into the convective scale model COSMO-DE at DWD
Q. J. R. Meteorol. Soc. (9999), 999, pp. 1 999 doi: 10.1256/qj.99.9 Assimilation of radar derived rain rates into the convective scale model COSMO-DE at DWD By K. STEPHAN, S. KLINK and C. SCHRAFF Deutscher
Introduction to the forecasting world Jukka Julkunen FMI, Aviation and military WS
Boundary layer challenges for aviation forecaster Introduction to the forecasting world Jukka Julkunen FMI, Aviation and military WS 3.12.2012 Forecast for general public We can live with it - BUT Not
Financing Community Wind
Financing Community Wind Wind Data and Due Diligence What is the Project's Capacity Factor? Community Wind Energy 2006 March 8, 2006 Mark Ahlstrom mark@windlogics.com Slide 1 The Need for Wind Assessment
GOES-R Product Training Document
GOES-R Product Training Document for the GOES-R Proving Ground Aviation Weather Testbed 2012 Summer Experiment Chad Gravelle (UW-CIMSS / NWS PG) Amanda Terborg (UW-CIMSS / AWC) With contributions by: Bob
18.2 TRACKING NWS AREA FORECAST DISCUSSIONS TO STUDY THE SUBJECTIVE USE OF WIND AND TEMPERATURE PROFILER DATA
18.2 TRACKING NWS AREA FORECAST DISCUSSIONS TO STUDY THE SUBJECTIVE USE OF WIND AND TEMPERATURE PROFILER DATA Douglas W. van de Kamp and Daphne M. Grant NOAA Research-Forecast Systems Laboratory, Boulder,
Active and Passive Microwave Remote Sensing
Active and Passive Microwave Remote Sensing Passive remote sensing system record EMR that was reflected (e.g., blue, green, red, and near IR) or emitted (e.g., thermal IR) from the surface of the Earth.