NOGAPS: Top color scale – Terrain height in meters Lower color scale – Surface frictional velocity (cm/s) Barbs – Surface wind direction and speed (m/s)

Slides:



Advertisements
Similar presentations
Chapter 13 – Weather Analysis and Forecasting
Advertisements

SPoRT Products in Support of the GOES-R Proving Ground and NWS Forecast Operations Andrew Molthan NASA Short-term Prediction Research and Transition (SPoRT)
Accessing and Interpreting Web-based Weather Data Clinton Rockey National Weather Service Portland, Oregon.
1 GOES Users’ Conference October 1, 2002 GOES Users’ Conference October 1, 2002 John (Jack) J. Kelly, Jr. National Weather Service Infusion of Satellite.
Navy Meteorology and Oceanography Support for Homeland Security Thomas J. Cuff Deputy Technical Director Oceanographer of the Navy 28 November 2001.
SCIENCE NEWS Magnitude CENTRAL ITALY Magnitude CENTRAL ITALY.
Fusion of SeaWIFS and TOMS Satellite Data with Surface Observations and Topographic Data During Extreme Aerosol Events Stefan Falke and Rudolf Husar Center.
Meteorological Data Systems Instrument History, Techniques and Applications By: Daniel Ruth.
Satellites and Radar – A primer ATMO 203. Satellites Two main types of satellite orbits – Geostationary Earth Orbiting Satellite is 35,786 km (22,236.
Ocean-atmosphere simulations of the Eastern Mediterranean using COAMPS TM /NCOM Objectives  Simulate Mediterranean and subregional (e.g., Adriatic and.
Forecasting Weather After completing this section, students will analyze weather maps and the resulting regional weather (Standard PI – 061)
Rapid Update Cycle Model William Sachman and Steven Earle ESC452 - Spring 2006.
Real Time High-Resolution McGill University J. Gyakum 1, R. McTaggart- Cowan 1, P. Sisson 2 1 McGill University 2 National Weather Service.
Remote Sensing of Mesoscale Vortices in Hurricane Eyewalls Presented by: Chris Castellano Brian Cerruti Stephen Garbarino.
Satellite Imagery Meteorology 101 Lab 9 December 1, 2009.
Climate, Meteorology and Atmospheric Chemistry.
Chapter 2: Satellite Tools for Air Quality Analysis 10:30 – 11:15.
Hongli Jiang, Yuanfu Xie, Steve Albers, Zoltan Toth
GOES-R Synthetic Imagery over Alaska Dan Lindsey NOAA/NESDIS, SaTellite Applications and Research (STAR) Regional And Mesoscale Meteorology Branch (RAMMB)
Assessment of the vertical exchange of heat, moisture, and momentum above a wildland fire using observations and mesoscale simulations Joseph J. Charney.
Surface Transportation Weather: Assessment of Current Capabilities and Future Trends William Mahoney Richard Wagoner National Center For Atmospheric Research.
Visualization, Exploration, and Model Comparison of NASA Air Quality Remote Sensing data via Giovanni Ana I. Prados, Gregory Leptoukh, Arun Gopalan, and.
Satellite Imagery and Remote Sensing NC Climate Fellows June 2012 DeeDee Whitaker SW Guilford High Earth/Environmental Science & Chemistry.
Forecasting and Numerical Weather Prediction (NWP) NOWcasting Description of atmospheric models Specific Models Types of variables and how to determine.
Sensitivity of High-resolution Tropical Cyclone Intensity Forecast to Surface Flux Parameterization Chi-Sann Liou, NRL Monterey, CA.
UNCLASSIFIED Navy Applications of GOES-R Richard Crout, PhD Naval Meteorology and Oceanography Command Satellite Programs Presented to 3rd GOES-R Conference.
Earth Science Division National Aeronautics and Space Administration 18 January 2007 Paper 5A.4: Slide 1 American Meteorological Society 21 st Conference.
Trajectory validation using tracers of opportunity such as fire plumes and dust episodes Narendra Adhikari March 26, 2007 ATMS790 Seminar (Dr. Pat Arnott)
Maj Richard “Krash” Krasner Directorate of Requirements Headquarters Air Force Space Command Air Force Space Command's Environmental Monitoring Requirements.
UNCLASSIFIED Initial Study of HPAC Modeled Dispersion Driven by MM5 With and Without Urban Canopy Parameterizations Dr. Chris Kiley – NGC Dr. Jason Ching.
20.5 Forecasting Weather Objectives
3rd NSTWS, Vienna VA, July Research to Operations in the Joint Center for Satellite Data Assimilation Lars Peter Riishojgaard Director, JCSDA.
An evolving resource collection by a virtual community Gobi Dust Storms March 15 –31, 2002 Would you like to contribute? DownloadDownload this PPT Add.
Polar Communications and Weather Mission Canadian Context and Benefits.
IMPROVING VERY-SHORT-TERM STORM PREDICTIONS BY ASSIMILATING RADAR AND SATELLITE DATA INTO A MESOSCALE NWP MODEL Allen Zhao 1, John Cook 1, Qin Xu 2, and.
Non-hydrostatic Numerical Model Study on Tropical Mesoscale System During SCOUT DARWIN Campaign Wuhu Feng 1 and M.P. Chipperfield 1 IAS, School of Earth.
Earth-Sun System Division National Aeronautics and Space Administration SPoRT SAC Nov 21-22, 2005 SPoRT Extensions to Coastal WFOs EOS Data and Products.
Accounting for Uncertainties in NWPs using the Ensemble Approach for Inputs to ATD Models Dave Stauffer The Pennsylvania State University Office of the.
Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:
An air quality information system for cities with complex terrain based on high resolution NWP Viel Ødegaard, r&d department.
3 rd Annual WRF Users Workshop Promote closer ties between research and operations Develop an advanced mesoscale forecast and assimilation system   Design.
How well can we model air pollution meteorology in the Houston area? Wayne Angevine CIRES / NOAA ESRL Mark Zagar Met. Office of Slovenia Jerome Brioude,
23 May 2001 UNCLASSIFIED VALUE OF FUTURE GOES TO NAVY OPERATIONS CAPT Mike Pind Asst. Chief-of-Staff for Plans and Programs HQ, NAVAL METEOROLOGY AND OCEANOGRAPHY.
Comparing GEM 15 km, GEM-LAM 2.5 km and RUC 13 km Model Simulations of Mesoscale Features over Southern Ontario 2010 Great Lakes Op Met Workshop Toronto,
A NASA / NSF / NRL airborne field campaign focusing on atmospheric composition, chemistry, and climate over Southeast Asia. Programmatic Context, Issues.
Importance of Low Light Sensing Lunar reflection-based features: Terrestrial/atmospheric emission-based features:
Preparing for GOES-R: old tools with new perspectives Bernadette Connell, CIRA CSU, Fort Collins, Colorado, USA ABSTRACT Creating.
AMS Annual Meeting - January NRL Global Model Adaptive Observing During TPARC/TCS-08 Carolyn Reynolds Naval Research Laboratory, Monterey, CA OUTLINE:
171 PC-HYSPLIT WORKSHOP Workshop Agenda Model Overview Model history and features Computational method Trajectories versus concentration Code installation.
Transitioning unique NASA data and research technologies to the NWS 1 In-House Utilization of AIRS Data and Products for Numerical Weather Prediction Will.
The Potential Role of the GPM in Activities at the Naval Research Laboratory Joe Turk and Jeff Hawkins Naval Research Laboratory Marine Meteorology Division.
Transport Simulation of the April 1998 Chinese Dust Event Prepared by: Bret A. Schichtel And Rudolf B. Husar Center for Air Pollution Impact and Trend.
Fly - Fight - Win 2 d Weather Group Mr. Evan Kuchera HQ AFWA 2 WXG/WEA Template: 28 Feb 06 Approved for Public Release - Distribution Unlimited AFWA Ensemble.
Improving Short-term Predictions and the Identification of Hazardous Weather using NASA/SPoRT Transitioned Satellite Products Deirdre Kann Brian Guyer.
High impact weather nowcasting and short-range forecasting using advanced IR soundings Jun Li Cooperative Institute for Meteorological.
Forecasting smoke and dust using HYSPLIT. Experimental testing phase began March 28, 2006 Run daily at NCEP using the 6Z cycle to produce a 24- hr analysis.
Transport Simulation of the April 1998 Chinese Dust Event Prepared by: Bret A. Schichtel And Rudolf B. Husar Center for Air Pollution Impact and Trend.
U.S. GODAE: Global Ocean Prediction with Community Effort: Community Effort: NRL, U. of Miami, FSU, NASA-GISS, NOAA/NCEP, NOAA/AOML, NOAA/PMEL, PSI, FNMOC,
Assessment of Upper atmospheric plume models using Calipso Satellites and Environmental Assessment and Forecasting Chowdhury Nazmi, Yonghua Wu, Barry Gross,
NOAA, May 2014 Coordination Group for Meteorological Satellites - CGMS NOAA Activities toward Transitioning Mature R&D Missions to an Operational Status.
Roger A. Stocker 1 Jason E. Nachamkin 2 An overview of operational FNMOC mesoscale cloud forecast support 1 FNMOC: Fleet Numerical Meteorology & Oceanography.
For GAW SSC meeting, 18 February Alexander Baklanov, ARE WMO.
Unit 4 Lesson 5 Weather Maps and Weather Prediction
Rosenstial School of Marine and Atmospheric Science
Tadashi Fujita (NPD JMA)
  Robert Gibson1, Douglas Drob2 and David Norris1 1BBN Technologies
Not Approved for Public Release
Naval Research Laboratory
COAMPS Coupled Ocean Atmosphere Prediction System Developed by FNMOC and NRL (1996) Operational - MEL/FNMOC Experimental - NRL-MRY 27 km Spatial.
Orographic Influences on Rainfall Associated with Tropical Cyclone
Presentation transcript:

NOGAPS: Top color scale – Terrain height in meters Lower color scale – Surface frictional velocity (cm/s) Barbs – Surface wind direction and speed (m/s) Dashed lines – Ground Wetness NRL Marine Meteorology Division’s Science and Technology was a major contributor to the war effort. Operational and R&D meteorological products from NRL played a role in supporting DOD’s tactical operations…and dust was one of the biggest problems encountered. Baghdad at Noon Numerical Weather and Aerosol PredictionRemote Sensing Data Applications NOGAPS Forecast NAAPS Forecast NAAPS: Surface Dust Concentration (ug-m**3) In addition to providing the traditional weather forecasts that are always needed to support military operations, our NWP models were also used to provide forcing for a new breed of aerosol prediction models developed at NRL. The NRL Aerosol Analysis and Prediction System (NAAPS) provides the first-ever global capability to predict dust, smoke, and sulfate concentration, movement, and dissipation. NAAPS requires databases for initializing the aerosol source regions, and it uses forecast fields from the Navy Atmospheric Global Operational Prediction System (NOGAPS) for forcing. Surface dust concentration (mg m -3 ) 9-km COAMPS TM 48-h forecast valid 00 UTC 26 Mar 2003 Surface stress (m/s) and 10-m streamlines Dust Front NRL’s mesoscale aerosol prediction capability is embedded directly into our high-resolution nested nonhydrostatic NWP model COAMPS™ (the Coupled Ocean/Atmosphere Mesoscale Prediction System). COAMPS fields at every model time step are used to define dust lifting, movement, and dissipation. COAMPS TM dust erodible fraction Iraq The high-resolution source database required to support this modeling effort is a unique 1-km resolution database developed by NRL for SW Asia. Through cooperation of NASA and NOAA, research data from MODIS and SeaWIFS were made available to NRL in near-real time (1-3 hours latency). From those data, NRL produced true-color imagery and enhancements for dust and smoke over land and water and provided those products over the Secure Internet connection at FNMOC to operational customers both afloat and ashore. Support of EM/EO and CBW Decision Aids Ducting products help the Fleet predict locations where they might encounter extended signal ranges or radar holes based upon atmospheric conditions forecast by COAMPS TM. COAMPS 6-hr Forecast of Duct Base Height (m) Ultimately, our goal is to use atmospheric information from COAMPS and NAAPS to provide direct input to tactical decision aids. Links have already been made, for example, between COAMPS and chemical/biological transport and dispersion models. Target Acquisition Weather System (TAWS) New targets added for OIF at customer request. NRL aerosol research aimed at improving information about weather effects on EO propagation. Hypothetical HPAC Forecast of Anthrax Surface Dosage 13 hours after release Atmospheric forcing is provided by COAMPS-OnScene run with an inner nest at 1-km resolution. COAMPS-OS™ is a portable, GUI- based version of COAMPS. A massive dust storm fans through the Margow desert of southern Afghanistan. This true color imagery example demonstrates the inherent difficulties in identifying the details of dust via analysis of unenhanced imagery The corresponding enhancement, highlighting areas of significant dust as shades of bright pink and orange, provides a markedly improved sensitivity and level of detail to the many dust features present in the scene NRL also developed and provided a variety of additional satellite data products to assist in distinguishing environmental features of interest. The products included low cloud, convective cloud top, contrails, snow, smoke and “hot spots”, and satellite/model overlays. The products were presented collectively on Secure Internet via NRL’s Satellite Focus webpage, hosted in coordination with FNMOC and available to all DoD assets. Low Clouds at Night Snow Cover High Clouds Low Clouds Smoke Plume Smoke Plume Tanker Location Aircraft Contrails Cloud Top Altitudes in Kilofeet Oil Tanker Attack Off Yemen Coast