2 METOC Consulting, Monterey, CA 93943,

Slides:



Advertisements
Similar presentations
Ekman Transport and Tidal Variations in the Gulf of Maine Norman J. Shippee Judd Gregg Meteorology Institute at Plymouth State University (
Advertisements

Fusion of SeaWIFS and TOMS Satellite Data with Surface Observations and Topographic Data During Extreme Aerosol Events Stefan Falke and Rudolf Husar Center.
J. Cook, G. Love, Q. Zhao, T. Tsui, P. Harasti, and S. Potts L. Phegley, D. Geiszler, M. Frost, L. N. McDermid, J. Kent, D. Martinez, F. Franco, G. Sprung.
Mrs. Smith’s 7th Grade Reading Blue Class Mrs. Smith’s 7th Grade Reading Blue Class Mrs. Smith’s 7th Grade Reading Blue Class.
Real-Time Sea Ice Detection from Coastal Radars 3 rd Ice Analyst WorkshopTuomas Niskanen of June, 2011Oceanographic Services Copenhagen, DenmarkFinnish.
Modeling study of the coastal upwelling system of the Monterey Bay area during 1999 and I. Shulman (1), J.D. Paduan (2), L. K. Rosenfeld (2), S.
Moisture observation by a dense GPS receiver network and its assimilation to JMA Meso ‑ Scale Model Koichi Yoshimoto 1, Yoshihiro Ishikawa 1, Yoshinori.
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.
Weather Forecasting Chapter 9 Dr. Craig Clements SJSU Met 10.
Chapter 9: Weather Forecasting Surface weather maps 500mb weather maps Satellite Images Radar Images.
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,
WRAP – System Overview 1 WRAP 0868G. 2 Introducing WRAP This is WRAP: A highly efficient system for radio network planning and spectrum management Provides.
Philippe Moinat MACC regional air quality multi-model forecasts: rationale and alternatives to the median ensemble November 29 - December 1, 2011 Potomac,
Update on Assessment of the Major Causes of Dust-Resultant Haze in the WRAP Vic Etyemezian, Jin Xu, Dave Dubois, and Mark Green.
AWIPS DATA VISUALIZATION AND MONITORING SYSTEM FOR OPERATIONAL RECORDS ADVISOR AWIPS DATA VISUALIZATION AND MONITORING SYSTEM FOR OPERATIONAL RECORDS September.
Nathalie Voisin 1, Florian Pappenberger 2, Dennis Lettenmaier 1, Roberto Buizza 2, and John Schaake 3 1 University of Washington 2 ECMWF 3 National Weather.
Violet:  m Blue:  m Green:  m Yellow:  m Orange:  m Red:
Bailey Wright.  Tornadoes are formed when the vertical wind shear, vertical vorticity, and stream line vorticity conditions are favorable. ◦ Storms and.
Assessment on Phytoplankton Quantity in Coastal Area by Using Remote Sensing Data RI Songgun Marine Environment Monitoring and Forecasting Division State.
Roger A. Stocker 1 Jason E. Nachamkin 2 An overview of operational FNMOC mesoscale cloud forecast support 1 FNMOC: Fleet Numerical Meteorology & Oceanography.
SmartMet Lea Saukkonen FMI What is SmartMet? A software tool for visualizing and editing meteorological data.
COAMPS ® Ducting Validation Wallops-2000 William Thompson and Tracy Haack Naval Research Laboratory Marine Meteorology Division Monterey, CA COAMPS ® is.
of Temperature in the San Francisco Bay Area
Weather Tools & Measurements
Evaluation of operational altimeter-derived ocean currents for shelf sea applications: a case study in the NW Atlantic D. Vandemark1, H. Feng1, and.
Examining the connection between the dynamics of western boundary shelves and the deep sea using satellite altimetry data Nicholas Trefonides and James.
Skillful Arctic climate predictions
Grade 11 Week 5 Percentages.
Safety Colors Quiz.
Intelligent pricing plans for de-icing companies
Fuzzy verification using the Fractions Skill Score
Evaluation of the Cuban Wind Atlas
Systematic timing errors in km-scale NWP precipitation forecasts
Verifying Precipitation Events Using Composite Statistics
Ozone Exceedances and Elevated PM2.5 Connecticut June 11, 2015
IMAGERY DERIVED CURRENTS FROM NPP Ocean Color Products 110 minutes!
Dan Petersen Bruce Veenhuis Greg Carbin Mark Klein Mike Bodner
Composite-based Verification
of Temperature in the San Francisco Bay Area
Dmitriy Aronov, David W. Tank  Neuron 
Road Weather Information Systems (RWIS)
Journal #58 List the instruments used to measure lower atmospheric conditions: List the the instruments used to measure upper atmospheric conditions:
Quality Assurance Measures for High Frequency Radar Systems
Aquarius Data Are Valuable for El Niño Forecasts
Forecast Pressure.
Naval Research Laboratory
Introduction to Remote-Sensing
Heather L. Dean, Maureen A. Hagan, Bijan Pesaran  Neuron 
Color of m&m's in One 1.69 Oz. Bag
Smart Water Fund Products
Forecasts and Warnings
Percent %.
Anne Karin Magnusson Norwegian Meteorological Institute, met.no Bergen
Volume 88, Issue 2, Pages (October 2015)
Histograms of grades in two classes, each of 200 students
RED 1 a % d % b % e. 0.71% c % f %
Composite Method Results Artificial Cases April 2008
OS 72B-0355 Analysis of Acoustic Signals from Ship Traffic at Pioneer Seamount Carl O. Vuosalo,1 Craig Huber,1 Michael D. Hoffman,1 Newell Garfield,2 and.
Safety Colors Quiz.
Heather L. Dean, Maureen A. Hagan, Bijan Pesaran  Neuron 
LCDR George Wright, USN OC 3570 – Winter 2008 Friday, March 14th 2008
An Inter-comparison of 5 HRPPs with 3-Hourly Gauge Estimates
The Dynamics of Signal Triggering in a gp130-Receptor Complex
Sharon C. Furtak, Omar J. Ahmed, Rebecca D. Burwell  Neuron 
Volume 5, Issue 3, Pages (November 2013)
Volume 28, Issue 8, Pages e3 (April 2018)
Coastal Ocean Dynamics Radar (CODAR) Mapping of
The Innovative Coastal-Ocean Observing Network (ICON)
Color Box Button - Gray Type : object Type : object Type : object
Discussion Questions to all Questions to SRNWP consortia
Presentation transcript:

2 METOC Consulting, Monterey, CA 93943, swadley@nrlmry.navy.mil MONITORING COAMPS® SURFACE FORECAST QUALITY G. Love1* and S. Swadley2   1 Naval Research Laboratory, Marine Meteorology Division, Monterey, CA 93943, love@nrlmry.navy.mil 2 METOC Consulting, Monterey, CA 93943, swadley@nrlmry.navy.mil The METQC monitor provides top-level signaling of forecast quality with links to maps and timelines of station differences configured in a classic drill-down schema. Forecast innovation vectors are graded both in terms of exceeding set caution and warning thresholds and in terms of a confidence level METQC Quality Control Monitor BLUE: High (>= 90 Percent), GREEN: Good (>= 75 Percent), YELLOW: Fair (>= 60 Percent), RED: Poor (< 60 Percent), GRAY: Missing Confidence Levels for the Last 12 Hours ending at 200506032300 Individual stations are further graded in terms and frequencies relative to being best or worst at a given time. The COAMPS innovation vector is the difference between the observation and the COAMPS forecast interpolated to the location and time of the observation. The confidence history over the West Coast reveals the hourly confidence level of the reporting group of stations. Since forecasts can lead or lag, the phase of each station is calculated and plotted based upon the offset time with the highest forecast-to-observation history correlation. The scoring distribution and related innovation histogram provide insight into why the confidence is low, due either to a large bias, a large spread or bimodal behavior. The METQC monitor has revealed terrain, coastal, diurnal, phase and seasonal effects in COAMPS forecast quality. Maps of innovation differences, confidence levels and phase offsets can also be overlaid with model grids, satellite images and radar scans to aid diagnosis of observed forecast quality. COAMPS® and COAMPS-OS® are registered trademarks of the Naval Research Laboratory