Part II  Access to Surface Weather Conditions:  MesoWest & ROMAN  Surface Data Assimilation:  ADAS.

Slides:



Advertisements
Similar presentations
1st National Weather and Climate Enterprise Partnership Summit John Horel NOAA Cooperative Institute for Regional Prediction Department of Meteorology.
Advertisements

Chapter 13 – Weather Analysis and Forecasting
59.4 m AGL wind: 14 UTC 3 May 2012 – 14 UTC 4 May 2012 Ramp-up event ~22-01 UTC; ramp-down event ~5-7 UTC 15.2 m and 59.4 m Wind Speed: 0-24 UTC 22 Feb.
Observing, Analyzing, and Simulating the Boundary Layer in Northern Utah and Beyond John Horel, Erik Crosman, Xia Dong, Matt Lammers, Neil Lareau, Dan.
Acknowledgments Jennifer Fowler, University of Montana, Flight Director UM-BOREALIS Roger DesJardins, Canadian East Fire Region, Incident Meteorologist.
2012: Hurricane Sandy 125 dead, 60+ billion dollars damage.
For the Lesson: Eta Characteristics, Biases, and Usage December 1998 ETA-32 MODEL CHARACTERISTICS.
ROMAN: Real-Time Observation Monitor and Analysis Network John Horel, Mike Splitt, Judy Pechmann, Brian Olsen NOAA Cooperative Institute for Regional Prediction.
Case Study: Using GIS to Analyze NWS Warnings Ken Waters NWS Phoenix June 5, 2008.
Update on the Regional Modeling System NASA Roses Meeting April 13, 2007.
RTMA (Real Time Mesoscale Analysis System) NWS New Mesoscale Analysis System for verifying model output and human forecasts.
Update on the Regional Modeling System NASA Roses Meeting April 13, 2007.
MesoWest - Monitoring Weather Conditions around the West and the Nation
Rapid Update Cycle Model William Sachman and Steven Earle ESC452 - Spring 2006.
Daniel P. Tyndall and John D. Horel Department of Atmospheric Sciences, University of Utah Salt Lake City, Utah.
Update on the Regional Modeling System Cliff Mass, David Ovens, Richard Steed, Mark Albright, Phil Regulski, Jeff Baars, David Carey Northwest Weather.
ROMAN: Real-Time Observation Monitor and Analysis Network John Horel, Mike Splitt, Judy Pechmann, Brian Olsen NOAA Cooperative Institute for Regional Prediction.
Brian Ancell, Cliff Mass, Gregory J. Hakim University of Washington
Transitioning unique NASA data and research technologies to the NWS 1 Evaluation of WRF Using High-Resolution Soil Initial Conditions from the NASA Land.
Weather Model Background ● The WRF (Weather Research and Forecasting) model had been developed by various research and governmental agencies became the.
Consortium Meeting June 3, Thanks Mike! Hit Rates.
Analysis of Record Issues: Research Perspective John Horel NOAA Cooperative Institute for Regional Prediction Department of Meteorology University of Utah.
Robert LaPlante NOAA/NWS Cleveland, OH David Schwab Jia Wang NOAA/GLERL Ann Arbor, MI 22 March 2011.
Chapter 13 – Weather Analysis and Forecasting. The National Weather Service The National Weather Service (NWS) is responsible for forecasts several times.
Applied Meteorology Unit 1 An Operational Configuration of the ARPS Data Analysis System to Initialize WRF in the NWS Environmental Modeling System 31.
Chapter 9: Weather Forecasting
Geostatistical approach to Estimating Rainfall over Mauritius Mphil/PhD Student: Mr.Dhurmea K. Ram Supervisors: Prof. SDDV Rughooputh Dr. R Boojhawon Estimating.
3DVAR Retrieval of 3D Moisture Field from Slant- path Water Vapor Observations of a High-resolution Hypothetical GPS Network Haixia Liu and Ming Xue Center.
Woody Roberts Tom LeFebvre Kevin Manross Paul Schultz Evan Polster Xiangbao Jing ESRL/Global Systems Division Application of RUA/RTMA to AWIPS and the.
VERIFICATION OF NDFD GRIDDED FORECASTS IN THE WESTERN UNITED STATES John Horel 1, David Myrick 1, Bradley Colman 2, Mark Jackson 3 1 NOAA Cooperative Institute.
Slide 1 Impact of GPS-Based Water Vapor Fields on Mesoscale Model Forecasts (5th Symposium on Integrated Observing Systems, Albuquerque, NM) Jonathan L.
Part III: ROMAN and MesoWest: resources for observing surface weather  MesoWest and ROMAN are software that require ongoing maintenance and development.
Managing Complexity with Multi-scale Travel Forecasting Models Jeremy Raw Office of Planning Federal Highway Administration May 11, 2011.
Analysis of Record Andy Devanas Jeff Medlin Charlie Paxton Pablo Santos Dave Sharp Irv Watson Pat Welsh Suggestions and Concerns from the Florida Science.
VERIFICATION OF NDFD GRIDDED FORECASTS USING ADAS John Horel 1, David Myrick 1, Bradley Colman 2, Mark Jackson 3 1 NOAA Cooperative Institute for Regional.
Higher Resolution Operational Models. Operational Mesoscale Model History Early: LFM, NGM (history) Eta (mainly history) MM5: Still used by some, but.
Integration of Surface Weather Observations with MODIS Imagery for Fire Weather Applications Mike Splitt, Brian Olsen, John Horel, Judy Pechmann NOAA Cooperative.
Synthesizing Weather Information for Wildland Fire Decision Making in the Great Lakes Region John Horel Judy Pechmann Chris Galli Xia Dong University of.
National Weather Service Goes Digital With Internet Mapping Ken Waters National Weather Service, Honolulu HI Jack Settelmaier National Weather Service,
P1.7 The Real-Time Mesoscale Analysis (RTMA) An operational objective surface analysis for the continental United States at 5-km resolution developed by.
Robert LaPlante NOAA/NWS Cleveland, OH David Schwab Jia Wang NOAA/GLERL Ann Arbor, MI 15 March 2012.
VERIFICATION OF NDFD GRIDDED FORECASTS USING ADAS John Horel 1, David Myrick 1, Bradley Colman 2, Mark Jackson 3 1 NOAA Cooperative Institute for Regional.
Central Region Snowfall Analysis Brian P. Walawender NWS Central Region Headquarters Matt W. Davis NWS WFO La Crosse, WI 5/26/2011.
1 NOHRSC Challenges of using Snow Data Carrie Olheiser Office of Hydrologic Development National Weather Service, NOAA U.S. Department of Commerce National.
Applied Meteorology Unit 1 High Resolution Analysis Products to Support Severe Weather and Cloud-to-Ground Lightning Threat Assessments over Florida 31.
Wind Gust Analysis in RTMA Yanqiu Zhu, Geoff DiMego, John Derber, Manuel Pondeca, Geoff Manikin, Russ Treadon, Dave Parrish, Jim Purser Environmental Modeling.
ROMAN: Real-Time Observation Monitor and Analysis Network John Horel, Mike Splitt, Judy Pechmann, Brian Olsen NOAA Cooperative Institute for Regional Prediction.
Local Analysis and Prediction System (LAPS) Technology Transfer NOAA – Earth System Research Laboratory Steve Albers, Brent Shaw, and Ed Szoke LAPS Analyses.
Rapid Update Cycle-RUC. RUC A major issue is how to assimilate and use the rapidly increasing array of offtime or continuous observations (not a 00.
Local Analysis and Prediction System (LAPS) Technology Transfer NOAA – Earth System Research Laboratory Steve Albers, Brent Shaw, and Ed Szoke LAPS Analyses.
The National Weather Service Goes Geospatial – Serving Weather Data on the Web Ken Waters Regional Scientist National Weather Service Pacific Region HQ.
Intelligent Use of LAPS • By • Ed Szoke • 16 May 2001.
NOAA Northeast Regional Climate Center Dr. Lee Tryhorn NOAA Climate Literacy Workshop April 2010 NOAA Northeast Regional Climate.
Translating Advances in Numerical Weather Prediction into Official NWS Forecasts David P. Ruth Meteorological Development Laboratory Symposium on the 50.
1 Application of MET for the Verification of the NWP Cloud and Precipitation Products using A-Train Satellite Observations Paul A. Kucera, Courtney Weeks,
7. Air Quality Modeling Laboratory: individual processes Field: system observations Numerical Models: Enable description of complex, interacting, often.
Hydrogeodesy Training Session Approximately 15 attendees, Three seminars 1. Introduction to Hydrogeodesy 2. Introduction to Data Assimilation 3. Introduction.
A new way of looking at things, doing things…and some new things.
Web Services for Fire Weather Applications
Oil Spill Model Systems
WRF Four-Dimensional Data Assimilation (FDDA)
Rapid Update Cycle-RUC
Overview of Deterministic Computer Models
NWS Forecast Office Assessment of GOES Sounder Atmospheric Instability
Winter storm forecast at 1-12 h range
Alexander A. Jacques, John D
Rapid Update Cycle-RUC Rapid Refresh-RR High Resolution Rapid Refresh-HRRR RTMA.
Spatial interpolation
P2.5 Sensitivity of Surface Air Temperature Analyses to Background and Observation Errors Daniel Tyndall and John Horel Department.
Presentation transcript:

Part II  Access to Surface Weather Conditions:  MesoWest & ROMAN  Surface Data Assimilation:  ADAS

MesoWest and ROMAN (Real-time Observation Monitor and Analysis Network) MesoWest/ROMAN Development Team: John Horel Mike Splitt Judy Pechmann Brian Olsen

 Real-time collection of weather observations from over 5000 stations and 120 participating organizations  Data processing, QC, and graphics generation every 15 min  Observations in areas not sampled by NWS/FAA or RAWS networks  Improved analysis/diagnosis of local and regional wind systems  Specialized interfaces for fire weather, RWIS, wind power applications  Distributed to WFOs by LDM MesoWest Horel et al. 2002: Bull Amer. Meteor. Soc.

MesoWest User Interface Redesign

ROMAN  Software developed at CIRP to assist entire fire weather community, including NWS forecasters at WFOs and IMETs, to obtain access to current surface weather information  Support for development of ROMAN from NWS through CIRP base funding and from fire agencies in support of NIFC and GACC meteorologists  Builds upon MesoWest database to store and display observations nationwide  Tools designed for fire weather applications can be used for many other purposes Geographic Area Coordination Centers

MesoWest/ROMAN  Designed for quick access to data from variety of networks  Tabular and graphical formats geared to operational fire weather needs  Structured by  GACCs  NWS CWAs  NWS Fire Weather Zones  States  Intuitive, easily navigable interface  Clickable maps  Station Weather  Weather Summary  Trend Monitor  Weather Monitor  5 Day Temp/RH Summary  Precip Summary/Monitor  Weather Near Fires  Search by zip code, geographic location

State Map

Station Interface

Weather Near Fires

Weather Near Biscuit Fire

Location Search

Current Weather Summary

Trend Monitor

MODIS Interface

Plan for 2004 Fire Season CIRP Data Sources Fire Wx User Community Dedicated Comms Web Server Boise WFO/ NIFC LDM AWIPS/ FX-NET/ GFE RAWS

Local Data Assimilation: ADAS  Utah ARPS Data Assimilation System (ADAS)  Mesoscale analyses require different assimilation techniques than those on a national scale, especially in complex terrain  Local analysis serves as a visual and numerical integrator of the MesoWest surface observations  Background and terrain fields help to build spatial & temporal consistency in the surface fields  Analyses serve as an additional quality control step to the MesoWest observations

What is ADAS?  ADAS is short for the Advanced Regional Prediction System (ARPS) Data Assimilation System (Xue et al. 2000, 2001a,b)  At CIRP, ADAS is run in near-real time to create analyses of meteorological variables over the complex topography of the western U. S.  10km analysis every 15 minutes; 2.5 km analysis once per hour  ADAS employs the Bratseth method of successive corrections (Bratseth 1986) to complete the objective analysis  The 20km Rapid Update Cycle (RUC; Benjamin et al. 2002) is used for the background field  ADAS can be used for nowcasting and as a verification tool by National Weather Forecast offices

Use of MesoWest in Data Analysis  Integration of weather resources into single analysis product  Many local data sources are not used in national-scale data assimilation systems  Local analysis graphics serve as a visual integrator of the MesoWest surface observations  Weather over complex terrain of Intermountain West depicted more accurately

Maximum Temperature: Monday. April Tax Day Storm: April 15, 2002

Tax Day Storm: April 15, 2002 Bagley. Salt Lake Tribune Maximum Sustained Wind Speed (mph)

ADAS Graphical Interface

 Depends on:  the application  Initializing numerical forecast?  Specifying atmospheric state for verification?  the dominant scales of motion  data spacing  Mesonet observations  Radar/satellite observations  available computational resources  Successive corrections, OI, 3/4-D Var  See Kalnay (2003) and Lazarus et al. (2002) for more details What is a Good Analysis?

Data Analysis Analysis value = Background value + observation Correction - A good analysis requires a good background field - Background fields are supplied by a model forecast - A good analysis requires a good previous model forecast - Observation correction depends upon weighted differences between observations & background values at observation locations - Weights typically depend upon: - distance of observations from analysis grid point - Expected error of observations - Expected error of background field

An analysis is more than spatial interpolation  Background field provides  Information where few observations  Avoids extrapolation far from observations  Provides detail between observations  Introduces dynamical consistency  Typical errors of observations and background field are considered  Data used in analysis are not limited to analysis/ forecast variables  Knowledge of atmospheric behavior can be used to relate 1 variable to another  Scales of motion too small to be resolved by forecast model can be removed

Data Assimilation in Complex Terrain Data Assimilation in complex terrain must be able to handle a wide range of scale interactions: Strongly forcedWeakly forcedElevated Valley Inversions O O ?O O ? O O ? T z

Key Points  High resolution analysis based upon coarse background field and sparse data is simply downscaling to specified grid terrain  High resolution analysis adds value IFF:  high resolution data sources are available  OR the background field is at high resolution  Spatial scales specified by weighting functions determine degree to which observed local weather variations can be resolved by the analysis

What added value does ADAS provide?

Part II: Summary  MesoWest/ROMAN/ADAS under development for use by weather professionals  Government server with 24/7 support by next summer  Tools can be adjusted to meet needs for office and field use  Feedback:

Mini-Lab  Goal- increase familiarity with MesoWest/ROMAN/ADAS tools  Evaluate and apply tools to your CWA  What observations do you have access to at your WFO that are not available in MesoWest/ROMAN?