1. Jason Levit NOAA NextGen Weather Program June, 2013
Real-Time Mesoscale Analysis Review and Plans for Rapid Updating Analysis
Jason Levit
NOAA NextGen Weather Program
June, 2013

2. Agenda RTMA Evolution: 2006  Today Drivers for RTMA Background
Development Organization
Analysis Domains and Resolution
RTMA Techniques
Plans for Near Term Enhancements to RTMA
Plans for Long Term Transition to Rapid Updating Analysis
Characteristics of the Multiple-Radar Multiple-Sensor project
Discussion

3. RTMA 2006 - Today Drivers for RTMA Development: Background:
Verification of NDFD forecasts
Initialization of gridded forecasts at Weather Forecast Offices
Situational awareness for sensible weather
Background:
Requirements defined in OSIP
Initial analyses for CONUS in 2006
OCONUS analyses added in 2008
Official NWS product in 2011
NextGen Program funding in FYs 2010 and 2011
Development Organizations:
NCEP, Environmental Modeling Center
Geoff DiMego, Federal Manager
Manuel Pondeca, Developer
Steve Levine, Developer
Yanqiu Zhu, Developer
Stan Benjamin, ESRL/GSD, Developer

4. Analyses + Resolution + Domains
Wind Speed and Direction
Temperature
Dew Point Temperature
Surface Pressure
Effective Cloud Amount – (remapped GOES by NESDIS)
Accumulated precipitation (remapped Stage 2 by Ying Lin)
Analysis Uncertainty:
Wind Speed
Wind Direction
Cross-validation:
A subset of observations are withheld
Scores computed for each analysis
Model Terrain:
Fixed field
Hourly Domains:
CONUS (5 and 2.5 km)
Hawaii (2.5 km)
Alaska (6 km)
Puerto Rico (2.5km)
3 hourly Domain:
Guam (2.5km)

5. RTMA Techniques Analysis Generation:
First Guess obtained from Rapid Refresh downscaled to NDFD domain
First Guess at the Appropriate Time (FGAT)
Terrain following background error covariance
Gridded Statistical Interpolation (GSI) used in Two Dimensional Variation (2D Var) mode
Observations from Meteorological Assimilation Data Ingest System (MADIS):
Satellite derived winds
ASOS – METAR
Mesonet
Buoy, ship, tide gage and Coastal-Marine Automated Network (CMAN)
Approximately 15,000 observations used per analysis
Quality control of observations beyond that done in MADIS:
Gross error check
Predefined reject list of sites from WFOs
Reject selected mesonet winds

6. Cross-Validation
Make multiple disjoint datasets for each ob type, each containing about 10% of the data. Datasets contain representative data from all the geographical regions observed but without the redundancy of close pairs or tight clusters
For each analysis, randomly pick one of the disjoint datasets to use for cross-validation

7. PARALLEL RTMA CONUS-2.5 km

8. PARALLEL RTMA CONUS-2.5 km
Note: Analyzed at the 10-m level

9. PARALLEL RTMA CONUS-2.5 km

10. RTMA Enhancements Planned 2013
3 km Analysis upgrade for Alaska
1.5 KM Analysis domain for Juneau
2.5 km Analyses for Northwest RFC domain
Science and quality control technique improvements:
Improved handling of snowpack in RAP
Winds from Hurricane WRF added to improve analyses of tropical cyclones
New analysis variables:
Wind gusts
Visibility

11. Next Steps for RTMA Development
Explore potential for additional Aviation impact analysis variables:
Total cloud cover
Cloud base heights
Mean sea level pressure
Continue to enhance quality control of observations:
Real-time monitoring system
Real-time data mining
Add metadata into GSI
Improved Land sea mask
Delayed Mesoscale Analysis:
Run 4 hours after RTMA
Collects more complete set of observations
Improved product verification
RTMA will continue to be available
Enables transition to Analysis of Record capability

12. Transition to Rapid Updating Analysis
Rapid Updating Analysis (RUA)
Benefits:
Enhance forecaster situational awareness
Enable issuance of warnings and forecasts with greater lead time and accuracy
Provide a more accurate data set for model and forecast verification
Concepts:
Updated every five minutes
1km horizontal resolution
Expands coverage to atmosphere
Uses satellite, radar and soundings (aircraft, etc.)
Phased Implementation: Use the MRMS in the initial phase and then moving to a GSI-based system
Multiple-Radar-Multiple-Sensor (MRMS) system serves as the initial backbone
VIL
Vertical wind shear
Precipitating species (hail)
Lightning
Reflectivity and radar quality
(see spreadsheet for list of variables)
Products will execute on NCEP mainframe
At full capability, will generate the most state-of-the-art analyses of the atmosphere currently possible, with the best scientific techniques
RUA data will serve as both a real-time analysis and eventually as initialization for high resolution models for Warn-on-Forecast applications
Utilizes the GSI framework for code compatibility across NOAA

13. What is MRMS? MRMS = Multiple-Radar / Multiple-Sensor
Multi-Radar: Exploits the overlapping coverage of the WSR-88D network and the Level-II real-time data feeds to build a seamless rapidly-updating high-resolution three-dimensional cube of radar data.
Multi-Sensor: Objectively blends data from the multiple-radar 3D cubes with surface, upper air, lightning, satellite, rain gauges, and NWP environmental data, to produce highly-robust decision assistance products.
Improvements demonstrated in QPE, severe weather diagnosis, warning decision efficiency, NWP, etc.
MRMS

14. MRMS Outputs 3D Reflectivity CONUS cube 3D Azimuthal Shear CONUS cube