EMC Operational Models
Real Time Mesoscale Analysis/
UnRestricted Mesoscale Analysis
Manuel Pondeca, Jacob Carley, Steve Levine,
Runhua Yang, Ying Lin, and Annette Gibbs
Environmental Modeling Center
IMSG and NOAA / NWS / NCEP
Manuel.Pondeca@NOAA.gov
UMAC data call
Real Time Mesoscale Analysis and UnRestricted Mesoscale Analysis
page 1 of 6
The RTMA/URMA
Real Time Mesoscale Analysis
Un-Restricted Mesoscale Analysis
•
Hourly 2DVar 2.5 km surface analysis for
National Digital Forecast Database
– GSI (Wu et. al. 2002)
–
•
URMA is the analysis of record in the National
Blend of Global Models project
–
•
–
–
–
•
•
•
URMA runs 6 hours later to account for latearriving data
Guess/Background field
–
•
CONUS, AK (3 km), PR, HI, Guam
CONUS: Downscaled NAM CONUS nest +
HRRR blend
HI and PR: Downscaled NAM nests
AK: Downscaled RAP and 6 km NAM nest
Downscaled GFS for Guam
NCEP Gridpoint Statistical Interpolation (GSI)
Analysis (Wu et al, 2002)
Use all available surface observations
(METAR, surface synop, ship, buoy, mesonet)
Satellite obs for sky cover, near-sfc winds,
surface wave height
Recent implementation briefing (April 2015)
UMAC data call
Real Time Mesoscale Analysis and UnRestricted Mesoscale Analysis
page 2 of 6
Future Plans
• Near term:
– Variational quality control
– New analysis variables (ceiling, significant wave height, max T, and Min T)
• Long(er) term:
– See Rapidly Updating Analysis draft white paper
• Presentations from June 2015 workshop featuring attendees from a
variety NOAA stakeholders: http://ruc.noaa.gov/rua/
• Go from 2-D to 3-D Rapid Update Analysis
• More frequent updates (at least every 15 mins! Closer to 5 mins
eventually)
• Major need from a diverse set of customers (aviation, helicopter
emergency response, surface transportation, situational awareness,
air quality)
– Observational quality control → Always ongoing
– New and diverse sources of data
– Advanced analysis methods (ensemble component, multigrid, etc.)
UMAC data call
Real Time Mesoscale Analysis and UnRestricted Mesoscale Analysis
page 3 of 6
How to View RTMA/URMA
Official Public Source: NCEP MAG Page
Real-time plots: Developer maintained RTMA and URMA
(parallels when testing)
Interactive viewer (intra-NOAA): RTMA/URMA comparison
UMAC data call
Real Time Mesoscale Analysis and UnRestricted Mesoscale Analysis
page 4 of 6
Field/Research feedback
• AOR/RTMA listserv (aor-rtma@infolist.nws.noaa.gov)
– A source for discussion, suggestions, questions, comments about
RTMA/URMA with users and RTMA/URMA developers
– An absolutely vital resource in facilitating improvement in the
RTMA/URMA
• Monthly conference calls with field, NWS regions
• Implementation briefings to NWS regions, SOOs, and
DOHs
• Possible VLab page at some point
A significant number of upgrades occur in RTMA/URMA as a direct result of
interacting with users.
This will continue and has been a fundamental aspect of RTMA/URMA
development.
Especially with the most recent upgrade.
UMAC data call
Real Time Mesoscale Analysis and UnRestricted Mesoscale Analysis
page 5 of 6
References
GSI:
Wu, W.-S., R. J. Purser, and D. F. Parrish, 2002: Three-dimensional variational analysis with spatially inhomogeneous
covariances. Mon. Wea. Rev., 130, 2905–2916.
RTMA:
De Pondeca, M. S. F. V., and Coauthors, 2011: The Real-Time Mesoscale Analysis at NOAA's National Centers for
Environmental Prediction: Current status and development. Wea. Forecasting, 26, 593–612.
Anisotropic Recursive Filters:
Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts, 2003a: Numerical aspects of the application of recursive filters to
variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances. Mon. Wea. Rev., 131, 1524–
1535.
Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts, 2003b: Numerical aspects of the application of recursive filters to
variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances. Mon. Wea. Rev., 131, 1536–
1548.
Purser, R. J., 2005: A geometrical approach to the synthesis of smooth anisotropic covariance operators for data assimilation.
NOAA/NCEP Office Note 447, 60 pp.
Analysis Error Estimation (Lanczos method):
Fisher, M., and P. Courtier, 1995: Estimating the covariance matrices of analysis and forecast error in variational data assimilation.
ECMWF Tech. Memo. 220, 29 pp.
UMAC data call
Real Time Mesoscale Analysis and UnRestricted Mesoscale Analysis
page 6 of 6