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

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

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

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)

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

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

PARALLEL RTMA CONUS-2.5 km

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

PARALLEL RTMA CONUS-2.5 km

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

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

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

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

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