1. Storm Prediction Center Requirements for RUA Steven Weiss and Russell Schneider RUA/Nowcasting Workshop Boulder, CO June 3, 2015

2. Introductory Comments Views represent SPC severe storm analysis and forecast perspective –Requires close attention to available observational data to assess current state of the atmosphere w.r.t convective storms –Yes, SPC forecasters continue to perform hand analysis of charts because linking observations to analysis is important But they only show part of the picture –Aligned with FACETs, Warn on Forecast, and WRN SPC’s RUA convective storm analysis vision should also apply to hazardous winter weather, heavy rain/flash flooding, and other significant weather events

3. Historical Background SPC’s traditional focus on short-range severe storm forecasting has emphasized use of observational data to assess the current state of the atmosphere Analysis of current atmospheric state and recent trends is foundational for short-term forecasting Ideally, we need to know the current atmospheric state and how it reached this point to determine future state Past (Trends) Current (Analysis) Future (Forecast) From Bosart, WAF (2003) “Whither the Analysis and Forecasting Process”

4. Historical Background Surface and radiosonde data formed basis for initial analysis and short-term forecasting efforts Surface Analysis 850 mb Analysis Sounding Analysis

5. Historical Background Surface and radiosonde data formed basis for initial analysis and short-term forecasting efforts As new observational data became available, these supplemented & filled gaps in basic observing network –Radar, satellite, lightning, mesonetworks, aircraft, etc.

6. Historical Background Surface and radiosonde data formed basis for initial analysis and short-term forecasting efforts As new observational data became available, these supplemented and filled gaps in basic observing network –Radar, satellite, lightning, mesonetworks, aircraft But gaps in observational data still exist –Example: Detailed 3-D distribution of water vapor remains a key missing ingredient for accurate thunderstorm prediction GOES WV Imagery Satellite Blended TPW

7. Historical Background Surface and radiosonde data formed basis for initial analysis and short-term forecasting efforts As new observational data became available, these supplemented and filled gaps in basic observing network –Radar, satellite, lightning, mesonetworks, aircraft But gaps in observational data still exist –Example: Detailed 3-D distribution of water vapor remains a key missing ingredient for more accurate thunderstorm prediction Since the 1990s, SPC has attempted to fill these gaps through the use of NWP model 00-hr analyses and short-term forecasts in local analysis programs

8. Supplementing Observational Data with RAP Model Data SPC Hourly Mesoscale Analysis (Bothwell et al. 2002) –Blends hourly surface data OA with RAP “free atmosphere” analysis (or 1-hr forecast) to create “best” 3-D estimate of environment –Each grid point profile processed by N-SHARP sounding analysis program to compute hundreds of parameters –Parameter fields displayed in N-AWIPS and distributed on SPC web page (www.spc.noaa.gov/exper/mesoanalysis/)

9. NWP Model Analyses The primary goal of NWP models is to produce the best forecast possible Data assimilation schemes/initialization procedures are fine-tuned to meet this goal, which can be inconsistent with a secondary goal of producing the best “analysis” 00-h Models Analyses Compared to 00z Raobs (2013) NAM GFS RAP Heights Relative Humidity RMSE BIAS

10. NWP Model Analyses The primary goal of NWP models is to produce the best forecast possible Data assimilation schemes/initialization procedures are fine-tuned to meet this goal, which can be inconsistent with a secondary goal of producing the best “analysis” The “best” initial conditions for a model forecast may not be the same as the “best” analysis for forecaster diagnostic purposes

11. RAP Data Assimilation The RAP has dual purposes to provide the “best” analyses and short-term forecasts In most instances, the RAP provides SPC forecasters with very useful mesoscale environment information But challenges exist in the multi-sensor data assimilation process, especially for thermodynamic variables –How do you extend surface METAR data vertically to provide “representative” boundary layer profiles? –How do you partition GPS IPW into “appropriate” vertical profiles of moisture?

12. RAP 00-h Analysis 21z 2 March 2012 Ohio Valley Tornado Outbreak RAP 00-h Sounding at SDF Mesoanalysis MLCAPE RAP soundings in this region are too dry immediately above the surface resulting in underestimate of MLCAPE during major tornado outbreak Max Value 500 J/kg

13. 2017 - SPC Potential Products/Services and Attributes of Operational Supporting NWP Probabilistic Outlooks of Tornado, Severe Hail, and Severe Wind Hazards extended to Day 3 Day 1 Probabilistic Outlooks of Tornado, Severe Hail, and Severe Wind Hazards for 4-hr periods for Days1 and 2 Hourly Updated, Short Term Probability Forecasts for Tornado, Severe Hail, Severe Wind, and Significant Severe Hazards to support transition to continuously evolving public severe weather watches Week 2 and Monthly Severe Weather Outlooks (with CPC) * NWP capabilities are dependent on improvements in NOAA high performance computing resources and advances in data assimilation, physics, ensemble perturbation strategies, etc. For “Warn on Forecast” vision to be met, a dedicated community- wide Severe Weather Modeling focus is needed Storm Scale 3D Analysis: 2 km EnKF/hybrid or newer state-of-the-art DA, CONUS storm scale analysis updated every 15 min North America Short-Range Mesoscale Ensemble: 12-15 km ~40 member multi- model/multi-physics/multi-IC ensemble with EnKF/hybrid or newer-state-of-the-art DA, run every 6 hrs with forecasts to Day 4* CONUS Storm Scale Ensemble Forecast (SSEF): 3 km CONUS ~15 member multi- model/multi-physics/multi-IC storm scale ensemble with EnKF/hybrid or newer state-of- the-art DA, issued every 6-hrs with forecasts to 48-60 hrs* Movable domain update SSEF: 2 km movable regional domain 10-15 member storm scale ensemble with EnKF/hybrid or newer state-of- the-art DA,run every 1-2 hrs with forecasts to 18-24 hrs*, focused on “severe weather of the day” areas Appropriate RAP/HRRR, Global deterministic / ensemble, and CFS capacities

14. SPC Vision for RUA The Basics RUA should be the best representation in real-time of the current state of the mesoscale & storm scale atmosphere RUA should use the power of advanced data assimilation to incorporate traditional observations and remote sensing data such as –Radar (reflectivity, dual-pol hydrometeors, velocity) –Satellite (radiances, cloud water path) –Total lightning (flash rate density) Long-term goal is to provide fully integrated and dynamically consistent real-time view of the mesoscale/storm scale environment –Including storm attribute fields themselves and feedback from convective storms to the near-storm environment

15. NSSL 3DVAR Real-Time Analysis 2120z 6 May 2015 http://www.nssl.noaa.gov/users/jgao/public_html/analysis/ Storm Reports 12-12z Reflectivity, Vorticity, Wind at 3 km (See Calhoun et al. WAF 2014) Vertical Vorticity Maxima Tornado Hail Wind

16. NSSL 3DVAR Real-Time Analysis 1100z 27 April 2015 http://www.nssl.noaa.gov/users/jgao/public_html/analysis/ (See Calhoun et al. WAF 2014) Vertical Vorticity Maxima Tornado Hail Wind Vertical Vorticity Maxima (Bookend Vortex) Severe Reports 09-12z Reflectivity, Vorticity and Wind at 3 km Vertical Vorticity Maxima (Embedded Mesocyclone)

17. SPC Vision for RUA How to Get There? MRMS may be part of early process –Integration of radar data with other observational data –But data assimilation is necessary to provide scale-appropriate atmospheric structures RUA will likely be successful initially on MCS scales –What will it take to move downscale toward individual convective storms and supercells in order to accurately resolve Convective storm attributes Storm feedback to surrounding environment Dynamically consistent near-storm environment fields Underlying premise –RUA requires high degree of fidelity with actual observations

18. Conceptual Model of Linear MCS Can We Analyze Radar, Mass Fields and Airflow in Storms and the Near-Storm Environment?

19. SPC Vision for RUA Final Phase Forecasters would look primarily at RUA instead of individual observational datasets (including radar) –Ultimately includes radar integrated into storm scale analyses When the difference between mesoscale/convective scale “Best Analysis” and “Best IC” becomes increasingly smaller –Forecasters will use RUA for both analysis, and –Short-term hazardous weather decision making RUA will lead to improvements in –Conceptual models of convective storms and storm systems –Understanding of relationship between storms and environment –Forecaster ability to assess threats as part of WoF and WRN

20. Surface Mesoanalysis (1956)

21. Surface Mesoanalysis Preface to Mesoanalysis, by Fujita, Newstein, and Tepper (1956) “The primary purpose of this paper is to present a significant scale of meteorological events. This scale, the mesoscale, is either overlooked or intentionally ignored in much of the analysis being done today. The reason normally given for this omission is that these meteorological motions are essentially “noise” superimposed on larger-scale circulations, and it is with these larger scale circulations that most meteorologists efforts have been concerned. It is our opinion, however, that this “noise” is directly tied up with the local weather, and the meteorologist will have to despair of ever being able to predict this local weather…unless he has an accurate knowledge of what is going in the mesometeorological scale of motion.”

22. Surface Mesoanalysis Preface to Mesoanalysis, by Fujita, Newstein, and Tepper (1956) “The primary purpose of this paper is to present a significant scale of meteorological events. This scale, the convective storm scale, is either overlooked or intentionally ignored in much of the analysis being done today.. The reason normally given for this omission is that these meteorological motions are essentially “noise” superimposed on larger-scale circulations, and it is with these larger scale circulations that most meteorologists efforts have been concerned. It is our opinion, however, that this “noise” is directly tied up with the local weather, and the meteorologist will have to despair of ever being able to predict this local weather…unless he has an accurate knowledge of what is going in the convective storm scale of motion.”