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SPC Mesoscale Analysis (aka “sfcOA”) Performance and Validation Efforts Steven Weiss, Israel Jirak, Andy Dean, Greg Carbin, Phillip Bothwell, and Corey.

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Presentation on theme: "SPC Mesoscale Analysis (aka “sfcOA”) Performance and Validation Efforts Steven Weiss, Israel Jirak, Andy Dean, Greg Carbin, Phillip Bothwell, and Corey."— Presentation transcript:

1 SPC Mesoscale Analysis (aka “sfcOA”) Performance and Validation Efforts Steven Weiss, Israel Jirak, Andy Dean, Greg Carbin, Phillip Bothwell, and Corey Mead RUA/Nowcasting Workshop Boulder, CO June 4, 2015

2 Assessment of the SPC Mesoscale Analysis (sfcOA) SPC has examined aspects of the RUC/RAP-based sfcOA performance over the last decade –Primarily associated with short-term severe storm forecasting –Is a component of SPC forecaster training –Part of SPC feedback to GSD and EMC Including RUC/RAP model upgrade evaluation input Emphasis has been on subjective assessment of sfcOA performance during selected severe weather cases Some statistical measures are also being developed –Comparison with METAR and raob data

3 Supplementing Observational Data with RAP Model Data SPC Hourly Mesoscale Analysis (Bothwell et al. 2002) –Blends hourly surface data 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/)

4 Supplementing Observational Data with Hourly Model Data The availability of hourly 3-D guidance fields can substantially improve forecaster situational awareness prior to/during severe weather episodes In many instances, it provides very useful diagnostic guidance for short-term severe weather forecasting –It can help forecasters “recover” from inaccurate mesoscale model guidance (such as outlook-scale products) But, it can also give a false sense of security –Forecasters must be cautious in treating these hourly fields as if they are actual observational data –Hourly model grids can and will have errors in key features (e.g., PBL structure)

5 A Few Case Examples RUC/RAP Sounding Impacts on sfcOA Thermodynamic Fields

6 Slidell LA Raob RUC Analysis of Boundary Layer Moisture Severe Weather in Southern Mississippi

7 00z Radar and sfcOA MLCAPE MLCAPE = 100 J/kg 250 500

8 1-hr RUC Sounding for Slidell (6RO) Modified with Observed T/Td Valid 00z 8 Jan 05 Dry low levels in RUC 1-hr fcst MLCAPE=0 -

9 Observed Slidell (LIX) Raob 00z 8 Jan 05 High RH in Raob MLCAPE = 480 Jkg -1

10 Raob (Red/Green) & RUC 1-hr PFC with METAR Slidell Soundings Valid 00z 8 Jan 05 Inaccurate PBL moisture in RUC 1-hr fcst

11 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

12 SPC RAPv2 Evaluation Case: 17 Nov 2013 – High Risk RAP-based sfcOARAPv2-based sfcOA12Z A major severe weather outbreak occurred on this date, including numerous significant tornadoes across IL, IN, and KY. MLCAPE was generally lower across the region in the RAPv2 for this case though the environment was still supportive of a significant tornado threat. MLCAPE/CIN & EFF. SHEAR

13 SPC RAPv2 Evaluation Case: 17 Nov 2013 – High Risk RAP ILX RAPv2 ILX 12Z F00 Difference in MLCAPE is caused by an unrealistic saturated cool layer above the surface in the RAPv2 that was not evident in the RAP or observed sounding. Neither have inversions as strong/sharp as observed. MLCAPE = 1130 J/kgMLCAPE = 812 J/kgMLCAPE = 1189 J/kg OBS ILX 12Z

14 12z Raob CRP Sounding 26 May 2015 MLCAPE 3413 J/kg MLCIN 123 J/kg Note sharp inversion and large MLCIN in observed sounding

15 12z RAP 00-h CRP Sounding 26 May 2015 MLCAPE 4316 J/kg MLCIN 4 J/kg Note larger CAPE, much weaker inversion and lower value of CIN in RAP 00-h sounding

16 12z RAP Precipitation Forecasts Valid 13z 26 May 2015 1-h Accumulated Pcpn Observed Radar Strong CAPE and minimal cap in RAP sounding resulted in early convective development over central/southeast TX CRP

17 12z RAP 01-h CRP Sounding Valid 13z 26 May 2015 RAP forecast soundings maintain large CAPE and minimal CIN through the next 6 hours

18 12z RAP 02-h CRP Sounding Valid 14z 26 May 2015 RAP forecast soundings maintain large CAPE and minimal CIN through the next 6 hours

19 12z RAP 03-h CRP Sounding Valid 15z 26 May 2015 RAP forecast soundings maintain large CAPE and minimal CIN through the next 6 hours

20 12z RAP 04-h CRP Sounding Valid 16z 26 May 2015 RAP forecast soundings maintain large CAPE and minimal CIN through the next 6 hours

21 12z RAP 05-h CRP Sounding Valid 17z 26 May 2015 RAP forecast soundings maintain large CAPE and minimal CIN through the next 6 hours

22 12z RAP 06-h CRP Sounding Valid 18z 26 May 2015 RAP forecast soundings maintain large CAPE and minimal CIN through the next 6 hours

23 12z RAP Precipitation Forecasts Valid 13z 26 May 2015 1-h Accumulated Pcpn Observed Radar Strong CAPE and minimal cap in RAP sounding resulted in erroneous convective storm development over central/southeast TX

24 12z RAP Precipitation Forecasts Valid 14z 26 May 2015 1-h Accumulated Pcpn Observed Radar Strong CAPE and minimal cap in RAP sounding resulted in erroneous convective storm development over central/southeast TX

25 12z RAP Precipitation Forecasts Valid 15z 26 May 2015 1-h Accumulated Pcpn Observed Radar Strong CAPE and minimal cap in RAP sounding resulted in erroneous convective storm development over central/southeast TX

26 12z RAP Precipitation Forecasts Valid 16z 26 May 2015 1-h Accumulated Pcpn Observed Radar Strong CAPE and minimal cap in RAP sounding resulted in erroneous convective storm development over central/southeast TX

27 12z RAP Precipitation Forecasts Valid 17z 26 May 2015 1-h Accumulated Pcpn Observed Radar Strong CAPE and minimal cap in RAP sounding resulted in erroneous convective storm development over central/southeast TX

28 12z RAP Precipitation Forecasts Valid 18z 26 May 2015 1-h Accumulated Pcpn Observed Radar Strong CAPE and minimal cap in RAP sounding resulted in erroneous convective storm development over central/southeast TX

29 Effects of Surface Data QC A Recent Case Illustrating Impacts of Incorrect METAR Data

30 RAP 00-h Dew Point Analysis 20z 9 April 2015 Note dew point minima (<50F) in western/middle TN

31 RAP 00-h SBCAPE Analysis 20z 9 April 2015 This translates into SBCAPE minima in western/middle TN

32 sfcOA Dew Point Analysis 20z 9 April 2015 The dew point minima also is evident in hourly sfcOA Minimum Contour of 48F

33 sfcOA MLCAPE Analysis 20z 9 April 2015 There is a minima in sfcOA MLCAPE as well

34 RAP 00-h Sounding at Franklin, TN 20z 9 April 2015 The RAP sounding displays spurious low-level PBL structure Surface T/Td = 83/43

35 Surface METAR Plot 21z 9 April 2015 The RAP sounding displays spurious low-level PBL structure Two METAR stations in TN had been reporting “frozen” dew points of 32F for several hours and these became part of the RAP and sfcOA analyses The QC for the RAP and sfcOA were unable to “toss out” noticeably erroneous data

36 A New Objective Validation Measure Under Development at SPC sfcOA Parameter Comparisons with Raobs

37 sfcOAp Comparison to Raobs Tool Example – ML Mixing Ratio 00z 31 May 2015 15 Convective Parameters for sfcOA-raob comparison Icons indicate sign and magnitude of difference field Data list and difference field for each raob site Scatter-plot and statistical measures

38 sfcOA Comparison to Raobs Tool Example – ML Mixing Ratio 00z 31 May 2015 This tool is in first stages of development Data collection began April 14 Plans include creation of cumulative statistical performance metrics and stratification by raob time, region, and months/seasons

39 Summary – SPC Validation of sfcOA sfcOA provides SPC & NWS forecasters with enhanced situation awareness of the mesoscale environment –Focused applications for severe storms, heavy rain, winter weather and fire weather Significant challenges are associated with resolving an accurate PBL structure and its short-term temporal evolution –Improvements are needed in observing systems (especially for vertical details of water vapor distribution), data assimilation, and quality control of data


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