# Presentation: 869872

## Ways of Viewing and Interpreting Ensemble Forecasts:Applications in Severe Weather Forecasting  

**David Bright**
*NOAA/NWS/Storm Prediction Center*
*Norman, OK*
**AMS Short Course on **
**Ensemble Prediction: Conveying Forecast Uncertainty**
- 14 January 2007
- San Antonio, TX

*Where Americas Climate and Weather Services Begin*

## Outline

Introduction 

Applications in Severe Weather Forecasting

Fire Weather
Winter Weather
Severe Convective Weather

Summary

- Introduction
- Applications in Severe Weather Forecasting
- Fire Weather
- Winter Weather
- Severe Convective Weather
- Summary

**Notes:**
Begin with an introduction as to where I’m coming from so you can understand my perspective on the problem of high-impact, mesoscale forecasting.  

## Outline

Introduction 

Applications in Severe Weather Forecasting

Fire Weather
Winter Weather
Severe Convective Weather

Summary

**Introduction**
- Applications in Severe Weather Forecasting
- Fire Weather
- Winter Weather
- Severe Convective Weather
- Summary

**Notes:**
If you’re not familiar with the NCEP... it is within the NWS and the SPC serves as one of the nine centers for national guidance and prediction services.

## STORM  PREDICTION CENTER 

**MISSION STATEMENT**
-  **The Storm Prediction Center (SPC) exists **
**solely to protect life and property of the American people**
**through the issuance of timely, accurate watch and forecast products **
**dealing with tornadoes, wildfires and other hazardous mesoscale weather phenomena**.

**MISSION STATEMENT**
-  **The Storm Prediction Center (SPC) exists **
**solely to protect life and property of the American people**
**through the issuance of timely, accurate watch and forecast products dealing with hazardous mesoscale weather phenomena**.

## Hail, Wind, Tornadoes
Excessive rainfall
Fire weather 
Winter weather

- Hail, Wind, Tornadoes
- Excessive rainfall
- Fire weather
- Winter weather

**STORM  PREDICTION CENTER**

- HAZARDOUS PHENOMENA

**Notes:**
Flagship product is the severe convective weather watch and accompanying products...but also provide short-term, high resolution mesoscale forecasts in excessive convective pcpn, winter wx, and fire weather.  Also extending outlook services through 8 days.  When I speak of severe weather over the next 50 minutes, I’m referring to any of these high-impact, mesoscale events and not necessarily just convection.

## Severe Weather Forecasting 

-  _**The Challenge**_:  High impact events often occur on temporal and spatial scales below the resolvable resolutions of most **observing and forecasting** systems
-  _**Key premise**_:  We must use knowledge of the environment and non-resolved processes to determine the spectrum of severe weather possible, where and when it may occur, and how it may evolve over time

**Notes:**
Ignoring all possible error sources in fcstg and observing systems (i.e., perfect measurements and models!), high impact events still occur at time and space scales below the resolvable resolution of most obs and fcstg systems.  Thus...

## Severe Weather Forecasting

**Observational data and diagnostic tools**
- Key input for short-term prediction, i.e., “Nowcast”
- But high-impact weather events typically occur on scales smaller than standard observational data
- Environment not sampled sufficiently to resolve key fields (especially 4D distribution of water vapor)
**Model forecasts**
- Supplement observational data in short term
- Increasing importance beyond 6-12 hr
- Typically do not resolve severe phenomena
- Deterministic model errors are related to analysis and physics errors
**Uncertainty addressed through probabilistic-type products**

**Notes:**
For the very short term forecast of severe weather, obs data is the key input into a “nowcast” using radar, satellite trends, surface obs and mesoscale analysis.  This, combined with a conceptual understanding of high-resolution processes (e.g., supercell evolution/splitting, right/left mover, etc.) can downscale the environment to make a high-resolution nowcast. But, even the best observing systems are not enough so we rely on model forecasts to fill in “data sparse” areas and of course extend predictability. However, model errors occur as a result of numerical approximations, errors or approximations to the model physics, and analysis (IC) error.

## CONVECTIVE OUTLOOKSCategorical and Probabilistic: Operational through Day 3; Exp through Day 8

**Tornado (Hatched area 10% **_**>**_** F2)**

**Wind**

**Hail (Hatched area 10% **_**>**_** 2”)**

**Notes:**
These known sources of error are handled by probabilistic products that are now part of the operational suite of forecast products now issued by the SPC.  For example...

## OPERATIONAL WATCH PROBABILITIES

|  Tornadoes |  |
| --- | --- |
|  Probability of 2 or more tornadoes | Low (10%) |
|  Probability of 1 or more strong (F2-F4) tornadoes | Low (<5%) |
|  Wind |  |
|  Probability of 10 or more severe wind events | Mod (60%) |
|  Probability of 1 or more wind event > 65 knots | Low (10%) |
|  Hail |  |
|  Probability of 10 or more severe hail events | Low (10%) |
|  Probability of 1 or more hailstones >2 inches	 | Low (<5%) |
|  Combined Severe Hail/Wind |  |
|  Probability of 6 or more combined severe wind/hail events | Mod (60%) |

- Severe Thunderstorm Watch 688 Probability Table

**OPERATIONAL WATCH PROBABILITIES**

## Experimental Enhanced Thunderstorms Outlooks

- Thunderstorm Graphic valid until 3Z

- Thunderstorm Graphic valid 3Z to 12Z

**Notes:**
The union of the 10% line forms the categorical 24h general thunderstorm outlook

## Guidance Addressing Uncertainty

- Deterministic models reveal one end state, while ensembles
*Provide a range of plausible forecast solutions, yielding information on forecast confidence and uncertainty (probabilities)*
- Ensemble systems supplement traditional (higher resolution) deterministic models
- Ensemble systems aid in decision support
*Particularly if guidance calibrated (i.e., correct for systematic model bias and deficiencies in spread)*

**Notes:**
Guidance that expresses uncertainty can help produce better probabilistic forecast products!  Thus...we have worked on developing ensemble guidance for the SPC.

## Ensemble Guidance at the SPC

**Develop specialized guidance for the specific application (severe weather, fire weather, winter weather)**
**Design guidance that...**
- Helps blend deterministic and ensemble approaches
- Facilitates transition toward probabilistic forecasts
- Incorporates larger-scale environmental information to yield downscaled probabilistic                                          guidance
- Aids in decision support of high                                   impact weather
- Gauge confidence
- Alert for potentially                                             significant events

**Notes:**
The approach we’ve taken in developing ensemble guidance as well as the operational implementation of ensembles.

## Commonly Used Ensemble Guidance at the SPC

- Mean, Median, Max, Min, Spread, Exceedance Probabilities, and Combined Probabilities
- Basic Weather Parameters
*Temperature, Height, MSLP, Wind, Moisture, etc.*
- Derived Severe Weather Parameters
*CAPE, Shear, Supercell and Sig. Tornado Parameters, etc.*
- Calibrated Probability of Thunderstorms and Severe Thunderstorms

## NCEP/EMC Short Range Ensemble Forecast (SREF)

- EMC SREF system (21 members)
*87 hr forecasts four times daily (03, 09, 15, 21 UTC)*
*North American domain*
*Model grid lengths 32-45 km*
*Multi-model:  Eta, RSM, WRF-NMM, WRF-ARW*
*Multi-analysis:  NAM, GFS initial and boundary conds.*
*IC perturbations and physics diversity*

**Notes:**
Most of the examples I will show are from the SREF, so let me begin by presenting a quick review of the NCEP SREF.  I’m going to show several examples of how the SREF could be used to add confidence to the forecast.

## NCEP/EMC Medium Range Ensemble Forecast (MREF)

**GFS     ****	****T126* (~ 105 km)         28****	****    14**    ****	****    GFS physics****	****        Simple A-S**

**    Same as the operational GFS in 1998*
*** ****14**** statistically independent perturbations (using Ensemble Kalman filter method)*

**NCEP/EMC ****Medium Range Ensemble Forecast (MREF)**

## Outline

Introduction 

Applications in Severe Weather Forecasting

Fire Weather
Winter Weather
Severe Convective Weather

Summary

**Introduction**
**Applications in Severe Weather Forecasting**
**Fire Weather**
**Winter Weather**
**Severe Convective Weather**
- Summary

_**Fire **__***Weather***__** Ingredients**_**:**
- 1) Dry low-level airmass (i.e., low RH)
- 2) Windy
- 3) No precipitation
- 4) Warm temperatures
- 4) Dry Thunderstorms (i.e., natural ignition source)

**Notes:**
Now, some example applications in severe weather forecasting at the SPC.

## SREF 500 mb Mean Height, Wind, Temp

**Notes:**
Hot and dry in the west...with strong through moving through the region.  Focus on the region circled. Strong jet – 59 kts.
All forecasts at this valid time...F27 valid 00 Z 15 June 2006.

**SREF 500 mb Mean Height and SD (dash)**

**SD**

**Notes:**
Minimal uncertainty in the height forecast.

**SREF Mean PCPN, UVV, Thickness**

**Notes:**
Northern part of the area under “3HR mean” pcpn...not the southern/eastern sections.

**SREF Pr[P12I **_**>**_** .01”] and Mean P12I = .01” (dash)**

**Notes:**
Longer look at the pcpn in probabilistic sense...p12I prob >= .01 and mean.  All prob products have the mean shown in the thick, dark, dashed line.

**SREF Pr[RH **_**<**_** 15%] and Mean RH = 15% (dash)**

**Notes:**
Uncorrected RH.  Drier to the south.  

**SREF Pr[WSPD **_**>**_** 20 mph] and Mean WSPD = 20 mph (dash)**

**Notes:**
What about wind?

**SREF Combined or Joint Probability**

**Pr [P12I **_**<**_** 0.01”] X**
**Pr [RH **_**<**_** 15%] X**
**Pr [WSPD **_**>**_** 20 mph] X**
**Pr [TMPF **_**>**_** 60F]**

**Notes:**
Put it together.  Joint probability (approximated by a combo).  Probs are of course uncorrected, but still provide useful information.  Ingredients based approach using the ensemble!

**Pr [P12I **_**<**_** 0.01”] X**
**Pr [RH **_**<**_** 15%] X**
**Pr [WSPD **_**>**_** 30 mph] ****X**
**Pr [TMPF **_**>**_** 60F]**

**SREF Combined or Joint Probability**

**Notes:**
Adjust the wind.  By changing one parameter it’s easy to see the sensitivity to that parameter. 

## Diagnostics and Analysis

- Example: *Fosberg Fire Weather Index (FFWI)*
- A nonlinear empirical relationship between meteorological conditions and fire behavior.  (Fuels are not considered!)
- Derived to highlight the fire weather threat over “small” space and time scales
- FFWI = **F**(Wind speed, RH, Temperature)
-    0 _<_ FFWI _<_ 100
-    FFWI > ~50-60  *significant fire weather conditions*
-    FFWI > ~75  *extreme fire weather conditions*

**Notes:**
Considerably more information can be extracted from ensembles than just the mean and SD.  We like to apply some of the diagnostics and parameters we use in deterministic forecasting to all the ensemble members and then view the spread of the diagnostic. In this case, Fosberg fire wx index.

**SREF Median Fosberg Index + Union (red)**

**Union of all members (Fosberg = 50)**

**Median Fosberg Index**

**Notes:**
Median Fosberg with spread view spatially.  Red is at least one member...if perfect models then could view as range of possible coverage.

**SREF Maximum Fosberg Index (any member)**

- Extreme values

**Notes:**
Worst case scenario...max value at every grid point.

**SREF Pr[Fosberg Index **_**>**_** 60] and Mean FFWI = 60**

**Notes:**
Probability of Fosberg > 60...”good” fire weather conditions.  Similar picture to the joint probability of ingredients we saw earlier.

**SREF 3h Calibrated Probability of Thunderstorms**

**Thunderstorm = **
_**>**_** 1 CG Lightning Strike in 40 km grid box**

**What about dry thunderstorms?**

**Photo from John Saltenberger**

**Notes:**
Chance of thunderstorms?  What about wetting rain?

**SREF 3h Calibrated Probability of “Dry” Thunderstorms**

**Dry thunderstorms = **
**CG Lightning with < 0.10” precipitation**

**Notes:**
Dry lightning (experimental...still under development)

**SPC Operational Outlook****(Uncertainty communicated in accompanying text)**

**Notes:**
Forecast.  Indeed, did prove to be a “big day” with lots of new and ongoing activity in the west.  For example, news clipping is a new fire start near Flagstaff forcing a partial shutdown of I-40 and evacuation of many homes.

## Extended forecast exampleusing “Postage Stamps”

**Notes:**
Quick example into extended forecasting.  I wanted to show a Postage Stamp...we have avbl for short-range at SPC but here’s an example from our 3 to 8 day fire weather program.

## Postage Stamps: 500 mb HGHT

**Postage Stamps: 500 mb HGHT**

**GFS Ensemble at SPC: F216 valid 00 UTC 21 June 2006**

**Note deeper**
**troughs in**
**some fcsts**

**14 members**
**+ CTRL**

**Notes:**
Variability in H5 patter...four members indicative of pattern associated with fire weather in the West.

## Postage Stamps: Fosberg Index

**Postage Stamps: Fosberg Index**

**GFS Ensemble at SPC: F216 valid 00 UTC 21 June 2006**

**High**
**Fosberg**
**Index in**
**a few**
**members**

**14 members**
**+ CTRL**

**Notes:**
Run Fosberg on them...

## Outline

Introduction 

Applications in Severe Weather Forecasting

Fire Weather
Winter Weather
Severe Convective Weather

Summary

**Introduction**
**Applications in Severe Weather Forecasting**
**Fire Weather**
**Winter Weather**
**Severe Convective Weather**
- Summary

**Notes:**
Now...take a look at the end of the year winter storm in the Rockies/wrn Plains.

**SREF 500 mb Mean Height, Wind, Temp**

**Notes:**
Strong upper low.  Focus on the deformation area circled.  All fcsts F24 valid Saturday morning (Dec 30) 09Z.

**SREF 500 mb Height (Spaghetti = 5580 m)**

**Notes:**
Minimal variability...altho one of the RSM members showing different “look” to the weaker closed low to the north.

**SREF Mean PCPN, UVV, Thickness**

**SREF Pr[P06I **_**>**_** 0.25”] and Mean P06I = .25” (dash)**

**Notes:**
0.25”!!

## Example: Dendritic Growth Zone (DGZ)
 Very efficient growth (assuming water vapor is  replenished)
 Peak growth rate -14 to -15C in low-to-mid troposphere
 Accumulate rapidly

-  Very efficient growth (assuming water vapor is  replenished)
-  Peak growth rate -14 to -15C in low-to-mid troposphere
-  Accumulate rapidly

- _Search ensemble members for:_
-   Omega _>_ 3 cm/second
-   -11 C _<_ Temp _<_ -17 C
-   Layer depth _>_ 50 mb
-   RH in layer _>_ 85%

**Diagnostics and Analysis**

**Notes:**
Let’s do some more indepth analysis... dendrites...very efficient growth rate and “fluffy” so accumulate well.

**SREF Pr[DGZ **_**>**_** 50 mb deep]**

**Notes:**
Nern AZ looks like most members agree will support dendritic growth.

**SREF Mean 2-D Frontogenesis Function**

**Deep Layer Petterssen Frontogenesis (900 to 650 mb)**
**(Positive values indicate strengthening of **
**frontal circulation/vertical motion)**

**Notes:**
Deep layer frontogenesis farther north and east.

**Deep Layer Petterssen Frontogenesis (900 to 650 mb)**

**SREF Pr[2-D Frontogenesis Function] **_**>**_** 1**

**Notes:**
Again, the majority of members in agreement on this.

## MPV

**AMS (Emanuel 1985)**

- Enhancement in the
- upward branch of the
- frontal circulation in
- the presence of low MPV.

- Typical values
- of MPV

- Low values
- of MPV

**MPV**
***Moist Potential Vorticity***

**Notes:**
Using this conceptual model expect nice bands of pcpn...

**SREF Combined or Joint Probability**

**Pr [Frntogenesis **_**>**_** 1] &**
**Pr [MPV **_**<**_** 0.05]**

**SREF Likely PTYPE and Mean P03I (contours)**

**Czys Algorithm**
**WAF (1996)**

**Rain**

**Snow**

**ZR**

**IP**

**SREF Pr[Snowfall Rate **_**>**_** 1”/hour]**

_***Snow rate***_** is estimated**
**from three inputs:**
**1)  3h accumulated pcpn**
**2)  pcpn rate**
**3)  snow density (JHM, Boone and Etchevers 2001)**

**SREF Mean 3h Accumulated Snowfall**

**SREF *****Maximum***** (any member) 3h Accumulated Snowfall**

**Notes:**
Worst case scenario.

**NCEP (Baldwin) Algorithm**

**SREF Pr[Ptype = Snow] and Mean P03I (contours)**

**SREF Pr[Ptype = ZR] and Mean P03I (contours)**

**NCEP (Baldwin) Algorithm**

**NCEP (Baldwin) Algorithm**

**SREF Combined or Joint Probability**

**Pr [PTYPE = ZR] &**
**Pr [P03I **_**>**_** 0.05”]**

**SREF Combined or Joint Probability**

**NCEP (Baldwin) Algorithm**

**Pr [PTYPE = ZR] &**
**Pr [Duration **_**>**_** 3h]**

**SREF 6h Calibrated Probability of Snow/Ice Accum**

**Accumulation based on MADIS **
**road surface condition**

**Express as a fraction of normal, where**
**values **_**>**_** 1 meet or exceed past**
**conditions when snow/ice accumulated on roads**

**Mean 2m temp (dash)**

**Intersection 2m temp (solid)**

**SREF Relative to Normal for Snow/Ice Accumulation**

**> 20” snow across large portion **
**of CO and KS **
**> .5” to 1” ZR wrn KS, wrn/cntrl NE**

**> 20”**

**> 10”**

**From Mike Umscheid**
**www.underthemeso.com**

**From Mike Umscheid**
**www.underthemeso.com**

## Outline

Introduction 

Applications in Severe Weather Forecasting

Fire Weather
Winter Weather
Severe Convective Weather

Summary

**Introduction**
**Applications in Severe Weather Forecasting**
**Fire Weather**
**Winter Weather**
**Severe Convective Weather**
- Summary

**SREF 500 mb Mean Height, Wind, Temp**

**SREF 500 mb Mean Height and SD (dash)**

**SREF 850 mb Mean Height, Wind, Temp**

**SREF Precipitable Water (Spaghetti = 1”)**

**SREF Pr[MUCAPE **_**>**_** 2000 J/kg] & Mean MUCAPE=2000 (dash)**

**SREF Pr[ESHR **_**>**_** 40 kts] & Mean ESHR=40 kts (dash)**

***“******Effective Shear” (ESHR; Thompson et al. 2007, WAF) is the bulk shear in the lower half of the convective cloud***

**SREF Pr[C03I **_**>**_** .01”] and Mean C03I = .01” (dash)**

***C03I = 3hr Convective Precipitation***

**SREF Combined or Joint Probability**

**Pr [MUCAPE **_**>**_** 2000 J/kg] X**
**Pr [ESHR **_**>**_** 40 kts] X**
**Pr [C03I **_**>**_** 0.01”]**

***Probability of convection in high CAPE, high shear environment***
***(favorable for supercells) ***

## Diagnostics and Analysis

- Example: *Significant Tornado Parameter (STP)*
- A parameter designed to help forecasters identify supercell environments capable of producing significant (_>_ F2) tornadoes (Thompson et al. 2003)
- STP = **F**(MLCAPE, MLLCL, Helicity, Deep shear)*
-    STP _>_  ~1 indicative of environments that may
-        support strong or violent tornadoes (given that
-        convection occurs)

**** An updated version (not shown) includes CIN and effective depth***

**SREF Median STP, Union (red), Intersection (blue)**

**Union of all members (STP = 1)**

**Median STP**

**Intersection of all members (STP = 1)**

**SREF Pr[STP **_**>**_** 5] and Mean STP = 5 (dash)**

**SREF Pr[0-1KM HLCY **_**>**_** 150 m^2/s^2] & ****Mean 0-1KM HLCY=150 m^2/s^2(dash)**

**SREF Pr[MLLCL **_**<**_** 1000m] & Mean MLLCL = 1000 m (dash)**

**Pr [MLCAPE **_**>**_** 1000 J/kg] X**
**Pr [MLLCL **_**<**_** 1000 m] X**
**Pr [0-1KM HLCY **_**>**_** 100 m^2/s^2] X**
**Pr [0-6 KM Shear **_**>**_** 40 kts] X**
**Pr [C03I **_**>**_** 0.01”]**

**SREF Combined or Joint Probability: STP Ingredients**

***Probability of Significant Tornado Environment***

**SREF 12h Calibrated Probability of Severe Thunderstorms**

**Severe Thunderstorm = **
_**>**_** 1 CG Lightning Strike in 40 km grid box **
***and***
**Wind **_**>**_** 50 kts *****or***** Hail **_**>**_** 0.75” *****or***** Tornado**

## SREF Probability of STP Ingredients: Time Trends

_***48 hr***_* SREF Forecast Valid 21 UTC 7 April 2006*

- Prob (MLCAPE _>_ 1000 Jkg-1)
- X
- Prob (6 km Shear _>_ 40 kt)
- X
- Prob (0-1 km SRH _>_ 100 m2s-2)
- X
- Prob (MLLCL _<_ 1000 m)
- X
- Prob (3h conv. Pcpn _>_ 0.01 in)
*Shaded Area Prob *_*>*_* 5%*

- Max 40%

## SREF Probability of STP Ingredients: Time Trends

_***36 hr***_* SREF Forecast Valid 21 UTC 7 April 2006*

- Prob (MLCAPE _>_ 1000 Jkg-1)
- X
- Prob (6 km Shear _>_ 40 kt)
- X
- Prob (0-1 km SRH _>_ 100 m2s-2)
- X
- Prob (MLLCL _<_ 1000 m)
- X
- Prob (3h conv. Pcpn _>_ 0.01 in)
*Shaded Area Prob *_*>*_* 5%*

- Max 50%

## SREF Probability of STP Ingredients: Time Trends

_***24 hr***_* SREF Forecast Valid 21 UTC 7 April 2006*

- Prob (MLCAPE _>_ 1000 Jkg-1)
- X
- Prob (6 km Shear _>_ 40 kt)
- X
- Prob (0-1 km SRH _>_ 100 m2s-2)
- X
- Prob (MLLCL _<_ 1000 m)
- X
- Prob (3h conv. Pcpn _>_ 0.01 in)
*Shaded Area Prob *_*>*_* 5%*

- Max 50%

## SREF Probability of STP Ingredients: Time Trends

_***12 hr***_* SREF Forecast Valid 21 UTC 7 April 2006*

- Prob (MLCAPE _>_ 1000 Jkg-1)
- X
- Prob (6 km Shear _>_ 40 kt)
- X
- Prob (0-1 km SRH _>_ 100 m2s-2)
- X
- Prob (MLLCL _<_ 1000 m)
- X
- Prob (3h conv. Pcpn _>_ 0.01 in)
*Shaded Area Prob *_*>*_* 5%*

- Max 50%

## Severe Event of April 7, 2006

_**First ever**_ Day 2 outlook High Risk issued by SPC
- More than 800 total severe reports
- 3 killer tornadoes and 10 deaths
- SREF severe weather fields aided forecaster confidence

## Outline

Introduction 

Applications in Severe Weather Forecasting

Fire Weather
Winter Weather
Severe Convective Weather

Summary

**Introduction**
**Applications in Severe Weather Forecasting**
**Fire Weather**
**Winter Weather**
**Severe Convective Weather**
**Summary**

## What is the perfect forecast?

- Determinism

- Ensemble

**What is the perfect forecast?**

## Ensemble Applications in Severe Forecasting

**Ensemble approach to forecasting similar to the  deterministic approach**
** **Ingredients based inputs
-  Diagnostic and parameter evaluation
-  Tend to view diagnostics in probability space
**Ensembles contribute appropriate levels of confidence to the forecast process**
**Calibration of ensemble output can remove systematic biases and improve the spread**
**Ensemble techniques scale to the problem of interest (weeks, days, or hours)**

## Looking Ahead: Storm-Scale Ensembles

- Ensemble techniques scale to the problem of interest (weeks, days, or hours)
- Example from SPC/NSSL 2005 Spring Program:
- 3 very high resolution WRF models allowed for the creation of a “Poor person’s ensemble”
- WRF-ARW2 (2 km grid space; OU/CAPS)
- WRF-ARW4 (4 km grid space; NCAR)
- WRF-NMM (4.5 km grid space; NCEP/EMC)
- Explicit, convection allowing forecasts
- Interested in resolved storm-scale structures
- Initiation, Mode, Evolution, Decay

## WRF 2 to 4.5 km ForecastsValid: F02426 May 2005

**2 km ARW**

**4 km ARW**

**4.5 km NMM**

## WRF 2 to 4.5 km ForecastsValid: F02426 May 2005

- “Spaghetti” of _**automated **_
_**supercell detection:**_
*circles indicate a supercell *
*identified within 25 miles*
_**All three**_ WRF models
- contribute information to
- the supercell forecast

**2 km ARW**
**4 km ARW**
**4.5 km NMM**

## 2007 Spring Experiment will continue 2005 work
~15 April 2007 through ~15 June 2007
2008 and 2009 will incorporate WRF-NMM and various upgrades
Collaboration between SPC and NSSL
OU/CAPS (NWC)
 NCEP partners: AWC, EMC, HPC
 WFO Norman
10 members, 4 km explicit convection allowing mixed physics WRFs (eastern 2/3 CONUS)
2 km high-resolution deterministic WRF to accompany ensemble
Emphasis on revolved, high-impact hazardous weather (e.g., supercells, mode, coverage, QPF)
Prototype for future NCEP regional SREF system

- ~15 April 2007 through ~15 June 2007
- 2008 and 2009 will incorporate WRF-NMM and various upgrades
- Collaboration between SPC and NSSL
- OU/CAPS (NWC)
-  NCEP partners: AWC, EMC, HPC
-  WFO Norman
- 10 members, 4 km explicit convection allowing mixed physics WRFs (eastern 2/3 CONUS)
- 2 km high-resolution deterministic WRF to accompany ensemble
- Emphasis on revolved, high-impact hazardous weather (e.g., supercells, mode, coverage, QPF)
- Prototype for future NCEP regional SREF system

**Storm-Scale Ensemble****NOAA Hazadous Weather Testbed (HWT)**

## Combined Prob-o-Grams (SREF – Cntrl NV)

***Combined Prob-o-Grams (SREF – Cntrl NV)***

- Pcpn

- Wind

- RH

- Joint Prob

- 10% - CG LTG Strikes

## SPC SREF Products on WEB	

- http://www.spc.noaa.gov/exper/sref/

**Questions/Comments...**
**david.bright@noaa.gov**