CONVECTIVE FORECAST CHALLENGES FROM THE PERSPECTIVE OF THE STORM PREDICTION CENTER Steven Weiss...

CONVECTIVE FORECAST CHALLENGES FROM THE PERSPECTIVE OF THE STORM PREDICTION CENTER

Steven [email protected]

NCAR Advanced Studies Program Summer ColloquiumThe Challenges of Convective Forecasting

July 12, 2006Boulder, CO

Where Americas Climate and Weather Services Begin

Instructions From Morris

• “The colloquium is meant to be as much an open discourse as a class. Thus, it is just as important to present what we don’t know as what we do know.”

• How do we distinguish between the known and the unknown?

Morning Quiz - Who Said This??

• “As we know, there are known knowns. There are things we know we know.

• We also know there are known unknowns. That is to say we know there are some things we do not know.

• But there are also unknown unknowns, the ones we don't know we don’t know.”





Secretary of Defense Donald Rumsfeld discussing events in Iraq?





Morris gesturing to Lance about the large gaps in our convective knowledge?

Outline• Brief Overview of Storm Prediction Center• Severe Thunderstorm Forecasting

– Role of the environment in assessment of storm potential– Sampling and resolution issues

• Use of models to supplement observational data • NAM and RUC Errors (especially PBL and instability)

– Sensitivity of convection to environment details• Observed storms and convective mode• Modeled storms

– WRF, cloud models, and high resolution ensembles– Verification of high resolution models

– Summary of analysis and prediction limitations• Some focus areas that may result in improved short-term

forecasting of severe thunderstorms

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

STORM PREDICTION CENTER HAZARDOUS PHENOMENA

SPC Organization35 Total Staff

Director

Operations Br. Chief Science Br. ChiefAdmin Officer

Admin Coord.

Warning Coord Met

5 Lead Forecasters

10 Mesoscale/Outlook Forecasters

5 Assist. Mesoscale Forecasters

Science Ops. Officer

3 Tech. Dvlpmt Mets

2 Systems Analysts

Software Analyst

Contract Computer Tech

Contract Programmer

20 Full-Time Forecasters

3 Shifts per Day

4 Forecasters per Shift to Cover the Lower 48 States

Days 4-8 Outlook (1*)

Day 3 Outlook (1*)

Day 2 Outlook (2*)

Day 1 Outlook (5*)

MesoscaleDiscussions#

Watch#

Time

-Minutes

-Hours

- Days

- Weeks

SPC Convective Forecast Suite

* Number of routine product issuances per day # Event driven product issued as needed

Status Rpt#

• TOR / SVR TSTM WATCHES (~1000 / yr)• WATCH STATUS REPORTS (~5000 / yr)• CONVECTIVE OUTLOOKS (~3200 / yr)

– Day 1; Day 2; Day 3; Days 4-8• MESOSCALE DISCUSSIONS (~2000 / yr)

– Severe Thunderstorm Potential Preceding Watch Issuance

– Analysis of Severe Weather in Active Watches– Hazardous Winter Weather– Heavy Rainfall

• FIRE WEATHER OUTLOOK (~1200 / yr)– Day 1; Day 2; Days 3-8

• FORECASTS ARE BOTH DETERMINISTIC AND PROBABILISTIC

SPC Forecast Products

75% of all SPC products are valid for < 24h period

Severe Weather Outlooks

•Two Outlook Types

•Categorical•Slight Risk

•Moderate Risk

•High Risk

•Probabilistic•Tornadoes

•Hail

•Convective Winds

Example of High Risk Outlook DayDay 1 Outlook 7 April 2006

Categorical Risk Tornado Probability

Hail Probability Wind Probability

What Happened on 7 April 2006?

Mesoscale Convective Discussions (MD)

• Goal is to issue pre-watch MDs 1 to 3 hours prior to a Severe Thunderstorm or Tornado watch issuance.

- Define area(s) of concern - State expected watch type - Provide meteorological reasoning – most important

• Also issued ~ every 2 hours for each active watch to provide diagnostic/short-term forecast information (when WFOs are busy with warning activities)

Example of Pre-Watch MDZCZC MKCSWOMCD ALL;334,0996 373,0979 353,0979 314,0996;ACUS3 KMKC 032023MKC MCD 032023TXZ000_OKZ000_032300_

SPC MESOSCALE DISCUSSION #0345 FOR...SW OK/NW TX...CONCERNING...SEVERE THUNDERSTORM POTENTIAL...

WATER VAPOR IMAGERY SHOWS A LEAD MID LEVEL SHORT WAVE TROUGH MOVING ENEWD OVER E/NE NM THIS AFTERNOON...AND THIS IS CONFIRMED BY PROFILER TIME SERIES FROM AZC/GDA/TCC/JTN. MID/UPPER 60 DEWPOINTS ANDTEMPERATURES NEAR 80 ARE CONTRIBUTING TO SURFACE_BASED CAPE VALUES OF 3500_5000 J/KG OVER WRN OK AND NW TX TO THE E OF THE DRYLINE. CONVERGENCE ON THE DRYLINE IS NOT STRONG AND A CIRRUS SHIELD OVER THE TX PANHANDLE/NW TX/WRN OKSHOULD LIMIT ADDITIONAL SURFACE HEATING...BUT VISIBLE/RADARIMAGERY HAS SHOWN THE FIRST ATTEMPTS AT TCU OVER FAR NW TXAS OF 20Z WITHIN A BREAK IN THE CIRRUS. MID LEVEL FLOW ANDVERTICAL SHEAR WILL INCREASE OVER NW TX AND WRN OK THROUGH LATE AFTERNOON... WITH AN INCREASING THREAT OFSUPERCELLS NEAR THE DRYLINE FROM 00_03Z. THIS AREA IS BEINGMONITORED FOR A POSSIBLE TORNADO WATCH LATER THISAFTERNOON. ..THOMPSON.. 05/03/99

...PLEASE SEE WWW.SPC.NOAA.GOV/ FOR GRAPHIC PRODUCT...NNNN

Convective Watch GoalsConvective Watch Goals• WATCHES ATTEMPT TO CAPTURE:WATCHES ATTEMPT TO CAPTURE:

– ALL SIGNIFICANT SEVERE:ALL SIGNIFICANT SEVERE:• 2” OR GREATER HAIL2” OR GREATER HAIL• 65+ kt WIND65+ kt WIND• F2 OR GREATER TORNADOESF2 OR GREATER TORNADOES

– MULTIPLE SEVERE EVENTS FROM MULTIPLE SEVERE EVENTS FROM ORGANIZED CONVECTION.ORGANIZED CONVECTION.

• SUPERCELLSSUPERCELLS• SQUALL LINESSQUALL LINES• MULTICELL COMPLEXESMULTICELL COMPLEXES

• Isolated and/or marginal severe Isolated and/or marginal severe storms may storms may notnot occur in watches. occur in watches.

Lead Time Goal

• Watches should be issued prior to onset of severe weather

• WATCH IN EFFECT:– 1 HOUR PRIOR TO

FIRST SEVERE THUNDERSTORM.

– 2 HOURS PRIOR TO FIRST TORNADO.

Example of a Tornado Watch

SPC WATCH VERIFICATION(1970-2005)

0

20

40

60

80

100

120

1970

1975

1980

1985

1990

1995

2000

2005

Per Cent Watchesthat contain SevereWeatherPer Cent of SevereWeather in a Watch

I

CombinedTornado and Severe Thunderstorm

Reports (1970-2004)

0

5000

10000

15000

20000

25000

30000

1970 1975 1980 1985 1990 1995 2000SIGNIFICANT SEVERE EVENTS SEVERE EVENTS

SPC WATCH VERIFICATION Significant Tornadoes (F2+)

0

20

40

60

80

100

1970 1980 1990 2000

PerCent of F2 orGreater Tornadoes inAny WatchPer Cent of F2 orGreater Tornadoes inTornado WatchPer Cent ofTornadoes that areF2 or Greater

Severe Thunderstorm Severe Thunderstorm ForecastingForecasting

Some Differences Between Severe Weather Forecasting and Warning

• Detection (warning) of existing severe weather is not the same as prediction (forecasting) of future occurrence or evolution– Warnings have improved because of advances in:

• Technology (NEXRAD & Workstation Analysis Tools) • Science (Understanding of storm structure/processes)• Forecaster training and education• Delivery systems to the public

– But analogous technological advancement for severe weather prediction has not yet occurred

• Considerable uncertainty can exist in both the prediction and detection phases

Modified Forecast Funnel• SPC focuses on relationship between synoptic - mesoscale environment and subsequent thunderstorm development and evolution

• Must maintain awareness of mesoscale - synoptic scale interactions

• Severe weather events occur on scales smaller than standard observational data (and typical model data)

• The real atmosphere is more important than a model atmosphere

Severe Thunderstorm Forecasting• Assessment of convective potential is often

limited by insufficient sampling on the mesoscale in time and space (especially 3D water vapor)– Radiosondes

• High vertical resolution, poor time and space resolution

– Surface METARS• High horizontal and time resolution, no vertical information

– Wind profilers and VAD winds• High vertical and time resolution, moderate horizontal res.• No thermodynamic data

– Satellite retrievals (winds and thermodynamic)• Mod./high horizontal and time resolution, poor vertical res.

– GPS Integrated Water Vapor• High time res., mod./high horizontal res., poor vertical res.

The Link Between Observable Scales and Stormscale is not Necessarily Clear

Observable scales

Stormscale

Courtesy, Sydney Harris

Severe Weather Forecasting • Key premise - We must use our (incomplete) knowledge of the environment and convective processes to determine the spectrum of storms that are possible, where and when they may occur, and how they may evolve over time

Severe Thunderstorm Forecasting

• We utilize NWP model output to supplement the limited sampling of real atmosphere (e.g., NAM and RUC output)

• Model output forms the foundation for most SPC outlooks, and it also impacts watch decisions – But accounting for uncertainties in IC’s (inadequate

sampling) and model physics errors is not easy– Example: Eta forecast soundings exhibit characteristic

errors caused by:• Early / late activation of deep convection• Shallow convective scheme in BMJ

BMJ Convective Parameterization in the NAM Model

• Both deep and shallow convective processes alter the sounding structure leaving identifiable “footprints” when active

• Deep Convection - nudges temperature toward reference profiles where profile is slightly unstable, with high RH through column

• Shallow Convection – distributes moisture upward and heat downward through cloud layer

• Warming/drying near LCL

• Cooling/moistening near cloud top

• These processes impact evolution of the model environment (e.g. CAPE/CIN fields forecasters look at)

Impact of Eta Model Deep Convection on Forecasts of CAPE

Eta 24 hr forecast valid 12z 8 Nov 2000

3 hr Conv Pcpn CAPE

Impact of Eta Model Deep Convection on Forecasts of CAPE

Verifying Data 12z 8 Nov 2000

Radar Reflectivity CAPE

Impact of Eta Model Deep Convection on Forecast Soundings

MUCAPE 929 J/kg

Mean RH 75%

24 hr Eta Fcst Valid 12z 8 Nov Observed LCH Sounding 12z 8 Nov

MUCAPE 2634 J/kg

Mean RH 28%

12 hr Loop of Eta Forecast Sounding

Showing Impact of BMJ Shallow Convection

Impact of Eta BMJ Shallow Convection

Observed Verifying Sounding (Red/Green) and 12 hr Eta Fcst (Purple)

Short-Term Severe Thunderstorm Environmental Parameter Guidance

• Hourly Update Information on 3D Convective Parameters is routinely available– SPC “sfcoa” in N-AWIPS (Mesoscale Analysis Web Page)

– LAPS in AWIPS– MSAS/RSAS in AWIPS

• All utilize observational data blended with model data for atmosphere above the ground

Diagnosis of Instability• Measures of instability such as CAPE or LI can vary

depending on choice of lifted parcel– Surface-Based (SB)

• Allows use of high resolution hourly METAR obs• Assumes surface conditions representative of well-mixed PBL• Can overestimate instability

– Mean Layer (ML)• More representative of actual convective cloud processes• Requires accurate information about PBL profile • Default PBL depth is 100 mb in NSHARP

– Most Unstable (MU)• Uses level of maximum theta-e as lifted parcel level• Most useful in identifying elevated instability above PBL• May be identical to SB parcel (when max theta-e is at surface)• Overestimates instability if theta-e “spike” exists at one level

Diagnosis of Instability – ML vs SB• Craven, Brooks, and Jewell (2002) examined more

than 400 warm season 00z soundings

• They estimated convective cloud base height using 100 mb ML and SB parcels and compared with observed ASOS cloud base heights

SB parcels tended to underestimate convective cloud bases (SB parcel too warm/moist), whereas ML parcels better represented convective processes

ML parcels-little hgt bias SB parcels – low hgt bias

Craven et al. Findings (cont’d) • SBCAPE almost always larger than MLCAPE

– Suggests surface conditions do not typically represent late afternoon PBL structure

– Implies MLCAPE more representative of convective processes and potential (SBCAPE less useful)

Dilemma – high resolution surface data allows hourly updates to environment

But using lifted surface parcel may overestimate actual CAPE

April 20, 2004Challenges in Sfc Data Assim. and Fcstg PBL Evolution

34 Tornadoes Including One F38 Deaths, 21 Inj., $19 Million in Damage

Jim Krancic

12z Eta Model Guidance

12 hr Eta Model 500 mb ForecastsValid 00z 21 Apr 04

Height and Vorticity Height, Temperature, Wind

12 hr Eta Model 850 mb and Sfc ForecastsValid 00z 21 Apr 04

850 mb Height,Temperature, Wind MSLP Isobars, 2m Dewpoint

12 hr Eta CAPE/Shear/SRH ForecastsValid 00z 21 Apr 04

MLCAPE/SHR6/SRH3 MUCAPE/SHR6/SRH3

12 hr Eta 3h Accum. Pcpn/VV ForecastValid 00z 21 Apr 04

6 hr Eta PFC for Peoria, IL (PIA)Valid 18z 20 Apr 04

12z RUC Model Guidance

12 hr RUC ForecastsValid 00z 21 Apr 04

MSLP Isobars and 2m Dewpoint CAPE/SHR6/SRH3

12 hr RUC 3h Accum. Pcpn/VV ForecastValid 00z 21 Apr 04

6 hr RUC PFC for Peoria, IL (PIA)Valid 18z 20 Apr 04

SPC Meso Analysis

21z

100 mb MLCAPE and MUCAPE

21z Radar and MLCAPE

21z Radar and MUCAPE

What Can Cause MUCAPE >> MLCAPE?

• For observed soundings with “skin” moisture, surface-based parcels are not representative of true convective boundary layer

SBCAPE 1140

MLCAPE 61

Sounding Interpretation in Cases of “Skin” Moisture

• Although these types of soundings occur more frequently at 12z, they also are found at 00z

• We favor the MLCAPE as being more correct

SBCAPE 770

MLCAPE 33

Skin Moisture with Large CAPE

• Even with ample moisture/instability, we assume the SBCAPE is an overestimate and favor MLCAPE values (supported by Craven, Brooks, Jewell results)

SBCAPE 4973

MLCAPE 2936

CAPE Assessment

• To diagnose differences between MLCAPE and MUCAPE, examination of hourly RUC soundings and surface data are required– SPC Meso Analysis combines hourly surface data

with 1-hr forecasts from the previous hour RUC that provide environment information above the ground

• For example, the 21z analysis incorporates 21z METAR data with a 1-hr forecast from the 20z RUC

1 hr RUC PFC for Peoria, IL (PIA)Modified with Observed T/Td

Valid 19z 20 Apr 04


Valid 20z 20 Apr 04


Valid 21z 20 Apr 04


Valid 22z 20 Apr 04

Meso Analysis Summary of CAPE

• Looking at hourly RUC 1-hr forecast soundings at PIA as warm front lifted north of PIA indicates– Observed surface dew points did not blend

well with model PBL background field from 1-hr forecast

– Dry layer immediately above model ground during 20-22z period limited MLCAPE values to < 200 J/kg

RUC Model Upgrade in 2004• The RUC was upgraded in September 2004

– One change was designed to increase the vertical impact of observed surface T/Td data on model PBL profiles

• The RUC PBL-based data assimilation should result in more accurate T/Td profiles in low levels– This should improve RUC hourly analyses– Better PBL profiles should result in improved short-term

forecasts (including PFCs and 1-hr forecasts that feed the Meso Analyses)

• Let’s examine RUC soundings for a January 2005 case

SPC Meso Analysis

21z

MLCAPE and MUCAPE

21z Radar and MLCAPE

21z Radar and MUCAPE

RUC Soundings at Slidell

20-00z

1 hr RUC PFC for Slidell (6RO)Modified with Observed T/Td

Valid 20z 7 Jan 05


Valid 21z 7 Jan 05


Valid 22z 7 Jan 05


Valid 23z 7 Jan 05


Valid 00z 8 Jan 05

RUC 1-hr PFC Assessment

• Again, the RUC 1-hr soundings exhibit dry low levels

• For this case, we have a 00z observed raob at Slidell for comparison purposes

Observed Slidell (LIX) Raob 00z 8 Jan 05

Observed (Red/Green) and RUC 1-hr PFC with METARSlidell Soundings Valid 00z 8 Jan 05

Observed vs. Model Soundings

• Be cautious when interpreting model soundings as if they were observed soundings– We know that model physics errors can have negative

impacts on sounding structure (e.g., NAM soundings with BMJ scheme)

• However, blending observed surface data with RUC model soundings may occasionally result in appearances of “skin” moisture– For observed soundings, we discount the SB parcel in favor

of ML parcel-based parameters– But for RUC/surface observation blends, the SB parcel may

be more appropriate when the model PBL background structure is too dry

Use of Objective Parameter Guidance• The availability of hourly 3D guidance fields can

improve our situational awareness prior to and during severe weather episodes

• But, it can also give us a false sense of security– We must be cautious in treating these hourly fields and

model soundings as if they are actual observational data – Short-term model input can and will have errors in key fields

(e.g., PBL structure)

• Suggests considerable improvement in our real-time assessment of the environment (especially PBL structure) is needed.

How Sensitive is Convection to Environmental Conditions?

• Significant severe weather events can occur over a wide range of CAPE – Shear parameter space

• Predictability is increased within the middle of the parameter distributions

• Uncertainty is largest when either CAPE or shear are on the margins of the distributions

• For example, cool season environments characterized by low CAPE – high shear are problematic

• Shear profiles are often “supportive” of tornadoes

• How much CAPE is “enough”?

From Johns et al. (1993)

Results from Thompson et al. RUC Analysis Sample

MLCAPE/Effective SRH Scatterplot

Most Sig Tor Events Occur with MLCAPE > 1000 J/kg and ESRH > 100 m2s2

But many non-tornadic supercells also occur in that parameter space

Similar environments produce different storm types

Different environments produce similar storm types

Sig Tor Non-Tor Supercell

How Sensitive is Convection to Environmental Conditions?

• Operational experience and proximity sounding studies suggest relationship of storm character to environment is highly variable

Example - Numerous severe storms in Arkansas but only one produced a significant tornado

F3 tornado 1 death

18 UTC BMX 16 Dec 2000 18 UTC BMX 16 Feb 2001

Very Similar Environments May Support Very Different Convective Modes

Tornadic Tornadic supercell supercell (F4 - 11 (F4 - 11 deaths)deaths)

Bow Bow echo echo system system

Importance of Convective Mode

•Basic “rule” of severe weather forecasting:

– Correct prediction of convective mode is of paramount importance – If you don’t get the mode right, the predominant type of severe weather that occurs may be different than you expected

Importance of Convective Mode

• Corollary - even when the mode is the same, different events may occur in close proximity to each other

• 22 June 2003 severe storms in Nebraska

2245 UTC Visible Satellite and Analysis (From Guyer and Ewald 2004)

0.5 deg Reflectivity 2358 UTC

Aurora Supercell Produced Record 7” Hailstone

Deshler Supercell Produced F2 Killer Tornado

(from Guyer and Ewald 2004)

Elmore et al. Ensemble Cloud Model

Experiment (WAF 2002)

• An ensemble forecasting experiment generated 702 separate cloud-scale model runs– 531 storms for which maximum vertical velocity

exceeds 8 m s-1 for at least 6 min

• Input soundings from operational Eta model• Question – how much does simulated storm

lifetime vary relative sounding similarities or differences?

Operationally Distinguishable Sounding that Results in Similar

Storm Lifetimes (Inferring Similar Evolution)

Operationally Indistinguishable Sounding that Results in Different Storm Lifetimes

What is Happening?Is it the Cloud Model? The Atmosphere?

• If we assume the model realistically reflects atmospheric processes at arbitrarily small scales– Then the atmosphere itself is the root of the

sensitivity

• This would require great accuracy in resolving and predicting environmental conditions

What is Happening?Is It The Cloud Model? The Atmosphere?

• But, it could be model errors– Model physics may introduce parameterized processes that

depend on threshold trigger points• Choices for microphysics, PBL, radiation, etc.

– Selection of different thresholds or physics packages will move sensitivity points from one set of soundings to another

• No direct way to know which explanation is correct– But evidence suggests model physics and limited ability to

resolve and predict the atmosphere at smaller scales both play a role

NOAA Hazardous Weather Testbed(Can High Resolution Models Help?)

• Primary Objectives in 2004 and 2005– Can non-hydrostatic high resolution WRF models provide unique

and meaningful information about details of subsequent severe thunderstorms?

– Can forecasters use the WRF output to supplement current operational data and produce improved severe weather forecasts?

Why Examine HiRes WRF Models?• Severe weather types (tornadoes, hail, wind damage)

can be closely related to convective mode– Tornadoes (discrete supercells; embedded supercells in lines)– Damaging wind (bow echoes and bowing line segments)

• SPC working to increase lead time of watches, and provide probabilistic information about tornado, hail, and wind threats in day 1 outlooks

• We require information about “where” and “when” storms will develop and how they will evolve– There is a need to accurately predict convective mode and

character of storms (storm scale details)

– Environmental clues (CAPE/shear, etc.) may not be sufficient– Operational mesoscale models lack smaller scale details

SPC 06z Day 1 Convective Outlook 10 November 2002

“…Significant Tornadoes Are Possible IF Supercells Can Develop Along/Ahead of Cold Front…”

Tornado/Radar-Based Supercell Tracks

32 major supercells

Several lasting many hours and traveling over 250 miles.

Two tracks in southern group over 400 miles long.

Comparison of Eta 24 hr Fcst of 3 hr Pcpn and Vertical Velocity with Radar

Eta Pcpn/VV valid 00z Radar 2345z

Mesoscale Models Rarely Provide Sufficient Information About Convective Mode

Spring Experiments 2004 and 2005

• Experimental near-stormscale (dx~2-4 km) versions of the WRF examined (EMC, NCAR, OU/CAPS/PSC)– Explore impacts of grid resolution and parameterized

convection versus explicit microphysics• Determine usefulness of high resolution WRF output to

SPC severe storm forecasters– Can Hi Res WRF provide unique information on

convective initiation, evolution, and mode, e.g., supercells and bow echoes?

• Provide feedback to model developers so they can improve models

Model Domains in 2005

Example of Good WRF Forecast

28-29 May 2004

4 km WRF-ARW(F22)

12 km ETA (F10)1h BREF (22Z)

4.5 km WRF-NMM (F22)

1h Tot Pcp

1h Tot Pcp1h Tot Pcp

4 km WRF-ARW(F23)



1h Tot Pcp


4 km WRF-ARW(F24)



1h Tot Pcp


4 km WRF-ARW(F25)



1h Tot Pcp


4 km WRF-ARW(F26)



1h Tot Pcp


4 km WRF-ARW(F27)



1h Tot Pcp


Example of WRF Forecasts of Simulated Reflectivity

Generation of WRF supercells in Arkansas

(But atmosphere did not agree)

29 April 2005

ARW2 BREF

NMM4ARW4

0100 UTC 29 April 2005: 25 hr model reflectivity, NEXRAD BREF

ARW2 BREF

NMM4ARW4


0600 UTC 29 April 2005: 30 hr ARW2 Reflectivity

Good news is the ARW2 can create well-defined, realistic supercell structures

An even bigger challenge is to consistently generate storms in the right place at the right time

Use and Interpretation of WRF Convective Forecasts

• In general, the different WRF models exhibited similar mesoscale prediction skill of convective systems

• But on some days WRF forecasts may be different, especially on the stormscale– All forecasts may appear plausible

• Raises issues of what constitutes a useful forecast – May depend on different needs of specific users

• How do we reconcile model differences ahead of time when we don’t know “the stormscale answer”?– When do we “believe” details of model output and when do

we discount it?– Suggests role for high resolution ensembles

How Has the 4.5 km WRF-NMM Performed Recently in Strongly

Forced Outbreaks?

4.5 km WRF-NMM and Radar31 hr WRF forecast valid 07z 6 Nov 2005

WRF-NMM and SDI (circles) Radar

F3 tornado 0759z

23 deaths

SDI-indicated mesocyclones

4.5 km WRF-NMM and Radar21 hr WRF forecast valid 21z 15 Nov 2005


11 F2+ Tornadoes 20-22z

1 death 108 injuries

SDI-indicated mesocyclones

Comments from SPC Forecasters About

15 November WRF-NMM Guidance “One of the best pieces of information was the WRF-NMM 4 km "equivalent reflectivity" product. This product did a GREAT job of persistently depicting a line of forced convection along the front, along with bands of storms ahead

of the main line. With the WRF showing these bands…increasing in intensity by late morning well east of the front, we gained confidence that it would be prudent to go much further e than the frontal zone itself with the early watch.” Steve Goss - SPC Midnight Shift Mesoscale Forecaster

“The WRF-NMM4 provided very useful input regarding the mesoscale organization and character of storms. While there were certain details that the model missed, it was superb in predicting multiple convective lines and their rough extents. I used it to help delineate where/when watches would be required.” John Hart - SPC Day Shift Lead Forecaster

4.5 km WRF-NMM and Radar25 hr WRF forecast valid 01z 3 April 2005


5 killer tornadoes 23-02z 26 deaths

Future Course of Operational NWP

• There is considerable discussion about the best use of computer, communications bandwidth, and workstation display resources– Some have advocated development of single highest resolution

deterministic model (traditional approach)– Others favor coarser resolution ensembles to account for initial

condition and model physics uncertainties– What about combining both concepts with high resolution

ensembles?

• Let’s look at some results from a 5 member ARPS ensemble with dx=3 km (from Levit et al. 2004)

28 March 2000 Damage Summary

• Tornadoes: – Fort Worth – F2, 2 Fatalities, 80 Injuries– Arlington/Grand Prairie – F3– Hail – 3.50 Inch, 1 Fatality

Image from COMET Case Study

3 km ARPS Ensemble Results (from Levit et al.)

• Ensemble members “similar” to each other (underdispersive?) and radar

• Is this a “good” forecast?– Answer depends on your specific

time/space requirements

– Good from watch scale perspective

– Not as good from county warning perspective (esp. Tarrant County!)

• How should we verify HiRes models?

Image from COMET Case Study

Ensemble Postage Stamp Display

90 min Reflectivity Forecasts

Probability of 50 dBZ Reflectivity

90 min forecast

Actual Radar

Verification of High Res Models

• High resolution models also introduce new issues for verification of model forecasts– Gridded output (e.g., temperature and winds) is at much

higher resolution than standard observational data• Analysis of Record (AOR)

– Traditional precipitation measures such as Equitable Threat Score (ETS) may provide misleading information about model skill

• Makes it difficult to determine if new models or upgrades are really producing “better” forecasts

– Subjective verification methods are needed to complement existing statistical metrics and to guide development of new measures

Forecast #1: smooth

OBSERVED

FCST #1: smooth

FCST #2: detailed

OBSERVED

Courtesy: Mike Baldwin

Traditional “measures-oriented” approach to verifying these forecasts

(Almost all favor the smooth forecast)

Verification Measure Forecast #1 (smooth)

Forecast #2 (detailed)

Mean absolute error 0.157 0.159

RMS error 0.254 0.309

Bias 0.98 0.98

Threat score 0.214 0.161

Equitable threat score 0.170 0.102

Courtesy: Mike Baldwin

Summary – Improving Thunderstorm Forecasting – Part 1

• There are many issues that suggest accurate prediction of convective details will be a slow, incremental process– We don’t understand small-scale phenomena and processes as

well as on the synoptic scale• As we go down in scale the science is less and less mature

– We don’t sample the atmospheric structure in enough detail• Small differences in structure may impact storm evolution

– It is hard enough to accurately predict a single storm and its evolution

• Complexity increases (by orders of magnitude?) once multiple storms develop and begin to interact

– Large uncertainty is inherent in convective prediction and we can’t ignore it

• Strongly suggests probabilistic approaches are needed


• Improvements in short-term convective forecasting will require (among other things):– Substantially improved 4-D sampling of the environment

• Development and deployment of new technology• Focus on detailed water vapor distribution and PBL evolution

– Improved representation of PBL, convection, radiation, etc. processes in mesoscale and high resolution models

• Scientific understanding of smaller scale phenomena will not systematically improve until observing systems improve

– Data assimilation systems appropriate for high resolution models that incorporate enhanced sampling of 4-D environment

• Radar and other remote sensing datasets

– Development of cloud-resolving, rapid update, multi-analysis / multi-model ensemble systems that include microphysical stochastic processes

• Must not “wash out” high resolution observed data


• Improvements in short term convective forecasting will require (among other things):– New objective metrics to properly evaluate high resolution

model forecasts– More interaction between model developers and forecasters

• Greater understanding by model and systems developers of how forecasters use models and what information they need

– Collaboration with cognitive and computer scientists to help develop innovative displays that enhance information transfer to humans

• More education and training of forecasters on new modeling systems (including strengths/weaknesses and “why”)

– If forecasters do not play major roles in design and testing of new guidance/support systems, systems may become “black boxes”

• Otherwise we increase the risk of ultimately removing humans from the forecasting and warning process

CONVECTIVE FORECAST CHALLENGES FROM THE PERSPECTIVE OF THE STORM PREDICTION CENTER Steven Weiss...

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Transcript of CONVECTIVE FORECAST CHALLENGES FROM THE PERSPECTIVE OF THE STORM PREDICTION CENTER Steven Weiss...