Meteorological evolution and model performance for fire threat days over the northeast u s
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Meteorological Evolution and Model Performance for Fire Threat Days Over the Northeast U.S. Joe Pollina 1,2 , Brian A. Colle 1 , Mike Erickson 1 1 School of Marine and Atmospheric Sciences Stony Brook University – SUNY 2 National Weather Service, New York City and and Joseph Charney

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Meteorological Evolution and Model Performance for Fire Threat Days Over the Northeast U.S.

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Meteorological evolution and model performance for fire threat days over the northeast u s

Meteorological Evolution and Model Performance for Fire Threat Days Over the Northeast U.S.

Joe Pollina1,2, Brian A. Colle1, Mike Erickson1

1School of Marine and Atmospheric Sciences

Stony Brook University – SUNY

2National Weather Service, New York City

and

and Joseph Charney

USDA Forest Service, East Lansing, MI

1995 “Sunrise Fire”

Westhampton, NY

Image taken from

the Cutchogue Fire

Department web

site at

www.cutchoguefiredept.org


Meteorological evolution and model performance for fire threat days over the northeast u s

1999-Sep 2009 Fire Events (> 100 Acres) Over the Northeast U.S. (excluding PA and ME). 106 events total from the Northeast Interagency Coordination Center


Motivational questions

Motivational Questions

What is climatology of fire-threat days over the Northeast U.S.? No formal studies have been done for the entire Northeast.

What are the large-scale flow patterns associated with fire threat days across the Northeast, and how do they evolve?

What are the important physical processes that lead to the fire threat?

How well do mesoscale models predict these fire threat conditions?


Data and methods climatology

Data and Methods (Climatology)

  • Data (Jan. 1998-mid Sep. 2009)

    • Obtained 97 “fire threat” days which the National Fire Danger Rating System (NFDRS) indicates that the fire danger was “high, very high, or extreme”. This was obtained through the Wildland Fire Assessment System (WFAS) website: http://www.wfas.net/component/option,com_wrapper/Itemid,92/

  • Methods

    • Monthly climatology of Fire Threat Days

    • Synoptic Flow Classification

      • Based on Yarnal (1993)

    • Large scale flow composite of the “fire threat” days

      • Used daily North American Regional Reanalysis (NARR) (Mesinger et al. 2006)


Monthly climatology of fire threat days

Monthly Climatology of Fire Threat Days

Normalized Difference

Vegetation Index

for 2008 Apr 14

PERCENTAGE

MONTH


Classification of synoptic weather regimes

Classification of Synoptic Weather Regimes

  • Used Yarnal (1993) classification system that was developed to describe the different types of surface pressure patterns associated with West Virginia fire events.

  • Applied 8 different types of weather patterns:

    • Pre-high (PH)

    • Back of high (BH)

    • Extended high (EH)

    • High pressure cell to the south (HS)

    • High pressure cell to the north (HN)

    • Cyclonic conditions with rain (RC)

    • Cold front passage (CF)

    • Elongated low (EL)


Meteorological evolution and model performance for fire threat days over the northeast u s

H

L

H

H

H

H

Pre-high

Back of high

Extended high


Meteorological evolution and model performance for fire threat days over the northeast u s

L

H

L

H

High to the south

High to the north


Distribution of fire threat days for each yarnal synoptic type

Distribution of Fire Threat Days For Each Yarnal Synoptic Type

PH=pre-high

EH=extended high

BH=back of high

CF=cold front

HS=high to south

HN=high to north

PERCENTAGE

YARNAL CLASSIFICATION


Distribution of synoptic type by month

Distribution of Synoptic Type By Month

(pre-high)

(extended high)

(back of high)

(cold front)

(high to the south)

Percentage

(high to the north)

Overall:

PH: 39.2%

EH: 29.9%

BH: 20.6%

CF: 5.2%

HS: 3.1%

HN: 2.1%

Month


Narr composite of all fire threat days

NARR Composite of All Fire Threat Days

SLP

500Z

48 hours prior to

events

24 hours prior to

events

Day of the events


Relatively dry and deep pbl well mixed

NARR Composite of All Fire Threat Days

Relatively dry and Deep PBL (Well-Mixed)

850-mb Relative Humidity (%)

Composite PBL Height (m)


Additional subsidence from terrain

NARR Composite of All Fire Threat Days

Additional Subsidence from Terrain

Composite 900 mb Wind Vector

Composite 2m RH


15 17 april 2008 overlooks ny fire event

15-17 April 2008 Overlooks (NY) Fire Event

Overlooks

Fire

04/15/12z

04/16/12z

04/17/12z

04/15/12z

04/16/12z

04/17/12z


Drying and warming at albany ny 4 14 4 17

Drying and Warming at Albany, NY (4/14-4/17)

1200 UTC 14 April 2008

0000 UTC 18 April 2008

Surface RH

and wind

observations

for central

and southern

NY


Origin of the dry air

Origin of the Dry Air

Backward trajectory (every 24 h)

starting at 12 UTC 17 April 2008

Using Hybrid Single-Particle

Lagrangian Integrated

Trajectory (HYSPLIT) program

(Draxler and

Rolph 2003). This

shows where the dry air

was 315 hours prior to

the event, and the path

it took up to the time of

the event.

4/5 00 UTC

4/6

4/7

4/8

4/12

4/9

4/14

4/10

4/11

4/13

4/15

4/16

4/17

4/17 12 UTC

4/16 00 UTC

4/16 00 UTC

4/17 12 UTC

4/17 12 UTC


Meteorological evolution and model performance for fire threat days over the northeast u s

Short-Range Ensemble Systems

Stony Brook Univ. 13 Member Ensemble (00 UTC)

- 7 MM5, 6 WRFv2.2 members at 12 km grid spacing (NAM soil moisture and SST).

- IC: NAM, GFS, CMC and NOGAPS.

- CP: Grell, KF and Betts-Miller.

- PBL MM5: Blackadar, MRF, MY

- PBL WRF: MYJ and YSU.

- MP MM5: Sice, Reis2

- MP WRF: Ferrier, WSM3.

NCEP SREF 21 Member (21 UTC)

- 10 ETA members at 32 km grid spacing.

- 5 with BMJ CP and Ferrier MP.

- 5 with KF CP and Ferrier MP.

- 5 RSM members at 45 km grid spacing.

- 3 with SAS CP and Zhou GFS MP.

- 2 with RAS CP and Zhou GFS MP.

- 3 WRF-NMM members at 40 km grid spacing.

- 3 WRF-ARW members at 45 km grid spacing.

- IC's are perturbed using a breeding technique.

  • Verification of surface parameters (e.g., 2-m temp and 10-m wsp) for 109 NWS stations over NE for March-Sept 2006-2009


Meteorological evolution and model performance for fire threat days over the northeast u s

Surface Temperature Mean Error (12-36h) by Member

for Stony Brook and SREF Ensembles

MYJ

SBU 12-km Ensemble

NCEP SREF

(oC)

(oC)

SREF sub-group averages

WRF

MM5


Meteorological evolution and model performance for fire threat days over the northeast u s

Impact of Using Previous 5 Fire Threat Days

for Bias Correction of Surface Temperature (12-36 h avg)

SBU 12-km Ensemble

NCEP SREF

(oC)

(oC)

WRF

MM5

Note: MAE for SBU+SREF ens mean is 1.84 K (~0.10 K less than best member, but comparable to SBU mean).


Meteorological evolution and model performance for fire threat days over the northeast u s

NARR Composite of Top 10 Largest and Smallest Cool Bias on Fire Threat Days

Sea-level pressure (Pa)

Large T Bias

Small T Bias

Cloud Fraction (%): Cool bias associated with partly cloudy fire threat days – model too cloudy??.

Large T Bias

Small T Bias


Conclusions

Conclusions

  • Peak fire threat over the Northeast U.S. occurs during the climatological spring (~52% of events in April). This is the pre green-up period.

  • Fire threat days were classified by synoptic type. Pre-high (39%), extended high (30%), and back of high (21%) are the major synoptic patterns associated with a high fire threat.

  • NARR composites illustrate high pressure moving south from Canada to the Northeast U.S., with a corresponding ridge at 500 mb approaching the Great Lakes.

  • Downsloping from northwesterly flow, subsidence from high pressure, and mixing from large PBL heights are all likely important in bringing in warmer, drier air to the surface.

  • Ensembles have relatively large (cool) biases for fire threat days over the Northeast U.S., which are larger than the average warm season biases.

  • A standard 14-day bias correction (used by many studies) does not work that well for fire threat days (since weather is more anomalous). A bias correction using last 5 fire threat days removes most of ensemble bias.


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