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Evaluating the Boundary Layer Environment and Convective
Storm Evolution from FV3-SAR Simulations
NCEP Student Presentation
Tomer BurgEMC Mentor: Logan Dawson
8 August 2019
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Unified Forecast System (UFS)
NCEP is transitioning its modeling suite to the Finite-Volume Cubed-Sphere (FV3) Dynamical Core
Image credit: GFDL
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Unified Forecast System (UFS)
NCEP is transitioning its modeling suite to the Finite-Volume Cubed-Sphere (FV3) Dynamical Core
The Global Forecast System (GFS) model was upgraded from v14 to v15 on 12 June 2019, implementing the FV3 core
Image credit: GFDL
GFS v14 GFS v15
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Computationally expensive!
Limited Area FV3-SAR
• The current FV3-nest configuration requires the global model to run in conjunction with a convection allowing 3-km nest
• EMC developed a stand alone regional (SAR) model capability for the FV3
• The FV3-SAR is run over the continental U.S. while ingesting boundary conditions from the global GFS
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Limited Area FV3-SAR
MODEL CONFIGURATION
ICs GFSv15
LBCs GFSv15
MP Scheme GFDL
PBL Scheme Hybrid EDMF
Land Surface Model NOAH
Radiation RRTMG
Model Core FV3
Horiz. Resolution 3-km
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2019 Hazardous Weather Testbed
• Every spring, the SPC & NSSL run a testbed to evaluate emerging technologies from research to operations
Image credit: SPC
• In 2019, the EMC ran FV3-SAR simulations for evaluation alongside other operational & experimental models
SPC = Storm Prediction CenterNSSL = National Severe Storms Laboratory
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2019 Hazardous Weather Testbed
• 2019 has been one of the most active severe weather years on record, producing many candidate cases for evaluation
May 20
May 27
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Planetary Boundary Layer (PBL)
• The PBL is defined as “the bottom layer of the troposphere that is in contact with the surface of the earth.”
American Meteorological Society glossary
PBL
• Typically at its highest during the day as solar heating results in a growing mixed turbulent layer
• Minimized at night when a radiative inversion develops at the surface
• The FV3-SAR inherits the same PBL scheme from GFSv15 and GFSv14
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This project seeks to address the following questions:
Project Goals
How does the GFS PBL scheme affect the pre-convective environment within the FV3-SAR?
How does the FV3-SAR represent convective mode & evolution compared to observations?
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This project seeks to address the following questions:
Project Goals
How does the GFS PBL scheme affect the pre-convective environment within the FV3-SAR?
How does the FV3-SAR represent convective mode & evolution compared to observations?
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This project seeks to address the following questions:
Project Goals
How does the GFS PBL scheme affect the pre-convective environment within the FV3-SAR?
How does the FV3-SAR represent convective mode & evolution compared to observations?
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OUTLINE
Results
Conclusions
• FV3-SAR Systematic Biases• Case Studies
Methodology
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Methodology
CASE SELECTION
Date Range 18 May 2019 – 29 May 2019
Initialization 0000 UTC Daily
Forecast Hours Hour 12 (1200 UTC) & Hour 24 (0000 UTC)
Criteria • 3 RAOB sites within the warm sector for each initialization with verification soundings available for both hours 12 & 24
• Both model & observed soundings are not convectively contaminated for both hours 12 & 24, but are downstream of convection
Data Sources • Model: FV3-SAR simulations• Observation: RAOB radiosondes
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Methodology
ANALYSIS METHODS
Skew-T Soundings MetPy python package (py3.7)
Severe Indices SHARPpy python package (py2.7 converted to py3.7)
PBL Metrics • PBL Height: First identifiable height at which the virtual potential temperature is greater than the surface value + 0.5
• PBL Integral: Vertical integral of dewpoint depression throughout the PBL
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“”
Python Problems
In theory, practice equals theory, but in practice, practice does not equal theory…”
- Robert Fovell, SUNY Albany, many times in 2019
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Python Problems
Python 3.7
• Parse grib2 file
• Generate soundings
• Make radar maps
End
• Run jupyterhub
“”
In theory, practice equals theory, but in practice, practice does not equal theory…”
THEORY
- Robert Fovell, SUNY Albany, many times in 2019
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Python Problems
Python 3.7
• Parse grib2 file
• Generate soundings
• Make radar maps
End
Python 3.7
NCL
• Parse grib2 filePython 2.7
• Make radar maps Python 3.7
• Generate soundings with edited SHARPpy
Local desktop
End?
• Run jupyterhub
• Run jupyterhub
“”
In theory, practice equals theory, but in practice, practice does not equal theory…”
PRACTICETHEORY
- Robert Fovell, SUNY Albany, many times in 2019
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OUTLINE
Conclusions
Methodology
Results• FV3-SAR Systematic Biases• Case Studies
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1200 UTC Temperature Biases
• 1200 UTC (Hour 12)
• Surface exhibits slight warm bias
• 925-850 hPa exhibit cool bias
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1200 UTC Temperature Biases
• 1200 UTC (Hour 12)
• Surface exhibits slight warm bias
• 925-850 hPa exhibit cool bias
Morning slight warm bias
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1200 UTC Temperature Biases
Observed
Forecast
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1200 UTC Temperature Biases
Observed
Forecast
Inversion is stronger than modeled
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1200 UTC Temperature Biases
• FV3-SAR tends to depict smaller inversions than observed
• The FV3-SAR is often biased too cold in the top of the inversion layer
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0000 UTC Temperature Biases
• 0000 UTC (Hour 24)
• Surface exhibits cold bias
• 925-850 hPa also exhibit cold bias, but less so than surface
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0000 UTC Temperature Biases
Evening cool bias
• 0000 UTC (Hour 24)
• Surface exhibits cold bias
• 925-850 hPa also exhibit cold bias, but less so than surface
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0000 UTC Temperature Biases
Observed
Forecast
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0000 UTC Temperature Biases
Observed
Forecast
PBL is less moist than modeled
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0000 UTC PBL Systematic Biases
• #1: FV3SAR modeled PBL integral, and sometimes height, is too small compared to observation
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0000 UTC PBL Systematic Biases
• #2: FV3SAR appears to decouple the boundary layer too quickly in the evening
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0000 UTC PBL Systematic Biases
• Both PBL height and PBL integral are biased too low for 0000 UTC soundings
• Associated with surface cool bias decreasing with height
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Convective Available Potential Energy (CAPE)
“The maximum buoyancy of an … air parcel, related to the potential updraft strength of thunderstorms.”
CAPECIN
American Meteorological Society glossary
• Larger CAPE results from:• Large magnitude of positive
buoyancy• Deep layer of positive buoyancy
• Used as a metric to assess instability in pre-convective environments
• Higher CAPE, when combined with vertical wind shear & ascent, contributes to severe thunderstorms
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Convective Available Potential Energy (CAPE)
Surface-Based CAPE
CAPE for parcel lifted from the surface
SBCAPE
CAPECIN
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Convective Available Potential Energy (CAPE)
Mixed Layer CAPE
CAPE for a parcel representative of the average of lowest 100mb
MLCAPE
CAPECIN
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SBCAPE vs. MLCAPE
• Surface-based CAPE is biased too low as a result of the evening cool bias
• Mixed layer CAPE significantly improves this bias by removing the impact of the surface decoupling
• MLCAPE is still biased slightly low due to the background low tropospheric cool bias
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FV3-SAR Temperature Systematic Biases
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FV3-SAR Temperature Systematic Biases
Morning slight warm bias
Evening cool bias
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FV3-SAR Temperature Systematic Biases
Systematic cool bias just aloft
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Case Study #1
ObservedForecast
• 27 May 2019 Dayton, OH Tornadoes
• Analysis Location: Wilmington, OH (KILN)
Hour 12 | 1200 UTC 27 May 2019
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Case Study #1
ObservedForecast
• 27 May 2019 Dayton, OH Tornadoes
• Analysis Location: Wilmington, OH (KILN)
Hour 24 | 0000 UTC 28 May 2019
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Case Study #1
ObservedForecast
• 27 May 2019 Dayton, OH Tornadoes
• Analysis Location: Wilmington, OH (KILN)
Hour 25 | 0100 UTC 28 May 2019
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Case Study #1
ObservedForecast
• 27 May 2019 Dayton, OH Tornadoes
• Analysis Location: Wilmington, OH (KILN)
Hour 26 | 0200 UTC 28 May 2019
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Case Study #1
ObservedForecast
• 27 May 2019 Dayton, OH Tornadoes
• Analysis Location: Wilmington, OH (KILN)
Hour 27 | 0300 UTC 28 May 2019
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Case Study #1
• FV3SAR hour 12 underestimated the inversion magnitude
• FV3SAR hour 24 depicted an inversion not observed
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Case Study #1
• CAPE bias is substantially reduced when using MLCAPE vs. SBCAPE
Surface Based CAPE:
FV3-SAR 260 J/kg
Observed 1799 J/kg
Mixed Layer CAPE:
FV3-SAR 632 J/kg
Observed 601 J/kg
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Case Study #1 Conclusions
• Surface temperature bias & SBCAPE biases don’t appear to have a major detrimental impact on convective mode & evolution
• A possible impact of lower PBL height & more PBL moisture could be more widespread convective initiation
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Case Study #2
• 20 May 2019 Oklahoma & Texas Tornadoes
• Analysis Location: Norman, OK (KOUN)
Hour 12 | 1200 UTC 20 May 2019
ObservedForecast
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Hour 15 | 1500 UTC 20 May 2019
ObservedForecast
Case Study #2
• 20 May 2019 Oklahoma & Texas Tornadoes
• Analysis Location: Norman, OK (KOUN)
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Hour 18 | 1800 UTC 20 May 2019
ObservedForecast
Case Study #2
• 20 May 2019 Oklahoma & Texas Tornadoes
• Analysis Location: Norman, OK (KOUN)
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Hour 21 | 2100 UTC 20 May 2019
ObservedForecast
Case Study #2
• 20 May 2019 Oklahoma & Texas Tornadoes
• Analysis Location: Norman, OK (KOUN)
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Hour 24 | 0000 UTC 21 May 2019
ObservedForecast
Case Study #2
• 20 May 2019 Oklahoma & Texas Tornadoes
• Analysis Location: Norman, OK (KOUN)
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Case Study #2 Conclusions
• Too much convection was already ongoing at forecast hour 12 compared to observations
• Spurious modeled convection resulted in faster upscale growth & propagation than observed, shifting the boundary for subsequent supercell initiation in Texas too far east
• Discrete cells developed east of the dryline in Texas in the FV3-SAR, but rapidly merged with the modeled QLCS over Oklahoma rather than remaining discrete
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Conclusions
� FV3-SAR exhibits a cold bias aloft for hours 12 & 24
How does the GFS PBL scheme affect the pre-convective environment within the FV3-SAR?
� FV3-SAR exhibits a surface slight warm bias at hour 12 & cold bias at hour 24
� PBL height and PBL integral are biased low compared to observations
� Surface cool bias results in SBCAPE low bias; calculating MLCAPE mostly eliminates this bias
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Conclusions
� FV3-SAR has a more “blobby” reflectivity structure compared to observations
How does the FV3-SAR represent convective mode & evolution compared to observations?
� In a majority of cases the FV3-SAR exhibited more convection than observed, in some cases spurious
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Acknowledgements
� Genene Fisher� Ashley Burrell� Sharon Dee
� Logan Dawson� Geoff Manikin� Jacob Carley� EMC
� Fellow interns� NCEP
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Extra Slides
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FV3-SAR Systematic Biases
Image credit: Logan Dawson
• FV3-SAR has a well documented cold bias
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FV3-SAR Systematic Biases
• FV3-SAR has a well documented cold bias
Image credit: Logan Dawson
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Unified Forecast System (UFS)
NCEP is transitioning its modeling suite to the Finite-Volume Cubed-Sphere (FV3) Dynamical Core
The FV3 is a global model with the capability of inserting a nested higher resolution grid
Image credit: GFDL