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Severe Weather Workshop WFO CRP
Waylon Collins
DOC/NOAA/National Weather Service
Weather Forecast Office Corpus Christi
February-March 2019
Operational NWP Models: Convection
Allowing Models (CAM)
Operational NWP Models: Convection
Allowing Models (CAMs) for Convection:
OutlineWhat is a Convection-Allowing Model (CAM)?
Review of Operational and Experimental CAMsDETERMINISTIC
Model configurations, output variables, and categorization by dynamic core, microphysics/PBL parameterizations, output variable
Performance comparisons (FV3-NSSL, FV3-GFDL, HRRRv3, UM)
ENSEMBLES
Ensembles and Time-Lagged Ensembles: General Concepts
Ensemble configurations, output variables, and categorization by output variable
Performance comparisons (HREF, HRRRE, NCAR, HRRR-TL4, HRRR-TL6)
Issues of CAM Predictability and Spin-Up
What is a Convection-Allowing Model (CAM)?
Convection-Allowing Models (CAMs) versus
Convection-Resolving Models (CRMs)
CONVECTIVE ALLOWING: Simulate deep convection
without convective parameterization (explicit prediction of
grid-scale precipitation by microphysical parameterization)
≤ 4 km grid spacing (Weisman et al. 1997)
CONVECTIVE RESOLVING: Simulate deep convection and
resolving individual convective cells without convective
parameterization
≤ 0.1 km grid spacing (Bryan et al. 2003; Petch 2006)
≤ 0.5 km grid spacing (Jascourt S. 2010)
Convective/Cumulus Parameterization
Relationship between parameterized sub-grid scale convection and resolved-scale convection (Warner, 2011)
A. Precipitation can be produced as a byproduct of the activation of moist convection by the convective parameterization scheme and by explicit prediction of grid-scale precipitation by microphysical parameterization
B. Parameterized precipitation is generated within a sub-saturated grid box (since convection parameterized is of sub-grid scale), while resolved scale precipitation in the microphysics scheme requires grid box saturation
C. Convective parameterization generally does not produce cloud water/ice on the grid-scale, thus no cloud radiative effects
D. Parameterized and resolved scale precipitation may predominate at different regions of an event (e.g. MCS parameterize convection dominate convective region while microphysics scheme influences the stratiform rain region.)
Convective/Cumulus Parameterization:
NWP Model skill when Convective
Parameterization turned off (≤ 10-km)
5-10 km: Convective overturning develops/evolves too slowly; updraft/downdraft mass
fluxes and precipitation rates too strong during mature phase (Weisman et al. 1997)
4-km:
Adequately resolve squall line mesoscale structures (Weisman et al. 1997)
Exaggerates scale of individual convective cells contributing to a high QPF bias (e.g.
Deng and Stauffer, 2006)
Can accurately/skillfully predict convection occurrence and mode. Less skillful with
regard to timing and position (Fowle and Roebber, 2003; Weisman et al. 2008)
4-km 2-km: Miniscule improvement in prediction skill; added value likely not worth
the factor of 10 increase in computational expense (Kain, 2008)
2-km 0.25 km: Simulations of supercell very sensitive to grid spacing (2-km: steady
state/unicellular ≤ 1-km: cyclic mesocyclogenesis (Adlerman and Droegemeier, 2002)
Review of Operational CAMs
Select CAMs to Support the Forecast Process:
Convection
DETERMINISTIC
3-km High Resolution Rapid Refresh (HRRR)
3-4 km HIRES Window (ARW, NMM)
4-km NAMNEST (NEMS-NMMB)
3,4-km NSSL WRF
3-km Texas Tech WRF
ENSEMBLE
High Resolution Rapid Refresh Ensemble (HRRRE)
High Res Rapid Refresh Time-Lagged Ensemble (HRRR-TLE)
Short-range Ensemble Forecast (SREF)
High Resolution Ensemble Forecast (HREF)
High Resolution Rapid
Refresh (HRRR)
NOAA/NWS/NCEP, Model Analyses and Guidance, accessed 2/5/2019 via URL
https://mag.ncep.noaa.gov/model-guidance-model-area.php
Rapid Refresh
(RAP)
High Resolution Rapid Refresh (HRRR)
Model Domain Grid
spacing
Vertical
levels
Boundar
y
condition
Initialization
Frequency/
Prediction
RAPv3 North
American
13-km 50 GFS Hourly/24-h
(03,09,15,21z)/
39-h
HRRR CONUS 3-km 50 RAP Hourly/24-h
(00,067,12,18z)
/36-h
Model Dynamic
Core
Assimilation Radar DA Radiation
LW/SW
Microphysics
RAPv3 ARW
v3.8.1
GSI Hybrid 3D-
VAR/ Ensemble
(0.85/0.85)
13-km DFI +
low reflect(Weygandt and
Benjamin, 2007)
RRTMG
v3.6 (Iacono
et al. 2008)
Thompson-
aerosol v3.6.1(Thompson and
Eidhammer, 2014)
HRRR ARW
V3.8.1
GSI Hybrid 3D-
VAR/ Ensemble
(0.85/0.85)
3-km 15 min
LH+ low reflect(Weygandt and
Benjamin, 2007)
RRTMG
v3.6 (Iacono
et al. 2008)
Thompson-
aerosol v3.6.1(Thompson and
Eidhammer, 2014)
High Resolution Rapid Refresh (HRRR)
Model Cumulus Parameterization Planetary
Boundary
Layer
Land
Surface
Model
Initialization
(Balancing)
RAPv3 Deep:
Grell and Freitas (2014)
Shallow:
Grell-Freitas-Olson
MYNN
v3.6+(Nakanishi and
Niino, 2004,
2009)
RUC
v3.6+(Smirnova
et al. 2008)
Digital Filter
Initialization
(DFI)(Weygandt and
Benjamin, 2007)
HRRR Deep: NONE
Shallow: MYNN PBL Clouds
MYNN
v3.6+(Nakanishi and
Niino, 2004,
2009)
RUC
v3.6+(Smirnova
et al. 2008)
Digital Filter
Initialization
(DFI)(Weygandt and
Benjamin, 2007)
High Resolution Rapid Refresh (HRRR)
Select Variables/Parameters
CONVECTIVE SEVERITY
Composite Indices (Available in CAVE/D2D
Perspective/Volume Browser)
Non-Supercell Tornado Parameter
Significant Tornado Parameter
Supercell Composite Parameter
MCS Maintenance Probability
High Resolution Rapid Refresh (HRRR)
Select Variables/Parameters
STORM ATTRIBUTES
Composite Indices (Available in CAVE/D2D
Perspective/Volume Browser)
Maximum Hourly Updraft Helicity
Maximum Hourly Wind Gust Speed
Maximum Hourly Lightning Threat
NAMNEST
HIRESW
NOAA/NWS/NCEP, Model Analyses and Guidance, accessed 2/5/2019 via
URL https://mag.ncep.noaa.gov/model-guidance-model-area.php
NAMNEST, HIRESW
Model Domain Grid
spacing
Vertical levels Boundary
condition
Initialization
Frequency
HIRESW(CONUS):
(1) NEMS-NMMB
(2) WRF-ARW
CONUS (1) 3.2-km
(2) 3.2-km
50 (16 in lowest 120 hPa)
RAP (IC)
GFS (BC)
3-hr (cycled)
/48-h
NAMCONUSNEST:
NEMS-NMMB
CONUS 3-km 60(27 in 0-3km layer)
NAM 1-hr (cycled)
/60-h
Model Dynamic
Core
Assimilation Radar
DA
Radiation
LW/SW
Microphysics
HIRESW(CONUS):
(1) NEMS-NMMB
(2) WRF-ARW
(1) NMMB
(2) ARW
v3.6.1
Hybrid
ensemble
3DVar
Yes LW: RRTM
(Iacono, et al. 2008)
SW: RRTM
(Mlawer, et al. 1997)
(1) Ferrier-Aligo
(Aligo et al. 2014)
(2) Modified WSM6
(Grasso et al. 2014)
NAMCONUSNEST:
NEMS-NMMB
NMMB
1/2015
Hybrid
ensemble
3DVar
Yes LW: RRTM
(Iacono, et al. 2008)
SW: RRTM
(Mlawer, et al. 1997)
Ferrier-Aligo(Aligo et al. 2014)
NAMNEST, HIRESW
Model Cumulus
Parameteri
zation
Planetary
Boundary
Layer
Land Surface Model Initialization
(Balancing)
HIRESW(CONUS):
(1) NEMS-NMMB
(2) WRF-ARW
NONE MYJ Level 2.5(Janjic 1994, 2001)
Noah LSM (Ek et al. 2003)
(20 MODIS-IGBP land
use categories)
(1) Diabatic
Digital
Filter
NAMCONUSNEST:
NEMS-NMMB
NONE MYJ Level 2.5(Janjic 1994, 2001)
Noah LSM (Ek et al. 2003)
(20 MODIS-IGBP land
use categories)
Diabatic
Digital Filter
NAMNEST, HIRESW
Select Variables/Parameters
CONVECTIVE SEVERITY (HIRESW)
Composite Indices (Available in CAVE/D2D
Perspective/Volume Browser)
Layer Non-Supercell Tornado Parameter
Significant Tornado Parameter
Layer Supercell Composite Parameter
MCS Maintenance Probability
NAMNEST, HIRESW:
Select Variables/Parameters
CONVECTIVE SEVERITY (NAMNEST)
Select Composite Indices (Available on NCEP/EMC
website)
Maximum 400-1000mb Downdraft/Updraft W
Maximum 2-5km Updraft Helicity (
0-1km, 0-3km Storm-scale Helicity
NOAA/NWS/NCEP/EMC, NCEP NAM CONUS nest Graphics, accessed 1/28/2019 via URL
https://www.emc.ncep.noaa.gov/mmb/mmbpll/nam_conusnest_hourly60/
Texas Tech WRF
Texas Tech Atmospheric Science Department, 12km/3km Real-Time WRF Modeling System,
accessed 2/7/2019 via URL http://www.atmo.ttu.edu/bancell/real_time_WRF/ttuwrfhome.php/
12-km 3-km
NWP Model Configurations:
Texas Tech Real Time Prediction System
Model Domain Grid
spacing
Vertical
levels
Boundary
condition
Initialization
Frequency
TexasTech WRF TX/OK/KS (portions of
surrounding states)
3-km 38 Outer 12-km
Grid (which is
forced by GFS)
6 h (00, 06, 12,
18 UTC cycles)
/ 60-h
Model Dynamic
Core
Assimilati
on
Radar
DA
Radiation
LW/SW
Microphysics
TexasTech WRF ARW v3.5.1 GFS
Analysis
No LW: RRTM
SW: Dudhia
Thompson(Thompson et al.
2008)
Model Cumulus
Parameteriza
tion
Planetary
Boundary
Layer
Land
Surface
Model
Initialization
(Balancing)
Texas Tech WRF 3-km: NONE
12-km: Tiedtke
YSU Noah(Ek et al.
2003)
NOAA/NWS/NCEP/EMC, About the Texas Tech Real Time Weather Prediction System, accessed 2/5/2019
via URL http://www.atmo.ttu.edu/bancell/real_time_WRF/TTUWRF-about.html
Texas Tech WRF
Select Variables/Parameters
CONVECTIVE SEVERITY
Composite Indices
URL http://www.atmo.ttu.edu/bancell/real_time_WRF/ttuwrfhome.php/
Significant Tornado Parameter
Supercell Composite Parameter
Updraft Helicity
Texas Tech Atmospheric Science Department, 12km/3km Real-Time WRF Modeling System,
accessed 1/28/2019 via URL http://www.atmo.ttu.edu/bancell/real_time_WRF/ttuwrfhome.php/
Texas Tech WRF 3-km Example
Texas Tech Atmospheric Science Department, 12km/3km Real-Time WRF Modeling System,
accessed 2/7/2019 via URL http://www.atmo.ttu.edu/bancell/real_time_WRF/ttuwrfhome.php/
NSSL WRF
NOAA/NSSL, Realtime CAM Data, accessed 2/7/2019 via URL https://cams.nssl.noaa.gov
NWP Model Configurations:
NSSL Real Time WRF Modeling Systems
Model Domain Grid
spacing
Vertical
levels
Boundary
condition
Initialization
Freq/
Prediction
NSSL-WRF
(since 2007)
CONUS 4-km 35 12-km NAM 12 h (00,12
UTC)/36 h
NSSL-WRF 3-km
(since Fall 2018)
CONUS 3.2-km 41 12-km NAM 24 h (00
UTC)/60 h
Model Dynamic
Core
Assimilation Radar
DA
Radiation
LW/SW
Microphysics
NSSL-WRF
(since 2007)
ARW
v3.4.1
NAM Analysis
(12-km)
No LW:RRTM(Mlawer, et al. 1997)
SW: Dudhia
WSM6(Hong and Lim 2006)
NSSL-WRF 3-km
(since Fall 2018)
ARW
v3.4.1
NAM Analysis
(12-km)
No LW: RRTM(Mlawer, et al. 1997)
SW: Dudhia
WSM6(Hong and Lim 2006
Adam Clark NOAA/OAR/NSSL, Personal communication
“…more equitable comparison to the experimental FV3 runs”
NWP Model Configurations: NSSL Realtime WRF
versus Texas Tech Real Time Prediction System
Model Cumulus
Parameteriza
tion
Planetary
Boundary
Layer
Land Surface Model Initialization
(Balancing)
NSSL-WRF
(since 2007)
NONE MYJ Noah
NSSL-WRF 3-km
(since Fall 2018)
3-km: NONE
12-km: Tiedtke
MYJ Noah (Ek et al. 2003)
Adam Clark NOAA/OAR/NSSL, Personal communication
NSSL-WRF
Select Variables/Parameters
CONVECTIVE SEVERITY
Composite Indices (Available on NSSL Website)
STORM ATTRIBUTES
Updraft Helicity (4hr maximum 2-5km; m2/s2)
Composite reflectivity (dBZ) and 2-5 km UH > 75 m2/s2
Updraft W (4-hr maximum; m/s)
Downdraft W (4-hr minimum; m/s)
National Severe Storms Laboratory, Realtime CAM Data,
accessed 1/31/2019 via URL https://cams.nssl.noaa.gov/
NSSL-WRF 3-km Example
NOAA/NSSL, Realtime CAM Data, accessed 2/7/2019 via URL https://cams.nssl.noaa.gov
Categorize CAM Deterministic Runs:
Dynamic Core
ARW
(Advanced Research WRF)
NMMB (Non-hydrostatic
Multiscale Mesoscale
Model on Arakawa B grid)
HRRR NAMNEST
HIRES Window-ARW HIRES Window-NMMB
NSSL WRF
Texas Tech WRF
Categorize CAM Deterministic Runs:
Microphysics Parameterization
(Explicit prediction of grid scale
precipitation)THOMPSON (1)
(Thompson et al. 2008)
THOMPSON AEROSOL (2)
(Thompson & Eidhammer, 2014)
FERRIER-ALIGO
(Aligo et al. 2014)
WSM6 (1)
(Hong and Lin 2006)
MODIFIED WSM6 (2)
(Grasso et al. 2014)
HRRR (2) HIRESW-NMMB HIRESW-ARW (2)
Texas Tech WRF (1) NAMNEST NSSL-WRF (1)
Categorize CAM Deterministic Runs:
Planetary Boundary Layer Parameterization
(PBL Affects CAPE)
MYNN v3.6+ (local)
(Nakanishi and Niino, 2004, 2009)
MYJ (1) (local)
MYJ LEVEL 2.5 (2)
(Janjic 1994, 2001)
YSU (non-local)
(Hong et al, 2006)
HRRR HIRESW-NMMB (2) Texas Tech WRF
HIRESW-ARW (2)
NAMNEST (2)
NSSL-WRF (1)
Categorize CAM Deterministic Runs:
Select Convective VariablesModel /
Variable
Maximum
Updraft
Helicity (2-5km)
Significant
Tornado
Parameter
Supercell
Composite
Parameter
Non-supercell
Tornado
Parameter
Downdraft/
Updraft W
(400-1000mb)
HRRR X X X X
HIRESW
(NMMB)
X X X
HIRESW
(ARW)
X X X
NAMNEST X
Texas Tech
WRF
X X X
NSSL-WRF X X
Categorize CAM Deterministic Runs:
Select Convective VariablesModel /
Variable
MCS Maintenance
Probability
0-1,3 km
Storm Scale
Helicity
Composite
Reflectivity
Max hourly
lightning
threat
Max hourly
wind gust
speed
HRRR X X X
HIRESW
(NMMB)
X
HIRESW
(ARW)
X
NAMNEST X
Texas Tech
WRF
NSSL-WRF X
Skill of Deterministic CAMs: Convection
2018 Spring Forecasting Experiment: Preliminary Results
(Adam Clark, NOAA/OAR/National Severe Storms Laboratory)
NWP Model Ensembles
NWP Model Ensembles: Conceptual
Stochastic Dynamical Forecasting (Epstein, 1969)
Attempt to account for the uncertainty regarding the true initial atmospheric
state
Run a single NWP model on a probability distribution (PD) that describes initial
atmospheric state uncertainty
Impractical for forecast operations
Ensemble Forecasting (Leith, 1974)
Monte Carlo approximation to stochastic dynamical forecasting
Random sample of PD that describes initial atmospheric state uncertainty. The
members are collectively referred to as an ensemble of initial conditions.
The modeler conducts a run on each member of the ensemble
Advantage of Ensemble Forecasting over Single Deterministic Runs
Assess the level of forecast uncertainty
NWP Model Ensembles: Conceptual
Ensemble Forecasting to Support Forecast Operations
Each ensemble member represents a unique combination of model
numerics, initialization, dynamics, and/or physics.
Assume a positive correlation between uncertainty and divergence/spread
in the ensemble members
Prediction probabilities generated by post-processing the ensemble
Time-Lagged Ensembles: Motivation
Lu, C., H. Yuan, B.E. Schwartz, and S.G. Benjamin, 2007: Short-Range Numerical Weather Prediction Using Time-
Lagged Ensembles. Wea. Forecasting, 22, 580–595, https://doi.org/10.1175/WAF999.1
A MOTIVATION FOR TIME-LAGGED ENSEMBLES TO SUPPORT
SHORT-RANGE (0-48 h) FORECASTING
Short-range predictions generally strong dependency on initial
conditions (Lu et al. 2007)
One can interpret time-lagged ensembles as predictions from a set
of perturbed initial conditions (van den Dool and Rukhovets 1994)
Given that IC perturbations are generated from different
initialization cycles, time-lagged ensembles would conceptually
reflect forecast error covariance with time-evolving/flow
dependent information (Lu et al. 2007)
Time-Lagged Ensembles: Conceptual
Lu, C., H. Yuan, B.E. Schwartz, and S.G. Benjamin, 2007: Short-Range Numerical Weather Prediction Using Time-
Lagged Ensembles. Wea. Forecasting, 22, 580–595, https://doi.org/10.1175/WAF999.1
Single-model ensemble system:
Equivalent model numerics, dynamics,
physics, for each ensemble member
Only differences amongst ensemble
members are different prediction
projections initialized at different times
Eight Member Ensemble, 1-hour
Latency Example
High Resolution Rapid Refresh Ensemble (HRRRE)
NOAA/ESRL/GSD, HRRR Ensemble (HRRRE) Guidance2018 HWT Spring Experiment, accessed 2/4/2019 via
URL https://rapidrefresh.noaa.gov/internal/pdfs/2018_Spring_Experiment_HRRRE_Documentation.pdf
Trevor Alcott NOAA/ESRL/GSD (personal communication)
High Resolution Rapid Refresh Ensemble
(HRRRE): Description
NOAA/ESRL/GSD, HRRR Ensemble (HRRRE) Guidance2018 HWT Spring Experiment, accessed 2/4/2019 via
URL https://rapidrefresh.noaa.gov/internal/pdfs/2018_Spring_Experiment_HRRRE_Documentation.pdf
MOTIVATION
To improve 0-12 h high-resolution forecasts via ensemble-based,
multi-scale data assimilation
To test single-model ensemble design for 0-36 h forecasts
Provide foundation for Warn-on-Forecast1 and other on-demand,
high-resolution applications.
1NSSL project to develop on-demand, ensemble-based, high-resolution, 0-3 h NWP model
to support severe thunderstorm and flash flood warnings
High Resolution Rapid Refresh Ensemble
(HRRRE): Description
NOAA/ESRL/GSD, HRRR Ensemble (HRRRE) Guidance2018 HWT Spring Experiment, accessed 2/4/2019 via
URL https://rapidrefresh.noaa.gov/internal/pdfs/2018_Spring_Experiment_HRRRE_Documentation.pdf
Trevor Alcott (NOAA/ESRL) and Adam Clark (NSSL), personal communication
ENSEMBLE CONFIGURATION
9-member, 3-km ensemble
Domain: ~70% of CONUS
Same dynamic core/physics used in the deterministic HRRR
GEFS ensemble members atmospheric perturbations
15-km/3-km nested data assimilation ensemble
First 9 members of the 3-km data assimilation ensemble continue
integrating to 36-h
The 9 ensemble predictions initiated at different times
There is no path to an operational version
High Resolution Rapid Refresh Ensemble
(HRRRE): Description
DOC/NOAA/ESRL, HRRR Model Fields – Experimental, accessed 2/11/2019 via URL
https://rapidrefresh.noaa.gov/hrrr/HRRRE/
URL https://rapidrefresh.noaa.gov/hrrr/HRRRE/
High Resolution Rapid Refresh Ensemble (HRRRE):
Select Variables/Parameters
CONVECTIVE SEVERITY
(URL https://rapidrefresh.noaa.gov/hrrr/HRRRE/)
2-5 km max 1hr updraft helicity
2-5 km max 1hr updraft helicity (>75)
Neighborhood probability of 2-5 km UH > 75 m2/s2
4-h neighborhood probability of hail > 1 inch
4-h neighborhood probability of wind > 50 knots
4-h neighborhood probability of a tornado
DOC/NOAA/ESRL, High Resolution Rapid Refresh (HRRR), accessed 2/8/2019 via URL
https://rapidrefresh.noaa.gov/hrrr/HRRRE/
High Resolution Rapid Refresh Time-Lagged
Ensemble (HRRR-TLE): Description
DOC/NOAA/ESRL, HRRR Time-Lagged Ensemble - Experimental, accessed 2/10/2019 via URL
https://rapidrefresh.noaa.gov/hrrr/hrrrtle/
Trevor Alcott (NOAA/ESRL) and Adam Clark (NSSL), personal communication
Developed as a tool for testing postprocessing algorithms at
NOAA/ESRL/GSD; there is no path to operations
A combination of the 3 most recent deterministic HRRR runs with
a 2 hour latency (thus a 3-member HRRR time-lagged ensemble)
Probabilities generated based on a neighborhood of grid points
(predictions at 100 grid points within 40-km of a point of interest
are considered “members”; the probability is the fraction of
“members” that exceed a given threshold)
Website at URL https://rapidrefresh.noaa.gov/hrrr/hrrrtle
contains output based on a 3-member HRRRX time-lagged ensemble
High Resolution Rapid Refresh Time-Lagged
Ensemble (HRRR-TLE): Description
DOC/NOAA/ESRL, HRRR Time-Lagged Ensemble - Experimental, accessed 2/12/2019 via URL
https://rapidrefresh.noaa.gov/hrrr/hrrrtle/
URL https://rapidrefresh.noaa.gov/hrrr/hrrrtle
High Resolution Rapid Refresh Time-Lagged
Ensemble (HRRR-TLE): Select Output Parameters
DOC/NOAA/ESRL, HRRR Time-Lagged Ensemble - Experimental, accessed 2/10/2019 via URL
https://rapidrefresh.noaa.gov/hrrr/hrrrtle/
Trevor Alcott (NOAA/ESRL/GSD), Personal communication
CONVECTIVE SEVERITY
(URL https://rapidrefresh.noaa.gov/hrrr/hrrrtle/)
4-h Neighborhood probability of hail > 1 inch
4-h neighborhood probability of a tornado
(Threshold: UH > 75m2/s2, LCL <1.5km, 0-1km shear >10kt,
SBCAPE >0.75*MUCAPE)
4-h neighborhood probability of wind > 50 knots
1-h neighborhood probability of a thunderstorm
Short-Range Ensemble Forecast (SREF)
NOAA/NWS/NCEP, Model Analyses and Guidance, accessed 2/5/2019 via
URL https://mag.ncep.noaa.gov/model-guidance-model-area.php
Short-Range Ensemble Forecast (SREF): Description
Du J.,DiMego G., Javic D., Ferrier B., B. Yang, 2015: EMC Implementation Briefing of SREF,.v7 (Q4FY15), accessed
2/5/2019 via URL https://www.emc.ncep.noaa.gov/mmb/SREF/SREFv7_implementationBriefing.pdf
Jun Du (NOAA/NWS/NCEP/EMC), personal communication
Twenty-seven (27) member ensemble system
Multiple dynamic cores and model physics schemes to simulate
uncertainty
Control runs:
SREF NMMB uses NDAS analysis, SREF ARW uses RAP analysis,
GEFS uses GFS analysis
Blending performed in the initial condition (IC) perturbation
Ensemble members include positive and negative perturbations of all
state variables (pressure, temperature, specific volume)
Small scale perturbations generated by breeding vector and large
scale perturbations created by EnKF
Short-range Ensemble Forecast (SREF):
NMMB Members
Du J.,DiMego G., Javic D., Ferrier B., B. Yang, 2015: EMC Implementation Briefing of SREF,.v7 (Q4FY15),
accessed 2/5/2019 via URL https://www.emc.ncep.noaa.gov/mmb/SREF/SREFv7_implementationBriefing.pdf
Short-range Ensemble Forecast (SREF):
ARW Members
Du J.,DiMego G., Javic D., Ferrier B., B. Yang, 2015: EMC Implementation Briefing of SREF,.v7 (Q4FY15),
accessed 2/5/2019 via URL https://www.emc.ncep.noaa.gov/mmb/SREF/SREFv7_implementationBriefing.pdf
Short-range Ensemble Forecast (SREF):
Select Variables/Parameters (Experimental Prototype)
CONVECTIVE SEVERITY Select Composite Indices
(URL https://www.spc.noaa.gov/exper/sref/)
Craven-Brooks Significant Severe (Mean, Probability)
Supercell Composite Parameters (Median)
Significant Tornado Parameter (Median)
Calibrated Probability (3,12 hours) of Thunderstorm
Calibrated Probability (3,12 hours) of Severe Thunderstorm
Calibrated Conditional Probability (3 hours) of Severe
Thunderstorm
NOAA/NWS/NCEP/SPC, Short Range Ensemble Forecast (SREF) Products, accessed 1/28/2019 via URL
https://www.spc.noaa.gov/exper/sref/
High Resolution Ensemble Forecast (HREF)
High Resolution Ensemble Forecast (HREF): Description
NOAA/NWS/NCEP/EMC, Mesoscale Model/Analysis Systems Web Page Reference List, accessed 2/13/2019 via
https://www.emc.ncep.noaa.gov/mmb/mmbpll/eric.html#TAB4
Trevor Alcott (NOAA/ESRL/GSD), personal communication
8-member ensemble
Combines the WRF-ARW NAMNEST, HIRESW-ARW, and HIRESW-
NMM
A time-lagged ensemble (combines previous 6-h and 12-h of these 4
deterministic modeling systems)
High Resolution Ensemble Forecast (HREF):
Members
Member Grid Spacing IC/LBC PBL Microphysics Vertical Levels
HRW NSSL 3.2 km NAM/NAM -6h MYJ WSM6 40
HRW NSSL -12h 3.2 km NAM/NAM -6h MYJ WSM6 40
HRW ARW 3.2 km RAP/GFS -6h YSU WSM6 50
HRW ARW -12h 3.2 km RAP/GFS -6h YSU WSM6 50
HRW NMMB 3.2 km RAP/GFS -6h MYJ Ferrier-Aligo 50
HRW NMMB -12h 3.2 km RAP/GFS -6h MYJ Ferrier Aligo 60
NAM CONUS
Nest
3 km NAM/NAM MYJ Ferrier Aligo 60
NAM CONUS
Nest -12 h
3 km NAM/NAM MYJ Ferrier-Aligo 60
NOAA/NWS/SPC, SPC HREF Ensemble Viewer, accessed 1/24/2019 via URL
https://www.spc.noaa.gov/exper/href/
High Resolution Ensemble Forecast (HREF):
Select CAMs Variables/Parameters
STORM ATTRIBUTES
(URL https://www.spc.noaa.gov/exper/href/)
Simulated Radar
Instantaneous Composite Reflectivity (>40 dBZ ensemble members)
4-h, 24-h Maximum Reflectivity (1-km AGL) (ensemble max, ensemble
probability of >40 dBZ and MUCAPE >50 J/kg)
Updraft Helicity
4h, 24h Maximum Updraft Helicity (2-5km) (probability of >75
m2/s2 and >150 m2/s2)
NOAA/NWS/SPC, SPC HREF Ensemble Viewer, accessed 1/24/2019 via URL
https://www.spc.noaa.gov/exper/href/
High Resolution Ensemble Forecast (HREF):
Select CAMs Variables/Parameters
STORM ATTRIBUTES
(URL https://www.spc.noaa.gov/exper/href/)
Updraft
4h, 24h Maximum Updraft (2-5km) (ensembles for >10 m/s)
Wind
4h, 24h Maximum Wind Speed (2-5km) (ensemble max and ensembles
for >30 knots and >20 dBZ)
NOAA/NWS/SPC, SPC HREF Ensemble Viewer, accessed 1/24/2019 via URL
https://www.spc.noaa.gov/exper/href/
High Resolution Ensemble Forecast (HREF):
Select CAMs Variables/Parameters
CONVECTIVE SEVERITY
(URL https://www.spc.noaa.gov/exper/href/)
Instability
Surface-Based CAPE (ensemble mean, ensemble max, probability above 500 J/kg, 1000 J/kg, and 2000 J/kg SBCAPE)
Most Unstable CAPE (ensemble mean)
Shear
0-1km, 0-3km SRH (ensemble mean)
Composite Indices
Significant Tornado Parameter (STP) (ensemble mean)
NOAA/NWS/SPC, SPC HREF Ensemble Viewer, accessed 1/24/2019 via URL
https://www.spc.noaa.gov/exper/href/
Skill of Ensemble-Based CAMs
Skill of Ensemble-Based CAMs: Convection
Israel L. Jirak, B. Roberts, B. T. Gallo, and A. J. Clark, Comparison of the HRRR Time-Lagged Ensemble to Formal
CAM Ensembles during the 2018 HWT Spring Forecasting Experiment, 29th Conference on Severe Local Storms, Stowe,
VT, 22-26 October 2018
Skill of Ensemble-Based CAMs: Severe Weather Guidance
Israel L. Jirak, B. Roberts, B. T. Gallo, and A. J. Clark, Comparison of the HRRR Time-Lagged Ensemble to Formal
CAM Ensembles during the 2018 HWT Spring Forecasting Experiment, 29th Conference on Severe Local Storms, Stowe,
VT, 22-26 October 2018
Categorize Ensembles:
Select Convection-related Output Variables
Ensemble /
Variable
Maximum
Updraft
Helicity (2-5km)
Neighborhood
Probability of UH
(2-5km) >75 m2/s2
4-h Neighborhood
Probability of hail
> 1 inch
4-h Neighborhood
Probability of
tornado
HRRRE X X X X
SREF
HREF
HRRR-TLE X X
Categorize Ensembles:
Select Convection-related Output Variables
Ensemble /
Variable
4-h Neighborhood
Probability of wind
> 50 knots
Calibrated Probability of
Severe Thunderstorm
(3,12 h)
Calibrated Conditional
Probability of Severe
Thunderstorm (3-h)
HRRRE X
SREF X X
HREF
HRRR-TLE X
Categorize Ensembles:
Select Convection-related Output Variables
Ensemble /
Variable
Craven-Brooks
Significant Severe
(Mean, Probability)
Supercell Composite
Parameter (Median)
Significant Tornado
Parameter (Median or
Mean)
HRRRE
SREF X X X
HREF X
HRRR-TLE
Categorize Ensembles:
Select Convection-related Output Variables
Ensemble /
Variable
4,24 h Maximum Reflectivity
(1-km) Probability of >40
dBZ and MUCAPE > 50 J/kg
4,24 h Maximum Updraft
Helicity (2-5 km) Probability
of >75 m2/s2 and >150 m2/s2
HRRRE
SREF
HREF X X
HRRR-TLE
CAMs: Predictability and Spin-up
Data Assimilation and NWP model
predictive skill: The issue of Spin upSpin-up: “…Post-initialization development of realistic three-dimensional features during the model integration…” (Warner, 2011)
Cold Start: No spin up processes in initial condition: no vertical motions/ageostrophic circulations. Model initialized with an analysis from another model (static initialization)
NSSL WRF, Texas Tech WRF
Warm Start: Partially spun-up processes: vertical motions/ ageostrophic circulations. Model initialized from an NWP model prediction (dynamic initialization)
HIRESWindow, NAMNEST
Hot Start: Completely spun-up processes/spin up eliminated: vertical motions/ageostrophic circulations. Initial values for all microphysical species/variables and latent heat. Model initialized from NWP model prediction (dynamic initialization)
HRRR
Warner, T. T., 2010: Numerical Weather and Climate Prediction. Cambridge University Press: New
York. 526 p.
Data Assimilation and NWP model
predictive skill: The issue of Spin up
0003 UTC 4/14/2015 WSR-88D 0000 UTC 4/14/2015 HIRESW ARW
WARM START
0000 UTC 4/14/2015 HRRR HOT
START
0000 UTC 4/14/2015 NSSL COLD
START
Data Assimilation and NWP model
predictive skill: The issue of Spin up
0100 UTC 4/14/2015 WSR-88D 0100 UTC 4/14/2015 HIRESW ARW
WARM START
0100 UTC 4/14/2015 HRRR HOT
START
0100 UTC 4/14/2015 NSSL COLD
START
Artificial high frequency waves??
4.2 km
4.0 km3.0 km
Data Assimilation and NWP model
predictive skill: The issue of Spin up
0200 UTC 4/14/2015 WSR-88D 0200 UTC 4/14/2015 HIRESW ARW
WARM START
0200 UTC 4/14/2015 HRRR HOT START 0200 UTC 4/14/2015 NSSL COLD START
4.2 km
4.0 km3.0 km
Data Assimilation and NWP model
predictive skill: The issue of Spin up
0300 UTC 4/14/2015 WSR-88D 0300 UTC 4/14/2015 HIRESW ARW
WARM START
0300 UTC 4/14/2015 HRRR HOT START 0300 UTC 4/14/2015 NSSL COLD START
4.2 km
4.0 km3.0 km
Data Assimilation and NWP model
predictive skill: The issue of Spin up
0400 UTC 4/14/2015 WSR-88D 0400 UTC 4/14/2015 HIRESW ARW
WARM START
0400 UTC 4/14/2015 HRRR HOT
START
0400 UTC 4/14/2015 NSSL COLD
START
4.2 km
4.0 km3.0 km
Predictability
Practical and Intrinsic Predictability
Practical Predictability
Ability to predict based on procedures currently available
(Lorenz, 1969)
Can improve predictability by decreasing errors in initial
conditions via better data assimilation methods and higher
quality observations, or improve the NWP model
parameterizations (e.g. Zhang et al. 2006)
Practical and Intrinsic Predictability
Intrinsic Predictability
“Extent to which prediction is possible if an optimum
procedure is used” (Lorenz, 1969)
Predictability given both nearly perfect knowledge of the
initial atmospheric state and a nearly perfect NWP model
(Lorenz, 1969)
Small amplitude errors such as undetectable random noise
can rapidly grow and contaminate deterministic prediction
Practical and Intrinsic Predictability
(Melhauser and Zhang, 2012)
NWP Models: Predictability of Convection
Intrinsic Predictability: Specific studies
Similar NWP model Skew-T structure (differences approximately undetectable) produced divergence storm lifetimes (Elmore et al. 2002)
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