operational nwp models: convection allowing models (cam)nssl real time wrf modeling systems model...

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)

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

High Resolution Rapid Refresh (HRRR)

Model Domain Grid

spacing

Vertical

levels

Boundar

condition

Initialization

Frequency/

Prediction

RAPv3 North

American

13-km 50 GFS Hourly/24-h

(03,09,15,21z)/

HRRR CONUS 3-km 50 RAP Hourly/24-h

(00,067,12,18z)

Model Dynamic

Assimilation Radar DA Radiation

Microphysics

RAPv3 ARW

v3.8.1

GSI Hybrid 3D-

VAR/ Ensemble

(0.85/0.85)

13-km DFI +

low reflect(Weygandt and

Benjamin, 2007)

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)

v3.6 (Iacono

et al. 2008)

Thompson-

aerosol v3.6.1(Thompson and

Eidhammer, 2014)

Model Cumulus Parameterization Planetary

Boundary

Surface

Initialization

(Balancing)

RAPv3 Deep:

Grell and Freitas (2014)

Shallow:

Grell-Freitas-Olson

v3.6+(Nakanishi and

Niino, 2004,

v3.6+(Smirnova

et al. 2008)

Digital Filter

Initialization

(DFI)(Weygandt and

Benjamin, 2007)

HRRR Deep: NONE

Shallow: MYNN PBL Clouds

v3.6+(Nakanishi and

Niino, 2004,

v3.6+(Smirnova

et al. 2008)

Digital Filter

Initialization

(DFI)(Weygandt and

Benjamin, 2007)

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

STORM ATTRIBUTES

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)

NAMCONUSNEST:

NEMS-NMMB

CONUS 3-km 60(27 in 0-3km layer)

NAM 1-hr (cycled)

Model Dynamic

Assimilation Radar

Radiation

Microphysics

HIRESW(CONUS):

(1) NEMS-NMMB

(2) WRF-ARW

(1) NMMB

(2) ARW

v3.6.1

Hybrid

ensemble

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

1/2015

Hybrid

ensemble

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

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

CONVECTIVE SEVERITY (HIRESW)

Layer Non-Supercell Tornado Parameter

Layer Supercell Composite Parameter

MCS Maintenance Probability

NAMNEST, HIRESW:

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

Assimilati

Radiation

Microphysics

TexasTech WRF ARW v3.5.1 GFS

Analysis

No LW: RRTM

SW: Dudhia

Thompson(Thompson et al.

Model Cumulus

Parameteriza

Planetary

Boundary

Surface

Initialization

(Balancing)

Texas Tech WRF 3-km: NONE

12-km: Tiedtke

YSU Noah(Ek et al.

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

CONVECTIVE SEVERITY

Composite Indices

URL http://www.atmo.ttu.edu/bancell/real_time_WRF/ttuwrfhome.php/

Supercell Composite Parameter

Updraft Helicity

Texas Tech WRF 3-km Example

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

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

Assimilation Radar

Radiation

Microphysics

NSSL-WRF

(since 2007)

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)

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

Planetary

Boundary

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

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

(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

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)

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)

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)

HIRESW

NAMNEST X

Texas Tech

NSSL-WRF X X

Select Convective VariablesModel /

Variable

MCS Maintenance

Probability

0-1,3 km

Storm Scale

Helicity

Composite

Reflectivity

Max hourly

lightning

threat

Max hourly

wind gust

HRRR X X X

HIRESW

(NMMB)

HIRESW

NAMNEST X

Texas Tech

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

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

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

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

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):

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

Ensemble (HRRR-TLE): Description

URL https://rapidrefresh.noaa.gov/hrrr/hrrrtle

Ensemble (HRRR-TLE): Select Output Parameters

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

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

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-

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

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/

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)

STORM ATTRIBUTES

Updraft

4h, 24h Maximum Updraft (2-5km) (ensembles for >10 m/s)

4h, 24h Maximum Wind Speed (2-5km) (ensemble max and ensembles

for >30 knots and >20 dBZ)

CONVECTIVE SEVERITY

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)

0-1km, 0-3km SRH (ensemble mean)

Composite Indices

Significant Tornado Parameter (STP) (ensemble mean)

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

HRRR-TLE X X

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

HRRR-TLE X

Ensemble /

Variable

Craven-Brooks

Significant Severe

(Mean, Probability)

Supercell Composite

Parameter (Median)

Significant Tornado

Parameter (Median or

SREF X X X

HREF X

HRRR-TLE

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

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)

Warner, T. T., 2010: Numerical Weather and Climate Prediction. Cambridge University Press: New

York. 526 p.

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

0000 UTC 4/14/2015 NSSL COLD

WARM START

0100 UTC 4/14/2015 HRRR HOT

0100 UTC 4/14/2015 NSSL COLD

Artificial high frequency waves??

4.2 km

4.0 km3.0 km

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

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

WARM START

0400 UTC 4/14/2015 HRRR HOT

0400 UTC 4/14/2015 NSSL COLD

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)

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

(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)

THE END

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