Recent and Future Advancements in Convective-Scale Storm Prediction with the High- Resolution Rapid...

Recent and Future Advancements in Convective-Scale Storm Prediction

with the High-Resolution Rapid Refresh (HRRR) Forecast System

NOAA/ESRL/GSD/AMBCurtis Alexander, David Dowell, Steve Weygandt,

Stan Benjamin, Tanya Smirnova, Ming Hu, Patrick Hofmann, Eric James, John Brown,

and Brian Jamison

26th Conference on Severe Local Storms06 November 2012

13km Rapid Refresh (RAP) (mesoscale)

13km RUC (mesoscale)

3km HRRR (storm-scale)

High-Resolution Rapid Refresh Experimental 3km nest inside RAP, hourly 15-h fcst

Replaced RUC at NCEP 05/01/12WRF, GSI, RUC features

Hourly Updated NWP Models

AMS 26th SLS Conf 06 Nov 2012High-Resolution Rapid Refresh 2

Radar Reflectivity Data AssimilationModel Forecasts (WRF-ARW)

Reflectivity = Diagnostic variable ESTIMATE

Hydrometeors are prognostic variables in microphysics scheme(s)

Radar Observations (WSR-88D)

Reflectivity = SUM(Rain, Snow, Hail, etc…)

Not a direct observation of individual hydrometeors and distributions

Reflectivity observation type does not correspond to a state variableOptions:A. Specification of hydrometeors using some assumptionsB. Use implicit condensate formation as a model forcing functionC. Combination of A and BD. Others


Latent heating (LH) estimated from radar observed condensate (3-dimensional)

Applied during forward step of DFI within 13-km model forecast (RAP)

1. If outside radar coverage• use existing model LH

2. If observed reflectivity ≤ 0 dBZ • set LH = 0 to suppress convection or precipitation

3. If observed reflectivity > 0 dBZ• Replace model LH with value derived from obs reflectivity• Don’t apply in planetary boundary layer (PBL)• Radar-derived LH applied over forward DFI period• LH rate based on 10 min. convective time-scale

(we have evaluated 5 min, 20 min, and 30 min)

Radar Reflectivity Data Assimilation


3-km Interp

Hourly HRRR Initialization from RAP

GSI 3D-VAR

Obs

Cloud Anx

DigitalFilter

1 hr

fcst

HMObs

ReflObs18 hr fcst

15 hr fcst 3-km Interp

GSI 3D-VAR

Obs

Cloud Anx

DigitalFilter

1 hr

fcst

HMObs

ReflObs18 hr fcst

15 hr fcst 3-km Interp

GSI 3D-VAR

Obs

Cloud Anx

DigitalFilter

HMObs

ReflObs18 hr fcst

15 hr fcst

13 km RAP

3 km HRRR

13z 14z 15z

Reflectivity

Convergence Cross-Section

RAPHRRR

RADAR

RAPHRRR

no radar

00z 12 Aug 2011

00z init

Convergence Cross-Section

Reflectivity

01z 12 Aug 2011

+1h fcst

RAPHRRR

RADAR

RAPHRRR

no radar

Eastern US, Reflectivity > 25 dBZ11-21 August 2011

• 3km HRRR forecasts improve upon RAP 13km forecasts, especially at coarser scales much better upscaled skill

• Radar DDFI adds skill at both 13km and 3km

CSI 13 km CSI 40 km

HRRR no radarRAP no radar

HRRR radarRAP radar

HRRR no radarRAP no radar

HRRR radarRAP radar

Radar Reflectivity Verification


3-km Interp

Experiment #0: DDFI at 3 km

GSI 3D-VAR

Obs

Cloud Anx

DigitalFilter

1 hr

fcst

HMObs

ReflObs18 hr fcst

15 hr fcst

3-km Interp

GSI 3D-VAR

Obs

Cloud Anx

DigitalFilter

1 hr

fcst

HMObs

ReflObs18 hr fcst

3-km Interp

GSI 3D-VAR

Obs

Cloud Anx

DigitalFilter

HMObs

ReflObs18 hr fcst

3 km HRRR

DigitalFilter

ReflObs 15 hr fcstDigitalFilter

ReflObs 15 hr fcstDigitalFilter

ReflObs

13z 14z 15z13 km RAP

DDFI in 13-km RAP (parent model) AND in 3-km HRRREastern US, Reflectivity > 25 dBZ

14-24 July 2010

Application of DDFI at both scales results in spurious convectionSignificant loss of skill

2x Latent heating rate in RAP1x Latent heating rate in RAP1/3x Latent heating rate in RAP1x Latent heating in RR and HRRR

CSI 40 km BIAS 03 km

Optimal

2x Latent heating rate in RAP1x Latent heating rate in RAP1/3x Latent heating rate in RAP1x Latent heating in RR and HRRR

HRRR Reflectivity Verification


3-km Interp

Experiment #1: Cycled Refl at 3 km

GSI 3D-VAR

Obs

Cloud Anx

DigitalFilter

1 hr

fcst

HMObs

ReflObs18 hr fcst

3-km Interp

GSI 3D-VAR

Obs

Cloud Anx

DigitalFilter

1 hr

fcst

HMObs

ReflObs18 hr fcst

GSI 3D-VAR

Obs

Cloud Anx

DigitalFilter

HMObs

ReflObs18 hr fcst

3 km HRRR

13z 14z 15z13 km RAP

15 hr fcst1 hr fcst

Refl Obs

Experiment #1: LH Specification

Obse

rved

3-D

Ra

dar R

eflec

tivity

Ti

me

(min

)

-60

-45

-30

-15

0-60 -45 -30

-15 0Model Pre-Forecast Time (min)

Temperature Tendency (i.e. LH) = f(Observed Reflectivity)LH specified from reflectivity observations in four 15-min periodsThe observations are valid at the end of each 15-min period1-hr older mesoscale observationsLatency reduced to ~1 hrNO digital filtering


Equation forLH = f(dBZ)?

Experiment #1: LH Specification

Observed Radar Reflectivity (3-D)Based Latent Heating

Model MicrophysicsLatent Heating0

Wei

ght

1

-60 -45 -30-15 0Model Pre-Forecast Time (min)

0

Wei

ght

1

-60 -45 -30-15 0Model Pre-Forecast Time (min)

Experiment 1a “Fixed” Experiment 1b “Ramp”

0

Multiple options for weighting LH specification vs model LHTwo approaches including:(a)100% specification for the entire pre-forecast hour and (b) Time-varying with linear ramp down to 0% specification at 1 hrOption (b) permits more “free model” behavior for additional DA


0-h fcstwithout 3-km

radar DA

Obs0000 UTC11 June

2011

0-h fcstwith 3-kmradar DA(fixed)

0-h fcstwith 3-kmradar DA(ramp)

Benefit from pre-forecast 3-km model integration


radar DA

Obs0100 UTC11 June

2011

1-h fcstwith 3-kmradar DA(fixed)


Convective systems more mature even by 1-hr

3-day retrospective period June 2011Forecasts every 2 hours

> 25 dBZ Composite ReflectivityEastern half of US

Upscaled to 40-km grid

With 3-km fixed radar DAWith 3-km ramp radar DAWithout 3-km radar DA

Bias = 1.0

Native3-km grid

Greatly improved CSI and BIAS between 0-1 fcst hr

Benefit persists until 4 hrsVery similar skill at longer lead times


14-day retrospective period June 2011 (160 runs)Forecasts every 2 hours


Upscaled to 40-km grid

With 3-km ramp radar DAWithout 3-km radar DA

Bias = 1.0

Native3-km grid

Greatly improved CSI and BIAS between 0-1 fcst hr

Benefit persists until 4 hrsVery similar skill at longer lead times


Experiment #2: Full-Cycling at 3 km

Obs

Cloud Anx

HMObs

3 km HRRR

13z 14z 15z

GSI 3D-VAR

1 hr

fcst

Cloud Anx

GSI 3D-VAR

15 hr fcst15 hr fcst

Refl O

bs

Obs

HMObs

Cloud Anx

GSI 3D-VAR

15 hr fcst

Obs

HMObs

Refl O

bs

1 hr

fcst



radar DA

Obs1800 UTC29 May 2011


Benefit from pre-forecast 3-km model integration

0-h fcstwith fully

cycled 3-km DA


radar DA

Obs0000 UTC30 May 2011


6-h fcstwith fully

cycled 3-km DA

Fully Cycled 3-km DA shows improved structures

1-day retrospective period May 2011 (6 runs)Forecasts every 2 hours


Upscaled to 40-km grid Bias = 1.0

Native3-km grid

Small sample sizeFully-cycled 3-km DA appears promising

Runtime for 3-km DA is O(20 min)

With fully cycle 3-km DAWith 3-km ramp radar DAWithout 3-km radar DA



Experiment #3: Partial-Cycling at 3km

3 km HRRR

13z 14z 15z

13 km RAP

3-km Interp

1 hr fcst

Cloud Anx

GSI 3D-VAR

Obs

HMObs

1 hr

fcst

Cloud Anx

GSI 3D-VAR

15 hr fcst

Obs

HMObs

Refl O

bs

Cloud Anx

GSI 3D-VAR

15 hr fcst

Obs

HMObs

Refl O

bs

1 hr

fcst

BackgroundRadar

Specification of Hydrometeors

Scale at which Latent Heating

is appliedDimensionality Updated

2012 HRRR model

initialization13-km RAP No 13-km 3-D Hourly

2013 HRRR model

initialization13-km RAP No

3km in 60min spin-up (also using 13km

radar-LH-DFI)3-D Hourly

Rapidly Updating

Analysis (RUA)3-km HRRR

1 hr fcst Yes None 3-D Hourly

Real-TimeMeso Analysis

(RTMA)3-km HRRR

1 hr fcst No None 2-D 15 min

Time-Lagged HRRR (HCPF)

3-km HRRRFcsts No Same as HRRR 2-D Hourly

3-km HRRR Products


10-11 hr fcst

09-10 hr fcst

08-09 hr fcst

11-12 hr fcst

10-11 hr fcst

09-10 hr fcst

Forecasts valid 21-22z 27 April 2011 Forecasts valid 22-23z 27 April 2011

All six forecastscombined to formprobabilities valid22z 27 April 2011

HRRR 11z Init

HRRR 12z Init

HRRR 13z Init

Time-lagged Ensemble

Spatial radius 45 kmTime radius 1 hrUH threshold 25 m2/s2

13z + 09hr fcstValid 22z 27 April 2011

1630z SPC Tornado Probability

27 April 2011 Storm Reports

Tornado = Red DotsTornadic Storm Probability (%)

Example: 27 April 2011Valid 1630-1200 UTC 28 Apr

Valid 1200-1200 UTC 28 Apr

GSD Program Review 13 March 2012High-Resolution Rapid Refresh 25

Tornadic Storm Probability (%)Reflectivity (dBZ)

13z + 09hr fcstValid 22z 27 April 2011

27 April 2011 Storm Reports

Observed Reflectivity22z 27 April

Example: 27 April 2011

Tornado = Red Dots

Valid 1200-1200 UTC 28 Apr


13z + 11hr fcstValid 00z 23 May 2011

1300z SPC Tornado Probability

Example: 22 May 2011

Tornadic Storm Probability (%)

22 May 2011 Storm Reports

Tornado = Red Dots


Tornadic Storm Probability (%)Reflectivity (dBZ)

22 May 2011 Storm Reports

13z + 11hr fcstValid 00z 23 May 2011

Observed Reflectivity00z 23 May

Tornado = Red DotsAMS 26th SLS Conf 06 Nov 2012High-Resolution Rapid Refresh 28

Example: 22 May 2011

HRRR Case Studies19z 27 Apr

2011

NSSL mosaic

HRRR13z + 6 hr

fcstGSD Program Review 13 March 2012High-Resolution Rapid Refresh 29


2011

NSSL mosaic

HRRR13z + 7 hr

fcstGSD Program Review 13 March 2012High-Resolution Rapid Refresh 30

13z + 8 hr fcst


2011

NSSL mosaic

HRRRTuscaloosa-Birminghamtornadic supercell


13z + 9 hr fcst


2011

NSSL mosaic



13z + 10 hr fcst


2011

NSSL mosaic



• Current – 1 computer running HRRR– NOAA/ESRL – Boulder– Current reliability: 97% for last 12h months (allowing up to 3h gaps)

• 2012-14 – 2 computers running HRRR – interim solution– Boulder – computer 1– Fairmont, WV – computer 2– Expected reliability to increase further to 98.5-99% via coordination of

downtimes for Boulder vs. Fairmont computers• 2015 – NCEP running HRRR

– NOAA/NCEP computing budget – will allow no increase before 2015• Conclusion: Interim HRRR computing for 2012-14 on 2 sites to

provide “real-time experimental” HRRR from NOAA for NWS, FAA, DOE/energy users

HRRR Transition to NCEP


• Moist bias reduced in 2012 RAP and HRRR– Reduced false alarms, lower precipitation bias– GSI enhancements and WRF upgrade to v3.3.1– Reflectivity diagnostic consistent with microphysics

• Science: Focus on 3-km assimilation for 2013– 3-km variational analysis– 3-km non-variational cloud analysis– 3-km radar reflectivity data assimilation

• Technical: Reduced latency for 2013 (2-3 hrs now)– Approximate 1-hr reduction in execution time (1-2 hrs)– Faster post-processing with parallelization– Direct GRIB2 generation

Summary and Plans


Recent and Future Advancements in Convective-Scale Storm Prediction with the High- Resolution Rapid...

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