recent and future advancements in convective-scale storm prediction with the high- resolution rapid...
DESCRIPTION
Radar Reflectivity Data Assimilation Model 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 variable Options: A.Specification of hydrometeors using some assumptions B.Use implicit condensate formation as a model forcing function C.Combination of A and B D.Others AMS 26 th SLS Conf06 Nov 2012High-Resolution Rapid Refresh 3TRANSCRIPT
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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
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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
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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
AMS 26th SLS Conf 06 Nov 2012High-Resolution Rapid Refresh 3
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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
AMS 26th SLS Conf 06 Nov 2012High-Resolution Rapid Refresh 4
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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
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Reflectivity
Convergence Cross-Section
RAPHRRR
RADAR
RAPHRRR
no radar
00z 12 Aug 2011
00z init
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Convergence Cross-Section
Reflectivity
01z 12 Aug 2011
+1h fcst
RAPHRRR
RADAR
RAPHRRR
no radar
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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
AMS 26th SLS Conf 06 Nov 2012High-Resolution Rapid Refresh 8
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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
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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
AMS 26th SLS Conf 06 Nov 2012High-Resolution Rapid Refresh 10
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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
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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
AMS 26th SLS Conf 06 Nov 2012High-Resolution Rapid Refresh 12
Equation forLH = f(dBZ)?
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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
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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
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1-h fcstwithout 3-km
radar DA
Obs0100 UTC11 June
2011
1-h fcstwith 3-kmradar DA(fixed)
1-h fcstwith 3-kmradar DA(ramp)
Convective systems more mature even by 1-hr
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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
HRRR Reflectivity Verification
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14-day retrospective period June 2011 (160 runs)Forecasts every 2 hours
> 25 dBZ Composite ReflectivityEastern half of US
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
HRRR Reflectivity Verification
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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
AMS 26th SLS Conf 06 Nov 2012High-Resolution Rapid Refresh 18
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0-h fcstwithout 3-km
radar DA
Obs1800 UTC29 May 2011
0-h fcstwith 3-kmradar DA(ramp)
Benefit from pre-forecast 3-km model integration
0-h fcstwith fully
cycled 3-km DA
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6-h fcstwithout 3-km
radar DA
Obs0000 UTC30 May 2011
6-h fcstwith 3-kmradar DA(ramp)
6-h fcstwith fully
cycled 3-km DA
Fully Cycled 3-km DA shows improved structures
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1-day retrospective period May 2011 (6 runs)Forecasts every 2 hours
> 25 dBZ Composite ReflectivityEastern half of US
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
AMS 26th SLS Conf 06 Nov 2012High-Resolution Rapid Refresh 21
HRRR Reflectivity Verification
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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
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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
AMS 26th SLS Conf 06 Nov 2012High-Resolution Rapid Refresh 23
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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
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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
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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
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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
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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
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HRRR Case Studies19z 27 Apr
2011
NSSL mosaic
HRRR13z + 6 hr
fcstGSD Program Review 13 March 2012High-Resolution Rapid Refresh 29
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HRRR Case Studies20z 27 Apr
2011
NSSL mosaic
HRRR13z + 7 hr
fcstGSD Program Review 13 March 2012High-Resolution Rapid Refresh 30
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13z + 8 hr fcst
HRRR Case Studies21z 27 Apr
2011
NSSL mosaic
HRRRTuscaloosa-Birminghamtornadic supercell
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13z + 9 hr fcst
HRRR Case Studies22z 27 Apr
2011
NSSL mosaic
HRRRTuscaloosa-Birminghamtornadic supercell
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13z + 9 hr fcst
HRRR Case Studies22z 27 Apr
2011
NSSL mosaic
HRRRTuscaloosa-Birminghamtornadic supercell
GSD Program Review 13 March 2012High-Resolution Rapid Refresh 33
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13z + 10 hr fcst
HRRR Case Studies23z 27 Apr
2011
NSSL mosaic
HRRRTuscaloosa-Birminghamtornadic supercell
GSD Program Review 13 March 2012High-Resolution Rapid Refresh 34
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• 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
AMS 26th SLS Conf 06 Nov 2012High-Resolution Rapid Refresh 35
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• 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
AMS 26th SLS Conf 06 Nov 2012High-Resolution Rapid Refresh 36