1. NCEP Production Suite Review
9 December 2015
Radar Reflectivity Data Assimilation: Applications in NOAA’s hourly Updating Rapid Refresh (RAP) and High-Resolution Rapid (HRRR)
NOAA/ESRL/GSD/EMB
Curtis Alexander, David Dowell,
Steve Weygandt, Stan Benjamin,
Ming Hu, Tanya Smirnova

2. RAP and HRRR Radar DA History
Key RAP/HRRR Development
2007 CONUS 3-D Radar Mosaic Developed by NSSL
2009 Radar reflectivity assimilation in 13-km operational RUC
2012 Radar reflectivity assimilation in 13-km operational RAP
2013 Radar reflectivity assimilation in 3-km experimental HRRR
2014 Radar reflectivity assimilation in 3-km operational HRRR
2016 Radar radial velocity assimilation in 13-km RAPv3
2016 Radar radial velocity assimilation in 3-km HRRRv3 (experimental)
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

3. Radar Reflectivity Data Assimilation
Radar Observations (WSR-88D)
Reflectivity =
SUM(Rain, Snow, Hail, etc…)
Not a direct observation of
individual hydrometeors and/or
distributions
Model Forecasts (WRF-ARW)
Reflectivity =
Diagnostic variable ESTIMATE
Hydrometeors are prognostic variables in microphysical schemes
Reflectivity observation type does not correspond to a state variable
Underdetermined problem!!!!
Assimilation Options:
Variational/Ensemble DA of precipitation hydrometeors
Non-variational specification of precipitation hydrometeors
Model forcing function
Additive Noise
Latent Heat Nudging

4. HRRRv2 Initialization from RAPv3
13 km RAP
13z
14z
15z
Obs
GFS
Ens
Obs
GFS
Ens
GFS
Ens
Obs
GSI
Hybrid
GSI
Hybrid
GSI
Hybrid HM
Obs HM
Obs HM
Obs
GSI HM Anx
GSI HM Anx
GSI HM Anx
1 hr fcst
1 hr fcst
Refl
Obs
Refl
Obs
Refl
Obs
Digital
Filter
Digital
Filter
Digital
Filter
18 hr fcst
18 hr fcst
18 hr fcst
Obs
GFS
Ens
3 km
HRRR
3-km Interp
1 hr pre-fcst
GSI
Hybrid
Increased weight of GFS ensemble in RAP
Introduction in HRRR
HMObs
GSI HM Anx
Refl Obs
15 hr fcst

5. Rapid Refresh and HRRR Radar Reflectivity Assimilation
Radar Data Assimilation: Focus On Reflectivity Only
Pre-Process Radar Reflectivity Observations
Uses NCEP 3-D CONUS MRMS 1-km Reflectivity Mosaic
Horizontally and vertically interpolate reflectivity observations onto analysis grid
O(1-10) CPUs in a few minutes
Transform Radar Reflectivity Observations To Latent Heating Rates
Reads observations and writes model input
O(1-10) CPUs in a few minutes
Apply Latent Heating Rates During WRF-ARW Model Integration
Within forward portion of digital filtering (RAP) or in pre-forecast hour (HRRR)
First few minutes of RAP integration or O(10) min for HRRR
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

6. Rapid Refresh and HRRR Radar Reflectivity Assimilation
Transform Radar Reflectivity Observations To Latent Heating Rates
LH = Latent Heating Rate (K/s)
p = Pressure
Lv = Latent heat of vaporization
Lf = Latent heat of fusion
Rd = Dry gas constant
cp = Specific heat of dry air at constant p
f[Ze] = Reflectivity factor converted to rain/snow condensate
t = Time period of condensate formation (600s i.e. 10 min)
Focus on how much, how long and where to apply latent heating…
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

7. Rapid Refresh and HRRR Radar Reflectivity Assimilation
Transform Radar Reflectivity Observations To Latent Heating Rates
How Much?
Namelist Option:
radar_latent_heat_time_period
Time period (min) over which
condensate formation assumed
to have occurred or “t”
= 5.0 min (double forcing)
= 10.0 min (RAP)
= 20.0 min (HRRR - half forcing)
= 30.0 min (third forcing)
Latent Heating Rate (K/s)
3-D Observed Reflectivity (dBZ)
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

8. Rapid Refresh and HRRR Radar Reflectivity Assimilation
Transform Radar Reflectivity Observations To Latent Heating Rates
Where?
Namelist Option:
convection_refl_threshold
Observed reflectivity threshold
above which the latent heating is
directly specified
= 35.0 dBZ (convection only)
= 28.0 dBZ (RAP and HRRR)
= 20.0 dBZ (warm rain)
Latent Heating Rate (K/s)
3-D Observed Reflectivity (dBZ)
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

9. Rapid Refresh and HRRR Radar Reflectivity Assimilation
Apply Latent Heating Rates During WRF-ARW Model Integration
How Long?
WRF Namelist Option:
runlength_dfi_fwd
Determines the duration of
simulation time (min) when
radar-specified latent heating rates
are applied
= 1x20.0 min (RAP)
= 4x15.0 min (HRRR)
Latent Heating Rate (K/s)
3-D Observed Reflectivity (dBZ)
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

10. Radar Reflectivity Assimilation
Rapid Refresh and HRRR
Radar Reflectivity Assimilation
Model Pre-Forecast Time (min)
-60
-15
-45
-30
Model
Integrations
Observed
Radar
Valid at -45
Valid at -30
Valid at -15
Valid at 0
Observed Reflectivity ≤ 0 dBZ :
Observed Reflectivity ≥ 28 dBZ :
Zero heating rate to suppress spurious model precipitation.
Positive heating rate to promote convective development.
0 dBZ < Observed Reflectivity < 28 dBZ :
No radar coverage:
Model microphysics heating rate preserved.
During the 1-hour pre-forecast, reflectivity observations are used to specify latent heating rates (microphysics temperature tendency) in each previous 15-min period:

11. Radar Reflectivity Assimilation
Rapid Refresh and HRRR
Radar Reflectivity Assimilation
Model Pre-Forecast Time (min)
-60
-15
-45
-30
Model
Integrations
Observed
Radar
Valid at -45
Valid at -30
Valid at -15
Valid at 0
Depth of minimum reflectivity must exceed 200 mb when background temp < 5 ˙C
(avoids identifying bright bands as convection)
Vertically builds (extrapolates) temperature tendencies below radar horizon to
model PBL top (avoids “disemboweled” precipitating features)
Will keep convective parameterization (if present) deactivated for 30 min into free
forecast if “no echo” in radar coverage (attempts to suppress convection)
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

12. Radar Reflectivity Data Assimilation
Skill from Reflectivity Data Assimilation
HRRR Forecast Skill
for Reflectivity (30 dBZ)
Critical Success Index X 100
 Less Skill More Skill 
radar data assimilation
in RAP and HRRR
Essential for predicting clouds, precipitation, and other local phenomena
no radar data assimilation
Forecast Length (Hours)
Radar DA retention into forecast period approximately 4-6 hrs
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

13. Radar Reflectivity Data Assimilation
Changing Strength (how much) of Latent Heating
HRRR – RUC third
HRRR – RAP third
HRRR – RAP full
HRRR – RUC full
HRRR – RAP double
20-km
30dBZ
CSI
20-km
30dBZ
BIAS
 Lower Higher 
Optimal
Increasing skill
with more forcing
But also higher bias…
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

14. Radar Reflectivity Assimilation HRRR (and RAP) Future Milestones
HRRR Milestones
HRRR (and RAP) Future Milestones
Reflectivity
00z init
00z 12 Aug 2011
RAP
HRRR
no radar
RAP
HRRR
RADAR
Convergence
Cross-Section
Rapid convective
spin-up with 13-km
radar data
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015 14

15. Radar Reflectivity Assimilation HRRR (and RAP) Future Milestones
HRRR Milestones
HRRR (and RAP) Future Milestones
Reflectivity
Reflectivity
+1h fcst
01z 12 Aug 2011
RAP
HRRR
no radar
RAP
HRRR
RADAR
Convergence
Cross-Section
Rapid convective
spin-up with 13-km
radar data
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015 15

16. Improved 0-2 hr Convective Fcsts
Radar Obs
05:00z 18 May 2013
05z + 0 min
0-hr fcst 3-km radar DA
0-hr fcst NO 3-km radar DA
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

17. Improved 0-2 hr Convective Fcsts 15-min fcst NO 3-km radar DA
Radar Obs
05:15z 18 May 2013
05z + 15 min
15-min fcst 3-km radar DA
15-min fcst NO 3-km radar DA
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

18. Improved 0-2 hr Convective Fcsts 30-min fcst NO 3-km radar DA
Radar Obs
05:30z 18 May 2013
05z + 30 min
30-min fcst 3-km radar DA
30-min fcst NO 3-km radar DA
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

19. Improved 0-2 hr Convective Fcsts 45-min fcst NO 3-km radar DA
Radar Obs
05:45z 18 May 2013
05z + 45 min
45-min fcst 3-km radar DA
45-min fcst NO 3-km radar DA
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

20. Improved 0-2 hr Convective Fcsts
Radar Obs
06:00z 18 May 2013
05z + 1 hour
1-hr fcst 3-km radar DA
1-hr fcst NO 3-km radar DA
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

21. Improved 0-2 hr Convective Fcsts 1-hr 30m fcst NO 3-km radar DA
Radar Obs
06:30z 18 May 2013
05z + 1:30 min
1-hr 30m fcst 3-km radar DA
1-hr 30m fcst NO 3-km radar DA
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

22. Improved 0-2 hr Convective Fcsts
Radar Obs
07:00z 18 May 2013
05z + 2hr min
2-hr fcst 3-km radar DA
2-hr fcst NO 3-km radar DA
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

23. Reflectivity Forecast Skill
Rapid Refresh and HRRR
Reflectivity Forecast Skill
Stats for east of 100°W longitude
(good radar coverage for verification)
Average CSI for Reflectivity ≥ 35 dBZ
More skill at short lead times – radar reflectivity DA has greatest impact
More skill in evening and overnight hours – after convective initiation/upscale growth
More skill in strongly forced environments – more favorable mesoscale environment
Spring (Apr-June)
Strongly forced environments
Summer (July-Sept)
Weakly forced environments
More skill at short lead times.
More skill in evening and overnight hours.
More skill at short lead times.
Less skill
More skill

24. Radar Reflectivity Assimilation
Rapid Refresh and HRRR
Radar Reflectivity Assimilation
Observations
Map to cloud field
No cloud
Cloud
Unknown
Merge cloud field
Update hydrometeors
based on the cloud field
Last opportunity for corrections from observations

25. Radar Reflectivity Assimilation
Rapid Refresh and HRRR
Radar Reflectivity Assimilation
Summary:
RAP and HRRR use three-dimensional observed radar reflectivities as a proxy for latent heating through specification (replacement) of the model’s microphysics temperature tendencies
Advantages:
Computationally inexpensive that can run “in-line” with model integration and offers several adjustable parameters for case/user specific applications
Often improves short-term forecast skill including reflectivity and precipitation
Limitations:
Can lead to high forecast bias (precip, reflectivity) early in forecast period in some strongly-forced situations or during periods of decaying convection
Effective mostly after convective initiation when convective coverage is increasing
Convective suppression is limited in capability for explicit convection applications…mesoscale forcing dominates
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

26. HRRRv3 Initialization NCEP Production Suite Review RAP/HRRR Radar DA
3 km
HRRR
Refl Obs
Refl Obs
Obs
GSI
Hybrid
GSI
Hybrid
GSI
Hybrid
1 hr pre-fcst
1 hr pre-fcst
RAP
Ens
RAP
Ens
HM Obs
HM Obs
GSI HM Anx
GSI HM Anx
GSI HM Anx
18 hr fcst
18 hr fcst
Hourly full-atmosphere cycling
More effective ensemble data assimilation (RAP)
No longer “spinning up” convection in first hour(s)
More balanced state should permit more even bias across shorter lead times
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

27. HRRRv4 Initialization NCEP Production Suite Review RAP/HRRR Radar DA
3 km
HRRR
HRRR
Ens
All
Obs
HRRR
Ens
HRRR
Ens
All
Obs
All
Obs
GSI
Hybrid
GSI
Hybrid
GSI
Hybrid
1 hr fcst
1 hr fcst
18 hr fcst
18 hr fcst
All hydrometeors included in variational/ensemble data assimilation
More advanced data assimilation ensemble (HRRR)
NCEP Production Suite Review
RAP/HRRR Radar DA
09 December 2015

28. Possible HRRR storm-scale ensemble system HRRR Milestones
HRRR (and RAP) Future Milestones
Prototype 3-km storm-scale HRRR ensemble domain
Single core (ARW)
Ensemble DA
Stochastic physics
HRRR domain
Assimilation Forecast
40 members 6 members
1 hr forecast 12 hr forecast
24 fcsts / day 24 fcsts / day
2 nodes / member 60 nodes / member
RAP v2 domain
RAP v3 expanded domain
More accurate
storm-details
from ensemble
data assimilation