Post on 16-Mar-2018
Applications in situational awareness high-resolution NWP
-- Ideas for the Blueprint DA discussion
8 March 2016 Stan Benjamin, David Dowell, Curtis Alexander
N O A A / E S R L / G L O B A L S Y S T E M S D I V I S I O N
Situational awareness - Blueprints for Next-Gen DA Systems 8-10 March 2016
DA extensions for situational awareness, retention in short-range NWP
Already • Cloud/hydrometeor • Radar reflectivity • Land-surface – simple
coupling • Ensemble DA – 13km, 3km,
HRRRE • Aerosols – RAP-chem,
HRRR-smoke
Some things needed • Sub-grid-scale, PDF
representations • Clouds, PBL, turbulence
• Hydrometeor/aerosol linkage • 30→15 →5 min updating • In-memory continuous DA,
minimize I/O • Full earth-system coupling • Global rapid refresh
Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016 1
3-km Interp
RAPv3 and HRRRv2 Initialization
GSI Hybrid
GSI HM Anx
Digital Filter
18 hr fcst
GSI Hybrid
GSI HM Anx
Digital Filter
18 hr fcst
GSI Hybrid
GSI HM Anx
Digital Filter
18 hr fcst
3 km HRRR
13z 14z 15z 13 km RAP
Refl Obs
1 hr pre-fcst
GSI HM Anx
GSI Hybrid
15 hr fcst
RAP Vr assimilation
RAP reflect. Assimilation (no spin-up)
(DFI)
HRRR reflect. Assimilation
(pre-fcst) (No DFI)
HRRR Vr assim
RAP/HRRR: Hourly-Updating Weather Forecast Models
Initial & Lateral Boundary
Conditions
Initial & Lateral Boundary
Conditions
Expanded RAP to match NAM for
SREF
(May 2016)
13-km Rapid Refresh (RAP) – to 21h (May 2016)
3-km High-Resolution Rapid Refresh (HRRR) –
to 18h (May 2016)
750-m HRRR nest Wind Forecast
Improvement Project Experiment (ongoing)
Prototype 3-km storm-scale HRRR ensemble (HRRRE)
(Spring 2016)
3-km High-Resolution Rapid Refresh Alaska
Testing (HRRR-AK) w/MRMS radar data
(Spring 2016)
3
3-km High-Resolution Time Lagged Ensemble (HRRR-TLE)
Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
RAPv3/HRRRv2 Observation Data Assimilation Changes
New in RAPv3/HRRRv2 Radial Velocity (RAPv3) Lightning (RAPv3) Mesonet (RAPv3/HRRRv2) RARS Radiances (RAPv3)
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Hourly Observation Type Variables Observed Observation Count Rawinsonde Temperature, humidity, wind, pressure 125
Profiler – NOAA/ 915 MHz Wind, virtual temperature 2 / 20-30 Radar – VAD Wind 125
Radar Radial velocity 125 radars Radar reflectivity – CONUS 3-d reflLatent htg, rain,snow,graupel ~1,500,000
Lightning – NLDN / GOES-R Flash density Proxy reflectivity 10K Aircraft Wind, temperature 3,000 -25,000
Aircraft - WVSS Humidity 0 - 800
Surface/METAR Temperature, moisture, wind, press, clouds/ceiling, visibility, current weather 2200 - 2500
Surface/Mesonet Temperature, moisture, wind ~5K-12K Buoys/ships Wind, pressure 200 - 400 GOES AMVs Wind 10K
AMSU/HIRS/MHS (RARS) Radiances (direct readout) 250K/200K/200KK GOES Radiances (fire location/intensity-smoke) ~3,000K
GOES-R Lightning, cloud-top cooling >3,000K GOES cloud-top press/temp Brightness temp ~380K
GPS – Precipitable water Humidity 300 METOP-B AScat Winds ~30,000
2016
Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
Crossover in forecast skill between Nowcasting/Extrapolation vs Numerical Weather Prediction
Forecast Length (Hours)
Fore
cast
Ski
ll 2013-2014 HRRR 3-km Radar Data Assimilation
2005-2008 Pre-Radar Data
Assimilation
2009-2012 RUC 13-km Radar Data Assimilation
-- Extrapolation -- Persistence
L
ess
Skill
M
ore
Skill
Improving forecast skill and halving crossover period every ~3-4 years
RUC/RAP/HRRR: Improving Forecast Skill
5 Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
Forecast Length (Hours)
Crit
ical
Suc
cess
Inde
x X
100
L
ess
Skill
Mor
e Sk
ill
radar data assimilation in RAP and HRRR
no radar data assimilation
HRRR Forecast Skill for Reflectivity (30 dBZ)
RAP/HRRR: Improving Forecast Skill
reflectivity heating rate: low-cost assimilation,
significant forecast improvement
6 Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
RAP/HRRR Development History
7
HRRR precipitation location skill improves by 50% over past 5 years
HRRR precipitation bias reduced by 60% over past 5 years
L
ess
Skill
Mor
e Sk
ill
U
nder
fore
cast
O
verf
orec
ast
Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
Digital filter-based assimilation initializes ongoing / developing
convection / precipitation regions
Forward integration,full physics with obs-based latent heating
-20 min -10 min Initial +10 min + 20 min
RUC / RAP model forecast
Backwards integration, no physics
Initial fields with improved balance, storm-scale circulation
Radar reflectivity Lightning Satellite cloud-top cooling rate
Use for following obs types:
RUC/RAP: Diabatic Digital Filter Assimilation
8 Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
U wind component difference (radar – no radar)
Low-Level Upper-Level Observed Reflectivity
1400 UTC 22 Oct 2008
Z = 3 km
Enhanced Divergence Enhanced Convergence
RUC/RAP: Diabatic Digital Filter Assimilation
9
Latent Heating Promotes Mesoscale
Circulations in Regions of
Precipitation
Quick. Baseline for ens
DA including HRRRE
Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
Observations Merge cloud field
Update hydrometeors based on the cloud field
Map to cloud field
No cloud
Cloud
Unknown
RAP/HRRR Cloud and Precip Hydrometeor Analysis
10 Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
Quick. Baseline for ens
DA including HRRRE
Rapid Refresh GSI Options – Surface Obs
Special treatments for surface observations
Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
Quick. Baseline for ens
DA including HRRRE
Namelist explanation Default value
RAP value
dfi_radar_latent_heat_time_period DFI forward integration window in minutes 30 30 metar_impact_radius METAR cloud obs impact radius in grid number 10 10 metar_impact_radius_lowCloud METAR low cloud observation impact radius in
grid number 4 4
l_gsd_terrain_match_surfTobs if .true., GSD terrain match for surface temperature observation
.false. .true.
l_sfcobserror_ramp_t namelist logical for adjusting surface temperature observation error
.false. .true.
l_sfcobserror_ramp_q namelist logical for adjusting surface moisture observation error
.false. .true.
l_PBL_pseudo_SurfobsT if .true. produce pseudo-obs in PBL layer based on surface obs T
.false. .false.
l_PBL_pseudo_SurfobsQ if .true. produce pseudo-obs in PBL layer based on surface obs Q
.false. .true.
l_PBL_pseudo_SurfobsUV if .true. produce pseudo-obs in PBL layer based on surface obs UV
.false. .false
pblH_ration percent of the PBL height within which to add pseudo-obs
0.75 0.75
GSI namelists from GSD
Namelist explanation Default value
RAP value
dfi_radar_latent_heat_time_period DFI forward integration window in minutes 30 30 metar_impact_radius METAR cloud obs impact radius in grid number 10 10 metar_impact_radius_lowCloud METAR low cloud observation impact radius in
grid number 4 4
l_gsd_terrain_match_surfTobs if .true., GSD terrain match for surface temperature observation
.false. .true.
l_sfcobserror_ramp_t namelist logical for adjusting surface temperature observation error
.false. .true.
l_sfcobserror_ramp_q namelist logical for adjusting surface moisture observation error
.false. .true.
l_PBL_pseudo_SurfobsT if .true. produce pseudo-obs in PBL layer based on surface obs T
.false. .false.
l_PBL_pseudo_SurfobsQ if .true. produce pseudo-obs in PBL layer based on surface obs Q
.false. .true.
l_PBL_pseudo_SurfobsUV if .true. produce pseudo-obs in PBL layer based on surface obs UV
.false. .false
pblH_ration percent of the PBL height within which to add pseudo-obs
0.75 0.75
GSI namelists from GSD
l_use_2mQ4B if .true. use 2m Q/T as part of background to calculate surface Q observation innovation
.false. .true.
Improved forward model for 2m surface obs when available, improved information matching
Z=0m, atmos/sfc interface
Z=2m, shelter height for temp/dewpoint obs
Z=8m, k=1 level for RAP (σ=0.998)
Z >>8m, k=1 level for NAM, GFS
Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
Purposes for RUA – Rapidly Updated Analysis high-frequency environmental nowcasts
1. Pure situational awareness – use all observations as precisely as possible. (Use high-res fcst as background, e.g., HRRR )
2. Initial conditions for extrapolation model (e.g., AutoNowcaster, CIWS)
3. Initial conditions for hydrodynamic model (may require multivariate “equilibrium” not needed for #1 or #2).
• Note: #3 is approaching #1 and #2 but not there yet.
Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
Vision: Unification for 3-d nowcasting under RUA
• Diagnose all other fields from 3-d best estimate of atmosphere/earth-system –
• Cloud cover • PBL height • 80m winds
• RUA should include • Water in all forms –
• Atmosphere: water vapor, hydrometeor types (mixing ratio, number concentration, bins, etc.)
• Land-surface field – soil, vegetation, snow cover • Contribution to QPE • 3-d aerosol/smoke/chemistry
Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
Current nowcast components • Observations
• Remotely sensed • Radar –
• MRMS national/international composite • Ancillary radar – CASA, etc. • PBL profiler
• Satellite • GOES, polar-orbiter – radiance, cloud, scat
• Camera – road cams, all-sky • Ceilometer, visibility - cloud
• In situ – surface, aircraft, raob, tower) • Modern data assimilation merger – GSI
initializing 3km HRRR – one start
Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
RAP/HRRR – variables updated in data assimilation
Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
• 9 soil layers, 2 snow layers • Surface observations are used to update the LSM through the data
assimilation step. For example, the soil temperature is decreased and soil moisture is increased where the model is too warm and too dry compared to the surface observations.
Soil Temperature Soil Moisture
Example Soil Adjustments 20 UTC
03 June 2013
Cooling
Warming Moistening
Drying
DA for Land Surface Model (LSM) for HRRR/RAP
Snow-cover updating HRRRv2 – full land-sfc/snow cycling
Snow water equivalent – 06z 20 May 2015 – inches
Colorado
Wyoming Nebraska
Denver
HRRRv2-exp ESRL
HRRRv1-oper NCEP
NOHRSC
2013 Warm Season (June-August) HRRR 0-6 hr precipitation forecast Difference against Stage IV
Dry Moist 21
HRRRv2 Real-Time Evaluation: Precipitation
Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
Dry Moist
Reduction in high precipitation bias
22
HRRRv2 Real-Time Evaluation: Precipitation
2015 Warm Season (June-August) HRRR 0-6 hr precipitation forecast Difference against Stage IV
Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
24
RAP-chem – assimilation of PM2.5 data Rapid Refresh with Chemistry Real time
forecasts predicting weather and air quality – based on WRF-Chem, GSI (Mariusz Pagowski) • WRF-Chem coupled with chemistry via
RACM chemical mechanism including MOSAIC/VBS for prediction of secondary organic aerosols (SOA).
• MEGAN biogenic emissions, • NEI and RETRO/EDGAR
anthropogenic emissions, • Chemical deposition, • Convective and turbulent chemical
transport, • Photolysis, • Advective chemical transport performed
simultaneously with meteorology ("online"),
• Lateral chemical boundary conditions obtained from RAQMS model real time forecasts.
Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
HRRRE (HREF)
Prototype 3-km storm-
scale HRRR ensemble
domain 2016
• Single core (ARW) • Ensemble DA • Stochastic physics Assimilation Forecast 20-40 members 9 members 1 hr forecast 12-15 hr forecast 21 cycles / day 4+ fcsts / day 21z Prev Day Start 00z, 12z, 15z, 18z
More accurate storm-details from ensemble data assimilation
Beginning development of formal 3-km data assimilation and forecast ensemble
25 Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
HRRRE (HREF)
26
Observations Six Member Ensemble 12 hr Forecasts Valid 00 UTC 8 Mar 2016
Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
Challenges of storm-scale DA • number of radar and cloud observations highly variable in space/time:
• fair weather only a few observations, • convective storms many observations
• methods needed to deal with non-Gaussian ensemble distributions and nonlinear observation operators:
• variable transformations and/or advanced DA methods • examples of non-Gaussian distributions:
• (1) bimodal distributions (some ens members have conv storm, some don’t), • (2) raindrop number concentration
• example of nonlinear observation operator: reflectivity (proportional to log q if assimilated observation is in dBZ)
27 Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016
Future for situational awareness DA • Requirement - 5-min DA w/ radar and satellite data while maintaining
“quick speed” • keeping all data (grids esp.) in memory all the time is essential. • Design toward an 80-mem 1km ensemble updating every 5 min, even global
• [Note: some evidence that if we do a good job computing the obs operators (ensemble priors) at the exact observation time, 10-min assimilation windows will be good enough for storm-scale DA]
• Coupled land-surface/chem/atmos DA - incl. sub-grid repr (e.g., clouds) • “on-demand” capability for very high resolution applications: DA and
NWP grids generated where there are risks of severe weather, heavy rain / snow, fire, etc.; grid discarded when risk is gone, perhaps after only a few hours
• Use of JEDI-like, community next-gen DA 28 Situational awareness - Blueprints for Next-Gen DA Systems 8 March 2016