applications in situational awareness high … in situational awareness high-resolution nwp -- ......

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)

4

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


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


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


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


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


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



DA including HRRRE

Rapid Refresh GSI Options – Surface Obs

Special treatments for surface observations



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

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


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.


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


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


RAP/HRRR – variables updated in data assimilation


• 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


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


23

HRRR-chem – assimilation of WFABBA HRRR-chem-3km PM25- sfc

11h fcst valid 17z 24 Feb 2015

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.


http://raqms-ops.ssec.wisc.edu/

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


HRRRE (HREF)

26

Observations Six Member Ensemble 12 hr Forecasts Valid 00 UTC 8 Mar 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)


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

applications in situational awareness high … in situational awareness high-resolution nwp -- ......

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