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RUC/Rapid Refresh Development and Testing Including TAMDAR and Radar Reflectivity Impact Studies. NOAA Earth System Research Lab (ESRL) Global Systems Division (GSD) Boulder, CO. NOAA/ESRL/GSD Stan Benjamin Steve Weygandt John M. Brown Tanya Smirnova Dezso Devenyi Kevin Brundage

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noaa earth system research lab esrl global systems division gsd boulder co

RUC/Rapid Refresh Development and Testing Including TAMDAR and

Radar Reflectivity Impact Studies

NOAA Earth System Research Lab (ESRL) Global Systems Division (GSD)Boulder, CO

NOAA/ESRL/GSD

Stan Benjamin

Steve Weygandt

John M. Brown

Tanya Smirnova

Dezso Devenyi

Kevin Brundage

Georg Grell

Steven Peckham

Tom Schlatter

Tracy Lorraine Smith

NCEP/EMC – Geoff Manikin

  • Major transitions:
  • RUC13 change package – ~Jan 2008
    • – radar reflectivity assimilation, TAMDAR, current status, testing
  • RUC changes in current testing
  • Rapid Refresh WRF testing in 2007
  • - planned testing in 2007
    • - Current impl plan – FY09 – 4Q
slide2

RUC 3dvar

3dvar

Obs

Obs

RUC Hourly Assimilation Cycle

Cycle hydrometeor, soil temp/moisture/snow

plus atmosphere state variables

Hourly obs in 2008 RUC

Data Type ~Number

Rawinsonde (12h) 80

NOAA profilers 30

VAD winds 110-130

PBL – prof/RASS ~25

Aircraft (V,temp) 1400-4500 TAMDAR (V,T,RH) 0-1000

Surface/METAR 1500-1700

Buoy/ship 100-150

GOES cloud winds 1000-2500

GOES cloud-top pres 10 km res

GPS precip water ~300

Mesonet (temp, dpt) ~7000

Mesonet (wind) ~ 600

METAR-cloud-vis-wx ~1500

Radar / lightning 2km

1-hr

fcst

1-hr

fcst

1-hr

fcst

Background

Fields

Analysis

Fields

Time

(UTC)

11 12 13

slide3

RUC

  • Rapid
  • Refresh
  • (2009)
  • Hourly NWP
  • Update for:
  • CONUS
  • AK/Can
  • Pac/Atl
  • Caribbean

Planned approx Rapid Refresh domain

Current RUC CONUS domain

  • NWP updated hourly w/ latest obs
  • Aviation / transportation
  • Severe weather
  • Decision support tools
slide4

RUC History – NCEP (NMC) implementations

  • - First operational implementation of RUC
      • - 60km resolution, 3-h cycle
  • 1998 – 40km resolution, 1-h cycle,
  • - cloud physics, land-sfc model
  • 2002 – 20km resolution
  • - addition of GOES cloud data in assimilation
  • 2005 – 13km resolution, new obs (METAR clouds, GPS moisture), new model physics
  • 2008 (Jan) – Assimilation of radar reflectivity, improved thunderstorm forecasting
  • 2009 – WRF-based Rapid Refresh to replace RUC

NOAA/GSD testing – precedes NCEP ops by 0.5-3 years

slide5

Jan 2008 Changes for oper RUC upgrade

  • Assimilation
    • Use of radar reflectivity in RUC diabatic digital filter initialization in RUC model
    • Mesonet winds using mesonet provider uselist
    • TAMDAR aircraft observations
      • (TAMDAR impact para RUC tests at GSD)
  • Model physics
    • RRTM longwave radiation
    • Mod to Grell-Devenyi – decrease areal coverage
    • Mod to RUC land-sfc model – fresh snow density
  • Post-processing – add reflectivity fields
slide6

RUC change package – Jan2008 - continued

  • Model changes
  • RRTM longwave radiation, replacing Dudhia LW (largely eliminates warm bias in RUC)
  • Mod to Grell-Devenyi convective scheme
  • (reduce excessive areal coverage for light precip)
  •  Mod to snow component of RUC land-sfc model for snow density
  • (decrease excessively cold 2m air temps over fresh snow cover at night)
slide7

RRTM Longwave Radiation in RUC Upgrade

  • Effect on 2-m temperature forecasts
  • Much decreased warm bias, esp. at nighttime

2-m temp bias (obs – forecast)

1-month comparison

14 May –13 June 07

Eastern US only

12h

RUC oper – Dudhia LW

3h

12h

RUC para – RRTM LW

COLD WARM

3h

0 3 6 9 12 15 18 21

UTC

slide8

9h fcst - Oper RUC

9h fcst – para RUC

Better 2m temp forecast

From para RUC w/ RRTM LW

Anx valid 09z

slide9

9h fcst - Oper RUC

9h fcst – para RUC

Today

Better 2m temp forecast

From para RUC w/ RRTM LW

Anx valid 09z

slide11
RUC cloud analysis since 2005 – Use of cloud / hydrometeor observations to modify cycled cloud / hydrometeor fields

Background

Cloud water + cloud ice

Cloud assessment

(YES/NO/UNKNOWN)

from observations

(METAR/sat/radar)

slide12

RUC change package – 2007 – one more thing

  • Assimilate mesonet winds from accepted provider uselist
    • Allows about 600 additional wind observations to be used hourly in RUC
    • Primary mesonet providers (e.g., Citizens Weather, AWS) have common siting problems
      • Mesonet winds turned off for RUC13 implementation in 2005
    • New RUC treats each mesonet provider separately
    • Station-by-station reject list will allow use of many Citizens-Wx, AWS stations in future

Poster – Mesonet QC monitoring web site – Benj et al

http://ams.confex.com/ams/pdfpapers/124829.pdf

the thunderstorm prediction problem for aviation and severe weather
The Thunderstorm Prediction Problem for Aviation and Severe Weather
  • Most significant weather problem for aviation operations.
  • Requirement for improved hourly-updated 2-8h forecast for air traffic management.
  • Current situation
    • Large uncertainty even for short lead time
    • Very small-scale, deterministic forecasts difficult
  • Severe convective weather -- significant impact on human activities
the thunderstorm prediction problem
The Thunderstorm Prediction Problem
  • Two separate, but related problems:
    • Convective initiation
    • Convective evolution (including decay)
the thunderstorm prediction problem15
The Thunderstorm Prediction Problem
  • Two separate, but related problems:
    • Convective Initiation
    • Convective evolution

Need very accurate

forecast of mesoscale

environment

the thunderstorm prediction problem16
The Thunderstorm Prediction Problem
  • Two separate, but related problems:
    • Convective Initiation
    • Convective evolution
    • Excellent near-surface physics
    • High frequency assimilation of asynoptic observations (surface METAR and mesonet, wind profilers, aircraft)

Need very accurate

forecast of mesoscale

environment

the thunderstorm prediction problem17
The Thunderstorm Prediction Problem
  • Two separate, but related problems:
    • Convective Initiation
    • Convective evolution
    • Excellent near-surface physics
    • High frequency assimilation of asynoptic observations (surface METAR and mesonet, wind profilers, aircraft)

Need very accurate

forecast of mesoscale

environment

Nighttime convection often elevated – even harder

once convection is ongoing
Once convection is ongoing…
  • WSR-88D provides invaluable observations
  • Much improved operational warning, nowcasting
  • Operational NWP community slow to utilize radar reflectivity data in models
    • Real-time Level II (3-d) data access issues
    • Difficulties assimilating reflectivity
diabatic digital filter initialization ddfi
Diabatic Digital Filter Initialization (DDFI)

-30 min -15 min Init +15 min

Backwards integration, no physics

Forward integration,

full physics

Obtain initial fields with improved balance

RUC model forecast

diabatic digital filter initialization ddfi add assimilation of radar data
Diabatic Digital Filter Initialization (DDFI)add assimilation of radar data

-30 min -15 min Init +15 min

Backwards integration, no physics

Forward integration,

full physics

Apply latent

heating

from radar

reflectivity,

lightning

data

Obtain initial fields with improved balance, vertical circulations associated with

ongoing convection

RUC model forecast

ruc reflectivity assimilation procedure
RUC reflectivity assimilation procedure
  • Force precipitation in areas with radar echoes
    • Specify 3D latent heat from reflectivity
    • Apply latent heat within forward integration of diabatic digital filter initialization (DDFI)
    • Replace temperature tendency from cumulus parameterization and explicit microphysics
    • Moisten echo region, do not add hydrometeors
  • Suppress convection in echo-free areas
    • Create convective suppression mask (> 300 mb deep layer > 100 km from existing convection
    • Inhibit cumulus parameterization during DDFI and 1st 30 min. of model integration
advantages of radar assimilation procedure
Advantages of radar assimilation procedure
  • Minimal shock to model
    • Coherent wind, temperature and moisture fields evolve in response to heating within DDFI
  • Very little additional computer cost
    • DDFI already used to control noise
  • Independent of model or physics packages
    • Can be applied to Rapid Refresh WRF, other models
slide23

RUC radar assimilation test case

NSSL radar

reflectivity mosaic

Test case 00z 8 Jan 2007

Experiment (EXP)

  • LH temperature tendency in DDFI

(no moistening, no suppression yet)

Control (CNTL)

  • Standard initialization

(no radar assimilation)

slide24

NSSL 3-km radar reflectivity (dbz)

K=15 LH temp. tend. (K / 15 min)

00z 8 Jan 2007

Contour interval = 0.5 K

slide25

Reflectivity experiments within RUC

  • CNTL

standard initialization – no radar init

  • EXP

LH nudging within DDFI

no adding hydrometeors from reflectivity

no moistening in reflectivity region

slide26

K=15 U-comp. diff (EXP - CNTL)

K=35 U-comp. diff (EXP - CNTL)

Contour interval = 0.2 m/s

Low-level

Convergence

Upper-level

Divergence

slide27

“Pyramids of silence”

complementing “cones of radar data”

Latent heating in diabatic

forward DFI step specified only where 3-d radar data available

NSSL mosaic

Z = 5 kft

1700 UTC

27 Jan 2004

NSSL mosaic

Z = 20 kft

NSSL mosaic

Z = 10 kft

slide28

00z 3-km refl (dBZ)

EXP 45-60 min. prec.

cint = 0.5 mm

NSSL

mosaic

slide29

CNTL 45-60 min. prec.

EXP 45-60 min. prec.

cint = 0.5 mm

cint = 0.5 mm

slide30

CNTL 105-120 min. prec.

EXP 105-120 min. prec.

cint = 0.5 mm

cint = 0.5 mm

slide31

CNTL 165-180 min. prec.

EXP 165-180 min. prec.

cint = 0.5 mm

cint = 0.5 mm

slide32

02z 3-km refl (dBZ)

EXP 115-120 min. prec.

cint = 0.5 mm

NSSL

mosaic

slide33

105-120 min. accum. precipitation

NSSL reflectivity

CNTL

EXP

02z 8 Jan 2007

Contour interval = 0.5 mm

slide34

Radar obs – 00z 25 Mar 2007

Real-time parallel testing at GSD

(started February 2007)

Added simulated RUC radar reflectivity field

Assimilation improvement most clear in reflectivity field(smeared in precipitation field)

RUC 3-h forecasts valid 00z 25 Mar 2007

No radar

assimilation

With radar

assimilation

slide35

03z 1 Mar 2007

03z 1 Mar 2007

Analyzed

700 mb

vert. vel.

from RUC

with and

without

radar

assimilation

0-h vert. vel.

0-h vert. vel.

Radar

assim

No radar

assim

Real-time case from

03z 1 March

2007

NSSL

Reflectivity mosaic

Obs. reflectivity

03z 1 Mar 2007

slide36

3-h precip.

3-h precip.

3-h fcst

accum.

precip.

from RUC

with and

without

radar

assimilation

No radar

assim

Radar

assim

06z 1 Mar 2007

06z 1 Mar 2007

Real-time case from

03z 1 March

2007

NSSL

Reflectivity mosaic

Obs. reflectivity

06z 1 Mar 2007

slide37

Suppress

convection

Allow

convection

No coverage

Radar reflectivity assimilation

Part 2 – convection suppression

  • Define suppression areas as follows:
  • No reflectivity > 20 dbZ within 100 km
  • Depth of radar data > 300 hPa
  • Complemented by GOES fully clear areas

Accomplish in RUC model:

Specify minimum cap depth as 0 hPa in DFI step and first 30 min in actual forecast

slide38

Convective suppression example

Suppress

convection

Allow

convection

No coverage

NSSL reflectivity

Suppression mask

Real-time

case from

12z 7 June

2007

slide39

Convective suppression example

Radar

Assimilation

NSSL 3-h

precipitation

Control

Real-time 3-h forecasts valid 15z 7 June 2007

Valid 15z 7 June 2007

convective suppression - How does it work? – Reduces latent heating, vert. motion in erroneous conv areas

slide40

RUC “analysis”

composite refl

(actually 1h fcst)

0900z

NSSL Q2 composite refl

0900z

Tues 17 July 2007

slide41

RUC “analysis”

composite refl

(actually 1h fcst)

0900z

NSSL Q2 composite refl

0900z

Tues 17 July 2007

slide42

RUC 3h forecast

composite refl

1200z

NSSL Q2 composite refl

Valid 1200z

Tues 17 July 2007

slide43

RUC 3h forecast

with RADAR ASSIM

NSSL Q2 QPE

Precip – 3h –09-12z

Tues 17 July 2007

slide44

RUC 3h forecast

with RADAR ASSIM

NSSL Q2 QPE

Precip – 3h –09-12z

Tues 17 July 2007

slide45

RUC 3h forecast

without RADAR ASSIM

NSSL Q2 QPE

Precip – 3h –09-12z

Tues 17 July 2007

slide46

Precip – 3h –09-12z

Tues 17 July 2007

NSSL Q2 QPE

RUC 3h forecasts

with RADAR ASSIM

without RADAR ASSIM

slide47

RUC “analysis”

composite refl

(actually 1h fcst)

1200z

NSSL Q2 composite refl

1200z

Tues 17 July 2007

slide48

RUC 3h forecast

with RADAR ASSIM

1500z

NSSL Q2 composite refl

1800z

Tues 17 July 2007

slide49

RUC forecast unable to hold onto squall line

- result of Grell-Dev limitations, not assim

RUC 6h forecast

with RADAR ASSIM

1800z

NSSL Q2 composite refl

1500z

Tues 17 July 2007

slide50

Precip – 3h –1215z

Tues 17 July 2007

NSSL Q2 QPE

RUC 3h forecasts

12-15z

without RADAR ASSIM

with RADAR ASSIM

slide51

Precip – 3h –15->18z

Tues 17 July 2007

NSSL Q2 QPE

RUC 6h forecasts

1800z

without RADAR ASSIM

with RADAR ASSIM

slide52

Sfc Temp – 21z

Tues 17 July 2007

Evaporative cooling

- improved cold pool

2100z

RUC 9h forecasts

without RADAR ASSIM

with RADAR ASSIM

slide53

No radar

assim.

3-h fcst

accum.

precip.

from RUC

with and

without

radar

assimilation

Radar

assim.

3-h fcst. precip.

12z 12 Feb 2007

3-h fcst. precip.

12z 12 Feb 2007

  • RUC Radar refl assim w/ DDFI
  • effective on winter and summer events
  • no added CPU

Real-time case from

09z 12 Feb

2007

Obs. reflectivity

11z 12 Feb 2007

slide54

Radar assimilation impact on

3-h precipitation skill scores

  • Significant improvement in ETS and bias
  • Spring - daytime
slide55

Radar assimilation impact on

3-h precipitation skill scores

  • Summer - overnight
slide56

Radar assimilation impact on

3-h precipitation skill scores

  • 4 x 0-3h vs. 1x0-12h
  • Summer - daytime
slide57

WSI

Nowrad

0500 UTC

23 Mar 2005

Radar reflectivity (dbz)

slide58

Lightning data

0500 UTC

23 Mar 2005

(strikes/40 min/gridbox)

NLDN

slide59

-------------------

Radar data

Lightning data

-----------------

0500 UTC

23 Mar 2005

------------------------

Rainwater from

radar/lightning data

------------------------

slide60

---- one more time -----

  • 2007 Changes for oper RUC upgrade
  • Assimilation
    • Use of radar reflectivity in RUC diabatic digital filter initialization in RUC model
    • Mesonet winds using mesonet provider uselist
    • TAMDAR aircraft observations
      • (TAMDAR impact para RUC tests at GSD)
  • Model physics
    • RRTM longwave radiation
    • Mod to Grell-Devenyi – decrease areal coverage
    • Mod to RUC land-sfc model – fresh snow density
  • Post-processing – add reflectivity fields
slide61

Verification regions for GSD-RUC TAMDAR impact

Large region (eastern half of US) -- 38 RAOB sites

Small region (Great Lakes) includes 14 RAOBs

amdar and tamdar
AMDAR and TAMDAR
  • “AMDAR” (Automated Meteorological Data and Recording) – are automatically sent from commercial aircraft, mostly large jets
  • “TAMDAR” (Tropospheric AMDAR) – automatic reports from (currently) ~50 turboprops flying regionally in the US Midwest
    • Provided by AirDat LLC
    • Agreement between Northwest Airlines (Mesaba – regional subsidiary) and AirDat LLC
    • New agreement between NWS/FAA and AirDat for use of TAMDAR
tamdar variables
TAMDAR Variables
  • TAMDAR measures temperature and winds aloft, as does the rest of the AMDAR fleet
  • In addition, TAMDAR measures
    • water vapor
    • turbulence (not discussed)
    • icing (not discussed)
parallel real time ruc cycles to monitor tamdar impact since 2005
Parallel real-time RUC cycles to monitor TAMDAR impact since 2005
  • “dev2” – includes TAMDAR and all other data typically assimilated by the RUC
  • “dev” – lacks only TAMDAR data
  • Both cycles use NAM boundary conditions
  • Both run at 20-km, but are otherwise similar to the operational 13-km runs
  • Background fields are set equal every 48 h
slide67

Great Lakes Region includes 13 RAOBs

Eastern US Region includes 38 RAOBs

National Region is the RUC domain (CONUS and adjacent)

Results today are from the Great Lakes region, where most current TAMDAR-equipped aircraft fly

slide68

3h Fcst errors – RUCdev (no TAMDAR), RUCdev2 (w/ TAMDAR)

Temp

RH

wTAM

noTAM

wTAM

noTAM

  • TAMDAR – regional aircraft
  • with V/T/RH obs
  • GSD impact study with RUC parallel cycles
  • 2005-2007 (ongoing)
  • 10-30% reduction in
  • RH, temperature, wind fcst error w/ TAMDAR assimilation

Wind

wTAM

noTAM

slide69

Temperature RMS error time series, 3-h forecasts, Great Lakes Region, surface to 500 mb

Red: dev RMS error

Blue: dev2 RMS error

Black: difference

TAMDAR impact up to 0.2 K

slide70

Temperature RMS error profile, 3-h forecasts, Great Lakes Region

For 4 months in Jan-May 2007

  • Thus TAMDAR impact represents about 35% of the maximum expected 3-h T forecast error reduction at 900 mb.

TAMDAR impact max 0.4K at 900 mb -- Inversion level, cloud ceiling

slide71

Wind RMS error profile, 3-h forecasts, Great Lakes Region

TAMDAR impact:

0.25 m/s at 700 mb

  • Analysis fit to RAOBs is ~2.2 m/s
  • Thus, TAMDAR impact on 3-h wind forecasts represents a 15% reduction in 3-h wind forecast error at 700 mb
slide72

RH error time series, 3-h forecasts, Great Lakes Region, surface to 500 mb

TAMDAR impact up to 2% RH

a look ahead 2
A look ahead (2)
  • AirDat will install TAMDAR on additional fleets over the next several months
  • Covering Alaska and the Western US
  • These fleets include some jet aircraft
    • higher altitudes, speeds (implications??)
    • better heading => reduced wind errors
  • GSD will evaluate the quality and impact of these data (with FAA funding)
  • (Unfortunately, these new data will not be available beyond GSD, per AirDat)
ed szoke gsd tamdar evals overview

TAMDAR soundings have been shown to be useful for forecasting

Talks at the last SLS Conference and previous Annual Meetings

WFO Green Bay helps maintain the official NOAA TAMDAR web page at http://www.crh.noaa.gov/tamdar/

In this talk we focus on the impact on NWP:

Evaluation of RUC precipitation forecasts for runs with and without TAMDAR for significant weather events

Mostly a subjective evaluation, but objective scoring for 2007 cases

Ed Szoke (GSD) TAMDAR evals - Overview
slide76

Case 1: 4 October 2005 – 2100 UTC Surface analyses and reflectivity

Still one of the better cases for TAMDAR impact...

4-5 Oct 2005: heavy precip in

the Upper Midwest.

Flooding reported in

Minnesota to northern

Wisconsin.

slide77

NPVU estimated precipitation for 6-h ending 0000 UTC 5 October 2005

Very sharp

cut off to the

precip in WI

and a huge

gradient with

a 2-3” max.

slide78

RUC forecasts from the 4 October 2005 1800 UTC runs

6-h total precipitation ending 0000 UTC 5 October

No TAMDAR

With TAMDAR

Both runs forecast too much precip in southern half of Wisconsin, but the RUC run with TAMDAR correctly forecasts more precip (small spots of >1.00”) across the northern half of the state.

slide79

Sounding comparison: RUC 6-h forecasts with (labeled dev2) and without

(labeled dev1, in black) TAMDAR, compared to the RAOB for Detroit (green)

at 0000 UTC 5 Oct 05. Incorrect dry layer in the dev1 (noTAM) forecast.

slide80

Same comparison but for Peoria, Illinois. The RUC run with TAMDAR

is closer to the RAOB especially at and below 700 mb.

slide81

Case 1/part 2: 5 October 2005 – 0300 UTC Surface analyses and reflectivity

Heavy precip continues

in the same areas

slide83

RUC forecasts from the 5 October 2005 0000 UTC runs

6-h total precipitation ending 0600 UTC 5 October

No TAMDAR

With TAMDAR

No TAMDAR

With TAMDAR

For this period the RUC run that used the TAMDAR data is a much better forecast with a very sharp cut off to the precipitation in Wisconsin and a better location for the heavy precip.

slide84

Case 4: 22 March 2007 – 0000 UTC Surface analyses and reflectivity

Strong spring storm with lots of severe weather

slide87

RUC forecasts from the 22 March 2007 0000 UTC runs

6-h total precipitation ending 0600 UTC 22 March

No TAMDAR

With TAMDAR

No TAMDAR

With TAMDAR

Some differences are seen – these are outlined in the forecasts

The RUC forecast that uses TAMDAR is generally better except within the

orange oval area, where no precipitation fell.

slide88

Case 5: 21 June 2007 – 2100 UTC Surface analyses and reflectivity

Strong convection with many reports of severe weather

slide91

RUC forecasts from the 21 June 2007 1800 UTC runs

6-h total precipitation ending 0000 UTC 22 June

No TAMDAR

With TAMDAR

Main difference is the precipitation in IL and IN predicted by the RUC run without TAMDAR compared to almost nothing in the run with TAMDAR.

Verification showed that no precipitation fell in the IL/IN area.

slide92

Sounding comparison for 6-h forecasts for RUC with TAMDAR (dev2) vs RUC without

TAMDAR (dev) compared to the DVN RAOB at 0000 UTC 22 June 2007

slide93

Sounding comparison for 6-h forecasts for RUC with TAMDAR (dev2) vs RUC without

TAMDAR (dev) compared to the ILX RAOB at 0000 UTC 22 June 2007

summary

When we began to examine precipitation forecasts in late 2005 were impressed by the 4-5 October 2005 case with significantly better forecasts by the RUC run that used TAMDAR

But that remains our best case

More typically, we see much smaller impacts

These tend to favor the RUC run that uses TAMDAR, but not always

And sometimes mixed...forecast better in some spots but not in others

Objective scoring of the precipitation forecasts that began in 2007 agrees with our overall subjective impression

Longer-term statistics show relatively small differences generally favoring the RUC run that uses TAMDAR

But on a case by case basis can see greater differences in the scores

Summary
slide95

Effect of vertical resolution on TAMDAR 3-h Temperature forecast impact

Each curve shows the amount that TAMDAR reduces the RMS error.

Low-res reduces the error less => less TAMDAR impact.

Decreased vertical resolution decreases TAMDAR impact on

3-h T forecasts by

~30% at 750 mb

~10% at 900 mb

slide96

RUC

  • Rapid-
  • Refresh
  • (2008-09)
  • Continental situational awareness
  • model
  • Hourly NWP
  • Update for:
  • CONUS
  • AK/Can
  • Pac/Atl
  • Caribbean

Planned Rapid Refresh domain

Current RUC CONUS domain

slide97

RUC to Rapid Refresh

  • North American domain
  • (13km)
  • GSI (Gridpoint Statistical
  • Interpolation)
  • WRF model
  • (ARW dynamic core almost certainly)
  • CONUS domain
  • (13km)
  • RUC 3dvar
  • RUC model
slide98

GSI

GSI

Obs

Obs

Rapid Refresh Hourly Assimilation Cycle

Cycle hydrometeor, soil temp/moisture/snow

plus atmosphere state variables

Hourly obs used in RR

Data Type ~Number

Rawinsonde (12h) 80

NOAA profilers 30

VAD winds 110-130

PBL – prof/RASS ~25

Aircraft (V,temp) 1400-4500 TAMDAR (V,T,RH) 0-1000

Surface/METAR 1500-1700

Buoy/ship 100-150

GOES cloud winds 1000-2500

GOES cloud-top pres 10 km res

GPS precip water ~300

Mesonet (temp, dpt) ~7000

Mesonet (wind) ~ 600

METAR-cloud-vis-wx ~1500

Radar / lightning 2km

Sat radiances – AMSU-A/B, GOES

QuikSCAT

1-hr

fcst

1-hr

fcst

1-hr

fcst

Background

Fields

Analysis

Fields

Time

(UTC)

11 12 13

slide99

WRF physics options

  • -- All available with both ARW and NMM cores w/ WRFv2.2
  • -- All combinations tested with WRF-RR core-test
  • Phase 1 - Default NMM physics
  • Phase 2 - RUC-like physics
rr application of gsi assimilation
RR application of GSI assimilation
  • 1-h cycling of atmospheric (including hydrometeor)

and land surface model fields

  • Update cycled fields with all available hourly observations
  • Utilize GSI satellite radiance assimilation scheme
  • Build in “RR-specific” components:

1) ‘pre-forecast’ diabatic digital filter initiation (DDFI)

2) cloud analysis (satellite, METAR, radar, LTG obs)

3) surface obs assimilation (BL depth, coast-lines)

4) Force convection from radar, lightning data in model DDFI after GSI pre-processing

example from the preliminary rapid refresh real time cycle
Example from the preliminary Rapid Refresh real time cycle

Analysis at 1200 UTC 29 May 2007 over RR domain. Wind field at model level 15 colored according to potential temperature. Each color represents a 5-K interval of potential temperature, with purple representing from 285-290K (north) and red representing from 330-335K (south).

slide102

More preliminary

RR results

12h forecast

- 2m temperature

Rapid

Refresh

RUC

12z 12 May 2007

slide103

Rapid Refresh Data Assimilation timeline

Fall 2006 – summer 2007

– Cycle WRF using GSI over RR domain

- Use of WRF version 1.2 (WRFSI)

- Testing of new surface assimilation and

cloud analysis modules

Fall 2007

Cycling over NAmerica with all observations

- Update to new GSI, WRF versions; new computer

- Use of NCEP prepbufr files, increase cycling frequency

- Comparison with other systems, continue refinement

Fall/Winter 2007/08

Full system with RR modifications

- DDFI in place for chosen WRF model core

- Detailed examination of cold season near surface aspects

- Refinement of system toward operation skill

slide104

Rapid Refresh Planned Timeline

Spring/Summer 2008

Real-time and retro cycles

- Testing of DDFI radar data assimilation

- Focus on performance for convective situations

- Transfer code to NCEP

Winter 2008 - 09

Testing of complete RR at NCEP and GSD

Summer/Fall 2009

Operational implementation at NCEP

slide105

Transitioning to operations (RUC, RR)

  • Must run at NCEP
  • Must run within available computer resources

and time constraints (5 min – assim, 17 min- 12h fcst)

  • Must be built into existing code infrastructure

(e.g.: Build assimilation capability in GSI, develop probabilistic products within SREF framework)

slide106

Major transition:

  • Rapid Refresh planned implementation
    • 2009
  • Evaluation at NCEP - 2008

Upcoming GSD tasks

  • Develop Rapid Refresh – North American 1h update 4-d assim/model – toward NextGen 4-d database
  • Tied with EMC more than before
  • Test and recommend physics options
  • Developed diabatic DFI (digital filter initialization) in WRF-ARW to allow RR 1h cycle
  • GSI assimilation with RUC-specific enhancements

Work with EMC, NCEP centers, NWS, other RUC/FAA/AWRP users, DTC, WRF community on forecast evaluation and improvement

This is where you folks come in!

future high resolution rapid refresh hrrr
FUTURE: High Resolution Rapid-Refresh (HRRR)
  • Proposal for 2009-11 time frame
  • Nested high-resolution domain (2-4 km) within RR
  • Explicit depiction of convection (no cum. param.)
  • Hourly or sub-hourly observation updating

Need to initialize storm scale details…

Use radial velocity (3DVAR, 4DVAR, retrieval techniques),

reflectivity (polarimetric information), and lightning data

to specify wind, temperature, and hydrometeors

Storm building, adjustment, and removal

slide108

06z obs refl

NSSL Q2 product

HRRR-ARW 6h fcst

radRUC IC

HRRR- ARW 6h fcst

Oper RUC IC

  • Results from sample real-time HRRR test
  • Forecasts initialized 0000z 16 Aug 07
  • Radar-enhanced RUC critical for HRRR forecast
slide109

RUC/Rapid Refresh Development and Testing Including TAMDAR and

Radar Reflectivity Impact Studies

[email protected]

http://Ruc.noaa.gov

303-497-6387

  • Major transitions:
  • RUC13 change package – ~Jan 2008
    • – radar reflectivity assimilation
    • - TAMDAR
    • Improved radiation, convection physics in RUC
  • Rapid Refresh planned for FY09
    • WRF ARW, GSI, North America
  • Ensemble Rapid Refresh
    • proposed by 2012, to use ESMF framework
  • High-Res Rapid Refresh (HRRR) – proposed to NCEP by 2012
    • 3km hourly updated 12h forecast
    • In testing at GSD
    • Covering NE Corridor
slide110

Planned implementation of

TAMDAR sensors by

AirDAT LLC into Horizon

and PenAir Saab 340s

slide111

First Alaska TAMDAR

data

- 12 June 2007

slide112

AMDAR

-24h

12-13 Mar 07

slide113

SATELLITE DATA EXPERIMENTS FOR RAPID REFRESH

Both HIRS and MSU -- Use of Community Radiative Transfer Model (CRTM)

Preliminary experiments with NAM satellite radiance and bias files over CONUS domain using RUC background fields. Case of 11 April 2006, 1200 UTC.

Difference

satellite minus no-satellite data; specific humidity.

Model level=10.

SATELLITE DATA EXPERIMENTS FOR RAPID REFRESH

  • Both HIRS and MSU
  • Usage of CRTM
  • Preliminary experiments with NAM satellite files over CONUS domain using RUC background fields
  • Case of 11 April 2006, 1200 UTC.

SATELLITE DATA EXPERIMENTS FOR RAPID REFRESH

  • Both HIRS and MSU
  • Usage of CRTM
  • Preliminary experiments with NAM satellite files over CONUS domain using RUC background fields
  • Case of 11 April 2006, 1200 UTC.
  • SATELLITE DATA EXPERIMENTS FOR RAPID REFRESH
  • Both HIRS and MSU
  • Usage of CRTM
  • Preliminary experiments with NAM satellite files over CONUS domain using RUC background fields
  • Case of 11 April 2006, 1200 UTC.
slide114

Some Special Challenges – Aviation Forecasts with Expanded Domain

Arctic low stratus – will be difficult to predict explicitly

-- but we will try

Sea ice – Leads, ponding, etc.

Taiga in spring—snow cover and frozen ground under

warm forest canopy

Tundra in the land-surface model – prediction of

surface conditions in the Far North

Tropical cyclones—initializing, track prediction, intensity

Sparse dataover land – risk of “climate drift” in the model

slide115

Joint GSD-NCAR plans for HRRR model development

  • High-Res Rapid Refresh (HRRR)
    • 2-3 km, hourly updating, assimilation of hourly radar reflectivity
    • Based off radar-assimilating Rapid Refresh (currently radar-assimilation RUC)
  • Plans for FY08 1Q-2Q
    • GSD, NCAR
      • Rerun cases and test periods summer 2007
      • 2 sources of external model grids
        • GSD radar-assimilating RUC
        • NCEP operational RUC (no radar assim)
      • 3 variations of 3-km WRF-ARW
        • ARW as is (already tested in real-time by GSD)
        • ARW with FDDA (NCAR lead on development)
        • ARW with radar-enhanced DFI (GSD lead)
real time hrrr for cospa for 3q 4q fy08
Real-time HRRR for CoSPA for 3Q, 4Q FY08
  • Real-time HRRR forecasts over regional domain over NE Corridor area for May-August period
    • Forecasts out to 12h, reinitialized every 1-3h using radar-enhanced RUC or Rapid Refresh initial conditions
    • 15-min output for selected 2-d fields including surface fields (2m temp/dewpoint, 10m winds), reflectivity, precipitation
    • Run at GSD, backup at NCAR
  • Experimental HRRR output to MIT/LL, NCEP Storm Prediction Center, AWC (possible), others
other fy08 activities toward hrrr
Other FY08 activities toward HRRR
  • Case studies from summer 2007 cases
    • NCAR - lead, GSD collaborating
  • Case study testing of revised radar assimilation methods
  • Development of simple forecast metrics for development retrospective tests and case studies
    • NCAR and GSD
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