NOAA ESRL GSDAssimilation and Modeling Branch

Hyperspectral soundings and the pre-storm environment:

Assimilation of AIRS data into theRapid Refresh + a little on

satellite Convective Initiation / Lightning DA

Steve Weygandt, Haidao Lin, Ming Hu,

Jun Li, Jinlong Li, Tim Schmit,Tracy Smith, Stan Benjamin,

Curtis Alexander, John Brown,David Dowell, Brian Jamison,

John Mecikalski

RAP: Data assimilation engine for HRRR

2

RAP

Data Assimilation cycle

Observations

Hourly cycling model

HRRR

Use of GSI for Rapid Refresh

NCEP, NASA GMAO supported “full” system• Primary development by NCEP for operational DA • Advanced satellite radiance assimilation• GSI used by NCEP for GFS, NAM, and RTMA• NASA GMAO work to create GSI-based 4DVAR• Framework for hybrid ensemble system

Community analysis system• Many users and code contributors• DTC work to make code available to research community• Community-wide SVN code management

1-hrfcst

1-hrfcst

1-hrfcst

11 12 13Time (UTC)

AnalysisFields

3DVAR

Obs

3DVAR

Obs

Back-groundFields

Partial cycle atmospheric fields – introduce GFS information 2x/dayCycle hydrometeorsFully cycle all land-sfc fields(soil temp, moisture, snow)

Hourly Observations RAP 2012 N. Amer

Rawinsonde (T,V,RH) 120

Profiler – NOAA Network (V) 21

Profiler – 915 MHz (V, Tv) 25

Radar – VAD (V) 125

Radar reflectivity - CONUS 1km

Lightning (proxy reflectivity) NLDN, GLD360

Aircraft (V,T) 2-15K

Aircraft - WVSS (RH) 0-800

Surface/METAR (T,Td,V,ps,cloud, vis, wx) 2200- 2500

Buoys/ships (V, ps) 200-400

Mesonet (T, Td, V, ps) flagged

GOES AMVs (V) 2000- 4000

AMSU/HIRS/MHS radiances Used

GOES cloud-top press/temp 13km

GPS – Precipitable water 260

WindSat scatterometer 2-10K

Nacelle/Tower/Sodar 20/100/10

Rapid RefreshHourly Update Cycle

Challenges for regional, rapid updating satellite assimilation

• Data availability-- Long data latency, short data cut-off, small domain

Limited data availability

• Bias correction-- Cycled predictive bias correction in GSI

-- Limited and non-uniform data coverage degrades BC

• Lower model top-- Many channels sense at levels near RAP model top (10 hPa)

-- Use of these high peaking channels can degrade forecast

AIRS Data

• Launched May 2002 on NASA Earth Observing System (EOS) polar-orbiting Aqua platform

• Twice daily, global coverage• 13.5 km horizontal resolution (Aumann et al. 2003)• 2378 spectral channels (3.7-15.4 µm) • 281 channel subset is available for operational

assimilation

AIRS Brightness Temperature (BT) simulated from Community Radiative Transfer Model (CRTM)

AIRS Radiance Coverage in RAP • 3 h time window (+/- 1.5 h), in 3-h cycle RAP retro run

00Z 03Z 06Z

09Z 12Z 15Z

18Z 21Z 08 May 2010

Brightness Temperature (BT) from AIRS channel 791

AIRS SFOV Data• Single Field of View (SFOV) soundings derived using

CIMSS hyperspectral IR sounder retrieval (CHISR) algorithm (Li et al. 2000)

• Clear sky only soundings • SFOV data from CIMSS

Sample retrieved soundings compared to radiosondes

SFOVRaob

Typical moistureand temperature biases for SFOV

Warm

cold

Dry

Less vertical structure inSFOV profiles

Westof AK

---(north)

EasternNA

CentralNA

WesternNA/AK

AK / Grnlnd

EasternNA

WesternNA/AK

Diurnal aspects SFOV T innovations (O-B)

00z 03z 06z 09z 12z 15z 18z 21z

SFOV Temperature innovations – horiz., daily avg.

dependence on height, and time of daySample SFOV profiles compared with raobs

SFOV assimilation400 -- 800 mb 400 mb

800

Compare AIRS SFOV with RaobsConditions for matched profiles: 3-h time window, less than 15 km horizontal distance under clear-sky

Tempbias

TempRMS

Mixing Ratiobias

Mixing RatioRMS

3 SFOV data sets obtained from UW CIMSS:

V1 – first set V2 – improvedV3 – best set

Cool

Warm

Cool

Warm

Cool

Improvements in SFOV retrievals

All results Shown from V3

CNTLNo SFOV

T SFOVno biascorrect

Comparison of SFOV Tto radiosonde data

Overall Temperature bias (vs. raobs)

+3h fcst T bias (00z,12z)

warmer

cooler

Cool WarmCool Warm

– Correspondence between raob comparison and fcst impact– Overall average masks diurnal signal– Model bias as well as observation bias

V1 – first set V2 – improvedV3 – best set

Radiosonde

Large dry bias, correction needed?

AIRS SFOV

Gaussian distribution,

small bias

Dry Moist Dry Moist

Histograms of Moisture Innovations (O-B) : Radiosonde vs. SFOV retrievals

SFOV Moisture Bias Correction

RAP verificationagainst raobs

Moistureinnovations+15% biascorrection

Dry Moist

MoistureinnovationsNo biascorrection

CNTLNo SFOV

SFOVWITH BC

SFOV NO BC

Dry Moist

Analysis

12-h forecast

SFOVWITH BC

SFOVNO BC

12-h fcst

CNTLNo SFOV

Combined assimilation ofSFOV T and Q (400-800 hPa)with bias corrections reduces +12h forecast RMS (relative to rawinsonde data)for all variables, most levels

SFOV T + Qv assimilation: forecast impact

SFOV WITH Bias CorrectSFOV NO bias correct

CNTL (NO SFOV data)

Wind

+12h fcst RMS

Temperature

Relative Humidity

9-dayretro

average

Normalize Errors

EN = (CNTL – EXP)

CNTL

SFOV T + Qv assimilation: forecast impact

SFOV WITH Bias CorrectSFOV NO bias correct

CNTL (NO SFOV data)

Wind

+12h fcst RMS

Temperature

Relative Humidity

9-dayretro

average

SFOV T + Qv assim: normalized RMS errors

+3h +6h +9h +12h

WITH BCNO BC

Wind

Temp -erature

Relative Humidity

Combined assimilation ofSFOV T and Q (400-800 hPa)with bias corrections

Small positive impact

Vertical average 400-800 mb

SFOV water vapor mixing ratio (g/kg) at 750 hPa

Analysis 850-500 hPa mean relative humidity (%) from RAP AIRS SFOV run

Analysis 850-500 hPa mean relative humidity (%) from RAP control run

HRRR case study initialized from RAP 2100 UTC 10 May 2010

NO SFOV WITH SFOVSFOV data

0-hr 850-500 hPa mean relative humidity (%)

Observed radar composite reflectivity

HRRR forecast reflectivity initialized from AIRS SFOV RAP run

HRRR forecast reflectivity initialized from control RAP

HRRR case study initialized from RAP 2100 UTC 10 May 2010

NO SFOV WITH SFOVRadar data

+2 hr Forecast Reflectivity

Challenges for regional, rapid updating satellite assimilation

• Data availability-- Long data latency, short data cut-off, small domain

Very limited data availability

• Bias correction-- Cycled predictive bias correction in GSI

-- Limited and non-uniform data coverage degrades BC

• Lower model top-- Many channels sense at levels near RAP model top (10 hPa)

-- Use of these high peaking channels can degrade forecast

Two month time series bias coefficients

AIRS channel 261 (CO2 channel, PWF ~ 840 mb)

How long a period to adequately spin-up bias coefficient corrections predictors?

• Highly variable for different predictors and channels

• Some can take two months or more

• Problems due to big differences in data coverage for successive cycles (in contrast to global models)

Temperature and Moisture JacobiansStandard profile (0.01 hPa top) RAP profile (10 hPa top)

Artificial sensitivity due to low model top in RAP

dBT/dT (K/K)

Artificial sensitivity due to low model top in RAP

(dBT/dq) * q (K)

Temperature

Moisture

AIRS radiance assimilationwith GSI bias correction

and channel selection reduces +6h forecast RMS

(relative to rawinsonde data)for all variables, most levels

Radiance assimilation: forecast impact

AIRS – 68 channelsAIRS – 120 channels

CNTL (NO AIRS data)

Wind

+6h fcst RMS

Temperature

Relative Humidity

9-dayretro

average

1.Map lightning density to proxy reflectivity-- sum ground flashes per grid-box over 40 min period (-30 +10 min)

REFLmax = min [ 40, 15 + (2.5)(LTG)]

Sin distribution in vertical

RAP assimilation of lightning data

LTG and REFLmax

REFLmax and vertical REFL profile

OLD specified relationship:

NEW seasonally averaged empirical relationships:

Summer

WinterOLDspecificationin RUC

NEWSeasonallydependentempirical

Lightning Flash Rate max reflectivity

SUMMER

Reflectivity profile as a function of column maximum reflectivity Max dbz 35-40

Max dbz 40-45

Max dbz 45-50

Max dbz 30-35

WINTER

Reflectivity profile as a function of column maximum reflectivity

Max dbz 30-35

Max dbz 35-40

Max dbz 40-45

Max dbz 45-50

44dBz

36dBz

40dBz

30dBz

Max dbz30 - 35

Max dbz35 - 40

Max dbz40 - 55

Max dbz45 - 50

AVERAGE

Reflectivity profile as a function of column maximum reflectivity

Summer

Winter

Summer

Winter

Summer

Winter

Summer

Winter

Applications lightning DA technique

Can apply technique to lightning data and satellite-based indicators of convective initiation GLD-360 lightning data

-- good long-range coverageEspecially helpful for oceanic convection

SATCAST cloud top cooling rate data -- good Convective Initiation (CI) indicatorAvoiding model delay in storm development

SATCAST work by Tracy Smith using data provided by John Mecikalski

proxy flash rate = - 2 x cloud-top cooling rate (K/15 min)

Radarcoverage

Observedreflectivity

Sat obs24 Apr 2012

16z

Latent heating-based temper-ature tendency

No radarecho

No radarcoverageLightning flash

rate

16z

Rapid Refreshoceaniclightning assimilation example

Observedreflectivity

Sat obs24 Apr 2012

16z

No radarecho

No radarcoverage

Rapid Refreshoceaniclightning assimilation example

LTG DA slightimpact on RAP forecast storm clusters

16z +1hGSD RAP forecasts

17z17z

16z

Assimilation of “satcast” cloud-top

cooling rate CI-indicator data

17zSATCAST cooling rate

(K / 15 min)

18z

IR image

18z

5 July 2012

Cloud-top cooling rate helpful for initializing developing convection in GSD RAP retro tests

5 July 2012

WITHsatcast assim

NOsatcast assim

18z+1h

18z+1h19z

Obs Reflect

Assimilation of “Satcast” cooling

rates provides more realistic short-range

forecast of convective initiation and development

18z+2h

18z+2h20z

Assimilation of “Satcast” cooling

rates provides more realistic short-range

forecast of convective initiation and development

Obs Reflect

WITHsatcast assim

NOsatcast assim

AIRS Assimilation Summary / Future Work

• Small positive impact in RAP forecasts obtained from assimilating of AIRS SFOV data with application of simple bias correction (competing with full mix of conventional observations)

• Assimilation of AIRS radiance data in RAP produces small positive impact for winds, temperature, relative humidity and heavy precipitation

• Work to address low model top issue(better channel selection, blend with GFS model, raise RAP top)

• Examination bias correction issues and cloud contamination, re-scripting RAP partial cycle to increase cutoff time

• Evaluate sensitivity AIRS data in conjunction with other satellite data types

LTG / satellite CI DA SummaryPreliminary evaluation of impact from assimilation of two novel convection indicators:GLD-360 lightning data

-- good long-range coverageHelpful for oceanic convection

Satcast cloud top cooling rate data -- good Convective Initiaation Avoid model delay in storm development

Qualitative assessment ongoing

Plan HRRR runsfrom RAP w/ andw/o LTG, satcast