NOAA ESRL GSD Assimilation and Modeling Branch
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Transcript of NOAA ESRL GSD Assimilation and Modeling Branch
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
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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