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GSI applications within the Rapid Refresh and
High Resolution Rapid Refresh
17th IOAS-AOLS Conference93rd AMS Annual Meeting
9 January 2013
Patrick Hofmann1, M. Hu1, S. G. Benjamin2, S. S. Weygandt2, C. R.
Alexander1
1Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado2NOAA/ESRL/Global Systems Division – Assimilation and Modeling Branch
Rapid Refresh and HRRRNOAA hourly updated models
— Advanced community codes (ARW and GSI)— Retain key features from RUC analysis / model system ( hourly cycle -- radar DFI assimilation -- cloud analysis )— RAP short-range guidance for aviation, severe weather, energy applications
RUC Rapid Refresh (01 May 2012)
Rapid Refresh v2• Many improvements, target NCEP implement early 2014?
HRRR
NCEP
GSD
GSD• Runs as nest within
RAP v2
Rapid RefreshHourly Update
Cycle
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/day
Fully cycle all land-sfc fields
Hourly observations (stations for raobs/profiles)
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 2km
Lightning (proxy reflectivity) NLDN
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 pressure/temp
13km
WindSat scatterometer 2-10K
Rapid Refresh – specific analysis features Special treatments for
surface observations Cloud and hydrometeor analysis
Digital filter-based reflectivity assimilation
Cloud analysis-related changes– Improved full column cloud building using emissivity from
satellite data– Conservation of virtual potential temperature during cloud
building
Improved use of existing observations– Assimilation of surface moisture pseudo-obs in PBL– Soil adjustment based on surface temperature and moisture
increments– Elevation correction, innovation limitation for PW observations– Closer fit to rawinsondes
Other improvements– GFS ensemble background error covariance specification– Merge with recent GSI trunk– Addition of tower, nacelle, and sodar observations– Addition of GLD 360 lightning data (proxy for radar reflectivity)– Radiance bias correction
Rapid Refresh version 2
data assimilation upgrades
Cloud analysis-related changes– Improved full column cloud building using emissivity from
satellite data– Conservation of virtual potential temperature during cloud
building
Improved use of existing observations– Assimilation of surface moisture pseudo-obs in PBL– Soil adjustment based on surface temperature and moisture
increments– Elevation correction, innovation limitation for PW
observations– Closer fit to rawinsondes
Other improvements– GFS ensemble background error covariance specification– Merge with recent GSI trunk– Addition of tower, nacelle, and sodar observations– Addition of GLD 360 lightning data (proxy for radar
reflectivity)– Radiance bias correction
Rapid Refresh version 2
data assimilation upgrades
Cloud Building ExperimentsRetro Period: 29 May – 12 June 2011
CONTROL: RAPV2, with cloud building below 1200mFULL BUILDING: Full column building using a cloud top pressure-based cloud fractionECA BUILDING: Full column building using effective cloud amount (ECA), which uses cloud emissivity as a proxy for true cloud fractionECA BUILDINGv2: ECA BUILDING, but no clearing from partially cloudy regions
NESDIS CLAVR-x data provided courtesy of Andrew Heidinger (UW/CIMSS/NOAA-AWG)
CLAVR-x is NOAA's operational cloud processing system for the AVHRR on the NOAA - POES and EUMETSAT-METOP series of polar orbiting satellites
Cloud Building Experiments29 May - 11 June
2011Relative Humidity
Bias
CTRLFULLECAECAv2
3HR
Cloud Building Experiments29 May - 11 June
2011Relative Humidity
Bias6HR
CTRLFULLECAECAv2
Cloud Building Experiments29 May - 11 June
20113000ft Ceiling TSS 1HR
CTRLFULLECAECAv2
Cloud Building Experiments29 May - 11 June
20113000ft Ceiling TSS 3HR
CTRLFULLECAECAv2
Full Column Cloud Building
Low (<1200m) Cloud Building
Cloud Top Comparison12Z 7 June 2011
Full Column Cloud Building3000ft Ceiling StatsCSI= 0.56BIAS= 1.3
Low Cloud Building3000ft Ceiling StatsCSI= 0.55BIAS= 1.3
Ceiling Comparison12Z 7 June 2011
3-km Interp
Hourly HRRR Initialization from RAP
GSI 3D-VAR
Obs
Cloud Anx
DigitalFilter
1 hr
fcs
tHMObs
ReflObs
18 hr fcst
15 hr fcst 3-km Interp
GSI 3D-VAR
Obs
Cloud Anx
DigitalFilter
1 hr
fcs
t
HMObs
ReflObs
18 hr fcst
15 hr fcst 3-km Interp
GSI 3D-VAR
Obs
Cloud Anx
DigitalFilter
HMObs
ReflObs
18 hr fcst
15 hr fcst
13 km RAP
3 km HRRR
13z 14z 15z
Background
Radar Specification
of Hydrometeo
rs
Scale at which Latent
Heating is applied
Dimensionality Updated
2013 RAP model
initialization
BCs from GFS No 13-km 3-D Hourly
2013 HRRR model
initialization13-km RAP No
3km in 60min spin-
up (also using 13km radar-LH-
DFI)
3-D Hourly
Rapidly Updating
Analysis (RUA-HRRR)
3-km HRRR1 hr fcst Yes None 3-D Hourly
Real-TimeMeso Analysis (RTMA-HRRR)
3-km HRRR1 hr fcst No None 2-D
Hourly(15 min
planned)
GSI Applications
RTMA-HRRR– Real Time Meso-scale Analysis– 1hr HRRR forecast used as background field– Anisotropic error covariance fields– Currently run hourly; plan to produce 15min
output
RUA-HRRR– Rapidly Updated Analysis– Full cloud analysis based on HRRR background
field– Includes cloud, radar, and surface analyses
– Specifies hydrometeors from radar observations– Improves initial reflectivity field
GSI Applications
RTMA-HRRR– Real Time Meso-scale Analysis– 1hr HRRR forecast used as background field– Anisotropic error covariance fields– Currently run hourly; plan to produce 15min
output
RUA-HRRR– Rapidly Updated Analysis– Full 3D GSI analysis based on HRRR
background field– Includes cloud, radar, and surface analyses
– Specifies hydrometeors from radar observations– Improves initial reflectivity field
GSI Applications
HRRR AnxRTMA
1-hr HRRR Fcst(Background)Valid 19 UTC30 Nov 2012
RTMA-HRRRValid 19 UTC30 Nov 2012
10 m Winds
Analysis Increments
3km RTMA-HRRR
3km RTMA-HRRR30 Nov – 4 Dec 2012
2m Temperature RMS
1 Day Avgs
RTMAHRRR 0HRHRRR 1HR
3km RTMA-HRRR30 Nov – 4 Dec 20122m Dewpoint RMS
1 Day Avgs
RTMAHRRR 0HRHRRR 1HR
3km RTMA-HRRR30 Nov – 4 Dec 2012
10m Winds RMS
1 Day Avgs
RTMAHRRR 0HRHRRR 1HR
RTMA-HRRR– Real Time Meso-scale Analysis– 1hr HRRR forecast used as background field– Anisotropic error covariance fields– Currently run hourly; plan to produce 15min
output
RUA-HRRR– Rapidly Updated Analysis– Full 3D GSI analysis based on HRRR
background field– Includes cloud, radar, and surface analyses
– Specifies hydrometeors from radar observations– Improves initial reflectivity field
GSI Applications
3km RUA-HRRR
1-hr HRRR Forecast (Background)Valid 22 UTC
03 November 2012
0-hr HRRR Analysis (RUA)Valid 22 UTC
03 November 2012
Obs22 UTC
03 Nov 2012
GSICloud Anx
RapidlyUpdatingAnalysis(RUA)
Specifies HydrometeorsFrom Radar Observations
ConclusionCompleted RAP v2 Changes- GFS ensemble background error covariance specification- Improved cloud building- Assimilation of surface moisture pseudo-obs in PBL- Soil adjustment based on near-surface temperature / moisture increments- Elevation correction, innovation limitation for PW
observations- Conservation of virtual potential temperature during cloud
building- Radiance bias correction- Merge with latest version of GSI from NCEP community trunk- Additional observations (radial wind, wind tower/nacelle,
lightning)
GSI 3km applications- RTMA-HRRR provides improved first-guess fields for NDFD- RUA-HRRR results in more realistic initial model state,
greatly improving reflectivity and 3-D hydrometeor fields