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