Assimilation of AIRS Radiance Data within the Rapid Refresh

Rapid Refresh domainHaidao Lin

Steve WeygandtMing Hu

Stan BenjaminPatrick HofmannCurtis Alexander

Assimilation and Modeling Branch

Global Systems DivisionNOAA Earth System Research

LabBoulder, CO

Cooperative Institute for Research in the Atmosphere

Colorado State University

http://rapidrefresh.noaa.gov

Presentation Outline

1. Background on Rapid Refresh (RAP) system

2. Background on Atmospheric Infrared Sounder (AIRS) data

3. AIRS radiance assimilation in RAP Bias correction Channel selection RAP retrospective runs and forecast

verification HRRR case runs initialized from RAP

4. Real-time RAP data availability issues

5. Summary and future work

Background on Rapid RefreshNOAA/NCEP’s hourly updated model

Rapid Refresh13

RUC-13

– Advanced community codes (ARW and GSI)– Retain key features from RUC analysis / model system

(hourly cycle, cloud analysis, radar DFI assimilation)– Domain expansion consistent fields

over all of N. America- RAP guidance for aviation, severe

weather, energy applications

High-Resolution Rapid Refresh (HRRR) - 3-km nested domain for storm predictions - New 15-hour forecast each hour -- Real-time experimental runs at ESRL

RUC Rapid Refresh -- May 1, 2012

HRRR

Rapid Refresh Hourly Update Cycle

1-hrfcst

1-hrfcst

1-hrfcst

11 12 13Time (UTC)

AnalysisFields

3DVAR

Obs

3DVAR

Obs

Back-groundFields

Rawinsonde (12h) 150NOAA profilers 35VAD winds ~130PBL profilers / RASS ~25

Aircraft (V,T) 3500 – 10,000METAR surface 2000 -2500Mesonet (T,Td) ~8000Mesonet (V) ~4000Buoy / ship 200-400GOES cloud winds 4000-8000METAR cloud/vis/wx ~1800

GOES cloud-top P,T 10 km res.Satellite radiances (AMSUA, HIRS, MHS)Radar reflectivity 1 km res.

Data types – counts/hr

Partial cycle atmospheric fields – introduce GFS information 2x per dayFully cycle all land-sfc fields

- Hourly cycling of land surface model fields - 6-hour spin-up cycle for hydrometeors, surface fields

RAP Hourly cycling throughout the day

RAP spin-upcycle

GFSmodel

RAP spin-upcycle

GFSmodel

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

Observationassimilation

Observationassimilation

Rapid Refresh Partial Cycling

RAP Benchmarking / Retro Configuration

• 9 day retro period (8-16 May 2010)

• Use 3-h cycle, no partial cycling

• Benchmark against R/T and perform raob denial

3-h RAP retro cycle results as expected-- 1-h RAP slightly better -- 3-h RAP similar to R/T RUC

RMS errorimpact

Raob denial retro run

Benj. et al. MWR 2010

6-h fcst T 0.06 K 0.05 K

12-h fcst T 0.11 K 0.15 K

6-h fcst RH 0.77% 1.25 %

12-h fcst RH 1.11% 1.75%

6-h fcst wind 0.13 m/s 0.1 m/s

12-h fcst wind 0.17 m/s 0.18 m/s

Raob denial results closely match previous OSE study

1-hourly R/T RUC

3-hourly RAP retro1-hourly RAP retro(partial cycle)

12-h fcst wind RMS Error (100-1000 mb mean)

Assimilate all standardobservations

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

Radiance Assimilation for RAPChallenges for regional, rapid updating radiance assimilation

•Bias correction-- Cycled predictive bias correction in GSI

-- Spin-up period, complicated by non-uniform data coverage

•Channel Selection•Many channels sense at levels near RAP model top (10 hPa)•Use of these high peaking channels can degrade forecast•Jacobian / adjoint analysis to select channels for exclusion

•Data availability issues for real-time use•Rapid updating regional models: short data cut-off, small domain •Above combined with large data latency little data availability•Complicates bias correction, partial cycle assimilation options

Observation Operator (CRTM)

Air mass bias Angle bias

are the coefficients of predictors (updated at every cycle)

ip = predictors

mean constantscan anglecloud liquid water (for microwave)square of T lapse rateT lapse rate

B Bias parameter background error covariance matrix

Variational Satellite Bias Correction in GSI

BT O-B Difference

AIRS channel 261 (CO2 channel) with PWF height around 840 hPa

Diff. before and after application of bias correction during

retrospective cycle, after 2-week spin-up

Mean BT diff without BC

Mean BT diff with BC

Histogram

0.0

-1.0

-1.0 0.0

0.0

Two month time series bias coefficients

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

How long a period to adequately spin up bias- correction predictor coefficients?

• 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)

• The CRTM K-matrix model (Jacobian model) computes the radiance derivatives with respect to the input-state variables, such as temperature and gas concentration

• Forward model• TL model

• AD model• K-matrix model • is the input K-matrix radiance input variable and is the transpose of

the ith row of the H matrix:

• Setting for (i=1,….,m), the matrix returned from the K-matrix model contains the Jacobians

The matrix H contains the jacobian element

Channel Selection Because of Low Model Top

Jacobian calculation in CRTM to find problem channels

Spurious warming from low model

top

warm

Sample RAP Temperature Analysis Increment and Jacobian

cool

Background (B) and analysis (A) temperature

Temperature increment (A-B)

Temp Jacobian from standard

profile

AIRS channel 22 T Jacobian for this profile

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

Adjoint Sensitivity Channel Selection

)( ii

jbj

b qq

TT

The brightness temperature sensitivity for channel j

The total contribution above the top of the model (10 hPa for RAP) is

Channels with larger than 0.06 K were discarded

More details in McCarty et al. 2009

Threshold 0.06 K is conservativeand tunable

Channel arranged by PWF Height

Removed Channels

68 selected channels

Removed channels

Settings for Retrospective Runs • Previous two-week warm up retro run

• April 23 – May 7, 2010

• 3-h AIRS radiance data with bias coefficients cycled (the very first bias coefficients were set to be zeroes)

• Control run (CNTL) – NO AIRS RADIANCE DATA• 3-h cycle run, 9 day retro run (May 8 2010 – May 16 2010)

• Conventional data

• AIRS experiment one (AIRS Ex. 1) -- NO CHANNEL SELECTION• CNTL + AIRS radiance data (60 km thinning in GSI)

• Use updated bias coefficients from warm up retro run, cycle the bias

• Use the 120 GDAS channel set

• AIRS experiment two (AIRS Ex. 2) – CHANNEL SELECTION• CNTL + AIRS radiance data (60 km thinning in GSI)

• Use updated bias coefficients from warm up retro run, cycle the bias

• Use the 68 selected channel set based on adjoint analysis

Mean BT Differences & RMS Errors before and after Assimilation

Mean Difference

RMS

Results from Ex. 2

* Background * Assimilated

BT Differences & RMS Errors before and after Assimilation Vertically Arranged by PWF

Height

Results from Ex. 2

* Background * Assimilated

Mean Difference RMS

6-h Forecast RMS Error (against raob）

AIRS Ex. 2 (selected 68 channels)

CNTL

AIRS Ex. 1 (default 120 channels

Temperature

Relative Humidity

Wind

AIRS Radiance Assimilation Summary

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

• Use of Jacobian / adjoint sensitivity test to eliminating channels with maximum sensitivity near RAP model top (10 hPa) improves forecasts

• Lengthy spin-up of GSI variational bias correction needed for some channels and predictors (issues with limited data coverage)

• Slightly improved longer lead time reflectivity forecast from several case HRRR runs

• Key data availability challenges for real-time use of data in rapidly updating, regional models

Future Work• Improve radiance bias correction in RAP context

• Investigate the cloud contamination issues • Re-scripting RAP partial cycles to increase the data cutoff

time to include more real-time AIRS data (and other polar-orbiting satellite data)

• Increase RAP model top

• Incorporate AIRS radiance data into operational hourly updated Rapid Refresh at NCEP

24-h (2 X 12h) CPC Precipitation VerificationCSI by precip threshold(avg. over eight 24h periods)

Slight improvement for heavy

precipitation thresholds from

AIRS radiance data

AIRS Ex. 2 (selected 68 channels)

CNTL (no AIRS)

AIRS Ex. 1 (default 120 channels

HRRR Radar Reflectivity Verification

3 case HRRR runs Initialization time from RAP: 21Z May 10, 2010 06Z May 13, 201009Z May 13, 2010(with good airs coverage)

AIRS Ex. 2 (selected 68 channels)

CNTL

25 dBZ 3-kmCONUS

30 dBZ 3-kmCONUS

| | | | | | | 0-h 2-h 4-h 6-h 8-h 10-h 12-h

| | | | | | | 0-h 2-h 4-h 6-h 8-h 10-h 12-h

3 case HRRR runs averaged

AIRS Data Coverage in RAP June 18 2012

Real time +/- 3 hour data window

Real time +/- 1.5 hour data window

00Z 01Z 02Z 03Z 04Z 05Z

Ideal +/- 3 hour data window

Ideal +/- 1.5 hour data window

Satellite Data Availability IssuesFor Rapid Refresh models: short data cutoff times combined with long data availability latency times lead to minimal satellite data availability for model assimilation

W = Data Window Time L = Data Latency TimeC = Data Cutoff Time

W = 180 minL = 60 minC = 30 min

% of data used = (W/2 - L + C)/W

% of data used = (180/2 - 60 + 30)/180 = 60/180 33%

obsused

after cutoffdata

latency

cutoff time

Diagram and equationfollowing Steve Lord

Samplevalues

data window initial time03z02z 04z 05z 06z

dataavailable

0130z 0230z 0330z 0430z Obs time

Satellite Data Availability IssuesWorst case for RAP model: W = Data Window Time

C = Data Cutoff TimeL = Data Latency TimeW = 90 min

L = 80 minC = 25 min

% of data used = (90/2 - 80 + 25)/90 = -10/180 0%

NOTE: Data latency time is variable, basedon proximity of satellite to download station

W = 90 minL = 80 minC = 180 min

% of data used = (90/2 - 80 + 180)/90 = 145/90 100%

Assimilation in partial cycle:Delay cycles 3-4 hrs longer cutoff

NO data used

00z 03z 06z 09z

RAP spin-upcycle

ALL data used

% of data used = (W/2 - L + C)/W