1. Overview of Community Radiative Transfer Model (CRTM) Fuzhong Weng, Yong Chen and Min-Jeong Kim NOAA/NESDIS/Center for Satellite Applications and Research and Joint Center for Satellite Data Assimilation High Impact Weather Working Group Workshop, February 24, 2011, Norman, OK

2. CRTM Application Areas CRTM was initially proposed to support primarily the JCSDA partners to assimilate satellite radiance data into global/regional forecast systems It is now also supporting the US satellite program developments through generating a high quality proxy data for algorithm tests, developments and integrations It has been used in the NOAA/NESDIS microwave sounding product system It can be used to generate the synthetic satellite radiances from NWP nature runs for observation system simulation experiments (OSSE) It is linked to other key projects such as climate reanalysis and satellite cal/val Joint Center for Satellite Data Assimilation (JCSDA) Partner Organizations 2

3. Requirements on CRTM Perform fast and accurate forward, tangent linear/adjoint calculations Support all the satellite instruments (US and foreign) that are used in NWP models Work under all atmospheric and surface conditions Have a flexible interface with different NWP models such as GFS, NOGAPS, and WRF Allow future expansion for broader applications CRTM supports more than 100 Sensors GOES-R ABI Metop IASI/HIRS/AVHRR/AMSU/MHS TIROS-N to NOAA-19 AVHRR TIROS-N to NOAA-19 HIRS GOES-8 to 14 Imager GOES-8 to 14 sounder IR channel 08-13 Terra/Aqua MODIS Channel 1-10 MSG SEVIRI Aqua AIRS, AMSR-E, AMSU-A,HSB NOAA-15 to 19 AMSU-A NOAA-15 to 17 AMSU-B NOAA-18/19 MHS TIROS-N to NOAA-14 MSU DMSP F13 to15 SSM/I DMSP F13,15 SSM/T1 DMSP F14,15 SSM/T2 DMSP F16-20 SSMIS Coriolis Windsat TiROS-NOAA-14 SSU FY-3 IRAS, MWTS,MWHS,MWRI NPP/JPSS CrIS/ATMS 3

4. Highlights on CRTM Software Architecture, Sciences and Physical Processes Atmospheric gaseous absorption  Band absorption coeff trained by LBL spectroscopy data with sensor response functions  Variable gases ( H 2 O, CO 2, O 3 etc).  Zeeman splitting effects near 60 GHz Cloud/precipitation scattering and emission  Fast LUT optical models at all phases including non-spherical ice particles  Gamma size distributions Aerosol scattering and emission  GOCART 5 species (dust, sea salt, organic/black carbon, )  Lognormal distributions with 35 bins Surface emissivity/reflectivity  Two-scale microwave ocean emissivity  Large scale wave IR ocean emissivity  Land mw emissivity including vegetation and snow  Land IR emissivity data base Radiative transfer scheme  Tangent linear and adjoints  Inputs and outputs at pressure level coordinate  Advanced double and adding scheme  Other transfer schemes such as SOI, Delta Eddington “Technology transfer made possible by CRTM is a shining example for collaboration among the JCSDA Partners and other organizations, and has been instrumental in the JCSDA success in accelerating uses of new satellite data in operations” – Dr. Louis Uccellini, Director of National Centers for Environmental Prediction 4

5. CRTM Infrared Spectroscopy Corresponding to AIRS, IASI and CrIS CRTM simulated brightness temperature (BT) spectrum for hyper-spectral infrared sensors IASI (black line), AIRS (red line), and CrIS (blue line). 5

6. CRTM Validation using CloudSat data (non-precipitating weather) CloudSat Data SetNCEP, ECMWF Data Set CRTM Forward Model Cloud profiles ( IWC, LWC) Atmospheric profiles, surface conditions Radiances and Brightness Temperatures Coincidental/Collocated Satellite Data Set Bias calculations and analysis, find the causes for the biases in the context of radiative physics and improve the CRTM performance. Satellite zenith angles, Solar zenith angles 6

7. Simulations Using Cloudsat Data and GDAS Profiles Cloudsat data are averaged along the track of NOAA-18 satellite within each AMSU, MHS and AVHRR IFOVs and then used as inputs to CRTM GDAS temperatures and water vapor profiles matched with Cloudsat profiles Simulations are compared with NOAA-18 AMSU-A, MHS, AVHRR observations It is shown that both bias and RMS errors are reduced with Cloudsat data used in CRTM AMSUA FOV (~50km diameter) MHS FOV ~50 CloudSat FOVs CloudSat FOV AVHRR FOV (GAC) ~4km diameter (Chen et al., 2008, JGR) 7

8. Histograms of the Observed and Simulated for AMSUA, MHS BTs over Ocean Observation Simulation Reasonable agreements of observed and simulated BT distributions at all frequencies. 8

9. Histograms of the BT Difference (Observation – Simulation) over Ocean under Clear and Cloudy Conditions Cloudy Clear The distributions are in Gaussian shapes with maximum observation at or near zero, which confirm that the agreement between observed and simulated BTs are very good under clear and cloudy conditions. There are clear-sky biases in certain surface sensitive microwave channels of the order of 1–2 K which is due to the sea-surface emission model used in CRTM. 9

10. CRTM Jacobian Calculations Compared with RTTOV RTTOV is another fast radiative transfer model used by NWP community for satellite data assimilation Radiance Jacobians at 6.2 and 7.2 micron water vapor channels (GOES-R ABI and MSG SEVIRI) are derived from CRTM & RTTOV Both models produce Jacobian profiles peaked at the same altitude But the magnitudes are slightly different between two fast models Assumption: surface emissivity = 0.98, local zenith angle = 0 deg., and skin temperature = 300 K 10

11. Inter-comparison of CRTM with RTTOV at MSG SEVIRI Water Vapor Channels Simulated vs observed brightness temperatures using 457 radiosonde profiles 11

12. CRTM Simulated GOES-R ABI Visible Channel Using WRF-Chem Model Outputs MODIS 0.645 µm GOES-R ABI 0.64 µm 1.Hourly GOES-R ABI proxy data simulated for the period of 10:00 UTC 24 to 03 UTC 25 August 2006 is produced with WRF-Chem air quality simulations and visible-enabled version of the CRTM. 2.The dataset covers CONUS domain with all 16 ABI bands. The high resolution (4km) aerosol and ozone data sets have been created over the continental US. 3.Compared with MODIS observations, simulated reflectance over land appears lower than observed and cloud reflectance is somewhat brighter Brad Pierce, 2009, GOES-R AWG workshop 12

13. Microwave Surface Emissivity Models in CRTM Oceans – two-scale roughness theory Sea ice – Coherent reflection Canopy – Four layer clustering scattering Bare soil – Coherent reflection and surface roughness Snow/desert – Random media Weng et al (2001, JGR) 13

14. Infrared Land Emissivity Data Base in CRTM 14

15. CRTM Applications in GOES-R Retrieval Algorithms 15

16. 16 Progress in Cloudy Radiance Assimilation  CRTM was implemented in NCEP GSI for clear sky satellite data assimilation and will be used for cloudy radiance assimilation Need to ensure a best trade-off between accuracy and computational efficiency  T o achieve improved forecast scores through cloudy radiance assimilation, we need: Linearity of models Appropriate background and observation errors Error statistics (non-Gausian vs. Gausian pdf) Quality control Representativeness of observations and model Bias correction The fundamental works such as bias characterization, observation error covariance in cloudy conditions just started.

17. AMSU Observation – Background (O-B) from GFS Clear and cloudy sky over the ocean Clear sky over the ocean 17 O-B pdfs for all sky conditions appear very similar to those for clear-only conditions

18. First Guess Departure as a Function of Cloud Liquid Water Using average CLW, it seems that the bias is less dependent on cloud liquid water which will simplify the bias correction algorithm in GFS 18

19. 9 K 13 K7K 10 K Observation Error Covariance as a Function of Cloud Liquid Water 1.3 K 0.55 K 19 However, observation error covariances remain highly dependent on CLW

20. Summary and Conclusions A new generation of radiative transfer model (Community Radiative Transfer Model (CRTM)) has been developed for the JCSDA partner’s NWP satellite data assimilation CRTM Version 2 upgrades include radiance calculations in pressure coordinate, new microwave snow and sea ice emissivity, trace gas absorption, aerosol scattering and absorption Independent assessments indicate an excellent performance of CRTM in both forward and Jacobian computations O-B bias and error covariance from CRTM in GFS under cloudy conditions are characterized for AMSU with which the AMSU cloudy radiances will be first tested for impact studies 20