Overview of the Advances in CRTM: - Applications to Support JPSS Sensors

Cal/Val and Assimilation Activities

Quanhua (Mark) Liu1,4, Paul van Delst1,2, Yong Chen1,4, David Groff1,2, Ming Chen1, Andrew Collard2, Fuzhong Weng3, John Derber2, Sid-Ahmed Boukabara1,3

1 Joint Center for Satellite Data Assimilation

2 NOAA/NCEP

3 NOAA/NESDIS Center for Satellite Applications and Research

4 ESSIC, University of Maryland, College Park, MD

11th JCSDA Science Workshop on Satellite Data Assimilation, College Park, MD

June 5-7, 2013

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OUTLINE

• CRTM – Radiance Interpreter

• CRTM Functionalities

• CRTM Achievements

• CRTM 2.1.1 Release

• SNPP Measurements

• CRTM Support to SNPP Validation and Monitoring

• Discussion and Summary

• Future Plan

What is CRTM? --- Radiance interpreter

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Satellite Radiance

Sensor monitoring

Radiance assimilation

Reanalysis

Radiative Transfer (CRTM)forward

adjoint

Physical retrieved satellite products

Geophysical Parameters

Areas CRTM may apply

• Satellite radiance data assimilations for NWP

• Radiometric data impact assessment in Observing System Simulation Experiments (OSSEs)

• Radiometric instrument design, calibration and monitoring

• Physical retrievals of atmospheric and surface state variables

• Air-quality monitoring and forecast

• Reanalysis and climate studies

• Aircraft campaign

• Scientific research and education

CRTM 2.1.1 ReleaseCRTM 2.1.1 was released on Dec. 06, 2012 and can be

downloaded from ftp.emc.ncep.noaa.gov . New features include

– Non-LTE for hyperspectral infrared sensors

– Successive Order of Interaction (SOI) radiative transfer algorithm

– Updated microwave sea surface emissivity model

– Updated microwave land surface emissivity model

– Aerosol optical depth functions

– Channel subseting

– Number of streams option for scattering atmospheres

– Scattering switch option for clouds and aerosols

– Aircraft instrument capability

– Option structure I/O

• Contact the CRTM team at ncep.list.emc.jcsda_crtm.support@noaa.gov5

Transmittance Models• Transmittance module

– ODAS: Optical Depth Absorber Space (O3, H2O, good performance for water vapor absorption)

– ODPS: Optical Depth Pressure Space (H2O, CO2, O3, N2O, CO, CH4)

– SSU model

– Fast Zeeman model for SSMIS UAS channels

– NLTE

CRTM simulated brightness temperature spectra for hyper-spectral infrared sensors IASI (black), AIRS (red) and CrIS (blue).

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Fast Transmittance Model for Stratospheric Sounding Unit (SSU)

• The SSU channel spectral response function (SRF) is a combination of the instrument filter function and the transmittance of a CO2 cell.

• The SRF varies due to the cell CO2 leaking problem.

• CRTM-v2 includes schemes to take the SRF variations into account (Liu and Weng, 2009; Chen et al. 2011)

CO2 cell pressure variations, which causes SSU SRF variations.

CRTM simulations compared with SSU observations for SSU noaa-14.

FILTER

DETECTOR

CO

2 c

ell

atm

osp

heric

rad

iatio

n

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Fast Transmittance Model for SSMIS Upper Atmospheric Sounding (UAS) Channels

• Zeeman-splitting can have an effect up to 10 K on SSMIS UAS channels.

• The fast transmittance model is implemented to take both effects into account (Han et al., JGR 2007).

Zeeman effect:

The O2 transition lines are split into many sublines and the radiation is polarized.

8Without using Earth magnetic field

CRTM NLTE simulation vs observation, Solar zenith angle = 30o, sensor zenith angle = 0.7o

AIRS

22110 ),(),(),(),( msunsen

msunsensunsensunsen

ch TcTccR Tm1 (0.005-0.2hPa) , Tm2 (0.2-52hPa) 9

Clouds

• Liquid

MW, IR, VIS: Mie

• Rain

MW, IR, VIS: Mie, Spheroid

• Ice

MW: Mie, IR, VIS: (non-spherical particle Yang et al., 2005)

• Snow:

MW, IR, VIS: Mie

• Graupel

MW, IR, VIS: Mie

• Hail:

(non-spherical particle Yang et al., 2005)

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Aerosol Models Global Model, Goddard Chemistry Aerosol Radiation and Transport (GOCART) Dust Sea Salt ( dry (hydrophobic), wet (hydrophilic) ) Organic carbon Black carbon Sulfate

To be considered:Regional Model WRF-NMM, Community Multiscale Air Quality (CMAQ) Sulfate mass Ammonium mass Nitrate mass Organic mass Unspecified anthropogenic mass Elemental carbon mass Marine mass Soil derived mass

CRTM Model for GOES-R Applications (preliminary )

  Continental   Urban   Generic l   Heavy smoke l   Dust 5 Coarse mode aerosol   4 Fine mode aerosol

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Surface emissivity/reflectivity model

The surface is categorized as

Land

IR: ASTER spectral library (NPOESS LUT )

MW: Physical model (EMC land group and STAR are working on improvement)

UV/VIS: ASTER spectral library (NPOESS LUT )

Ocean

IR: Wu-Smith, Nalli

MW: Fastem-1+low frequency model, Fastem-5

UV/VIS: ASTER spectral library (NPOESS LUT )

BRDF model

Ice

IR: ASTER spectral library (NPOESS LUT )

MW: from sensor data derived

UV/VIS: ASTER spectral library (NPOESS LUT )

Snow

IR: ASTER spectral library (NPOESS LUT )

MW: from sensor data derived

UV/VIS: ASTER spectral library (NPOESS LUT )

FASTEM-5

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CRTM Support to JPSS Radiance Validation and Monitoring

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ATMS Weighting Function

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ATMS Striping

Courtesy of Ninghai Sun in JPSS ATMS SDR Team

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CrIS -1

Red: all ocean cases; green uses ch. 3 homogeneity (0.7 K); black also with Ch. 3 one sigma central points.

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CrIS -2

The nine FOV to FOV (FOV-2-FOV) relative radiometric variability by removing the mean bias between observations and CRTM simulations.

VIIRS and CRTM Modeling for M12 Striping Investigation

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The STAR team applied the CRTM to simulate the VIIRS SDR data.It is found that the M12 striping reported by the SST EDR team iscaused by the difference in VIIRS azimuth angles among detectors.

M1, M4, and M11 measured (R-Rm)/Rm *100

Detector # BRDF A B R

Brightness temperature

1 0.73685 80.368 0.04253 0.51055 0.10590 0.61645 302.666

2 0.73649 80.543 0.04309 0.50923 0.10717 0.61641 302.648

3 0.73700 80.717 0.04365 0.51022 0.10873 0.61894 302.738

4 0.73645 80.892 0.04422 0.50964 0.10999 0.61962 302.769

5 0.73705 81.066 0.04479 0.51114 0.11159 0.62273 302.871

6 0.73628 81.241 0.04537 0.51147 0.11280 0.62427 302.931

7 0.73701 81.415 0.04596 0.51164 0.11448 0.62612 302.987

8 0.73596 81.589 0.04656 0.51074 0.11566 0.62640 303.020

9 0.73673 81.764 0.04715 0.51175 0.11739 0.62914 303.115

10 0.73557 81.938 0.04776 0.51124 0.11855 0.62978 303.153

11 0.73641 82.113 0.04837 0.51120 0.12036 0.63157 303.230

12 0.73509 82.287 0.04901 0.51134 0.12155 0.63289 303.316

13 0.73562 82.461 0.04962 0.51180 0.12325 0.63505 303.396

14 0.73486 82.636 0.05026 0.51057 0.12461 0.63518 303.417

15 0.73526 82.810 0.05089 0.50993 0.12629 0.63622 303.439

16 0.73565 82.985 0.05154 0.50998 0.12812 0.63810 303.560

)(),,()()cos(])1()()[( 0__ satsatsunsatsunsunsunuatmdatmssat BRDFFRRTBR

sat)( sat

A B

Detailed CRTM Calculation for the striping

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Blackbody Temperature Warm Up and Cool Down

Objective: To test non-linaerity and stability.

Result: NEdT depends on BB temperature (Solid and dashed line), as our model predicted (red line).

Black solid and dashed lines are for measured values at HAM A and B sides. Lines in red are predicted based on single operational BB temperature (see green triangle).

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Scene-dependent NEdT

Cao et al., 2013)

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CRTM capability for OMPS application

OMPS nadir mapper and profiler radiance between observations (black line) and CRTM-uvspec calculations (red line). The ECMWF forecasting profiles including ozone is used.

Discussion and Summary

• CRTM is a fast and accurate model to compute satellite radiance and radiance derivatives for IR, MW, Visible and UV sensors.

• It includes advanced RT components to compute absorption, emission and scattering from various gases, clouds, aerosols and surfaces.

• It has been extensively validated against its base models and observations.

• The user interface and program structure are designed for easy use and future expansion.

• CRTM has been applied in data assimilation in supporting of weather forecast, satellite product retrieval, air quality analysis, climate studies, and sensor monitoring and calibration.

• Scene-dependent measurement error needs be further investigated.

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Requests Highlights

• Improve computation efficiency for cloud and aerosol radiance assimilation.

• MonoRTM, MW sensor response function data

• Integrate advances in the surface emissivity and reflectivity models, integrated snow/ice empirical model, BRDF, ocean-bio-optic model

• New aerosol models, CMAQ, GOES-R (MODIS, VIIRS)

• Limb-scan simulation, no zenith angle

• Extend the CRTM capability for radiation energy calculations for satellite radiation flux measurements (CERES)

• Polarimetric (full Stokes) RT model

• Parallel computation in the CRTM

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