Cirrus Cloud Boundaries from the Moisture ProfileCirrus Cloud Boundaries from the Moisture ProfileQ-6: HS Sounder Constituent Profiling CapabilitiesQ-6: HS Sounder Constituent Profiling CapabilitiesW. SmithW. Smith1,21,2, B. Pierce, B. Pierce33, and Z. Chen, and Z. Chen22

11University of Wisconsin, University of Wisconsin, 22Hampton University, Hampton University, 33NOAA/CIMSSNOAA/CIMSSSatellite Hyperspectral Sensor Workshop

Rosentiel School of Marine & Atmospheric Science (March 29-31, 2011

Content of Presentation• Review Hyperspectral sounding capability for Green House Gases (GHG) measurements.

• Present a physical/statistical technique for GHG sounding utilizing global chemistry model (RAQMS) profiles and the RTTOVS Radiative Transfer Model to prescribe GHG-profile/Radiance-spectrum covariance

• Demonstrate the capability of current satellite Hyperspectral sounders (e.g., IASI) profile T, H2O, O3, CH4, CO, N2O.

• Discuss the need for Geo-Hyperspectral sounders to observe boundary layer GHG concentrations and tropospheric gas fluxes.

IASIWater Vapor

Surface Skin / Surface Air Temperature Contrast Increases Satellite Sensitivity in Lower Atmosphere

Tskin – Tair = 15 K

IASIWater Vapor

Tskin – Tair = 0 K

IASICarbon Dioxide

Tskin – Tair = 15 K

IASICarbon Dioxide

Tskin – Tair = 0 K

Sensitivity of IASI to GHGs

Question #6Atmospheric Constituent Profiles From IR Radiance Spectra

Motivation of this Retrieval Approach• Use statistical covariance between gas mixing ratio profiles and the radiance spectrum to obtain GHG gas profiles• Procedure adds statistical information as needed for profile retrieval through the cross-gas and cross-temperature correlations contained in the radiance spectrum• Statistical information provided by a global scale model with interactive chemistry/weather dynamics physics and radiative transfer model.

High-performance Instrumented Airborne Platform for Environmental Research (HIAPER), Pole-to-Pole Observation

(HIPPO) III

PI: Steven C. Wofsy, Harvard University

http://www.eol.ucar.edu/deployment/field-deployments/field-projects/hippo_global_3

National Science Foundation (NSF)-sponsored effort to study the distribution of greenhouse gases and black carbon in the atmosphere.

High-accuracy measurements of greenhouse gases and black carbon particles from the top of the troposphere to the earth's surface and pole-to-pole.

NCAR G-V aircraftMarch 20-April 20, 2010

RAQMS O3 curtain with HIPPO 3 in-situ O3 (Spackman, NOAA/ESRL)

Denver to Anchorage 03/24/2010Developed by Brad Pierce (NOAA/CIMSS)

IASI Granules Overlaying HIPPO Flight Tracks

GHG Profile Retrieval Algorithmqret = qo + (rm-ro)C

C = (R’TR’ + λETE)-1R’TQ’ q = atmospheric profiles (Temp.H2O, O3, CH4, CO, N2O)

C = global ensemble statistical covariance about the mean profile qo

R = global ensemble of Principal Component (PC) scores (deviation from their ensemble mean values) of the radiances calculated from atmospheric profiles, q.

Q & R = global ensemble of T/GHG profiles & calculated radiance spectra PC scores.

( )’ = deviation from the ensemble mean values, qo and ro

ETE = statistical covariance of spectral radiance PC score noise

λ = matrix conditioner assumed to be 0.05 % of the variance of the error for the first (i.e., most important) PC score.

Spectral Range: 650-2250 cm-1 & Spectral Spacing: 0.25 cm-1 = 6401 Channels300 Radiance Principal Components Used for Retrieval

Statistical BIAS Correction

Cor= (qknown – qret/known)

When using global ensemble statistics, the retrieved profile will have significant bias towards the global mean for scale vertical structure modes not resolved by the radiance observations (i.e., modes within the null space ). This bias, or null space error, can be minimized by producing a retrieval from a error-free radiance spectrum simulated from a known atmospheric condition, which is more representative of the atmospheric condition being retrieved than is the mean profile. In practice, a numerical prediction model background field can be used to provide a more representative, than the global mean, known reference atmospheric condition.

Thus, an estimate of the “null-space” error correction is given by:

where in this case the RAQMS profile is used to represent a known profileand rknown is the vector of PC scores corresponding to the known noise freeradiance calculated from the known profile

Cloud Height EstimationCloud Height Estimation

Retrieved values Set Equal to Missing Below

Cloud Level

Cloud Level ( Tiasi < Traqms for p>pcld )

Example ProfilesExample Profiles

Corrected Vs Uncorrected

Observed Vs Simulated (Q)

Total Column ConcentrationIASI Vs RAQM (U)

Gas

IASI – RAQMS Difference (% std) Mean Std

H2O - 13 28

O3 5 22

CO - 26 28

CH4 - 20 23

N2O - 8 20

IASI Vs Aircraft Measurements (CO)

IASI Vs Aircraft Measurements (O3)

Geo-HS Is What is Really Needed!

Conclusion

I dedicate this work to my colleague for more than 40 years, Moustafa Chahine. Mous was a tenacious remote sensing

scientist/experimenter/leader; as a competitor he made my professional life much richer than it would have been without him. Thank you Mous,

I will miss you. May you keep active in your new life.

• A retrieval technique has been developed to produce vertical profiles of GHG using the statistical covariance between the GHGs and the radiance spectrum

• The technique was applied here by applying the statistical structure of GHG profiles and simulated IASI radiances provided by the global scale RAQMS for the HIPPO experiment where aircraft ground truth is available

• RAQMS predicted GHG profiles were successfully validated by comparison of the profiles with retrievals from IASI observations obtained during the HIPPO experiment

• Finally, a mesoscale WARF model Hurricane Ike simulation was used to illustrate how the four dimensional water vapor distribution can be observed by a Geostationary Hyperspectral Imaging Spectrometer