AIRS (Atmospheric Infrared Sounder) Regression Retrieval (Level 2)

Slides:



Advertisements
Similar presentations
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course
Advertisements

A fast physical algorithm for hyperspectral sounding retrieval Zhenglong Li #, Jun Li #, Timothy J. and M. Paul Menzel # # Cooperative Institute.
AIRS (Atmospheric Infrared Sounder) Level 1B data.
Validation of CrIMSS sounding products of Cloud contamination and angle dependency Zhenglong Li, Jun Li, and Yue Li University of Wisconsin -
Atmospheric Sounding Temperature and water vapor profiling Atmospheric sounding Atmospheric sounding is retrieving vertical profiles of temperature trace-
ATS 351 Lecture 8 Satellites
Quick Review of Remote Sensing Basic Theory Paolo Antonelli CIMSS University of Wisconsin-Madison South Africa, April 2006.
MODIS Atmospheric Profiles Suzanne Wetzel Seemann, CIMSS MOD07 Developer Retrievals are performed in 5x5 FOV (approximately 5km resolution) clear-sky radiances.
Geostationary Imaging Fourier Transform Spectrometer An Update of the GIFTS Program Geostationary Imaging Fourier Transform Spectrometer An Update of the.
Cirrus Cloud Boundaries from the Moisture Profile Q-6: HS Sounder Constituent Profiling Capabilities W. Smith 1,2, B. Pierce 3, and Z. Chen 2 1 University.
Cooperative Institute for Meteorological Satellite Studies University of Wisconsin - Madison E. Eva Borbas, Zhenglong Li and W. Paul Menzel Cooperative.
CRN Workshop, March 3-5, An Attempt to Evaluate Satellite LST Using SURFRAD Data Yunyue Yu a, Jeffrey L. Privette b, Mitch Goldberg a a NOAA/NESDIS/StAR.
Experiments with the microwave emissivity model concerning the brightness temperature observation error & SSM/I evaluation Henning Wilker, MIUB Matthias.
Extending HIRS High Cloud Trends with MODIS Donald P. Wylie Richard Frey Hong Zhang W. Paul Menzel 12 year trends Effects of orbit drift and ancillary.
Diagnosing Climate Change from Satellite Sounding Measurements – From Filter Radiometers to Spectrometers William L. Smith Sr 1,2., Elisabeth Weisz 1,
AIRS (Atmospheric Infrared Sounder) Regression Retrieval (Level 2)
High Spectral Resolution Infrared Land Surface Modeling & Retrieval for MURI 28 April 2004 MURI Workshop Madison, WI Bob Knuteson UW-Madison CIMSS.
Infrared Interferometers and Microwave Radiometers Dr. David D. Turner Space Science and Engineering Center University of Wisconsin - Madison
Intercomparisons of AIRS and NAST retrievals with Dropsondes During P- TOST (Pacific Thorpex Observational System Test) NASA ER-2 NOAA G-IV Dropsonde.
A physical basis of GOES SST retrieval Prabhat Koner Andy Harris Jon Mittaz Eileen Maturi.
New Products from combined MODIS/AIRS Jun Li, Chian-Yi Liu, Allen Huang, Xuebao Wu, and Liam Gumley Cooperative Institute for Meteorological Satellite.
Satellite-derived Sea Surface Temperatures Corey Farley Remote Sensing May 8, 2002.
CrIS Use or disclosure of data contained on this sheet is subject to NPOESS Program restrictions. ITT INDUSTRIES AER BOMEM BALL DRS EDR Algorithms for.
University of Wisconsin - Madison Space Science and Engineering Center (SSEC) High Spectral Resolution IR Observing & Instruments Hank Revercomb (Part.
March 18, 2003 MODIS Atmosphere, St. Michaels MD Infrared Retrieval of Temperature, Moisture, Ozone, and Total Precipitable Water: Recent Update and Status.
IGARSS 2011, July 24-29, Vancouver, Canada 1 A PRINCIPAL COMPONENT-BASED RADIATIVE TRANSFER MODEL AND ITS APPLICATION TO HYPERSPECTRAL REMOTE SENSING Xu.
USE OF AIRS/AMSU DATA FOR WEATHER AND CLIMATE RESEARCH Joel Susskind University of Maryland May 12, 2005.
Advanced Sounder Capabilities- Airborne Demonstration with NAST-I W.L. Smith, D.K. Zhou, and A.M. Larar NASA Langley Research Center, Hampton, Virginia.
Hyperspectral Infrared Alone Cloudy Sounding Algorithm Development Objective and Summary To prepare for the synergistic use of data from the high-temporal.
Testing LW fingerprinting with simulated spectra using MERRA Seiji Kato 1, Fred G. Rose 2, Xu Liu 1, Martin Mlynczak 1, and Bruce A. Wielicki 1 1 NASA.
AIRS Radiance and Geophysical Products: Methodology and Validation Mitch Goldberg, Larry McMillin NOAA/NESDIS Walter Wolf, Lihang Zhou, Yanni Qu and M.
Jinlong Li 1, Jun Li 1, Christopher C. Schmidt 1, Timothy J. Schmit 2, and W. Paul Menzel 2 1 Cooperative Institute for Meteorological Satellite Studies.
Cloud optical properties: modeling and sensitivity study Ping Yang Texas A&M University May 28,2003 Madison, Wisconsin.
Cloud Properties from MODIS, AIRS, and MODIS/AIRS observations S. A. Ackerman, D. Tobin, P. Anotonelli, P. Menzel CRYSTAL meeting.
Satellite based instability indices for very short range forecasting of convection Estelle de Coning South African Weather Service Contributions from Marianne.
Using MODIS and AIRS for cloud property characterization Jun W. Paul Menzel #, Steve Chian-Yi and Institute.
Radiative transfer in the thermal infrared and the surface source term
Considerations for the Physical Inversion of Cloudy Radiometric Satellite Observations.
Charles L Wrench RCRU Determining Cloud Liquid Water Path from Radiometer measurements at Chilbolton.
Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.
A step toward operational use of AMSR-E horizontal polarized radiance in JMA global data assimilation system Masahiro Kazumori Numerical Prediction Division.
Retrieval of cloud parameters from the new sensor generation satellite multispectral measurement F. ROMANO and V. CUOMO ITSC-XII Lorne, Victoria, Australia.
UCLA Vector Radiative Transfer Models for Application to Satellite Data Assimilation K. N. Liou, S. C. Ou, Y. Takano and Q. Yue Department of Atmospheric.
1 Atmospheric Radiation – Lecture 13 PHY Lecture 13 Remote sensing using emitted IR radiation.
CIMSS Hyperspectral IR Sounding Retrieval (CHISR) Processing & Applications Jun Elisabeth Jinlong Hui Liu #, Timothy J. Schmit &,
AIRS Land Surface Temperature and Emissivity Validation Bob Knuteson Hank Revercomb, Dave Tobin, Ken Vinson, Chia Lee University of Wisconsin-Madison Space.
© 2014 RAL Space Study for the joint use of IASI, AMSU and MHS for OEM retrievals of temperature, humidity and ozone D. Gerber 1, R. Siddans 1, T. Hultberg.
1 Moisture Profile Retrievals from Satellite Microwave Sounders for Weather Analysis Over Land and Ocean John M. Forsythe, Stanley Q. Kidder*, Andrew S.
Radiance Simulation System for OSSE  Objectives  To evaluate the impact of observing system data under the context of numerical weather analysis and.
© Crown copyright Met Office OBR conference 2012 Stephan Havemann, Jean-Claude Thelen, Anthony J. Baran, Jonathan P. Taylor The Havemann-Taylor Fast Radiative.
Workshop on Soundings from High Spectral Resolution Infrared Observations May 6-8, 2003 University of Wisconsin-Madison.
© Crown copyright Met Office Assimilating infra-red sounder data over land John Eyre for Ed Pavelin Met Office, UK Acknowledgements: Brett Candy DAOS-WG,
June 20, 2005Workshop on Chemical data assimilation and data needs Data Assimilation Methods Experience from operational meteorological assimilation John.
1 Synthetic Hyperspectral Radiances for Retrieval Algorithm Development J. E. Davies, J. A. Otkin, E. R. Olson, X. Wang, H-L. Huang, Ping Yang # and Jianguo.
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course
What is atmospheric radiative transfer?
SPLIT-WINDOW TECHNIQUE
Vicarious calibration by liquid cloud target
Hyperspectral IR Clear/Cloudy
Geostationary Sounders
M. Goldberg NOAA/NESDIS Z. Cheng (QSS)
In the past thirty five years NOAA, with help from NASA, has established a remote sensing capability on polar and geostationary platforms that has proven.
Component decomposition of IASI measurements
Clear Sky Forward Model & Its Adjoint Model
PCA based Noise Filter for High Spectral Resolution IR Observations
Use of ATOVS at DAO Joanna Joiner, Donald Frank, Arlindo da Silva,
Early calibration results of FY-4A/GIIRS during in-orbit testing
Development of inter-comparison method for 3.7µm channel of SLSTR-IASI
AIRS (Atmospheric Infrared Sounder) Level 1B data
Fabien Carminati, Stefano Migliorini, & Bruce Ingleby
Presentation transcript:

AIRS (Atmospheric Infrared Sounder) Regression Retrieval (Level 2)

Level 0 to Level 2 Level 0: raw data Level 1A: geolocated radiance in counts Level 1B: calibrated radiance in physical units Level 2: retrieved physical variables (temperature, humidity and ozone profiles, surface skin temperature, total precipitable water, total ozone content, cloud top height...)

Regression Model X = C Y T C = X Y (Y T Y) Regression Model 2. Least squares regression solution Y…measurements [nprofs x nchannels] C...Regression coefficients [nlevels x nchannels] X... Atmospheric variables [nlevels x nprofs]

Regression Retrieval (1) C = X tr Y tr (Y tr T Y tr ) -1 X = C Y T 1. Calculate Regression Coefficients 2. Perform Retrieval (RTV) Y… Measurements [nprofs x nchannels] C... Regression coefficients [nlevels x nchannels] X... Atmospheric variables [nlevels x nprofs] Subscript tr refers to trainingset

Regression Retrieval (2) C =  X tr  Y tr (  Y tr T  Y tr ) -1 with  X tr = X tr – mean(X tr )  Y tr = Y tr – mean(Y tr )  X = C  Y T or X = mean(X tr ) + C  Y T with  X = X – mean(X tr )  Y = Y – mean(Y tr ) 1. Calculate Regression Coefficients 2. Perform Retrieval (RTV)

Principal Components (PC) Regression Retrieval 1. Calculate Regression Coefficients M = Cov(Y tr ) U = eig(M) A tr =  Y tr U C =  X tr A tr (A tr T A tr ) -1 X = mean(X tr ) + C A T with A=  Y U,  Y = Y – mean(Y tr ) 2. Perform Retrieval (RTV) M… covariance matrix [nchannels x nchannels] U... First few eigenvectors of M [nchannels x npc] A... Projection Coefficients (or amplitudes) [nsamples x npc]

The Trainingset X tr... Representative set of atmospheric variables including temperature, moisture, ozone, surface pressure, surface skin temperature and surface skin emissivities Y tr... Corresponding set of simulated radiances, calculated by a fast RT (radiative transfer) forward model

Radiance received by AIRS  Upwelling IR radiation from surface  Upwelling IR radiation from atm. layers  Reflected downwelling IR radiation  Reflected solar radiation R…radiance,  …wavenumber, s…surface, p…pressure, sun…solar, T…temperature, B…Planck function,  …emissivity,  …level to space transmittance, ...local solar zenith angle r…reflectivity, with r = (1-  )/ ,  *…level to surface (downwelling) transmittance [  *=   2 (p s )/   (p)]

Fast Radiative Transfer Forward Model Fast Model Regression : - Computation of line-by-line Transmittance  for FM training data set - Convolve with AIRS SRF (spectral response function) - Solve regression scheme  = AC for coefficients C using predictors A (predictors are functions of T, p, absorber amount, scanang …) Calculate transmittance  for any other profile Solve RTE to get radiance R 

IMAPP AIRS Regression Retrieval Results: Comparison with co-located radiosonde observations (RAOBs)

Co-located RAOB / AIRS single profile retrieval (RTV) Temperature (pixel 1259) RAOB Residual (Obs – Calc BT) Retrieval Residual (Obs – Calc BT ) residual = observed minus calculated spectrum RAOB – RTV Brightness Temperature (BT) residual

Co-located RAOB / AIRS single profile retrieval (RTV) RAOB Residual (Obs – Calc BT) Retrieval Residual (Obs – Calc BT ) Humidity (pixel 1516) Brightness Temperature (BT) residual RAOB – RTV residual = observed minus calculated spectrum

HumidityTemperature RMS of RAOB – RTV STDEV of RAOB – RTV Co-located RAOB / AIRS retrieval statistics (1899 profiles)

RMS of Residual (Obs – Calc BT) Stdev of Residual (Obs – Calc BT) Mean of Residual (Obs – Calc BT) Radiosonde ObservationsAIRS Retrieval

IMAPP AIRS Regression Retrieval Results: Global (240 granules) retrievals and retrievals over the CIMSS direct broadcast area

Globally Retrieved Temperature at 850 mbar With CloudmaskWithout Cloudmask Ascending Grans Descending Grans

Globally Retrieved Moisture at 850 mbar Ascending Grans Descending Grans With CloudmaskWithout Cloudmask

CIMSS Direct Broadcast area: AIRS measurements ( ) Brightness Temperature [K] at 1000 cm -1 (no cloudmask)

Temperature [K] at 700 mbar (no cloudmask) Humidity [g/kg] at 700 mbar (no cloudmask) CIMSS Direct Broadcast area: IMAPP AIRS Retrieval ( )

Surface Skin Temperature [K] (no cloudmask) Total Precipitable Water [cm] (no cloudmask) CIMSS Direct Broadcast area: IMAPP AIRS Retrieval ( )

Ozone [ppmv] at 9.5 mbar (no cloudmask) Total Ozone [Dobson Units] (no cloudmask) CIMSS Direct Broadcast area: IMAPP AIRS Retrieval ( )

Surface Reflectivity at 2641 cm -1 (no cloudmask) Surface Emissivity at 908 cm -1 (no cloudmask) CIMSS Direct Broadcast area: IMAPP AIRS Retrieval ( )

IMAPP AIRS Regression Retrieval Results: Comparison with ECMWF analysis fields

AIRS RTV vs. ECMWF Analysis: Temperature at 700 mbar (G192, ) With CloudmaskWithout Cloudmask AIRS RTV ECMWF ANL

AIRS RTV vs. ECMWF Analysis: Temperature at 500 mbar and at selected pixel AIRS RTV ECMWF ANL IMAPP RTV ECMWF – IMAPP RTV Pixel 28/49

AIRS RTV vs. ECMWF Analysis: Humidity at 750 mbar and at selected pixel ECMWF ANL IMAPP RTV ECMWF – IMAPP RTV Pixel 78/53 AIRS RTV ECMWF ANL

AIRS RTV vs. ECMWF Analysis: Humidity along scanline 65 (without cloudmask) Scanline 65 IMAPP AIRS RetrievalECMWF Analysis

AIRS RTV vs. ECMWF Analysis: Humidity along scanline 65 (with cloudmask) Scanline 65 IMAPP AIRS RetrievalECMWF Analysis

RMS and STDEV of ECMWF minus AIRS RTV (G192, , 4758 clear pixels) HumidityTemperature Root Mean Square (RMS) Error Standard Deviation Surface Skin Temperature [K] Total Precipitable Water [cm]

AIRS RTV vs. ECMWF Analysis: Spatial mean of Brightness Temperature (BT) residual RMS of Residual (Obs – Calc BT) Stdev of Residual (Obs – Calc BT) Mean of Residual (Obs – Calc BT) IMAPP AIRS RetrievalECMWF Analysis

AIRS RTV vs. ECMWF Analysis: Spectral mean of Brightness Temperature (BT) residual AIRS RTV ECMWF Analysis Mean Long WaveMean Short Wave Mean Mid Wave

IMAPP AIRS Regression Retrieval Results: Comparison with ECMWF analysis fields and L2 operational product

AIRS RTV vs. ECMWF Analysis vs. Operational Product: Temperature at 500 mbar (G192, ) IMAPP AIRS RTV ECMWF ANL Operational AIRS RTV available on AMSU footprint (=3x3 AIRS FOVs) only missing areas  retrieval not successful or not validated yet

AIRS RTV vs. ECMWF Analysis vs. Operational Product: Temperature at selected pixel ECMWF ANL IMAPP RTV OP RTV ECMWF – IMAPP RTV ECMWF – OP RTV Pixel 22/6

AIRS RTV vs. ECMWF Analysis vs. Operational Product: Humidity at 600 mbar (G192, ) IMAPP AIRS RTV ECMWF ANL Operational AIRS RTV available on AMSU footprint (=3x3 AIRS FOVs) only missing areas  retrieval not successful or not validated yet

AIRS RTV vs. ECMWF Analysis vs. Operational Product: Humidity at selected pixel ECMWF ANL IMAPP RTV OP RTV ECMWF – IMAPP RTV ECMWF – OP RTV Pixel 14/15

IMAPP AIRS Regression Retrieval Results: Comparison with MODIS and GOES retrievals

MODIS RTV vs. AIRS RTV vs. GOES RTV: Temperature and Humidity at 620 mbar (G192, ) AIRS RTVGOES RTVMODIS RTV