AIRS Sounding and Cloud Property Study

Slides:

Advertisements

Similar presentations

A fast physical algorithm for hyperspectral sounding retrieval Zhenglong Li #, Jun Li #, Timothy J. and M. Paul Menzel # # Cooperative Institute.

Advertisements

A New A-Train Collocated Product : MODIS and OMI cloud data on the OMI footprint Brad Fisher 1, Joanna Joiner 2, Alexander Vasilkov 1, Pepijn Veefkind.

The Utility of GOES-R and LEO Soundings for Hurricane Data Assimilation and Forecasting Jun Timothy J. Schmit #, Hui Liu &, Jinlong and Jing.

MOD06 Cloud Top Properties Richard Frey Paul Menzel Bryan Baum University of Wisconsin - Madison.

GOES Cloud Products and Cloud Studies Height Techniques Introduction GOES Sounder Currently there are three techniques being used to generate cloud top.

Review of Remote Sensing Fundaments IV Infrared at High Spectral Resolution – Basic Principal & Limitations Allen Huang Cooperative Institute for Meteorological.

GOES-12 Sounder SFOV sounding improvement Zhenglong Li, Jun Li, W. Paul Menzel, Timothy J. Schmit and other colleagues Cooperative Institute for Meteorological.

Improving Severe Weather Forecasting: Hyperspectral IR Data and Low-level Inversions Justin M. Sieglaff Cooperative Institute for Meteorological Satellite.

MODIS Regional and Global Cloud Variability Brent C. Maddux 1,2 Steve Platnick 3, Steven A. Ackerman 1,2, Paul Menzel 1, Kathy Strabala 1, Richard Frey.

Cooperative Institute for Meteorological Satellite Studies University of Wisconsin - Madison E. Eva Borbas, Zhenglong Li and W. Paul Menzel Cooperative.

1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.

1 Tropical cyclone (TC) trajectory and storm precipitation forecast improvement using SFOV AIRS soundings Jun Tim Schmit &, Hui Liu #, Jinlong Li.

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,

Orbit Characteristics and View Angle Effects on the Global Cloud Field

High Spectral Resolution Infrared Land Surface Modeling & Retrieval for MURI 28 April 2004 MURI Workshop Madison, WI Bob Knuteson UW-Madison CIMSS.

New Products from combined MODIS/AIRS Jun Li, Chian-Yi Liu, Allen Huang, Xuebao Wu, and Liam Gumley Cooperative Institute for Meteorological Satellite.

AGU 2002 Fall Meeting NASA Langley Research Center / Atmospheric Sciences Validation of GOES-8 Derived Cloud Properties Over the Southeastern Pacific J.

Measurement of cirrus cloud optical properties as validation for aircraft- and satellite-based cloud studies Daniel H. DeSlover (Dave Turner, Ping Yang)

Towards retrieving 3-D cloud fractions using Infrared Radiances from multiple sensors Dongmei Xu JCSDA summer colloquium, July August

IGARSS 2011, July 24-29, Vancouver, Canada 1 A PRINCIPAL COMPONENT-BASED RADIATIVE TRANSFER MODEL AND ITS APPLICATION TO HYPERSPECTRAL REMOTE SENSING Xu.

1 Using water vapor measurements from hyperspectral advanced IR sounder (AIRS) for tropical cyclone forecast Jun Hui Liu #, Jinlong and Tim.

Chian-Yi Liu 1,*, Jun Li 1, and Timothy J. Schmit 2 1 Cooperative Institute for Meteorological Satellite Studies (CIMSS) / University of Wisconsin-Madison.

Andrew Heidinger and Michael Pavolonis

Hyperspectral Infrared Alone Cloudy Sounding Algorithm Development Objective and Summary To prepare for the synergistic use of data from the high-temporal.

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, Timothy J. Schmit 2, Fang Wang 1, James J. Gurka 3, and W. Paul Menzel 2 1 Cooperative Institute for Meteorological Satellite Studies.

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.

Linear Optimization as a Solution to Improve the Sky Cover Guess, Forecast Jordan Gerth Cooperative Institute for Meteorological Satellite Studies University.

High impact weather studies with advanced IR sounder data Jun Li Cooperative Institute for Meteorological Satellite Studies (CIMSS),

Cloud optical properties: modeling and sensitivity study Ping Yang Texas A&M University May 28,2003 Madison, Wisconsin.

BBHRP Assessment Part 2: Cirrus Radiative Flux Study Using Radar/Lidar/AERI Derived Cloud Properties David Tobin, Lori Borg, David Turner, Robert Holz,

Using MODIS and AIRS for cloud property characterization Jun W. Paul Menzel #, Steve Chian-Yi and Institute.

Bryan A. Baum, Richard Frey, Robert Holz Space Science and Engineering Center University of Wisconsin-Madison Paul Menzel NOAA Many other colleagues MODIS.

ITSC-1227 February-5 March 2002 Use of advanced infrared sounders in cloudy conditions Nadia Fourrié and Florence Rabier Météo France Acknowledgement G.

Validation of Satellite-derived Clear-sky Atmospheric Temperature Inversions in the Arctic Yinghui Liu 1, Jeffrey R. Key 2, Axel Schweiger 3, Jennifer.

Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.

Cirrus Cloud Boundaries from the Moisture Profile Hyperspectral (i.e., Ultraspectral) IR Sounders W. L. Smith 1,2, Jun-Li 2, and E, Weisz 2 1 Hampton University.

Retrieval of cloud parameters from the new sensor generation satellite multispectral measurement F. ROMANO and V. CUOMO ITSC-XII Lorne, Victoria, Australia.

Caroline Poulsen ATSR-2 Group Cloud parameters estimated by variational analysis of visible and infrared measurements from ATSR-2 Caroline Poulsen, Richard.

Jun Li 1, Zhongdong Yang 1, W. Paul Menzel 2, and H.-L. Huang 1 1 Cooperative Institute for Meteorological Satellite Studies (CIMSS), UW-Madison 2 NOAA/NESDIS/ORA.

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.

Satellite Data Assimilation Activities at CIMSS for FY2003 Robert M. Aune Advanced Satellite Products Team NOAA/NESDIS/ORA/ARAD Cooperative Institute for.

May 15, 2002MURI Hyperspectral Workshop1 Cloud and Aerosol Products From GIFTS/IOMI Gary Jedlovec and Sundar Christopher NASA Global Hydrology and Climate.

CIMSS Hyperspectral IR Sounding Retrieval (CHISR) Processing & Applications Jun Elisabeth Jinlong Hui Liu #, Timothy J. Schmit &,

High impact weather nowcasting and short-range forecasting using advanced IR soundings Jun Li Cooperative Institute for Meteorological.

MODIS, AIRS, and Midlevel Cloud Phase Shaima Nasiri CIMSS/SSEC, UW-Madison Brian Kahn Jet Propulsion Laboratory MURI Hyperspectral Workshop 7-9 June, 2005.

AIRS Land Surface Temperature and Emissivity Validation Bob Knuteson Hank Revercomb, Dave Tobin, Ken Vinson, Chia Lee University of Wisconsin-Madison Space.

1 Hyperspectral IR Cloudy Fast Forward Model J. E. Davies, X. Wang, E. R. Olson, J. A. Otkin, H-L. Huang, Ping Yang #, Heli Wei #, Jianguo Niu # and David.

© Crown copyright Met Office Assimilating infra-red sounder data over land John Eyre for Ed Pavelin Met Office, UK Acknowledgements: Brett Candy DAOS-WG,

Toward Continuous Cloud Microphysics and Cloud Radiative Forcing Using Continuous ARM Data: TWP Darwin Analysis Goal: Characterize the physical properties.

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.

Visible vicarious calibration using RTM

Hyperspectral Cloud-Clearing Allen Huang, Jun Li, Chian-Yi Liu, Kevin Baggett, Li Guan, & Xuebao Wu SSEC/CIMSS, University of Wisconsin-Madison AIRS/AMSU.

A-Train Symposium, April 19-21, 2017, Pasadena, CA

Yinghui Liu1, Jeff Key2, and Xuanji Wang1

Observing System Simulation Experiments at CIMSS

Meteorological Satellite Center Japan Meteorological Agency

Vicarious calibration by liquid cloud target

DCC inter-calibration of Himawari-8/AHI VNIR bands

Winds in the Polar Regions from MODIS: Atmospheric Considerations

Hyperspectral IR Clear/Cloudy

RECENT INNOVATIONS IN DERIVING ATMOSPHERIC MOTION VECTORS AT CIMSS

NPOESS Airborne Sounder Testbed (NAST)

Hyperspectral Wind Retrievals Dave Santek Chris Velden CIMSS Madison, Wisconsin 5th Workshop on Hyperspectral Science 8 June 2005.

Zhang Hailong, Xin Xiaozhou, Liu Qinhuo

Using dynamic aerosol optical properties from a chemical transport model (CTM) to retrieve aerosol optical depths from MODIS reflectances over land Fall.

Andrew Heidinger and Michael Pavolonis

Studying the cloud radiative effect using a new, 35yr spanning dataset of cloud properties and radiative fluxes inferred from global satellite observations.

Off-line 3DVAR NOx emission constraints

Presentation transcript:

AIRS Sounding and Cloud Property Study Xuebao Wu, Jun Li, Paul Menzel,Allen Huang In collaboration with CIMSS colleagues National Satellite Meteorology Center, Beijing, China CIMSS, University of Wisconsin-Madison, Madison, U.S.A. Mar.6, 2006

Outline ( Clear Sounding Study) 1. Algorithm---Regression&Physical 2. Two Experiments 3. Validation (Cloud Property Study) 4. MR Retrieval Method 5. Case Study: MPACE 6. Validation

(Courtesy of Lee et al., 2005）

Part 1. Clear IR Sounding Retrieval Regression Physical retrieval (regularization) which needs --Radiance measurements and observation errors --First guess from regression or other sources --Fast Radiative Transfer Model -- Others

Regularization Inversion Equations Unknowns Solutions Newton Iteration Discrepancy Principle (Li and Huang 1999)

Discrepancy Principle γ Residual = Observation error

394 AIRS Channels 1688 AIRS Channels

Sept.6, 2002, G193 1km MODIS Cloud Mask

Against ECMWF

500 hPa T difference 500 hPa q (g/kg) difference

Validation with match up dataset Match up dataset at ARM SGP Site AIRS/Best Estimate (Sept. of July 2002 – Aug. of 2004) ) Use AIRS retrieval algorithm developed at CIMSS (regression + physical)

Part 2 Cloud Property Study Some features: Minimum Residual Retrieval Method Ping Yang’s fast cloud RT model MODIS cloud mask and cloud phase to characterize the AIRS SFOV

1DVAR algorithm for cloud retrieval Fast Cloudy Radiative Transfer Model: coupled Observed AIRS Radiance Measurements CTP: Cloud-Top Pressure; ECA: Effective Cloud Amount CPS: Cloud Particle Size; COT: Cloud Optical Thickness at 0.55µm CTP and ECA: 720 – 790 cm**-1 COT and CPS: 790 – 1130 cm**-1 Background Information from MODIS Minimum Residual (MR) algorithm for cloud retrieval

MODIS Cloud Phase ( Courtesy of Chianyi LIU)

AIRS CTH (m) Barrow

Conclusion? Clear sky sounding retrieval algorithm has been tested using AIRS data. Physical retrieval work reliably. One day MPACE study. Ongoing works and Remaining issues(Esfc,BiasCorr) Acknowledge: HalW, KevinB, JinglongL,SeeBor, DaveT

Reference: Wu Xuebao, Li Jun, Zhang Wenjian, and Wang Fang, Atmospheric Profile Retrieval with AIRS Data and Validation at the ARM CART Site, Advances in Atmospheric Sciences, 2005, Vol. 22, No. 5, 647–654. Wu, X., J. Li, W. P. Menzel, A. Huang, K. Baggett, and H. Revercomb, Evaluation of AIRS cloud properties using MPACE data, Geophys. Res. Lett., 2005, 32, L24819.