Feasibility of Deriving Surface and Atmospheric Parameters over Land using TRMM-TMI B. S. Gohil, Atul K. Varma and A. K. Mathur Oceanic Sciences Division.

Slides:

Advertisements

Similar presentations

VII Riunione annuale CeTeM-AIT – Bari – 4,5 Dicembre 2012 Elevation and Climatological Dependence of the SSI Capabilities to Discriminate Atmospheric Propagation.

Advertisements

1 An Assessment of AMSU-A Moisture Retrievals over Land and Ocean Stan Kidder 20 June 2006.

6th Aquarius/SAC-D Science Meeting Seattle. L2 Algorithms. S. Masuelli 1 of 26 July 19-21, th Aquarius/SAC-D Science Meeting July 2010 Seattle,

Meteorology and Physical Oceanography

Extension and application of an AMSR global land parameter data record for ecosystem studies Jinyang Du, John S. Kimball, Lucas A. Jones, Youngwook Kim,

A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.

The Aquarius Salinity Retrieval Algorithm Frank J. Wentz and Thomas Meissner, Remote Sensing Systems Gary S. Lagerloef, Earth and Space Research David.

Passive Measurements of Rain Rate in Hurricanes Ruba A.Amarin CFRSL December 10, 2005.

Global Warming and Climate Sensitivity Professor Dennis L. Hartmann Department of Atmospheric Sciences University of Washington Seattle, Washington.

MICROWAVE RAINFALL RETRIEVALS AND VALIDATIONS R.M. GAIROLA, S. POHREL & A.K. VARMA OSD/MOG SAC/ISRO AHMEDABAD.

TRMM/TMI Michael Blecha EECS 823.  TMI : TRMM Microwave Imager  PR: Precipitation Radar  VIRS: Visible and Infrared Sensor  CERES: Cloud and Earth.

Near Surface Soil Moisture Estimating using Satellite Data Researcher: Dleen Al- Shrafany Supervisors : Dr.Dawei Han Dr.Miguel Rico-Ramirez.

Atmospheric Sounding Temperature and water vapor profiling Atmospheric sounding Atmospheric sounding is retrieving vertical profiles of temperature trace-

Using Scatterometers and Radiometers to Estimate Ocean Wind Speeds and Latent Heat Flux Presented by: Brad Matichak April 30, 2008 Based on an article.

Xin Kong, Lizzie Noyes, Gary Corlett, John Remedios, Simon Good and David Llewellyn-Jones Earth Observation Science, Space Research Centre, University.

Millimeter and sub-millimeter observations for Earth cloud hunting Catherine Prigent, LERMA, Observatoire de Paris.

Single Column Experiments with a Microwave Radiative Transfer Model Henning Wilker, Meteorological Institute of the University of Bonn (MIUB) Gisela Seuffert,

J. S. Parihar Agricultural Resources Group Space Applications Centre (ISRO) Ahmedabad, India Land applications from Megha.

Intercalibration of AMSR-E and WindSat TB over Tropical Forest Scenes Thomas Meissner adapted by Marty Brewer for AMSR Science Team Meeting Huntsville,

1 EE 543 Theory and Principles of Remote Sensing Derivation of the Transport Equation.

Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.

Princeton University Development of Improved Forward Models for Retrievals of Snow Properties Eric. F. Wood, Princeton University Dennis. P. Lettenmaier,

SMOS+ STORM Evolution Kick-off Meeting, 2 April 2014 SOLab work description Zabolotskikh E., Kudryavtsev V.

Retrieving Snowpack Properties From Land Surface Microwave Emissivities Based on Artificial Neural Network Techniques Narges Shahroudi William Rossow NOAA-CREST.

Precipitation Retrievals Over Land Using SSMIS Nai-Yu Wang 1 and Ralph R. Ferraro 2 1 University of Maryland/ESSIC/CICS 2 NOAA/NESDIS/STAR.

A Combined Radar/Radiometer Retrieval for Precipitation IGARSS – Session 1.1 Vancouver, Canada 26 July, 2011 Christian Kummerow 1, S. Joseph Munchak 1,2.

Passive Microwave Remote Sensing

A NON-RAINING 1DVAR RETRIEVAL FOR GMI DAVID DUNCAN JCSDA COLLOQUIUM 7/30/15.

Satellite-derived Sea Surface Temperatures Corey Farley Remote Sensing May 8, 2002.

Level 2 Algorithm. Definition of Product Levels LevelDescription Level 1 1A Reconstructed unprocessed instrument data 1B Geolocated, calibrated sensor.

OSTST, March 12-15, 2007 – 1 Charles Desportes (CLS)& Estelle Obligis (CLS), Laurence Eymard (LOCEAN) On the Wet Tropospheric Correction for Altimetry.

Application of in situ Observations to Current Satellite-Derived Sea Surface Temperature Products Gary A. Wick NOAA Earth System Research Laboratory With.

3rd Indo-French Workshop on Megha-Tropiques under ISRO-CNES Programme on Atmosphere, Climate and Oceanography, Ahmedabad, India October 17-20, Convection,

WATER VAPOR RETRIEVAL OVER CLOUD COVER AREA ON LAND Dabin Ji, Jiancheng Shi, Shenglei Zhang Institute for Remote Sensing Applications Chinese Academy of.

PASSIVE MICROWAVES Figure 5-2 Sensitivity of brightness temperature to geophysical parameters over ocean surface.

Towards parametrized GEC current sources for the CESM model FESD project meeting February 2014 Wiebke Deierling, Andreas Baumgaertner, Tina Kalb.

Satellite-based Land-Atmosphere Coupled Data Assimilation Toshio Koike Earth Observation Data Integration & Fusion Research Initiative (EDITORIA) Department.

A New Inter-Comparison of Three Global Monthly SSM/I Precipitation Datasets Matt Sapiano, Phil Arkin and Tom Smith Earth Systems Science Interdisciplinary.

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.

An evaluation of satellite derived air-sea fluxes through use in ocean general circulation model Vijay K Agarwal, Rashmi Sharma, Neeraj Agarwal Meteorology.

Improvement of Cold Season Land Precipitation Retrievals Through The Use of Field Campaign Data and High Frequency Microwave Radiative Transfer Model IPWG.

Response of active and passive microwave sensors to precipitation at mid- and high altitudes Ralf Bennartz University of Wisconsin Atmospheric and Oceanic.

EARTH SCIENCE. THE FOUR BRANCHES OF EARTH SCIENCE Geology – the scientific study of the origin, history, and structure of the Earth and the processes.

AMSR-E Vapor and Cloud Validation Atmospheric Water Vapor –In Situ Data Radiosondes –Calibration differences between different radiosonde manufactures.

Charles L Wrench RCRU Determining Cloud Liquid Water Path from Radiometer measurements at Chilbolton.

The Inter-Calibration of AMSR-E with WindSat, F13 SSM/I, and F17 SSM/IS Frank J. Wentz Remote Sensing Systems 1 Presented to the AMSR-E Science Team June.

RADIANT SURFACE TEMPERATURE OF A DECIDUOUS FOREST – THE EFFECTIVENESS OF SATELLITE MEASUREMENT AND TOWER-BASED VALIDATION.

Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.

Geophysical Ocean Products from AMSR-E & WindSAT Chelle L. Gentemann, Frank Wentz, Thomas Meissner, Kyle Hilburn, Deborah Smith, and Marty Brewer

Basis of GV for Japan’s Hydro-Meteorological Process Modelling Research GPM Workshop Sep. 27 to 30, Taipei, Taiwan Toshio Koike, Tobias Graf, Mirza Cyrus.

A step toward operational use of AMSR-E horizontal polarized radiance in JMA global data assimilation system Masahiro Kazumori Numerical Prediction Division.

A Combined Radar-Radiometer Approach to Estimate Rain Rate Profile and Underlying Surface Wind Speed over the Ocean Shannon Brown and Christopher Ruf University.

November 28, 2006 Derivation and Evaluation of Multi- Sensor SST Error Characteristics Gary Wick 1 and Sandra Castro 2 1 NOAA Earth System Research Laboratory.

A New Ocean Suite Algorithm for AMSR2 David I. Duncan September 16 th, 2015 AMSR Science Team Meeting Huntsville, AL.

Apr 17, 2009F. Iturbide-Sanchez A Regressed Rainfall Rate Based on TRMM Microwave Imager Data and F16 Rainfall Rate Improvement F. Iturbide-Sanchez, K.

Dependence of SMOS/MIRAS brightness temperatures on wind speed: sea surface effect and latitudinal biases Xiaobin Yin, Jacqueline Boutin LOCEAN.

Synthesis of work on Budget of Water Vapor and Trace gases in Amazonia Transport and Impacts of Moisture, Aerosols and Trace Gases into and out of the.

1 Moisture Profile Retrievals from Satellite Microwave Sounders for Weather Analysis Over Land and Ocean John M. Forsythe, Stanley Q. Kidder*, Andrew S.

“CMORPH” is a method that creates spatially & temporally complete information using existing precipitation products that are derived from passive microwave.

Passive Microwave Remote Sensing

Retrieval of Land Surface Temperature from Remote Sensing Thermal Images Dr. Khalil Valizadeh Kamran University of Tabriz, Iran.

Xie, X., U. Löhnert, S. Kneifel, and S. Crewell

Microwave Assimilation in Tropical Cyclones

SOLab work description

GPM Microwave Radiometer Vicarious Cold Calibration

Wei Huang Class project presentation for ECE539

Saturn temperature and H2 profiles from Solar EUV occultations

RegCM3 Lisa C. Sloan, Mark A. Snyder, Travis O’Brien, and Kathleen Hutchison Climate Change and Impacts Laboratory Dept. of Earth and Planetary Sciences.

Peering through Jupiter’s clouds with radio spectral imaging

A Coastal Forecasting System

Presentation transcript:

Feasibility of Deriving Surface and Atmospheric Parameters over Land using TRMM-TMI B. S. Gohil, Atul K. Varma and A. K. Mathur Oceanic Sciences Division Meteorology and Oceanography Group Space Applications Centre (ISRO) Ahmedabad , India.

TB ( ,p) = TB DN  (  )(1-  (T S, ,p)) +T S  (T S, ,p)  (  )+TB UP The brightness temperature received by microwave radiometer looking towards earth in non-scattering atmosphere in a thermodynamic equilibrium is given by: TB DN TBUP Surface Ts,  TB TB S    For attenuation by atmospheric gases – Liebe (1992) model  Absorption by non-precipitating clouds – Paris (1971)

Emissivities of (1) first year ice, (2) multiyear ice and (3) open water at H-polarization and 50 o incidence angle (Pedersen, 1988) Assumption:  V19   V23   V37 =  V  H19   H23   H37 =  H  V or  H  0

Constitution of database , T S, TB UP, TB DN, CLW, WV T S – from Climatology Pressure profiles simulated from hydrostatic equation  V and  H are proxy  V is moved from 0.4 tp 1.0 and  H such that  H <  V  V-H =  V -  H = 0 to 0.6 ParameterMinMaxMeanSD SST (K) WV (g/cm 2 ) CLW (g/cm 2 )

Simulation of Water Vapor profiles RH is linearly varied from surface to tropopause RH is varied at the surface Temperature Lapse Rate of Standard Atmosphere are adopted WV profile is derived using RH and T profiles If clouds present RH = 100% at the base of the clouds Surface Tropopause (16 km) RH=0

Simulation of Clouds Case: 1 Case: 2 Case: 3 Freezing level  CLW is maximum at freezing level  CLW (max) = 5% or 10% of cloud thickness in gm/m 3  Raining clouds have not been considered

Minimization Wi is weight, that for water vapor taken as: 0.5 for 19 GHz 0.8 for 23 GHz 0.7 for 37 GHz

TMI Characteristics

Examples of IWV

IWV-July

IWV - July

IWV - July

IWV - June

IWV - June

Examples of Emissivity

Emissivity (V) -July

Emissivity (V) - July

Emissivity - July

Emissivity - June

Emissivity - June

Examples of Emissivity Diff. (V-H)

Emissivity Difference (V-H) -July

Emissivity Difference (V-H) -July

Emissivity Difference (V-H) -July

Emissivity Difference (V-H) -June

Emissivity Difference (V-H) -June

Examples of Land Surface Temperature (LST)

LST -July

LST -July

LST -July

LST -June

LST -June

Examples of CLW

CLW - July

CLW – July

CLW - July

CLW - June

CLW - June

TMI DERIVED GEOPHYSICAL PARAMETERS OVER LAND (JUN 3, ’03-15GMT) (Gohil, et al, 2003)Abs

Comparison NCEP Reanalysis Jun 03, 2003 (Daily Mean) TMI Derived WVC over Land (Jun 03, 2003/15 GMT)(Over ocean – Wentz Product)

Conclusion Study shows good prospects for estimation of Atmospheric and Surface parameters, especially water vapor over land. Study needs to be more refined with more case studies and inter-comparison/validation.

Thanks