A Time-varying Radiometric Bias Correction for the TRMM Microwave Imager. Kaushik Gopalan Thesis defense : Oct 29, 2008

Similar documents
VALIDATION OF WIDEBAND OCEAN EMISSIVITY RADIATIVE TRANSFER MODEL. SONYA CROFTON B.S. University of Florida, 2005

RAIN RATE RETRIEVAL ALGORITHM FOR AQUARIUS/SAC-D MICROWAVE RADIOMETER. ROSA ANA MENZEROTOLO B.S. University of Central Florida, 2005

Creating a Consistent Oceanic Multi-decadal Intercalibrated TMI-GMI Constellation Data Record

Radiometric Calibration of RapidScat Using the GPM Microwave Imager

URSI-F Microwave Signatures Meeting 2010, Florence, Italy, October 4 8, Thomas Meissner Lucrezia Ricciardulli Frank Wentz

MWR Rain Rate Retrieval Algorithm. Rosa Menzerotolo MSEE Thesis defense Aug. 29, 2010

Ocean Vector Winds in Storms from the SMAP L-Band Radiometer

Inter-satellite Microwave Radiometer Calibration

The Effect of Clouds and Rain on the Aquarius Salinity Retrieval

APPENDIX 2 OVERVIEW OF THE GLOBAL PRECIPITATION MEASUREMENT (GPM) AND THE TROPICAL RAINFALL MEASURING MISSION (TRMM) 2-1

A New Satellite Wind Climatology from QuikSCAT, WindSat, AMSR-E and SSM/I

3D.6 ESTIMATES OF HURRICANE WIND SPEED MEASUREMENT ACCURACY USING THE AIRBORNE HURRICANE IMAGING RADIOMETER

An Emissive Antenna Correction for The Tropical Rainfall Measuring Mission Microwave Imager (TMI)

IN-FLIGHT vicarious calibration using on-earth targets is

Version 7 SST from AMSR-E & WindSAT

"Cloud and Rainfall Observations using Microwave Radiometer Data and A-priori Constraints" Christian Kummerow and Fang Wang Colorado State University

A two-season impact study of the Navy s WindSat surface wind retrievals in the NCEP global data assimilation system

Post-Launch Calibration of the TRMM Microwave

MEETING REPORT GPM Intersatellite Calibration Working Group (X-CAL) University of Michigan July 11-12, 2012

MWR Calibration pre-launch & post launch

Post-Launch Calibration of the TRMM Microwave Imager

WindSat Ocean Surface Emissivity Dependence on Wind Speed in Tropical Cyclones. Amanda Mims University of Michigan, Ann Arbor, MI

NPP ATMS Instrument On-orbit Performance

THE first space-based fully polarimetric microwave radiometer,

THE SPECIAL Sensor Microwave/Imager (SSM/I) [1] is a

Results of intercalibration between AMSR2 and TMI/AMSR-E (AMSR2 Version 1.1)

CFRSL A STATISTICAL ALGORITHM FOR INFERRING RAIN RATE FROM THE QUIKSCAT RADIOMETER. Yanxia Wang

Interpretation of Polar-orbiting Satellite Observations. Atmospheric Instrumentation

The Global Precipitation Measurement (GPM) Mission: Arthur Hou. NASA Goddard Space Flight Center

An Effort toward Assimilation of F16 SSMIS UPP Data in NCEP Global Forecast System (GFS)

Global Precipitation Measurement Mission Overview & NASA Status

METOC Consulting 1, The Aerospace Corporation 3, Met Office 4 Marine Meteorology Division of the Naval Research Laboratory 2, Monterey, CA USA

ASSIMILATION EXPERIMENTS WITH DATA FROM THREE CONICALLY SCANNING MICROWAVE INSTRUMENTS (SSMIS, AMSR-E, TMI) IN THE ECMWF SYSTEM

Atmospheric Profiles Over Land and Ocean from AMSU

MARCH 2010 VOLUME 48 NUMBER 3 IGRSD2 (ISSN )

Orbit Design Marcelo Suárez. 6th Science Meeting; Seattle, WA, USA July 2010

Long-Term Time Series of Water Vapour Total Columns from GOME, SCIAMACHY and GOME-2

CEOS SST-VC. Passive Microwave Radiometer Constellation for Sea Surface Temperature

Status of GCOM-W1 and

From L1 to L2 for sea ice concentration. Rasmus Tonboe Danish Meteorological Institute EUMETSAT OSISAF

Future Opportunities of Using Microwave Data from Small Satellites for Monitoring and Predicting Severe Storms

For those 5 x5 boxes that are primarily land, AE_RnGd is simply an average of AE_Rain_L2B; the ensuing discussion pertains entirely to oceanic boxes.

Evaluation of the Hurricane Imaging Radiometer (HIRAD) Brightness Temperatures

A radiative transfer model function for 85.5 GHz Special Sensor Microwave Imager ocean brightness temperatures

Improving Sea Surface Microwave Emissivity Model for Radiance Assimilation

Welcome and Introduction

Microwave Satellite Observations

Parameterization of the surface emissivity at microwaves to submillimeter waves

Hurricane Wind Vector Estimates from WindSat Polarimetric Radiometer

Calibrating SeaWinds and QuikSCAT scatterometers using natural land targets

Aquarius L2 Algorithm: Geophysical Model Func;ons

Two Prototype Hail Detection Algorithms Using the Advanced Microwave Sounding Unit (AMSU)

OCEAN VECTOR WINDS IN STORMS FROM THE SMAP L-BAND RADIOMETER

Radiometric correction of observations from microwave humidity sounders

Passive Microwave Sea Ice Concentration Climate Data Record

GLOBAL PRECIPITATION MEASUREMENT UPDATE

IMPROVED MICROWAVE REMOTE SENSING OF HURRICANE WIND SPEED AND RAIN RATES USING THE HURRICANE IMAGING RADIOMETER (HIRAD)

Characteristics of Global Precipitable Water Revealed by COSMIC Measurements

SNOWFALL RATE RETRIEVAL USING AMSU/MHS PASSIVE MICROWAVE DATA

Use of FY-3C/GNOS Data for Assessing the on-orbit Performance of Microwave Sounding Instruments

remote sensing ISSN

Uncertainty of Atmospheric Temperature Trends Derived from Satellite Microwave Sounding Data

Atmospheric Lidar The Atmospheric Lidar (ATLID) is a high-spectral resolution lidar and will be the first of its type to be flown in space.

Q-Winds Hurricane Retrieval Algorithm using QuikSCAT Scatterometer

SSS retrieval from space Comparison study using Aquarius and SMOS data

A New Microwave Snow Emissivity Model

RSS SMAP Salinity. Version 2.0 Validated Release. Thomas Meissner + Frank Wentz, RSS Tony Lee, JPL

SMOSIce L-Band Radiometry for Sea Ice Applications

A Microwave Snow Emissivity Model

Description of Precipitation Retrieval Algorithm For ADEOS II AMSR

Outline of 4 Lectures

Keiji Imaoka Earth Observation Research Center (EORC) Japan Aerospace Exploration Agency (JAXA) GSICS/GRWG Meeting Darmstadt, Germany March 25, 2014

Long-term Water Cycle Observation by the Advanced Microwave Scanning Radiometer (AMSR) Series: AMSR-E, AMSR2 and Follow-on

Impact of QuikSCAT Surface Marine Winds on Wave Hindcasting

P6.13 GLOBAL AND MONTHLY DIURNAL PRECIPITATION STATISTICS BASED ON PASSIVE MICROWAVE OBSERVATIONS FROM AMSU

Toward a Fully Parametric Retrieval of the Nonraining Parameters over the Global Oceans

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 51, NO. 9, SEPTEMBER

Generating a Climate data record for SST from Passive Microwave observations

Inter-tropical Convergence Zone (ITCZ) analysis using AIRWAVE retrievals of TCWV from (A)ATSR series and potential extension of AIRWAVE to SLSTR

Decadal Trends and Variability in Special Sensor Microwave / Imager (SSM/I) Brightness Temperatures and Earth Incidence Angle

The Status of NOAA/NESDIS Precipitation Algorithms and Products

Determination of an Amazon Hot Reference Target for the On-Orbit Calibration of Microwave Radiometers

RADIO SCIENCE, VOL. 38, NO. 4, 8065, doi: /2002rs002659, 2003

for the Global Precipitation Mission

Latest Development on the NOAA/NESDIS Snowfall Rate Product

On the Satellite Determination of Multilayered Multiphase Cloud Properties. Science Systems and Applications, Inc., Hampton, Virginia 2

Sea water dielectric constant, temperature and remote sensing of Sea Surface Salinity

P2.12 VARIATION OF OCEANIC RAIN RATE PARAMETERS FROM SSM/I: MODE OF BRIGHTNESS TEMPERATURE HISTOGRAM

Summary The present report describes one possible way to correct radiometric measurements of the SSM/I (Special Sensor Microwave Imager) at 85.5 GHz f

T. Meissner and F. Wentz Remote Sensing Systems

Bringing Consistency into High Wind Measurements with Spaceborne Microwave Radiometers and Scatterometers

The assimilation of AMSU and SSM/I brightness temperatures in clear skies at the Meteorological Service of Canada

P1.23 HISTOGRAM MATCHING OF ASMR-E AND TMI BRIGHTNESS TEMPERATURES

Lecture 19: Operational Remote Sensing in Visible, IR, and Microwave Channels

Ultra-High Resolution ASCAT Products & Progress in Simultaneous Wind/Rain Retrieval

Recalibration of DMSP SSMI/S for Weather and Climate Applications

AN IMPROVED MICROWAVE RADIATIVE TRANSFER MODEL FOR OCEAN EMISSIVITY AT HURRICANE FORCE SURFACE WIND SPEED

Aeolus. A Mission to Map the Winds of Mars. Anthony Colaprete Amanda Cook NASA Ames Research Center

GMES: calibration of remote sensing datasets

State of Passive Microwave Polar Stereographic Products. Walt Meier and polar stereographic product team

Transcription:

A Time-varying Radiometric Bias Correction for the TRMM Microwave Imager Kaushik Gopalan Thesis defense : Oct 29, 2008

Outline Dissertation Objective Initial research Introduction to GPM and ICWG Inter-satellite radiometric calibration Current research TMI time-varying bias correction Conclusions and Future work

Dissertation Objective Original Objective To develop inter-calibration techniques for a constellation of satellite radiometers over ocean, Amazon and polar regions Current Work Detection of systematic errors using intercalibration and development of correction techniques that eliminate systematic errors detected in TMI

Motivation GPM contains a constellation of similar, but not identical, radiometers Satellites of opportunity Inter-calibration is required in order to obtain self-consistent retrievals from all the radiometers in the constellation Separating long term environmental change from instrument errors due to aging

Calibration for GPM constellation GPM observatory : Non sun-synchronous tropical orbit Constellation satellites : Sun-synchronous polar orbits Image Courtesy : NASA

ICWG Objectives Common Dataset TMI as proxy for GMI WindSat and SSMI on F13, F14 are the polar orbiting radiometers Verify the accuracy of TMI as the transfer standard Using the gold standard radiometer to calibrate polar orbiting radiometers Generation of a standard, repeatable protocol for use in GPM

ICWG Methodology Four teams using independent techniques BESS : Tom Wilheit CSU : Chris Kummerow and Wes Berg Michigan : Chris Ruf UCF : Linwood Jones Common radiative transfer model Common dataset : July 2005 June 2006 Contributions by Fuzhong Weng and others

TMI Scan geometry

TMI channels Freq GHz Channels EIA, deg BW, MHz 10.65 V,H 52.3 100 19.35 V,H 52.3 200 21.3 V 52.3 500 37.0 V,H 52.3 2000 85.5 V,H 52.3 3000

SSMI scan geometry

SSMI channels Freq GHz Channels EIA, deg BW, MHz 19.35 V,H 53.4 250 22.3 V 53.4 250 37.0 V,H 53.4 1000 85.5 V,H 53.4 1500

WindSat Sensor Assembly Feed Horn Array

WindSat channels Freq GHz Channels EIA, deg BW, MHz 6.8 V,H 53.5 125 10.7 V,H,±45,lc,rc 49.9 300 18.7 V,H,±45,lc,rc 55.3 750 23.8 V,H 53.0 500 37.0 V,H,±45,lc,rc 53.0 2000

Match-up Data Sets

Match-up Data Sets ASCII files containing Tb meas, incidence and azimuth angles, & envir parameters Match-ups with temporal tolerance of ±1 hour and spatial quantization of 1º Lat x 1 Lng Three collocation files generated per day TMI-WSat, TMI-SSMI-F13 & TMI-SSMI-F-14 Match-up file formats available via SSH to the research community

Match-up Environmental Data Sets Atmospheric parameter profiles from GDAS Pressure, temperature, water vapor @ 21 atmos pressure layers Global @ 1 lat/lon grid, every 6 hours Oceanic surface parameters from GDAS Sea surface temperature, wind speed & direction Ocean salinity from NOAA climatology Columnar cloud liquid water from TMI retrievals Remote Sensing Systems

Match-up locations SSMI F-13 WindSat

Match-up Data Sets : Tb Filtering Filtering of match-up Tb samples to remove nonhomogeneous environmental scenes & bogus outliers In each 1 lat/lon bin, std dev of: V-pol 2K, H-pol 3K Upper limits of Tb s applied to remove rainy and land pixels TMI: 10V (185K), 10H (115K), 19V (230K), 19H (200K), 21V (260K), 37V (240K), 37H (210K) WindSat: 6V (200K), 6H (120K), 10V (200K), 10H(150K), 18V (250K), 18H (200K), 23V (260K), 23H (230K), 37V (250K ), 37H (200K) Conservative Land Mask applied

Radiative Transfer Theory Antenna T app T ex T b_down T b_up sea surface T b_surf T T refl app T Image courtesy : Liang Hong ( 1 )( Tex Tb _ down b _ up ( Tb _ surf Trefl ) )

Radiative Transfer Model - ICWG Subroutines 100 atmos layers, each of thickness 200m Elsaesser model for ocean isotropic emissivity Weng model for ocean directional emissivity Anisotropic with relative direction Calc from GDAS wind dir & Tb observation azimuth Rosenkranz model for WV, CLW, O2 and N2 absorption Implemented in MATLAB and C

Emissivity SST dependence of 10.7 GHz emissivity 0.65 0.6 0.55 Vertical 0.5 0.45 0.4 Flat for tropical SSTs 0.35 0.3 0.25 270 275 280 285 290 295 300 305 310 SST (K) Horizontal

TMI/WindSat Radiometric Biases Tb _ SS norm Tb _ SS obs Tb _ TMI pred Tb _ SS pred Tb _ diff Tb _ TMI obs Tb _ SS norm Tb_SSnorm : WindSat/SSMI Tb normalized to TMI freq. and angle Tb_diff : Unexplained radiometric bias between TMI and WindSat/SSMI

WindSat Measured Tb (K) Example : 10V 180 175 TMI = WS 170 165 160 155 162 164 166 168 170 172 174 176 TMI Measured Tb (K)

Predicted TMI - WindSat Difference (K) Normalized WindSat Tb (K) Example : 10V Predicted Tb difference Normalized Tb 7 180 6.8 178 6.6 176 6.4 174 6.2 172 6 170 5.8 168 5.6 166 5.4 164 5.2 162 5 162 164 166 168 170 172 174 176 TMI Measured Tb (K) 160 162 164 166 168 170 172 174 176 TMI Measured Tb (K)

Inter-Sat Tb Comparison Results Relative Biases for TMI and WindSat analyzed for 1 year (ICWG period) The radiometric bias between TMI and WSat radiometers must be independent of envir parameters Anomaly: Results indicate apparent correlation between the bias and SST.

Inter-Sat Tb Comparison Results 1600 Two distinct random error sources? 1400 1200 1000 800 600 400 200 0-5 -4-3 -2-1 0 1 2 3 4 5 10V TMI-WindSat relative bias (K)

TMI - WindSat 10V bias (K) 10V Bias between WindSat & TMI Concern: bias is f(envir pars)??? 0.5 0.4 0.3 Step of ~0.5K 0.2 0.1 0-0.1-0.2 284 286 288 290 292 294 296 298 300 302 304 SST (K)

TMI - WindSat (K) Predicted and measured differences 7.4 7.2 Predicted Tb difference Measured Tb difference 7 6.8 6.6 6.4 6.2 6 284 286 288 290 292 294 296 298 300 302 304 SST (K)

TMI - WindSat Measured Tb (K) Latitude dependence of measured Tb differences 7.4 7.2 Abs lat <=20 Abs lat > 20 7 6.8 6.6 6.4 6.2 6 284 286 288 290 292 294 296 298 300 302 304 SST (K)

TMI - WindSat Measured Tb (K) WV dependence of measured Tb differences 7.6 7.4 WV <=25 WV > 25 7.2 7 6.8 6.6 6.4 6.2 6 5.8 5.6 284 286 288 290 292 294 296 298 300 302 304 SST (K)

Evening Tb Measured - Morning Tb Measured (K) (Evening Morning) measured Tb diff. 2 1.5 TMI WS 1 Mean bias ~ 1.4K for TMI 0.5 0 Mean bias ~ 0.1K for WSat -0.5 284 286 288 290 292 294 296 298 300 302 304 SST (K)

Evening/Morning separation Evening Morning Unimodal

TMI Time of Day bias variation TMI reflector has been estimated to have a reflectivity of about 96% [Wentz et. al. 2001] Bias varies with reflector physical temperature, which has a diurnal cycle Challenge : WindSat, F13 and F14 have very similar equator crossing times TMI daily bias variation cannot be estimated from the match-up dataset Solution : Compare TMI meas. with RTM model

Wentz Correction (Implemented in V5 TMI data product) T T ( 1 ) T ant sc phy Constant

5 minute averaging over TMI orbit

TMI bias variation with orbit time

Bias variation with Solar Beta angle

Daily Average Bias (K) TMI Daily Average Bias Plot For Positive Beta Angles [2004-2006] Normalized Beta Angle Day Number

Daily Average Bias (K) TMI Daily Average Bias Plot For Negative Beta Angles [2004-2006] Absolute Value of Normalized Beta Angle Day Number

Effect of TMI time-varying bias correction on Inter-Cal results

WindSat Uncorrected Evening Morning

WindSat Corrected Evening Morning

SSMI Uncorrected Evening Morning

SSMI Corrected Evening Morning

SSMI V-pol biases (Uncorrected) Tb bias F-13 F-14 Evening Morning Evening Morning 19V -0.44-1.86-0.07-1.43 21V -1.61-2.99-1.23-2.62 37V -0.44-1.82-0.68-2.06

SSMI V-pol biases (Corrected) Tb bias F-13 F-14 Evening Morning Evening Morning 19V -1.50-1.58-1.12-1.13 21V -2.67-2.71-2.28-2.32 37V -1.50-1.54-1.73-1.76

TMI-WindSat V-pol biases Tb bias Uncorrected Corrected Evening Morning Evening Morning 10V 0.87-0.54 0.00-0.01 19V 0.16-1.38-0.71-0.85 21V -0.95-2.58-1.82-2.05 37V -2.53-4.01-3.40-3.49

V7 Radiometric Bias Estimated bias applied in the form of look-up table based on orbit time after eclipse and beta angle Beta angle : 1 degree (or finer) quantization Orbit time : 1 minute quantization Separate look-up tables for pre-boost and post-boost periods

V7 Radiometric Bias Post-boost bias estimate table to be created by averaging estimated bias for all 1 degree pixels from 2004-2007 for every beta/orbit time bin Pre-boost bias estimate table to be created by averaging estimated bias for all available 1 degree pixels for every beta/orbit time bin Smoothing applied to table for both beta and time

Summary Radiometric Inter-calibration performed for ICWG dataset Time-varying TMI bias discovered, and corrected using an empirical method based on comparing TMI observations to RTM TMI error estimated as function of orbit time after eclipse and sun angle Correction will be applied to TRMM 1B11 v7, scheduled for release in 2010

Publications K. Gopalan, L. Jones, T. Wilheit, and T. Kasparis, Inter-Satellite Radiometer Calibration of Windsat, TMI AND SSMI, IGARSS 2008. K. Gopalan, L. Jones, S. Biswas, S. Bilanow T. Wilheit, and T. Kasparis, A Time-Varying Radiometric Bias Correction for the TRMM Microwave Imager, submitted to TGARSS (accpted by GRSL)

Future work Analysis of TMI bias for pre-boost period Direct comparisons between WindSat and SSMI in polar regions Comparison of WindSat and RTM Detection of possible TMI pointing errors through comparison with RTM

Back-up slides

TMI bias dependence on sun angles Analysis was primarily performed by Sayak Biswas Direct contributions from Linwood Jones, Kaushik Gopalan and Steve Bilanow

?

Courtesy: Steve Bilanow Body or Instrument Coordinate Angle Definitions Y X X Typical TRMM Orientation at yaw = 0 deg. Typical TRMM Orientation at yaw = 180 deg. Y Z Z Flight Direction Solar beta angle : Sun elevation above X-Y plane positive toward Z

TRMM Solar Beta Angle Variation over 1 year is a combination of +/- 35 degrees due to the orbit inclination on a 46 or 47 day cycle, and the annual variation of +/- 23.5 degrees due to the Earth s axis tilt. Courtesy: Steve Bilanow

Cycle 1 Cycle 2 Cycle 3 Cycle 5 Cycle 6 Cycle 7 Cycle 4

Yaw Flip Beta > 0 0 +X body axis forward yaw angle = 0 0 Beta < 0 0 -X body axis forward yaw angle = 180 0

View From The Sun (negative beta angle) Spacecraft Aft Structure Shadowing of TMI - Jul 18, 2005 [β= -4.4 0 ] Flt Dir TRMM exits eclipse (TMI in shadow) A TMI exits S/C shadow B TMI in sunlight C TMI in sunlight D TMI in sunlight TMI reflector face illuminated before eclipse TMI in eclipse TMI in eclipse E F G H

D E Earth Shadow S/C Aft Shadow C B F A G H

TMI Observations RTM Predictions (K) Spacecraft Aft Structure Shadowing of TMI - Jul 18, 2005 [β= -4.4 0 ] F D E G H A B C Orbit Time Since Start of Eclipse (min)

View From The Sun (positive beta angle) Spacecraft Aft Structure Shadowing of TMI Aug 02, 2005 [β= +4.3 0 ] Flt Dir TRMM exits eclipse (TMI in sunlight) A TMI in sunlight B TMI in sunlight C TMI in sunlight D TMI enters S/C shadow TMI in S/C shadow before eclipse TMI in eclipse TMI in eclipse E F G H

C B Earth Shadow S/C Aft Shadow D E A F H G

TMI Observations RTM Predictions (K) Spacecraft Aft Structure Shadowing of TMI Aug 02, 2005 [β= +4.3 0 ] D E F G H A B C Orbit Time Since Start of Eclipse (min)