Level-1A to Level-1B Spacecraft data to Geolocation Data and Antenna Temperature Level-1B to Level-2A Antenna Temperatures Converted to TOA Brightness.

Slides:



Advertisements
Similar presentations
F. Wentz, T. Meissner, J. Scott and K. Hilburn Remote Sensing Systems 2014 Aquarius / SAC-D Science Team Meeting November ,
Advertisements

L1a to L2 Aquarius Processor Frank Wentz and Thomas Meissner Aquarius Algorithm Workshop, Santa Rosa, CA, March 9-11, 2010.
SMOS L2 Ocean Salinity Level 2 Ocean Salinity Using TEC estimated from Stokes 3 24 October 2012 ACRI-st, LOCEAN & ARGANS SMOS+polarimetry.
1 © ACRI-ST, all rights reserved – 2012 TEC estimation Jean-Luc Vergely (ACRI-ST) Jacqueline Boutin (LOCEAN)
Aquarius Status Salinity Retrieval and Applications D. M. Le Vine NASA/GSFC, Greenbelt, MD E. P. Dinnat, G. Lagerloef, P. de Matthaeis, H. Kao, F.
The Aquarius Salinity Retrieval Algorithm Frank J. Wentz and Thomas Meissner, Remote Sensing Systems Gary S. Lagerloef, Earth and Space Research David.
Combined Active & Passive Rain Retrieval for QuikSCAT Satellite Khalil A. Ahmad Central Florida Remote Sensing Laboratory University of Central Florida.
Maintaining and Improving the AMSR-E and WindSat Ocean Products Frank J. Wentz Remote Sensing Systems, Santa Rosa CA AMSR TIM Agenda 4-5 September 2013.
Sea water dielectric constant, temperature and remote sensing of Sea Surface Salinity E. P. Dinnat 1,2, D. M. Le Vine 1, J. Boutin 3, X. Yin 3, 1 Cryospheric.
Cold Sky Calibration Aquarius: D. M. Le Vine MWR: J. C. Gallo.
Frank J. Wentz and Thomas Meissner Remote Sensing Systems Aquarius Salinity Algorithm and Simulations Aquarius/SAC-D Science Workshop July 2010,
Scatterometer Algorithm Simon Yueh, Alex Fore, Adam Freedman, Julian Chaubell Aquarius Scatterometer Algorithm Team July 19, 2010 Seattle.
Aquarius/SAC-D Mission Mission Simulators - Gary Lagerloef 6 th Science Meeting; Seattle, WA, USA July 2010.
Aquarius/SAC-D Mission Error Validation and Early Orbit Corrections Gary Lagerloef 6 th Science Meeting; Seattle, WA, USA July 2010.
This poster concerns the on-orbit validation of the antenna beam pointing and corresponding instantaneous field of view (IFOV) earth location for the CONAE.
ESTEC July 2000 Estimation of Aerosol Properties from CHRIS-PROBA Data Jeff Settle Environmental Systems Science Centre University of Reading.
MWR Algorithms (Wentz): Provide and validate wind, rain and sea ice [TBD] retrieval algorithms for MWR data Between now and launch (April 2011) 1. In-orbit.
Initial Results on the Cross- Calibration of QuikSCAT and Oceansat-2 Scatterometers David G. Long Department of Electrical and Computer Engineering Brigham.
Galactic noise model adjustment Jean-Luc Vergely (ACRI-ST) Jacqueline Boutin (LOCEAN) Xiaobin Yin (LOCEAN)
Aquarius Algorithm Meeting To Do Lists. From Frank Wentz:  Implement Ruf RFI flagging  Implement other QC flags  Further test review, and finalize.
MWR Roughness Correction Algorithm for the Aquarius SSS Retrieval W. Linwood Jones, Yazan Hejazin, Salem Al-Nimri Central Florida Remote Sensing Lab University.
EECS 823 MACHARIA.  Four-frequency, linearly-polarized, passive microwave radiometric system which measures atmospheric, ocean and terrain microwave.
COST 723 Training School - Cargese October 2005 OBS 2 Radiative transfer for thermal radiation. Observations Bruno Carli.
1 EE 543 Theory and Principles of Remote Sensing Derivation of the Transport Equation.
Aquarius Ancillary Data Ancillary data types and sources were extracted from the Level 3 Science Algorithm Requirements. Today’s objectives are to: –Agree.
Aquarius Algorithm Workshop March 2007 College of Engineering Department of Atmospheric, Oceanic & Space Sciences Chris Ruf Space Physics Research.
Evaluation of Microwave Scatterometers and Radiometers as Satellite Anemometers Frank J. Wentz, Thomas Meissner, and Deborah Smith Presented at: NOAA/NASA.
Calibration and Validation Studies for Aquarius Salinity Retrieval PI: Shannon Brown Co-Is: Shailen Desai and Anthony Scodary Jet Propulsion Laboratory,
SCIENCE PROCESSING OVERVIEW David Le Vine Aquarius Deputy PI 07 July 2009.
25 June 2009 Dawn Conway, AMSR-E TLSCF Lead Software Engineer AMSR-E Team Leader Science Computing Facility.
Satellite-derived Sea Surface Temperatures Corey Farley Remote Sensing May 8, 2002.
T. Meissner, F. Wentz, J. Scott, K. Hilburn Remote Sensing Systems ESA Ocean Salinity Science and Salinity Remote Sensing Workshop November.
Level 2 Algorithm. Definition of Product Levels LevelDescription Level 1 1A Reconstructed unprocessed instrument data 1B Geolocated, calibrated sensor.
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.
Aquarius Algorithm Workshop Santa Rosa, CA 9 March 2010 College of Engineering Department of Atmospheric, Oceanic & Space Sciences Chris Ruf University.
Space Reflecto, November 4 th -5 th 2013, Plouzané Characterization of scattered celestial signals in SMOS observations over the Ocean J. Gourrion 1, J.
Sea Surface Salinity from Space Simulation of Aquarius brightness temperature, Tb, at spacecraft during one orbit Top Panel –Ground track (red) –Outer.
Workshop Agenda: Day One 9:30 IntroductionLagerloef / Le Vine 9:45 Workshop objectivesG. Feldman 10:00 Overview of the Aquarius Data Processing System:G.
Simulator Wish-List Gary Lagerloef Aquarius Principal Investigator Cal/Val/Algorithm Workshop March GSFC.
Mission Operations Review February 8-10, 2010 Cordoba, ARGENTINA SECTION 16.x Aquarius Science Commissioning and Acceptance Draft 2 Prepared by: Gary Lagerloef,
Aquarius Mission Simulation A realistic simulation is essential for mission readiness preparations This requires the ability to produce realistic data,
T. Meissner and F. Wentz Remote Sensing Systems 2014 Aquarius / SAC-D Science Team Meeting November , 2014 Seattle. Washington,
Use of AMSR-E Land Parameter Modeling and Retrievals for SMAP Algorithm Development Steven Chan Eni Njoku Joint AMSR Science Team Meeting Telluride, Colorado.
Level 2 Scatterometer Processing Alex Fore Julian Chaubell Adam Freedman Simon Yueh.
Aquarius Simulation Studies Gary Lagerloef Aquarius Principal Investigator Algorithm Workshop 9-11 March 2010.
ADPS Science Software Development Bryan Franz NASA Ocean Biology Processing Group Aquarius Data Processing Workshop, NASA/GSFC, March 2007.
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.
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
A step toward operational use of AMSR-E horizontal polarized radiance in JMA global data assimilation system Masahiro Kazumori Numerical Prediction Division.
Recent SeaWiFS view of the forest fires over Alaska Gene Feldman, NASA GSFC, Laboratory for Hydrospheric Processes, Office for Global Carbon Studies
Aquarius Level 0-to-1A Processing Rule #1: save everything from the Level 0 data. Rule #2: never forget Rule #1! The objective is to ensure that the Level.
Radiometer Calibration: Implementation of Counts to TA Processor Frank Wentz and Thomas Meissner Aquarius Algorithm Workshop, Santa Rosa, CA, March 9-11,
SMOS Science Meeting September 2011 Arles, FR Simulating Aquarius by Resampling SMOS Gary Lagerloef, Yann Kerr & Eric Anterrieu and Initial Results.
Impact of sea surface roughness on SMOS measurements A new empirical model S. Guimbard & SMOS-BEC Team SMOS Barcelona Expert Centre Pg. Marítim de la Barceloneta.
Obs-sim[ECMWF] obs-sim[AIRS] Dashed curve = ECMWF curve shifted to AIRS curve at nadir This is our best estimate of scan bias Motivation: AIRS-retrieval.
Dependence of SMOS/MIRAS brightness temperatures on wind speed: sea surface effect and latitudinal biases Xiaobin Yin, Jacqueline Boutin LOCEAN.
Status and plans for AMSR-E calibration Keiji Imaoka JAXA Earth Observation Research Center (EORC) X-CAL Meeting University of Central Florida, Orlando.
Radiance Simulation System for OSSE  Objectives  To evaluate the impact of observing system data under the context of numerical weather analysis and.
Passive Microwave Remote Sensing
T. Meissner, F. Wentz, J. Scott, K. Hilburn Remote Sensing Systems 2014 Aquarius / SAC-D Science Team Meeting November , 2014.
Cassini Huygens EECS 823 DIVYA CHALLA.
HSAF Soil Moisture Training
Calibration Activities of GCOM-W/AMSR2
Roughness Correction for Aquarius (AQ) Sea Surface Salinity (SSS) Algorithm using MicroWave Radiometer (MWR) W. Linwood Jones, Yazan Hejazin Central FL.
Extreme Wind Speed Measurements from NASA’s SMAP L-Band Radiometer
An Update on the Activities of the Precipitation Measurement Missions (i.e. TRMM/GPM) XCAL Team PMM XCAL Team Wesley Berg, Rachael Kroodsma, Faisal Alquaeid,
Frank Wentz and Carl Mears Remote Sensing Systems, Santa Rosa CA, USA
Soil Moisture Active Passive (SMAP) Satellite
AMSR-E RFI Update - Towards RFI Adaptive Algorithms 1.0
Presentation transcript:

Level-1A to Level-1B Spacecraft data to Geolocation Data and Antenna Temperature Level-1B to Level-2A Antenna Temperatures Converted to TOA Brightness Temperature Level-2A to Level-2B Sea-surface Salinity found from TOA TB plus Ancillary Data Based on Aquarius Science Pre-CDR Level 1 and 2 Algorithms, Frank J. Wentz July, 2006 Algorithm Theoretical Basis Document Aquarius Level-2 Radiometer Algorithm: Revision 1 January Aquarius Data Processing

Aquarius Sun sunlat, sunlon sundis Solar Reflection refllat refllon reflinc Gain Angles Direct and Reflected Solar tht_global_sun(2) phi_global_sun(2) Boresight cellat cellon celinc celazm celpra Solar Backscatter suninc sunazm sunglt Galaxy Big Bang glxlat glxlon (J2KM) Moon moonlat, moonlon moondis Earth Surface Radiation Seen by Aquarius Atmosphere

Level 1A Data Essentially Organized Version of Level 0  Data organized in orbital files Start near the South Pole where z-velocity=0.  Orbit contain 20% overlap on both ends Accommodate count averaging  Spacecraft Position in MJ2K Coordinates: [x, y, z, t] Coordinates have been verified with CONAE  Spacecraft Attitude [roll, pitch, yaw, t]  Radiometer Counts: Earth and Calibration  Thermistor Counts  Housekeeping Counts

Level-1A to Level-1B Processing Geolocation S/C subtrack latitude, longitude altitude, zang. For each horn observation every 1.44 sec: 1. Geodetic latitude and east longitude 2. Boresight incidence angle and azimuth angle 3. Sun incidence and azimuth angle (for backscattering computation) 4. Polarization rotation angles relative to Earth 5. Zenith and azimuth angles for sun and moon 6. Zenith and azimuth angles for reflected ray (MJ2K coordinates) Sun and Moon Contamination Approximate increase in TA due to (requires antenna pattern) 1. Direct and Reflected Sun (assuming nominal flux) 2. Reflected Moon RFI Detection Flags applied to radiometer counts Earth maps of number of occurrences stratified by horn number, pol, and asc/dsc Radiometer Count Averaging Earth Counts averaged 1.44 sec (one complete radiometer cycle) Calibration Counts averaged for longer time periods (TBD) Convert Thermistor Counts to Temperature (K) Level-1A Data All time tagged, MJ2K coordinates S/C Position, Velocity, Attitude Radiometer Counts Thermistor Counts Compute Antenna Temperature TAv, TAh, TAp, TAm (one value for each complete 1.44 s radiometer cycle)

Geolocation  Oblate Spheroid Earth Model: WGS-84 RE= D3, RP= D3  Sensor Point Geometry for 3 Horns Nadir Angle = [ o, o, o ] Azimuth Angle= [ 9.76 o, o, 6.50 o ]  Rotation Matrix for Sensor/Spacecraft Misalignment  Vector formulation in MJ2K coordinates  Plenty of Heritage (SSM/I,TMI,AMSR, etc.)

Sun and Moon Contamination  Direct and reflected solar radiation is estimated using antenna pattern measurements  Reflected lunar radiation is also found  Uncertainty in pattern measurements is large  May just be useful as a quality flag

RFI Detection  Done before any averaging of Earth counts  Statistical outlier analysis  Earth maps of persistent sources  Suspected Earth counts are flagged and excluded from averaging

Count Averaging and Thermistor Temperatures  Earth counts are averaged for 1.44 sec (one complete radiometer cycle)  Calibration counts are averaged over longer intervals  Thermistor counts are converted to physical temperatures using laboratory derived coefficients

Antenna Temperature Calculation

Direct solar radiation included in T B,Space. Reflected and backscattered solar radiation included in T B (  ) The Antenna Temperature Equation

Level-1B to Level-2A Processing Remove Radiation from Far Sidelobes Option: Fixed table computed by running simulator: Tb_land_correction(1440,720,2,12,3,2) Tb_land_correction(nlon,nlat,nasc,nmon,nhorn,npol) Interpolated via. 3-D linear interp. Fixed table computed by running simulator: Ta_space(3,360,360,3); Ta_space(nch,nday,nzang,nhorn) Interpolated via. 2-D linear interp. Option: Dynamic table of solar flux TAsund_actual=TAsund*(flux/flux0) Faraday Rotation Correction Compute TOA TB Remove Radiation from Space Level-1B Data Geolocation Parameters TAsund, TAsunr, TAmoonr TAv, TAh, TAp, TAm Flags (rfi, TBD) Option: TAsunr from L1B Remove Coastline Contamination TB_TOA = a1*TA1 + a2*TA2

Removal of Radiation from Space  Cosmic background, TB=2.73 K  Galactic radiation (smoothed by antenna pattern)  Earth limb (very small, TA=0.006)  Pre-computed tables a function of day-of-year and orbit position  Direct solar radiation (expected to be <0.05 K, optional, computed during Level-1)

Removal of Reflected Solar and Lunar Radiation  Reflected solar radiation Expected to be <0.05 K, optional, computed during Level-1  Reflected lunar radiation In mainlobe and may be correctable

Top of the Atmosphere (TOA) TB (slide 1) Definition of TOA TB: A simple average (no weighting) of the upwelling brightness at the top of the atmosphere The average is just over the 3-dB footprint The incidence angle is constant An effective incidence angle can be used in place of the boresight incidence angle. In other words: TOA TB is independent of antenna characteristics and is just a function of the environment and specified incidence angle. Over the Open Ocean: Very detailed and elaborate simulations show: TB_TOA = a1*TA1 + a2*TA2 to a 1-sigma accuracy of 0.04 K TA1 and TA2 and the first and second TA stokes measurements after removing space contribution and doing Faraday Rotation Correction. See The Estimation of TOA T B from Aquarius Observations, RSS Report , January 30, 2006 Coefficients a1 and a2 determined before launch using scale-model antenna patterns. They may be revised after launch to remove any global, absolute difference between Aquarius TOA TB and TOA TB coming from the RTM.

Over Extended Land Areas: The expression: TB_TOA = a1*TA1 + a2*TA2 still works very well, although a1 and a2 are slightly different Areas Containing a Mixture of Land and Water: Very difficult to maintain accuracies required for salinity retrievals Possibly one can extended salinity retrievals towards the coast by 100 km (?). We propose using the simulator to produce correction tables. Tb_land_correction(1440,720,2,12,3,2) {0.5 GB} by 720 is a 0.25  latitude, longitude map 2 is ascending/descending orbit 12 is months of year 3 is horns 2 is polarizations Table can be redone at the end of the mission using more realistic L-band land temperatures. Top of the Atmosphere (TOA) TB (slide 2)

Coastline Correction Table for Ascending Orbit Segments for 1 st Stokes Coastline Correction Map

Level-2A to Level-2B Processing Remove Reflected and Scattered Radiation 1. Galactic reflected 2. Solar backscatter 3. Moon reflected NCEP Profiles of temperature, pressure, vapor Interpolated via. 3-D linear interp. Compute atmospheric upwelling and downwelling radiation (TBup, TBdw) and transmittance t. Salinity Retrieval Algorithm Remove Radiation from the Atmosphere Level-2A Data Geolocation Parameters TBtoav, TBtoah Flags (TBD) NCEP 10-m wind Interpolated via. 3-D linear interp. Fixed table giving galactic maps with varying amounts of smoothing TB_gal(1440,720,21) TB_gal(nlon,nlat,nwind) Interpolated via. 3-D linear interp. Options: solar backscatter=f(tht,thtsun,azm-azmsun,wind) TAmoonr from L1B. !!! Salinity !!! NCEP 10-m wind (TBR) NCEP SST (TBR) Interpolated via. 3-D linear interp.

Sea-Surface Emission  Atmospheric parameters come from NCEP 6-hour fields  Spatial averaging of galactic radiation is an important consideration  Option for computing backscattered solar radiation

Regression algorithm trained with simulated data (Possibly more terms will be added to account for non-linearities)  SST comes from best available souces (MISST/GHRSST)  Wind from scatterometer and/or ancillary data  Inc. angle knowledge is critical See: Salinity Error due to Surface Roughness Effects RSS Memorandum Estimation of Sea Surface Salinity

Earth Scene Ocean: Salinity, SST, Wind fields Land: Soil moisture, vegetation type, LST Ice: Ice type and temperature Atmosphere (including limb): NCEP profiles TA Integration Full 4-Stokes Integration over Earth and Space Sun Year 2000 actual values Easily scalable Cosmic Background 2.7 K Galaxy To be implemented Faraday Rotation Actual TEC values Earth Magnetic Field Orbiting Antenna CONAE Orbit Parameters Roll/Pitch/Yaw now included Aquarius Scale Model patterns Radiometer Piepmeier Forward Model for TA to counts TBTB TBTB TBTB TBTB T B rotated TATA Radiometer Counts Orbiting Thermal Model Simple harmonic of orbit position Thermistor Response Func. Linear with temperature Temperatures Thermistor Counts Orbit Position End-to-End Aquarius On-Orbit Simulator: Part 1

End-to-End Aquarius On-Orbit Simulator: Part 2 Radiometer CountsThermistor Counts Pre-Formatter Format in Group, Block, and Sub-Block Structure Telemetry Formatter Format in Group, Block, and Sub-Block Structure Scatterometer DataPlatform Data Simulated Downlink Telemetry Level-0 to Level-1A Processing Level-1A to Level-1B Processing Level-1B to Level-2 Processing Level-2 to Level-3 Processing Antenna Temperature TOA Brightness Temperature Swath Salinity, SST, wind, etc Time-Averaged Salinity Fields

Scale Model Gain Pattern

Includes –surface temperature and moisture from NCEP (simultaneous) –Surface type (bare, ice, grass, crop, tree (tropical, deciduous, conifer)) from EUROCLIMAP monthly/annual climatology –Soil roughness effect –Vegetation effect –L-band dielectric model of Dobson et al Land Emissivity Model

Simulates, based on ATBD (Piepmeier/Pellerano/Wilson/Yueh 2005) –radiometer (Ta  counts) –Ta retrieval (counts  Ta) Used minimum 2 calibration looks for v-/h-pol and 4 calibration looks for 3 rd Stokes Fully used correlated noise diodes Accuracy is better than 0.01K Testing TA  Counts  TA

Components of Aquarius On-orbit Simulator 1. Complete integration of the 4-Stokes parameters over the complete 4p steradians 2. Direct and reflected solar radiation for year Cosmic and galactic spillover contribution (galactic TBI) 4. Earth Limb Contribution 5. Faraday rotation in the ionosphere 6. Full slant-path integration through NCEP atmospheres 7. Surface emissivity from NCEP wind fields, Reynolds SST fields, & ECCO salinity model. 8. Intensive numerics with integration error < 0.01 K

Error Modeling for Aquarius On-orbit Simulator 1. Year 2000 (maximum of last solar cycle) used for ionosphere electron density 2. Worst case Faraday rotation (Julian day 303 in 2003) 3. NEDT for 6-sec average = 0.08 K 3. Incidence angle knowledge error is 0.03 deg std. dev. error added to incidence angle 4. SST knowledge error is 0.3 C 5. Wind speed knowledge error is 0.5 m/s (also 1.0 m/s) 6. Six-hour variability added to atmospheric model 7. No correction for solar radiation (included in simulation but not retrieval)

Atmospheric Absorption at 1.4 GHz

Global Results for Salinity Retrievals (7-days) Reference Retrievals

Salinity Retrieval Performance (7days) Mean Std. Dev.

Temp Loss T ND =500K, T DL =290K, T CND =500K TA to Voltage (count) Forward Simulation

3 rd Stokes Calibration: gain and offset Estimating G pv, G ph, o p, G mv, G mh, o m –3 calibration looks are needed (used 4 looks - overdetermined) Estimating G pU (same for G mU ) –4 th calibration look (v CND ) is used –v p,earth =earth count at 10milisec interval –T CND,v and T CND,h are set to T CND /2

3 rd Stokes Calibration: gain and offset T U produces v p and v m signals Thus v p and v m are used to estimate T U Yet v p and v m are affected also by T v and T h Manipulating the forward equation yields –First, retrieve earth-view T v and T h –Then, estimate G pv, G ph, G mv, G mh, G pU, G mU, o p, o m. –Then, remove contributions of T v and T h to v p and v m –Finally, account for G pU, G mU, o p, o m