AMSR Bias Busting Find, Beat, Repeat, Report

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Presentation transcript:

AMSR Bias Busting Find, Beat, Repeat, Report Marty Brewer Frank J. Wentz, Kyle Hilburn, Thomas Meissner Carl Mears, Chelle Gentemann Remote Sensing Systems, Santa Rosa CA Research Supported by NASA’s Earth Science Division AMSR Science Team Meeting Huntsville, Alabama September 22-23, 2014

Detecting 6.9 GHz Ocean Reflected RFI RTM Methods Polarization Ratios Detecting and Evading 10.65 GHz RFI Adaptive Algorithm Wind Speeds through Rain =======================The Fold============================ Intercalibrating AMSR-2 with AMSR-E Using WindSat as a Bridge Adapted from Tokyo, January, 2014

Communication Satellite Detecting 6.9 GHz Ocean Reflected RFI RTM Methods Polarization Ratios Detecting and Evading 10.65 GHz RFI Adaptive Algorithm Wind Speeds through Rain =======================The Fold============================ Intercalibrating AMSR-2 with AMSR-E Using WindSat as a Bridge Adapted from Tokyo, January, 2014

Communication Satellite Detecting 6.9 GHz Ocean Reflected RFI RTM Methods Polarization Ratios Detecting and Evading 10.65 GHz RFI Adaptive Algorithm Wind Speeds through Rain =======================The Fold============================ Intercalibrating AMSR-2 with AMSR-E Using WindSat as a Bridge Adapted from Tokyo, January, 2014

SST in the Gulf of Mexico Linked to Drought Area in U.S. AMSR-E, WindSat, & AMSR-2 SST in the Gulf of Mexico Linked to Drought Area in U.S. “In 2010, the area of severe drought is only 3.3%, compared with the average of 19.5%. This indicates that variability in the Loop Current can have profound impacts on precipitation (and hence agriculture) in the United States.” -Hilburn

AMSR-2 RFI Mask – Implemented Jan, 2014

AMSR-2 RFI Mask – Implemented Jan, 2014 Something’s Missing

Diurnal Warming of SST in low wind speeds Yes, but… Stronger in AMSR-2 than AMSR-E? Suspicious. At Night? Preposterous!!!

Diurnal Warming of SST in low wind speeds Yes, but… Stronger in AMSR-2 than AMSR-E? Suspicious. At Night? Preposterous!!! Low winds, high SSTs: Ocean Reflected RFI

6.9GHz Space-Based Ocean-Reflected RFI Night

6.9GHz Space-Based Ocean-Reflected RFI Day

6.9GHz Space-Based Ocean-Reflected RFI Night

6.9GHz Space-Based Ocean-Reflected RFI Day

6.9GHz Space-Based Ocean-Reflected RFI Night

6.9GHz Space-Based Ocean-Reflected RFI

6.9GHz Space-Based Ocean-Reflected RFI

6.9GHz Space-Based Ocean-Reflected RFI

Reported by JAXA in AMSR-E: Globalstar satellite telephone service 52+ satellites in Low Earth Orbit (LEO) 6.825-7.075 GHz ICO 10+ satellites in Medium Earth Orbit (MEO) 6.875-7.055 GHz 6GHz RFI from space Makoto Yoshikawa, Yasuhiro Fujimoto Mitsubishi Space Software Co., Ltd. Keiji Imaoka, and Akira Shibata Japan Aerospace Exploration Agency, EORC Joint AMSR Science Team Meeting 13-15 September 2005 University of Hawaii at Manoa, Honolulu, HI

Detecting 6.9 GHz Ocean Reflected RFI RTM Methods Polarization Ratios

6.9 10.65 18.7 23.8 36.5 SST Very Low X SST Low Res Wind Very Low Wind Low Res

AMSR-2 2014.04.25 SST 6.9

AMSR-2 2014.04.25 SST 6.9 – 10.6

AMSR-2 2014.04.25 wind 6.9 – 10.6

AMSR-E 2011.05.09 SST 6.9 – 10.6

AMSR-E 2011.05.09 SST 6.9 – 10.6

AMSR-E 2011.05.09 SST 6.9 – 10.6

AMSR-E 2011.05.09 wind 6.9 – 10.6

AMSR-E 2011.05.09 wind 6.9 – 10.6

AMSR-E 2011.05.09 wind 6.9 – 10.6

AMSR-E 2011.05.09 wind 6.9 – 10.6

AMSR-E 2011.05.09 6.9V

AMSR-E 2011.05.09 6.9V Ascension Island Negative RFI?!?!?

AMSR-E 2011.05.09 6.9V

AMSR-E 2011.05.09 6.9H

AMSR-E 2011.05.09 6.9 H/V Polarization ratio

AMSR-E 2011.05.09 10.65 H/V Polarization ratio

AMSR-E 2011.05.09 6.9 H/V - 10.65 H/V Polarization ratio difference

AMSR-E 2011.05.09 wind 6.9 – 10.6

Detecting 6.9 GHz Ocean Reflected RFI RTM Methods Polarization Ratios

Detecting 6.9 GHz Ocean Reflected RFI RTM Methods – hard to beat Polarization Ratios – 7.3 GHz TBD

Detecting 6.9 GHz Ocean Reflected RFI RTM Methods – hard to beat Polarization Ratios – 7.3 GHz TBD Detecting and Evading 10.65 GHz RFI RTM Methods Adaptive Algorithm

SST “No 10” Algorithm 6.9 10.65 18.7 23.8 36.5 SST Very Low X SST Low Res Wind Very Low Wind Low Res

SST “No 10” – SST ALL 6.9 10.65 18.7 23.8 36.5 SST ALL X SST “No 10” SST Low Res Wind Very Low Wind Low Res

SST “No 10” – SST ALL 6.9 10.65 18.7 23.8 36.5 SST ALL X SST “No 10”

SST “No 10” – SST ALL 6.9 10.65 18.7 23.8 36.5 SST ALL X SST “No-10”

SST “No 10” – SST ALL 6.9 10.65 18.7 23.8 36.5 SST ALL X SST “No-10”

SST “No 10” – SST ALL 6.9 10.65 18.7 23.8 36.5 SST ALL X SST “No-10” TB Perturbation (1K) SST ALL – SST “No 10” (K) None -0.008 6V -0.416 6H 0.239 10V 0.647 10H -0.598 18V -0.185 18H 0.133 6V and 6H -0.192 10V and 10H 0.064 18V and 18H 0.000

SST “No 10” – SST ALL 6.9 10.65 18.7 23.8 36.5 SST ALL X SST “No-10” TB Perturbation (1K) SST ALL – SST “No 10” (K) None -0.008 6V -0.416 6H 0.239 10V 0.647 10H -0.598 18V -0.185 18H 0.133 6V and 6H -0.192 10V and 10H 0.064 18V and 18H 0.000

SST “No 10” – SST ALL 6.9 10.65 18.7 23.8 36.5 SST ALL X SST “No-10” TB Perturbation (1K) SST ALL – SST “No 10” (K) 10V 0.647 10H -0.598

SST “No 10” – SST ALL 6.9 10.65 18.7 23.8 36.5 SST ALL X SST “No-10” TB Perturbation (1K) SST ALL – SST “No 10” (K) 10V 0.647 10H -0.598

RFI: 10V 10H SST “No 10” – SST ALL TB Perturbation (1K) SST ALL – SST “No 10” (K) 10V 0.647 10H -0.598

SST “No 10” – SST ALL RFI: 10V 10H

RFI: 10H SST “No 10” – SST ALL Astra Constellation One of many footprints Astra Constellation

SST “No 10” – SST ALL RFI: 10V 10H

SST “No 10” – SST ALL RFI: 10V 10H

SST “No 10” – SST ALL

SST “No 10” – SST ALL

SST “No 10” – SST ALL 6.9 10.65 18.7 23.8 36.5 SST ALL X SST “No-10” TB Perturbation (1K) SST ALL – SST “No 10” (K) None -0.008 6V -0.416 6H 0.239 10V 0.647 10H -0.598 18V -0.185 18H 0.133 6V and 6H -0.192 10V and 10H 0.064 18V and 18H 0.000

SST “No 10” – SST ALL 6.9 10.65 18.7 23.8 36.5 SST ALL X SST “No-10” TB Perturbation (1K) SST ALL – SST “No 10” (K) 18V -0.185 18H 0.133

SST “No 10” – SST ALL 6.9 10.65 18.7 23.8 36.5 SST ALL X SST “No-10” TB Perturbation (1K) SST ALL – SST “No 10” (K) 18V -0.185 18H 0.133

SST “No 10” – SST ALL TB Perturbation (1K) SST ALL – SST “No 10” (K) 18V -0.185 18H 0.133

SST “No 10” – SST ALL

WindSat: The 2 Look Advantage

Detecting 6.9 GHz Ocean Reflected RFI RTM Methods Polarization Ratios Detecting and Evading 10.65 GHz RFI Adaptive Algorithm Wind Speeds through Rain =======================The Fold============================ Intercalibrating AMSR-2 with AMSR-E Using WindSat as a Bridge Adapted from Tokyo, January, 2014

Detecting 6.9 GHz Ocean Reflected RFI RTM Methods Polarization Ratios Detecting and Evading 10.65 GHz RFI Adaptive Algorithm Wind Speeds through Rain aka Wind All Weather (AW) Wind AW =======================The Fold============================ Intercalibrating AMSR-2 with AMSR-E Using WindSat as a Bridge Adapted from Tokyo, January, 2014

AMSR-E Wind AW HRD Winds (6 km) HRD winds (resampled to 50 km) AMSR-E 6.9 GHz TB H-pol AMSR-E 10.7 GHz TB H-pol AMSR-E Rain (25 km)

Hurricane Katrina, 2005-08-28T07:30Z

Arigato Gozai Mas Thank You Detecting 6.9 GHz Ocean Reflected RFI RTM Methods Polarization Ratios Detecting and Evading 10.65 GHz RFI Adaptive Algorithm Wind Speeds through Rain =======================The Fold============================ Intercalibrating AMSR-2 with AMSR-E Using WindSat as a Bridge Adapted from Tokyo, January, 2014 Thank You

Geophysical Retrievals from GCOM-W AMSR-2 Radiative Transfer Model (RTM) Inversion (RTM-1) SST, Wind, Vapor, Cloud, Rain Frank J. Wentz Marty Brewer Remote Sensing Systems, Santa Rosa CA Research Supported by NASA’s Earth Science Division

RSS Inventory of Satellite Microwave Observations: AMSR-2 Needs to be Consistently Calibration into Existing Record

RSS Inventory of Satellite Microwave Observations: AMSR-2 Needs to be Consistently Calibration into Existing Record

RSS Inventory of Satellite Microwave Observations: AMSR-2 Needs to be Consistently Calibration into Existing Record

RSS Inventory of Satellite Microwave Observations: AMSR-2 Needs to be Consistently Calibration into Existing Record

RSS Inventory of Satellite Microwave Observations: AMSR-2 Needs to be Consistently Calibration into Existing Record

RSS Inventory of Satellite Microwave Observations: AMSR-2 Needs to be Consistently Calibration into Existing Record

RSS Inventory of Satellite Microwave Observations: AMSR-2 Needs to be Consistently Calibration into Existing Record

RSS Inventory of Satellite Microwave Observations: AMSR-2 Needs to be Consistently Calibration into Existing Record

RSS Inventory of Satellite Microwave Observations: AMSR-2 Needs to be Consistently Calibration into Existing Record

RSS Inventory of Satellite Microwave Observations: AMSR-2 Needs to be Consistently Calibration into Existing Record 6:00 PM 1:30 PM ~4.5 hours (daytime)

6:00 PM 1:30 PM 6:00 AM 1:30 AM ~7.5 hours (night) ~4.5 hours (day)

6:00 PM 1:30 PM 6:00 AM 1:30 AM ~7.5 hours (night) ~4.5 hours (day)

6:00 AM ~4.5 hours (night) 1:30 AM

Geophysical Retrievals from GCOM-W AMSR-2 Radiative Transfer Model (RTM) Inversion (RTM-1) SST, Wind, Vapor, Cloud, Rain Chelle Gentemann Frank J. Wentz, Kyle Hilburn Marty Brewer Remote Sensing Systems, Santa Rosa CA

Radiative Transfer Model (RTM) Inversion (RTM-1) SST, Wind, Vapor, Cloud, Rain

Radiative Transfer Model (RTM) Inversion (RTM-1) SST, Wind, Vapor, Cloud, Rain

Radiative Transfer Model (RTM) Inversion (RTM-1) SST Wind Speed Rain Rate Water Vapor Cloud Liquid

SST Wind Speed Rain Rate Water Vapor Cloud Liquid

SST Wind Speed Rain Rate Water Vapor Cloud Liquid

SST Wind Speed Rain Rate Rain Free Scenes Water Vapor Cloud Liquid

SST Wind Speed Rain Rate Rain Free Scenes Water Vapor Cloud Liquid

W T V L

WindSat W T V L

TA WindSat W T TB V L

TB TA WindSat W T TB V L

TB TA WindSat RTM-1 T,W,V,L W T TB V L

T W V L TB T,W,V,L TA RTM-1 TB WindSat Adjust: Frequency, Incidence Angle T,W,V,L W T TB V L

T W V L TB T,W,V,L TA RTM-1 TB AMSR-2 WindSat Adjust: Frequency, Incidence Angle T,W,V,L W T TB V L

T W V L TB T,W,V,L TA RTM-1 RTM TB TB Simulated AMSR-2 AMSR-2 WindSat Adjust: Frequency, Incidence Angle T,W,V,L W T TB V L

T W V L TB T,W,V,L TA RTM-1 RTM TB TB + 4.5 hours Simulated AMSR-2 WindSat RTM-1 RTM Adjust: Frequency, Incidence Angle + 4.5 hours T,W,V,L W T TB V L

T W V L TB TB T,W,V,L TA RTM-1 RTM TA TB TB + 4.5 hours Simulated AMSR-2 TB TA TA AMSR-2 WindSat RTM-1 RTM Adjust: Frequency, Incidence Angle + 4.5 hours T,W,V,L W TB T TB V L

T W V L TB TB T,W,V,L TA RTM-1 RTM TA TB TB + 4.5 hours Simulated Find best fit: Simulated AMSR-2 TB NL, HL, spillover TA TA AMSR-2 WindSat RTM-1 RTM Adjust: Frequency, Incidence Angle + 4.5 hours T,W,V,L W TB T TB V L

T W V L TB TB T,W,V,L TA RTM-1 RTM TA TB TB + 4.5 hours Simulated Find best fit: Simulated AMSR-2 TB NL, HL, spillover TA TA NL, HL, spillover AMSR-2 WindSat RTM-1 RTM Adjust: Frequency, Incidence Angle + 4.5 hours T,W,V,L W TB T TB V L

T W V L TB TB T,W,V,L TA RTM-1 RTM TA TB TB TB + 4.5 hours Simulated Find best fit: Simulated AMSR-2 TB NL, HL, spillover TA TA NL, HL, spillover TB AMSR-2 WindSat RTM-1 RTM Adjust: Frequency, Incidence Angle + 4.5 hours T,W,V,L W TB T TB V L

T W V L TB TB T,W,V,L TA RTM-1 RTM TA TB TB TB + 4.5 hours Simulated Find best fit: Simulated AMSR-2 TB NL, HL, spillover TA TA NL, HL, spillover TB AMSR-2 WindSat RTM-1 RTM Compare: ‘Observed’ to ‘Simulated’ Adjust: Frequency, Incidence Angle + 4.5 hours T,W,V,L W TB T TB V L

T W V L TB TB T,W,V,L TA RTM-1 RTM TA TB TB TB + 4.5 hours Simulated Find best fit: Simulated AMSR-2 TB NL, HL, spillover TA TA NL, HL, spillover TB AMSR-2 WindSat RTM-1 RTM Compare: ‘Observed’ to ‘Simulated’ Adjust: Frequency, Incidence Angle + 4.5 hours T,W,V,L W TB T TB V L

T W V L TB TB T,W,V,L TA RTM-1 RTM TA TB TB TB + 4.5 hours Simulated Find best fit: Simulated AMSR-2 TB NL, HL, spillover TA TA NL, HL, spillover TB AMSR-2 WindSat RTM-1 RTM Adjust: Frequency, Incidence Angle + 4.5 hours T,W,V,L W TB T TB V L

T W V L TB TB T,W,V,L T,W,V,L TA RTM-1 RTM TA TB TB TB + 4.5 hours Find best fit: Simulated AMSR-2 TB NL, HL, spillover TA TA NL, HL, spillover TB AMSR-2 WindSat RTM-1 RTM Adjust: Frequency, Incidence Angle + 4.5 hours T,W,V,L W TB T TB T,W,V,L V L

T W V L TB TB T,W,V,L T,W,V,L TA RTM-1 RTM TA TB TB TB + 4.5 hours Find best fit: Simulated AMSR-2 TB NL, HL, spillover TA TA NL, HL, spillover TB AMSR-2 WindSat RTM-1 RTM Adjust: Frequency, Incidence Angle + 4.5 hours T,W,V,L W TB T TB T,W,V,L V L Tune: Vapor, Cloud

T W V L TB TB T,W,V,L T,W,V,L TA RTM-1 RTM TA TB TB TB + 4.5 hours Find best fit: Simulated AMSR-2 TB NL, HL, spillover TA TA NL, HL, spillover TB AMSR-2 WindSat RTM-1 RTM Adjust: Frequency, Incidence Angle + 4.5 hours T,W,V,L W TB T TB T,W,V,L V L Tune: Vapor, Cloud

T W V L TB TB T,W,V,L T,W,V,L TA RTM-1 RTM TA TB TB TB + 4.5 hours TB Find best fit: Simulated AMSR-2 TB NL, HL, spillover TA TA NL, HL, spillover TB AMSR-2 WindSat RTM-1 RTM Adjust: Frequency, Incidence Angle + 4.5 hours TB T,W,V,L RTM W TB T TB T,W,V,L V L Tune: Vapor, Cloud

T W V L TB TB T,W,V,L T,W,V,L TA RTM-1 RTM TA TB TB TB + 4.5 hours TB Find best fit: Simulated AMSR-2 TB NL, HL, spillover TA TA NL, HL, spillover TB AMSR-2 WindSat RTM-1 RTM Adjust: Frequency, Incidence Angle + 4.5 hours TB T,W,V,L RTM W TB T TB T,W,V,L V L Tune: Vapor, Cloud

Do ALL channels “tune” together? TB Find best fit: Simulated AMSR-2 TB NL, HL, spillover TA TA NL, HL, spillover TB AMSR-2 WindSat RTM-1 RTM Do ALL channels “tune” together? Adjust: Frequency, Incidence Angle + 4.5 hours TB T,W,V,L RTM W TB T TB T,W,V,L V L Tune: Vapor, Cloud

T W V L TB TB T,W,V,L TA RTM-1 RTM TA TB TB TB + 4.5 hours Simulated Find best fit: Simulated AMSR-2 TB NL, HL, spillover TA TA NL, HL, spillover TB AMSR-2 WindSat RTM-1 RTM Adjust: Frequency, Incidence Angle + 4.5 hours T,W,V,L W TB T TB V L

T W V L TB TB T,W,V,L T,W,V,L TA RTM-1 RTM TA TB TB TB + 4.5 hours Find best fit: Simulated AMSR-2 TB NL, HL, spillover TA TA NL, HL, spillover TB AMSR-2 WindSat RTM-1 RTM Adjust: Frequency, Incidence Angle + 4.5 hours RTM-1 T,W,V,L W TB T TB T,W,V,L V L

T W V L TB TB T,W,V,L T,W,V,L TA RTM-1 RTM TA TB TB TB TB + 4.5 hours Find best fit: Simulated AMSR-2 TB NL, HL, spillover TA TA NL, HL, spillover TB TB AMSR-2 WindSat RTM-1 RTM Adjust: Frequency, Incidence Angle + 4.5 hours RTM-1 RTM T,W,V,L W TB T TB T,W,V,L V L

T W V L TB TB = T,W,V,L T,W,V,L TA ? RTM-1 RTM TA TB TB TB TB Find best fit: Simulated AMSR-2 TB NL, HL, spillover TA TA NL, HL, spillover = TB TB AMSR-2 WindSat ? RTM-1 RTM Closure Adjust: Frequency, Incidence Angle + 4.5 hours RTM-1 RTM T,W,V,L W TB T TB T,W,V,L V L

RTM Calibration Methodology Ocean Radiative Transfer Model (RTM) is Calibration Reference for all MW Radiometers 0.2 K absolute (TBD), and 0.1 K relative Meissner and Wentz (2012): IGARSS Paper of the Year Award Publicly available Inputs for RTM are the WindSat Retrievals of SST, Wind, Vapor, and Cloud (Rain excluded) WindSat is highly stable Observation period from 2003 to present Overlaps both AMSR-E and AMSR-2 and hence serves as a Calibration Bridge Ocean retrievals have been thoroughly validated Co-location window (6:00 AM -> (~4.5 hours ) -> 1:30 AM) 1:30 PM <-> 6:00 PM not used (large diurnal variability) RTM[ T,W,V,L from WindSat ]  Simulated AMSR-2 Brightness Temperatures Amazon Forest calibration needed because of Receiver Non-Linearity.

Calibration Model RTM Brightness Temperature TB (k) Calibration Parameters Non-Linear coefs: a1-5 Hot Load Offset Antenna Spillover δ Cross-Pol NOT adjusted 3 80 250 280 Cold Space Range of Ocean Values Amazon Forest AMSR2 Antenna Temperature TA (K)

Hot Load Offset and Antenna Spillover Adjustments Hot Load Temperature Offset: -1.3 K Adjustments to the antenna spillover value has the same effect as adding an offset to the hot load temperature. A single hot load offset for all channels is found that minimizes the required changes to the spillover values. The adjustment for AMSR2 is to subtract 1.3 K from the temperature reported by the hot load thermistors. This adjustment is consistent with the -1.0 K offset found for previous radiometers Spillover Adjustments (290 K times fractional spillover value)

Non-Linear Correction: Red Curves are JAXA Non-Linear Correction ( Marehito Kasahara 21 Feb 2013 X-Cal presentation) Black Curves are values coming from RSS analysis.

Brightness Temperature (TB) Validation/Evaluation of Calibration In-Situ comparisons not available, so use a variety of methods, including, but not limited to… Amazon Canopy (~ land temperatures) - compare monthly/yearly averages: AMSR-2 TB <-> TB WindSat, SSMIs Ocean Comparisons 1. Observed AMSR-2 – AMSR-2 Predicted by WindSat: Measured AMSR-2 TB (NL, HL, δ) <-> Simulated AMSR-2 TB (RTM(WSAT(T,W,V,L))) Look at Mission Plots for V, H: - before vapor+cloud adjustment - after vapor+cloud adjustment - zoom in, look for systematic errors 2. Observed AMSR-2 – AMSR-2 Predicted by AMSR-2: Measured AMSR-2 TB (NL, HL, δ) <-> Simulated AMSR-2 TB (RTM(AMSR-2(T,W,V,L))) aka “Closure Analysis” Look at Earth Gridded mission averages: - ascending - descending minus ascending

Amazon Forest Calibration Before Adjusting Hot-Load Temperature, APC, and Non-Linear Correction AMSR-2 WSAT AMSR-2 WSAT Black diamonds are WindSat. Red diamonds are AMSR-E. Green diamonds are AMSR-2. Colored squares are the 6 SSM/Is Same months used for averages, but averaging years are different.

Amazon Forest Calibration After Adjusting Hot-Load Temperature, APC, and Non-Linear Correction AMSR-2 WSAT AMSR-2 WSAT Black diamonds are WindSat. Red diamonds are AMSR-E. Green diamonds are AMSR-2. Colored squares are the 6 SSM/Is Same months used for averages, but averaging years are different.

AMSR-2 TB Minus RTM(WindSat T,W,V,C) TB Over Ocean Before Vapor/Cloud Adjustment 6V 7V 11V 19V 24V 37V 89aV 89bV

AMSR-2 TB Minus RTM(WindSat T,W,V,C) TB Over Ocean After Vapor/Cloud Adjustment 6V 7V 11V 19V 24V 37V 89aV 89bV

AMSR-2 TB Minus RTM(WindSat T,W,V,C) TB Over Ocean After Vapor/Cloud Adjustment 6V 7V 11V 19V 24V 37V 89aV 89bV

AMSR-2 TB Minus RTM(WindSat T,W,V,C) TB Over Ocean Before Vapor/Cloud Adjustment 6H 7H 11H 19H 24H 37H 89aH 89bH

AMSR-2 TB Minus RTM(WindSat T,W,V,C) TB Over Ocean After Vapor/Cloud Adjustment 6H 7H 11H 19H 24H 37H 89aH 89bH

AMSR-2 TB Minus RTM(WindSat T,W,V,C) TB Over Ocean After Vapor/Cloud Adjustment 6H 7H 11H 19H 24H 37H 89aH 89bH

AMSR-2 TB minus RTM with AMSR-2 Ocean Retrievals Closure Analysis: AMSR-2 TB minus RTM with AMSR-2 Ocean Retrievals Each image shows a separate channel. All 16 channels are shown. Only Ascending Orbit Segments

AMSR-2 TB minus RTM with AMSR-2 Ocean Retrievals Closure Analysis: AMSR-2 TB minus RTM with AMSR-2 Ocean Retrievals Each image shows a separate channel. All 16 channels are shown. Descending Minus Ascending Orbit Segments

Summary Most calibration issues for AMSR-2 are typical for satellite microwave radiometers Receiver non-linearity is a bit unusual and needs to be better understood First Round of TB Calibration is completed Ocean Products have been Validate with other satellite retrievals and buoys Ocean Products have been available for download at www.remss.com since February, 2014 ftp://ftp.remss.com/amsr2 RFI Continues to be worrisome but Adaptive Mitigation Strategies are being employed

Arigato Gozai Mas Thank You Geophysical Retrievals from GCOM-W AMSR-2 Radiative Transfer Model (RTM) Inversion (RTM-1) SST, Wind, Vapor, Cloud, Rain Chelle Gentemann Frank J. Wentz, Kyle Hilburn Marty Brewer Remote Sensing Systems, Santa Rosa CA Research Supported by NASA’s Earth Science Division Joint PI Workshop Global Environment Observation Mission 2013 Earth Observation Research Center, JAXA TKP Gardencity Takebashi, Tokyo JAPAN January 17, 2014