Presentation is loading. Please wait.

Presentation is loading. Please wait.

AMSR Bias Busting Find, Beat, Repeat, Report

Published byMagdalen Copeland Modified over 8 years ago

Similar presentations

Presentation on theme: "AMSR Bias Busting Find, Beat, Repeat, Report"— Presentation transcript:

1 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

2 Detecting 6.9 GHz Ocean Reflected RFI
RTM Methods Polarization Ratios Detecting and Evading 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

3 Communication Satellite
Detecting 6.9 GHz Ocean Reflected RFI RTM Methods Polarization Ratios Detecting and Evading 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

4 Communication Satellite
Detecting 6.9 GHz Ocean Reflected RFI RTM Methods Polarization Ratios Detecting and Evading 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

5 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

6 AMSR-2 RFI Mask – Implemented Jan, 2014

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

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

9 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

10 6.9GHz Space-Based Ocean-Reflected RFI
Night

11 6.9GHz Space-Based Ocean-Reflected RFI
Day

12 6.9GHz Space-Based Ocean-Reflected RFI
Night

13 6.9GHz Space-Based Ocean-Reflected RFI
Day

14 6.9GHz Space-Based Ocean-Reflected RFI
Night

15 6.9GHz Space-Based Ocean-Reflected RFI

16 6.9GHz Space-Based Ocean-Reflected RFI

17 6.9GHz Space-Based Ocean-Reflected RFI

18 Reported by JAXA in AMSR-E:
Globalstar satellite telephone service 52+ satellites in Low Earth Orbit (LEO) GHz ICO 10+ satellites in Medium Earth Orbit (MEO) 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

19 Detecting 6.9 GHz Ocean Reflected RFI
RTM Methods Polarization Ratios

20 6.9 10.65 18.7 23.8 36.5 SST Very Low X SST Low Res Wind Very Low Wind Low Res

21 AMSR SST 6.9

22 AMSR SST – 10.6

23 AMSR wind 6.9 – 10.6

24 AMSR-E SST 6.9 – 10.6

25 AMSR-E SST 6.9 – 10.6

26 AMSR-E SST 6.9 – 10.6

27 AMSR-E wind 6.9 – 10.6

28 AMSR-E wind 6.9 – 10.6

29 AMSR-E wind 6.9 – 10.6

30 AMSR-E wind 6.9 – 10.6

31 AMSR-E V

32 AMSR-E V Ascension Island Negative RFI?!?!?

33 AMSR-E V

34 AMSR-E H

35 AMSR-E H/V Polarization ratio

36 AMSR-E H/V Polarization ratio

37 AMSR-E H/V H/V Polarization ratio difference

38 AMSR-E wind 6.9 – 10.6

39 Detecting 6.9 GHz Ocean Reflected RFI
RTM Methods Polarization Ratios

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

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

42 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

43 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

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

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

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

47 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

48 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

49 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

50 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

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

52 SST “No 10” – SST ALL RFI: 10V 10H

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

54 SST “No 10” – SST ALL RFI: 10V 10H

55 SST “No 10” – SST ALL RFI: 10V 10H

56 SST “No 10” – SST ALL

57 SST “No 10” – SST ALL

58 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

59 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

60 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

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

62 SST “No 10” – SST ALL

63 WindSat: The 2 Look Advantage

64 Detecting 6.9 GHz Ocean Reflected RFI
RTM Methods Polarization Ratios Detecting and Evading 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

65 Detecting 6.9 GHz Ocean Reflected RFI
RTM Methods Polarization Ratios Detecting and Evading 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

66 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)

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

68 Arigato Gozai Mas Thank You
Detecting 6.9 GHz Ocean Reflected RFI RTM Methods Polarization Ratios Detecting and Evading 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

69 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

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

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

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

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

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

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

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

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

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

79 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)

80 6:00 PM 1:30 PM 6:00 AM 1:30 AM ~7.5 hours (night) ~4.5 hours (day)

81 6:00 PM 1:30 PM 6:00 AM 1:30 AM ~7.5 hours (night) ~4.5 hours (day)

82 6:00 AM ~4.5 hours (night) 1:30 AM

83 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

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

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

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

87 SST Wind Speed Rain Rate Water Vapor Cloud Liquid

88 SST Wind Speed Rain Rate Water Vapor Cloud Liquid

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

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

91 W T V L

92 WindSat W T V L

93 TA WindSat W T TB V L

94 TB TA WindSat W T TB V L

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

96 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

97 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

98 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

99 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

100 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

101 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

102 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

103 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

104 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

105 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

106 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

107 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

108 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

109 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

110 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

111 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

112 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

113 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

114 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

115 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

116 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

117 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.

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

119 Hot Load Offset and Antenna Spillover Adjustments
Hot Load Temperature Offset: 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)

120 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.

121 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

122 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.

123 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.

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

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

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

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

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

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

130 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

131 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

132 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 since February, 2014 ftp://ftp.remss.com/amsr2 RFI Continues to be worrisome but Adaptive Mitigation Strategies are being employed

133 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

Download ppt "AMSR Bias Busting Find, Beat, Repeat, Report"

Similar presentations

Calibration Method of Microwave Tb for Retrieving Accurate SST Akira Shibata JAXA / EORC Advances of Satellite Oceanography at Vladivostok, Oct. 3-6, 2007.

Calibration Method of Microwave Tb for Retrieving Accurate SST Akira Shibata JAXA / EORC Advances of Satellite Oceanography at Vladivostok, Oct. 3-6, 2007.
F. Wentz, T. Meissner, J. Scott and K. Hilburn Remote Sensing Systems 2014 Aquarius / SAC-D Science Team Meeting November 11- 14,

F. Wentz, T. Meissner, J. Scott and K. Hilburn Remote Sensing Systems 2014 Aquarius / SAC-D Science Team Meeting November ,
Extension and application of an AMSR global land parameter data record for ecosystem studies Jinyang Du, John S. Kimball, Lucas A. Jones, Youngwook Kim,

Extension and application of an AMSR global land parameter data record for ecosystem studies Jinyang Du, John S. Kimball, Lucas A. Jones, Youngwook Kim,
Communicating Uncertainties for Microwave-Based ESDRs Frank J. Wentz, Carl A. Mears, and Deborah K. Smith Remote Sensing Systems, Santa Rosa CA Supported.

Communicating Uncertainties for Microwave-Based ESDRs Frank J. Wentz, Carl A. Mears, and Deborah K. Smith Remote Sensing Systems, Santa Rosa CA Supported.
Aquarius Status Salinity Retrieval and Applications D. M. Le Vine NASA/GSFC, Greenbelt, MD 20771 E. P. Dinnat, G. Lagerloef, P. de Matthaeis, H. Kao, F.

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.

The Aquarius Salinity Retrieval Algorithm Frank J. Wentz and Thomas Meissner, Remote Sensing Systems Gary S. Lagerloef, Earth and Space Research David.
All-Weather Wind Vector Measurements from Intercalibrated Active and Passive Microwave Satellite Sensors Thomas Meissner Lucrezia Ricciardulli Frank Wentz.

All-Weather Wind Vector Measurements from Intercalibrated Active and Passive Microwave Satellite Sensors Thomas Meissner Lucrezia Ricciardulli Frank Wentz.
Passive Measurements of Rain Rate in Hurricanes Ruba A.Amarin CFRSL December 10, 2005.

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

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.

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.
ATS 351 Lecture 8 Satellites

ATS 351 Lecture 8 Satellites
Analysis and Mitigation of Atmospheric Crosstalk Kyle Hilburn Remote Sensing Systems May 21, 2015.

Analysis and Mitigation of Atmospheric Crosstalk Kyle Hilburn Remote Sensing Systems May 21, 2015.
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.

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.
Recent activities on utilization of microwave imager data in the JMA NWP system - Preparation for AMSR2 data assimilation - Masahiro Kazumori Japan Meteorological.

Recent activities on utilization of microwave imager data in the JMA NWP system - Preparation for AMSR2 data assimilation - Masahiro Kazumori Japan Meteorological.
Intercalibration of AMSR-E and WindSat TB over Tropical Forest Scenes Thomas Meissner adapted by Marty Brewer for AMSR Science Team Meeting Huntsville,

Intercalibration of AMSR-E and WindSat TB over Tropical Forest Scenes Thomas Meissner adapted by Marty Brewer for AMSR Science Team Meeting Huntsville,
Yimin Ji - Page 1 October 5, 2010 Global Precipitation Measurement (GPM) mission Precipitation Processing System (PPS) Yimin Ji NASA/GSFC,

Yimin Ji - Page 1 October 5, 2010 Global Precipitation Measurement (GPM) mission Precipitation Processing System (PPS) Yimin Ji NASA/GSFC,
September 4 -5, 2013Dawn Conway, AMSR-E / AMSR2 TLSCF Lead Software Engineer AMSR-E / AMSR2 Team Leader Science Computing Facility Current Science Software.

September 4 -5, 2013Dawn Conway, AMSR-E / AMSR2 TLSCF Lead Software Engineer AMSR-E / AMSR2 Team Leader Science Computing Facility Current Science Software.
SMOS+ STORM Evolution Kick-off Meeting, 2 April 2014 SOLab work description Zabolotskikh E., Kudryavtsev V.

SMOS+ STORM Evolution Kick-off Meeting, 2 April 2014 SOLab work description Zabolotskikh E., Kudryavtsev V.
Evaluation of Microwave Scatterometers and Radiometers as Satellite Anemometers Frank J. Wentz, Thomas Meissner, and Deborah Smith Presented at: NOAA/NASA.

Evaluation of Microwave Scatterometers and Radiometers as Satellite Anemometers Frank J. Wentz, Thomas Meissner, and Deborah Smith Presented at: NOAA/NASA.
D istributed I nformation S ervices for C limate and O cean Products and V isualizations for E arth R esearch We provide multi-sensor, multi-platform highly.

D istributed I nformation S ervices for C limate and O cean Products and V isualizations for E arth R esearch We provide multi-sensor, multi-platform highly.

Similar presentations

Ads by Google