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 Mandalay Beach, CA

 Retrieve accurate wind speeds when rain is present  Mitigate RFI contamination  Remove sun glitter contamination  Assign error bars to each and every retrieval AMSR-E and WindSat Algorithm Improvements

Winds Through Rain

New Winds-Through-Rain Product Rain Rate WindSat – BUOY Wind Speed [m/s] Bias Standard Deviation no rain light rain 0 – 3 mm/h moderate rain 3 – 8 mm/h heavy rain > 8 mm/h

RFI Mitigation

Sun Glitter Removal Based on Recent Work Removal of Galactic Reflections for Aquarius

Assignment of Error Bars Error Bars and Dynamic Quantities

 Retrieve accurate wind speeds when rain is present  Mitigate RFI contamination  Remove sun glitter contamination  Assign error bars to each and every retrieval Proposed Future Work for AMSR-E and WindSat

Using WindSat as a Calibration Bridge from AMSR-E to AMSR-2 Frank J. Wentz Remote Sensing Systems, Santa Rosa CA AMSR TIM Agenda 4-5 September 2013 Mandalay Beach, CA

Climate Change: Hydrologic Cycle and General Circulation Probably the Greatest Consequences of Our Warming Climate will be Related to Changes to Hydrologic Cycle and General Circulation: Drought, Floods, Severe Storms Is the Hydrologic Cycle Accelerating? Is the Walker Circulation Intensifying? Is the Hadley Cell becoming More Energetic? How will Precipitation Increase with Global Warming? Slide 10

35-Years of Microwave Earth Observations GCOM-W and GCOM-W2 Continues the Advancement Slide 11

Quarter Century Trend Maps of Wind and Vapor Regional Trend Patterns are 5+ times larger than the estimated 2-sigma error. They are real.

Intensification of Walker Circulation as Evidenced by Increasing Surface Winds in the Tropical Pacific Sea-Surface Height, SST Trend = K per decade Wind Trend = m/s per decade

High Wind Trends from Altimeters Trend Discrepancies in NINO4 Region Discrepancies in Wind Trends Satellite Wind Trends ( )Mean CMIP-3 Wind Trends ( ) MERRA Wind Trends ( )ERA-Interim Wind Trends ( ) Nino-4

Climate Models Do Not Produce True Large-Scale, Quarter-Century Climate Features Slide 15

Standard Error in Satellite Trend Estimated to be 0.05 mm/decade (0.2%/decade) Discrepancies in Vapor Trends

Geophysical Retrievals Validation EP Adjustments ( i.e., clear sky bias, high vapor bias) Retrieval Algorithm Radiative Transfer Model Simulated Antenna Temperatures Sensor Antenna Temperatures Sensor AdjustmentsRTM Adjustments Automatic Calibration Cycle Time ≈ ½ Year Engineering Climate Data Records Version-7 Calibration Methodology  Precision of 0.1 K or smaller  Use same RTM for calibrating all satellites  Use RTM -1 for same retrieval algorithm for all satellites

F16 SSM/I Problem Inter-Comparison of Radiometer Wind Time Series F13 SSMI, F16 & F17 SSM/IS, WindSat (F31), and AMSR-E (F32) Agreement is at 0.1 m/s Level

Inter-Comparison of Radiometer Vapor Time Series F13 SSMI, F16 & F17 SSM/IS, WindSat (F31), and AMSR-E (F32) Agreement is at 0.1 mm Level AMSR-E > WindSat: Vapor in Rain

WindSat as a Calibration Bridge to AMSR-2  Both AMSR-E and WindSat are at the V-7 Calibration Standard  WindSat is Very Stable  Years of Analysis have gone into comparing WindSat and AMSR-E  Diurnal differences are mostly understood  Goal: Make AMSR-2 versus WindSat look like AMSR-E versus WindSat

Proposed Calibration Methodology WindSat Ocean Products are accurate: SST, Wind, Vapor, and Cloud (T,W,V,L) They have been thoroughly validated and will be continue to be validated Ocean Radiative Transfer Model (RTM) is highly accurate 0.2 K absolute (TBD), and 0.1 K relative Meissner and Wentz (2012): IGARSS Paper of the Year Award Publically available RTM [ T,W,V,L from WindSat ]  Highly accurate simulated AMSR-2 Brightness Temperatures Same Version-7 Calibration Method use for other MW radiometer: 6 SSM/I, 2 SSM/IS, AMSR-E, and WindSat (soon TMI) Primary Calibration Adjustments: 1.Mean Hot Load Temperature: -1.8 K for 6-37 GHz; -0.8 K for 89 GHz 2.APC 3.Non-Linear correction Amazon Forest calibration needed because of non-linearity issue.

Red Curves are JAXA Non-Linear Correction ( Marehito Kasahara 21 Feb 2013 presentation) Black Curves are preliminary values coming from our analysis. Receiver Non-Linearity is an Important Issue for AMSR-2 Each image shows a separate channel. All 16 channels are shown.

Ocean Calibration Difference of AMSR-2 TB Minus RTM TB using WindSat Retrievals Before Vapor/Cloud Diurnal Adjustment After Vapor/Cloud Diurnal Adjustment 6.9 H 10.7H 18.7H 23.8H 37 H

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

Black triangles are WindSat. Red triangles are AMSR-E. Green triangles 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

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

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

Conclusions  We Expect AMSR-2 will Significantly Advanced Our Understanding of Climate Change.  The Various Calibration Issues are Typical for Satellite Microwave Radiometers, Although the Receiver Non-Linearity is a Bit Unusual.  RFI Continues to be Worrisome but Adaptive Mitigation Strategies Can be Employed