Maintaining and Improving the AMSR-E and WindSat Ocean Products

Frank J. WentzRemote Sensing Systems, Santa Rosa CA

AMSR TIM Agenda4-5 September 2013Mandalay 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

AMSR-E WindSat Improvement Section Pending Tested Implemented Pending Tested Implemented V7 TB Calibration 1.2.7 X X X X Winds through Rain 1.2.2 X X X RFI Mitigation 1.2.3 X X Sun Glitter Removal 1.2.4 X N/A N/A N/A Error Estimates 1.2.4 X X

Figure 2. An example of retrieving winds through rain for a WindSat pass over Super Storm Sandy. The left panel shows the WindSat vector wind retrievals and the right panel shows the HRD wind field.

Winds Through Rain

New Winds-Through-Rain Product Rain-free WindSat All-weather WindSat Rain rate

Rain RateWindSat – BUOY Wind Speed

[m/s] Bias Standard Deviation

no rain 0.04 0.9

light rain 0 – 3 mm/h 0.70 1.6

moderate rain3 – 8 mm/h 0.02 2.0

heavy rain> 8 mm/h -0.05 2.5

Figure 3. An example of RFI mitigation for descending AMSR-E passes over Europe during 2010. The left (right) panel shows the AMSR-E minus Reynolds SST retrieval before (after) RFI mitigation.

RFI Mitigation

Figure 4. An example of the effect of sun glitter (dark red streaks) on the daytime AMSR-E SST retrievals in the southern oceans. AMSR-E minus Reynolds SST differences are shown.

Sun Glitter Removal

Based on Recent Work Removal of Galactic Reflections for Aquarius

Figure 5. An example of error bars being placed on AMSR-E wind speed retrievals

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. WentzRemote Sensing Systems, Santa Rosa CA

AMSR TIM Agenda4-5 September 2013Mandalay 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 ObservationsGCOM-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, 1993-2011

SST Trend = -0.155 K per decadeWind Trend = 0.387 m/s per decade

High Wind Trends from Altimeters Trend Discrepancies in NINO4 Region

Discrepancies in Wind TrendsSatellite Wind Trends (1988-2011) Mean CMIP-3 Wind Trends (1976-1999)

MERRA Wind Trends (1988-2011) ERA-Interim Wind Trends (1988-2011)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 Adjustments RTM Adjustments

Automatic

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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

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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 accurate0.2 K absolute (TBD), and 0.1 K relativeMeissner and Wentz (2012): IGARSS Paper of the Year AwardPublically available

RTM[ T,W,V,L from WindSat ] Highly accurate simulated AMSR-2 Brightness TemperaturesSame 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 GHz2. APC3. Non-Linear correction

Amazon Forest calibration needed because of non-linearity issue.

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Receiver Non-Linearity is an Important Issue for AMSR-2

Each image shows a separate channel.

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Ocean CalibrationDifference of AMSR-2 TB Minus RTM TB using WindSat Retrievals

Before Vapor/Cloud Diurnal Adjustment After Vapor/Cloud Diurnal Adjustment

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Amazon Forest CalibrationAfter 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

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Each image shows a separate channel.

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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