View
225
Download
1
Category
Preview:
Citation preview
Remote Sensing Systems Climate Satellite Program
Frank J. Wentz and Carl MearsRemote Sensing Systems, Santa Rosa, CA
Supported in part by : NASA’s Earth Science Division
NOAA’s Scientific Data Stewardship and Climate Program Office
Presented at: CICS Mtg., Univ. of Maryland, September 8, 2010
Engineering Climate Data Records: The Challenges
Large Volume: Over 150 satellite-years of observations from Microwave Radiometers
Microwave Imagers (13) SSM/I: F08, F10, F11 F13 ,F14, F15 SSM/IS: F16, F17, F18 TMI AMSR-E and AMSR WindSat
Microwave Sounders (16) MSU: Tiros-N, NOAA 6, 7, 8, 9, 10, 11, 12, and 14 AMSU: NOAA 15, 16 ,17 18, and 19, Aqua, MetOP A
Difficult Calibration: Each sensor has its unique set of problems
Sensor pointing and S/C attitude errors Antenna pattern knowledge error Scan-dependent errors Sun intrusion and thermal gradients in Hot Load Emissive Antenna
High Precision Required: Climate Variability typical 1% of the Mean
Engineering Climate Data Records: The Method
Maintain a High Level of ConsistencyThe steadfast adherence to the same principles and methods in data processing all the way from radiometer counts to climate time series, including:
GeolocationRadiometer calibrationGeophysical retrievals
On-Going and Comprehension ValidationRadiosondes temperature and vaporGPS vaporOcean buoy winds and SSTScatterometer wind retrievalsSST, Cloud and Rain retrievals from satellites IR sensors
Much of this validation is done by the User community via peer reviewed papers.
Sensor Calibration and Geophysical Retrievals Use Same Radiative Transfer Model = Same Retrieval Algorithm for all Sensors
All MW imagers are calibrated to the same RTMAll retrievals come from the inverse of the RTM (RTM-1)
The Fundamental, Fundamental Data Record: Radiometer Counts
Consistency in data processing requires the antenna temperatures or brightness temperature obtained from various data providers like TDRs from NOAA, NASA Level 1 dataset , JAXA L1A data be reversed engineered back to the original radiometer counts in the telemetry downlink. Also satellite position, velocity, and attitude vectors are required
RSS maintains a complete archive of radiometer counts (both earth view and calibration), and associated spacecraft ephemeris and housekeeping data (thermistors).
Dataset is fully q/c and organized into time-ordered orbital files.
Volume size is relatively small
Non-Controversial
Stable
Basis for all product generation at RSS (Ta, Tb, CDR, gridded products)
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
Calibration
Methodology: Continuous Updating and Reprocessing
Cycle Time ≈ ½ Year
Radiometer Counts
Same ΔTa (simulated minus measured) plotted versus different parameters Same color scale: ΔTa goes from -3K to +3K
0.5 1 1.5 2 2.5
x 104
0102030405060708090
100110120130140150160170180190200210220230240250260270280290300310320330340350360
-3
-2
-1
0
1
2
3
0.5 1 1.5 2 2.5
x 104
0102030405060708090
100110120130140150160170180190200210220230240250260270280290300310320330340350360
-3
-2
-1
0
1
2
3
0.5 1 1.5 2 2.5
x 104
0102030405060708090
100110120130140150160170180190200210220230240250260270280290300310320330340350360
-3
-2
-1
0
1
2
3
0.5 1 1.5 2 2.5
x 104
0102030405060708090
100110120130140150160170180190200210220230240250260270280290300310320330340350360
-3
-2
-1
0
1
2
3
0 10 20 300
10
20
30
-2
0
2
0 10 20 300
10
20
30
-2
0
2
0 10 20 300
10
20
30
-2
0
2
0 10 20 300
10
20
30
-2
0
2
0 20 40 600
10
20
30
-2
0
2
0 20 40 600
10
20
30
-2
0
2
0 20 40 600
10
20
30
-2
0
2
0 20 40 600
10
20
30
-2
0
2
0 10 20 300
20
40
60
-2
0
2
0 10 20 300
20
40
60
-2
0
2
0 10 20 300
20
40
60
-2
0
2
0 10 20 300
20
40
60
-2
0
2
40 60 80 100 120 140
40
60
80
100
120
140
-3
-2
-1
0
1
2
3
40 60 80 100 120 140
40
60
80
100
120
140
-3
-2
-1
0
1
2
3
40 60 80 100 120 140
40
60
80
100
120
140
-3
-2
-1
0
1
2
3
40 60 80 100 120 140
40
60
80
100
120
140
-3
-2
-1
0
1
2
3
Y=Wind, X=SST Y=Wind, X=Vapor Y=Vapor, X=SST
Y=Orbit Position, South Pole to South Poel, X=Orbit number (5 years) Y=Sun Polar Angle, X=Sun Azimuth Angle
RTM Error Diagnostics
Sensor CalibrationError Diagnostics
Distinguishing Sensor Errors from RTM Errors
Sun intrudingInto hot load
240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 27520
21
22
23
24
25
Wa
ter
Va
por
(mm
)
Months (240=Dec 2006)
240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 27520
21
22
23
24
25
Wa
ter
Va
por
(mm
)
Months (240=Dec 2006)
240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 27520
21
22
23
24
25
Wa
ter
Va
por
(mm
)
Months (240=Dec 2006)
F17F13
F17F13
F17F13
Comparing F17 and F13 Water Vapors (Monthly Averages)
Version 7
Version 7
Version 7
Bias (SSMI minus GPS) = -0.07 mmStd. Dev. = 1.9 mmNum Obs = 53,730Credit: Carl Mears (to be published)
Geophysical Validation: Columnar Water Vapor
Version – 7 100 Satellite-Years of Earth System Data Records Consistently Processed with Common AlgorithmsSame RTM Used for Calibration and Retrievals for all Sensors
The Products
SST (not all sensors) Wind Speed Columnar Water Vapor Columnar Liquid Water Rain Rate
The Sensors
SSM/I: F08, F10, F11, F13, F14, F15 SSM/IS: F16, F17, F18 TMI AMSR-E and AMSR-A WindSat
AvailabilityBackbone: F13, WindSat, F16, and F17 now available
Remaining Sensors will be made publicly available end of 2010.
Products will be hosted at www.remss.com
MSU/AMSU Climate Data Records
Trend Map, Middle Troposphere (1979-2009)
Time Series for Each Channel
MSU/AMSU Uncertainty AnalysisTwo types of Analysis
“Bottom Up”Estimated Errors Propagated Through Merging Procedure using Monte-Carlo Techniques• Sampling• Adjustments (local time, etc)• Residual Calibration Errors
“Top Down”Compare to other measurements of the same parameters
• Other sounder datasets
• Radisondes
• GPS-RORange of simulated errors
Satellite, Radiosonde and ReanalysisComparison
BACKUP SLIDES
Individual overpasses: RMS variation of SSMI minus bouy difference = 1.0 m/sMonthly Averages: RMS variation of SSMI minus bouy difference = 0.1 m/s ( lag-1 correlation =0.46)Estimate error bar is 0.05 m/s/decade at 95% confidence SSM/I trend minus the buoy trend is 0.02 m/s/decade.
Yearly corrections (0.05-0.10 m/s) are applied to SSMI winds (equivalent to 50 mK TB adjustments)These results indicate we are doing better than 0.1 K/decade
Geophysical Validation: Wind Speed Trends
Figure from : Wentz, Ricciardulli, Hilburn, Mears: Science, July 13, 2007, How Much More Rain Will Global Warming Bring?
Climate Constraint: Constant Relative Humidity
Correlation Coef.W::TS = 0.84W::TA = 0.71TA::TS = 0.69
Correlation Coef.W::TS = 0.98W::TA = 0.93TA::TS = 0.94
Scaling Coef.W/TS = 9.2%/KW/TA = 6.7%/KTA/TS = 1.4
Scaling Coef.W/TS = 8.5%/KW/TA = 6.8%/KTA/TS = 1.2
Scaling Coef.W/TS = 8.6%/KW/TA = 4.6%/KTA/TS = 1.9
Correlation Coef.W::TS = 0.73W::TA = 0.36TA::TS = 0.56
WTS
TA/1.6
Decadal TrendW = 1.9%/decTS = 0.20K/decTA = 0.04K/dec
WTS
TA/1.6
WTS
TA/1.6
Decadal TrendW = 2.1%/decTS = 0.23K/decTA = 0.22K/dec
Decadal TrendW = 1.0%/decTS = 0.03K/decTA = 0.00K/dec
Tem
per
atu
re (
K)
Vap
or
(%)
-0.4
-0.2
0.0
0.2
0.4
-4
-2
0
2
420N to 60N
Tem
per
atu
re (
K)
Vap
or
(%)
-0.4
-0.2
0.0
0.2
0.4
-4
-2
0
2
420S to 20N
Year
Tem
per
atu
re (
K)
Vap
or
(%)
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98
-0.4
-0.2
0.0
0.2
0.4
-4
-2
0
2
460S to 20S
Figure from : Wentz and Schabel: Nature, 403,2000, Precise climate monitoring using complementary satellite data sets.
Nearly all climate models predict a near constant Relative Humidity with global warming.This means total water vapor will increase with rising air temperatures at a rate ≈ 7%/K
Original MSU air temperatures showed little warming while SSM/I water vapors showed significant moistening.
This contradiction was due to errors in the original calibration of MSU.
Results here show with proper calibration MSU air temperature and SSM/I water vapors agree.
The “MSU controversy” was resolved.
Parameter Mean Std. Dev. Trend Evaporation 961 mm/yr 10.1 mm/yr (1.1%) 12.6 mm/yr/decade (1.3%/decade) Precipitation 950 mm/yr 12.7 mm/yr (1.3%) 13.2 mm/yr/decade (1.4%/decade) Total Water 28.5 mm 0.292 mm (1.0%) 0.354 mm/decade (1.2%/decade)
Climate Constraint: Evaporation = Precipitation
Figure from : Wentz, Ricciardulli, Hilburn, Mears: Science, July 13, 2007, How Much More Rain Will Global Warming Bring?
On global, monthly time scales, Evaporation must equal Precipitation (Variability in storage term is extremely small)
Climate Constraint: Radiative Cooling Limit on Evaporation
Climate Models: 4 mm/year/decadeWentz et al., Science, July 13, 2007: 13 mm/year/decade (C-C rate)Yu and Weller, BAMS, Vol 88, No 4, April 2007: 50 mm/year/decade J. R. Christy et al., Geophys. Res. Lett., 28, 183, 2001: 21 mm/year/decade Hamburg Ocean Atmos. Parameters & Fluxesfrom Satellite Data (IGARSS 2006 presentation) very large
Explanation of Christy Result
Found a trend in the Tropical Air-Sea Temperature Difference: -0.1 K/decade (1979-1999)
Used to support their MSU Lower Tropospheric Temperature Retrieval, which indicated: In the tropics, the troposphere is not warming as fast as the surface. (Later was found to be incorrect)
From the standpoint of evaporation:A trend of -0.1 K/decade in the air-sea temperature difference would greatly increase the trend in global evaporation
Nearly all climate models predict an enhanced radiative cooling that is balance by an increase in latent heat from precipitation
Geolocation
Analysis
Attitude
Adjustment
Along-Scan
Correction
Absolute
Calibration
Hot Load
Correction
Antenna
Emissivity
Resampling
Algorithm
Rain
Threshold
OOB
Q/C
SSM/I F08, F10, F11 F13 ,F13, F15
NRL/RSS No Yes APC Yes 0 Opt. Intrep. 0.18 mm Ocean RTM
TMI Goddard Dynamic Yes TA Offsets No 3.5% Opt. Intrep. 0.18 mm Ocean RTM
AMSR-E RSS Fixed Yes APC Yes 0 Opt. Intrep. 0.18 mm Ocean RTM
AMSR-A RSS Dynamic Yes APC Yes 0 Opt. Intrep. 0.18 mm Ocean RTM
WindSat NRL/RSS Fixed Yes APC Yes 0 Earth-Grid Weighted Average
0.18 mm Ocean RTM
SSM/IS
F16, F17
RSS No Yes APC Yes 1 - 4%
Note: 1
Opt. Intrep. 0.18 mm Ocean RTM
STEP-1: All Level-1 (NASA) or TDR (DMSP) datasets are reversed engineered back to radiometer counts
Level-1 Processing: Counts-to-Antenna Temperature
STEP-2: Apply a completely consistent set of Level-1 Routines to produce calibrated antenna temperatures
Note 1: Over 19 – 92 GHz
Preliminary Results Over Land
266 268 270 272 274 276 278 280 282 284 2860
0.2
0.4
0.6
0.8
1
Antenna Temperaure (K)
PD
F w
ith m
axim
um
se
t to
1
evening F13 SSMIevening F16 SSMISevening F17 SSMISevening Windsat
266 268 270 272 274 276 278 280 282 284 2860
0.2
0.4
0.6
0.8
1
Antenna Temperaure (K)
PD
F w
ith m
axim
um
se
t to
1
morning F13 SSMImorning F16 SSMISmorning F17 SSMISmorning Windsat
270 272 274 276 278 280 282 284 286 288 2900
0.2
0.4
0.6
0.8
1
Brightness Temperaure (K)
PD
F w
ith m
axim
um
se
t to
1
evening F13 SSMIevening F16 SSMISevening F17 SSMISevening Windsat
270 272 274 276 278 280 282 284 286 288 2900
0.2
0.4
0.6
0.8
1
Brightness Temperaure (K)
PD
F w
ith m
axim
um
se
t to
1
morning F13 SSMImorning F16 SSMISmorning F17 SSMISmorning Windsat
Calibration is done just over ocean.Land can be considered a withheld data set.How well does the ocean calibration extrapolate to land?Key factors: Radiometer linearity and accuracy of ocean RTM
Histograms of 19 GHz V-pol at the high end: TA above and TB below.F13, F16, F17 consistent to about 0.25 K. Windsat has different spatial resolution
Recommended