hirs upper tropospheric humidity and humidity profiles

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Frascati, Italy March 8-10, 20111 NOAA’s National Climatic Data Center

HIRS Upper Tropospheric Humidity and Humidity Profiles

Lei Shi

NOAA National Climatic Data CenterAsheville, NC, U.S.A.

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Outline of Upper Tropospheric Humidity

• Motivation for intersatellite calibration– Time series discontinuity from satellite to satellite, particularly

from HIRS/2 to HIRS/3– Upper tropospheric water vapor (UTWV) is an important

fundamental climate data record (CDR)– UTWV is a key component to water vapor feedback

• Approach– Intersatellite calibration based on overlaps of zonal means

• Result to achieve– Extended time series of the fundamental CDR to present

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Motivation – Uncorrected Intersatellite Differences of UTWV (Channel 12)

• Due to the independence of individual HIRS instrument’s calibration, biases exist from satellite to satellite.

• These intersatellite biases have become a common source of uncertainty faced by long-term studies.

Start of HIRS/3

HIRS/2

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Spectral Filter Functions

• Differences between HIRS/2 and HIRS/3 are expected due to different filter functions.

• In-orbit performance still has biases unexplained by filter functions.• Thus empirical approach is considered.

0

0.2

0.4

0.6

0.8

1

1350 1400 1450 1500 1550 1600

N06

N07

N08

N09

N10

N11

N12

N14

N15

N16

N17

Tran

Wave Number (per cm)

HIRS/2 HIRS/3

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• More than half of satellites have bias variations larger than 0.5 K.

Temperature-dependent Intersatellite Differences from Zonal Mean Approach

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Intersatellite Calibrated to N-12(showing clear-sky 30S – 30N)

• Biases minimized.

• Temperature dependent biases accounted for.

• Similar overall variances between HIRS/2 and HIRS/3/4.

• Time series can be extended as variance preserved.

Pairs T_N-N06

N06-N07

N07-N08

N09-N10

N10-N11

N11-N12

N12-N14

N14-N15

N15-N16

N16-N17

N17-M02

Ave. Diff.

0.076 -0.065 0.073 0.008 0.059 0.006 0.020 0.005 -0.024 0.050 0.020

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Climatologic Mean for Jan, Apr, Jul, and Oct

January April

July October

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Climatological Monthly Mean and Variance

• There are two peaks of mean UTWV brightness temperatures in 30N-30S, one in winter (January) and another one in summer (June).

• During these two seasons the subtropics are dominated by a belt of strong subsidence (in northern hemisphere during winter and in southern hemisphere during summer).

• The lows of the UTWV brightness temperatures are found in April and November, indicating weaker descending branch of the general circulation during spring and fall.

• Large variances occur during the winter and early spring months.

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For an easier comparison of UTWV with other conventional observations, Soden and Bretherton [1993] derived a formula to calculate the upper tropospheric humidity (UTH) based on UTWV brightness temperature as

UTH = cos(θ)exp(31.5-0.115Tb) (1)

In which Tb is the brightness temperature of HIRS channel 12 and θ is the satellite zenith angle.

Upper Tropospheric Humidity

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Time Series of High and Low UTH

• There are usually two peaks of large area coverages in a year, one in summer and another one in winter.

• The area peaks of large UTH values and small UTH values usually occur in the same month, indicating when there are large organized convections in some parts of tropics, there are enhanced descending areas in other parts of the tropics.

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UTH values

Trend (grids/yr)

Std Dev (grids) Lag-1

Yrs Sig (yrs)

Greater than 40% 2.5613 0.536 0.504 23.4

Less than 20% 2.1989 0.787 0.139 33.5

Trends of high and low UTH

• There is an increase of 2.6 grids/yr for the area with UTH values greater the 40%, and an increase of 2.2 grids/yr in the area of UTH with values less than 20%.

• The long-term area increases in both high and low UTH values reveal the likelihood of enhanced convective activities in the tropics.

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MJO

ER

Monitoring Tropical Waves

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Madden-Julian oscill. Kelvin waves Eq. Rossby waves

Monitoring Tropical Waves

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Outline of Humidity Profile

• Inter-satellite calibration.

• Neural network scheme for deriving temperature and water vapor profiles.

• Comparisons with surface observations.

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ChannelNumber

CentralWavenumber (cm-1 )

Wavelength(micrometers)

1 669 14.95

2 680 14.71

3 690 14.49

4 703 14.22

5 716 13.97

6 733 13.64

7 749 13.35

8 900 11.11

9 1,030 9.71

10 802 12.47

11 1.365 7.33

12 1,533 6.52

13 2,188 4.57

14 2,210 4.52

15 2,235 4.47

16 2,245 4.45

17 2,420 4.13

18 2,515 4.00

19 2,660 3.76

20 (visible channel) 14,500 0.690

High-Resolution Infrared Radiation Sounder (HIRS)/3 Spectral Characteristics.

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Non-Inter-calibrated Clear-sky HIRS Channel Brightness Temperatures

Non-Inter-calibrated Clear-sky HIRS Channel Brightness Temperatures

260

261

262

263

264

265

266

267

268

1980 1985 1990 1995 2000 2005

HIRS.SATS.TB.REGION

N05N06N07N08N09N10N11N12N14

Year

212

213

214

215

216

217

218

219

220

1980 1985 1990 1995 2000 2005

20S-20N, 160W-100W, 0-3UTC

N05N06N07N08N09N10N11N12N14

Year

259

259.5

260

260.5

261

261.5

262

262.5

263

1980 1985 1990 1995 2000 2005

20S-20N, 160W-100W, 0-3UTC

N05N06N07N08N09N10N11N12N14

Year

292

292.5

293

293.5

294

294.5

295

295.5

1980 1985 1990 1995 2000 2005

20S-20N, 160W-100W, 0-3UTC

N05N06N07N08N09N10N11N12N14

Year

CH 2 CH 6

CH 8 CH 11

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Non-Inter-calibrated Clear-sky HIRS Channel-5 Brightness

Temperatures

Non-Inter-calibrated Clear-sky HIRS Channel-5 Brightness

Temperatures

246

247

248

249

250

251

252

1980 1985 1990 1995 2000 2005

20S-20N, 160W-100W, 0-3UTC

N05N06N07N08N09N10N11N12N14

Year

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Simultaneous Nadir Overpass (SNO)

http://www.orbit.nesdis.noaa.gov/smcd/spb/calibration/intercal/

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-4

-3

-2

-1

0

1

2

3

4

200 210 220 230 240 250

CH1

T(K)

-1

-0.5

0

0.5

1

1.5

190 200 210 220 230 240

CH2

T(K)

-1.5

-1

-0.5

0

0.5

1

1.5

2

190 200 210 220 230 240

CH3

T(K)

-1.5

-1

-0.5

0

0.5

1

1.5

2

200 205 210 215 220 225 230 235 240

CH4

T(K)

-1

-0.5

0

0.5

1

210 215 220 225 230 235 240 245 250

CH5

T(K)

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

200 210 220 230 240 250

CH6

T(K)

-1.5

-1

-0.5

0

0.5

1

1.5

200 210 220 230 240 250 260

CH7

T(K)

-0.4

-0.2

0

0.2

0.4

0.6

200 210 220 230 240 250 260 270 280

CH8

T(K)

-0.4

-0.2

0

0.2

0.4

0.6

200 210 220 230 240 250 260

CH9

T(K)

-10

-5

0

5

10

200 210 220 230 240 250 260 270

CH10

T(K)

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

200 210 220 230 240 250 260

CH11

T(K)

-1

0

1

2

3

4

5

205 210 215 220 225 230 235 240 245

CH12

T(K)

-1

0

1

2

3

4

5

205 210 215 220 225 230 235 240 245

CH12

N6-N7

N7-N8

N9-N10

N10-N11

N11-N12

N12-N14

N14-N15

N15-N16

N16-N17

Inter-satellite Biases of 12 HIRS Longwave Channels

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Seasonal Variation of Inter-satellite Differences

0.6

0.7

0.8

0.9

1

1.1

1.2

240

245

250

255

260

2002.5 2003 2003.5 2004 2004.5 2005

Seasonal Difference (N16-N17), CH07, Northern

Diff (K)

Tb (K)

Year

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CO2 Increase

From : http://scrippsco2.ucsd.edu/images/graphics_gallery/original/mlo_record.pdf

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CO2 Impact

• Averaged from Tb’s derived from 13,459 global profiles.• The only variable changed in the simulation is CO2 amount.• For mid-tropospheric channels (channels 4-6), this means if there

were no temperature change in the past 30 years, one would see decrease in the observed Tb’s.

• It is important to consider CO2 impact in the retrieval!

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Statistics of Training Data

160 180 200 220 240 260 280 300 320

0

200

400

600

800

1000


minmaxmean

Temperature (K)

0 5 10 15 20 25 30

0

200

400

600

800

1000


minmaxmean

Mixing Ratio (g/kg)

Temperature Specific Humidity

(K) (g/kg)

Training data: A diverse sample of ECMWF profiles selected from the 1st and 15th of each month between January 1992 and December 1993 (Chevallier, 2001)

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Model Simulation

• RTTOV-9 is used to simulate HIRS channel brightness temperatures.

– Advantage: model simulation of CO2 effect.

– Disadvantage: relationship can be affected by errors in pre-launch instrument measurements and model errors. However, the errors can be reduced by adjustment to observations (for example, adjusted to co-located HIRS and homogenized radiosonde samples).

• The selected profiles and corresponding HIRS brightness temperatures are randomly divided into three data sets:

- 60% training set- 20% testing set- 20% independent validation set

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Neural Network

• Separate neural networks for temperature and water vapor.

• Input: HIRS channels 2-12 and CO2 concentration.

• Output: Tskin, Ta, and temperature profiles from 1000 to 50 hPa and water vapor profiles from 1000 to 300 hPa.

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Retrieval Root Mean Square (RMS) Errors

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Comparison with a Buoy Observation

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Comparisons of Retrievals with and without HIRS Inter-satellite Calibration

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Comparison with Drifting and Moored Buoy Observations

Drifting Moored

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Conclusions

• Upper Tropospheric Humidity based on almost-all-sky data– Temperature dependent inter-calibration– Extension of time series to current

• HIRS/2 and HIRS/3 series connected

• 30 years of global data

• Humidity Profiles based on clear-sky data– The HIRS data are intersatellite-calibrated using data from

simultaneous nadir observations (SNOs).

– CO2 effect needs to be considered in the retrieval scheme.

– A temperature and specific humidity retrieval scheme is developed based on neural network technique.

– The retrievals are consistent with sea surface observations. Comparisons with land surface observations and with profile observations are being planned.

hirs upper tropospheric humidity and humidity profiles

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