GlobVapour Frascati, Italy March 8-10, 2011 1 NOAA’s National Climatic Data Center HIRS Upper Tropospheric Humidity and Humidity Profiles Lei Shi NOAA National Climatic Data Center Asheville, NC, U.S.A.
Dec 23, 2015
GlobVapour
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.
GlobVapour
Frascati, Italy March 8-10, 20112 NOAA’s National Climatic Data Center
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
GlobVapour
Frascati, Italy March 8-10, 20113 NOAA’s National Climatic Data Center
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
GlobVapour
Frascati, Italy March 8-10, 20114 NOAA’s National Climatic Data Center
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
GlobVapour
Frascati, Italy March 8-10, 20115 NOAA’s National Climatic Data Center
• More than half of satellites have bias variations larger than 0.5 K.
Temperature-dependent Intersatellite Differences from Zonal Mean Approach
GlobVapour
Frascati, Italy March 8-10, 20116 NOAA’s National Climatic Data Center
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
GlobVapour
Frascati, Italy March 8-10, 20117 NOAA’s National Climatic Data Center
Climatologic Mean for Jan, Apr, Jul, and Oct
January April
July October
GlobVapour
Frascati, Italy March 8-10, 20118 NOAA’s National Climatic Data Center
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.
GlobVapour
Frascati, Italy March 8-10, 20119 NOAA’s National Climatic Data Center
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
GlobVapour
Frascati, Italy March 8-10, 201110 NOAA’s National Climatic Data Center
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.
GlobVapour
Frascati, Italy March 8-10, 201111 NOAA’s National Climatic Data Center
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.
GlobVapour
Frascati, Italy March 8-10, 201112 NOAA’s National Climatic Data Center
MJO
ER
Monitoring Tropical Waves
GlobVapour
Frascati, Italy March 8-10, 201113 NOAA’s National Climatic Data Center
Madden-Julian oscill. Kelvin waves Eq. Rossby waves
Monitoring Tropical Waves
GlobVapour
Frascati, Italy March 8-10, 201114 NOAA’s National Climatic Data Center
Outline of Humidity Profile
• Inter-satellite calibration.
• Neural network scheme for deriving temperature and water vapor profiles.
• Comparisons with surface observations.
GlobVapour
Frascati, Italy March 8-10, 201115 NOAA’s National Climatic Data Center
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.
GlobVapour
Frascati, Italy March 8-10, 201116 NOAA’s National Climatic Data Center
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
GlobVapour
Frascati, Italy March 8-10, 201117 NOAA’s National Climatic Data Center
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
GlobVapour
Frascati, Italy March 8-10, 201118 NOAA’s National Climatic Data Center
Simultaneous Nadir Overpass (SNO)
http://www.orbit.nesdis.noaa.gov/smcd/spb/calibration/intercal/
GlobVapour
Frascati, Italy March 8-10, 201119 NOAA’s National Climatic Data Center
-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
GlobVapour
Frascati, Italy March 8-10, 201120 NOAA’s National Climatic Data Center
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
GlobVapour
Frascati, Italy March 8-10, 201121 NOAA’s National Climatic Data Center
CO2 Increase
From : http://scrippsco2.ucsd.edu/images/graphics_gallery/original/mlo_record.pdf
GlobVapour
Frascati, Italy March 8-10, 201122 NOAA’s National Climatic Data Center
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!
GlobVapour
Frascati, Italy March 8-10, 201123 NOAA’s National Climatic Data Center
Statistics of Training Data
160 180 200 220 240 260 280 300 320
0
200
400
600
800
1000
Statistics of Training Data
minmaxmean
Temperature (K)
0 5 10 15 20 25 30
0
200
400
600
800
1000
Statistics of Training Data
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)
GlobVapour
Frascati, Italy March 8-10, 201124 NOAA’s National Climatic Data Center
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
GlobVapour
Frascati, Italy March 8-10, 201125 NOAA’s National Climatic Data Center
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.
GlobVapour
Frascati, Italy March 8-10, 201126 NOAA’s National Climatic Data Center
Retrieval Root Mean Square (RMS) Errors
GlobVapour
Frascati, Italy March 8-10, 201127 NOAA’s National Climatic Data Center
Comparison with a Buoy Observation
GlobVapour
Frascati, Italy March 8-10, 201128 NOAA’s National Climatic Data Center
Comparisons of Retrievals with and without HIRS Inter-satellite Calibration
GlobVapour
Frascati, Italy March 8-10, 201129 NOAA’s National Climatic Data Center
Comparison with Drifting and Moored Buoy Observations
Drifting Moored
GlobVapour
Frascati, Italy March 8-10, 201130 NOAA’s National Climatic Data Center
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.