Surface temperature variation from satellite homogenized SSMR-SSM/I microwave dataset
and re-analysis over Northern America
Alain Royer, Stéphane Poirier, Michel Fily and Ghislain Picard
Abstract
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1. Introduction
Land surface temperature (Ts) is one of the important factors needed in climate change analysis. Lack
of surface temperature data can induce uncertainties in these analysis. Satellite data with improved
spatial coverage can narrow these uncertainties. Surface temperature derivation from infrared sensors
is limited by clouds and fog. Re-analysis without cross-referencing data could be biased. Therefore,
infrared sensor data must usually be composited over weekly, bi-weekly or even monthly periods.
NOAA/NASA Pathfinder Nimbus 7-Scanning Multichannel Microwave Radiometer (SMMR) and
Defense Meteorological Satellite Program-Special Sensor Microwave/Imager (SSM/I) sensors provide
daily measurements independently of the presence of clouds and fog. In this article, we present a new
30-years land surface temperature database derived from microwave satellite brightness temperature
measurements on which a procedure for homogenization is applied and we compare this data to
ground-based meteorological observations, to European Centre for Medium-Range Weather
Forecasts (ECMWF) 40-years Reanalysis (ERA-40) data and to NOAA's National Center for
Atmospheric Prediction (NCEP) North American Regional Reanalysis (NARR) data.
2. Datasets
More than 30 years of spaceborn microwave radiometer observations of the Earth’s surface are now
available from Nimbus 7-Scanning Multichannel Microwave Radiometer (SMMR), which operated from
1979 to 1987, and the Defense Meteorological Satellite Program-Special Sensor Microwave/Imager
(SSM/I), from 1987 up to present. Both NOAA/NASA Pathfinder SMMR and SSM/I sensors provide
brightness temperatures measurements (TBs) gridded onto polar stereographic projection called
Equal-Area Scalable Earth (Ease-Grid) at a resolution of 25Km and were obtained from the EOSDIS
National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center, University of Colorado
at Boulder, USA. Up to two measurements per day (ascending and descending pass) are available for
our study area. The SMMR and SSM/I instruments provide TBs at several frenquencies and at both
vertical (V) and horizontal (H) polarizations. The SMMR operated at 6.6, 10.7, 18, 21, and 37 GHz.
The SSM/I operates at 19.3, 22.2, 37 and 85.5 GHz. Our study is restricted to the 19 (18) and 37 GHz
frequencies because they are available on both sensors and are less affected by atmospheric effects
than the 22.2 (21) or 85 GHz channels. NSIDC’s SMMR and SSM/I databases were analysed directly
on the EASE-Grid projection. Re-analysis ECMWF ERA-40 (ERA-40) data used in our study has been
provided by ECMWF and has been obtained from the ECMWF Data Server. NCEP North American
Regional Reanalysis (NARR) data has been provided by the NOAA/OAR/ESRL PSD, Boulder,
Colorado, USA. We have used the temperature 2-meters above ground at a resolution of 74Km and
frequency of 4 measurements daily (each 6 hours) for ERA-40 and at a resolution of 32Km and
frequency of 8 measurements daily (each 3 hours) for NARR. ECMWF ERA-40 and NCEP NARR
databases were resampled on the EASE-Grid projection. Table 1 presents a list of the datasets used
in this article.
Environment Canada ground-based meteorological observations used in our study has been provided
by Canadian Daily Climate Data (CDCD), ground-based meteorological observations 1979-2007,
Meteorological Service of Canada, National Archives and Data Management Branch,
Downsview, Ontario, Canada. Temperatures are measured in a louvered box called a Stevenson
screen, mounted 1.5-meter above the ground, which is usually a level, grassy surface. At most
ordinary stations the maximum temperature is the highest recorded in a 24-hour period ending in the
morning of the next day. The minimum values are for a period of the same length, beginning in the
evening of the previous day. Mean temperature is the average of the two. At most principal stations
the maximum and minimum temperatures are for a day beginning at 0600 Greenwich (or Universal)
Mean Time, which is within a few hours of midnight local standard time in Canada. Figure 1 shows the
distribution of meteorological stations used for validation over a topographic gradient map of Canada,
obtained convoluting a 3 x 3 pixels window computing the standard deviation over can3d300 dem data
projected on 25km x 25km resolution EASE grid.
Data
Period
SMMR-Nimbus7
SSM/I-DMSP F8
SSM/I-DMSP F11
SSM/I-DMSP F13
ERA-40
NARR
Meteo Stations
1979-1987
1987-1991
1991-1995
1995-2008
1979-2002
1979-2008
1979-2007
Time of acquisition
/frequency
/daily
/twice daily
/twice daily
/twice daily
Each 6 hours/daily
Each 3 hours/daily
Hourly/daily
Resolution
25Km
25Km
25Km
25Km
74Km
32Km
-
Sources
NSIDC
NSIDC
NSIDC
NSIDC
ECMWF
NCEP
Env. Canada
Table 1 – Description of the databases analysed. NSIDC’s SMMR and SSM/I databases were
analysed directly on the EASE-Grid projection. ECMWF ERA-40 and NCEP NARR databases were
resampled on the EASE-Grid projection.
Figure 1 Meteorological stations used for validation over a topographic gradient map of Canada,
obtained convoluting a 3 x 3 pixels window computing the standard deviation over can3d300 dem data
projected on 25km x 25km resolution EASE grid.
(C:\ar\sp\presentation\stations_70-stations.tif)
(C:\ar\sp\presentation\stations_70-stations.fig)
(C:\ar\sp\presentation\stations_70-stations.txt)
(C:\ar\sp\presentation\stations_70-stations.xls)
3. Surface temperature retreival from satellite measurements
Land surface temperatures are derived from brightness temperatures using the method presented in
Fily et al., 2003. Then, these land surface temperatures are normalized for time acquisition change
and drift.
Land surface temperatures are derived from brightness temperatures using the method presented in
Fily et al., 2003. We have used both vertical and horizontal polarization of the 37GHz SMMR and
SSM/I bands. Vertical-horizontal emissivity relationship’s coefficients (eV=a*eH+b) a=0.5290 and
b=0.4572 are used for SMMR while coefficients a=0.5020 and b=0.4840 are used for SSM/I. The
atmospheric transmission (t) and the atmospheric temperature components (Ta up and Ta down) used
for SMMR and SSM/I were the same: t=0.888, Ta up=29.3 and Ta down=31.8. We derived the land
surface temperatures (Ts) using the equation:
Ts= (TBV - a x TBH - (1 - b - a) x t x Ta down – Ta up x (1 - a))/(t x b)
Where TBV and TBH are the vertical and horizontal brightness temperatures respectively.
The second step of the procedure consists in normalizing surface temperatures according to ECMWF
ERA-40 temporal resolution (00h,06h,12h,18h) and according to NCEP NARR temporal resolution
(00h,03h,06h,09h,12h,15h,18h,21h). A spline algorithm is used to project the satellite passage
measures onto the models temporal references (ar\sp\tsnorm_era40.m, ar\sp\tsnorm_narr.m).
The third step of the procedure consists in deriving daily average surface temperatures from the
normalized surface temperatures for all SMMR and SSM/I covered period (1978-2008) as well as both
ECMWF ERA-40 and NCEP NARR models (ar\sp\tsmoy_era40.m and ar\sp\tsmoy_narr.m).
The fourth step of the procedure consists in applying a correction so satellite data from different
periods can be compared. The SSM/I-DMSP F8 daily average surface temperatures (1987-1991) are
corrected against the SSM/I-DMSP F11 and F13 daily average surface temperatures (1992-2008). A
correction matrix is obtained by substracting the difference between the measured average surface
temperature and the ECMWF ERA-40 modeled average temperature of period F8 from period F11.
Each SSM/I-DMSP F8 derived daily average surface temperatures are then corrected adding this
correction matrix. In similar fashion, SMMR-Nimbus 7 derived daily average surface temperatures
(1978-1987) are also corrected against the SSM/I-DMSP F11 and F13 daily average surface
temperatures (1992-2008). However, no correction matrix is applied to years 1978 to 1982 inclusively.
One correction matrix is obtained for years 1983, 1984 and 1985. And two others distinct corrections
matrices are obtained, one for year 1986 and an other for year 1987 (ar\sp\ts_corr.m).
4. Results
4.1 Intercalibration between satellites
Figure 2 Comparison between Canadian summer temperature averages from 1979 to 2006
(C:\ar\sp\presentation\ts_corr-sscv.tif)
(C:\ar\sp\presentation\ts_corr-sscv.fig)
(C:\ar\sp\presentation\ts_corr-sscv.txt)
Figure 3 Overlap SMMR-SSM/I
(C:\ar\sp\presentation\overlap_42.tif)
(C:\ar\sp\presentation\overlap_42.fig)
(C:\ar\sp\presentation\overlap_42.txt)
4.2 Validation against meteorological data and re-analysis
Table 2. Meteorological stations list (*Meteo stations meeting WMO specifications)
Figure 4a) Temperatures comparison for Katinniq meteo station in year 2000
Figure 4b) Correlation between Tera and Ts as a function of Tstation for Katinniq in year 2000
Table 3. Error analysis relative to figure 4a) and 4b).
Figure 5a) Example of summer temperature differences for station FORT SIMPSON A
(C:\ar\sp\presentation\tscorr-sscsv_80.tif)
(C:\ar\sp\presentation\tscorr-sscsv_80.fig)
(C:\ar\sp\presentation\tscorr-sscsv_80.txt)
Figure 5b) Example of summer temperature differences for station BRANDON A
(C:\ar\sp\presentation\tscorr-sscsv_106.tif)
(C:\ar\sp\presentation\tscorr-sscsv_106.fig)
(C:\ar\sp\presentation\tscorr-sscsv_106.txt)
Figure 5c) Example of summer temperature differences for station KAPUSKASING A
(C:\ar\sp\presentation\tscorr-sscsv_116.tif)
(C:\ar\sp\presentation\tscorr-sscsv_116.fig)
(C:\ar\sp\presentation\tscorr-sscsv_116.txt)
Figure 6) Inter-annual differences with respect to each meteorological station. Only stations having a
topographic gradient less than 75 meters were used).
(C:\ar\sp\presentation\tscorr-sscsv_162.tif)
(C:\ar\sp\presentation\tscorr-sscsv_162.fig)
(C:\ar\sp\presentation\tscorr-sscsv_162.txt)
Table 4. Error analysis: mean diff, altitude effect.
Figure 7) Moyenne relative des différences entre les températures NARR et ERA-40 pour les jours
182 à 243 de 2002. Les différences sont calculées pour les températures aux heures correspondantes
aux 2 modèles (0h00, 06h00, 12h00 et 18h00).
(C:\ar\sp\presentation\era40_narr_comparison.tif)
(C:\ar\sp\presentation\era40_narr_comparison.fig)
(C:\ar\sp\presentation\era40_narr_comparison.txt)
Figure 7) Moyenne relative des différences entre les températures NARR et ERA-40 pour les jours
182 à 243 de 2002 avec masque de sous-régions. Les différences sont calculées pour les
températures aux heures correspondantes aux 2 modèles (0h00, 06h00, 12h00 et 18h00).
(C:\ar\sp\presentation\era40_narr_comparison_region-mask.tif)
(C:\ar\sp\presentation\era40_narr_comparison_region-mask.fig)
(C:\ar\sp\presentation\era40_narr_comparison_region-mask.txt)
Figure 8) Anomalies trends
(C:\ar\sp\presentation\ts_corr-anomalies_ssav.tif)
(C:\ar\sp\presentation\ts_corr-anomalies_ssav.fig)
(C:\ar\sp\presentation\ts_corr-anomalies_ssav.txt)
Figure 9) Map of anomalies
(C:\ar\sp\presentation\ts-corr_anomalies_1979-2006.tif)
(C:\ar\sp\presentation\ts-corr_anomalies_1979-2006.fig)
Table 6. Trends analysis for different regions
5. Discussion and conclusion
Acknowledgement
References
Knowles, Kenneth W., Ein G. Njoku, Richard L. Armstrong, and Mary Jo Brodzik. 2002. Nimbus-7
SMMR Pathfinder Daily EASE-Grid Brightness Temperatures, [1979-1987]. Boulder, Colorado USA:
National Snow and Ice Data Center. Digital media, ftp://sidads.colorado.edu.
Maslanik, J., and J. Stroeve. 1990, updated current year. DMSP SSM/I daily polar gridded brightness
temperatures, [1987-2008]. Boulder, Colorado USA: National Snow and Ice Data Center. Digital
media, ftp://sidads.colorado.edu.
Brodzik, M. J. and R.L. Armstrong. 2008, updated daily. Near Real Time DMSP SSM/I Pathfinder Daily
EASE-Grid Brightness Temperatures, [2008]. Boulder, Colorado USA: National Snow and Ice Data
Center. Digital media, ftp://sidads.colorado.edu.
ECMWF ERA-40 data used in this study/project have been provided by ECMWF/have been obtained
from the ECMWF Data Server, http://data-portal.ecmwf.int/data/d/era40_daily/.
NCEP Reanalysis data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their
Web site at http://www.cdc.noaa.gov/
Canadian Daily Climate Data (CDCD), ground-based meteorological observations 1979-2007,
Meteorological Service of Canada, National Archives and Data Management Branch,
Downsview, Ontario, Canada, ftp://arcdm20.tor.ec.gc.ca/pub/dist/CDCD/.
Canada3D300 Product Standard, 2001. Centre for Topographic Information, Sherbrooke, Quebec,
Canada, http://ftp2.cits.rncan.gc.ca/pub/canada3D/.
Fily, M., Royer, A., Goïta, K. and Prigent, C. (2003). A simple retreival method for land surface
temperature and fraction of water surface determination from satellite microwave brightness
temperatures in sub-arctic areas. Remote Sensing of Environment, 85, 328-338.