Letter

Potential of SSMIS to derive cloud liquid water over sea ice

H. LAUE*{, S. ANDERSEN{, C. MELSHEIMER{ and G. HEYGSTER{{Institute of Environmental Physics, University of Bremen, Otto-Hahn-Allee 1,

D - 28359 Bremen, Germany

{Danish Meteorological Institute, Lyngbyvej 100, DK – 2100 København Ø, Denmark,

e-mail: san@dmi.dk

(Received 29 November 2006; in final form 25 July 2007 )

Passive microwave measurements from satellite sensors like SSM/I deliver

valuable information on sea ice and atmospheric conditions in polar regions. In

late 2003, the successor of SSM/I, the SSMIS on board the DMSP satellite F16,

was launched. It combines the channels of SSM/I, SSM/T and SSM/T2 into a

single instrument with a constant incidence angle on the surface of 53.3u. At the

time of this writing, SSMIS data are still to be released to the broader scientific

user community. However, radiative transfer modelling and comparisons to

algorithms for similar sensors like SSM/I can give an idea of how the retrieval of

atmospheric parameters become more accurate or be extended to additional

surface types once the data are available. This letter discusses the possibilities of

retrieving cloud liquid water with this new sensor based on the results of radiative

transfer modelling for typical Arctic atmospheres. Moreover, the increase of

accuracy for an existing cloud liquid water algorithm, the R-factor method, with

additional water vapour information is shown.

1. Introduction

Satellite sensor observations of meteorological quantities provide an important

additional source of information to conventional meteorological observations and

modelling. Especially in regions with sparse regular meteorological measurements

like open oceans or the polar regions, satellite sensor data can fill a large gap ofinformation. For instance, passive microwave radiometers like the Special Sensor

Microwave Imager (SSM/I) and Humidity Sounders (SSM/T, SSM/T2) provide

information on total water vapour (TWV), liquid water path (LWP), temperature

profile and wind speed over open ocean. These estimated measures are generally not

applicable over the partly or completely sea ice covered ocean. The new Special

Sensor Microwave Imager Sounder (SSMIS) on board the Defense Meteorological

Satellite Program (DMSP) satellite F16 provides the potential for estimating the

influence of surface emissivity on existing methods and algorithms more accuratelysince it combines measurements at frequencies used by microwave imagers and

sounders (see table 1).

LWP is the main source of error for the sea ice coverage retrieval with the91.65 GHz channels (Kern and Heygster, 2001) and is difficult to retrieve

with conventional meteorological measurements. Miao et al. (2000) developed the

*Corresponding author. Now at MeVis Research, Center for Medical ImageComputing, Bremen, Germany. Email: hendrik.laue@mevis.de

International Journal of Remote Sensing

Vol. 28, No. 20, 20 October 2007, 4693–4700

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2007 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160701596131

R-factor method, an algorithm to retrieve LWP over sea ice. The R-factor can be

written as an expression involving both LWP and TWV. Therefore, it does not

provide quantitative estimates of LWP.

The following section will present radiative transfer model results for brightness

temperatures of the SSMIS channels under the influence of liquid water for 223

example polar atmospheres. In combination with emissivities for ice taken from the

literature the potential of the different SSMIS channels for cloud liquid water

retrieval will be discussed. Section 3 combines the R-factor method of Miao et al.

(2000) based on SSM/I data and the TWV-algorithm by Miao (2001) based on SSM/

T2 data for a pure LWP retrieval determined from the R-factor.

2. Radiative transfer modelling for the SSMIS channels

2.1 Atmospheric profile generation and selection

The example profiles of the polar atmosphere for the model calculations were

selected from radiosonde profiles acquired by the research vessel Polarstern in the

Arctic summer between June 13, 1984 and August 20, 1985, and between August 18,

1994 and May 11, 2003. Since Cloud Liquid Water (CLW) is not directly measured

by radiosondes, CLW profiles were added to each profile using thresholds of the

relative humidity U(U.85%) as indicators for clouds. Additionally, synoptic cloud

observations from Polarstern were used to adjust this threshold (U.80%) for

profiles with observed cloud coverage (Karstens et al., 1994). Only profiles were

chosen for which Polarstern observation reported any ice cover six hours before or

after the radiosonde launch. These criteria left 223 atmospheric profiles for

temperature, pressure, relative humidity and CLW.

Table 1. Comparison of the SSMIS frequencies and channels to those of SSM/I and SSM/T2.The polarization is noted as H for horizontal, M for mixed and H + V for left circular (Feldeand Pickle, 1995). The channel specifications were compiled from Kramer (2002) and Bell

(personal communication).

SSMIS Channel SSMIS n [GHz] Pol. SSM/I n [GHz] Pol. SSM/T2 n [GHz] Pol.

1 50.3V2 52.8V3 53.6V4 54.4V5 55.5V6 57.3H + V7 59.4H + V8 150.0H 150M9 183¡6.6H 183¡7M10 183¡3.0H 183¡3M11 183¡1.0H 183¡1M12 19.35H 19.35H13 19.35V 19.35V14 22.235V 22.235V15 37.0H 37.0H16 37.0V 37.0V17 91.655H 85.5H 91.65M18 91.655V 85.5V

4694 H. Laue et al.

2.2 Radiative transfer modelling

The radiative transfer model Atmospheric Radiative Transfer Simulator (ARTS)

allows the modelling of brightness temperature at the sensor for atmospheric

gaseous absorption as well as for the absorption by CLW (Buehler et al., 2005). It is

used to determine the brightness temperature for all selected radiosonde profiles,

with surface emissivity varying from 0.65 to 0.9.

2.3 Modelling results

In polar regions, use of the above frequencies for atmospheric retrieval are subject to

a serious drawback since the surface emissivity for sea ice at these frequencies is very

high (see table 2) and variable. Generally, this emissivity is about 0.85 and above for

most types of sea ice, except multi-year (MY) ice. However, recent measurements

obtained through aircraft based radiometers and radiative modelling based on drop

sound data show that for frequencies above 100 GHz the emissivity of sea ice

decreases to a value of about 0.75 at 157 GHz (Selbach, 2003). Figure 1 shows the

results of the model calculation performed for the frequencies 91.65, 150 and

183.31¡7 GHz.

The 91.65 and 150 GHz channels show a high dependence on the LWP and these

channels might be used for LWP retrieval. Nonetheless, the brightness temperature

for these channels saturates for larger LWP values (see figure 1). The clearly visible

dependence for TWV for both channels suggests that both quantities should be

determined simultaneously.

The low emissivity and the high dependence on LWP at 150 GHz might be

exploited when combined with another channel to eliminate the influence of

changing surface emissivities. The most likely channel for this is the 91.65 GHz

channel since we expect the smallest emissivity difference to 150 GHz. Most

promising could be an additional channel for the vertical polarization of 150 GHz,

because the constant emissivity polarization difference at 37 and 91.65 GHz might

also be present there (Miao et al., 2000).

It might also be useful to consider a combination of the existing R-factor and

TWV algorithms previously introduced. The practical combination was previously

hampered by the different observing geometries of the SSM/I and SSM/T2. With

SSMIS these problems are largely solved.

Table 2. First year ice surface emissivities for a selection frequencies similar to the SSMISchannels for first year ice (FY) and multi-year ice (MY). Compiled from Eppler et al. (1992);

Selbach (2003); Felde and Pickle (1995).

IceType

Frequency[GHz]Polarization

21.0 37.0 90153

V H V H V H M

FY Emissivity eStd. Deviationde

0.96 0.91 0.955 0.913 0.926 0.913 0.730.019 0.019 0.015 0.013 0.045 0.031 0.04

MY Emissivity eStd.Deviation de

0.79 0.64 0.76 0.71 0.68 0.65 0.710.08 0.125 0.08 0.115 0.11 0.011 0.04

Remote Sensing Letters 4695

3. Correction of the R-factor method with total water vapour

The R-factor is defined as the logarithm of the ratio of brightness temperature

polarization differences DT(n)5Tv(n)2Th(n):

R n1, n2ð Þ~lnDT n1ð ÞDT n2ð Þ

ð1Þ

Figure 1. Simulated brightness temperatures for 223 atmospheric profiles, includingapproximate cloud liquid water for the new channels available on SSMIS for 91.65, 150and 183.31 GHz. The surface emissivity is set to value of 0.75 as an estimate for the emissivityof sea ice at 157 GHz (see table 2). Left: Dependency on the cloud liquid water. Right:Dependency on total water vapour.

4696 H. Laue et al.

with the frequencies n1537 GHz and n2585.5 GHz for SSM/I and n2591.65 GHz for

SSMIS, respectively.

According to Miao et al. (2000), the LWP L and TWV W are related to the

R-factor as follows:

p½ �LzaWLW~1

bR{Rsð Þ{ Dtd

DkL

ð2Þ

Rs is the surface term of the R-factor and can be calculated by Equation (1) based on

the polarization differences of the surface emissivities. b and aWL are constants and

defined by b52secHDkL and aWL5DkW/DkL, where DkL is the difference of the

liquid water mass absorption coefficients for the frequencies n1 and n2. Accordingly

DkW is the difference of the corresponding water vapour mass absorption

coefficients and Dtd the difference of the dry atmosphere opacities at these

frequencies. H is the satellite zenith angle at the ground. The water vapour content is

available through the TWV-algorithm, and allows compensation for the TWV

dependence in the R-factor and to determine L from Equation (2). This leads to the

water vapour corrected R-factor R9:

R0~R{baWLW ð3Þ

The correction of the R-factor method allows determination of L directly but more

importantly, it is used to determine a new background term unaffected by water

vapour.

3.1 Corrected background R-factor

The background term is necessary to account for the surface emissivity. This is

especially important in polar regions, firstly because of varying fractions of ice cover

and secondly because of the snow and ice emissivity variations induced by

temperature, rain and snow. In the original version of the R-factor method, the

background term is also used to compensate for the influence of water vapour by

including the mean water vapour Wm into a background R-factor Rb:

Rb~Rszb aWLWmð ÞzbDtd

DkL

ð4Þ

Here, Rb is determined from a time series of 41 preceding observations. Rb is

chosen as the median of all values smaller than the averaged R-factor for each pixel,

which allows identification of the measurement mostly unaffected by clouds (Miao

et al., 2000). However, the knowledge of W makes the assumption of a mean water

vapour content unnecessary by using the water vapour corrected R-Factor R9

instead of R. The new background term, R9b, is determined according to the

conventional Rb.

With R9 and R9b, the R-factor based LWP LR can be determined by

LR0~R0{ R0b

bð5Þ

The algorithm is designed for usage over ice covered areas and the marginal ice

zone. Like for the original algorithm, the application was limited to a maximum

wind speed of 14 m s21 for open ocean (Miao et al., 2000).

Remote Sensing Letters 4697

3.2 Validation of the algorithm

To apply the water vapour correction without SSMIS data available, the NWP

model based TWV fields from the European Centre for Medium-Range Weather

Forecasts (ECMWF) were used to simulate the results of the satellite based TWV

algorithm. Generally, NWP models have more difficulties representing the LWP

information that varies on small spatial scales compared to the TWV information

that varies more smoothly. Thus, this approach, apart from being valid for TWV

values exceeding the limits of the TWV algorithm, is an efficient way to combine the

strengths of remote sensing with those of numerical modelling. The brightness

temperatures necessary for the R-factor are taken from the SSM/I instruments on-

board the DMSP satellites F13, F14 and F15.

Figure 2 shows an example of the corrected R-factor value on January 29, 2005 in

the Arctic winter. In this figure, clouds can be seen over ice covered ocean northeast

of Greenland. Moreover, the R-factor shows cloud coverage south of Svalbard over

open ocean. The performance of the water vapour corrected R-factor method and

the estimation of its accuracy is evaluated by a comparison with the LWP algorithm

for open ocean developed by Karstens et al. (1994). Therefore, the comparison is

performed for two open ocean test areas in and close to the Arctic, which are shown

by the white rectangles in figure 2.

Figure 2. The water vapour corrected R-factor value calculated for January 29, 2005 at23:00 UTC for a clip showing the north polar sea. Two cloudy areas are indicated by the twowhite ellipses. Rectangles show areas of comparison to an open water CLW algorithm.

4698 H. Laue et al.

In total, 22907 brightness temperature measurements could be used for this

comparison. They are shown in the scatter diagrams in figure 3 as R/b and R9/b to

allow a direct comparison of the LWP retrieved to the LWP obtained from

the algorithm developed by Karstens et al. (1994). It can be seen that the adjustment

to TWV results in a thinning of the scatter cloud in the left diagram of figure 3. This

is also reflected in the correlation coefficients r listed in table 3 which show an

increase from 0.73 to 0.83. Moreover, the slope of the regression line is increased

from 0.58 to 0.71. An even larger increase of the slope can be found, by using

weights for the regression (see table 3). In this case, the slope for the improved R-

factor is increased to 0.73 and decreased to 0.57 for the uncorrected R-factor. Thus,

the use of TWV information, either retrieved by the TWV-algorithm or from NWP

data, refines the results of the R-factor method and allows the quantitative

estimation of LWP over sea ice. This was previously not possible with microwave-

based algorithms.

4. Discussion and conclusion

The radiative transfer modelling suggests that the 91.65 and 150 GHz channels of

SSMIS are suitable candidates for a LWP retrieval algorithm over sea ice.

Nevertheless, the high and variable surface emissivity might still be an obstacle for a

reliable algorithm. Such development will require access to satellite sensor observed

brightness temperatures as long as we do not have reliable simulated surface

emissivities representative of the horizontal scale of space borne passive microwave

sensor footprints.

Figure 3. Comparison of the R-factor CLW retrieval with the algorithm by Karstens et al.(1994) for measurements below a wind speed of 14 m s21. Left: without TWV correction.Right: with the TWV correction.

Remote Sensing Letters 4699

Meanwhile, we have demonstrated that the combination of the R-factor and the

TWV algorithm improves the LWP retrieval over sea ice and the marginal ice zone.This is mainly achieved by removing the water vapour influence from the R-factor,

which as an important side effect leads to an improved determination of the surface

influences represented in the background term. This result is significant as LWP is

an important quantity for numerical weather prediction and climate research.

Acknowledgments

This research was supported by the visiting scientist programme of EUMETSAT’s

Satellite Application Facility on Ocean an Sea Ice and by the EU project IOMASA(Integrated Observing and Modelling of the Arctic Surface and Atmosphere, EVK3-

2001-00116). The radiosonde profile data and synoptic observations were provided

by the Alfred Wegner Institute (AWI) in Bremerhaven, Germany; the SSM/I data

used were provided by the Danish Meteorological Institute (DMI), Copenhagen.

Moreover, we would like to thank William Bell of UK Met Office for clarifying the

configuration of the SSMIS channels.

ReferencesBUEHLER, S.A., ERIKSSON, P., KUHN,, vON ENGELN, A. and VERDES, C., 2005, ARTS, the

atmospheric radiative transfer simulator. Journal of Quantitative Spectroscopy &

Radiative Transfer, 91, pp. 65–93.

EPPLER, D., FARMER, L.D., LOHANICK, A.W.L., ANDERSON, M.R., CAVALIERI, D.J.,

COMISO, D.J., GLOERSEN, P., GARRITY, C., GRENFELL, T.C., HALLIKAINEN, M.,

MASLANIK, J.A., MATZLER, C., MELLOH, R.A., RUBINSTEIN, I. and SWIFT, C.T., 1992,

Passive microwave signatures of sea ice. In Microwave Remote Sensing of Sea Ice,

F. Carsey (Ed.), pp. 47–71 (Washington, DC: American Geophysical Union).

FELDE, G.W. and PICKLE, J.D., 1995, Retrieval of 91 and 150 GHz surface emissivities.

Journal of Geophysical Research, 100, pp. 20855–20866.

KARSTENS, U., SIMMER, C. and RUPRECHT, E., 1994, Remote sensing of cloud liquid water.

Meteorology and Atmospheric Physics, 54, pp. 157–171.

KERN, S. and HEYGSTER, G., Sea ice concentration retrieval in the AntArctic based on the

SSM/I 85 GHz polarisation. Annals of Glaciology, 33, pp. 109–114.

KRAMER, H.J., 2002, Observation of the Earth and Its Environment (Berlin: Springer-Verlag).

MIAO, J., JOHNSEN, K.P., KERN, S., HEYGSTER, G. and KUNZI, K., 2000, Signature of clouds

over AntArctic sea ice detected by the Special Sensor Microwave/Imager. IEEE

Transactions on Geoscience and Remote Sensing, 38, pp. 2333–2343.

MIAO, J., KUNZI, K., HEYGSTER, G., LACHLAN-COPE, T.A. and TURNER, J., 2001,

Atmospheric water vapour over Antartica derived from Special Sensor Microwave/

Temperature 2 data. Journal of Geophysical Research, 106, pp. 10187–10203.

SELBACH, N., 2003, Determination of total water vapour and surface emissivity of sea ice at

89 GHz, 157 GHz and 183 GHz in the Arctic winter. PhD thesis, University of

Bremen; Reports of the Institute of Environmental Physics, 21, (Berlin: Logos-Verlag).

Table 3. Results for the comparison of the corrected and uncorrected R-factor to aconventional CLW retrieval over open water. r is the correlation coefficient, a the slope and bthe offset of a linear regression. aw and bw are the corresponding coefficients for a weightedlinear regression with the weights determined from the number of measurements for R/b in

0.1 kg m22 intervals.

Method r a b [kg m22] aw bw [kg m22]

Without TWV correction 0.73 0.58 20.066 0.57 20.064With TWV correction 0.83 0.71 20.038 0.73 20.040

4700 Remote Sensing Letters