Introduction

Historically, global land surface temperatures and wetness have been obtained from in situ point sources located mainly in populated and industrialized regions. Unfortunately, these stations are neither located evenly nor densely around the globe. Specifically, observations are sparse over large regions of Africa, tropical South America, southeastern and central Asia, and large sections of the Arctic and Antarctic. Therefore, we developed a technique to derive the global distribution of land surface temperature and wetness from satellite observations. In order to derive global surface temperature anomalies at 1 degree resolution, we merged that land values (derived from

Remote Sensing and Climate Modeling: Synergies and Limitations, 181-202. ©2001 Kluwer Academic Publishers. Printed in the Netherlands.

the procedure described below) with sea surface values provided by Reynolds and Smith (1994).

This technique uses channel measurements from the SSM/I sensors on three separate Defense Meteorological Satellite Program (DMSP) polar orbiting satellites (F08, Fll, and F13) from 1987 to 1997. Background information on the SSM/I instrument can be obtained from the world wide web at: http://www.ngdc.noaa.gov/dmsp/descriptions/doc_ssmi.html. These DMSP satellites provide sun synchronized overpasses at 6 A.M. and 6 P.M. These twice daily satellite overpasses are processed into 1/3 x 1/3 degree "pixels" by NESDIS and archived at the National Climatic Data Center (NCDC) in near real time. From August 1988 to the end of 1991, erratic signals from the F08 85GHz channels forced the removal of the data from our analysis. The SSM/I instrument measures the brightness temperature at four frequencies: 19, 37, and 85 GHz with vertical and horizontal polarization and 22 GHz with only vertical polarization. All of these frequencies are in atmospheric window regions with the 22 and 85 GHz channels having weak water vapor absorption. Various signatures among the seven channel measurements were used to identify surface types and calculate dynamic emissivity adjustments. In this paper will distinguish between the various SSM/I channels by their frequency in GHz, where V stands for vertical and H stands for horizontal polarization (i.e. the 37 GHz horizontally polarized channel will be denoted as the 37H channel.

Observations of passive microwave radiation by polar orbiting satellites can be used to measure many of the Earth's atmospheric and geophysical properties. In particular, brightness temperatures from the Special Sensor Microwave Imager (SSM/I) have been used over land to derive: surface wetness (Basist et al. 1998), snow cover (Grody and Basist 1996), surface emis-sivities (Prigent et al. 1997), precipitation (Ferraro and Marks 1994), and soil moisture (Vinnikov et al. 1999). Ferraro et al. (1996) give an excellent overview of numerous surface and atmospheric products developed from the SSM/I instrument. Land surface temperature has been derived having different accuracies depending on surface conditions (McFarland et al. 1990, Neale et al. 1990, Njoku 1994, Weng and Grody 1998). Basist et al. (1998) developed a technique that dynamically adjusts the SSM/I algorithm coefficients for the effect of liquid water on surface emissivity, resulting in improved temperature estimates.

The primary difficulty in deriving surface temperature from passive microwave measurements is the variable emissivity associated with different surfaces. For the microwave spectrum the emissivity of soil depends on its water and/or mineral content, as well as the effects of vegetation and surface roughness. Since the microwave emissivity is variable, the brightness temperature is not a function of surface temperature alone. Therefore, any algo rithm that attempts to estimate surface temperature must first infer the particular surface condition for a microwave measurement, and either make appropriate emissivity adjustments to the microwave measurement, or filter the measurement if reliable adjustments are not currently possible. The approach used here assumes no a priori information about the surface conditions, allowing the satellite observations to provided a dynamic assessment of the surface type and current emissivity. The technique will be explained more fully in the methodology section below.

The Basist Wetness Index (BWI) is simply the emissivity adjustment associated with water in the radiating surface. However, wetness values also comes from the magnitude of precipitation, which can is derived directly from the SSM/I instrument (Ferraro et al. 1996). The wavelength of microwave frequencies are near the diameter of precipitation. Consequently, large hydrometeors (i.e. large raindrops and snow flakes) exceed the wavelength at 85 GHz, allowing them to scatter the high frequencies channel measurements, while the low frequency channel measurements (19and 22 GHz) remain transparent to these hydrometeors. This reduction of upwelling radiation at high frequencies is known as a scattering signature, where the greater the difference between the low and high frequencies, the greater the magnitude of precipitation. Moreover, large ice grains in the anvil tops of deep convection can also scatter emission at the lower microwave frequencies (i.e. 19 GHz wavelengths). Since all channel measurements are affected by deep convection, there is no base line for removing for the influence of hydrometeors at the higher frequency channels, forcing us to remove these observations from the temperature product. However, the gradient in scattering across the frequencies still provides as a reliable magnitude of precipitation. Therefore, when the temperature is above freezing (Grody and Basist 1996), we associate the scatter index to a wetness value. Unfortunately, these scattering values do not have the same scaling as the surface wetness measurements; none-the-less, they are included in the BWI, after weighting the consequence of not associating a rainfall event with an underlying wet surface.

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