Alfred T. C. Chang

Nimbus-7 SMMR Derived Global Snow Cover Parameters

Snow covers about 40 million km 2 of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/ NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2 . Preliminary analysis is performed to evaluate the accuracy of these products. Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978-1984). Intercomparisons of SMMR, NOAA/ NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product. Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas. INTRODUCTION Remotely acquired microwave data in conjunction with essential ground observations will most likely lead to advanced extraction of snow properties beyond conventional techniques. Landsat visible and near-infrared data have recently become near operational for use in measurements of snow covered areas (Rango 1975, 1978). Operational NOAA satellites provide continuous global coverage with 4 km spatial resolution. Both Landsat and NOAA data acquisition are hampered by cloud cover, sometimes at critical times when a snowpack is ripe and ready to melt. Furthermore, information on water equivalent, free water content and other snowpack properties germane to accurate snow melt run-off prediction is not currently available using visible and near-infrared data because only surface and near surface snow contribute to the measured reflectances. Microwave remote sensors which have the capability to penetrate the snowpack and respond to variations in snow properties, could provide information about snow depth and snow water equivalent (Ran go and others 1979; Chang and others 1982). However, due to the coarse spatial resolution of the present microwave radiometers, combinations of vegetation, terrain and snow information within a pixel greatly complicate the retrieval algorithm development. Algorithms need to be developed that are specific to physiographic areas like the Colorado River basin and the north slope of Alaska. These algorithms will take into account additional parameters related to microwave signatures. Until these algorithms are operational, the use of remotely collected microwave data for global quantitative snowpack analysis will not be operational due to the complexities involved in the data analysis. MICROWA VE EMISSION FROM SNOW Microwave emission from a layer of snow over ground consists of two parts: (1) emission by the snow volume and (2) emission by the underlying ground . Snow particles act as scattering centers for microwave radiation from a snowpack. The scattering effect which redistributes the upwelling radiation according to snow thickness and crystal size, provides the physical basis for microwave detection of snow. Mie scattering theory is used to account for the energy redistribution by snow crystals . Although the snow crystal usually is not spherical in shape, its ensemble scattering properties can be mimicked by spheres (Chang and others 1976). Theoretical computations indicate that scattering by individual snow crystals can be the dominant modification factor of upwelling 37 GHz (0.8 cm) radiation in the dry snow cases (Chang and others 1982). The effect of scattering is lessened by using the longer wavelengths. Fig.1 shows the calculated brightness temperatures versus snow water equivalent for SMMR frequencies . The effective microwave penetration depth into a dry snowpack, typically 10-100 times the wavelength, depends on the wavelength used and the characteristic crystal size of the snowpack . When the wavelength is much larger than the crystal size (> 5 cm), absorption will be the dominant effect. The brightness temperature will resemble the physical temperature of the snowpack. When the wavelength is comparable to the snow crystal size « I cm), scattering becomes the dominant effect. Nimbus-7 SMMR is a five frequency, dualpolarized microwave radiometer which measures the upwelling microwave radiation at 6.6, 10.7, 18.0, 21.0, and 37.0 GHz (4 .6, 2.8, 1.7, 1.4 and 0.8 cm) while scanning 25 0 to either side of the spacecraft (approximately 780 km swath width) with a constant incidence angle of approximately 50 0 with respect to the Earths surface. The spatial resolution varies from 25 km for the 37 GHz (0.8 cm) to 150 km for the 6.6 GHz (4.6 cm). A detailed description of this instrument can be found in Gloersen and Barath (1977). The Nimbus-7 satellite was launched on October 24, 1978, into a sun-synchronous polar orbit with local noon/ midnight equatorial crossing. Using the multifrequency analysis approach, one may make inferences regarding not only the thickness of the snowpack, but the underlying soil (wet versus dry) condition. The shorter wavelengths, such as 0.8 cm (37 GHz), sense near surface (0-50 cm) temperature and emissivity, and

A prototype AMSR-E global snow area and snow depth algorithm

A methodologically simple approach to estimate snow depth from spaceborne microwave instruments is described. The scattering signal observed in multifrequency passive microwave data is used to detect snow cover. Wet snow, frozen ground, precipitation, and other anomalous scattering signals are screened using established methods. The results from two different approaches (a simple time and continentwide static approach and a space and time dynamic approach) to estimating snow depth were compared. The static approach, based on radiative transfer calculations, assumes a temporally constant grain size and density. The dynamic approach assumes that snowpack properties are spatially and temporally dynamic and requires two simple empirical models of density and snowpack grain radius evolution, plus a dense media radiative transfer model based on the quasicrystalline approximation and sticky particle theory. To test the approaches, a four-year record of daily snow depth measurements at 71 meteorological stations plus passive microwave data from the Special Sensor Microwave Imager, land cover data and a digital elevation model were used. In addition, testing was performed for a global dataset of over 1000 World Meteorological Organization meteorological stations recording snow depth during the 2000-2001 winter season. When compared with the snow depth data, the new algorithm had an average error of 23 cm for the one-year dataset and 21 cm for the four-year dataset (131% and 94% relative error, respectively). More importantly, the dynamic algorithm tended to underestimate the snow depth less than the static algorithm. This approach will be developed further and implemented for use with the Advanced Microwave Scanning Radiometer-Earth Observing System aboard Aqua.

Retrieval of monthly rainfall indices from microwave radiometric measurements using probability distribution functions

Abstract An algorithm for the estimation of monthly rain totals for 5° cells over the oceans from histograms of SSM/I brightness temperatures has been developed. Them are three novel features to this algorithm. First, it uses knowledge of the form of the rainfall intensity probability density function to augment the measurements. Second, a linear combination of the 19.35 and 22.235 GHz channels has been employed to reduce the impact of variability of water vapor. Third, an objective technique has been developed to estimate the rain layer thickness from the 19.35- and 22.235-GHz brightness temperature histograms. Comparison with climatologies and the GATE radar observations suggest that the estimates are reasonable in spite of not having a beam-filling correction. By-products of the retrievals indicate that the SSM/I instrument noise level and calibration stability am quite good.

Comparison of Snow Mass Estimates from a Prototype Passive Microwave Snow Algorithm, a Revised Algorithm and a Snow Depth Climatology

Abstract While it is recognized that no single snow algorithm is capable of producing accurate global estimates of snow depth, for research purposes it is useful to test an algorithrns performance in different climatic areas in order to see how it responds to a variety of snow conditions. This study is one of the first to develop separate passive microwave snow algorithms for North America and Eurasia by including parameters that consider the effects of variations in forest cover and crystal size on microwave brightness temperature. A new algorithm (GSFC 1996) is compared to a prototype algorithm (Chang et al., 1987) and to a snow depth climatology (SDC), which for this study is considered to be a standard reference or baseline. It is shown that the GSFC 1996 algorithm compares much more favorably to the SDC than does the Chang et al. (1987) algorithm. For example, in North America in February there is a 15% difference between the GSFC 1996 algorithm and the SDC, but with the Chang et al. (1987) algorithm the difference is greater than 50%. In Eurasia, also in February, there is only a 1.3% difference between the GSFC 1996 algorith-rn and the SDC, whereas with the Chang et al. (1987) algorithm the difference is about 20%. As expected, differences tend to be less when the snow cover extent is greater, particularly for Eurasia. The GSFC 1996 algorithln performs better in North America in each month than does the Chang et al. (1987) algorithm. This is also the case in Eurasia, except in April and May when the Chang et al. (1987)algorithm is in closer accord to the SDC than is the GSFC 1996 algorithm.

Dense media radiative transfer theory based on quasicrystalline approximation with applications to passive microwave remote sensing of snow

Dense media radiative transfer (DMRT) equations based on quasicrystalline approximation (QCA) for densely distributed moderate size particles are developed. We first compute the effective propagation constant and coherent transmission into a dense medium on the basis of the generalized Lorentz-Lorenz law and the generalized Ewald-Oseen extinction theorem. The absorption coefficient of the dense media is then calculated. The distorted Born approximation is next applied to a thin layer to determine the bistatic scattering coefficients and the scattering coefficient. The phase matrix in DMRT is then obtained as bistatic scattering coefficient per unit volume. The model is applied to multiple sizes and for sticky particles. Numerical results are illustrated for extinction and brightness temperatures in passive remote sensing using typical parameters in snow terrain. The QCA-based DMRT is also used to compare with satellite Special Sensor Microwave Imager (SSM/I) brightness temperatures for four channels at 19 and 37 GHz with vertical and horizontal polarizations and for two snow seasons. It shows reasonable agreement to snow depth of 1 m.

Snow water equivalent estimation by microwave radiometry

Abstract Snow water equivalent (SWE) is one of the most important parameters for accurate prediction of snowmelt runoff. Conventionally, SWE is monitored using observations made at widely scattered points in or around specific watersheds. Remote sensors, which provide data with better spatial and temporal coverage, can be used to improve the SWE estimates. Microwave radiation, which can penetrate through a snowpack, may be used to infer the SWE. Calculations made from a microscopic scattering model are used to simulate the effect of varying SWE on the microwave brightness temperature. Data obtained from truck mounted, airborne and spaceborne systems from various tests sites have been studied. The simulated SWE compares favorably with the measured SWE for dry snowpacks. In addition, whether or not the underlying soil is frozen or thawed may be discriminated using the polarization information obtained by spaceborne sensors.

Inversion of snow parameters from passive microwave remote sensing measurements by a neural network trained with a multiple scattering model

The inversion of snow parameters from passive microwave remote sensing measurements is performed with a neural network trained with a dense-media multiple-scattering model. The input-output pairs generated by the scattering model are used to train the neural network. Simultaneous inversion of three parameters, mean-grain size of ice particles in snow, snow density, and snow temperature from five brightness temperatures, is reported. It is shown that the neural network gives good results for simulated data. The absolute percentage errors for mean-grain size of ice particles and snow density are less than 10%, and the absolute error for snow temperature is less than 3 K. The neural network with the trained weighting coefficients of the three-parameter model is also used to invert SSMI data taken over the Antarctic region. >

Algorithms for the Retrieval of Rainfall from Passive Microwave Measurements.

The retrieval of rainfall intensity from radiances measured by spaceborne microwave radiometers can be understood in terms of well established physics. At frequencies below about 40 GHz over an ocean background the relationship between the rainfall and the observations is particularly well understood. In this part of the spectrum, the radiances are principally determined by the liquid hydrometeors with only a modest amount of ambiguity. In very intense convection, ice aloft may increase this ambiguity somewhat. At high frequencies, such as the 85.5 GHz channel of the SSM/I, scattering by the frozen hydrometeors becomes more significant and quantitative rainfall retrieval becomes more problematic. In spite of the ambiguities, the use of the higher frequencies is desirable on a number of counts including: applicability over land, spatial resolution and dynamic range. A total of 16 algorithms were submitted for the PIP‐1. These include algorithms that are based on high frequency (scattering) measurements and low frequency (emission) measurements with a few combinations and variations on these themes. The calibration of the algorithms varies from mostly empirical to essentially first principles with most falling somewhere in‐between. All of the algorithms retrieved rainfall and one also retrieved a profile of the liquid and frozen hydrometeors.

Microwave radiometric observations near 19.35, 92 and 183 GHz of precipitation in tropical storm Cora

Abstract Observations of rain cells in the remains of a decaying tropical storm were made by Airborne Microwave Radiometers at 19.35 and 92 GHz and three frequencies near 183 GHz. Extremely low brightness temperatures, as low as 140 K, were noted in the 92 and 183 GHz observations. These can be accounted for by the ice often associated with raindrop formation. Further, the 183 GHz observations can be interpreted in terms of the height of the ice. The brightness temperatures observed suggest the presence of precipitationsized ice as high as 9 km or more.

EFFECTS OF FOREST ON THE SNOW PARAMETERS DERIVED FROM MICROWAVE MEASUREMENTS DURING THE BOREAS WINTER FIELD CAMPAIGN

Passive microwave data have been used to infer the areal snow water equivalent (SWE) with some success. However, the accuracy of these retrieved SWE values have not been well determined for heterogeneous vegetated regions. The Boreal Ecosystem-Atmosphere Study (BOREAS) Winter Field Campaign (WFC), which took place in February 1994, provided the opportunity to study in detail the effects of boreal forests on snow parameter retrievals. Preliminary results reconfirmed the relationship between microwave brightness temperature and snow water equivalent. The pronounced effect of forest cover on SWE retrieval was studied. A modified vegetation mixing algorithm is proposed to account for the forest cover. The relationship between the microwave signature and observed snowpack parameters matches results from this model.