Richard E.J. Kelly

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.

Estimation of snow depth from AMSR-E in the GAME/CEOP Siberia experiment region

The Advanced Microwave Scanning Radiometer-EOS (AMSR-E) launched in 2002 aboard NASAs Aqua satellite, has improved spatial resolution capabilities compared with previous passive microwave instruments. Snow depth retrievals from AMSR-E are tested with GEWEX (Global Energy and Water Experiment) Asian Monsoon Experiment (GAME) and Coordinated Enhanced Observing Period (CEOP) data. Level 1B AMSR-E brightness temperatures are used in the study. Seven acoustic snow gauge sites recorded daily snow depth within a 100 km times 100 km domain located near Yakutsk in Siberia. These point snow depth measurements span a period between October 2002 and February 2003 and are used to test AMSR-E ascending and descending retrievals. The paper describes the accuracy of the snow depth estimates compared with the GAME/CEOP Siberia sites. It also assesses how World Meteorological Organization/Global Telecommunication System meteorological snow data from a station at Yakutsk compare with the GAME/CEOP site snow depth data and AMSR-E estimates

Estimation of global snow cover using passive microwave data

This paper describes an approach to estimate global snow cover using satellite passive microwave data. Snow cover is detected using the high frequency scattering signal from natural microwave radiation, which is observed by passive microwave instruments. Developed for the retrieval of global snow depth and snow water equivalent using Advanced Microwave Scanning Radiometer EOS (AMSR-E), the algorithm uses passive microwave radiation along with a microwave emission model and a snow grain growth model to estimate snow depth. The microwave emission model is based on the Dense Media Radiative Transfer (DMRT) model that uses the quasi-crystalline approach and sticky particle theory to predict the brightness temperature from a single layered snowpack. The grain growth model is a generic single layer model based on an empirical approach to predict snow grain size evolution with time. Gridding to the 25 km EASE-grid projection, a daily record of Special Sensor Microwave Imager (SSM/I) snow depth estimates was generated for December 2000 to March 2001. The estimates are tested using ground measurements from two continental-scale river catchments (Nelson River and the Ob River in Russia). This regional-scale testing of the algorithm shows that for passive microwave estimates, the average daily snow depth retrieval standard error between estimated and measured snow depths ranges from 0 cm to 40 cm of point observations. Bias characteristics are different for each basin. A fraction of the error is related to uncertainties about the grain growth initialization states and uncertainties about grain size changes through the winter season that directly affect the parameterization of the snow depth estimation in the DMRT model. Also, the algorithm does not include a correction for forest cover and this effect is clearly observed in the retrieval. Finally, error is also related to scale differences between in situ ground measurements and area-integrated satellite estimates. With AMSR-E data, improvements to snow depth and water equivalent estimates are expected since AMSR-E will have twice the spatial resolution of the SSM/I and will be able to characterize better the subnivean snow environment from an expanded range of microwave frequencies.

Mapping random and systematic errors of satellite-derived snow-water equivalent observations in Eurasia

Passive microwave sensors onboard satellites can provide global snow water equivalent (SWE) observations day or night, even under cloudy conditions. However, there are both systematic (bias) and random errors associated with the passive microwave measurements. While these errors are well known, they have thus far not been adequately quantified. In this study, unbiased SWE maps, random error maps and systematic error maps of Eurasia for the 1990-1991 snow season (November-April) have been examined. Dense vegetation, especially in the taiga region, and large snow crystals (>0.3 mm in radius), found in areas where the temperature/vapor gradients are greatest, (in the taiga and tundra regions) are the major source of systematic error. Assumptions about how snow crystals evolve with the progression of the season also contribute to the errors. In general, while random errors for North America and Eurasia are comparable, systematic errors are not as great for Eurasia as those observed for North America. Understanding remote sensing retrieval errors is important for correct interpretation of observations, and successful assimilation of observations into numerical models.

Seasonal Snow Extent and Snow Volume in South America Using SSM/I Passive Microwave Data

Abstract Seasonal snow cover in primarily non‐mountainous areas of South America was examined in this study using passive microwave satellite data from the Special Sensor Microwave Imagers (SSM/I) on board Defense Meteorological Satellite Program (DMSP) satellites. For the period 1992–1998, both snow‐cover extent and snow depth (snow mass) were investigated during the winter months (May‐August) in Argentina and Chile. Most of the seasonal snow in South America is in the Patagonia region of Argentina. Since winter temperatures in this region are often above freezing, the coldest winter month was the one with both the most extensive snow cover and greatest snow depth.

The effect of sub-pixel areal distribution of snow on the estimation of snow depth from spaceborne passive microwave instruments

The mapping of estimated snow depth (SD) or snow water equivalent (SWE) from spaceborne passive microwave imagery is usually achieved by detecting a snow scattering signal from the land surface and then by calibrating the magnitude of the scattering with snow depth or snow water equivalent. Scattering is estimated from the brightness temperature difference between 19 GHz and 37 GHz vertical polarization channels of a microwave radiometer (or frequencies not too dissimilar to these). If a snow scattering signal is present, it is generally assumed that snow covers the entire area of a coarse spatial resolution passive microwave pixel (or footprint). For seasonal snowpacks, this is a reasonable assumption because at high latitudes in general, snow cover is often spatially continuous over wide areas during the winter season. However, for spatially discontinuous snowpacks (such as early winter snow, ephemeral snow covers or perhaps regions marginal to mid-winter continental snow covers), snow might be detected in a microwave pixel but it may be inaccurate to assume that snow covers the entire pixel; snow might be spatially localized but dominant enough radiometrically to trigger a snow scattering signal. In this paper we investigate the effect of sub-pixel scale fractional snow extent on the microwave detection of snow. Under cloud-free conditions determined by the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10/spl I.bar/L2 product, we compare the snow scattering signal for various DMSP Special Sensor Microwave Imager pixels with the MODIS MOD10/spl I.bar/L2 snow product (500 m/spl times/500 m spatial resolution). Using the 25 km/spl times/25 km EASE grid projection for the passive microwave imagery, comparisons are made between the microwave scattering signal and the percentage of MODIS pixels classed as 100% snow within the 25 km/spl times/25 km. We also compare microwave snow scattering signals with the degree of MODIS snow pixel clustering/dispersion within 25 km/spl times/25 km pixels. This research has important implications for the errors of passive microwave mapping of SD or SWE in discontinuous snow covered regions.