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A. T. C. Chang, J. L. Foster, R. E. J. Kelly, E. G. Josberger, R. L. Armstrong, and N. M. Mognard


Accurate estimation of snow mass is important for the characterization of the hydrological cycle at different space and time scales. For effective water resources management, accurate estimation of snow storage is needed. Conventionally, snow depth is measured at a point, and in order to monitor snow depth in a temporally and spatially comprehensive manner, optimum interpolation of the points is undertaken. Yet the spatial representation of point measurements at a basin or on a larger distance scale is uncertain. Spaceborne scanning sensors, which cover a wide swath and can provide rapid repeat global coverage, are ideally suited to augment the global snow information. Satellite-borne passive microwave sensors have been used to derive snow depth (SD) with some success. The uncertainties in point SD and areal SD of natural snowpacks need to be understood if comparisons are to be made between a point SD measurement and satellite SD. In this paper three issues are addressed relating satellite derivation of SD and ground measurements of SD in the northern Great Plains of the United States from 1988 to 1997. First, it is shown that in comparing samples of ground-measured point SD data with satellite-derived 25 × 25 km2 pixels of SD from the Defense Meteorological Satellite Program Special Sensor Microwave Imager, there are significant differences in yearly SD values even though the accumulated datasets showed similarities. Second, from variogram analysis, the spatial variability of SD from each dataset was comparable. Third, for a sampling grid cell domain of 1° × 1° in the study terrain, 10 distributed snow depth measurements per cell are required to produce a sampling error of 5 cm or better. This study has important implications for validating SD derivations from satellite microwave observations.

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Alan Basist, Claude Williams Jr., Thomas F. Ross, Matthew J. Menne, Norman Grody, Ralph Ferraro, Samuel Shen, and Alfred T. C. Chang


The frequencies flown on the Special Sensor Microwave Imager (SSM/I) are sensitive to liquid water near the earth's surface. These frequencies are primarily atmospheric window channels, which receive the majority of their radiation from the surface. Liquid water near the surface depresses the emissivity as a function of wavelength. The relationship between brightness temperatures at different frequencies is used to dynamically derive the amount of liquid water in each SSM/I observation at 1/3° resolution. These data are averaged at 1° resolution throughout the globe for each month during the period of 1992–97, and the 6-yr monthly means and the monthly anomalies of the wetness index are computed from this base period. To quantify the relationship between precipitation and surface wetness, these anomalies are compared with precipitation anomalies derived from the Global Precipitation Climate Program. The analysis was performed for six agricultural regions across six continents. There is generally a good correspondence between the two variables. The correlation generally increases when the wetness index is compared with precipitation anomalies accumulated over a 2-month period. These results indicate that the wetness index has a strong correspondence to the upper layer of the soil moisture in many cultivated areas of the world. The region in southeastern Australia had the best relationship, with a correlation coefficient of 0.76. The Sahel, France, and Argentina showed that the wetness index had memory of precipitation anomalies from the previous months. The memory is shorter for southeastern Australia and central China. The weakest correlations occurred over the southeastern United States, where the surface is covered by dense vegetation. The unique signal, strengths, and weaknesses of the wetness index in each of the six study regions are discussed.

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