Nimbus-7 SMMR Derived Global Snow Cover Parameters

Abstract

Snow covers about 40 million km2of 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.

Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016

“…1) (Chang et al, 1987), with modifications for non-SSMI platforms as proposed by Armstrong and Brodzik The Cryosphere, 11, 2329-2343 www.the-cryosphere.net/11/2329/2017/ T ie n S h a n Pamir Hindu Kush K u n lu n S h a n K a r a k o r a m H im a la ya Figure 1. Topographic map of the study area across High Mountain Asia (HMA), with major catchment boundaries (black lines and labels in black font with white border) and major mountain ranges (white font).…”

“…To track the end of snowmelt, we leverage two additional datasets: (1) the raw Tb 37V time series, which rapidly increases as snowpack thins, and (2) a SWE time series calculated from the Tb 19 and Tb 37 GHz channels (Chang et al, 1987;Kelly et al, 2003;Tedesco et al, 2015;Smith and Bookhagen, 2016).…”

Evaluating global snow water equivalent products for testing land surface models

“…This can be converted to SWE by multiplying by snow density (assumed to be 300 kgm −2 ). This is known as the "Chang algorithm" (Chang et al 1987) and forms the basis of the AMSR-E and SSM/I inversions. The wavelengths will vary for different instruments and correction factors can be applied to account for this (Brodzik et al 2005).…”

“…The Chang algorithm has also been shown to saturate in deeper snow covers, SWE >120 mm (Chang et al 1987). This is due to the higher frequency microwaves no longer decreasing with increasing SWE .…”

Soil Moisture & Snow Properties Determination with GNSS in Alpine Environments: Challenges, Status, and Perspectives

“…The passive microwave-SWE conversion widely used [75] assumes constant snow density and constant grain size [66,76], hence snow grains, enlarged through snow metamorphosis, result in increased calculated SWE, and increased density (and in-particular the presence of liquid water) results in decreased calculated SWE [77]. These two parameters are particularly important in alpine and sub-alpine regions, where snow is often around its melting point and temperature gradients within the snowpack may be large.…”