The third-generation, polar orbiting, TIROS-N series of environmental satellites provides two thermal IR channels for twice-daily monitoring of the Earth's surface. Part of the Advanced Very High Resolution Radiometer (AVHRR), the 3. 8 mu m thermal IR channel was designed to supplement the 11 mu m thermal IR channel in the computation of sea surface temperature by providing corrections for atmospheric water vapor and cloud contamination. Initial examination of the 3. 8 mu m data over land areas, however, revealed differences between them and the 11 mu m data. It is concluded that the 3. 8 mu m channel is also useful for identification of ″hot″ sources. Employing the algorithm of J. Dozier (1981) it is possible to determine the blackbody temperature of these sources and the portions of the 1. 1-km pixels covered by the ″hot″ targets.

A theoretically based model accurtely calculates incoming longwave radiation under clear sky conditions in a remote alpine area. Data requirements are relatively simple and because the model is theoretically based it can be widely applied. The model calculates atmospheric radiation using a form of Brutsaert's method that is adjusted for variations in air pressure and radiation from adjacent slopes. Tests of both point and areal applications indicate that the method gives reliable results. When applied as a segment of an energy balance snowmelt model, the model provides information on energy exchange in an alpine environment and improved snowmelt runoff prediction. Radiation from cloud cover and canopy cover can be more easily isolated by using the model to account for clear sky radiation. It is possible that it can also calibrate atmospheric effects in thermal satellite data, thus enhancing the possibilities for using satellite radiometry in alpine areas. © 1979 Springer-Verlag.

Pore space in a dry snow specimen is filled with a water‐insoluble liquid which is frozen solid. The specimen is microtomed, polished with carbon dust, and micrographed through a photomicroscope mounted directly over the microtome. Serial sections are prepared and micrographed at the rate of about ten per hour. The micrographs are automatically digitized, and converted to binary images for microstructural analysis. 1986 Blackwell Science Ltd

Do the April 1 snow course measurements underestimate the snow accumulation peak in the Sierra Nevada, thus possibly introducing biases in studies of trends and interannual variability that use the April 1 data? Although their period of record is many decades shorter, measurements of snow water equivalent (SWE) from automated snow pillows provide daily records throughout each year, enabling us to examine the timing and magnitude of the maximum SWE accumulation. Using generalized additive mixed models, on average, we find that the peak occurs earlier than April 1 at the lowest elevations and later than April 1 at the highest elevations and that the degree to which April 1 SWE under-measures maximum SWE is greatest at the lowest and highest elevations. In addition, our results show that the biases are associated with cold and warm phase interactions of certain climate indices.

An algorithm is developed for calculating the horizons for each point in a digital terrain grid in order N iterations, whereas all previous methods seem to be of O(N2) time complexity. The new method makes horizon computations reasonable, and ought to improve the accuracy of surface climate models in rugged terrain. © 1981.

A radiance value of a target pixel recorded by a remote sensor can be decomposed into three components: (1) attenuated target signature, (2) pure atmospheric radiation, and (3) the contribution made by the ground through the atmospheric scattering process. Given the meteorological and optical parameters of a layer-structured atmosphere, its transmittance and radiance distribution can be accurately calculated with a plane-parallel radiative transfer model. For a uniform surface, the ground contribution can be obtained by comparing radiances for an atmosphere over a black but non-emitting surface and the same atmosphere with an underlying ground of given albedo or temperature. For an inhomogeneous surface, the first two components remain the same as long as the surface is a plane. The third may be estimated using the locally averaged top-of-atmosphere radiance. An atmospheric point spread function is calculated by a Monte Carlo approach and is used for retrieving the ground signature through a deconvolution procedure. © 1987 VNU science press.

Regression tree models have been shown to provide the most accurate estimates of distributed snow water equivalent (SWE) when intensive field observations are available. This work presents a comparison of regression tree models using different source digital elevation models (DEMs) and different combinations of independent variables. Different residual interpolation techniques are also compared. The analysis was performed in the 19.1 km2 Tokopah Basin, located in the southern Sierra Nevada of California. Snow depth, the dependent variable of the statistical models, was derived from three snow surveys (April, May and June 1997), with an average of 328 depth measurements per survey. Estimates of distributed SWE were derived from the product of the snow depth surfaces, the average snow density (54 measurements on average) and the fractional snow covered area (obtained from the Landsat Thematic Mapper and the Airborne Visible/Infrared Imaging Spectrometer). Independent variables derived from the standard US Geological Survey DEM yielded the lowest overall model deviance and lowest error in snow depth prediction. Simulations using the Shuttle Radar Topography Mission DEM and the National Elevation Dataset DEM were improved when northness was substituted for solar radiation in five of six cases. Co-kriging with maximum upwind slope and elevation proved to be the best method for distributing residuals for April and June, respectively. Inverse distance weighting was the best residual distribution method for May. Copyright © 2004 John Wiley & Sons, Ltd.

The mass and energy balance of the snowpack govern its evolution. Direct measurement of these fluxes is essential for modeling the snowpack, yet there are few sites where all the relevant measurements are taken. Mammoth Mountain, CA USA, is home to the Cold Regions Research and Engineering Laboratory and University of California – Santa Barbara Energy Site (CUES), one of five energy balance monitoring sites in the western US. There is a ski patrol study site on Mammoth Mountain, called the Sesame Street Snow Study Plot, with automated snow and meteorological instruments where new snow is hand-weighed to measure its water content. There is also a site at Mammoth Pass with automated precipitation instruments. For this dataset, we present a clean and continuous hourly record of selected measurements from the three sites covering the 2011–2017 water years. Then, we model the snow mass balance at CUES and compare model runs to snow pillow measurements. The 2011–2017 period was marked by exceptional variability in precipitation, even for an area that has high year-to-year variability. The driest year on record, and one of the wettest years, occurred during this time period, making it ideal for studying climatic extremes. This dataset complements a previously published dataset from CUES containing a smaller subset of daily measurements. In addition to the hand-weighed SWE, novel measurements include hourly broadband snow albedo corrected for terrain and other measurement biases. This dataset is available with a digital object identifier: https://doi.org/10.21424/R4159Q.