[1] Thirty-three snowpack models of varying complexity and purpose were evaluated across a wide range of hydrometeorological and forest canopy conditions at five Northern Hemisphere locations, for up to two winter snow seasons. Modeled estimates of snow water equivalent (SWE) or depth were compared to observations at forest and open sites at each location. Precipitation phase and duration of above-freezing air temperatures are shown to be major influences on divergence and convergence of modeled estimates of the subcanopy snowpack. When models are considered collectively at all locations, comparisons with observations show that it is harder to model SWE at forested sites than open sites. There is no universal ''best'' model for all sites or locations, but comparison of the consistency of individual model performances relative to one another at different sites (and vice versa). Calibration of models at forest sites provides lower errors than uncalibrated models at three out of four locations. However, benefits of calibration do not translate to subsequent years, and benefits gained by models calibrated for forest snow processes are not translated to open conditions.

Marked increases in active‐layer and upper permafrost temperatures occurred in the central Lena River basin in association with abrupt increases in active‐layer soil moisture following the summer of 2005. The positive trend in soil temperature‐moisture relations was observed at monitoring sites in the Yakutsk area, regardless of vegetation and soil type. The increase in soil temperature appears to have started in response to the large amounts of snow that accumulated in the winter of 2004. Abnormally high pre‐winter rainfall and snowfall in the following three years accelerated soil warming through the effects of greater latent heat of freezing and insulation from atmospheric cooling in winter. The consecutive positive anomalies of snow depth and rainfall, which occurred widely in the central and southern Lena River basin during this three‐year period, increased soil moisture and appear to have altered the active‐layer thermal properties, which likely induced widespread warming of the surface layer of permafrost in this region. Copyright © 2009 John Wiley & Sons, Ltd.

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A method to detect cloud cover in the Antarctic using only the infrared channels of AVHRR is discussed. From the data of NOAA-7 received at Syowa Station, the difference in the brightness temperature of each channel appeared to be useful for the identification of clouds. The brightness temperature of the channels 3 (3.7*m) and 4 (11*m) shows the positive difference when the thickness of clouds are in some particular range, and then tends to show negative difference for the thick cloud. Thin clouds have the difference in the brightness temperature between channels 4 and 5 (12 * m). These tendencies are explained by the radiative properties of model clouds theoretically calculated. On the graph of these temperature difference against the channel 4 brightness temperature, pixels of the same cloud distribute on the particular arch starting from the clear pixel. From the arch, clouds can be distinguished from the ground surface. The particle size, temperature and thickness of the cloud can also be inferred. At the low temperature over the inland snow surface, many troubles arise. The channel 3 brightness temperature accompanies poor resolution and large noise at the low temperature. The brightness temperature difference between channels 4 and 5 shows strong dependence on temperature and viewing angle at the low temperature due to the nonlinearity error and variation of snow surface emissivity. An empirical correction is applied to the low temperature data for the automatic cloud detection.

As the world is witnessing the epidemic of coronavirus disease 2019, emerging genetics and clinical pieces of evidence suggest a similar immunopathology to those of severe acute respiratory syndrome and Middle East respiratory syndrome. Staying at home to prevent the spread of the virus and consequently being largely inactive is associated with unintended consequences. These can actually enhance the infection risk and exacerbate poor health conditions including impaired immune function. Physical activity is a feasible way of improving health, particularly physical and mental health in a time of social isolation. However, people with certain health conditions in these circumstances may need a special physical activity programme in addition to any exercise they may already be performing via online programmes. This review aims to provide practical guidelines during the COVID-19 quarantine period. We suggest performing aerobic, resistance training, respiratory muscle training and yoga in the healthy, and in those with upper respiratory tract illness, patients with lower respiratory tract illness should be restricted to respiratory muscle training and yoga. In addition, vitamins D and C, omega-3 fatty acids, and regular consumption of fruit and vegetables might be considered as nutritional aids to support the immune system in those affected by COVID-19.

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The objective of this review is to introduce significant research findings mainly on cytokine responses to exercise. First, some basic background information on cytokines is provided. Then, some of our data according to exercise modes and key experimental research on the factors affecting cytokine responses to exercise are explained. Furthermore, the mechanisms and modulations of the cytokine responses are described to understand stress and inflammatory reactions and their prevention/recovery.

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