ALBERT

Description: High resolution snow depth maps (1 × 1 m) obtained from terrestrial laser scanner measurements in a small catchment (0.55 km 2 ) in the Pyrenees were used to assess small scale variability of the snowpack at the catchment and sub-grid scales. The coefficients of variation are compared for various plot resolutions (5 × 5, 25 × 25, 49 × 49 and 99 × 99 m), and 8 different days in two snow seasons (2011–2012 and 2012–2013). We also studied the relation between snow variability at the small scale and snow depth, topographic variables, and small scale variability in topographic variables. The results showed that there was marked variability in snow depth, and it increased with increasing scales. Days of seasonal maximum snow accumulation showed the least small scale variability, but this increased sharply with the onset of melting. The coefficient of variation in snowpack depth showed statistically significant consistency amongst the various spatial resolutions studied, although it declined progressively with increasing difference between the grid sizes being compared. Snow depth best explained the spatial distribution of sub-grid variability. Topographic variables including slope, wind sheltering, sub-grid variability in elevation, and potential incoming solar radiation were also significantly correlated with the coefficient of variation of the snowpack, with the greatest correlation occurring at the 99 × 99 m resolution. At this resolution, stepwise multiple regression models explained more than 70% of the variance, whereas at the 25 × 25 m resolution they explained slightly more than 50%. The results highlight the importance of considering small scale variability of the snow depth for comprehensively representing the distribution of snowpack from available punctual information, and the potential for using snow depth and other predictors to design optimized surveys for acquiring distributed snow depth data. This article is protected by copyright. All rights reserved.

Description: We provide an analysis of the galactic cosmic ray (GCR) radiation environment of Earth's atmosphere using measurements from the Cosmic Ray Telescope for the Effects of Radiation (CRaTER) aboard the Lunar Reconnaissance Orbiter (LRO) together with the Badhwar-O'Neil model and dose lookup tables generated by the Earth-Moon-Mars Radiation Environment Module (EMMREM). This study demonstrates an updated atmospheric radiation model that uses new dose tables to improve the accuracy of the modeled dose rates. Additionally, a method for computing geomagnetic cutoffs is incorporated into the model in order to account for location-dependent effects of the magnetosphere. Newly available measurements of atmospheric dose rates from instruments aboard commercial aircraft and high-altitude balloons enable us to evaluate the accuracy of the model in computing atmospheric dose rates. When compared to the available observations, the model seems to be reasonably accurate in modeling atmospheric radiation levels, overestimating airline dose rates by an average of 20%, which falls within the uncertainty limit recommended by the ICRU. Additionally, measurements made aboard high-altitude balloons during simultaneous launches from New Hampshire and California provide an additional comparison to the model. We also find that the newly incorporated geomagnetic cutoff method enables the model to represent radiation variability as a function of location with sufficient accuracy.

Description: This quantitative study evaluates how 71 Spanish undergraduate students perceive and interpret the uncertainty inherent to deterministic forecasts. It is based on several questions that asked participants what they expect given a forecast presented under the deterministic paradigm for a specific lead time and a particular weather parameter. In this regard, both normal and extreme weather conditions were studied. Students’ responses to the temperature forecast as it is usually presented in the media expect an uncertainty range of ±1°–2°C. For wind speed, uncertainty shows a deviation of ±5–10 km h−1, and the uncertainty range assigned to the precipitation amount shows a deviation of ±30 mm from the specific value provided in a deterministic format. Participants perceive the minimum night temperatures as the least-biased parameter from the deterministic forecast, while the amount of rain is perceived as the most-biased one. In addition, participants were then asked about their probabilistic threshold for taking appropriate precautionary action under distinct decision-making scenarios of temperature, wind speed, and rain. Results indicate that participants have different probabilistic thresholds for taking protective action and that context and presentation influence forecast use. Participants were also asked about the meaning of the probability-of-precipitation (PoP) forecast. Around 40% of responses reformulated the default options, and around 20% selected the correct answer, following previous studies related to this research topic. As a general result, it has been found that participants infer uncertainty into deterministic forecasts, and they are mostly used to take action in the presence of decision-making scenarios. In contrast, more difficulties were found when interpreting probabilistic forecasts.