Suicide Basin is a partly glacierized marginal basin of Mendenhall Glacier, Alaska, that has released glacier lake outburst floods (GLOFs) annually since 2011. The floods cause inundation and erosion in the Mendenhall Valley, impacting homes and other infrastructure. Here, we utilize in-situ and remote sensing data to assess the recent evolution and current state of Suicide Basin. We focus on the 2018 and 2019 melt seasons, during which we collected most of our data, partly using unmanned aerial vehicles (UAVs). To provide longer-term context, we analyze DEMs collected since 2006 and model glacier surface mass balance over the 2006-2019 period. During the 2018 and 2019 outburst flood events, Suicide Basin released ∼ 30 × 10 6 m 3 of water within approximately 4-5 days. Since lake drainage was partial in both years, these ∼ 30 × 10 6 m 3 represent only a fraction (∼ 60%) of the basin's total storage capacity. In contrast to previous years, subglacial drainage was preceded by supraglacial outflow over the ice dam, which lasted ∼ 1 day in 2018 and 6 days in 2019. Two large calving events occurred in 2018 and 2019, with submerged ice breaking off the main glacier during lake filling, thereby increasing the basin's storage capacity. In 2018, the floating ice in the basin was 36 m thick on average. In 2019, ice thickness was 29 m, suggesting rapid decay of the ice tongue despite increasing ice inflow from Mendenhall Glacier. The ice dam at the basin entrance thinned by more than 5 m a-1 from 2018 to 2019, which is approximately double the rate of the reference period 2006-2018. While ice-dam thinning reduces water storage capacity in the basin, that capacity is increased by declining ice volume in the basin and longitudinal lake expansion, with the latter process challenging to predict. The potential for premature drainage onset (i.e., drainage before the lake's storage capacity is reached), intermittent drainage decelerations, and early drainage termination further complicates prediction of future GLOF events.

What: More than 80 participants, representing local government, Alaska Native communities, state and federal agencies, nongovernmental organizations, academia, businesses, utilities, and the media, met in person and remotely to gather information about drought impacts in southeast Alaska and improve understanding of what constitutes a drought in a temperate rain forest.

A new data product calculates snowfall rates from weather data beamed directly from several satellites, helping meteorologists provide fast, accurate weather reports and forecasts.

Sea spray presents a significant hazard to vessels in the high latitudes. At issue is the accumulation of ice, which can destabilize, and at times, sink a ship. Many studies have focused on icing prediction systems, but a knowledge gap exists in the detection of sea spray using remote sensing data. The recent availability of data from new and advanced imagers onboard NOAA satellites, specifically the GOES-R series Advanced Baseline Imager (ABI) and JPSS Visible Infrared Imaging Radiometer Suite (VIIRS), offers new tools for the detection and tracking of sea spray for forecasters. While ABI provides superior temporal coverage in order to capture the near real-time evolution of sea spray, VIIRS contributes higher spatial detail, allowing for improved analysis of sea spray extent, particularly within smaller bodies of water. Forecasters can implement these detection techniques to help verify sea spray related forecast products, and to pass along potentially life-saving information to their mariner core partners. This paper discusses the freezing sea spray hazard, and introduces newly-identified methods for detecting and tracking sea spray using NOAA satellite data.

Abstract. Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We train and evaluate 136 statistical models with a rainfall dataset with five landslide-triggering rainfall events recorded near Sitka, Alaska, USA, as well as >6,000 days of non-triggering rainfall (2002–2020). We use Akaike, Bayesian, and leave-one-out information criteria to compare models trained on cumulative precipitation at timescales ranging from 1 hour to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as with testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without. Although frequentist and Bayesian inference produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. Three-hour precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale combined with the limited role of antecedent conditions reflects the rapid draining of porous colluvial soils on very steep hillslopes around Sitka. We use the resulting estimates of daily landslide probability to establish two decision boundaries for three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system (sitkalandslide.org). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.