Outlier Detection In Depth Of Snow Data

In many places in the United States, buildings need to be built to withstand extreme snow events, without making the construction overly expensive. Often, fitting a probability distribution based on annual maximum measurements of snow depth (or snow water equivalent) will be used in an extreme value analysis. Because the maximum annual snow depths are used in fitting the probability distributions, it is crucial that those maximum values are legitimate. Manually searching through about 100 different snow measurement locations scattered in four different states, there are four patterns that large, but legitimate, maximum snow values tend to follow. The patterns are characterized by the degree of build up to, or build down from, the maximum observation. We use these patterns as part of a two-step filtering process. The first step of the filter naively flags potential outliers. The second step then looks through each potential outlier and compares the set of days around the max to the four patterns previously identified. Any potential outliers that closely follow one of the four patterns are not thrown out, but those that do not follow any pattern are removed. This method of outlier removal protects the probability distribution fitting process from anomalous high values while still ensuring that buildings are designed to withstand true, extreme snow load events

The 2020 National Snow Load Study

The United States has a rich history of snow load studies at the state and national level. The current ASCE 7 snow loads are based on studies performed at the Cold Regions Research and Engineering Laboratory (CRREL) ca. 1980 and updated ca. 1993. The map includes large regions where a site-specific case study is required to establish the load. Many state reports attempt to address the case-study regions designated in the current ASCE 7 design snow load requirements. The independently developed state-specific requirements vary in approach, which can lead to discrepancies in requirements at state boundaries. In addition, there has been great interest to develop site-specific reliability-targeted loads that replace the current load and importance factors applied to 50-year snow load events as defined in ASCE 7-16. This interest stems from the fact that the relative variability in extreme snow load events is not constant across the country, leading to a non-constant probability of failure for a given design scenario. This report describes the creation of a modern, universal, and reproducible approach for estimating reliability-targeted design ground snow loads for the conterminous United States. This new approach significantly reduces the size of case-study regions as currently designated in ASCE 7-16 and resolves discrepancies in design snow load requirements that currently exist along western state boundaries

Supplementary Files For: Interactive Modeling of Bear Lake Elevations in a Future Climate

The water level, or elevation, of Bear Lake has a significant impact on agriculture, power, infrastructure, and recreation for communities around the lake. Climatological variables, such as precipitation, temperature, and snowfall, all have an impact on the elevation of Bear Lake. As the climate changes due to greenhouse gas emissions, the typical behaviors of these climate variables change, leading to new behaviors in Bear Lake elevation. Because of the importance of Bear Lake, it is vital to be able to model and understand how Bear Lake\u27s elevation may change in the face of different climate scenarios and to gain further insights into the sensitivity of Bear Lake\u27s elevation to these changes. One tool to aid in this pursuit is the creation of interactive plots that allow a user to easily visualize the effect that different climate scenarios have on our model of Bear Lake\u27s elevation. Therefore our project has two primary objectives. First is the modeling of Bear Lake elevation using statistical models, and state-of-the-art Neural Networks. Second is the creation of an interactive application that could be hosted online which makes accessible the results of our modeling efforts, as well as visual access to the historical climatological data. This report details our modeling efforts and a proof-of- concept for an interactive application accessible through a web browser