Unmanned aerial vehicles are increasingly used to study atmospheric structure and dynamics. While much emphasis has been on the development of fixed-wing unmanned aircraft for atmospheric investigations, the use of multirotor aircraft is relatively unexplored, especially for capturing atmospheric winds. The purpose of this article is to demonstrate the efficacy of estimating wind speed and direction with 1) a direct approach using a sonic anemometer mounted on top of a hexacopter and 2) an indirect approach using attitude data from a quadcopter. The data are collected by the multirotor aircraft hovering 10 m above ground adjacent to one or more sonic anemometers. Wind speed and direction show good agreement with sonic anemometer measurements in the initial experiments. Typical errors in wind speed and direction are smaller than 0.5 and 30°, respectively. Multirotor aircraft provide a promising alternative to traditional platforms for vertical profiling in the atmospheric boundary layer, especially in conditions where a tethered balloon system is typically deployed.
Snow is a critical component of the climate system, provides fresh water for millions of people globally, and affects forest and wildlife ecology. Snowy regions are typically data sparse, especially in mountain environments. Remotely-sensed snow cover data are available globally but are challenging to convert into accessible, actionable information. SnowCloudMetrics is a web portal for on-demand production and delivery of snow information including snow cover frequency (SCF) and snow disappearance date (SDD) using Google Earth Engine (GEE). SCF and SDD are computed using the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow Cover Binary 500 m (MOD10A1) product. The SCF and SDD metrics are assessed using 18 years of Snow Telemetry records at more than 750 stations across the Western U.S. SnowCloudMetrics provides users with the capacity to quickly and efficiently generate local-to-global scale snow information. It requires no user-side data storage or computing capacity, and needs little in the way of remote sensing expertise. SnowCloudMetrics allows users to subset by year, watershed, elevation range, political boundary, or user-defined region. Users can explore the snow information via a GEE map interface and, if desired, download scripts for access to tabular and image data in non-proprietary formats for additional analyses. We present global and hemispheric scale examples of SCF and SDD. We also provide a watershed example in the transboundary, snow-dominated Amu Darya Basin. Our approach represents a new, user-driven paradigm for access to snow information. SnowCloudMetrics benefits snow scientists, water resource managers, climate scientists, and snow related industries providing SCF and SDD information tailored to their needs, especially in data sparse regions.
Snow is Earth's most climatically sensitive land cover type. Traditional snow metrics may not be able to adequately capture the changing nature of snow cover. For example, April 1 snow water equivalent (SWE) has been an effective index for streamflow forecasting, but it cannot express the effects of midwinter melt events, now expected in warming snow climates, nor can we assume that station‐based measurements will be representative of snow conditions in future decades. Remote sensing and climate model data provide capacity for a suite of multi‐use snow metrics from local to global scales. Such indicators need to be simple enough to “tell the story” of snowpack changes over space and time, but not overly simplistic or overly complicated in their interpretation. We describe a suite of spatially explicit, multi‐temporal snow metrics based on global satellite data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and downscaled climate model output for the U.S. We describe and provide examples for Snow Cover Frequency (SCF), Snow Disappearance Date (SDD), At‐Risk Snow (ARS), and Frequency of a Warm Winter (FWW). Using these retrospective and prospective snow metrics, we assess the current and future snow‐related conditions in three hydroclimatically different U.S. watersheds: the Truckee, Colorado Headwaters, and Upper Connecticut. In the two western U.S. watersheds, SCF and SDD show greater sensitivity to annual differences in snow cover compared with data from the ground‐based Snow Telemetry (SNOTEL) network. The eastern U.S. watershed does not have a ground‐based network of data, so these MODIS‐derived metrics provide uniquely valuable snow information. The ARS and FWW metrics show that the Truckee Watershed is highly vulnerable to conversion from snowfall to rainfall (ARS) and midwinter melt events (FWW) throughout the seasonal snow zone. In comparison, the Colorado Headwaters and Upper Connecticut Watersheds are colder and much less vulnerable through mid‐ and late‐century.
Satellite-based C-band synthetic aperture radar (SAR) imagery is an effective tool to map and monitor river ice on regional scales because the SAR backscatter is affected by various physical properties of the ice, including roughness, thickness, and structure. Validation of SAR-based river ice classification maps is typically performed using expert interpretation of aerial or ground reference images of the river ice surface, using visually apparent changes in surface roughness to delineate different ice classes. Although many studies achieve high classification accuracies using this qualitative technique, it is not possible to determine if the river ice information contained within the SAR backscatter data originates from the changes in surface roughness used to create the validation data, or from some other ice property that may be more relevant for ice jam forecasting. In this study, we present the first systematic, quantitative investigation of the effect of river ice surface roughness on C-band Sentinel-1 backscatter. We use uncrewed aerial vehicle-based Structure from Motion photogrammetry to generate high-resolution (0.03 m) digital elevation models of river ice surfaces, from which we derive measurements of surface roughness. We employ Random Forest models first to repeat previous ice classification studies, and then as regression models to explore quantitative relationships between ice surface roughness and Sentinel-1 backscatter. Classification accuracies are similar to those reported in previous studies (77–96%) but poor regression performance for many surface roughness metrics (5–113% mean absolute percentage errors) indicates a weak relationship between river ice surface roughness and Sentinel-1 backscatter. Additional work is necessary to determine which physical ice properties are strong controls on C-band SAR backscatter.
Snow is Earth's most climatically sensitive land cover type. Air temperature and moisture availability are first-order controls on snowfall. Maximum snowfall occurs at temperatures very near 0°C, so even a slight increase in temperature will shift a snowy winter to one with midseason rainfall and melt events. Traditional snow metrics are not able to adequately capture the changing nature of snow cover. For example, April 1 snow water equivalent (SWE, the amount of water represented by the snowpack) is used as a streamflow predictor. Still, it cannot express the effects of midwinter melt events, now expected in warming snow climates. The multiple impacts of a changing snowpack require a suite of climate indicators derived from readily measured or modelled data that serve as proxies for relevant snow-related and climate-driven processes. Such indicators need to be simple enough to "tell the story" of snowpack changes over space and time, but not overly simplistic as to be conflated with other variables or, conversely, overly complicated in their interpretation. This paper describes a targeted set of spatially explicit, multi-temporal snow metrics for multiple sectors, stakeholders, and scientists. They include metrics based on satellite data from NASA's Moderate Resolution Imaging Spectroradiometer, meteorological observations and snow data from ground-based stations, and climate model output. We describe and provide examples for Snow Cover Frequency (SCF), Snow Disappearance Date (SDD), snowstorm temperature (ST), At-Risk Snow (ARS), and Frequency of a Warm Winter (FWW).
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