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Using global Sentinel-1 radar backscatter data, we systematically map the locations of glaciers with surge-type activity during 2017–22. Patterns of pronounced increases or decreases in the strongest backscatter between two winter seasons often indicate large changes in glacier crevassing, which we treat here as a sign of surge-type activity. Validations against velocity time series, terminus advances and crevassing found in optical satellite images confirm the robustness of this approach. We find 115 surge-type events globally between 2017 and 2022, around 100 of which on glaciers already know as surge-type. Our data reveal a pronounced spatial clustering in three regions, (i) Karakoram, Pamirs and Western Kunlun Shan (~50 surges), (ii) Svalbard (~25) and (iii) Yukon/Alaska (~9), with only a few other scattered surges elsewhere. This spatial clustering is significantly more pronounced than the overall global clustering of known surge-type glaciers. The 2017–22 clustering may point to climatic forcing of surge initiation. [ABSTRACT FROM AUTHOR]

While many studies have been conducted regarding wind-driven Ka-band scattering on the ocean and sea surfaces, few have identified the impacts of Ka-band scattering on small inland water bodies, and fewer have identified the influence of wind on coherence over water. These previous studies have been limited in spatial scale, covering only large water bodies >25 km2. The recently launched Surface Water and Ocean Topography (SWOT) mission is the first Ka-band InSAR satellite designed for mapping water surface elevations and open water areas for rivers as narrow as 100 m and lakes as small as 0.0625 km2. Because measurements of these types are novel, there remains some uncertainty about expected backscatter amplitudes given wind-driven water surface roughness variability. A previous study using the airborne complement to SWOT, AirSWOT, found that low backscatter and low coherence values were indicative of higher errors in the water surface elevation products, recommending minimum thresholds for backscatter and coherence for filtering the data to increase the accuracy of averaged data for lakes and rivers. We determined that the global average wind speed over lakes is 4 m/s, and after comparing AirSWOT backscatter and coherence data with ERA-5 wind speeds, we found that the minimum required speed to retrieve high backscatter and coherence is 3 m/s. We examined 11,072 lakes across Canada and Alaska, with sizes ranging from 350 m2 to 156 km2, significantly smaller than what could be measured with previous Ka-band instruments in orbit. We found that small lakes (0.0625–0.25 km2) have significantly lower backscatter (3–5 dB) and 0.20–0.25 lower coherence than larger lakes (>1 km2). These results suggest that approximately 75% of SWOT observable lake areas around the globe will have consistently high-accuracy water surface elevations, though seasonal wind variability should remain an important consideration. Despite very small lakes presenting lower average backscatter and coherence, this study asserts that SWOT will be able to accurately resolve the water surface elevations and water surface extents for significantly smaller water bodies than have been previously recorded from satellite altimeters. This study additionally lays the foundation for future high-resolution inland water wind speed studies using SWOT data, when the data become available, as the relationships between wind speed and Ka-band backscatter reflect those of traditional scatterometers designed for oceanic studies. [ABSTRACT FROM AUTHOR]

Climate and numerical weather prediction models require assumptions to represent the vertical distribution of subgrid‐scale clouds, which have radiative transfer implications. In this study, nearly 25 years of ground‐based radar and lidar observations of vertical cloud profiles at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site are utilized to derive cloud vertical overlap characteristics from the Cloud Type (CLDTYPE) data product. The cloud vertical overlap characteristics are further separated by cloud regime by considering seven cloud types (i.e., low cloud, congestus, deep convection, altocumulus, altostratus, cirrostratus, and cirrus) as well as periods of shallow cumulus. The decorrelation length scale (i.e., exponential transition from maximum to random overlap with layer separation) is found to vary by cloud regime, ranging between 0.04 km for cirrostratus paired with cirrus to 4.58 km for low cloud paired with cirrus at SGP. Cloud vertical overlap characteristics are also considered for other ARM sites including the Tropical Western Pacific (TWP), North Slope of Alaska (NSA), and Eastern North Atlantic (ENA) sites among other shorter term ARM deployments globally. The decorrelation length scale ranged globally from 1.03 km in the Arctic Ocean to 3.06 km in Manacapuru, Brazil. Globally, the decorrelation length scale by cloud regime exhibited similarities (e.g., for cirrus paired with cirrus) and differences (e.g., congestus paired with cirrus). The results could help inform development of cloud vertical overlap assumptions within operational numerical weather prediction models and potentially improve prediction of radiative fluxes for weather, climate, and renewable energy forecasting. Plain Language Summary: The complexity of cloud vertical structure requires approximations in weather and climate models, which has implication for the forecast of how much sunlight reaches the surface. Cloud observations from radars and lidars help to inform how cloud layers overlap vertically. The cloud observations ultimately inform what model assumptions best capture the observed cloud vertical structure. In this study, cloud profiles from ground‐based radars and lidars are considered to calculate cloud vertical overlap characteristics at 18 long‐term and short‐term sites across the globe. The cloud vertical overlap characteristics are also considered by cloud types to understand if cloud type provides additional information on the model assumptions to use. The cloud vertical overlap characteristics are found to vary by site, season, and cloud type. The results could help inform development of cloud vertical overlap assumptions within weather and climate models and potentially improve prediction of sunlight reaching the surface, which has implications for weather, climate, and renewable energy forecasting. Key Points: Decorrelation length scales varied by cloud regime at the Atmospheric Radiation Measurement Program Southern Great Plains site between 0.04 and 4.58 kmDecorrelation length scales varied by site with values ranging from near 1 km in the Arctic to near 3 km in BrazilDecorrelation length scales for the same cloud regime across the sites were similar, although some cloud regimes exhibited differences [ABSTRACT FROM AUTHOR]

During the concluding phase of the NASA Operation IceBridge (OIB), we successfully completed two airborne measurement campaigns (in 2018 and 2021, respectively) using a compact S and C band radar installed on a Single Otter aircraft and collected data over Alaskan mountains, ice fields, and glaciers. This paper reports seasonal snow depths derived from radar data. We found large variations in seasonal radar-inferred depths with multi-modal distributions assuming a constant relative permittivity for snow equal to 1.89. About 34 % of the snow depths observed in 2018 were between 3.2 and 4.2 m, and close to 30 % of the snow depths observed in 2021 were between 2.5 and 3.5 m. We observed snow strata in ice facies, combined percolation and wet-snow facies, and dry-snow facies from radar data and identified the transition areas from wet-snow facies to ice facies for multiple glaciers based on the snow strata and radar backscattering characteristics. Our analysis focuses on the measured strata of multiple years at the caldera of Mount Wrangell (K'elt'aeni) to estimate the local snow accumulation rate. We developed a method for using our radar readings of multi-year strata to constrain the uncertain parameters of interpretation models with the assumption that most of the snow layers detected by the radar at the caldera are annual accumulation layers. At a 2004 ice core and 2005 temperature sensor tower site, the locally estimated average snow accumulation rate is ∼2.89 m w.e. a -1 between the years 2003 and 2021. Our estimate of the snow accumulation rate between 2005 and 2006 is 2.82 m w.e. a -1 , which matches closely to the 2.75 m w.e. a -1 inferred from independent ground-truth measurements made the same year. The snow accumulation rate between the years 2003 and 2021 also showed a linear increasing trend of 0.011 m w.e. a -2. This trend is corroborated by comparisons with the surface mass balance (SMB) derived for the same period from the regional atmospheric climate model MAR (Modèle Atmosphérique Régional). According to MAR data, which show an increase of 0.86 ∘ C in this area for the period of 2003–2021, the linear upward trend is associated with the increase in snowfall and rainfall events, which may be attributed to elevated global temperatures. The findings of this study confirmed the viability of our methodology, as well as its underlying assumptions and interpretation models. [ABSTRACT FROM AUTHOR]

In this work, we evaluate landfast sea ice dynamics using ground-based radar interferometry. During two field campaigns in Utqiaġvik, Alaska, we collected three ∼24-h series of measurements on 16 May 2012 and 21–23 April 2021 using the Gamma portable radar interferometer (GPRI). These data enable examination of progressive strain of landfast ice on the sub-cm scale. The results indicate near spatially uniform divergence strains exceeding 10−6 in response to an increase in offshore winds from 0 to 7 m s−1. Exceptions to uniform divergence are grounded ridges with either negligible or reduced motion. We also track uniform vertical shifts in response to cm-scale sea level change correlated with NOAA model tidal predictions. We evaluate and take steps to remove contributions from moisture variability, thereby reducing potential errors from atmospheric effects to less than 5 mm. The results suggest that the GPRI can be a valuable tool for monitoring ice-covered coastal zones. The system has possible applications for monitoring tides and ocean surges and tracking small-scale deformation of otherwise stationary ice that can lead to fracture and destabilization. • We resolve wind-induced ice strain over kilometers with cm-scale accuracy. • We track vertical displacement of sea ice in microtidal environment. • We obtain minute-scale temporal resolution and sub-meter range resolution. • Allows accurate location and timing of episodic events e.g., cracks and detachments. • Atmospheric effects can be mitigated by removing phase change from static targets. [ABSTRACT FROM AUTHOR]