SEARCH · Engineering Papers

Results for “Snow”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,315 records · Page 73

All sources NASA NTRS DOE OSTI

NASA NTRS2020

Winter Arctic Sea Ice Thickness from ICESat-2 Freeboards

NASA’s ICESat-2 mission was launched in September 2018 with the primary goal of monitoring our rapidly changing polar regions. The sole instrument onboard, the Advanced Topographic Laser Altimeter System (ATLAS), is now providing routine, very high-resolution, surface elevation data across the globe, including the Arctic and Southern Oceans. In this study we demonstrate our new processing chain for converting the along-track ICESat-2 sea ice freeboard product (ATL10) into sea ice thickness, focusing our initial efforts on the Arctic Ocean. For this conversion we primarily make use of snow depth and density data from the NASA Eulerian Snow on Sea Ice Model (NESOSIM). The coarse resolution (~100 km) snow data are redistributed onto the high resolution (~30 - 100 m) ATL10 freeboards using relationships obtained from snow depth and freeboard data collected by NASA’s Operation IceBridge mission. We present regional sea ice thickness distributions and highlight their seasonal evolution through our first winter-season of data collection. We include ice thickness uncertainty estimates, while also acknowledging the limitations of these estimates. We generate a gridded monthly thickness product and compare this with various monthly sea ice thickness estimates obtained from ESA’s CryoSat-2 satellite mission, with ICE 33 Sat-2 showing consistently lower thicknesses. Finally, we compare our February/March 2019 thickness estimates to ICESat February/March (19th February – 21st March) 2008 ice thickness estimates using the same input assumptions, which show a ~0.40 m or ~21% thinning across an inner Arctic Ocean domain in this 11-year time period.

Alek A Petty↗

NASA NTRS2021

The Scientific Legacy of NASA’s Operation IceBridge

The National Aeronautics and Space Administration (NASA)’s Operation IceBridge (OIB) was a 13-year (2009–2021) airborne mission to survey land and sea ice across the Arctic, Antarctic, and Alaska. Here, we review OIB’s goals, instruments, campaigns, key scientific results, and implications for future investigations of the cryosphere. OIB’s primary goal was to use airborne laser altimetry to bridge the gap in fine-resolution elevation measurements of ice from space between the conclusion of NASA’s Ice, Cloud, and land Elevation Satellite (ICESat; 2003–2009) and its follow-on, ICESat-2 (launched 2018). Additional scientific requirements were intended to contextualize observed elevation changes using a multisensor suite of radar sounders, gravimeters, magnetometers, and cameras. Using 15 different aircraft, OIB conducted 968 science flights, of which 42% were repeat surveys of land ice, 42% were surveys of previously unmapped terrain across the Greenland and Antarctic ice sheets, Arctic ice caps, and Alaskan glaciers, and 16% were surveys of sea ice. The combination of an expansive instrument suite and breadth of surveys enabled numerous fundamental advances in our understanding of the Earth’s cryosphere. For land ice, OIB dramatically improved knowledge of interannual outlet-glacier variability, ice-sheet, and outlet-glacier thicknesses, snowfall rates on ice sheets, fjord and sub-ice-shelf bathymetry, and ice-sheet hydrology. Unanticipated discoveries included a reliable method for constraining the thickness within difficult-to-sound incised troughs beneath ice sheets, the extent of the firn aquifer within the Greenland Ice Sheet, the vulnerability of many Greenland and Antarctic outlet glaciers to ocean-driven melting at their grounding zones, and the dominance of surface-melt-driven mass loss of Alaskan glaciers. For sea ice, OIB significantly advanced our understanding of spatiotemporal variability in sea ice freeboard and its snow cover, especially through combined analysis of fine-resolution altimetry, visible imagery, and snow radar measurements of the overlying snow thickness. Such analyses led to the unanticipated discovery of an interdecadal decrease in snow thickness on Arctic sea ice and numerous opportunities to validate sea ice freeboards from satellite radar altimetry. While many of its data sets have yet to be fully explored, OIB’s scientific legacy has already demonstrated the value of sustained investment in reliable airborne platforms, airborne instrument development, interagency and international collaboration, and open and rapid data access to advance our understanding of Earth’s remote polar regions and their role in the Earth system.

Joseph A. MacGregor↗

NASA NTRS2021

Quantifying Uncertainties in Nighttime Light Retrievals From Suomi-NPP and NOAA-20 VIIRS Day/Night Band Data

Satellite observations of nighttime lights (NTL) from Suomi-NPP and NOAA-20 VIIRS Day/Night Band data have been widely used to estimate human activities. Long-term changes such as urban development and abrupt short-term changes such as power outages have been monitored from temporal NTL acquired by satellites. While high temporal NTL variation has been found across NTL data of varying temporal scale (e.g., daily, monthly, and annual composites), the sources of measurement error and uncertainty are poorly understood. This paper quantifies the sources of VIIRS-derived NTL uncertainty due to view-illumination geometry, surface Bidirectional Reflectance Distribution Function (BRDF)/albedo, and the effects of snow cover, lunar irradiance, aerosol loading, cloud mask, vegetation, geometry, and ephemeral artifacts (e.g., the Aurora Borealis). Based on this current assessment of NASA Black Marble retrievals (VNP46, Collection V001), we found that angular and atmospheric effects dominate retrieval uncertainty. Errors introduced by upstream data inputs (e.g., a coarser nighttime snow cover flag and misclassification errors in the existing VIIRS nighttime cloud mask) were also found to impact retrieval quality. Despite these challenges, a consistent daily NTL time series record can be routinely generated from top-of-atmosphere VNP46 radiances. Key recommendations include: (1) the use of lunar-BRDF adjusted and atmospherically corrected NTL (i.e., as identified as high-quality retrievals in the VNP46 QA fields), (2) development and improvement to the VIIRS snow cover and cloud masks algorithms to accurately reflect NTL retrieval conditions, (3) characterizing seasonal variations in NTL due to vegetation and snow, (4) reducing geometric effects due to the spatial mismatch of gridded pixel and observation footprint, (5) employing angularly-consistent NTL observations from multiple VIIRS instruments (i.e., Suomi-NPP and NOAA-20) to reduce pixel-based uncertainties and address persistent data gaps, and (6) being mindful of surface-reflected radiance from aurora events at mid-to-high latitudes.

Zhuosen Wang↗

NASA NTRS2022

An Empirical Algorithm to Map Perennial Firn Aquifers and Ice Slabs Within the Greenland Ice Sheet Using Satellite L-band Microwave Radiometry

Perennial firn aquifers are subsurface meltwater reservoirs consisting of a meters-thick water-saturated firn layer that can form on spatial scales as large as tens of kilometers. They have been observed within the percolation facies of glaciated regions experiencing intense seasonal surface melting and high snow accumulation. Widespread perennial firn aquifers have been identified within the Greenland Ice Sheet (GrIS) via field expeditions, airborne ice-penetrating radar surveys, and satellite microwave sensors. In contrast, ice slabs are nearly continuous ice layers that can also form on spatial scales as large as tens of kilometers as a result of surface and subsurface water-saturated snow and firn layers sequentially refreezing following multiple melting seasons. They have been observed within the percolation facies of glaciated regions experiencing intense seasonal surface melting but in areas where snow accumulation is at least 25 % lower as compared to perennial firn aquifer areas. Widespread ice slabs have recently been identified within the GrIS via field expeditions and airborne ice-penetrating radar surveys, specifically in areas where perennial firn aquifers typically do not form. However, ice slabs have yet to be identified from space. Together, these two ice sheet features represent distinct, but related, sub-facies within the broader percolation facies of the GrIS that can be defined primarily by differences in snow accumulation, which influences the englacial hydrology and thermal characteristics of firn layers at depth. Here, for the first time, we use enhanced-resolution vertically polarized L-band brightness temperature () imagery (2015–2019) generated using observations collected over the GrIS by NASA's Soil Moisture Active Passive (SMAP) satellite to map perennial firn aquifer and ice slab areas together as a continuous englacial hydrological system. We use an empirical algorithm previously developed to map the extent of Greenland's perennial firn aquifers via fitting exponentially decreasing temporal L-band signatures to a set of sigmoidal curves. This algorithm is recalibrated to also map the extent of ice slab areas using airborne ice-penetrating radar surveys collected by NASA's Operation IceBridge (OIB) campaigns (2010–2017). Our SMAP-derived maps show that between 2015 and 2019, perennial firn aquifer areas extended over 64 000 km2, and ice slab areas extended over 76 000 km2. Combined together, these sub-facies are the equivalent of 24 % of the percolation facies of the GrIS. As Greenland's climate continues to warm, seasonal surface melting will increase in extent, intensity, and duration. Quantifying the possible rapid expansion of these sub-facies using satellite L-band microwave radiometry has significant implications for understanding ice-sheet-wide variability in englacial hydrology that may drive meltwater-induced hydrofracturing and accelerated ice flow as well as high-elevation meltwater runoff that can impact the mass balance and stability of the GrIS.

Julie Z Miller↗

NASA NTRS2023

Seasonal Forecasting Skill for the High Mountain Asia Region in the Goddard Earth Observing System

Seasonal variability of the global hydrologic cycle directly impacts human activities, including hazard assessment and mitigation, agricultural decisions, and water resources management. This is particularly true across the High Mountain Asia (HMA) region, where availability of water resources can change depending on local seasonality of the hydrologic cycle. Forecasting the atmospheric states and surface conditions, including hydrometeorological relevant variables, at subseasonal-to-seasonal (S2S) lead times of weeks-to-months is an area of active research and development. NASA’s 15 Goddard Earth Observing System (GEOS) S2S prediction system has been developed with this research goal in mind. Here, we benchmark the forecast skill of GEOS-S2S (version 2) hydrometeorological forecasts at 1-3 month lead times in the HMA region, including a portion of the Indian Subcontinent, during the retrospective forecast period, 1981-2016. To assess forecast skill, we evaluate 2-m air temperature, total precipitation, fractional snow cover, snow water equivalent, surface soil moisture, and terrestrial water storage forecasts against the Modern-Era Retrospective analysis for Research and 20 Applications, Version 2 (MERRA-2) and independent reanalysis data, satellite observations, and data fusion products. Anomaly correlation is highest when the forecasts are evaluated against MERRA-2 and particularly in variables with long memory in the climate system, likely due to similar initial conditions and model architecture used in GEOS-S2S and MERRA-2. When compared to MERRA-2, results for the 1-month forecast skill range from anomaly correlation of R anom =0.18 for precipitation to R anom =0.62 for soil moisture. Anomaly correlations are consistently lower when forecasts are 25 evaluated against independent observations; results for the 1-month forecast skill range from R anom =0.13 for snow water equivalent to R anom =0.24 for fractional snow cover. We find that, generally, hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology.

GEOS↗

NASA NTRSDate not supplied

Observing Ice Phase Clouds and Precipitation with Millimeter- and Submillimeter-wave Radiometry during the IMPACTS field campaign: Algorithm Development for the Emerging Class of Microwave Radiometers

Millimeter- and submillimeter-wave (mmWave, submm) radiometers provide significant information content on atmospheric water in liquid, ice, and vapor phases. Recent sensors, such as the Global Precipitation Measurement (GPM) or the Temporal Experiment for Storms and Tropical Systems Technology – Demonstration (TEMPEST-D), use mmWave bands to improve estimation of ice-phase precipitation, and future missions, such as the Atmosphere Observing System (AOS), will extend to submm to provide additional information on thinner ice clouds, linking clouds and precipitation and therefore elucidating the links between weather, climate, and the water cycle. To support the expanded use of mmWave/submm radiometry and exploit the available information content on condensed water mass, we demonstrate ice and liquid water path retrievals for NASA Goddard Space Flight Center's airborne radiometers the Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) and the Configurable Scanning Submillimeter-wave Instrument/Radiometer (CoSSIR, formerly the Compact Scanning Submillimeter Imaging Radiometer). The analysis is based on data collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Storms (IMPACTS) field campaign. A simulation study compares and contrasts retrievals for CoSMIR and CoSSIR, with CoSSIR demonstrating higher skill in ice retrievals, particularly for thin clouds, and the CoSMIR frequencies performing better for liquid. Additionally, we look at dual-polarized radiances observed by CoSMIR during the first two IMPACTS deployments to understand the polarization signatures induced by oriented hydrometeors. Results will be presented in the context of upcoming missions and sensors including AOS and the Ice Cloud Imager (ICI). We will describe the objectives for the expected first flight of the newly upgraded CoSSIR during the final IMPACTS deployment, and we will share novel science investigations we plan to undertake with CoSSIR's new capabilities. Popular Summary: Ice in clouds and falling snow is an important component of weather, climate, and water resources. To better use a new class of sensors that provide information on atmospheric ice, we developed an approach to measure ice clouds and falling snow using airborne data taken during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Storms (IMPACTS), a field campaign to study winter storms. We look at the results for two similar instruments to understand how those differences affect these measurements of clouds and snow, and we further investigate the information the sensors can provide on properties of the clouds and snow.

Ian S Adams↗

NASA NTRSDate not supplied

Observing Ice Phase Clouds and Precipitation with Millimeter- and Submillimeter-wave Radiometry during the IMPACTS field campaign: Algorithm Development for the Emerging Class of Microwave Radiometers

Ian S Adams↗

NASA NTRS2024

Applications of ArcticDEM for measuring volcanic dynamics, landslides, retrogressive thaw slumps, snowdrifts, and vegetation heights

Topographical changes are of fundamental interest to a wide range of Arctic science disciplines faced with the need to anticipate, monitor, and respond to the effects of climate change, including geohazard management, glaciology, hydrology, permafrost, and ecology. This study demonstrates several geomorphological, cryospheric, and biophysical applications of ArcticDEM – a large collection of publicly available, time-dependent digital elevation models (DEMs) of the Arctic. Our study illustrates ArcticDEM's applicability across different disciplines and five orders of magnitude of elevation derivatives, including measuring volcanic lava flows, ice cauldrons, post-failure landslides, retrogressive thaw slumps, snowdrifts, and tundra vegetation heights. We quantified surface elevation changes in different geological settings and conditions using the time series of ArcticDEM. Following the 2014–2015 Bárðarbunga eruption in Iceland, ArcticDEM analysis mapped the lava flow field, and revealed the post-eruptive ice flows and ice cauldron dynamics. The total dense-rock equivalent (DRE) volume of lava flows is estimated to be (1431 ± 2) million m 3 . Then, we present the aftermath of a landslide in Kinnikinnick, Alaska, yielding a total landslide volume of (400 ± 8) × 103 m 3 and a total area of 0.025 km 2 . ArcticDEM is further proven useful for studying retrogressive thaw slumps (RTS). The ArcticDEM-mapped RTS profile is validated by ICESat-2 and drone photogrammetry resulting in a standard deviation of 0.5 m. Volume estimates for lake-side and hillslope RTSs range between 40,000 ± 9000 m 3 and 1,160,000 ± 85,000 m 3 , highlighting applicability across a range of RTS magnitudes. A case study for mapping tundra snow demonstrates ArcticDEM's potential for identifying high-accumulation, late-lying snow areas. The approach proves effective in quantifying relative snow accumulation rather than absolute values (standard deviation of 0.25 m, bias of −0.41 m, and a correlation coefficient of 0.69 with snow depth estimated by unmanned aerial systems photogrammetry). Furthermore, ArcticDEM data show its feasibility for estimating tundra vegetation heights with a standard deviation of 0.3 m (no bias) and a correlation up to 0.8 compared to the light detection and ranging (LiDAR). The demonstrated capabilities of ArcticDEM will pave the way for the broad and pan-Arctic use of this new data source for many disciplines, especially when combined with other imagery products. The wide range of signals embedded in ArcticDEM underscores the potential challenges in deciphering signals in regions affected by various geological processes and environmental influences.

Chunli Dai↗

DOE OSTI2019

Enhancing Hydrologic Design by Next-Generation Intensity-Duration-Frequency Curves Considering Snowmelt and Climate Nonstationarity

Precipitation intensity-duration-frequency (PREC-IDF) curves are a standard tool to derive design floods for hydraulic infrastructure design worldwide. In snow-dominated regions where a large percentage of flood events are caused by snowmelt and rain-on-snow events, precipitation alone can be a poor predictor of flood risk. Meanwhile, anthropogenic climate change is expected to intensify hydrological cycle and alter hydrologic extremes, potentially increasing the likelihood of infrastructure failure in their design life period. To enhance future hydrologic design, we propose the next-generation IDF (NG-IDF) curves with consideration of both snow process and climate nonstationarity. The NG-IDF curves, which characterize the actual water reaching the land surface (melt + rain), use of the concept of “water available for runoff” rather than “rainfall” for hydrologic design. This paper shows that in the mountainous regions of the western United States, the standard PREC-IDF curves without considering snow process can substantially underestimate the input water intensities and the consequent peak design floods (i.e., the PREC-IDF curves may lead to underestimation of peak design flood by as much as 324%). Under a warming climate, we also observe statistically significant declining trends in both snowpack mass and extreme water available for runoff across the mountainous regions of the western United States from 1979 to 2017, confirming the proclamation that “stationarity is dead” and demonstrating the need to develop nonstationary NG-IDF curves for future hydrologic design.

Yan, Hongxiang↗

DOE OSTI2025

Fine-scale vegetation composition and structure shape spatiotemporal variation in surface albedo across a low Arctic tundra landscape

The unprecedented rate of warming in the Arctic is driving changes in the structure and composition of tundra vegetation. Increases in deciduous tall shrub cover, height, and density are of particular concern, as these changes alter local surface albedo in ways that could amplify effects on the regional surface energy budget (SEB). Despite this importance, significant uncertainties remain in understanding the interplay between fine-scale vegetation patterns and emergent albedo dynamics across space and time. Here, we address these uncertainties by (1) quantifying spatiotemporal variation in surface shortwave albedo and (2) determining the relative influence of fine-scale vegetation composition, structure, and environmental conditions on albedo across a representative low-Arctic tundra landscape on Alaska’s Seward Peninsula. To do this, we synthesized multi-scale, multi-platform remote sensing observations, including a novel Landsat-derived albedo time series, a fine-scale map of Arctic plant functional type (PFT) fractional cover, and airborne LiDAR estimates of canopy height and topography. We show that there are substantial reductions in winter albedo for pixels dominated by tall, woody PFTs (28.13%) relative to pixels dominated by non-woody vegetation, but almost no change in summer albedo (3% increase). Further, we identified a unimodal trend in the relationship between canopy height and the timing of the springtime transition from high (snowy) to low (leafy) albedo (peak at 5.5 m), possibly because of competing ‘snow-fence’ and ‘protrusion’ snow-shrub interactions. To explore the primary drivers of albedo, we constructed a random forest model and found that canopy height and the fractional cover of woody PFTs were as- or more important predictors of winter albedo than topographic features. These findings provide strong evidence for the impacts of local vegetation characteristics on regional surface albedo, highlighting the need for better quantification of snow-shrub interactions to accurately predict the Arctic’s SEB under future environmental change.

Arctic↗

DOE OSTI2022

Tracking snowmelt during hydrological surface processes using a distributed hydrological model in a mesoscale basin on the Tibetan Plateau

We report that mountain snowpack is an important water resource for the high altitude and latitude regions where the terrain is complex. However, the snowmelt pathway and its actual contribution to streamflow and soil moisture are rarely reported and remain unclear in such regions. To fill in this knowledge gap, we incorporate a snowmelt pathway tracking algorithm to a high-resolution physics-based distributed-hydrology-soil-vegetation model (DHSVM), to track snowmelt movement and quantify snowmelt contributions in the surface hydrologic processes. A simple reservoir operation scheme is also incorporated in the model. The modified model is applied to a dammed meso-scale watershed in the northeast Tibetan Plateau, China to study the snow and reservoir effects. The results show that annual snow contribution to soil moisture (SC-SM) and snow contribution to streamflow (SC-S) significantly decrease over 1965-2019. At a monthly scale, SC-SM has the largest amplitude at the top soil layer and its peak in the deeper layer lags behind the upper layer, and mean monthly SC-S at all stations show bimodal distributions corresponding to snowfall season. Reservoir regulation has subtle impacts (≤2.0%) on SC-S. If the current climate change rate continues, monthly and annual streamflow at the outlet will decrease primarily due to snowpack reduction. To mitigate climate change impacts, better water resource management is needed in this watershed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Impact of El Niño‐Southern Oscillation and Madden‐Julian Oscillation on the US Puget Sound Regional Hydroclimate

El Niño-Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO) are two major modes of climate variability with global hydroclimate impacts. However, their impacts often depend on the local climate and geography, resulting in large regional differences. In this study, we examined the connection of ENSO and MJO to the hydroclimate conditions and extremes in the Puget Sound (PS) basin located in the US Pacific Northwest coast. The results indicate that ENSO significantly modulates the cold season temperature and temperature-mediated hydrologic processes. El Niño cold seasons feature less snow accumulation and intensified surface runoff, even if the precipitation amount is similar to La Niña cold seasons. Therefore, El Niño causes more snow drought (in the form of compound dry and warm snow drought) and shifts the surface runoff seasonality by reducing runoff in the subsequent warm season. MJO phases 6–7 trigger more extreme precipitation, temperature, snowmelt, and runoff in the PS region at 0–9–day lags, and such connections are robust regardless of how the ENSO signals are removed. Meanwhile, MJO modulates large-scale extreme weather systems (e.g., atmospheric rivers) with significant enhancement during phases 6–7. ENSO impacts have intensified in the 2001–2020 period, whereas MJO impacts showed some phase shift in this period. This study reveals ENSO and MJO phases 6–7 as useful predictors of the PS hydroclimate anomalies/extremes at seasonal and daily scales, respectively. Utilizing these findings holds the potential to improve regional water resources prediction and management.

ENSO↗

DOE OSTI2022

Spatiotemporal evolution of melt ponds on Arctic sea ice

Melt ponds on sea ice play an important role in the Arctic climate system. Their presence alters the partitioning of solar radiation: decreasing reflection, increasing absorption and transmission to the ice and ocean, and enhancing melt. The spatiotemporal properties of melt ponds thus modify ice albedo feedbacks and the mass balance of Arctic sea ice. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition presented a valuable opportunity to investigate the seasonal evolution of melt ponds through a rich array of atmosphere-ice-ocean measurements across spatial and temporal scales. In this study, we characterize the seasonal behavior and variability in the snow, surface scattering layer, and melt ponds from spring melt to autumn freeze-up using in situ surveys and auxiliary observations. We compare the results to satellite retrievals and output from two models: the Community Earth System Model (CESM2) and the Marginal Ice Zone Modeling and Assimilation System (MIZMAS). During the melt season, the maximum pond coverage and depth were 21% and 22 ± 13 cm, respectively, with distribution and depth corresponding to surface roughness and ice thickness. Compared to observations, both models overestimate melt pond coverage in summer, with maximum values of approximately 41% (MIZMAS) and 51% (CESM2). This overestimation has important implications for accurately simulating albedo feedbacks. During the observed freeze-up, weather events, including rain on snow, caused high-frequency variability in snow depth, while pond coverage and depth remained relatively constant until continuous freezing ensued. Both models accurately simulate the abrupt cessation of melt ponds during freeze-up, but the dates of freeze-up differ. MIZMAS accurately simulates the observed date of freeze-up, while CESM2 simulates freeze-up one-to-two weeks earlier. This work demonstrates areas that warrant future observation-model synthesis for improving the representation of sea-ice processes and properties, which can aid accurate simulations of albedo feedbacks in a warming climate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025

This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the hillslope of East River (ER) near Pump House (PH) at Mount Crested Butte (ER-PHS1 to 4), and the other four are in the Snodgrass Mountain (SG) area (SG-EHS5 to 8). In terms of vegetation cover, three sites are in montane grasslands (ER-PHS1, ER-PHS2, and SG-EHS5), three are below evergreen conifer canopy (ER-PHS3, SG-EHS6, and SG-EHS7), and two are below deciduous aspen canopy (ER-PHS4 and SG-EHS8). The monitoring period began in October 2019 at the East River sites, in October 2020 at SG-EHS5 and SG-EHS6, and in October 2021 at SG-EHS7 and SG-EHS8. In September 2024, all four East River sites were fully retired. The four Snodgrass Mountain sites remain active. Each site is equipped with a comprehensive suite of meteorological sensors on a tripod and soil sensors that measure weather, energy fluxes, and soil variables. This data package includes measurements from ten different types of sensors and up to thirteen individual sensors per site, including (1) a weather station (measurement height ranges from 2.8~3.8 meters (m) above ground), (2) a quantum sensor for photosynthetic active radiation (PAR) (2.4~3.3m), (3) a net radiometer (1.7~2.1m), (4) an infrared radiometer (1.6~2.2m), (5) a sonic distance sensor (1.5~1.9m), (6) a soil carbon dioxide (CO2) flux chamber (0m), (7) a soil heat flux plate (-0.05m below ground), (8) a soil oxygen sensor (-0.3m), (9) a soil water potential sensor (-0.3m), and (10) soil water content sensors at 3~4 depths (-1.15 ~ -0.1m). A total of twenty-three variables is reported in this data package, including (1) atmospheric variables: air temperature (TA), atmospheric pressure (PA), vapor pressure (VP), and vapor pressure deficit (VPD), (2) precipitation variables: rain precipitation (P) and snow depth (D_SNOW), (3) energy fluxes variables: four-component net radiation (NETRAD) (shortwave/longwave incoming/outgoing radiation, SW_IN, SW_OUT, LW_IN, LW_OUT), photosynthetic photon flux density (PPFD), and soil heat flux (G), (4) soil variables: soil water content (SWC), soil water potential (SWP), soil temperature (TS), soil bulk electrical conductivity (COND_SOIL), and soil gaseous oxygen concentration (O2_SOIL), (5) wind variables: two-dimensional wind speed (WS), gust speed (WS_MAX), and wind direction (WD), and (6) surface variables: surface infrared temperature (T_CANOPY) and soil CO2 flux (CO2_SOIL). Please see the Methods section for data processing and QA/QC steps taken to generate the hourly datasets. The following files are included in this data package (notes on version: v{x}-{y}, where x is the metadata version, and y is the data version, when applicable): (1) “metadata_site_v{x}-{y}.csv” - a site metadata file that summarizes location information of all sites, including site ID, description, coordinates, timeframe, elevation, and vegetation cover, (2) “metadata_instrument_v{x}-{y}.csv” - an instrument metadata file that summarizes sensor information of all sites, including sensor manufacturer and model, measurement height, and sampling and averaging interval of all variables, (3) "data_{SITE_ID}_v{x}-{y}.csv" - eight data files that contain hourly data of each site indicated by {SITE_ID} in the filename, (4) “/figure/data_{SITE_ID}_v{x}-{y}.png" - eight figures that help visualize data of each site indicated by {SITE_ID} in the filename, (5) “/photo/*” - photos of each site indicated by {SITE_ID} in the filename, and (6) four file level metadata (flmd.csv) and data dictionary (*_dd.csv) files that summarize file, header, column, and variable information of all files. Notes: (1) Measurement height: Each variable name is followed by conventional positional qualifiers “H_V_R”, where H indicates the relative horizontal positions of that specific variable, V the vertical positions, and R the replicates. In this data package, only the vertical qualifier V varies, and V increases from the highest vertical position (V=1) to the lowest. Variables with the same qualifier are not necessarily measured by the same sensor, and the same variable with the same qualifier across different sites are not necessarily measured at the same height. Please refer to “metadata_instrument.csv” for the sensor information and measurement heights, and whether a variable is measured below the canopy. (2) Variable availability: Snow depth is not available at ER-PHS3 and SG-EHS7. SWC, soil temperature, and soil bulk EC at the deepest depth (<-1m) are not available at SG-EHS6 and SG-EHS7. The missing value code for numeric variables is -9999, except for SWP. For SWP, the missing value code is +9999, because SWP values are negative. (3) Sampling frequency: Please refer to “metadata_instrument.csv” for the increase of sampling frequency of some variables from 30-min to 1-min at ER-PHS1 to 4 in July 2020. (4) Sensors: While the methods of each sensor are not detailed, all sensors are commercially available, and their methods can be found in their manuals. Please refer to “metadata_instrument.csv” for the sensor manufacturer and model information. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Lake-Effect Snowstorm Events and Associated Snowfall Totals Integrated from NOAA Storm Reports, ERA5, and HRRR for the Laurentian Great Lakes (1997–2024)

Lake-effect snowstorms are localized, impactful winter weather phenomena that can generate substantial snowfall totals and pose significant challenges for forecasting, transportation, and regional infrastructure. To support the analysis and modeling of these events, this dataset compiles observational reports of lake-effect snowstorms alongside corresponding snowfall estimates derived from gridded atmospheric datasets. The observational component of the data originates from the National Weather Service (NWS) winter storm report, subset to lake-effect snow event type, covering 1997–2024. For each lake-effect snow event, this data provides the impacted county, event start and end datetimes at an hourly resolution, as well as relevant storm narratives. The complementary reanalysis-derived data is sourced from European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and High-Resolution Rapid Refresh (HRRR) gridded data. For both gridded datasets, the maximum total snowfall (in units mm) was extracted, constrained by the county and datetimes specified by the observational report. ERA5 data covers the entire observational period (1997–2024), whereas HRRR data is only available from November 2016 – December 2024. Three CSV files are provided here: (1) the observational lake-effect snow event report, (2) ERA5 maximum snowfall detections for each event, and (3) HRRR maximum snowfall detections for each event. Relevant data from the observational files, such as impacted state and county, event datetimes, and event IDs, were included for convenience. Users can inspect and visualize the data using tools such as Microsoft Excel and Python pandas/matplotlib packages. This dataset may support a variety of applications, including climatological analyses of lake-effect snowfall, evaluation of snowfall representation in atmospheric datasets and numerical weather prediction models, and the development of machine learning approaches for detecting or predicting lake-effect snowfall events.

EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > SOLID↗

DOE OSTI2020

Next-Generation Intensity-Duration-Frequency Curves for Climate-Resilient Infrastructure Design: Advances and Opportunities

National and international security communities (e.g., U.S. Department of Defense) have shown increasing attention for innovating critical infrastructure and installations due to recurring high-profile flooding events in recent years. The standard infrastructure design approach relies on local precipitation-based intensity-duration-frequency (PREC-IDF) curves that do not account for snow process and assume stationary climate, leading to high failure risk and increased maintenance costs. This paper reviews the recently developed next-generation IDF (NG-IDF) curves that explicitly account for the mechanisms of extreme water available for runoff including rainfall, snowmelt, and rain-on-snow under nonstationary climate. The NG-IDF curve is an enhancement to the PREC-IDF curve and provides a consistent design approach across rain- to snow-dominated regions, which can benefit engineers and planners responsible for designing climate-resilient facilities, federal emergency agencies responsible for the flood insurance program, and local jurisdictions responsible for developing design manuals and approving subsequent infrastructure designs. Further, we discuss the recent advances in climate and hydrologic science communities that have not been translated into actional information in the engineering community. To bridge the gap, we advocate that building climate-resilient infrastructure goes beyond the traditional local design scale where engineers rely on recipe-based methods only; the future hydrologic design is a multi-scale problem and requires closer collaboration between climate scientists, hydrologists, and civil engineers.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2013

Influence of Persistent Wind Scour on the Surface Mass Balance of Antarctica

Accurate quantification of surface snow accumulation over Antarctica is a key constraint for estimates of the Antarctic mass balance, as well as climatic interpretations of ice-core records. Over Antarctica, near-surface winds accelerate down relatively steep surface slopes, eroding and sublimating the snow. This wind scour results in numerous localized regions (< or = 200 sq km) with reduced surface accumulation. Estimates of Antarctic surface mass balance rely on sparse point measurements or coarse atmospheric models that do not capture these local processes, and overestimate the net mass input in wind-scour zones. Here we combine airborne radar observations of unconformable stratigraphic layers with lidar-derived surface roughness measurements to identify extensive wind-scour zones over Dome A, in the interior of East Antarctica. The scour zones are persistent because they are controlled by bedrock topography. On the basis of our Dome A observations, we develop an empirical model to predict wind-scour zones across the Antarctic continent and find that these zones are predominantly located in East Antarctica. We estimate that approx. 2.7-6.6% of the surface area of Antarctica has persistent negative net accumulation due to wind scour, which suggests that, across the continent, the snow mass input is overestimated by 11-36.5 Gt /yr in present surface-mass-balance calculations.

Antarctica↗

NASA NTRS2019

NCA-LDAS: Overview and Analysis of Hydrologic Trends for the National Climate Assessment

Terrestrial hydrologic trends over the conterminous United States are estimated for 1980-47 2015 using the National Climate Assessment-Land Data Assimilation System (NCA-LDAS) reanalysis. NCA-LDAS employs the uncoupled Noah Version 3.3 land surface model at 0.125 degreesx0.125 degrees forced with NLDAS-2 meteorology, rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth and irrigation products. Mean annual trends are reported using the nonparametric Mann-Kendall test at p<0.1 significance. Results illustrate the interrelationship between regional gradients in forcing trends, and trends in other land energy and water stores and fluxes. Mean precipitation trends range from +3 to +9 mm/yr in the Upper Great Plains and Northeast to -1 to -9 in the West and South; net radiation flux trends range from +0.05 to +0.20 W/m(exp2)/yr in the East to -0.05 to -0.20 in the West; U.S.-wide temperature trends average about +0.03K/yr. Trends in soil moisture, snow cover, latent and sensible heat fluxes and runoff are consistent with forcings, contributing to increasing evaporative fraction trends from West to East. Evaluation of NCA-LDAS trends compared to independent data indicates mixed results. The RMSE of U.S.-wide trends in number of snow cover days improved from 3.13 to 2.89 days/yr while trend detection increased 11%; Trends in latent heat flux were hardly affected, RMSE decreasing only 0.17 to 0.16 W/m(exp 2)/yr, while trend detection increased 2%; NCA-LDAS runoff trends degraded significantly from 2.6 to 16.1 mm/yr while trend detection was unaffected. Analysis also indicated that NCA-LDAS exhibits relatively more skill in low precipitation station density areas, suggesting there are limits to the effectiveness of satellite data assimilation in densely gaged regions. Overall, NCA-LDAS demonstrates capability for quantifying physically consistent, U.S. hydrologic climate trends over the satellite era.

Snow depth↗