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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.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Methodology for deriving the telescope focus function and its uncertainty for a heterodyne pulsed Doppler lidar

Doppler lidars provide two measured parameters, radial velocity and signal-to-noise ratio, from which winds and turbulent properties are routinely derived. Attenuated backscatter, which gives quantitative information on aerosols, clouds, and precipitation in the atmosphere, can be used in conjunction with the winds and turbulent properties to create a sophisticated classification of the state of the atmospheric boundary layer. Calculating attenuated backscatter from the signal-to-noise ratio requires accurate knowledge of the telescope focus function, which is usually unavailable. Inaccurate assumptions of the telescope focus function can significantly deform attenuated backscatter profiles, even if the instrument is focused at infinity. Here, we present a methodology for deriving the telescope focus function using a co-located ceilometer for pulsed heterodyne Doppler lidars. The method was tested with Halo Photonics StreamLine and StreamLine XR Doppler lidars but should also be applicable to other pulsed heterodyne Doppler lidar systems. The method derives two parameters of the telescope focus function, the effective beam diameter and the effective focal length of the telescope. Additionally, the method provides uncertainty estimates for the retrieved attenuated backscatter profile arising from uncertainties in deriving the telescope function, together with standard measurement uncertainties from the signal-to-noise ratio. The method is best suited for locations where the absolute difference in aerosol extinction at the ceilometer and Doppler lidar wavelengths is small.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Beryllium Isotopes in Rainfall for Stratospheric-Tropospheric Exchange Dynamics Experiment (BIRDIE) (Final Report)

The Beryllium Isotopes in Rainfall for Stratospheric-Tropospheric Exchange Dynamics Experiment or BIRDIE was a precipitation collection campaign designed to measure cosmogenic isotopes of beryllium in rainfall and other forms of precipitation across the DOE Southern Great Plains (SGP) Atmospheric Radiation Measurement (ARM) site. Precipitation samples were collected over three weeks (May 21-June 10, 2022) at 11 SGP sites across a 120 km 2 domain in northern and central Oklahoma. The field campaign targeted summer-time convection rain events. We aimed to capture any spatial and/or temporal variability in the concentrations of beryllium-10 (10Be) and beryllium-7 (7Be) across the SGP ARM site during these rainfall events. The ratio of 10Be to 7Be (10/7Be) has been shown to be a powerful tracer for transport processes in the stratosphere-troposphere exchange (STE) system (e.g., Koch and Rind 1998). Any observed variability is analyzed to see if it could be linked to STE processes, including overshooting of deep convection clouds which penetrate the lower stratosphere. Six wet weather system events were collected within our three-week span. Five out of six of these rainfall events are classified as moderately to strongly convective. Those events are studied in more detail. The five systems also follow each other over a short time period of eleven days. In our rainfall samples we observed spatial and event variability in the concentrations of beryllium-10 and -7 which resulted in variability of 10/7 Be ratios. Each weather event appeared to exhibit a unique 10/7Be pattern across the domain that was not directly linked to storm total precipitation amount. To better understand the causes for this spatial variability, cloud characteristics in the domain were looked at to assess cloud type, cloud height and depth information. We also ran Hysplit back trajectories to identify possible air mass and moisture origins across our events. Detailed analysis is still on-going. The purpose of this technical report is to provide background on using cosmogenic isotopes as atmospheric tracers, describe the experimental design, and present the beryllium concentration and ratio data.

58 GEOSCIENCES↗
DOE OSTI2021

Machine learning to generate gridded extreme precipitation data sets for global land areas with limited in situ measurements

There is a strong need for gridded observational data sets that describe the climatology of extreme precipitation across the globe; such data sets are critical for quantifying precipitation extremes and their corresponding influence on large perturbations on surface and groundwater systems and flooding. Machine learning (ML) methods can be used to generate pseudo in situ measurements of the climatology of extreme precipitation for land regions with poor sampling by matching relevant geographic (e.g., orography) and atmospheric (e.g., surface temperature, precipitation, and pressure) variables for densely sampled regions and those with limited geographic sampling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

GUC CSU X-Band Precipitation Radar Surface QUantitatIve pRecipitation Estimation (SQUIRE)

For the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign, a scanning X-Band dual-polarimetric radar provided by Colorado State University (CSU) was deployed to estimate precipitation within the Upper Colorado River Basin. In order to evaluate precipitation estimates from the CSU X-Band Radar (XPRECIPRADAR), Surface QUantitatIve pRecipitation Estimation (SQUIRE) was created to provide the SAIL community with a gridded dataset of precipitation estimates for comparison with hydrology and atmospheric models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Supporting Advancement in Weather and Water Prediction in the Upper Colorado River Basin: The SPLASH Campaign

Water is a critical resource that causes significant challenges to inhabitants of the western United States. These challenges are likely to intensify as the result of expanding population and climate-related changes that act to reduce runoff in areas of complex terrain. To better understand the physical processes that drive the transition of mountain precipitation to streamflow, the National Oceanic and Atmospheric Administration has deployed suites of environmental sensors throughout the East River watershed of Colorado as part of the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH). This includes surface-based sensors over a network of five different observing sites, airborne platforms, and sophisticated remote sensors to provide detailed information on spatiotemporal variability of key parameters. With a 2-yr deployment, these sensors offer detailed insight into precipitation, the lower atmosphere, and the surface, and support the development of datasets targeting improved prediction of weather and water. Initial datasets have been published and are laying a foundation for improved characterization of physical processes and their interactions driving mountain hydrology, evaluation and improvement of numerical prediction tools, and educational activities. SPLASH observations contain a depth and breadth of information that enables a variety of atmospheric and hydrological science analyses over the coming years that leverage collaborations between national laboratories, academia, and stakeholders, including industry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Understanding Subseasonal Moisture Recycling from Extreme MCSs Using a Land–Atmosphere Coupled Water Tracer Model

Extreme precipitation events often produce severe flooding and pose significant threats to our society. However, their subseasonal impacts on the terrestrial and atmospheric systems are not well understood. Here, we use a new land–atmosphere coupled water tracer tool embedded in the Weather Research and Forecasting (WRF) Model to “tag” precipitation produced by a series of extreme mesoscale convective systems (MCSs) in May 2015 over the southern Great Plains to reveal their contributions to different terrestrial water storages and moisture recycling during May–July. During May, we find that precipitation from earlier MCSs can contribute to 20%–50% of total evapotranspiration (ET) in later May and contribute ∼10% of local precipitation for later MCSs. After May, the water “tagged” to preceding May MCS events has the most active contribution to moisture recycling during the first 5 days in June, but this contribution diminishes quickly afterward. Both periods indicate a quick turnover of tagged precipitation, which may be representative of the southern Great Plains region. The quick turnover is closely associated with the direct evaporation from the soil surface due to the predominant shrubland and grassland coverage over this region. Over some forested areas, however, the tracer-ET flux is dominated by transpiration that stably supplies a small fraction (<1%) of moisture to the atmosphere throughout June and July. In conclusion, the tracer-contributed plume (through evaporation and transpiration) in atmospheric moisture can extend 500–700 km downstream between May and July, highlighting the far-reaching contribution to moisture recycling and precipitation from the tagged MCS events in the southern Great Plains.

Atmosphere-land interaction↗
DOE OSTI2023

Bayesian Cloud Property Retrievals from ARM Active and Passive Measurements

The optimum use of the continuous measurements of thermodynamics, radiation, aerosols, clouds and precipitation from the DOE Atmospheric Radiation Measurement (ARM) program is key to achieve the DOE Atmospheric System Research (ASR)’s objectives. One of the key mission requirements is to retrieve cloud and precipitation properties, as well as vertical motion parameters, along the vertical cross- section defined by the profiling active sensors. Such retrievals are challenging to perform continuously in the entire spectrum of cloud and precipitation conditions due to the large natural microphysical and dynamical variability, the often-limited information content in the measurements, and the lack of proper characterization of measurement quality and uncertainty. Today, the acquisition of new remote and in-situ sensors by the ARM program creates opportunities to address the microphysical retrieval problem by exploiting new, more robust retrieval techniques and integrating various scattered advancements in both sensor techniques and retrieval algorithms. During this project, we constructed a robust Bayesian Markov chain Monte Carlo (MCMC) cloud property retrieval algorithm that includes a state of the art radar forward model. Our MCMC-based retrieval produces both the best estimate of height-resolved cloud and precipitation properties in the radar profile, as well as an estimate of the in-cloud vertical motion and turbulence. In addition, the MCMC algorithm automatically produces robust and flexible estimates of retrieval uncertainty. We tested the algorithm on several synthetic cloud profiles obtained from large eddy simulation (LES) models with bin-resolved microphysics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Which Combinations of Environmental Conditions and Microphysical Parameter Values Produce a Given Orographic Precipitation Distribution?

This study applies an idealized modeling framework, alongside a Bayesian Markov chain Monte Carlo (MCMC) algorithm, to explore which combinations of upstream environmental conditions and cloud microphysical parameter values can produce a particular precipitation distribution over an idealized two-dimensional, bell-shaped mountain. Simulations focus on orographic precipitation produced when an atmospheric river interacts with topography. MCMC-based analysis reveals that different combinations of parameter values produce a similar precipitation distribution, with the most influential parameters being relative humidity (RH), horizontal wind speed (U), surface potential temperature (θ sfc ), and the snow fall speed coefficient (A s ). RH, U, and A s exhibit interdependence: changes in one or more of these factors can be mitigated by compensating changes in the other(s) to produce similar orographic precipitation rates. The results also indicate that the parameter sensitivities and relationships can vary for spatial subregions and given different environmental conditions. In particular, high θ sfc values are more likely to produce the target precipitation rate and spatial distribution, and thus the ensemble of simulations shows a preference for liquid precipitation at the surface. The results presented here highlight the complexity of orographic precipitation controls, and have implications for flood and water management, observational efforts, and climate change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 OSTI2025

A Unified Interpretation of Variability in Precipitation Isotope Ratios

Abstract Several mechanisms have been proposed to explain why the isotope ratios of precipitation vary in space and time and why they correlate with other climate variables like temperature and precipitation. Here, we argue that this behavior is best understood through the lens of radiative transfer, which treats the depletion of atmospheric vapor transport by precipitation as analogous to the attenuation of light by absorption or scattering. Building on earlier work by Siler et al., we introduce a simple model that uses the equations of radiative transfer to approximate the two-dimensional pattern of the oxygen isotope composition of precipitation ( δ p ) from monthly mean hydrologic variables. The model accurately simulates the spatial and seasonal variability in δ p within a state-of-the-art climate model and permits a simple decomposition of δ p variability into contributions from gradients in evaporation and the length scale of vapor transport. Outside the tropics, δ p is mostly controlled by gradients in evaporation, whose dependence on temperature explains the positive correlation between δ p and temperature (i.e., the temperature effect). At low latitudes, δ p is mostly controlled by gradients in the transport length scale, whose inverse relationship with precipitation explains the negative correlation between δ p and precipitation (i.e., the amount effect). This suggests that the temperature and amount effects are both mostly explained by the variability in upstream rainout, but they reflect distinct mechanisms governing rainout at different latitudes. Significance Statement The isotopic composition of precipitation has long been used to make inferences about past climates based on its observed relationship with precipitation in the tropics and with temperature at higher latitudes. These relationships—known as the “amount effect” and “temperature effect,” respectively—have been attributed to many different mechanisms, most of which are thought to operate at either high or low latitudes but not both. Here, we present a unified framework for interpreting the isotope variability that can explain the latitude dependence of the temperature and amount effects despite making no distinction between high and low latitudes. Although our results are generally consistent with certain interpretations of the amount effect, they suggest that the temperature effect is widely misunderstood.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The atmospheric effect of aerosols on future tropical cyclone frequency and precipitation in the Energy Exascale Earth System Model

This study uses experiments from the Energy Exascale Earth System Model (E3SM) to compare the influence on tropical cyclone (TC) activity of: (i) the atmospheric effect of aerosols under specified sea-surface temperatures (SSTs); and (ii) the net effect of greenhouse gases (GhGs) (including changes in SSTs). The experiments were performed using the CMIP6 Shared Socioeconomic Pathway SSP5-8.5 emissions scenario with GhG-induced SST warming specified and atmospheric aerosol effects simulated but without explicit ocean coupling. Insignificant changes in global TC frequency are found in response to the atmospheric effect of future aerosols and GhGs, as significant regional responses in TC frequency counteract each other. Future GhGs contribute to more frequent TCs in the North Atlantic, and reductions over the Northwestern Pacific and Southern Indian Ocean. The atmospheric effect of future aerosols drives more frequent TCs over the Northwestern Pacific and reductions over the Northeast Pacific and North Atlantic. Along with increases in TC intensity, global TC precipitation (TCP) is projected to increase by 52.8% (14.1%/K) due to the combined effect of future aerosols and GhGs. Although both forcings contribute to TCP increases (14.7–19.3% from reduced aerosols alone and 28.1–33.3% from increased GhGs alone), they lead to different responses in the spatial structure of TCP. TCP increases preferentially in the inner-core due to increased GhGs, whereas TCP decreases in the inner-core and increases in the outer-bands in response to the atmospheric effects of decreased aerosols. These changes are distinct from those caused by aerosol-induced SST changes, which have been considered in other studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Rising atmospheric moisture escalates the future impact of atmospheric rivers in the Antarctic climate system

Abstract Despite their relative rarity, atmospheric rivers are key contributors to the surface mass balance of Antarctica. However, the future role of atmospheric rivers in modulating Antarctic sea-level contributions is a major area of uncertainty. Here, we leverage high-resolution climate simulations to show that Antarctic atmospheric rivers are highly sensitive to future increases in atmospheric moisture, leading to a doubling of atmospheric river frequencies and 2.5 × increase in precipitation from 2066–2100 under present-day thresholds for atmospheric river detection. However, future precipitation impacts are critically dependent on the detection threshold: accounting for moisture increases in the threshold produces smaller, regional changes in atmospheric river frequency, primarily resulting from an eastward shift in the polar jet maximum wind speeds. Our results underscore the importance of using large ensembles to quantify Antarctic atmospheric river responses to variability in projected moisture, which may not be captured when using only a few ensemble members.

Environmental Sciences & Ecology↗
DOE OSTI2022

Evaluating Tropical Precipitation Relations in CMIP6 Models with ARM Data

Abstract A set of diagnostics based on simple, statistical relationships between precipitation and the thermodynamic environment in observations is implemented to assess phase 6 of the Coupled Model Intercomparison Project (CMIP6) model behavior with respect to precipitation. Observational data from the Atmospheric Radiation Measurement (ARM) permanent field observational sites are augmented with satellite observations of precipitation and temperature as an observational baseline. A robust relationship across observational datasets between column water vapor (CWV) and precipitation, in which conditionally averaged precipitation exhibits a sharp pickup at some critical CWV value, provides a useful convective onset diagnostic for climate model comparison. While a few models reproduce an appropriate precipitation pickup, most models begin their pickup at too low CWV and the increase in precipitation with increasing CWV is too weak. Convective transition statistics compiled in column relative humidity (CRH) partially compensate for model temperature biases—although imperfectly since the temperature dependence is more complex than that of column saturation. Significant errors remain in individual models and weak pickups are generally not improved. The conditional-average precipitation as a function of CRH can be decomposed into the product of the probability of raining and mean precipitation during raining times (conditional intensity). The pickup behavior is primarily dependent on the probability of raining near the transition and on the conditional intensity at higher CRH. Most models roughly capture the CRH dependence of these two factors. However, compensating biases often occur: model conditional intensity that is too low at a given CRH is compensated in part by excessive probability of precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Idaho National Laboratory Site Environmental Surveillance Program Report: First Quarter 2023

This report for the first quarter of 2023 contains results from the INL Site environmental surveillance program’s monitoring of the U.S. Department of Energy’s Idaho National Laboratory (INL) Site’s onsite, boundary and offsite location environment, January 1 through March 31, 2023. All sample types (media) and the sampling schedule followed during 2023 are listed in Appendix A. This report contains results for the following sample types: •Air, including particulate air filters, charcoal cartridges, and atmospheric moisture •Quarterly composites •Precipitation •Milk •Large game animal sampling.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Idaho National Laboratory Site Environmental Surveillance Report: First Quarter 2022

This report for the first quarter of 2022 contains results from the INL Site environmental surveillance program’s monitoring of the U.S. Department of Energy’s Idaho National Laboratory (INL) Site’s onsite, boundary and distant location environment, January 1 through March 31, 2022. All sample types (media) and the sampling schedule followed during 2022 are listed in Appendix A. This report contains results for the following sample types: • Air, including particulate air filters, charcoal cartridges, and atmospheric moisture, • Quarterly composites, • Precipitation, • Milk, and • Large game animals

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Limitations of Bin and Bulk Microphysics in Reproducing the Observed Spatial Structure of Light Precipitation

Abstract Coarse-gridded atmospheric models often account for subgrid-scale variability by specifying probability distribution functions (PDFs) of process rate inputs such as cloud and rainwater mixing ratios ( q c and q r , respectively). PDF parameters can be obtained from numerous sources: in situ observations, ground- or space-based remote sensing, or fine-scale modeling such as large-eddy simulation (LES). LES is appealing to constrain PDFs because it generates large sample sizes, can simulate a variety of cloud regimes/case studies, and is not subject to the ambiguities of observations. However, despite the appeal of using model output for parameterization development, it has not been demonstrated that LES satisfactorily reproduces the observed spatial structure of microphysical fields. In this study, the structure of observed and modeled microphysical fields are compared by applying bifractal analysis, an approach that quantifies variability across spatial scales, to simulations of a drizzling stratocumulus field that span a range of domain sizes, drop concentrations (a proxy for mesoscale organization), and microphysics schemes (bulk and bin). Simulated q c closely matches observed estimates of bifractal parameters that measure smoothness and intermittency. There are major discrepancies between observed and simulated q r properties, though, with bulk simulated q r consistently displaying the bifractal properties of observed clouds (smooth, minimally intermittent) rather than rain while bin simulations produce q r that is appropriately intermittent but too smooth. These results suggest fundamental limitations of bulk and bin schemes to realistically represent higher-order statistics of the observed rain structure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Surface Atmosphere Integrated Field Laboratory (SAIL) Campaign

The science of mountainous hydrology spans the atmosphere through the bedrock and inherently crosses physical and disciplinary boundaries: land-atmosphere interactions in complex terrain enhance clouds and precipitation, while watersheds retain and release water over a large range of spatial and temporal scales. Limited observations in complex terrain challenge efforts to improve predictive models of the hydrology in the face of rapid changes. The Upper Colorado River exemplifies these challenges, especially with ongoing mismatches between precipitation, snowpack, and discharge. Consequently, the U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility has deployed an observatory to the East River Watershed near Crested Butte, Colorado between September 2021 and June 2023 to measure the main atmospheric drivers of water resources, including precipitation, clouds, winds, aerosols, radiation, temperature and humidity. This effort, called the Surface Atmosphere Integrated Field Laboratory (SAIL), is also working in tandem with DOE-sponsored surface and subsurface hydrologists and other federal, state, and local partners. SAIL data can be benchmarks for model development by producing a wide range of observational information on precipitation and its associated processes, including those processes that impact snowpack sublimation and redistribution, aerosol direct radiative effects in the atmosphere and in the snowpack, aerosol impacts on clouds and precipitation, and processes controlling surface fluxes of energy and mass. Preliminary data from SAIL’s first year showcase the rich information content in SAIL’s many data-streams and support testing hypotheses that will ultimately improve scientific understanding and predictability of Upper Colorado River hydrology in 2023 and beyond.

54 ENVIRONMENTAL SCIENCES↗