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Readings corrected for temperature, pressure, and humidity of air. Program for handheld computer developed to ensure accuracy of oxygen monitors in National Transonic Facility, where liquid nitrogen stored. Calibration values, determined daily, based on entries of data on barometric pressure, temperature, and relative humidity. Output provided directly in millivolts.
The joint research interchange effort was directed at the following principal areas: u further development of NASA-Ames' Mars Micro-meteorology mission concept as a viable NASA space mission especially with regard to the science and instrument specifications u interaction with the flight team from NASA's New Millennium 'Deep-Space 2' (DS-2) mission with regard to selection and design of micro-pressure sensors for Mars u further development of micro-pressure sensors suitable for Mars The research work undertaken in the course of the Joint Research Interchange should be placed in the context of an ongoing planetary exploration objective to characterize the climate system on Mars. In particular, a network of small probes globally-distributed on the surface of the planet has often been cited as the only way to address this particular science goal. A team from NASA Ames has proposed such a mission called the Micrometeorology mission, or 'Micro-met' for short. Surface pressure data are all that are required, in principle, to calculate the Martian atmospheric circulation, provided that simultaneous orbital measurements of the atmosphere are also obtained. Consequently, in the proposed Micro-met mission a large number of landers would measure barometric pressure at various locations around Mars, each equipped with a micro-pressure sensor. Much of the time on the JRI was therefore spent working with the engineers and scientists concerned with Micro-met to develop this particular mission concept into a more realistic proposition.
During the last ten years, two major experiments have elucidated the factors determining acclimatization and tolerance to extreme altitude (over 7000 m). These were the American Medical Research Expedition to Everest, and the low pressure chamber simulation, Operation Everest II. Extreme hyperventilation is one of the most important responses to extreme altitude. Its chief value is that it allows the climber to maintain an alveolar PO2 which keeps the arterial PO2 above dangerously low levels. Even so, there is evidence of residual impairment of central nervous system function after ascents to extreme altitude, and maximal oxygen consumption falls precipitously above 7000 m. The term 'acclimatization' is probably not appropriate for altitudes above 8000 m, because the body steadily deteriorates at these altitudes. Tolerance to extreme altitude is critically dependent on barometric pressure, and even seasonal changes in pressure probably affect climbing performance near the summit of Mt Everest. Supplementary oxygen always improves exercise tolerance at extreme altitudes, and rescue oxygen should be available on climbing expeditions to 8000 m peaks.
The weather systems, cyclones, and anticyclones, along with air trajectories and cloud forms, are compared to past studies of the Arctic to assess compatibility of the four month study of the Arctic Cloud Experiment flights of the First ISCCP Regional Experiment (FIRE/ACE) with past climatologies. The frequency and movement of cyclones (lows) and anticyclones (highs) followed the general eastward and northeastward directions indicated by past studies. Most cyclones (lows) came from eastern Siberia and the Bering Sea to the south and moved north across the Bering Straight or Alaska into the Arctic Ocean. They generally weakened in central pressure as they moved poleward. Anticyclones (highs) were most common in the eastern Beaufort Sea near Canada in June and July as predicted from previous studies. However, many cyclones and anticyclones moved in westward directions which is rare in other latitudes. Erratic changes in shape and intensity on a daily basis also were observed. The National Center for Environmental Prediction (NCEP) analysis generally reflected the Surface Heat Budget in the Arctic (SHEBA) Ship World Meteorological Organization (WMO) observations which it used. However, NCEP temperatures were biased warm by 1.0 to 1.5 C in April and early May. In July when the surface temperature were at the freezing/thawing point, the NCEP analysis changed to a cold bias of -1.0 C. Dew points had smaller biases except for July where they were biased cold by -1.4 C. Wind speeds had a -2 m/s low bias for the six windiest days. Surface barometric pressures had consistently low biases from -1.2 to -2.8 hPa in all four months. Air parcel historical trajectories were mainly from the south or from local anticyclonic gyres in the Beaufort Sea. Most air came to the SHEBA Ship from the north Pacific Ocean or from Alaska and Canada and occasionally from eastern Siberia. Very few trajectories traced back across the pole to Europe and Central Asia. Cloud cover was high, as expected, from 69-86% of the time. Satellite data also indicate frequent stratus, altostratus, and cirrus clouds (occurring 61% of the time) above the expected boundary layer fog and Arctic stratus clouds.
Abstract Both the source of methane on Mars and the mechanism for transmission from the subsurface to the atmosphere are not fully understood. Previous seepage simulations have invoked relatively shallow subsurface sources to explain observed methane signatures on Mars. We propose that barometric‐pressure pumping through fracture networks could be an effective mechanism for methane transport from the deep subsurface on Mars. Using atmospheric pressure data gathered by Curiosity as input, we simulate methane gas transport from depths of 200 m to the surface. Even with such a deep source, our model reproduces the observed seasonality of methane, and the simulated surface methane fluxes fall within the range of previous estimates derived from atmospheric observations. Because 200 m is the likely minimum hospitable depth for living methanogenic microbes, our fracture network model indirectly reinvigorates the possibility of a microbial source of methane on Mars.
Gobbling activity of Eastern wild turkeys (Meleagris gallopavo silvestris; hereafter, turkeys) has been widely studied, focusing on drivers of daily variation. Weather variables are widely believed to influence gobbling activity, but results across studies are contradictory and often equivocal, leading to uncertainty in the relative contribution of weather variables to daily fluctuations in gobbling activity. Previous works relied on road-based auditory surveys to collect gobbling data, which limits data consistency, duration, and quantity due to logistical difficulties associated with human observers and restricted sampling frames. Development of new methods using autonomous recording units (ARUs) allows researchers to collect continuous data in more locations for longer periods of time, providing the opportunity to delve into factors influencing daily gobbling activity. We used ARUs from 1 March to 31 May to detail gobbling activity across multiple study sites in the southeastern United States during 2014–2018. We used state-space modeling to investigate the effects of weather variables on daily gobbling activity. Our findings suggest rainfall, greater wind speeds, and greater temperatures negatively affected gobbling activity, whereas increasing barometric pressure positively affected gobbling activity. Therefore, when using daily gobbling activity to make inferences relative to gobbling chronology, reproductive phenology, and hunting season frameworks, stakeholders should recognize and consider the potential influences of extended periods of inclement weather.
We measured methane and carbon dioxide fluxes at natural gas well pad soils and undisturbed soils in the Rocky Mountain and Gulf Coast regions of the United States, including producing and gas storage wells. We collected both short-term (15 min) and multi-day (between 3 and 8), continuous measurements at 47 well pads and two undisturbed locations. Methane fluxes varied by more than an order of magnitude over periods as short as 30 min (e.g., 19–593 mg m -2 h -1 in one instance), and diurnal and seasonal variability was also significant (e.g., spring-to-fall change from 509 to 14174 mg m -2 h -1 ). We hypothesize that short-term flux variability was caused by pulsed flow of methane during its migration through the subsurface. Barometric pressure and well conditions likely impacted fluxes, but we found only weak evidence for this. Bacterial methanotrophy appeared to impact methane flux magnitude and variability. We injected methane into the subsurface at one well, and we found that, while fluxes of methane and carbon dioxide, and combustible soil gas concentrations, increased in response to the injection, the response was not uniform, and fluxes exhibited high hourly-scale variability, in spite of a constant injection rate. Methane fluxes tended to be higher at well pad soils compared to background soils (often much higher), and fluxes tended to be higher at well pad locations closer to the well head.
Understanding frequency and variation of cave-exiting activity after arousal from torpor of hibernating bats is important for bat ecology and conservation, especially considering white-nose syndrome. In winter from 2011 to 2018, we acoustically monitored, and counted in hibernacula, two species of conservation concern—western small-footed myotis ( Myotis ciliolabrum ) and Townsend’s big-eared bats ( Corynorhinus townsendii )—in 9 caves located in important habitat for these species in western North America. We investigated if cave-exiting activity differed by species, cave, number of hibernating bats, moon phase, and weather variables. Both species exited hibernacula during all winter months, but most activity occurred in March followed by November. Although we counted almost 15 times more Townsend’s big-eared bats during hibernacula surveys, we documented western small-footed myotis exiting caves 3 times more than Townsend’s big-eared bats. Cave-exiting activity increased with increasing number of hibernating bats, but more so for western small-footed myotis. Both species of bats were active during warm weather and low wind speeds. Western small-footed myotis were more active during colder temperatures, higher wind speeds, and greater change in barometric pressure than Townsend’s big-eared bats. Our results provide a long-term dataset of cave-exiting activity after arousal from torpor during hibernation for these species before the arrival of white-nose syndrome.
Autocollimators are used in profilometers for the precise form measurement of beam-shaping optics for synchrotron light sources and x-ray free-electron lasers. This application requires using an aperture stop, typically 2.5 mm in diameter, which limits the footprint of the autocollimator’s measuring beam on the optics to achieve a sufficiently high lateral resolution. Central to the development of profilometry has been the availability of commercial autocollimators, such as the Elcomat 3000 from Möller–Wedel Optical. Although autocollimators are generally not designed for use with small apertures, this device has proven capable and has become the de facto standard. Now that it has been replaced by the Elcomat 5000, we evaluate the performance of several autocollimators of this type at small apertures, including the characterization of instrument transfer functions with a chirped height profile. We demonstrate that the Elcomat 5000 is capable of repeatable angle measurements with a standard deviation of 0.014 arcsec at an aperture diameter of 1.6 mm, whereas the Elcomat 3000 achieves 0.03 arcsec at 2.5 mm, comparing models with modified reticle designs. Here, we also investigate the influence of optical aberrations of the autocollimator’s objective and their changes with the path length to the surface under test. We characterize the sensitivity of the angle measurement to changes in environmental parameters, in particular, barometric pressure. These efforts are aimed at approaching fundamental limits in the characterization of the shape of optical surfaces with autocollimator-based profilometers so that these are ready for characterizing the next generation of synchrotron and XFEL beamline optics.
Abstract Many hibernating bats in thermally stable, subterranean roosts have experienced precipitous declines from white-nose syndrome (WNS). However, some WNS-affected species also use thermally unstable roosts during winter that may impact their torpor patterns and WNS susceptibility. From November to March 2017–19, we used temperature-sensitive transmitters to document winter torpor patterns of tricolored bats (Perimyotis subflavus) using thermally unstable roosts in the upper Coastal Plain of South Carolina. Daily mean roost temperature was 12.9 ± 4.9°C SD in bridges and 11.0 ± 4.6°C in accessible cavities with daily fluctuations of 4.8 ± 2°C in bridges and 4.0 ± 1.9°C in accessible cavities and maximum fluctuations of 13.8 and 10.5°C, respectively. Mean torpor bout duration was 2.7 ± 2.8 days and was negatively related to ambient temperature and positively related to precipitation. Bats maintained non-random arousal patterns focused near dusk and were active on 33.6% of tracked days. Fifty-one percent of arousals contained passive rewarming. Normothermic bout duration, general activity and activity away from the roost were positively related to ambient temperature, and activity away from the roost was negatively related to barometric pressure. Our results suggest ambient weather conditions influence winter torpor patterns of tricolored bats using thermally unstable roosts. Short torpor bout durations and potential nighttime foraging during winter by tricolored bats in thermally unstable roosts contrasts with behaviors of tricolored bats in thermally stable roosts. Therefore, tricolored bat using thermally unstable roosts may be less susceptible to WNS. More broadly, these results highlight the importance of understanding the effect of roost thermal stability on winter torpor patterns and the physiological flexibility of broadly distributed hibernating species.
Abstract High transmissivity aquifers typically have low hydraulic gradients (i.e., a flat water table). Measuring low gradients using water levels can be problematic because measurement error may be greater than the true difference in water levels (i.e., a low signal‐to‐noise ratio). In this study, the feasibility of measuring a hydraulic gradient in the range of 10 −6 to 10 −5 m/m was demonstrated. The study was performed at a site where the depth to water from land surface ranged from 40.1 to 94.2 m and the aquifer transmissivity was estimated at 41,300 m 2 /d (hydraulic conductivity of 18,800 m/d). The goals of the study were to reduce measurement error as much as practicable and assess the importance of factors affecting water level measurement accuracy. Well verticality was the largest source of error (0.000 to 0.168 m; median of 0.014 m), and geodetic survey of casing elevations was the next most important source of error (0.002 to 0.013 m; median of 0.005 m). Variability due to barometric pressure fluctuations was not an important factor at the site. Hydraulic heads were measured to an accuracy of ±0.0065 m, and the average hydraulic gradient was estimated to be 8.0 × 10 −6 (±0.9 × 10 −6 ) m/m. The improvement in accuracy allowed for two reversals in the groundwater flow direction to be identified, after which the gradient averaged 2.5 × 10 −5 (±0.4 × 10 −5 ) m/m. This study showed it is possible to sufficiently control sources of error to measure hydraulic gradients in the 10 −6 to 10 −5 m/m range.
As part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), four autonomous seasonal ice mass balance buoys were deployed in first- and second-year ice. These buoys measured position, barometric pressure, snow depth, ice thickness, ice growth, surface melt, bottom melt, and vertical profiles of temperature from the air, through the snow and ice, and into the upper ocean. Observed air temperatures were similar at all four sites; however, snow–ice interface temperatures varied by as much as 10°C, primarily due to differences in snow depth. Observed winter ice growth rates (November to May) were <1 cm day -1 , with summer melt rates (June to July) as large as 5 cm day -1 . Air temperatures changed as much as 2°C hour -1 but were dampened to <0.3°C hour -1 at the snow–ice interface. Initial October ice thicknesses ranged from 0.3 m in first-year ice to 1.2 m in second-year ice. By February, this range was only 1.20–1.46 m, due in part to differences in the onset of basal freezing. In second-year ice, this delay was due to large brine-filled voids in the ice; propagating the cold front through this ice required freezing the brine. Mass balance results were similar to those measured by autonomous buoys deployed at the North Pole from 2000 to 2013. Winter average estimates of the ocean heat flux ranged from 0 to 3 W m -2 , with a large increase in June 2020 as the floe moved into warmer water. Estimates of average snow thermal conductivity measured at two buoys during periods of linear temperature profiles were 0.41 and 0.42 W m -1 °C -1 , higher than previously published estimates. Results from these ice mass balance buoys can contribute to efforts to close the MOSAiC heat budget.
This dataset contains meteorological output from the Weather Research and Forecasting (WRF) version 3.8.1. This dataset has been created to 1) investigate hydrometeorological processes impacting the East River and water-delivery to the Critical Zone, and 2) provide meteorological forcing data for distributed Earth-science modeling applications in the East River watershed. Variables have a 1 kilometer spatial resolution and hourly temporal resolution and encompass a rectangular region encompassing the East and Taylor River watersheds, Colorado, near the town of Crested Butte. WRF was forced using Climate Forecast System Reanalysis (CFSR) lateral boundary conditions. Each .zip file contains one "water year" of data (October 1 -- September 30; i.e. water year 2017 starts October 1, 2016 and ends September 30, 2017). Each zip folder contains 12 netcdf (.nc) files containing one month of hourly data each and are approximately 250mb. Model timestamps are in UTC time.The files contain the following data variables:EAST_MASK: binary mask of the watershed regionTAYLOR_MASK: binary mask of the watershed regionGLW downwelling longwave radiation (w/m2)HR_PRCP: Hourly Precipitation Rate (mm/hr). Includes all hydrometeors (solid+liquid). HFX NoahMP LSM total grid-cell modelled sensible heat flux (w/m2) [positive towards atmosphere]LH NoahMP LSM total grid-cell modelled latent heat flux (w/m2) [positive towards atmosphere; can be converted to ET]PSFC Surface Barometric Pressure (hPa)Q2 Two-meter specific humidity (kg/kg)SWDOWN Downwelling shortwave solar radiation (w/m2)SWNORM Terrain-normal downwelling shortwave radiation (w/m2)T2 Two-meter air temperature (deg K)U10 10-m U-component of wind velocity (m/s)V10 10m V-component of wind velocity (m/s)XLAT Latitude of grid-center point XLONG Longitude of grid-center point XTIME Model timestamp, **in UTC**
This data package includes a time series of meteorological data (air temperature, relative humidity, barometric pressure, precipitation, wind speed, soil heat flux, incoming shortwave radiation, incoming longwave radiation, outgoing shortwave radiation and outgoing longwave radiation) from the Slate River floodplain outside Crested Butte, Colorado, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The package also includes reference evapotranspiration calculated via the Penman-Monteith equation and an estimate of willow (Salix spp.) evapotranspiration using the method of Irmak et al., J. Hydrology, 2013. Due to two malfunctioning sensors (incoming shortwave radiation from Oct 2020-May 2021 and incoming longwave radiation from May 2021-March 2022), observed net radiation and calculated evapotranspiration are only included from July-October 2020. However, users may be able to extend calculated evapotranspiration using estimates of these two parameters from other sources. Both sensors have been replaced and data beyond 2021 be uploaded to this repository when it is available.Within the data package, "FLMD.csv" describes file-level metadata and "dd.csv" defines column headers and universal terms across the dataset. The data package includes 3 "*data.csv" files, two for meteorological data for 2020 and 2021 and one for reference evapotranspiration and transpiration data from 2020 ("SLT_OBJ2_PenmanMonteith_2020_data.csv"). Each "*data.csv" file of meteorological data has a corresponding "*_InstallationMethods.csv" file that describes the location, sensor model, sensor serial number and other metadata corresponding to each measured parameter. We also include a Jupyter notebook ("PenmanMonteithET_Calculations.ipynb") that details the assumptions and equations used to calculate daily and hourly reference evapotranspiration and daily willow transpiration from raw meteorological data.
This dataset supports a broader study examining the drivers of spatial variability in sediment respiration rates in the Yakima River Basin. The dataset provides data and photos generated from sample collection during the same one-week period at 48 sites within multiple rivers throughout the Yakima River Basin in Washington, USA. The contents include surface water geochemistry data; river substrate grain size photos; stream depth data; manual chamber open channel respiration data; and field metadata (including qualitative information on instream and river corridor characteristics). Grain size photos can be used to improve estimates of channel substrate D50 data. The dataset also includes tensile strength and photos from cotton strip field degradation experiments; five-week sensor time series temperature, dissolved oxygen, pressure, pH, specific conductance, chlorophyll A, and turbidity data; plots of the sensor data; and R scripts used to generate the plots. Samples collected during this study were labeled as “Second Spatial Study” or “SSS.” A subset of data from the SSS samples were published in the contiguous United States (CONUS)-Scale Model-Sample (CM) study data package available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1923689 that presents data from across the CONUS. SSS data published in the CM data package were not included in this data package. They include dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC), total nitrogen (TN), grain size, aerobic sediment respiration, dissolved oxygen (DO), and temperature. Parent IDs and Site IDs are consistent between the SSS and CM data packages, and they can be mapped directly so data across packages can be used together. Additionally, sensor data from a similar 2021 spatial study can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1892052 and 2021 sample data can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1898914. The 2021 spatial study had some sites in common with this 2022 spatial study. This dataset is comprised of three photo folders and one main data folder with six subfolders. The photo folders contain photographs and videos of cotton strip retrieval and sediment quadrats. The main data folder consists of (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) total suspended solids (TSS) data and cotton strip tensile strength data and averages; (5) field protocol; (6) readme; (7) methods codes; (8) international generic sample number (IGSN) mapping file; (9) sensor installation methods summary; (10) stream depth and averages; and (11) Ultrameter data and averages. The Sonar subfolder consists of Sonar time-series depth data and a processing script. The BarotrollAtm, DepthHOBO, MantaRiver, miniDOT, and miniDOTManualChamber subfolders contain time-series data, plots, and summary files. All files are .csv, .pdf, .txt, .R, .Rmd, .jpg, .jpeg, .AVI, .mp4, or .mov. The data package was originally published in April 2023. It was updated in August 2023 (v2; modified files) and September 2024 (v3; modified files). See the change history section in the readme for details. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.
This dataset is from the Department of Energy Office of Science funded project, Community Research on Urban and Climate Science (CROCUS) (https://crocus-urban.org/). Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements.Datasets are stored in the netCDF data format, and we we encourage users to make use the associated toolkits available from Unidata (https://www.unidata.ucar.edu/software/netcdf/), Project Pythia (https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html), and our “Instrument Cookbooks” (https://crocus-urban.github.io/instrument-cookbooks) for more information on how to process the metadata-rich datasets.
Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements. These measurements are collected at Chicago State University (CSU) at a prarie site on campus in Chicago, Illinois. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html. The data is aggregated into daily frequency to make it easier to process multiple days, and compress the higher-resolution fields. Each file contains one day's worth of data (24 hours, starting at 0000 UTC). File naming convention includes the project (CROCUS), location (CSU), data level (raw, a1), date (year, month, day), and hour (0000).
Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements. These measurements are collected at the Argonne Testbed for Multiscale Observational Science (ATMOS), a prairie field site at Argonne National Laboratory in Lemont, Illinois. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html. The data is aggregated into daily frequency to make it easier to process multiple days, and compress the higher-resolution fields. Each file contains one day's worth of data (24 hours, starting at 0000 UTC). File naming convention includes the project (CROCUS), location (atmos), data level (raw, a1), date (year, month, day), and hour (0000).