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34 results for “Physical profiling”
NOAA PSL thermodynamic profiles retrieved from ASSIST infrared radiances with the optimal estimation physical retrieval TROPoe during SPLASH
<p>This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (TROPoe, Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 min from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Rochette et al. 2009).</p> <p>The ASSIST was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. </p> <p>The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm<sup>-1</sup> and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer, temperature, water vapor mixing ratio, and pressure from colocated near-surface measurements and from hourly analysis profiles from the operational Rapid Refresh (RAP, Benjamin et al. 2021) weather prediction model at the closest grid point. The latter are used only outside the atmospheric boundary layer (ABL) above 4 km above ground level (AGL) and provide information in the middle and upper troposphere where little to no information content is available from the infrared radiances.</p> <p>In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) which provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see e.g. Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. For this study, we computed the prior from operational radiosondes launched near Denver, CO, and re-centered the mean profiles of water vapor and temperature to account for the elevation difference between the East River Valley and the launch site near Denver to get a more representative prior.</p> <p>The file format is netcdf and the file naming conventions are</p> <p>NOAA_PSL_ASSIST_RoaringJudy_yyyymmdd.cdf</p> <p>with</p> <p>yyyy: Year</p> <p>mm: Month</p> <p>dd: Day</p> <p> </p> <p>The time stamp of all data is in UTC.</p> <p>Selected basic variables are (many more provided):</p> <p> </p> <table> <tbody> <tr> <td> <p>Name</p> </td> <td> <p>Dimension</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>base_time</p> </td> <td> <p>Single value</p> </td> <td> <p>Seconds (since 00 UTC 1 Jan 1970)</p> </td> </tr> <tr> <td> <p>time_offset</p> </td> <td> <p>Time</p> </td> <td> <p>Second (since base_time)</p> </td> </tr> <tr> <td> <p>hour</p> </td> <td> <p>Time</p> </td> <td> <p>Hours since 00UTC this day</p> </td> </tr> <tr> <td> <p>height</p> </td> <td> <p>Height</p> </td> <td> <p>km AGL</p> </td> </tr> <tr> <td> <p><strong>temperature </strong></p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, temperature</p> </td> </tr> <tr> <td> <p><strong>waterVapor </strong></p> </td> <td> <p>Time, Height</p> </td> <td> <p>g/kg, water vapor mixing ratio</p> </td> </tr> <tr> <td> <p>theta</p> </td> <td> <p>Time, Height</p> </td> <td> <p>K, potential temperature</p> </td> </tr> <tr> <td> <p>pressure</p> </td> <td> <p>Time, Height</p> </td> <td> <p>hPa, pressure</p> </td> </tr> <tr> <td> <p>rh</p> </td> <td> <p>Time, Height</p> </td> <td> <p>%, relative humidity</p> </td> </tr> <tr> <td> <p>dewpt</p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, dew point temperature</p> </td> </tr> <tr> <td> <p>thetae</p> </td> <td> <p>Time, Height</p> </td> <td> <p>K, equivalent potential temperature</p> </td> </tr> <tr> <td> <p>sigma_temperature</p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, 1-sigma uncertainty temperature</p> </td> </tr> <tr> <td> <p>sigma_waterVapor</p> </td> <td> <p>Time, Height</p> </td> <td> <p>g/kg, 1-sigma uncertainty water vapor</p> </td> </tr> <tr> <td> <p>cdfs_temperature</p> </td> <td> <p>Time, Height</p> </td> <td> <p>cumulative degrees of freedom for temperature</p> </td> </tr> <tr> <td> <p>cdfs_waterVapor</p> </td> <td> <p>Time, Height</p> </td> <td> <p>cumulative degrees of freedom for water vapor</p> </td> </tr> </tbody> </table> <p>Bold variables are the main retrieved profiles, from which the other variables are derived.</p> <p>Note that the vertical resolution of the retrieved profiles decreases with height, because of the broadening of the weighting function as a function of height. Thus, there are relatively few independent pieces of information in the profiles, this is reflected in the cumulative degree of freedom variables. The majority of the information from the ASSIST is in the lowest 2-3 km, above that most information comes from the RAP model.</p> <p>Because of strong emission in the infrared from clouds, clouds strongly impact the ability to retrieve profiles from the ASSIST and care should be taken when analyzing the retrievals in the presence of clouds. </p> <p><strong>References: </strong></p> <p>Rochette, L., W. L. Smith, M. Howard, and T. Bratcher, 2009: ASSIST, atmospheric sounder spectrometer for infrared spectral technology: Latest development and improvement in the atmospheric sounding technology. Imaging spectrometry XIV, Vol. 7457 of, SPIE, 9–17.</p> <p>Turner, D. D., and U. Löhnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based atmospheric emitted radiance interferometer (AERI). J. Appl. Meteor. Climatol., 53, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1.</p> <p>Turner, D. D., and W. G. Blumberg, 2019: Improvements to the AERIoe thermodynamic profile retrieval algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1339–1354, https://doi.org/10.1109/JSTARS.2018.2874968.</p> <p>Turner, D. D., and U. Löhnert, 2021: Ground-based temperature and humidity profiling: Combining active and passive remote sensors. Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021.</p>
Mountain Lake Chemistry and Physics Profile Data since 2015 at Castle Lake
This data set contains long-term limnology data from Castle Lake (located 5440 ft above sea level in Northern California). The data contained can be broadly grouped into two different types: chemical and physical data. This data set contains dissolved oxygen saturation, dissolved oxygen concentration, chlorophyll-a concentration, CDOM, phycocyanin concentration, conductivity, specific conductivity, pH, salinity, water temperature, pressure, turbidity, sea pressure, density anomaly, and speed of sound at various depths. Sampling Frequency: Continuous measurements were made and recorded down to milliseconds. Sampling dates vary from year to year, starting as early as February and as late as November. This data collection is part of an ongoing project funded by the US National Science Foundation, private donors, US AID, and the University of Nevada's Global Water Center. This package was updated in February 2023 with data from 2021 and 2022. Dissolved O2 concentration data in the 2020 data file was updated to values in units of mg/L instead of umol/L and specific conductivity units in all files was revised to be in microSiemens per centimeter and not milliSiemens per centimeter.
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
<p>Information about the spatial distribution of soil hydraulic parameters is necessary for the accurate prediction of soil water flow and coupled movement of chemicals and heat at the field scale using a process-based model. Physics-informed neural networks (PINNs), which can provide physical constraints in deep learning to obtain a mesh-free solution, can be used to inversely estimate the soil hydraulic parameters from less and noisy training data. Previous studies using PINNs have successfully estimated soil hydraulic parameters for homogeneous soil but estimating such parameters of layered soil profiles where the interface depth and the parameters are unknown still has some difficulties. The objective of this study was to develop PINNs to inversely estimate the distribution of soil hydraulic parameters, such as saturated hydraulic conductivity and <em>α</em> and <em>n</em>, of the Mualem-van Genuchten model directly within layered soil profiles by predicting changes in pressure head from training data based on simulation results at given depths during infiltration. The impact of factors affecting PINNs performance, such as the weights assigned to each component of the loss function, the time range used in error computations, and the number of samples used to assess physical constraint was investigated. By assigning a larger weight to the physical constraint and excluding the earlier stage of infiltration in the loss function, the changes in pressure head and the three soil hydraulic parameter distributions within the layered soil profiles were successfully estimated. The developed PINNs can be further applied to more complex soils and can be improved.</p>
Comparison of children's physical activity profiles before and after COVID-19 lockdowns - dataset
<p>This dataset represents a subset of the Active-6 data used to produce the children's activity profiles reported in the manuscript 'Comparison of children's physical activity profiles before and after COVID-19 lockdowns: a latent profile analysis'. Included in this repository is the dataset and a data dictionary.</p> <p>This dataset has been made available so that future researchers can replicate the study findings using the data. If you wish to use the data for any purpose other than replicating the study findings, please contact the Principal Investigator Professor Russ Jago (russ.jago@bristol.ac.uk) to discuss this.</p>
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
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NOAA PSL thermodynamic profiles retrieved from a combination of active and passive remote sensors and numerical weather prediction models with the optimal estimation physical retrieval TROPoe at Platteville, CO, USA
<p>This dataset contains retrieved profiles of thermodynamic variables obtained using the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval from various combinations of input data collected by passive and active remote sensing instruments, in-situ surface platforms, and numerical weather prediction models deployed at the Platteville, CO, USA, site in fall 20221-winter 2022. Among the employed instruments are Microwave Radiometers (MWRs), Infrared Spectrometers (IRS), Radio Acoustic Sounding Systems (RASS), ceilometers, surface sensors, and information from the operational Rapid Refresh numerical weather prediction model.</p> <p>The dataset also includes 15 radiosounding launched for assessing the retrievals.</p> <p>For further information, please see:</p> <p>Bianco, L., Adler, B., Bariteau, L., Djalalova, I. V., Myers, T., Pezoa, S., Turner, D. D., and Wilczak, J. M.: Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-263, in review, 2024.</p>
Physical Profiling
<p>Profiling of physical capacity and physical performance in training and match in academy and first team football players.</p>
Health profile of elderly users of a town public program of physical activity: a cross-sectional study
<p>This repository contains the full database generated in the study entitled "Health profile of elderly users of public physical activity programs in the city of Porto Alegre" (Hospital de Clínicas de Porto Alegre). This project was approved by the Institutional Review Board (IRB) in 2018 and carried out between 2018-2020.</p> <p> </p>
Physical Activity on Neurophysiologic Gene Expression Profiles of Chronic Low Back Pain
ClinicalTrials.gov study NCT03637998. IPD Sharing: YES. Countries: 1. Publications: 1.
Recreational Physical Activity and Cardiometabolic Profile
ClinicalTrials.gov study NCT02982967. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Evolutionary patterns of adaptive acrobatics and physical performance predict expression profiles of androgen receptor – but not oestrogen receptor – in the forelimb musculature
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Transcriptional profiling to identify physical-chemical properties detrimental to nanomaterial-induced pulmonary response (part 1)
GEO Series GSE60797. Mus musculus. 70 samples. Type: Expression profiling by array.
Gene expression profiles of Vcap xenograft tumors with chemical or physical denervation.
GEO Series GSE28716. Homo sapiens. 4 samples. Type: Expression profiling by array.
Transcriptional profiling to identify physical-chemical properties detrimental to nanomaterial-induced pulmonary response
GEO Series GSE60801. Mus musculus. 278 samples. Type: Expression profiling by array.
Gene expression profiles of rat tissues surrounding Vcap xenograft tumors with chemical or physical denervation.
GEO Series GSE28717. Rattus norvegicus. 16 samples. Type: Expression profiling by array.
An unsupervised and broadly applicable method for physical cell interaction profiling of complex tissues
GEO Series GSE175664. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Protect-seq: Genome-wide profiling of nuclease inaccessible domains reveals physical properties of chromatin
GEO Series GSE135580. Homo sapiens. 40 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other.
Transcriptional profiling to identify physical-chemical properties detrimental to nanomaterial-induced pulmonary response (part 4)
GEO Series GSE60800. Mus musculus. 70 samples. Type: Expression profiling by array.
Genome-wide profiling of salt fractions maps physical properties of chromatin
GEO Series GSE13217. Drosophila melanogaster. 47 samples. Type: Genome binding/occupancy profiling by genome tiling array.
Transcriptional profiling to identify physical-chemical properties detrimental to nanomaterial-induced pulmonary response (part 2)
GEO Series GSE60798. Mus musculus. 69 samples. Type: Expression profiling by array.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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