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52 results for “surface states”
The Potential of GNSS-R for Freeze/Thaw Surface State Monitoring in the New Space Era
<p><em><strong>GNSS-R based F/T surface state maps over the Tibet Plateau, Andes Mountains, and Rocky Mountains covering two full F/T cycles 2019-2020:</strong> </em></p> <p><em><strong>(a) </strong>Number of frozen months during 2019</em></p> <p><em><strong>(b)</strong> number of thawed months during 2019</em></p> <p><em><strong>(c) </strong>number of frozen months during 2020</em></p> <p><em><strong>(d)</strong> number of thawed months during 2020</em></p> <p><em><strong>(e) </strong>difference of the number of frozen months between 2019 and 2020</em></p> <p><em><strong>(f) </strong>difference of the number of thawed months between 2019 and 2020</em></p> <p><em><strong>(g) </strong>number of frozen months during the 2019 freezing period (September to December)</em></p> <p><em><strong>(h) </strong>number of thawed months during the 2019 thawing period (January to June)</em></p> <p><em><strong>(i) </strong>number of frozen months during the 2020 freezing period (September to December)</em></p> <p><em><strong>(j)</strong> number of thawed months during the 2020 thawing period (January to June)</em></p> <p><em><strong>(k) </strong>difference of the number of frozen months between 2019 and 2020 during the freezing period (September to December)</em></p> <p><em><strong>(l) </strong>difference of the number of thawed months between 2019 and 2020 during the thawing period (January to June)</em></p>
A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China
<p>** VIC_forcings_4vars: 1/4 Degree Gridded Daily Meteorological VIC Forcing Data Set over China domain </p> <p>1. Data sources</p> <p>This dataset is from 1/1/1952 to 12/31/2012, which were derived by interpolating gauged daily precipitation, maximum temperature, minimum temperature and wind speed of 756 ground mornitoring stations from Chinese Meteorological Administration (CMA). </p> <p>** This section provides only a very brief description of the data source. For a full explanation, the user need refer to the published papers in the references ** </p> <p>2. References to Cite</p> <p>We request that users of this data set cite Zhang et al.(2014) in any reports or publications using it. </p> <p>Zhang, X., Tang, Q., Pan, M., Tang, Y., 2014. A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China. Journal of Hydrometeorology. (Accepted)</p> <p>3. Dataset format</p> <p>This data set is available in netCDF (which cannot be read directly by VIC). </p> <p>Please click the year number in the table below to download the corresponding daily forcings (i.e., precipitation, maximum temperature, minimum temperature and wind speed). </p> <p><br> ** VICoutput_fluxes: VIC Retrospective Land Surface Dadaset over China: 1952-2012</p> <p>1. Background</p> <p>The Variable Infiltration Capacity (VIC) model was driven using the gridded daily observed forcings (including precipitation, maximum temperature, minimum temperature, and wind speed; if necessary, please download the VIC forcings from http://hydro.igsnrr.ac.cn/public/vic_forcings_4vars.html) to simulate the land surface hydrological cycle from 1952-2012 over China. The modeling study was done at a 3-hourly time step and at a spatial resolution of 0.25 degree. Details can be found in the journal article:</p> <p>Zhang, X., Q. Tang, M. Pan, and Y. Tang, 2014: A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China. Journal of Hydrometeorology. doi: 10.1175/JHM-D-13-0170.1</p> <p>2. Archived data information and format</p> <p>This website provides access to parts of the model derived variables(including the water balance variables and states and energy balance varibales) at daily scale. This dataset is available in netCDF format. Please click the varible names below to download the corresponding variable.</p> <p>3. Download the dataset</p> <p>Model Derived Variables, 1952-2012 (Water Balance Variables and States)</p> <p>Evaporation<br> Runoff<br> Baseflow<br> Soil Moisture Layer 1<br> Soil Moisture Layer 2<br> Soil Moisture Layer 3<br> Snow Water Equivalent</p> <p>Contact: tangqh@igsnrr.ac.cn</p>
FIGURE 18. Bertolonia violacea. A. Fertile branch. B. C. Leaf base, abaxial surface. D in The Genus Bertolonia (Melastomataceae) In The State Of Bahia, Brazil
FIGURE 18. Bertolonia violacea. A. Fertile branch. B. C. Leaf base, abaxial surface. D. Purplish trichomes on the abaxial leaf surface. E. Inflorescence. F. Hypanthium and calyx, abaxial surface. G. Petals apex showing the apiculum with glands (arrow). H. Stamen, dorsal (left) and lateral (right) views, note the extrorse pore on dorsal view. I. Ovary and style. [A, C: Kollmann 11578; B, D, E, F, G, H, I: Fontana 5909].
FIGURE 6. Bertolonia carmoi. A. Fertile branch. B. Leaf base, abaxial surface. C in The Genus Bertolonia (Melastomataceae) In The State Of Bahia, Brazil
FIGURE 6. Bertolonia carmoi. A. Fertile branch. B. Leaf base, abaxial surface. C. Short-stalked glandular trichomes on the abaxial leaf surface. D. Inflorescence. E. Hypanthium and calyx, abaxial surface. F. Petals apex. G. Stamen, lateral (left) and dorsal (right) views. H. Ovary and style. [A, B, E, G: Aona 3286; C: Valadão 495; D. Mori 12853; F, H: Pinheiro 1678].
FIGURE 1. Hyptidendron albidum. A. Branch bearing leaves and inflorescenses. B−C. Leaves, adaxial surface with indumentum detail. D−E. Leaves, abaxial surface with indumentum detail. F. Immature cyme. G. Flower, side view. H. Calyx with bracteole, side view. I. Corolla, side view. J in Hyptidendron albidum (Lamiaceae, Hyptidinae), a remarkable new species from northern Minas Gerais state, Brazil
FIGURE 1. Hyptidendron albidum. A. Branch bearing leaves and inflorescenses. B−C. Leaves, adaxial surface with indumentum detail. D−E. Leaves, abaxial surface with indumentum detail. F. Immature cyme. G. Flower, side view. H. Calyx with bracteole, side view. I. Corolla, side view. J. Gynoecium and style, showing stylopodium. K. Mericarp. A−K. Illustration of Laura Montserrat based on Souza et al. 29588 (SPF).
FIGURE 2. Agapetes brevipedicellata Y. H. Tan & S.S. Zhou. A. Habit. B. Leaves showing abaxial and adaxial surfaces. C. Inflorescence. D in Agapetes brevipedicellata (Ericaceae), a new species from Putao, Kachin State, Northern Myanmar
FIGURE 2. Agapetes brevipedicellata Y. H. Tan & S.S. Zhou. A. Habit. B. Leaves showing abaxial and adaxial surfaces. C. Inflorescence. D. Flowers showing pedicel, calyx and corolla. E. Flowers showing pedicel, calyx and style. F. Corolla opened to show outer surface. G. Stamens in lateral and dorsal view. All from Myanmar Exped. 20160001 (HITBC) and drawn by Zheng-Meng Yang.
FIGURE 2. Paepalanthus ferrugineus line drawings. A. Habit. B. Scape and capitulum. C. Involucral bract, abaxial surface. D. Floral bract, adaxial surface. E in Novelties from the Serra Nova State Park (Minas Gerais, Brazil): two new endemic species of Eriocaulaceae
FIGURE 2. Paepalanthus ferrugineus line drawings. A. Habit. B. Scape and capitulum. C. Involucral bract, abaxial surface. D. Floral bract, adaxial surface. E. Staminate flower subtended by floral bract. F. Staminate flower with open corolla. G. Pistillate flower with floral bract. H. Pistillate flower dissected to expose the gynoecium. Illustration by: Klei Souza.
FIGURE. Myrcia cf. obversa. A: Habit; B–C: Fruits; D. Calyx indumentum in fruit: E: Infrutescence; F. Vegetative branch; G: Leaf abaxial surface; H: Distribution map. (A, B: Hatschbach 16698; C–G: Brotto 2561). in Myrcia (Myrtaceae) in the state of Paraná, Brazil
FIGURE. Myrcia cf. obversa. A: Habit; B–C: Fruits; D. Calyx indumentum in fruit: E: Infrutescence; F. Vegetative branch; G: Leaf abaxial surface; H: Distribution map. (A, B: Hatschbach 16698; C–G: Brotto 2561).
Signature of a pair of Majorana zero modes in superconducting gold surface states
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State estimation of surface and deep flows from sparse SSH observations of geostrophic ocean turbulence using Deep Learning
<p>This is a data repository in support of the publication "State estimation of surface and deep flows from sparse SSH observations of geostrophic ocean turbulence using Deep Learning" by Manucharyan et al. (2020), Journal of Advances in Modeling Earth Systems. The zipped file contains 10-day-separated snapshots of surface and deep ocean streamfunctions from the two-layer quasigeostrophic model of ocean turbulence. The included Python scripts demonstrate the efficacy of Deep Learning in temporal interpolation and state estimation given partial observations of the surface ocean turbulence.</p> <p> </p>
Water level elevation data for monitoring wells and surface water gages at the Colorado State University Mountain Campus, 2019 – 2023
<p>This dataset contains groundwater and surface water levels measured between 2019 and 2023 at the Colorado State University Mountain Campus. The study site is situated within a formerly glaciated valley traversed by a modern perennial stream, the South Fork Cache la Poudre River, and occurs near the subalpine-montane ecosystem transition at an approximate elevation of 2,750 meters above sea level. Water levels were measured at two transects (the upstream and downstream transects). Each transect includes a surface water gage, two shallow hand-augered monitoring wells on the valley floor within the riparian zone, and a deeper water table observation well located on the adjacent terrace. Water levels were measured at a subhourly frequency using a combination of vented and non-vented pressure transducers. All data from non-vented transducers were corrected for barometric pressure fluctuations.</p>
The state of the AMOC revealed from the Subpolar North Atlantic Sea Surface Salinity
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Data for article "Solid-State NMR Spectra of Protons and Quadrupolar Nuclei at 28.2 T: Resolving Signatures of Surface Sites with Fast Magic Angle Spinning"
<p>Solid-state NMR data for article:</p> <p>Solid-State NMR Spectra of Protons and Quadrupolar Nuclei at 28.2 T: Resolving Signatures of Surface Sites with Fast Magic Angle Spinning</p> <p> Zachariah J. Berkson, Snædís Björgvinsdóttir, Alexander Yakimov, Domenico Gioffrè, Maciej D. Korzyński, Alexander B. Barnes, and Christophe Copéret</p> <p>https://doi.org/10.1021/jacsau.2c00510</p>
Data from: A surface renewal model for unsteady-state mass transfer using the generalized Danckwerts age distribution function
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Water level elevation data for monitoring wells and surface water gages at the Colorado State University Mountain Campus, 2019 – 2023
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Sounder SIPS: Suomi NPP CrIMSS Level 2 CLIMCAPS Full Spectral Resolution: Atmosphere cloud and surface geophysical state V2 (SNDRSNIML2CCPRET) at GES DISC
WARNING: To users of the derived product “co_mmr_midtrop” (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply “co_mmr_midtrop” by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (“mol_lay/co_mol_lay”) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.govThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Full Spectral Resolution (FSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2000-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.The CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.A level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.
Sounder SIPS: JPSS-1 CrIS Level 2 CLIMCAPS: Atmosphere cloud and surface geophysical state V2 (SNDRJ1IML2CCPRET) at GES DISC
WARNING: To users of the derived product “co_mmr_midtrop” (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply “co_mmr_midtrop” by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (“mol_lay/co_mol_lay”) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.govThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the NOAA-20 platform, also known as JPSS-1. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR (Full Spectral Resolution) infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2100-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products. A level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.
Sounder SIPS: AQUA AIRS IR-only Level 2 CLIMCAPS : Atmosphere, cloud and surface geophysical state V2 (SNDRAQIL2CCPRET) at GES DISC
WARNING: To users of the derived product “co_mmr_midtrop” (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply “co_mmr_midtrop” by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (“mol_lay/co_mol_lay”) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.govThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS CLIMCAPS Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km. The CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphereprofiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.An AIRS level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.
Sounder SIPS: Suomi NPP CrIMSS Level 2 SiFSAP Standard: Atmosphere cloud and surface geophysical state per footprint V2 (at GESDISC)
This level 2 standard product is generated by the SiFSAP (Single Field-of-View Sounder Atmospheric Products) algorithm. The SIFSAP algorithm provides retrieval for each sounder Field of View (FOV), therefore, it has 3-times higher horizontal spatial resolution and 9-time denser products compared to other current IR sounder products. Since SiFSAP is an FOV-based algorithm, its product variables have an additional dimension which represents the number of FOVs. For CrIS instrument, there are 9 FOVs for each Field of Regard (FOR). The SiFSAP Level-2 retrieval products contain a variety of geophysical parameters retrieved from IR/MW sounder suites measurements, including profiles of temperature, water vapor and trace gas species as well as clouds and surface properties. This standard product provides retrievals on a reduced vertical profile (11 levels for water profiles and up to 27 for other profile variables). A level 2 granule has been set as 6 minutes of data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.
Sounder SIPS: Suomi NPP CrIMSS Level 2 SiFSAP Support: Atmosphere cloud and surface geophysical state per footprint V2 at GES DISC
This level 2 support product is generated by the SiFSAP (Single Field-of-View Sounder Atmospheric Products) algorithm. The SIFSAP algorithm provides retrieval for each sounder Field of View (FOV), therefore, it has 3-times higher horizontal spatial resolution and 9-time denser products compared to other current IR sounder products. Since SiFSAP is an FOV-based algorithm, its product variables have an additional dimension which represents the number of FOVs. For CrIS instrument, there are 9 FOVs for each Field of Regard (FOR). The SiFSAP Level-2 retrieval products contain a variety of geophysical parameters retrieved from IR/MW sounder suites measurements, including profiles of temperature, water vapor and trace gas species as well as clouds and surface properties. This support product provides height vertical sampling (up to 98 levels) and also includes more detailed Empirical Orthogonal Function (EOF) information like averaging kernels. A level 2 granule has been set as 6 minutes of data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.