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2,322 results for “precipitations”
Ka, W and G-band radar observations of clouds and light precipitation during the EPCAPE campaign in March and April 2023
<p>The files contained in the data sets include Ka, W and G-band radar observations of clouds and light precipitation for several days from March 23 to April 27, 2023, during the EPCAPE campaign in La Jolla, CA, USA. <br>The YYYYMMDD_HHMMSS file naming convention corresponds to the starting measurement time of the data set in UTC.<br>The CloudCube_EPCAPE_Gband_Spectra.zip folder contains G-band radar Doppler spectra in the form of calibrated reflectivity as a function of Doppler velocity and range, where the noise has been masked out. The CloudCube_EPCAPE_Gband_Spectra_Noise.zip folder contains G-band radar Doppler spectra, including noise and SNR. The CloudCube_EPCAPE_Gband_Moments.zip folder contains G-band radar Doppler spectra moments, i.e. calibrated reflectivity, mean Doppler velocity and Doppler spectrum width. The CloudCube_EPCAPE_Multifrequency.zip folder includes Ka, W and G-band calibrated reflectivity and dual-frequency reflectivity ratios. <br>These data were obtained from CloudCube, a Ka, W and G-band atmospheric profiling radar, to demonstrate synergies between multifrequency retrievals.<br>For more details about the data processing and description, please refer to: Socuellamos, J. M., Rodriguez Monje, R., Lebsock, M. D., Cooper, K. B., Beauchamp, R. M., and Umeyama, A.: Multifrequency radar observations of marine clouds during the EPCAPE campaign, Earth Syst. Sci. Data. https://doi.org/10.5194/essd-2023-454, 2024. </p>
The Repository for the Manuscript "Temperature and Precipitation Dominate Seasonal Variations in Seismic Velocity and Attenuation in Deserts"
<p><strong><span>Overview</span></strong></p> <p><span>This dataset contains the essential code and data for calculating the Horizontal-to-Vertical Spectral Ratio (HVSR), analyzing vehicle-generated seismic events, retrieving Q-values, and comparing them with meteorological data. It also includes waveform data from 20 seismic events.</span></p> <p><span>The seismic data originate from a temporary broadband seismic array deployed in the Tarim Basin, from July 2017 to October 2019 (Zuo et al., 2022). This dataset focuses on three seismic stations: T12, T52, and T23. Stations T12 and T23 recorded data from July 2017 to October 2019, while station T52 recorded from November 2018 to October 2019.</span></p> <p><span> </span></p> <p><strong><span>Code</span></strong></p> <p><span>The dataset includes Python scripts for calculating HVSR and retrieving Q-values. The HVSR calculation follows Li et al., (2023), while forward modeling is based on Antonio García-Jerez et al., (2016).</span></p> <p><span>The codes for Q-value estimation are stored in ‘Retrieving Q-value’ folder. The Q-value estimation process, demonstrated for station T12 in Jupyter Notebook, involves extracting single vehicle signals from continuous data, time-frequency spectrogram calculations, two-dimensional correlation coefficient of their time-frequency amplitude calculations, using hierarchical clustering algorithm to classify vehicle signals, vehicle speed estimation, and performing Q-value inversion.</span></p> <p><span> </span></p> <p><strong><span>Dataset </span></strong></p> <p><span>HVSR variations over time for three stations are calculated from continuous seismic recordings and are stored in the <em>‘HVSR’</em> folder under each station directory. </span></p> <p><span>Time-frequency spectrograms for Q-value estimation are stored in the <em>‘Spectrogram’</em> folder, with filenames indicating the record time of each vehicle signal. The Q-value is inverted using these signals, and for stability, we stacked every 100 individual results, which are stored in the 'Q-values' folder under the corresponding station name folder. Due to interference from wind and other sources, Q-value inversion using vehicle signals was unreliable for T23, so Q-values are only provided for T12 and T52.</span></p> <p><span>Meteorological data (temperature and soil water content) are stored in the <em>‘temperature’</em> and <em>‘soil water content’</em> folders under each station directory.</span></p> <p><span>Seismic event waveforms for 20 selected strong earthquakes are stored in the <em>‘events’</em> folder, with filenames indicating the start and end times of the events.</span></p> <p><span> </span></p>
Met Office UKCP Local CPM precipitation ML emulator dataset
<div> <div> <div> <h1>Met Office UKCP Local CPM precipitation ML emulator dataset</h1> <p>This is a collection of two datasets: one sourced from CPM data (bham64_ccpm-4x_12em_psl-sphum4th-temp4th-vort4th_pr.tar.gz) and one sourced from GCM data (bham64_gcm-4x_12em_psl-sphum4th-temp4th-vort4th_pr.tar.gz). Each dataset is made up of climate model variables extracted from the Met Office's storage system, combining many variables over many years. It consists of 3 NetCDF files (train.nc, test.nc and val.nc), a YML ds-config.yml file and a README (similar to this one but tailored to the source of the data). Code used to create the dataset can be found here: <a href="https://github.com/henryaddison/mlde-data">https://github.com/henryaddison/mlde-data</a> (specifically the james-submission tag).</p> <p>The YML file contains the configuration for the creation of the dataset, including the variables, scenario, ensemble members, spatial domain and resolution, and the scheme for splitting the data across the three subsets.</p> <p>Each NetCDF contains the same variables but split into different subsets (train, val and test) of the based on time dimension.</p> <p>Otherwise the NetCDF files have the sames dimensions and coordinates for ensemble_member, grid_longitude and grid_latitude.</p> <ul> <li>Spatial resolution: This has two parts - the resolution of the data and the grid resolution stored at in the file. For predictand variables this is 2.2km variables coarsened 4 times to 8.8km (this is the target grid). For predictor variables this is 2.2km variables conservatively regriddded to GCM 60km grid or variables from GCM (so already on 60km grid) then regrid (nearest neighbour) to the target grid of predictands. In the naming convention of resolution used in config files, 60km resolution is synonamous with the GCM grid and 2.2km resolution is synonamous with the CPM grid.</li> <li>Spatial domain: A 64x64 section of the 8.8km target grid covering England and Wales</li> <li>Time resolution: daily</li> <li>Time domain: 1st Dec 1980 to 30th Nov 2000; 1st Dec 2020 to 30th Nov 2040; 1st Dec 2060 to 30th Nov 2080. Uses a 360-day calendar.</li> <li>Scenario: RCP8.5</li> <li>Ensemble Members: 01, 04-13 & 15 (these correspond to the 12 ensemble member runs from the CPM but don't carry intrinsic meaning).</li> <li>Split scheme: 70% training, 15% validation, 15% testing, split by choosing complete seasons at random, with an equal number of each season from each of the 3 time periods.</li> </ul> <p> </p> <h2>Predictor variables</h2> <ul> <li>psl (hPa) - mean sea level pressure</li> <li>temp850, temp700, temp500, temp250 - air temperature (K) at 850, 700, 500 and 250 hPa</li> <li>vorticity850, vorticity700, vorticity500, vorticity250 - relative vorticity (s^-1) at 850, 700, 500 and 250 hPa</li> <li>spechum850, spechum700, spechum500, spechum250 - specific humidity at 850, 700, 500 and 250 hPa</li> </ul> <h2>Predictand variable</h2> <ul> <li>target_pr - precipitation rate (mm/day)</li> </ul> <p> </p> <p>UPDATE 2025-03-27: Dataset tars are renamed to make it clearer their source (ccpm for coarsened CPM and gcm for GCM).</p> </div> </div> </div>
The role of atmospheric drivers in a sudden transition of California precipitation in the 2012/13 winter
<p>This contains the data, presented in a publication entitled "The role of atmospheric drivers in a sudden transition of California precipitation in the 2012/13 winter" (JGR: Atmospheres). See the paper for details. See 'Readme.pdf' for the file descriptions.</p>
A Test of Energetic Particle Precipitation Models Using Simultaneous Incoherent Scatter Radar and Van Allen Probes Observations
<p>BERI modeling reuslts for "A Test of Energetic Particle Precipitation Models Using Simultaneous Incoherent Scatter Radar and Van Allen Probes Observations"</p>
Object-based evaluation of precipitation systems in convection-permitting regional climate simulation over eastern China
<p>Data used in the manuscript "<strong>Object-based evaluation of precipitation systems in convection-permitting regional climate simulation over eastern China</strong>" which was submitted to Journal of Geophysical Research: Atmospheres. </p>
Supporting data for "Understanding the impact of precipitation kinetics on the electrochemical performance of lithium–sulfur batteries by operando X-ray diffraction"
<p>This is the dataset of electrochemical and operando X-ray diffraction measurements for our publication "Understanding the impact of precipitation kinetics on the electrochemical performance of lithium–sulfur batteries by operando X-ray diffraction". This archive contains the raw data and scripts written in R used in the analysis and presentation of the results in this manuscript.</p> <p>Abstract of the manuscript:</p> <p>The complex reaction mechanism of the lithium–sulfur battery system consists of repetitive dissolution and precipitation of the sulfur-containing species in the positive electrode. In particular, the precipitation of lithium sulfide (Li<sub>2</sub>S) during discharge has been considered a crucial factor for obtaining a high degree of active material utilization. Here, the influence of electrolyte amount, electrode thickness, applied current and electrolyte salt on the formation of Li<sub>2</sub>S is systematically investigated in a series of operando X-ray diffraction experiments. Through a combination of simultaneous diffraction and resistance measurements, the evolution of Li<sub>2</sub>S is directly correlated to the variation in internal resistance and transport properties inside the positive electrode. The correlation indicates that at different stages, the Li<sub>2</sub>S precipitation both facilitates and impedes the discharge process. This information on the kinetics of Li<sub>2</sub>S formation offers mechanistic explanations for the strong impact of different electrochemical cell parameters on the cell performance and thus, directions for holistic optimizations to achieve high sulfur utilization.</p> <p> </p>
CAM Global RCE simulations TC track, radial profiles, and filtered precipitation files
<p>This dataset includes processed output from the Community Atmosphere Model (CAM), version 5, the atmospheric component of the Community Earth System Model (CESM2). CAM was run in a global rotating radiative convective equilibrium (RCE) aquaplanet configuration for 2 years, with the first 2 months discarded to allow for spin-up. The globally-uniform sea surface temperature (SST) was varied from 295 to 305 K in 1 K increments, producing a total of 11 model simulations. More details about CAM and the RCE configuration can be found in the associated manuscript in <em>JGR: Atmospheres.</em> The TempestExtremes software package (https://github.com/ClimateGlobalChange/tempestextremes) was used to track tropical cyclones (TCs) in the raw model output. Specifically DetectNodes and StitchNodes were used to locate potential TCs based on sea level pressure minima and then stitch these TC candidates into tracks based on spatial proximity. NodeFileEditor was used to calculate a radial wind profile at each timestep in each TCs' lifetime, and from these radial profiles, the TCs' outer sizes were estimated based on the radii of 8 m/s winds, outside the radii of maximum winds. Lastly NodeFileFilter was used to extract all precipitation within these calculated outer sizes at each timestep in each TC's lifetime. This dataset contains the TC track files, the TC radial wind profile and outer size files, and the filtered TC precipitation files. Note that while the track and radial profile files contain data from TCs all over the global domain, the filtered TC precipitation files only contain precipitation froms TCs between 40°S and 40°N because that's the domain we used for the TC precipitation analysis in the manuscript. </p>
Projection of hourly extreme precipitation over Eastern China
<p>Data used in the manuscript "<strong>Projection of hourly extreme precipitation over Eastern China</strong>" which will be submitted to Journal of Geophysical Research: Atmospheres.</p>
Tropospheric delays and precipitable water vapor retrieved from global radiosonde observations from 2014 to 2019
<p>The data consists of a set of meteorological quantities including tropospheric delays (zenith wet delay, zenith hydrostatic delay, and zenith total delay), precipitable water vapor, and surface temperature and pressure. The data is retrieved from the observations of 414 globally distributed radiosonde stations from 2014 to 2019. In addition to the geographic information of radiosonde stations, the profiles of tropospheric delays and precipitable water vapor are contained in the data file. This data has a wide range of applications, e.g., validating the tropospheric delays and precipitable water vapor derived from other techniques, investigating the spatial-temporal variations of water vapor, and acting as training data of machine learning to build tropospheric delay models.</p> <p>In the manuscript "Machine Learning-based Model for Real-time GNSS Precipitable Water Vapor Sensing", this data is used to train a machine learning model to map the zenith total delays to precipitable water vapor. The data is split into training data and test data, where the data from 2014 to 2018 are employed for model training, and the data of 2019 are used for testing. The developed models and the results for the manuscript are saved in the directories of Models and Results, respectively.</p>
1 km Monthly Precipitation Dataset for China from 1952 to 2019 (ChinaClim_time-series)
<p>ChinaClim_time-series is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.</p>
Data from: Climatic influences on winter precipitation use by trees in summer
<p>Trees in seasonal climates may use water originating from both winter and summer precipitation. However, the seasonal origins of water used by trees have not been systematically studied. We used stable isotopes of water to compare the seasonal origins of water found in three common tree species across 24 Swiss forest sites sampled in two different years. The data set provides information on the sites (e.g., latitude/longitude, site name), site characteristics (e.g., weather/climate), tree species studied (beech, spruce and oak), and corresponding observations of stable isotopes of hydrogen and oxygen in tree xylem water. </p>
Simulated cycles of East Asian temperature and precipitation over the past 425 ka
<p>These files contain the model outputs from NorESM-L and CESM that are used to analyse and draw the main parts of Figures in our paper entitled "Simulated cycles of East Asian temperature and precipitation over past 425 ka".</p>
Fig. 1 in Avian Diversity Along A Precipitation Gradient In Southern Africa
Fig. 1. Location of the transect (broken red line in the map above, yellow line in the map below).
Model data from GRL paper "Precipitation in Northeast Mexico Primarily Controlled by the Relative Warming of Atlantic SSTs"
<p>This folder includes monthly model data (Specified Chemistry version of the Whole Atmosphere Community Climate Model with CAM4 physics) of sea level pressure (SLP), zonal wind (U), meridional wind (V), convective precipitation rate (P), and vertical velocity (OMEGA) that were used in the Geophysical Research Letters paper "<strong>Precipitation in Northeast Mexico Primarily Controlled by the Relative Warming of Atlantic SSTs</strong>" by Wright et al. Boreal winter files include "DJFM" (December - March) in their title and boreal summer files include "JJAS" (June - September) in their title. Data from nine different experiments is included. These experiments were designed to isolate the large-scale atmospheric response to each combination of Atlantic Multidecadal sea surface temperature Variability (AMV) and Interdecadal Pacific sea surface temperature variability (IPV). Given that each climate mode can exist in a positive, negative, or neutral state, there are nine combinations of AMV and IPV. The first four letters of each filename denote the experiment ID with "A" referring to AMV, "I" referring to IPV, "C" referring to cold, "W" referring to warm, and "N" referring to neutral. For example, "ACIW_JJAS_SLP_1801-2000.nc" is the -AMV/+IPV sea level pressure during boreal summer. Vertical velocity data was lost for some experiments and we are providing what is left. Please refer to the manuscript by Wright et al. for further details on the experimental setup.</p>
Drought increased since the mid-20th century in the northern South American Altiplano revealed by a 389-year precipitation record
<p>In this study, we develop the first tree-ring based precipitation reconstruction for the northern South American Altiplano back to 1625 CE. We established the significance of our reconstruction by using it to determine that the occurrence rate of extreme dry events together with a shift in mean dry conditions for the late 20th-beginning 21st century is unprecedented in the past 389 years. Our reconstruction provides also valuable information about the ENSO influences in the local precipitation.</p>
The Relations between Summer Droughts/Floods and Oxygen Isotope Composition of Precipitation in Dongting Lake Basin
<p>This is the precipitation amount and stable isotope composition of precipitation data on monthly timescale from Jan 1979 to Dec 2017 over the Dongting Lake Basin, Central-shouthern China. This is simulated data from isoGSM2.</p> <p> </p>
The data for the article entitled "Nitrogen deposition drives response and recovery to precipitation change and its reversal in an arid ecosystem""
<p><strong>Title:</strong> Nitrogen deposition drives response and recovery to precipitation change and its reversal in an arid ecosystem</p> <p>Yu et al.</p> <p>Document 1: Data on plant community traits.</p> <p>Document 2: Data on soil features.</p>
wudalist/allmydata: Three-day precipitation data
<p>We have radar echo data in Yinchuan, Ningxia for 2018 and 2019, which is collected every six minutes. Due to the infrequent occurrence of precipitation, radar echo data from 84 rainy days were selected to form our dataset. We first processed the original data with noise reduction, and then used a sliding window of length 20 to evenly divide the training and test sets in a 4:1 ratio. The details of how the dataset was produced are clearly described in the paper. precipitation is the final dataset. Due to the size of the original dataset, only three days of the radar echo precipitation data are provided here for testing purposes.</p>
Data from: Increased precipitation attenuates shrub encroachment by facilitating herbaceous growth in a Mongolian grassland
<p>Widespread shrub encroachment is profoundly impacting the structures and functions of global drylands, and precipitation change is assumed to be one of the most critical factors affecting this phenomenon. However, there is little evidence to show how precipitation changes will affect the process. In this study, we conducted a 6-year precipitation manipulation experiment (-30%, ambient, +30%, and +50%) to investigate the effects of precipitation changes on the growth of shrubs and herbaceous plants in a shrub-encroached grassland in Inner Mongolia. We found that the increasing precipitation significantly increased the mean height, coverage, and aboveground biomass of herbaceous species, while the growth of shrub species did not exhibit a significant response to precipitation changes. With increasing precipitation, the relative coverage of shrubs decreased, while that of herbs increased. The native dominant herbaceous plant (Leymus chinensis) with more sensitive maximum photosynthetic rate to the precipitation change, showed higher photosynthetic nitrogen use efficiency and water use efficiency than those of the encroached shrub species (Caragana microphylla) at high soil moisture contents, reflecting that the ecophysiological characteristics of L. chinensis might provide it a competitive advantage under increased precipitation. Our findings suggest that increasing precipitation may slow down shrub encroachment by facilitating herbaceous growth in Mongolian grasslands, and consequently affect the forage value and carbon budget in these ecosystems. </p>
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