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The data comprises time series from three automatic meteorological and snow-hydrological stations situated in the Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria). The stations are situated at 2737, 2805, and 2919 m a.s.l. and include automatic measurements of meteorological (temperature, precipitation, humidity, wind speed, and radiation fluxes) and snow-hydrological variables (snow depth, snow water equivalent, volumetric solid and liquid water content, snow density, layered snow temperature profiles, snow surface temperature, and snow drift). The data are sampled by fully automatic weather stations (AWS). The data retrieved by the sensors is stored on a logger in 10 min. temporal resolution. The data is continuously transferred by GSM to a server. The data processing consists of transfer of the raw data from the logger to a data server, basic processing steps (e.g., temperature correction long-wave radiation, decumulating precipitation measurements), and a semi-automatic correction for erroneous values.
The W5E5 dataset was compiled to support the bias adjustment of climate input data for the impact assessments carried out in phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b).Version 1.0 of the W5E5 dataset covers the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2016. Data sources of W5E5 are version 1.0 of WATCH Forcing Data methodology applied to ERA5 data (WFDE5; Weedon et al., 2014; Cucchi et al., 2020), ERA5 reanalysis data (Hersbach et al., 2019), and precipitation data from version 2.3 of the Global Precipitation Climatology Project (GPCP; Adler et al., 2003).Variables (with short names and units in brackets) included in the W5E5 dataset are Near Surface Relative Humidity (hurs, %), Near Surface Specific Humidity (huss, kg kg-1), Precipitation (pr, kg m-2 s-1), Snowfall Flux (prsn, kg m-2 s-1), Surface Air Pressure (ps, Pa), Sea Level Pressure (psl, Pa), Surface Downwelling Longwave Radiation (rlds, W m-2), Surface Downwelling Shortwave Radiation (rsds, W m-2), Near Surface Wind Speed (sfcWind, m s-1), Near-Surface Air Temperature (tas, K), Daily Maximum Near Surface Air Temperature (tasmax, K), Daily Minimum Near Surface Air Temperature (tasmin, K), Surface Altitude (orog, m), and WFDE5-ERA5 Mask (mask, 1).W5E5 is a merged dataset. It combines WFDE5 data over land with ERA5 data over the ocean. The mask used for the merge is included in the dataset. The mask is equal to 1 over land and equal to 0 over the ocean.Over land, orog is the surface altitude used for elevation corrections in WFDE5. For all other variables already included in WFDE5 (huss, prsn, ps, rlds, rsds, sfcWind, tas), W5E5 data over land are equal to the daily mean values of the corresponding hourly WFDE5 data. W5E5 hurs over land is the daily mean of hourly hurs computed from hourly WFDE5 huss, ps, and tas using the equations of Buck (1981) as described in Weedon et al. (2010). W5E5 pr over land is the daily mean of the sum of hourly WFDE5 rainfall and snowfall. Note that W5E5 pr and prsn over land are based on WFDE5 rainfall and snowfall bias-adjusted using GPCC monthly precipitation totals. W5E5 psl over land is the daily mean of hourly psl computed from hourly WFDE5 orog, ps, and tas according to psl = ps * exp((g * orog) / (r * tas)), where g is gravity, and r is the specific gas constant of dry air. Lastly, W5E5 tasmax and tasmin over land are the daily maximum and minimum, respectively, of hourly WFDE5 tas.Over the ocean, W5E5 data are based on temporally (from hourly to daily resolution) and spatially (from 0.25° to 0.5° horizontal resolution) aggregated ERA5 data. The spatial aggregation using first-order conservative remapping was always done after the temporal aggregation. For tasmax and tasmin, hourly tas values were aggregated to daily maximum and minimum values, respectively. For all other variables, hourly values were aggregated to daily mean values. Variables unavailable in ERA5 (huss, hurs, sfcWind, orog) were first derived from available variables at hourly temporal and 0.25° horizontal resolution and then aggregated like all other variables. huss and hurs were derived from Near Surface Dewpoint Temperature, ps, and tas using the equations of Buck (1981) as described in Buck (2010). sfcWind was derived from Eastward Near-Surface Wind (uas) and Northward Near-Surface Wind (vas) according to sfcWind = sqrt(uas * uas + vas * vas). orog is equal to surface geopotential divided by gravity. Lastly, pr and prsn were bias-adjusted such that monthly W5E5 precipitation totals match GPCP version 2.3 values over the ocean. Monthly rescaling factors used for this purpose were computed following the scale-selective rescaling procedure described by Balsamo et al. (2010).
This dataset comprises numerical outputs from the whole atmospheric model GAIA (=Ground-to-topside model of Atmosphere and Ionosphere for Aeronomy) and associated simulations presented in the article "Whole atmosphere model simulations of ultra-fast Kelvin wave effects in the ionosphere and thermosphere" (Yamazaki et al., 2020).GAIA is a numerical model of the Earth’s whole atmosphere (e.g., Jin et al., 2011; Miyoshi et al., 2011). The model consists of mathematical equations appropriate for various physical and chemical processes in the troposphere, stratosphere, mesosphere, and thermosphere. The neutral atmosphere model (Miyoshi & Fujiwara, 2003) is coupled with an ionospheric model (Shinagawa, 2011) and electrodynamics model (Jin et al., 2008). The lower layers of the model below 30 km are constrained by meteorological reanalysis products by the Japan Meteorological Agency (Onogi et al., 2007; Kobayashi, et al., 2015).The model was run for the following three time intervals:1. 15 August 2010 - 15 October 20102. 01 August 2011 - 30 September 20113. 01 December 2012 - 31 January 2013The simulation outputs can be found in GAIA/2010, GAIA/2011, and GAIA/2013, respectively. In each directory, the model data are stored in a MATLAB format (.mat).List of model outputs:GZ: "Geopotential height" in [m] as a function of LONGITUDE [˚], LATITUDE [˚], PRESSURE, and TIMEGU: "Zonal wind" in [m/s] as a function of LONGITUDE, LATITUDE, PRESSURE, and TIME (positive eastward)GV: "Meridional wind" in [m/s] as a function of LONGITUDE, LATITUDE, PRESSURE, and TIME (positive northward)GT: "Temperature" in [K] as a function of LONGITUDE, LATITUDE, PRESSURE, and TIMEEEF: "Equatorial zonal electric field" in [V/m] as a function of LONGITUDE and TIME.TEC: "Total electron content" in [TECU] as a function of LONGITUDE, LATITUDE, and TIMEPRESSURE is given at: 10^2, 10^1, 10^0, 10^(-1), 10^(-2), 10^(-3), 10^(-4), 10^(-5), 10^(-6), 10^(-7), 10^(-8) [hPa]For the time period #1, additional controlled simulations "LARGE_WAVES" and "NO_UFKW" were run. See Yamazaki et al. (2020) for details of these runs. The simulation outputs can be found in LARGE_WAVES/2011 and NO_UFKW/2011, respectively.
VERSION HISTORY:- On June 26, 2018 all files were republished due to the incorporation of additional observational data covering years 2014 to 2016. Prior to that date, the dataset only covered years 1979 to 2013. Data for all years prior to 2014 are identical in this and the original version of the dataset.DATA DESCRIPTION:The EWEMBI dataset was compiled to support the bias correction of climate input data for the impact assessments carried out in phase 2b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b; Frieler et al., 2017), which will contribute to the 2018 IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways.The EWEMBI data cover the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2013. Data sources of EWEMBI are ERA-Interim reanalysis data (ERAI; Dee et al., 2011), WATCH forcing data methodology applied to ERA-Interim reanalysis data (WFDEI; Weedon et al., 2014), eartH2Observe forcing data (E2OBS; Calton et al., 2016) and NASA/GEWEX Surface Radiation Budget data (SRB; Stackhouse Jr. et al., 2011). The SRB data were used to bias-correct E2OBS shortwave and longwave radiation (Lange, 2018).Variables included in the EWEMBI dataset are Near Surface Relative Humidity, Near Surface Specific Humidity, Precipitation, Snowfall Flux, Surface Air Pressure, Surface Downwelling Longwave Radiation, Surface Downwelling Shortwave Radiation, Near Surface Wind Speed, Near-Surface Air Temperature, Daily Maximum Near Surface Air Temperature, Daily Minimum Near Surface Air Temperature, Eastward Near-Surface Wind and Northward Near-Surface Wind. For data sources, units and short names of all variables see Frieler et al. (2017, Table 1).
The EWEMBI dataset was compiled to support the bias correction of climate input data for the impact assessments carried out in phase 2b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b; Frieler et al., 2017), which will contribute to the 2018 IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways.The EWEMBI data cover the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2013. Data sources of EWEMBI are ERA-Interim reanalysis data (ERAI; Dee et al., 2011), WATCH forcing data methodology applied to ERA-Interim reanalysis data (WFDEI; Weedon et al., 2014), eartH2Observe forcing data (E2OBS; Calton et al., 2016) and NASA/GEWEX Surface Radiation Budget data (SRB; Stackhouse Jr. et al., 2011). The SRB data were used to bias-correct E2OBS shortwave and longwave radiation (Lange, 2018).Variables included in the EWEMBI dataset are Near Surface Relative Humidity, Near Surface Specific Humidity, Precipitation, Snowfall Flux, Surface Air Pressure, Surface Downwelling Longwave Radiation, Surface Downwelling Shortwave Radiation, Near Surface Wind Speed, Near-Surface Air Temperature, Daily Maximum Near Surface Air Temperature, Daily Minimum Near Surface Air Temperature, Eastward Near-Surface Wind and Northward Near-Surface Wind. For data sources, units and short names of all variables see Frieler et al. (2017, Table 1).
This publication contains the supplementary data set to Mikolaj et al. "Resolving geophysical signals by terrestrial gravimetry: a time domain assessment of the correction-induced uncertainty" (2019, JGR-Solid Earth). The aim of the article is to estimate the uncertainty of terrestrial gravity corrections applied to resolve small-scale gravity effects. The uncertainty of the gravity corrections is assessed using various models of the tidal effect, large-scale hydrology, non-tidal ocean loading, and atmosphere. Taken into account are widely recognized models with global spatial coverage, sufficient temporal resolution and coverage, and available to the public for research purposes. The uncertainty is expressed in terms of a root-mean-square and mean-absolute error of the deviations between all available models. The data set comprises models for 11 sites worldwide. The processing scripts are provided along with an explanatory file with all instructions for results reproduction and application of the uncertainty analysis for an arbitrary location. Please consult the readme file for further details on the data.
The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) is a community-driven modelling effort bringing together impact models across sectors and scales to create consistent and comprehensive projections of the impacts of different levels of global warming. This entry holds the input data of the ISIMIP Fast Track Initiative consisting of bias corrected daily data for from the following five CMIP5 Global Climate Models (GCMs): GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M. Bias corrections has been processed by Sabrina Hempel at PIK and is described in "A trend-preserving bias correction -- the ISIMIP approach" by Hempel et al. (2013) The input data section of the ESGF project referenced in this entry holds the initial version of the bias-corrected GCM input data and was used to force impact models in the ISIMIP Fast Track phase. It should only be used for the ISIMIP2 catch-up experiments for sectors that were already part of the Fast Track phase. For all other purposes, i.e. future runs for new ISIMIP 2 sectors and modeling exercises with no relation to ISIMIP, the corrected and extended version published under the ISIMIP 2 ESGF project should be used. It overcomes several limitations in adjusting the daily variability (denoted as ISIe in Hempel et al., 2013). Data access links are provided to the PIK node of the Earth System Grid Federation (ESGF, https://esg.pik-potsdam.de/). There is currently no directly linked data available, please take a look at the input data of the ISIMIP Fast Track Initiative via https://esg.pik-potsdam.de/search/isimip-ft/. For technical support please have a look at the ESGF FAQ (http://esgf.github.io/esgf-swt/index.html) and the tutorials (https://www.earthsystemcog.org/projects/cog/tutorials_web).
This dataset comprises numerical outputs from the whole atmospheric model GAIA (=Ground-to-topside model of Atmosphere and Ionosphere for Aeronomy) and associated simulations (EXP1, EXP2, and EXP3) presented in the article "Excitation mechanism of ionospheric 6-day oscillation during the 2019 September sudden stratospheric warming event" (Miyoshi and Yamazaki, 2020). Briefly, GAIA is a numerical model of the Earth’s whole atmosphere (e.g., Jin et al., 2011; Miyoshi et al., 2011, 2012). The model consists of mathematical equations that represent various physical and chemical processes in the troposphere, stratosphere, mesosphere, and thermosphere. The neutral atmosphere model (Miyoshi & Fujiwara, 2003) is coupled with an ionospheric model (Shinagawa, 2011) and electrodynamics model (Jin et al., 2008). The lower layers of the model below 40 km are constrained by meteorological reanalysis products by the Japan Meteorological Agency (Kobayashi, et al., 2015). The model was run for the period 1 September-10 October 2019, when there was a sudden stratospheric warming in the Antarctic region (Yamazaki et al., 2020). The GAIA simulation outputs can be found in the directory 'gaia', while the numerical outputs from the controlled simulations EXP1, EXP2, and EXP3 can be found in the directories 'exp1', 'exp2', and 'exp3', respectively. The model data for the temperature, zonal wind, meridional wind, and geopotential heigh are stored separately for each day in the NetCDF format. 'gt', 'gu', 'gv', and 'gz' in file name indicate the temperature, zonal wind, meridional wind, and geopotential heigh, respectively. For instance, the file 'gv20190915gcm.nc' contains the meridional wind data for 15 September 2019. The model data for the eastward current intensity, eastward electric field, and total electron content can be found as text files, namely, 'East_current_gaia.data', 'East_efield_gaia.data', and 'tec_gaia.data'.
This data publication contains global maps of the vertical total electron content (VTEC) of the Earth's ionosphere. They are computed at GFZ from ground GNSS data and provided on an operational basis. The dataset covers the period since the beginning of 2000 and is published in daily files.
This data publication presents global high-frequency mass variability that is induced by individual oceanic and atmospheric partial tides. While the atmospheric component is obtained by conducting a tidal analysis of numerical weather data data, the oceanic component has been produced using the hydro-dynamical ocean tide model TiME that was recently upgraded in the framework of the DFG-funded Research Group NEROGRAV and can be used for gravimetric applications. The overall goal of this project is to facilitate the analysis of gravimetric data sets (e.g. GRACE/GRACE-FO) by improving the understanding of sensor data, processing strategies, and background models. The data set presented herein contributes to this goal as the here described tidally induced mass variations are an important part of the described background models. As tidal variability is usually described as a superposition of so-called partial tides, the presented mass variations can be attributed to individual partial tide frequencies and are thus represented by individual files for each partial tide frequencies. Here, not only the effect of direct gravitation exerted by the ocean and atmospheric mass is included but also gravity variations due to the elastic yielding of the solid Earth in response to water and atmospheric mass redistribution (the load tide) are allowed for. The information describing the partial tides has been transformed to fully normalized Stokes Coefficients describing harmonic in-phase and quadrature component fields as those are especially handy for gravimetric purposes. Additionally, a set of files that allows further expansion of the ensemble of ocean partial tides via linear admittance theory is provided.
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