ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

Please note: Please use ds633.1 to access RDA maintained ERA-5 Monthly Mean data, see ERA5 Reanalysis (Monthly Mean 0.25 Degree Latitude-Longitude Grid), RDA dataset ds633.1. This dataset is no longer being updated, and web access has been removed.

After many years of research and technical preparation, the production of a new ECMWF climate reanalysis to replace ERA-Interim is in progress. ERA5 is the fifth generation of ECMWF atmospheric reanalyses of the global climate, which started with the FGGE reanalyses produced in the 1980s, followed by ERA-15, ERA-40 and most recently ERA-Interim. ERA5 will cover the period January 1950 to near real time, though the first segment of data to be released will span the period 2010-2016.

ERA5 is produced using high-resolution forecasts (HRES) at 31 kilometer resolution (one fourth the spatial resolution of the operational model) and a 62 kilometer resolution ten member 4D-Var ensemble of data assimilation (EDA) in CY41r2 of ECMWF's Integrated Forecast System (IFS) with 137 hybrid sigma-pressure (model) levels in the vertical, up to a top level of 0.01 hPa. Atmospheric data on these levels are interpolated to 37 pressure levels (the same levels as in ERA-Interim). Surface or single level data are also available, containing 2D parameters such as precipitation, 2 meter temperature, top of atmosphere radiation and vertical integrals over the entire atmosphere. The IFS is coupled to a soil model, the parameters of which are also designated as surface parameters, and an ocean wave model. Generally, the data is available at an hourly frequency and consists of analyses and short (18 hour) forecasts, initialized twice daily from analyses at 06 and 18 UTC. Most analyses parameters are also available from the forecasts. There are a number of forecast parameters, e.g. mean rates and accumulations, that are not available from the analyses. Together, the hourly analysis and twice daily forecast parameters form the basis of the monthly means (and monthly diurnal means) found in this dataset.

Improvements to ERA5, compared to ERA-Interim, include use of HadISST.2, reprocessed ECMWF climate data records (CDR), and implementation of RTTOV11 radiative transfer. Variational bias corrections have not only been applied to satellite radiances, but also ozone retrievals, aircraft observations, surface pressure, and radiosonde profiles.

NCAR's Data Support Section (DSS) is performing and supplying a grid transformed version of ERA5, in which variables originally represented as spectral coefficients or archived on a reduced Gaussian grid are transformed to a regular 1280 longitude by 640 latitude N320 Gaussian grid. In addition, DSS is also computing horizontal winds (u-component, v-component) from spectral vorticity and divergence where these are available. Finally, the data is reprocessed into single parameter time series.

Please note: As of November 2017, DSS is also producing a CF 1.6 compliant netCDF-4/HDF5 version of ERA5 for CISL RDA at NCAR. The netCDF-4/HDF5 version is the de facto RDA ERA5 online data format. The GRIB1 data format is only available via NCAR's High Performance Storage System (HPSS). We encourage users to evaluate the netCDF-4/HDF5 version for their work, and to use the currently existing GRIB1 files as a reference and basis of comparison. To ease this transition, there is a one-to-one correspondence between the netCDF-4/HDF5 and GRIB1 files, with as much GRIB1 metadata as possible incorporated into the attributes of the netCDF-4/HDF5 counterpart.

Introduction

This dataset's snow depth data was derived using elevation differencing, which is simply the snow surface elevation (ICESat-2 ATL08) minus the reference surface elevation (obtained from Digital Elevation Models):

DEM Co-registration: DEMs are co-registered to ICESat-2 ATL08 snow-off reference without vertical bias adjustment.

Elevation Bias Correction: The elevation bias between the DEMs and ICESat-2 is corrected using ICESat-2 ATL08 snow-off segments.

Snow Depth Calculation: Determining snow depth by subtracting the bias-free reference ground elevation(from Step 2) from ICESat-2 ATL08 snow-on segments.

This dataset is presented in a tabular format, which simplifies the preprocess for machine learning models. While co-registration has been done (1), users have the flexibility to train a bias correction model again (2) and retrieve snow depth measurements anew (3). Alternatively, the snow depth can be directly used for various analytical purposes. Detailed methodologies for the co-registration, bias correction, and snow depth determination are thoroughly documented in the paper (under submission) to support users in leveraging this dataset for their research needs.

Meta Information

Study Area: Mainland Norway

Acquisition Period (ICESat-2): October 2018 to October 2020

ICESat-2 data source: ATL08 (level3, version 5)

Reference DEMs: Norway DTM1, Norway DTM10, Copernicus GLO30, FABDEM. (see reference links)

Reference snow depth: ERA5 Land (hourly), ERA5 Land (monthly).

Snow condition: The dataset contains snow depth retrieved (snow_on_alt08_segments_and_snow_depth.csv) and snow-free observations (snow_free_alt08_segments_and_dems.csv).

Data Cleaning: No, this is a raw dataset that may contain outliers.

Mask: Excluded water surface and permanent ice at a spatial resolution of 100 m.

Description

This dataset encapsulates a wide array of attributes derived from ICESat-2 observations, alongside measurements pertinent to snow depth, terrain, and environmental conditions across Mainland Norway. For detailed attribute descriptions, refer to the ICESat-2 ATL08 documentation. The dataset is structured into several columns, each representing a specific attribute:

'latitude': Latitude coordinates of the data points in WGS 84.

'longitude': Longitude coordinates of the data points in WGS 84.

'segment_landcover': Land cover classification for each segment.

'segment_snowcover': Snow cover classification for each segment.

'h_te_best_fit': Best-fit elevation of the terrain.

'h_te_std': Standard deviation of terrain elevation.

'n_te_photons': Number of photons used for terrain elevation estimation.

'subset_te_flag': Quality flag (5 = all geosegments available, 4 = four geosegments...).

'segment_cover': Woody vegetation fractional cover derived from the 2019 Copernicus 100m shrub and forest fractional cover data product.

'h_canopy': Canopy height above terrain from ICESat-2 (only for snow-off segments).

'h_mean_canopy': Mean canopy height ICESat-2 (only for snow-off segments).

'canopy_openness': Canopy openness from ICESat-2 (only for snow-off segments).

'h_canopy_winter': Canopy height above terrain from ICESat-2 (only for snow-on segments).

'h_mean_canopy_winter':Canopy mean height from ICESat-2 (only for snow-on segments).

'canopy_openness_winter':Canopy openness from ICESat-2 (only for snow-on segments).

'tree_presence': the presence of trees in the segment (1 = tree, 0 = no tree, binary of h_canopy).

'pair': Pair flag for ICESat-2.

'beam': Beam flag for ICESat-2.

'p_b': Pair and beam flag for ICESat-2.

'region': Region identifier for ICESat-2.

'cloud_flag_atm': Atmospheric cloud flag for ICESat-2.

'urban_flag': Urban area flag for ICESat-2.

'h_te_skew': Skewness of terrain elevation of segments.

'snr': Signal-to-noise ratio for ICESat-2.

'terrain_slope': Slope of the terrain from ICESat-2.

'h_te_uncertainty': Uncertainty in terrain elevation estimation.

'night_flag': Flag indicating nighttime data.

'brightness_flag': Brightness flag for ICESat-2.

'h_te_interp': Interpolated terrain elevation.

'E': Easting coordinate in EPSG 32633.

'N': Northing coordinate in EPSG 32633.

'slope': Terrain slope computed from DTM10.

'aspect': Terrain aspect computed from DTM10.

'planc': Plan curvature computed from DTM10.

'profc': Profile curvature computed from DTM10.

'curvature': Overall terrain curvature computed from DTM10.

'tpi': Terrain Position Index computed from DTM10.

'tpi_9': TPI with a 90-meter radius.

'tpi_27': TPI with a 270-meter radius.

'wf_positive': Positive wind aspect index.

'wf_negative': Negative wind aspect index.

'smlt_acc': Snowmelt accumulation calculated from ERA5 Land monthly snow melting (currently not in use).

'sf_acc': Snowfall accumulation calculated from ERA5 Land monthly snowfall (currently not in use).

'sd_era': Snow depth from ERA5 Land reanalysis, coupled with ICESat-2 measurements at daily resolution,

'sde_era': Snow depth linear interpolated from ERA5 Land reanalysis.

'date': Date of data acquisition.

'date_': Date in Pandas Datatime data dype.

'month': Month of data acquisition.

'difference': The elevation difference between segment and subsegment at the midpoint ( 'h_te_best_fit_20m_2' minus 'h_te_best_fit'). If you want to use h_te_best_fit_20m_2 instead of h_te_best_fit as elevation from ICESat-2, you can do it by df_after_dtm1 - difference, snowdepth_dtm1 - difference.

Columns on elevation difference and snow depth (in meters):

'dh_after_dtm1': The elevation difference between the snow-free segment and DTM1 (ICESat-2 minus DTM1). This serves as an independent variable y in the bias correction model for DTM1. Here, 'after' means after co-registration.

'snowdepth_dtm1': The elevation difference between the snow-on segment and DTM1 (ICESat-2 minus DTM1), representing the raw snow depth as measured against DTM1.

'sd_correct_dtm1': Corrected snow depth using DTM1, adjusted by bias correction model.

'df_dtm1_era5': Difference betwen 'sd_correct_dtm1' and 'sde_era'. (sd_correct_dtm1 minus sde_era), providing a comparison between corrected snow depth from DTM1 and snow depth from ERA5 Land reanalysis

'dh_after_dtm10': The elevation difference between the snow-free segment and DTM10 (ICESat-2 minus DTM10), used in bias correction for DTM10.

'snowdepth_dtm10': The elevation difference between the snow-on segment and DTM10 (ICESat-2 minus DTM10).

'sd_correct_dtm10': Corrected snow depth using DTM10, adjusted by bias correction model.

'df_dtm10_era5': Difference between 'sd_correct_dtm10' and 'sde_era'.

'dh_after_cop30': The elevation difference between the snow-free segment and Copernicus GLO30 (ICESat-2 minus Copernicus GLO30).

'snowdepth_cop30': The elevation difference between the snow-on segment and Copernicus GLO30.

'sd_correct_cop30': The adjusted snow depth using Copernicus GLO30, adjusted by bias correction model.

'df_cop30_era5': The discrepancy between 'sd_correct_cop30' and 'sde_era'.

'dh_after_fab': The elevation difference between the snow-free segment and FABDEM (ICESat-2 minus FABDEM), used in bias correction for FABDEM.

'snowdepth_fab': The elevation difference between the snow-on segment and FABDEM, representing the uncorrected snow depth.

'sd_correct_fab': The corrected snow depth using FABDEM, adjusted by bias correction model.

'df_fab_era5': The difference between 'sd_correct_fab' and 'sde_era'.

More explanation (especially on how the parameters are calculated, such as wind aspect index) is available in related works and blog posts on snow depth, and DEM bias correction.

This dataset includes a comprehensive collection of snow depth data and correlated environmental variables for Mainland Norway. Researchers can use this dataset to investigate the following:

The difference between ICESat-2 and DEMs. For example, how 'df_after_dtm1' relates to terrain parameters.

The residual bias of ICESat-2 derived snow depth, for example, snowdepth_dtm1 and bias-corrected sd_correct_dtm1. You can train a better bias correction to retrieve snow depth again. You can compare your model with my model by 'dh_reg_dtm1', 'dh_reg_dtm10', 'dh_reg_cop30', and 'dh_reg_fab', which are the elevation differences after bias correction for each DEM.

The difference between ICESat-2-derived snow depth and snow depth from ERA5 Land, for example, 'df_dtm1_era5'.

The spatial distribution of snow depth or subgrid variability.

Reliable and detailed measurements of atmospheric and snow conditions in the Arctic are limited. While modern atmospheric reanalyses could potentially replace the former, the latter can be principally simulated by dedicated snow modelling. However, because the uncertainties of reanalyses and modelling are still exceptionally large at high latitudes, a thorough analysis of the performance of atmospheric reanalyses and the snow model simulations are required. Specifically, we aim to answer the following questions for Villum Research Station (VRS), northeast Greenland: (1) What are the predominant snow and meteorological conditions? (2) What are systematic differences between the modern atmospheric reanalysis ERA5 and in situ measurements? (3) Can the snow model Crocus simulate reliably snow depth and stratigraphy? We systematically compare atmospheric in situ measurements and ERA5 reanalysis (November 2015–August 2018) and evaluate simulated and measured snow depth (October 2014–September 2018). Moreover, modelled and measured vertical profiles of snow density and snow specific surface area (SSA) are analysed for two days where a survey had taken place. We found good agreement between in situ and ERA5 atmospheric variables with correlation coefficients >0.84 except for precipitation, wind speed, and wind direction. ERA5’s resolution is too coarse to resolve the topography in the study area adequately, leading presumably to the detected biases. Crocus can simulate satisfactorily the evolution of snow depth, but simulations of SSA and density profiles, whether driven by ERA5 or in situ measurements are biased compared to measurements. Unexpectedly, measured snow depth agrees better with ERA5 driven simulation than with simulation forced with in situ measurements (explained variance: 0.73 versus 0.23). This is due to differences in snowfall, humidity and air temperature between both forcing datasets. In conclusion, ERA5 has great potential to force snow models but the use of Crocus in the Arctic is affected by limitations such as inappropriate parametrisations for Arctic snowpack evolution, but also by lack of process formulations such as vertical water vapour transport. These limitations strongly affect the accuracy of the vertical profiles of physical snow properties.

This dataset provides globally continuous, daily snow and ice cover information at a high spatial resolution (0.1° latitude/longitude grid) for the period from January 1, 1980, to June 30, 1987. It extends the Global Automated Snow and Ice Mapping System (GMASI) dataset, which begins in July 1987. For access to GMASI snow and ice cover data starting in 1987, users can visit: https://www.star.nesdis.noaa.gov/pub/smcd/emb/snow/gmasi_reprocessing/dailymaps/data/.The extended dataset was developed using advanced machine learning techniques, specifically a Random Forest algorithm, applied to ERA5 reanalysis data. This dataset is designed to support diverse applications, including climate studies, hydrological modeling, and long-term precipitation analyses.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

The Crocus-ERA5 daily snow product is derived from the complex snow scheme Crocus coupled to the ISBA (Interactions between Soil–Biosphere–Atmosphere) land surface model (Brun et al., 2013) and embedded into the SURFEX numerical platform (https://www.umr-cnrm.fr/surfex/). The model is driven by a meteorological forcing (temperature, precipitation, humidity, winds, etc) derived from the ERA5 global atmospheric reanalysis (https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5). This product only concerns open field snowpack, i.e. only low vegetation is modeled (no forest). It covers the entire Northern Hemisphere at 0.25° resolution over the 1950-07-01 to 2023-06-30 period. All snow characteristics (see later) are available at a dailly frequency. This product is the successor of the Crocus-ERA-Interim daily snow product (Decharme, 2024). It is used by the NOAA Artic Report Card from 2021 to present for the annual survey of the Terrestrial Snow Cover anomalies over the Northern Hemisphere. An evaluation of the snow water equivalent product can be found in Mudryk et al. (2024) where it is compared to observations and to about twenty alternative datasets.

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land uses as input to control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'.
The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. The temporal and spatial resolutions of ERA5-Land makes this dataset very useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states.

ERA5-Land monthly averaged data January 2019

Dataset has been retrieved on the Copernicus Climate data Store (https://cds.climate.copernicus.eu/#!/home) and is meant to be used for teaching purposes only. This dataset is used in the Galaxy training on "Visualize Climate data with Panoply in Galaxy".

See https://training.galaxyproject.org/ (topic: climate) for more information.

Product type: Monthly averaged reanalysis

Variable:

10m u-component of wind, 10m v-component of wind, 2m temperature, Leaf area index, high vegetation, Leaf area index, low vegetation, Snow cover, Snow depth

Year:

2019

Month:

January

Time:

00:00

Format:

NetCDF (experimental)

This dataset contains hourly values of 2m air temperature, snow depth and total precipitation from the ERA5-land reanalysis from 2015-01-01 to 2022-12-31.

The geographical area of interest corresponds to the Troms and Finnmark counties in Norway.

Along with 10.5281/zenodo.8142734 this is to be used as input to forecast vegetation browning in Troms and Finnmark using machine learning.

Three long-term snow depth datasets (random forest, pixel-based, and fusion products) covering 35 years (1988–2022) were provided. To improve the reliability of snow depth estimates, an ensemble dataset of random forest, pixel-based, and ERA5-Land products covering 1988-2022 snowy season was generated.Compared to snow depth datasets based solely on remote sensing or climate reanalysis data, fusion dataset can achieve the complementary observations of different data sources and therefore provide a higher confidence in the magnitude of snow depth.The snow depth datasets are stored in *.hdf format. Its naming convention is “SnowDepth_YYYYMMDD_Daily_025km_ProductName.h5”. “SnowDepth” is the snow depth variable. The file name variable “YYYYMMDD” denotes the date stamp, for example, 20220101. The “Daily” means the revisit time. The “025km” denotes the spatial resolution. The “ProductName” is the dataset name, for example, RandomForest, PixelBased, and Fusion. The pixel values in the hdf files have specific meanings: “0–1000” represents the effective value of snow depth, and the unit is cm; “9999” means missing or no data. The code used to read *.hdf file.

Note: a new time-series dataset from ERA5 has been published — this one won't be updated/maintained anymore

Country averages of meteorological variables generated using the R routines available in the package panas based on the Copernicus Climate Change ERA5 reanalyses. The time-series are at hourly resolution and the included variables are:

• 2-meter temperature (t2m),
• snow depth (snow_depth),
• mean sea-level pressure (mslp),
• runoff,
• surface solar radiation (ssrd),
• surface solar radiation with clear-sky (ssrdc),
• temperature at 850hPa (t850),
• total precipitation (total_prec),
• zonal (west-east direction) wind speed at 10m (u10) and 100m (u100),
• meridional (north-sud) wind speed at 10m (v10) and 100m (v100),
• dew point temperature (dew)

The original gridded data has been averaged considered the national borders of the following countries (European 2-letter country codes are used, i.e. ISO 3166 alpha-2 codes with the exception of GB->UK and GR->EL): AL, AT, BA, BE, BG, BY, CH, CY, CZ, DE, DK, DZ, EE, EL, ES, FI, FR, HR, HU, IE, IS, IT, LT, LU, LV, MD, ME, MK, NL, NO, PL, PT, RO, RS, SE, SI, SK, UA, UK.

The unit measures here used are listed in the official page: https://cds.climate.copernicus.eu/cdsapp#!/dataset/era5-hourly-data-on-single-levels-from-2000-to-2017?tab=overview

The script used to generate the files is available on github here

land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.

This deposit contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the ERA-5 reanalysis. The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x) Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr) The simulations cover the period covered by ERA5 reanalysis : 1981-2020 (1979-1980=Spin-up years) Data are available at the daily frequency, with one variable (10 years of data) per file. The available variables in this deposit are : LWD: Surface downward longwave radiation, [W/m2] LWU: Surface upward longwave radiation, [W/m2] MB: Total snow water equivalent, [mm.We] MBrr: Daily rainfall, mm.We MBsf: Daily snowfall, mm.We

QQz: Near-surface specific humidity at constant height, g/kg

SWD: Surface downward shortwave radiation, [W/m2] SWU: Surface upward shortwave radiation, [W/m2]

TTmax: Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C] TTmin: Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C]

TTz: Near-surface mean air temperature at constant-height, [C] UUz: Near-surface zonal component of wind speed at constant height, [m/s] VVz: Near surface Meridional component of wind speed at constant height, [m/s] ZN3: Total snow height, [m]

Other variables are available upon request (see email above). AL : Surface albedo, [0-1] CC: Cloud cover, [0-1] CD: Low level Cloud cover, [0-1] CM: Middle level Cloud cover, [0-1] CU: High level Cloud cover, [0-1] SP: Surface pressure, [hPa] ST: Surface temperature, [C] TT: Near-surface mean air temperature for the first three model level above the surface (constant sigma), C TTp: Constant pressure-level mean air temperature, C ZZ: Surface geopotential for the first three model level above the surface (constant sigma), [m]

SHF: Surface sensible heat flux, [W/m2] LHF: Surface latent heat flux, [W/m2]

• Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUl.nc file

(1) For variable TT, ZZ model constant sigma level of 0.9997479 (2) For variables TTz, QQz constant height level at 2m (3) For variables UUz, VVz constant height level at 10m (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respectibe fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results. Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION 1:CROPS_LOW 2:CROPS_MEDIUM 3:CROPS_HIGH 4:GRASS_LOW 5:GRASS_MEDIUM 6:GRASS_HIGH 7:BROADLEAF_LOW 8:BROADLEAF MEDIUM 9:BROADLEAF_HIGH 10:NEEDLELEAF_LOW 11:NEEDLELEAF MEDIUM 12:NEEDLELEAF_HIGH 13:City

Due to its high surface albedo and low thermal conductivity, snowpack on sea ice can effectively adjust the change of sea ice (growth and melting) and control energy budgets. It is an important parameter for sea ice thickness estimation. This product provides the daily snow depth on Arctic sea ice from 2012 to 2020 (September to April). Based on the original reanalysis reconstruction model (NASA Eulerian Snow on Sea Ice Model), we add a melting process, and then combine with the particle filter method to construct the snow depth estimation model. The ERA5 data (snowfall, 2-m air temperature and wind speed data) provided by the ECMWF, sea ice drift data provided by the OSI SAF and sea ice concentration data provided by the NSIDC are used to force the reanalysis reconstruction model to obtain the simulated snow depth, and then the satellite-derived snow depths are assimilated into the model to obtain the cold-season snow depth on Arctic sea ice (October to April). Since there is no remote sensing data used for assimilation in September, the linear regression analysis is used to construct the relationship between the simulated snow depth and the assimilated snow depth to obtain the final snow depth data in September. Finally, the final snow depth on Arctic sea ice from 2012 to 2020 (September-April) is generated at a 50-km spatial resolution. This product can effectively integrate the advantages of satellite data and simulation data, and is in good agreement with three OIB data (i.e., the NSIDC OIB quick look product, NSIDC OIB L4 product and OIB product provided by the NOAA), with root mean square errors (RMSE) of 5.80 cm, 4.61 cm and 6.50 cm, respectively. This data set can provide accurate input parameters for the estimation of sea ice thickness and volume, help to analyze the Arctic mass balance and energy balance, and promote the future development of sea ice models.

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 2.0 of the W5E5 dataset covers the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2019. Data sources of W5E5 are version 2.0 of WATCH Forcing Data methodology applied to ERA5 data (WFDE5; Weedon et al., 2014; Cucchi et al., 2020), ERA5 reanalysis data (Hersbach et al., 2020), 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).

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Total precipitation: Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. The original ERA5-Land dataset (period: 2000 - 2020) has been reprocessed to: - aggregate ERA5-Land hourly data to daily data (minimum, mean, maximum) - while increasing the resolution from the native ERA5-Land resolution of 0.1 degree (~ 9 km) to 30 arc-sec (~ 1 km) by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate proportion of ERA5-Land / aggregated CHELSA 3. interpolate proportion with a Gaussian filter to 30 arc seconds 4. multiply the interpolated proportions with CHELSA Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA. Data available is the daily sum of precipitation. Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief) Original ERA5-Land dataset license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4 Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

Accurate snow depth datasets are of paramount importance for water resource management, comprehensive climate change assessments, and the sustainable development of the ice-and-snow economy. To create a high-resolution monthly snow depth dataset tailored for the Northern Hemisphere winter months (NHMSD), this study employed the Delta statistical downscaling method, in conjunction with a spatial feature transfer technique, to refine snow depth data derived from 21 major general circulation models and four shared socioeconomic pathways sourced from the CMIP6 project. The NHMSD stands as the world's pioneering long-term 0.05° snow depth dataset, encompassing the historical era from 1980 to 2014 and extending into future projections from 2015 to 2100. Validation using 2062 ground snow depth observations has confirmed that NHMSD outperforms reanalysis datasets, including ERA5-Land and GLDAS, in terms of root mean square error, bias, and mean absolute error for the periods 1980–2014 and 2015–2023.

Overview:
ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.

Total precipitation:
Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.

Processing steps:
The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:
1. spatially aggregate CHELSA to the resolution of ERA5-Land
2. calculate proportion of ERA5-Land / aggregated CHELSA
3. interpolate proportion with a Gaussian filter to 30 arc seconds
4. multiply the interpolated proportions with CHELSA
Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA.

The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis starting from Saturday for the time period 2016 - 2020.
Data available is the weekly average of daily sums and the weekly sum of daily sums of total precipitation.

File naming:
Average of daily sum: era5_land_prectot_avg_weekly_YYYY_MM_DD.tif
Sum of daily sum: era5_land_prectot_sum_weekly_YYYY_MM_DD.tif

The date in the file name determines the start day of the week (Saturday).

Pixel values:
mm * 10
Example: Value 218 = 21.8 mm

Coordinate reference system:
ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035)

Spatial extent:
north: 82:00:30N
south: 18N
west: 32:00:30W
east: 70E

Spatial resolution:
1km

Temporal resolution:
weekly

Period:
01/01/2016 - 12/31/2020

Lineage:
Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.

Software used:
GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)

Original ERA5-Land dataset license:
https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

CHELSA climatologies (V1.2):
Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4
Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

Other resources:
https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b

Format: GeoTIFF

Representation type: Grid

Processed by:
mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/)

Contact:
mundialis GmbH & Co. KG, info@mundialis.de

This data set is the annual grid data set of snow days in Mongolia from 1981 to 2020. The data format is TIF with a resolution of 0.1 ° × 0.1°。 According to the definition of Tejada and Gonzalez scholars, the snow day is the snow day when the snow depth reaches 0.5 cm. Snow days refer to the number of snow days in a year ( Tejada and Gonzalez，2006)。 This data set is processed by the reanalysis data era5 of the European Center for medium range weather forecasting. The long-time series snow days data can provide a reference for revealing the changes of snow cover in Mongolia.

This data set provides daily estimates of snow depth and snow density for snow-on-sea-ice in the Arctic Ocean over a 41-year period using a Lagrangian snow-evolution model forced with NASA’s Modern Era Retrospective-Analysis for Research Applications Version 2 (MERRA-2) and the European Centre for Medium Range Weather Forecasts (ECMWF) Reanalysis, generation 5 (ERA5).