This data set consists of ECMWF WRF T799 0.22deg Resolution Analysis model GRIB files for the period of the VOCALS project.

This series contains datasets related to the forecasting of a severe weather event, a derecho, in Poland on 11 August 2017. The simulations were conducted using the Weather Research and Forecasting (WRF) model version 4.2.1 with initial and boundary conditions from European Centre for Medium-Range Weather Forecasts (ECMWF). Simulation was performed for two starting hours: at 00:00 and 12:00 UTC. The datasets contain about 280 meteorological parameters stored as 2D or 3D fields in 3 domains with high-spatial (7.5 km, 2.5 km and 0.5 km domains) and temporal (1 hour, 10 minutes, 10 minutes) resolutions. The three-dimensional fields are calculated up to 50 hPa at 50 levels. All data are stored in easily accessible NetCDF files.

All the ECMWF operational analysis data forcing data (2008-2012) related to the thesis (https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/deliver/index/docId/81907/file/PhD_Zhang.pdf)

ERA-20C is ECMWF's first atmospheric reanalysis of the 20th century, from 1900-2010. It assimilates observations of surface pressure and surface marine winds only. It is an outcome of the ERA-CLIM project. ERA-20C was produced with IFS version CY38r1 and the same surface and atmospheric forcings as the final version of the atmospheric model integration ERA-20CM. A coupled atmosphere land surface and ocean wave model is used to reanalyze the weather, by assimilating surface observations. The ERA-20C products describe the spatial-temporal evolution of the atmosphere (on 91 vertical levels, between the surface and 0.01 hPa), the land-surface (on 4 soil layers), and the ocean waves (on 25 frequencies and 12 directions). The horizontal resolution is approximately 125 km (spectral truncation T159). Note that atmospheric data are not only available on the native 91 model levels, but also on 37 pressure levels (as in ERA-Interim), 16 potential temperature levels, and the 2 potential vorticity unit level. NCAR's Data Support Section (DSS) is performing and supplying a grid transformed version of ERA-20C, in which variables originally represented as spectral coefficients or archived on a reduced Gaussian grid are transformed to a regular 320 longitude by 160 latitude N80 Gaussian grid. In addition, DSS is also computing horizontal winds (u-component, v-component) from spectral vorticity and divergence where these are available. The data is being organized into single parameter time series. The assimilation methodology is 24-hour 4D-Var analysis, with variational bias correction of surface pressure observations. Analysis increments are at T95 horizontal resolution (approximately 210 km). The analyses provide the initial conditions for subsequent forecasts that serve as backgrounds to the next analyses. The spatial temporal evolution of background errors was provided by a 10-member ensemble produced a priori. The observations assimilated in ERA-20C include surface and mean...

A 4km resolution WRF domain over New Zealand seeded by the ECMWF Global Model

Summary This dataset contains postprocessed, high resolution (15 kilometers (km), 3 hours (h)) output from Polar Weather Research and Forecast (WRF) driven by Community Earth System Model (CESM) Large Ensemble Representative Concentration Pathway (RCP) 8.5 dataset, Jun-Jul-Aug, 2071-2080. These simulations used WRF-distributed ice sheet topography and General Circulation Model (GCM)-derived sea ice. Details These datasets are output from the regional forecast model Polar WRF (i.e., WRF-Advanced Research WRF (ARW) with polar modifications developed by the Polar Meteorology Group at The Ohio State University; Hines and Bromwich 2008; Skamarock et al 2008). Datasets are available as both original model output and as post-processed output, i.e, model output that has been processed into more user-friendly format with an National Center for Atmospheric Research (NCAR) Command Language (NCL) script. Post-processed files are also smaller after removing variables of lesser interest. All datasets are on a 15-km spatial grid with 39 vertical levels. Output is archived every 3 hours for the core melt-season months of June, July and August. WRF initial and boundary conditions were provided by three global datasets: the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA) Interim reanalysis (ECMWF, erai, Dee et al, 2011), the CESM Large Ensemble (NCAR, cesmle, Kay et al, 2015) and the CESM Low Warming Ensemble (NCAR, cesmlw, Sanderson et al 2016). Simulations cover two broad time periods: historical (or hindcast) and future (based on standard emissions scenarios). Future, GCM-based simulations cover a “high warming” scenario based on RCP 8.5 and a “low warming” scenario based on limiting future temperature increases to 1.5 degrees Celsius above pre-industrial levels. These simulations are limited to time periods where the driving variables required by WRF were archived for the GCM at sub-daily resolution. ERA Interim-based WRF simulations were done as overlapping 3-day runs with the first day discarded for spinup and the remaining days concatenated to produce the time series. These simulations used the Bootstrap Sea Ice dataset (Comiso 2000) from National Snow and Ice Data Center (NSIDC) for sea ice data. GCM-based WRF simulations were done as overlapping month-plus-one day runs with the first day discarded for spinup and the remaining days concatenated to produce the time series. These runs used upper level four dimensional data assimilation (grid nudging) to maintain the large- scale wave patterns from CESM during these long simulations. To be self-consistent, these runs used sea ice data from that component (Community Ice CodE (CICE)) of the GCM instead of the Bootstrap dataset. Model domain corner coordinates are: 55.2 North, 62.2 West (Southwest), 76.2 North, 117.2 West (Northwest), 76.2 North, 37.2 East (Northeast), 55.2 North, 17.8 West (Southeast). References Comiso, J. C. 2000, updated 2015. Bootstrap Sea Ice Concentrations from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I)-SSMIS, Version 2. Boulder, Colorado USA. National Aeronautics and Space Administration (NASA) National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/J6JQLS9EJ5HU. Dee, D.P., with 35 co-authors, 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart. J. R. Meteorol. Soc., 137, 553-597, doi: 10.1002/qj.828. Hines, K. M., and D. H. Bromwich, 2008: Development and testing of Polar WRF. Part I. Greenland ice sheet meteorology. Mon. Wea. Rev., 136, 1971-1989. Howat, I.M., A. Negrete, B.E. Smith, 2014: The Greenland Ice Mapping Project (GIMP) land classification and surface elevation datasets, The Cryosphere, 8, 1509-1518, doi:10.5194/tc-8-1509-2014. Kay, J. E., with 20 co-authors, 2015: CESM Large Ensemble Project: A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability, Bulletin of the American Meteorological Society, 96, 1333-1349, doi: 10.1175/BAMS-D-13-00255.1. Sanderson, B., B. O'Neill, and C. Tebaldi, 2016: What would it take to achieve the Paris temperature targets? Geophys. Res. Lett., doi: 10.1002/2016GL069563. Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., Duda, M. G., Huang, X.-Y., Wang, W., and Powers, J. G., 2008, A Description of the Advanced Research WRF Version 3 (No. NCAR/TN-475+STR). University Corporation for Atmospheric Research. doi:10.5065/D68S4MVH

ECMWF has implemented a significant resolution upgrade and methodology for high-resolution forecasts (HRES) and ensemble forecasts (ENS) beginning January of 2016. HRES is now performed via a transform grid with a nominal grid point spacing of 9 kilometers (0.08 degrees), and is carried out with IFS (Integrated Forecast System). Improvements in computational efficiency and effective resolution have been brought about by implementing a triangular cubic octahedral reduced Gaussian grid in which the shortest spatial wavelength is represented by at least four grid points anywhere on the globe, as opposed to the former linear arrangement whereby the shortest wavelength was represented by two grid points, while at the same time retaining the same number of spherical harmonics and triangular truncation. (The term "cubic" is due to the ability of the grid to represent cubic products in the dynamical equations.) In addition, the reduction of grid points along latitude circles as one approaches the poles is achieved using a triangular to octahedral mapping which corresponds to a poleward reduction of four points per latitude circle and an optimization of the total number of grid points and their local mesh resolution. ECMWF has documented superior filtering properties at higher resolution, an improved representation of orography, improved global mass conservation properties, substantial efficiency gains, and more scalable locally compact computations of derivatives and other properties that depend on nearest-neighbor information only. More details may be found in the publications cited below. NCAR's DECS is performing and supplying a grid transformed version of HRES IFS, in which variables originally represented as spectral coefficients or archived on a reduced Gaussian grid are transformed to a regular 5120 longitude by 2560 latitude N1280 Gaussian grid. In addition, DECS is also computing horizontal winds (u-component, v-component) from spectral vorticity and divergence...

Files used to set up the WRF-Hydro RAPID model, the ECMWF RAPID model, and the Streamflow Prediction Tool App.

Summary This dataset contains high resolution (15 kilometers (km), 3 hours (h)) output from Polar Weather Research and Forecast (WRF) driven by European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA), Jun-Jul-Aug, 1986-2015. These simulations used the Greenland Ice Mapping Project (GIMP) 90 meters (m) digital elevation model (DEM) from The Ohio State University (Howat et al 2014) for ice sheet topography and the Bootstrap Sea Ice dataset (NSIDC-0079). Details These datasets are output from the regional forecast model Polar WRF (i.e., WRF-Advanced Research WRF (ARW) with polar modifications developed by the Polar Meteorology Group at The Ohio State University; Hines and Bromwich 2008; Skamarock et al 2008). Datasets are available as both original model output and as post-processed output, i.e, model output that has been processed into more user-friendly format with an National Center for Atmospheric Research (NCAR) Command Language (NCL) script. Post-processed files are also smaller after removing variables of lesser interest. This dataset contains auxiliary files used in WRF simulations: option/parameter/namelist files, postprocessing scripts, Network Common Data Form (netCDF) file metadata (model output and postprocesed), and other supporting files. Summary of Auxiliary Files See specific folders within the tar archive for detailed information and files. 1. geogrid GEOGRID.TBL, modified GEOGRID.TBL.ARW to add new HGT_M entry for GIMP DEM. 2. intermediate files: conversion code and scripts a. Community Earth System Model (CESM) (from netCDF) README_CESM.pdf describes the CESM files needed to provide variables that WRF requires to run (as WRF Preprocessing System (WPS) intermediate format) and outlines processing steps to perform the conversion from netCDF. b. sea ice (from binary) README_ Sea ice.pdf describes the processing needed to convert Bootstrap Sea Ice v2 binary files to WPS intermediate format. 3. namelists a. WPS (geogrid/ungrib/metgrid) b. WRF (real/wrf) 4. Postproc Script: wrfout_postproc.ncl Utility scripts: make_wrf_postproc, run_wrf_postproc, wrf_postproc List of variables: other/wrfout_postproc_metadata.txt 5. Other metadata for WRF output files: wrfout_original_metadata.txt wrfout_postproc_metadata.txt

API access to NWP model data provided by MetraWeather

Files used to set up the WRF-Hydro RAPID model, the ECMWF RAPID model, and the Streamflow Prediction Tool App.

Contains WRF-ENS forecasts for the Arno River flooding event occurred in Italy in November 1966. Files format is binary with auxiliary description file.

This dataset contains model outputs and necessary files to replicate the simulations from the article "Simulating wake losses of the Danish Energy Island wind farm cluster" (https://doi.org/10.1088/1742-6596/2505/1/012015)

The modelling uses the Weather research and forecasting (WRF; https://github.com/wrf-model/WRF ) model using a wind farm parametrisation (EWP, https://doi.org/10.5194/gmd-8-3715-2015 ). Two modelling approaches are used: an ideal isolated case (one specific wind direction and wind speed) and real cases (two grid spacing) that cover the simulation of a full year (2016). The simulation aimed to compare methodologies for modelling very large offshore clusters and calculate their Anual Energy Production.

Input files To perform the simulations with WRF, input files are attached as zip files for real (modelfiles_real_*km) and ideal (modelfiles_ideal_1km) simulations. This zip file contains the namelist.input*, namelist.wps, and input_souding necessary to run the models on both modes and for the specified grid spacing (1 or 2 kilometres). For the real simulations, boundary conditions (ERA5 reanalysis) are needed and can be downloaded from ftp://ftp.ecmwf.int/pub/wrf. For ideal, the input_soundig file is the only requirement.

The files EnergyO.ideal, EnergyO.real, and GEN-15-236.turbine are provided to simulate the wind farms. The cluster comprises ten wind farms with 1 GW capacity. Each wind farm is simulated with 67 15 MW wind turbines. EnergyO.ideal, EnergyO.real are the locations for the ideal and real cases, respectively, and the thrust and power curves are given in GEN-15-236.turbine.

Model outputs files

The model's output is in NetCDF format (*.nc) and contains the wind speed ("WS") field and the power production (''POWER") generated by the wind farms. Quantities are expressed as temporal averages over 1 hour and 1 year (2016) for the ideal and real cases, respectively.

This dataset contains a range of parameters from a 1 km gridded output from runs of version 3.6.1 of the Weather Research and Forecasting (WRF) model deployed on the ARCHER UK National Supercomputing Service. These runs were part of the NERC funded BBUBL project (Biotelemetry/Bio-aerial-platforms for the Urban Boundary Layer - also known as City Flocks, NERC grant award NE/N003195/1). The domain of the model runs was over the set over Birmingham conurbation for all of 2015. This geo-temporal domain encompasses measurements of the urban boundary layer obtained from instrumentation attached to birds flown around the area. See related dataset. The WRF model set up followed that used by Heaviside et al. (2015) - see linked documentation for details - and was run on the ARCHER UK National Supercomputing Service. Meteorology data from the European Centre for Medium-range Weather Forecasts (ECMWF) ERA-interim reanalysis data for initial and lateral boundary conditions. The WRF v3.6.1 model set up implemented in this study included four nested domains. The domains had grid resolutions of 36 km x 36 km, 12 km x 12 km, 3 km x 3 km and 1 km x 1 km. The finest domain covered the West Midlands, centering over Birmingham. The multi-layer building energy parametrization (BEP) scheme with three land-use types (low-intensity residential, high-intensity residential and industrial/commercial) was also used.

This data set contains the hourly WRF-Chem v4.1.5 model output over two nested domains centered around Belgium. The inner domain has a horizontal resolution of 3 x 3 km and covers Belgium and part of its neighbouring countries. The parent domain has a horizontal resolution of 9 x 9 km and covers a large part of Western Europe. The simulation period is from 1 June 2018 until 1 September 2018. The model output files are in NetCDF format and contain a wide range of meteorological variables as well as concentration fields for CO2, CH4 and CO split in different tracers depending on the source sector: background, anthropogenic, biogenic, ocean etc. The meteorological and chemical initial and lateral boundary conditions are taken from ECMWF ERA5 and CAMS reanalysis (egg4, eac4). Anthropogenic emissions are from EDGAR v7.0 for CO2 and CH4, while they are from EDGAR v6.1 for CO. Biomass buring emissions are from the Fire INventory from NCAR (FINN v2.5, Wiedinmyer at al. 2023)), CH4 wetlands emissions are from WetCHARTs (Bloom et al., 2017) and CO2 ocean exchange from Landschützer et al. (2019) (https://doi.org/10.25921/9hsn-xq82). Remark that there was an offset added to the uptake tracer fields: 50 ppm for CO2_BIO and CO2_OCE, and 1.8 ppm for CH4_BIO. This should be subtracted when processing the data. The corresponding namelist.input text file is also provided. These simulations have been made in the context of the BELSPO BRAIN 2.0 project “VERBE” (Towards a greenhouse gas emission monitoring and VERification system for BElgium, https://verbe.aeronomie.be/ , 2022-2026). Size: 7 GB per day

While climate information from General Circulation Models (GCMs) are usually too coarse for climate impact modelers or decision makers from various disciplines (e.g., hydrology, agriculture), Regional Climate Models (RCMs) and Regional Earth System Models (RESMs) provide feasible solutions for downscaling GCM output to finer spatiotemporal scales. However, it is well known that the model performance depends largely on the choice of the physical parameterisation schemes, but optimal configurations may vary from region to region. Besides land-surface processes, the most crucial processes to be parameterised in ESMs include radiation (RA), cumulus convection (CU), cloud microphysics (MP), and planetary boundary layer (PBL), partly with complex interactions. Before conducting long-term climate simulations, it is therefore indispensable to identify a suitable combination of physics parameterisation schemes for these processes. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product ERA-Interim as lateral boundary conditions, we derived an ensemble of 16 physics parameterisation runs for a larger domain in Northern sub-Saharan Africa (NSSA), northwards of the equator, using two different CU, MP-, PBL-, and RA schemes, respectively, using the Weather Research and Forecasting (WRF) model (version v3.9) for the period 2006-2010 in a resolution of 0.1 degree horizontal resolution.

Conclusions about suitable physical parameterisation schemes may vary within the study area. We therefore want to stimulate the development of own performance evaluation studies for climate simulations or subsequent impact studies over specific (sub-)regions in NSSA. For this reason, selected climate surface variables of the physics ensemble (i.e. the 16 experiments from 2006-2010) are provided.

For more information about the setup of the experiments, please see: Laux et al., 2021: A high-resolution regional climate model physics ensemble for Northern sub-Saharan Africa. Frontiers in Earth Science (under revision).

The large-scale Land-Uses and Land-Cover Changes (LULCC) in India in the past several decades is primarily driven by anthropogenic factors that influence the climate from regional to global scales. Therefore, to understand the LULCC over the Indian region from 2002 to 2015 and its implications on temperature and precipitation, we performed Weather Research Forecast (WRF) model simulation using the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis data for the period 2009 to 2015 as a boundary condition with 2009 as spin-up time. The results showed moderate forest cover loss in major parts of northeast India, and the Himalayan region during 2002–2015. Such large LULC changes, primarily significant alteration of grassland and agriculture from the forest, led to increased precipitation due to increasing evapotranspiration (ET) similar to the forest-dominated regions. An increase in the precipitation patterns (>300 mm) was observed in the parts of eastern and western Himalayas, western Ghats, and the northwestern part of central India, while most parts of northeast Himalayas have an exceptional increase in precipitation (∼100–150 mm), which shows similar agreement with an increase of leaf area index (LAI) by ∼15%. The overall phenomenon leads to a greening-induced ET enhancement that increases atmospheric water vapor content and promotes downwind precipitation. In the case of temperature, warming was observed in the central to eastern parts of India, while cooling was observed in the central and western parts. The increase in vegetated areas over northwest India led to an increase in ET, which ultimately resulted in decreased temperature and increased precipitation. The study highlights the changes in temperature and precipitation in recent decades because of large LULCC and necessitates the formulation of sustainable land use-based strategies to control meteorological variability and augment ecological sustainability.

This dataset includes global bias-corrected climate model output data from version 1 of NCAR's Community Earth System Model (CESM1) that participated in phase 5 of the Coupled Model Intercomparison Experiment (CMIP5), which supported the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). The dataset contains all the variables needed for the initial and boundary conditions for simulations with the Weather Research and Forecasting model (WRF) or the Model for Prediction Across Scales (MPAS), provided in the Intermediate File Format specific to WRF and MPAS. The data are interpolated to 26 pressure levels and are provided in files at six hourly intervals. The variables have been bias-corrected using the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim) fields for 1981-2005, following the method in Bruyere et al. (2014) [http://dx.doi.org/10.1007/s00382-013-2011-6]. Files are available for a 20th Century simulation (1951-2005) and three concomitant Representative Concentration Pathway (RCP) future scenarios (RCP4.5, RCP6.0 and RCP8.5) spanning 2006-2100. NOTE: There are no bias-corrected data for RCP2.6, due to corrupted data caused by a model bug in CESM. Note to Microsoft Windows users: The executable metgrid.exe, which is required to ingest this data into WPS/WRF, is not compatible with Windows and can only be run in a Linux environment. It is recommended, therefore, that this dataset be used in Linux environments only.

While climate information from General Circulation Models (GCMs) are usually too coarse for climate impact modelers or decision makers from various disciplines (e.g., hydrology, agriculture), Regional Climate Models (RCMs) provide feasible solutions for downscaling GCM output to finer spatiotemporal scales. However, it is well known that the model performance depends largely on the choice of the physical parameterization schemes, but optimal configurations may vary e.g., from region to region. Besides land-surface processes, the most crucial processes to be parameterized in RCMs include radiation (RA), cumulus convection (CU), cloud microphysics (MP), and planetary boundary layer (PBL), partly with complex interactions. Before conducting long-term climate simulations, it is therefore indispensable to identify a suitable combination of physics parameterization schemes for these processes. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product ERA-Interim as lateral boundary conditions, we derived an ensemble of 16 physics parameterization runs for a larger domain in Northern sub-Saharan Africa (NSSA), northwards of the equator, using two different CU-, MP-, PBL-, and RA schemes, respectively, using the Weather Research and Forecasting (WRF) model for the period 2006–2010 in a horizontal resolution of approximately 9 km. Based on different evaluation strategies including traditional (Taylor diagram, probability densities) and more innovative validation metrics (ensemble structure-amplitude-location (eSAL) analysis, Copula functions) and by means of different observation data for precipitation (P) and temperature (T), the impact of different physics combinations on the representation skill of P and T has been analyzed and discussed in the context of subsequent impact modeling. With the specific experimental setup, we found that the selection of the CU scheme has resulted in the highest impact with respect to the representation of P and T, followed by the RA parameterization scheme. Both, PBL and MP schemes showed much less impact. We conclude that a multi-facet evaluation can finally lead to better choices about good physics scheme combinations.

Abstract

This database compiles the outputs of the global experiment performed with the Lagrangian particle dispersion model FLEXPART since 1980. The experiment was conducted using the ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) and homogeneously dividing the atmosphere into 30 million particles. The database can be used to investigate global moisture and heat transport and to establish sink-source relationships.

Input data

The data employed for FLEXPART running was the ERA5 reanalysis dataset from the ECMWF (Hersbach et al., 2020). To feed the model, the input data was downloaded and pre-processed by using the software Flex_extract v7.1 (Tipka et al., 2020).

The original available ERA5 resolution is 0.1-degree and 1-hour. For this experiment, ERA5 input data was retrieved for the global area (90ᵒS to 90ᵒN and 180ᵒW to 180ᵒE) at a 0.5-degree horizontal resolution for 137 level from the surface to 1 hPa and a 3-hour temporal resolution (00, 03, 06, 09, 12, 15,18 and 21 UTC).

The data is stored in individual GRIB files for each time step, following the name criteria "EAYYMMDDHH". The size of each file is approximately 530 MB. The variables included in each file are: temperature, specific humidity, u- and v-wind components, Eta-coordinate vertical velocity, divergence, specific cloud liquid water content, specific cloud ice water content, and the logarithm of surface pressure on model levels; and 2m temperature an dew-point temperature, 10 m u and v wind component, geopotential, land-sea mask, mean sea level pressure, snow depth, the standard deviation of orography, surface pressure, total cloud cover, convective precipitation, large-scale precipitation, surface sensitive heat flux, eastward and northward turbulent surface stress and surface net solar radiation at the surface level.

Software and running

The software used for the simulations is the Lagrangrian particle dispersion model FLEXPART on version 10.4 (Pisso et al., 2019). The software is configured for a global experiment, and the simulations were obtained from 1980 to the present with a temporal resolution of 3-h. For the experiment, 30 million particles were homogeneously distributed on the global area, and their trajectories were followed according to the model configuration specified in the COMMAND and RELEASES files. The complete period is distributed in individual annual experiments, with each annual experiment obtained continuously running the model from October of the previous year to December of that year.

Outputs characteristics

The outputs were stored in individual GRIB files for each time step, with the file name following the naming convention "partposit_YYYYMMDDHH". Each file has a size of 1,76 GB, and the total size of the annual experiment is 6 TB. Each file contains information about each particle of the experiment: the particle identification number (particle ID), the particle's position (latitude, longitude, and altitude), topographic height, potential vorticity, specific humidity, air density, atmospheric boundary layer height, and temperature. The file corresponding to the 1st January 2023 at 00UTC is provided in this repository as an example. Due to the size of each file, the complete dataset is accessible by personal contact (see Data Access section).

Post-process and applications

The dataset presented here allows for the analysis of moisture and heat transport in the atmosphere for any region of the world up to 3-h temporal resolution and different horizontal resolutions. The transport may be established between sources and sinks, both in a forward or backward tracking in time. Currently, two open-source post-processing options developed within the EPhyslab-UVigo group are available for the analysis of these data: TROVA (Fernadez-Alvarez et al., 2022) and LATTIN (Perez-Alarcón et al., 2024) with different moisture tracking calculation options, and the latter including tools for heat transport analysis. Both options allow different methodologies (those most widely used) for the moisture transport analysis. The studies can be configured for any region of the planet, specifying it by a NetCDF 2-D mask, and the moisture transport can be set for different time periods (from 1 to 15 days, being from 8 to 10 days the periods most commonly applied according to the mean residence time of water vapor in the atmosphere). For further discussion on the residence time of water vapor in the atmosphere and its application for Lagrangian studies see Gimeno et al. (2021) and Nieto and Gimeno (2019).

Example of application

J. C. Fernández-Álvarez, M. Vázquez, A. Pérez-Alarcón, R. Nieto, L. Gimeno (2023) Comparison of moisture sources and sinks estimated with different versions of FLEXPART and FLEXPART-WRF models forced with ECMWF reanalysis data, Journal of Hydrometeorology, doi: 10.1175/JHM-D-22-0018.1.

A. Pérez-Alarcón, R. Sorí, M. Stojanovic, M. Vázquez, R.M. Trigo, R. Nieto, L. Gimeno (2024) Assessing the Increasing Frequency of Heat Waves in Cuba and Contributing Mechanisms, Earth Systems and Environment, DOI: 10.1007/s41748-024-00443-8

Validation

The moisture transport analysis provided by this dataset was validated by Fernández-Alvarez et al. (2023) through an in-depth comparison with different versions of the model, horizontal resolutions and input data, including the ERA-Interim reanalysis from the ECMWF, which has been widely used for this purpose over the past decades.

Data Access

Data access is available by contacting the EPhysLab group via: rnieto[at]uvigo.gal or l.gimeno[at]uvigo.gal