NetCDF Format Research Articles

A database of deep convective systems derived from the intercalibrated meteorological geostationary satellite fleet and the TOOCAN algorithm (2012–2020)

Abstract. We introduce two databases, TOOCAN (Tracking Of Organized Convection Algorithm using a 3D segmentatioN) and CACATOES, aimed at facilitating the study of deep convective systems (DCSs) and their morphological characteristics over the intertropical belt during the period spanning from 2012 to 2020. The TOOCAN database is constructed using a tracking algorithm called TOOCAN applied on a homogenized GEOring infrared (IR) archive and enables the documentation of the morphological parameters of each DCS throughout their life cycles. The homogenized GEOring IR database has been built from level-1 data of a fleet of geostationary platforms originating from various sources and has been intercalibrated; spectrally adjusted; and limb-darkening corrected, specifically for high cold clouds, based on a common reference, the IR channel of the Scanner for Radiation Budget (ScaRaB) radiometer on board the Megha-Tropiques. The resulting infrared observations are then homogeneous for brightness temperatures (BT) <240 K, with a standard deviation lower than 1.5 K, throughout the GEOring. A systematic uncertainty analysis is carried out. First, the radiometric errors are shown to have little impact on the DCS characteristics and occurrences. We further evaluate the impact of missing data and demonstrate that a maximum of 3 h of consecutive missing images represents a favorable compromise for maintaining tracking continuity while minimizing the impact on the DCS morphological parameters. However, beyond this temporal threshold, the segmentation of DCS is significantly compromised, necessitating the interruption of the tracking process. The CACATOES database is derived from the TOOCAN database through a post-processing procedure, which involves projecting the morphological parameters of each deep convective system (DCS) onto a daily 1° × 1° grid. The resultant dataset provides a broader perspective, allowing for an Eulerian analysis of the DCS and facilitating comparisons with auxiliary gridded datasets on the same daily 1° × 1° grid box. Both the TOOCAN and CACATOES databases are provided in a common netCDF format that is compliant with the standards of Climate and Forecast (CF) conventions and the Attribute Convention for Dataset Discovery (ACDD). A total of 15×106 DCSs have been identified over the tropical regions and the 9-year period. The analysis of DCSs over the tropical oceans and continents reveals a large variety of DCS characteristics and organizations. They can last from few hours up to several days, and their cloud shield ranges from 1000 km2 to a few millions of squared kilometers. Oceanic DCSs are characterized by a longer lifetime duration and larger shields. Finally, the DCS geographical distribution is in line with previous DCS climatologies built from other algorithms and satellite observations. All datasets can be accessed via the repository under the following data DOIs: TOOCAN database: https://doi.org/10.14768/1be7fd53-8b81-416e-90d5-002b36b30cf8 (Fiolleau and Roca, 2023a) CACATOES database: https://doi.org/10.14768/98569eea-d056-412d-9f52-73ea07b9cdca (Fiolleau and Roca, 2023b).

FUME 2.0 – Flexible Universal processor for Modeling Emissions

Abstract. This paper introduces FUME 2.0, an open-source emission processor for air quality modeling, and documents the software structure, capabilities, and sample usage. FUME provides a customizable framework for emission preparation tailored to user needs. It is designed to work with heterogeneous emission inventory data, unify them into a common structure, and generate model-ready emissions for various chemical transport models (CTMs). Key features include flexibility in input data formats, support for spatial and temporal disaggregation, chemical speciation, and integration of external models like MEGAN. FUME employs a modular Python interface and PostgreSQL/PostGIS backend for efficient data handling. The workflow comprises data import, geographical transformation, chemical and temporal disaggregation, and output generation steps. Outputs for mesoscale CTMs CMAQ, CAMx, and WRF-Chem and the large-eddy-simulation model PALM are implemented along with a generic NetCDF format. Benchmark runs are discussed on a typical configuration with cascading domains, with import and preprocessing times scaling near-linearly with grid size. FUME facilitates air quality modeling from continental to regional and urban scales by enabling effective processing of diverse inventory datasets.

Lagrangian surface drifter observations in the North Sea: an overview of high-resolution tidal dynamics and surface currents

Abstract. A dataset of 85 Lagrangian surface drifter trajectories covering the central North Sea area and the Skagerrak from 2017–2021 of 17 deployments is presented. The data have been quality-controlled, uniformly structured, and assimilated in a standard NetCDF format (https://doi.org/10.1594/PANGAEA.963166, Meyerjürgens et al., 2023a). Using appropriate methods presented in detail here, surface currents were calculated from the drifter position data. Based on a drifter deployment in the Skagerrak, it is demonstrated that the Lagrangian measurements can be converted into an Eulerian representation by calculating mean current velocities. Tidal energy spectra were analyzed separately for the southern and northern areas of the North Sea, and tidal ellipses were calculated to determine the tidal impact on surface currents. Significant differences between the shallow shelf and the deeper areas of the North Sea are evident. While the shallow nearshore areas are dominated by tidal currents, deeper areas such as the Skagerrak record a high mean residual circulation driven by high-density gradients. Measurements using Eulerian approaches and remote sensing methods are restricted in temporal and spatial coverage, in particular, to capture fine-scale dynamics. For this reason, Lagrangian measurements, to a large extent, provide new insights into the complex submesoscale dynamics of the North Sea. Exemplarily, the Skagerrak region is used to demonstrate that high-resolution drifter observations capture both mesoscale and small-scale current patterns. This unique dataset, covering the entire southeastern North Sea and the Skagerrak, offers further analysis possibilities and can be used for the investigation of various hydrodynamic and environmental issues, e.g., the analysis of submesoscale current dynamics at ocean fronts, the determination of the kinetic eddy energy, and the propagation of pollutants in the North Sea.

The digital climate atlas of the Canary Islands: A tool to improve knowledge of climate and temperature and precipitation trends in the Atlantic islands

This paper presents the Interactive Digital Climate Atlas of the Canary Islands, a web portal based on geographic information systems for the dissemination of climate information. It provides citizens and public and private administrations with high spatial resolution maps (100 m) of different climate variables such as temperature, rainfall, relative humidity, cloudiness frequency and wind velocity. Also available are Köppen climate classification maps and decadal trends of temperature and precipitation in a context of climate change. This spatial resolution is suitable for the detailed climatic description of small and orographically complex islands. For this purpose, climate series from databases of different public institutions as well as data from remote sensing and reanalysis systems were employed. A statistical process involving data filtering and the detection and correction of inhomogeneous segments was carried out as a preparatory step. Then, a spatial interpolation model was developed using a multiple linear regression method for the generation of the high spatial resolution climate cartography. The results are annual and monthly maps of the climate variables in Canary Islands at 100 m spatial resolution and with different time periods depending on the availability of data on the different variables, but in most cases between 1991 and 2020. Following the European Directive INSPIRE (Directive 2007/2/CE, Infrastructure for Spatial Information in Europe), both map visualisation and downloading in GeoTIFF and NetCDF format are permitted, as well as consultation of the data and metadata of the climatic series used.

Lagoon hydrodynamics of pearl farming atolls: the case of Raroia, Takapoto, Apataki and Takaroa (French Polynesia)

Abstract. Between 2018 and 2022, four pearl farming Tuamotu atolls of French Polynesia were monitored with autonomous oceanographic instruments to measure the hydrodynamics of atoll lagoons and the ocean–lagoon water exchanges. These surveys were conducted within the framework of the ANR-funded MANA (Management of Atolls) project and its extensions to additional sites. The overarching goal was to improve knowledge on the processes influencing the spat collection of the pearl oyster Pinctada margaritifera, the oyster species used to produce black pearls. These data sets are also critical for the calibration and validation of 3D high spatial resolution hydrodynamic models used to study oyster larval dispersal within lagoons. The observational strategies focused on the characterization of ocean–lagoon exchanges through passes and hoa (i.e., shallow reef flats), lagoon circulation, incident waves breaking on the forereef, water elevation inside the lagoon as well as spatial temperature variability. Chronologically, the investigated atolls were first Raroia Atoll with 9 months of measurements between May 2018 and March 2019 during which the MALIS1 and MALIS2 cruises on-board the R/V ALIS took place. It was followed by a 4 month deployment in Takapoto Atoll (November 2021 to March 2022). In late April 2022, Apataki Atoll was instrumented until end of July, followed by Takaroa measurements between July and October 2022. Apataki (Leg2) and Takaroa Atoll were conjointly instrumented during the MALIS 3 oceanographic cruise. Altogether, those multi-atoll data bring a worldwide unique oceanographic atoll data set that is useful for addressing local pearl farming questions but potentially beneficial for other fundamental and applied investigations. Each data set was post-processed, quality controlled and converted into NetCDF format. Files are available in open source into dedicated repositories in the SEANOE marine data platform. Links (DOI) of individual data sets are provided in Table 2.

Analysis of Projected Temperature Changes in Aceh Province

The objective is to analyse temperature changes and their future projection in Aceh. The activities consist of collecting past and future temperature data, preparing materials for processing, and analyzing past and future temperature data (climate change projections). The data used are monthly average temperature data from data global climate model, e.g., csiromk3.6-hist-1986-2005-tas, csiromk3.6-rcp45- 2016-2035-tas, csiromk3.6-rcp45-2046-2065-tas, csiromk3.6-rcp45-2081-2100- tas, csiromk3.6-rcp85-2016-2035-tas, csiromk3.6-rcp85-2046-2065-tas, and csiromk3.6-rcp85-2081-2100-tas. The study began with reading climate data in NetCDF format using GRADS software, data processing using CDO software, providing a coordinate system using QGIS software, making climate change projection maps using ArcGIS software, and making climate change graphs using spreadsheet programs. Two scenarios, i.e., RCP4.5 and RCP8.5 are used to analyse the projected temperature changes in the short-term (2016 – 2035), medium-term (2046-2065), and long-term (2081-2100). The results show that the RCP4.5 projection shows a lower change in temperature rise than the RCP 8.5. A change in a temperature rise of up to 5°C was found in the RCP8.5 scenario.

Past and future discharge and stream temperature at high spatial resolution in a large European basin (Loire basin, France)

Abstract. This paper presents retrospective simulations (1963–2019) and future projections (1976–2100) of daily time series of discharge and stream temperature for 52 278 reaches (median length of 1.3 km) over the Loire River basin (105 km2) in France, using a physical process-based thermal model coupled with a semi-distributed hydrological model. Retrospective simulations are based on the 8 km gridded Safran meteorological reanalysis over France. Twenty-first century projections are based on a subset of the 8 km gridded and bias-corrected DRIAS-2020 dataset over France. The discharge and stream temperature dataset stands out from existing ones thanks to its large scale and its high spatial resolution and the use of a physical process-based thermal model. The whole dataset is freely available and can be downloaded in NetCDF format from https://doi.org/10.57745/LBPGFS (Seyedhashemi et al., 2022a).

FloPy Workflows for Creating Structured and Unstructured MODFLOW Models.

FloPy is a Python package for creating, running, and post-processing MODFLOW-based groundwater flow and transport models. FloPy functionality has expanded to support the latest version of MODFLOW (MODFLOW 6) including support for unstructured grids. FloPy can simplify the process required to download MODFLOW-based and other executables for Linux, MacOS, and Windows operating systems. Expanded FloPy capabilities include (1) full support for structured and unstructured spatial discretizations; (2) geoprocessing of spatial features and raster data to develop model input for supported discretization types; (3) the addition of functionality to provide direct access to simulated output data; (4) extension of plotting capabilities to unstructured MODFLOW 6 discretization types; and (5) the ability to export model data to shapefiles, NetCDF, and VTK formats for processing, analysis, and visualization by other software products. Examples of using expanded FloPy capabilities are presented for a hypothetical watershed. An unstructured groundwater flow and transport model, with several advanced stress packages, is presented to demonstrate how FloPy can be used to develop complicated unstructured model datasets from original source data (shapefiles and rasters), post-process model results, and plot simulated results.

Idealized hydrodynamical numerical model dataset with no-river runoff at the western tropical North Atlantic.

The western tropical North Atlantic (WTNA) is a very complex region, with the influence of intense western boundary currents in connection with equatorial zonal currents, important atmospheric forcings (e.g Intertropical Convergence Zone), mesoscale activities (e.g NBC rings), and the world's largest river discharge as the Amazon River runoff. The volume discharge is equivalent to more than one-third of the Atlantic river freshwater input, with a plume that spreads over the region reaching the northwestward Caribbean Sea and eastward longitudes of 30°W, andinfluencing from physical to biological structures. Therefore, in order to enable and encourage more understanding of the region, here we present a dataset based on an idealized scenario of no river runoff of the Amazon River and Par ´a River in the WTNA. The numerical simulations were conducted with a regional oceanicmodeling system (ROMS) model and three pairs of files were generated with themodel outputs: (i) ROMS-files, with the parameters of the ROMS-outputs raw data in a NetCDF format and monthly and weekly frequencies; (ii) MATLAB-files, which contain oceanographic parameters also in monthly and weekly frequencies; and (iii) NetCDF-files, with oceanographic parameters again in monthly and weekly frequencies. For each file, we present the coordinates and variable names, descriptions, and correspondent units. The dataset is available in the Science Data Bank repository (doi: https://doi.org/10.57760/sciencedb.02145).

Sea surface height anomaly and geostrophic current velocity from altimetry measurements over the Arctic Ocean (2011–2020)

Abstract. Satellite altimetry missions flying over the ice-covered Arctic Ocean have opened the possibility of further understanding changes in the ocean beneath the sea ice. This requires complex processing of satellite signals emerging from the sea surface in leads within the sea ice, with efforts to generate consistent Arctic-wide datasets of sea surface height ongoing. The aim of this paper is to provide and assess a novel gridded dataset of sea surface height anomaly and geostrophic velocity, which incorporates both the ice-covered and open ocean areas of the Arctic. Data from the CryoSat-2 mission in the period 2011–2020 were gridded at monthly intervals, up to 88∘ N, using the Data-Interpolating Variational Analysis (DIVA) method. To examine the robustness of our results, we compare our dataset to independent satellite data, mooring time series and Arctic-wide hydrographic observations. We find that our dataset is well correlated with independent satellite data at monthly timescales. Comparisons to in situ ocean observations show that our dataset provides reliable information on the variability of sea surface height and surface geostrophic currents over geographically diverse regions of the Arctic Ocean and different dynamical regimes and sea ice states. At all comparison sites we find agreement with in situ observed variability at seasonal to interannual timescales. Furthermore, we find that our geostrophic velocity fields can resolve the variability of boundary currents wider than about 50 km, a result relevant for studies of Arctic Ocean circulation. Additionally, large-scale seasonal features emerge. Sea surface height exhibits a wintertime Arctic-wide maximum, with the highest amplitude over the shelves. Also, we find a basin-wide seasonal acceleration of Arctic slope currents in winter. We suggest that this dataset can be used to study not only the large-scale sea surface height and circulation, but also the regionally confined boundary currents. The dataset is available in netCDF format from PANGAEA at https://doi.org/10.1594/PANGAEA.931869 (Doglioni et al., 2021d).

A database of marine macronutrient, temperature and salinity measurements made around the highly productive island of South Georgia, the Scotia Sea and the Antarctic Peninsula between 1980 and 2009

Abstract. We present a database from substantial collections of macronutrient data made on 20 oceanographic cruises, primarily from around the island of South Georgia and the Scotia Sea. This sector of the Southern Ocean was studied comprehensively during the Discovery Investigations between ∼1920 and 1950 and resulted in the hugely influential Discovery Reports. Following this pioneering research period, there was a lull of several decades prior to the British Antarctic Survey (BAS) initiation of an offshore biological programme to study the ecology of the South Atlantic sector of the Southern Ocean. These studies began in the late 1970s and have continued until the present day. Between 1980 and 2009, the programme included macronutrient measurements as part of an integrated ecosystem analysis. In addition to South Georgia and the Scotia Sea, measurements were also made in the Bellingshausen Sea and the waters to the west of the Antarctic Peninsula. Data were collected during all months of the year with the exceptions of May and June and compiled into a database. Vertical profile samples were taken from water bottles, while data along transects were collected by monitoring the ship's non-toxic seawater supply. Nutrients measured were silicate (Si(OH)4–Si), phosphate (PO4–P), nitrate (NO3–N), ammonium (NH4–N), and nitrite (NO2–N). Our database includes nutrient data along with contemporaneous temperature and salinity data where available. Further background and supporting information is included for context. The profile and underway sample data have been deposited at the UK Polar Data Centre and are publicly available in NetCDF and CSV formats from https://doi.org/10.5285/4014370F-8EB2-492B-A5F3-6DC68BF12C1E (Whitehouse et al., 2022).

Hydrodynamic and hydrological processes within a variety of coral reef lagoons: field observations during six cyclonic seasons in New Caledonia

Abstract. From 2014 to 2021 during the cyclone seasons, extensive monitoring of the hydrodynamics within a variety of lagoons of New Caledonia was conducted as a part of the PRESENCE project (PRESsures on coral Ecosystems of New CalEdonia). The PRESENCE project is aimed at building an efficient representation of the land–lagoon–ocean continuum at Grande Terre, New Caledonia's main island. Overall, coastal physical observations encompassed five different lagoons (four of which were never before monitored) and at least eight major atmospheric events ranging from tropical depressions to category 4 cyclones. The main objectives of this study were to characterize the processes controlling the hydrodynamics and hydrology of these lagoons (e.g., ocean–lagoon exchanges, circulation, level dynamics, temperature, and salinity variability), and to capture the magnitude of change during extreme events. An additional objective was to compile an adequate data set for future use in high-resolution hydrodynamic models. Autonomous oceanographic instruments were moored at strategic locations to collect time series of temperature, salinity, pressure, and Eulerian currents. Additionally, Lagrangian surface currents were observed through deploying drifter buoys, and cross-shore hydrological profile radials were carried out using CTDs (conductivity, temperature, depth). In total, five survey campaigns were conducted, beginning with the SPHYNX campaign which lasted 15 months (December 2014 to February 2016) in the Hienghène–Touho lagoon and ended with the 9 months NEMO campaign (September 2020 to April 2021) in Moindou lagoon. Between these were the 5 months NOUMEA campaign (December 2016 to April 2017) in Noumea lagoon, the 6 months ELADE campaign (February to August 2018) in the Poe lagoon, and the 5 months CADHYAK campaign (December 2019 to May 2020) in Koumac lagoon. In addition to characterizing these lagoons, the data set identifies important features and processes, such as the presence of internal waves on forereefs, wave-driven fluxes over reef barriers, and exchanges through passes. Signatures from strong events were also identified, including surges, thermal drops inside lagoons, and massive flash flood plume dispersion. Raw data sets were processed, controlled for quality, validated, and analyzed. Processed files are made publicly available in dedicated repositories on the SEANOE marine data platform in NetCDF format. Links (DOI) of individual data sets are provided herein.

Neighborhood Emission Mapping Operation (NEMO): A 1-km anthropogenic emission dataset in the United States

We present an unprecedented effort to map anthropogenic emissions of air pollutants at 1 km spatial resolution in the contiguous United States (CONUS). This new dataset, Neighborhood Emission Mapping Operation (NEMO), is produced at hourly intervals based on the United States Environmental Protection Agency (US EPA) National Emission Inventories 2017. Fine-scale spatial allocation was achieved through distributing the emission sources using 108 spatial surrogates, factors representing the portion of a source in each 1 km grid. Gaseous and particulate pollutants are speciated into model species for the Carbon Bond 6 chemical mechanism. All sources are grouped in 9 sectors and stored in NetCDF format for air quality models, and in shapefile format for GIS users and air quality managers. This dataset shows good consistency with the USEPA benchmark dataset, with a monthly difference in emissions less than 0.03% for any sector. NEMO provides the first 1 km mapping of air pollution over the CONUS, enabling new applications such as fine-scale air quality modeling, air pollution exposure assessment, and environmental justice studies.

GSPy: A new toolbox and data standard for Geophysical Datasets

The diversity of geophysical methods and datatypes, as well as the isolated nature of various specialties (e.g., electromagnetic, seismic, potential fields) leads to a profusion of separate data file formats and documentation conventions. This can hinder cooperation and reduce the impact of datasets researchers have invested in heavily to collect and prepare. An open, portable, and well-supported community data standard could greatly improve the interoperability, transferability, and long-term archival of geophysical data. Airborne geophysical methods particularly need an open and accessible data standard, and they exemplify the complexity that is common in geophysical datasets where critical auxiliary information on the survey and system parameters are required to fully utilize and understand the data. Here, we propose a new Geophysical Standard, termed the GS convention, that leverages the well-established and widely used NetCDF file format and builds on the Climate and Forecasts (CF) metadata convention. We also present an accompanying open-source Python package, GSPy, to provide methods and workflows for building the GS-standardized NetCDF files, importing and exporting between common data formats, preparing input files for geophysical inversion software, and visualizing data and inverted models. By using the NetCDF format, handled through the Xarray Python package, and following the CF conventions, we standardize how metadata is recorded and directly stored with the data, from general survey and system information down to specific variable attributes. Utilizing the hierarchical nature of NetCDF, GS-formatted files are organized with a root Survey group that contains global metadata about the geophysical survey. Data are then organized into subgroups beneath Survey and are categorized as Tabular or Raster depending on the geometry and point of origin for the data. Lastly, the standard ensures consistency in constructing and tracking coordinate reference systems, which is vital for accurate portability and analysis. Development and adoption of a NetCDF-based data standard for geophysical surveys can greatly improve how these complex datasets are shared and utilized, making the data more accessible to a broader science community. The architecture of GSPy can be easily transferred to additional geophysical datatypes and methods in future releases.

A dataset of Oceanographic and biogeochemical anomalies in the Caribbean Sea.

This article describes six ocean datasets consistent in anomalies of biogeochemical, physical, sea wave, biological, oceanic and chemical parameters (DACS-BGC, DACS-PHY, DACS-WAVE, DACS-BIO, DACS-OCE and DACS-CHEM) in several time scales from 3-hourly to monthly frequencies, either on the sea surface, downward/upward fluxes between the ocean and the atmosphere and the water column in the Caribbean basin (Gulf of Mexico, Caribbean Sea and Atlantic Ocean) in a geographical domain from latitudes 8 degrees to 35 degrees North and from longitudes 45 degrees to 100 degrees West, obtained, from several satellites, modeling services and observational programs. The datasets were created in NetCDF format conserving their original horizontal resolutions of 1.0, 0.5, 0.26, 0.08333 and 0.04 degrees in gridded structure; only the WAVEWATCH3 dataset has a non-uniform step in latitude and longitude. This internal data structure facilitates its handling due to a wide diversity of existent freeware tools, and it is mainly intended to support researchers to understand the evolution and cycles of physical, biogeochemical, chemical, sea wave, oceanic and biological parameters linked to global climate change.

Development of Spatial Peatland Fire Danger Index Using Coupled SWAT-MODFLOW Model

The Keetch–Byram Drought Index (KBDI) is a numerical value reflecting the dryness of the top layer of soils, deep forest litter, logs, and living vegetation. The KBDI is expressed as a scale from 0 to 200, where the number represents the amounts of rainfall (in millimeters) to return the soil to saturation. We proposed a method to integrate peatland groundwater as a key variable for the peatland forest fire hazard, and we called it mKBDI. The groundwater table was obtained from the SWAT-MODFLOW model simulation. The MODFLOW model uses a 200 m × 200 m grid. The SWAT-MODFLOW model was calibrated and validated using daily water level measurements in the river. The model failed to represent peak flow, but the model produced the average water level. Output from the simulation was read using the FloPy module, and then mKBDI was calculated. The daily calculations of the mKBDI for each grid for the catchment were saved in the NetCDF format using the x-array module. We applied this model in the Peatland Hydrological Unit (PHU) Merang-Kepahyang, South Sumatera, Indonesia, in 2015 (El-Niño year) and 2016 (La-Nina Year). The daily mKBDI index from all the grids in the catchment was classified into three classes: low (mKBDI < 100), moderate (mKBDI = 101–150), or high (mKBDI > 150). Then, the whole catchment was classified according to these classes. Therefore, every day the percentage of the area with low, moderate, or high class in the catchment dynamically changed. When these classes were verified with hotspot data, all hotspots only coincided with the high hazard classes, where more than 60% was area of the catchment. No hotspot data were reported on low/moderate levels throughout 2015/2016. In the larger area with high mKBDI classes, the frequency of hotspots substantially rose. Sixty-three hotspots occurred during August–October of 2015 when the area of high hazard classes was above 70%. Through this finding, we proposed to use a 60% area of the catchment with high mKBDI classes as a threshold value indicating that the area is prone to peatland fire. Therefore, the peatland restoration project in preventing the fire could be evaluated using this indicator. If the restoration projects could reduce the area with high mKBDI classes to less than 60% for the whole year, we could accept it as a successful project.

Hydrodynamic and TS Structure Dataset of the São Francisco and Parnaiba Brazilian Rivers

Running a numerical simulation with the ROMS model in the regions of the mouths and plumes of the São Francisco and Parnaiba rivers, this dataset was obtained. It is the result of two scenarios, one of them taking into account the tides and the other not. It consists of 50 files (in NetCDF format) divided into 25 for each of the mentioned scenarios. Among the 25 files, 1 corresponds to the spatial discretization parameters of the input; 12 corresponds to the numer-ical simulation in the Parnaiba River and the remaining 12 to the São Francisco River. These files contain the daily average of all input and output parameters of the simulations. The ROM-STOOLS scripting package was used to prepare the input data. The ROMS model input was the surface forcings from the COADS dataset; the WOA09 and SODA datasets were used as initial and lateral boundary conditions, respectively. The bathymetry was taken from ETOPO2 and the tide was acquired from the TPXO7 satellite product. The hydrodynamics and TS structure data contained in this dataset are useful for physical oceanographers to study the thermodynamics of the waters and other physical processes occurring in the mouth and plume region of the São Francisco and Parnaiba Rivers. It is also useful for chemical and biological oceanographers to study the behavior of nutrients, chlorophyll and primary productivity.

Console-Based Mapping of Mongolia Using GMT Cartographic Scripting Toolset for Processing TerraClimate Data

This paper explores spatial variability of the ten climatic variables of Mongolia in 2019: average minimal and maximal temperatures, wind speed, soil moisture, downward surface shortwave radiation (DSRAD), snow water equivalent (SWE), vapor pressure deficit (VPD), vapor pressure anomaly (VAP), monthly precipitation and Palmer Drought Severity Index (PDSI). The PDSI demonstrates the simplified soil water balance estimating relative soil moisture conditions in Mongolia. The research presents mapping of the climate datasets derived from TerraClimate open source repository of the meteorological and climate measurements in NetCDF format. The methodology presented the compiled observations of Mongolia visualised by GMT coding approach using Generic Mapping Tools (GMT) cartographic scripting toolset. The results present 10 new maps of climate data over Mongolia made using automated cartographic techniques of GMT. Spatial environmental and climate analysis were conducted which determine relative distribution of PDSI and temperature extremes, precipitation and soil moisture, wind speed and DSRAD. The DSRAD showed minimum at 40 Wm−2, maximum at 113 Wm−2 in the Gobi Desert region, SWE (up to 491 mm), VAP and VPD compared with landmass parameters represent powerful cartographic tools to address complex regional climate and environmental issues in Mongolia, a country with contrasting topography, extreme climate conditions and unique environmental setting.

CAMS-REG-v4: a state-of-the-art high-resolution European emission inventory for air quality modelling

Abstract. This paper presents a state-of-the-art anthropogenic emission inventory developed for the European domain for an 18-year time series (2000–2017) at a 0.05∘ × 0.1∘ grid resolution, specifically designed to support air quality modelling. The main air pollutants are included: NOx, SO2, non-methane volatile organic compounds (NMVOCs), NH3, CO, PM10 and PM2.5, and also CH4. To stay as close as possible to the emissions as officially reported and used in policy assessment, the inventory uses the officially reported emission data by European countries to the UN Framework Convention on Climate Change, the Convention on Long-Range Transboundary Air Pollution and the EU National Emission Ceilings Directive as the basis where possible. Where deemed necessary because of errors, incompleteness or inconsistencies, these are replaced with or complemented by other emission data, most notably the estimates included in the Greenhouse gas Air pollution Interaction and Synergies (GAINS) model. Emissions are collected at the high sectoral level, distinguishing around 250 different sector–fuel combinations, whereafter a consistent spatial distribution is applied for Europe. A specific proxy is selected for each of the sector–fuel combinations, pollutants and years. Point source emissions are largely based on reported facility-level emissions, complemented by other sources of point source data for power plants. For specific sources, the resulting emission data were replaced with other datasets. Emissions from shipping (both inland and at sea) are based on the results from a separate shipping emission model where emissions are based on actual ship movement data, and agricultural waste burning emissions are based on satellite observations. The resulting spatially distributed emissions are evaluated against earlier versions of the dataset as well as against alternative emission estimates, which reveals specific discrepancies in some cases. Along with the resulting annual emission maps, profiles for splitting particulate matter (PM) and NMVOCs into individual components are provided, as well as information on the height profile by sector and temporal disaggregation down to the hourly level to support modelling activities. Annual grid maps are available in csv and NetCDF format (https://doi.org/10.24380/0vzb-a387, Kuenen et al., 2021).

Database of oceanographic anomalies and atmospheric surface fluxes for the study of climate change in the Brazilian Northeast.

This paper presents three datasets of anomalies of oceanic physical parameters and surface atmospheric fluxes in the northeast region of Brazil, geographically limited by 42.5°W - 29.75°W / 20.5°S - 1.25°N, calculated from the SODA, TROPFLUX and GPCP datasets. The DASAF-BNE (Daily Anomalies of the Surface Atmospheric Fluxes of the Brazilian Northeast), DRA-BNE (Daily Rainfall Anomalies in the Brazilian Northeast), and MAOP-BNE (Monthly Anomalies of Oceanographic Parameters in the Brazilian Northeast) datasets are generated with 0.25° resolution bilinearly interpolating the original datasets. This database includes four files in NetCDF format, which facilitates its handling due to the diversity of freeware tools that exist, DASAF-BNE and DRA-BNE have a daily frequency, while MAOP-BNE has a monthly frequency. This database is mainly intended to research climate changes in the Brazilian Northeast.