NetCDF Files Research Articles

Observed Global Changes in Sector‐Relevant Climate Extremes Indices—An Extension to HadEX3

AbstractGlobal gridded data sets of observed extremes indices underpin assessments of changes in climate extremes. However, similar efforts to enable the assessment of indices relevant to different sectors of society have been missing. Here we present a data set of sector‐specific indices, based on daily station data, that extends the HadEX3 data set of climate extremes indices. These additional indices, which can be used singly or in combinations, have been recommended by the World Meteorological Organization and are intended to empower decision makers in different sectors with accurate historical information about how sector‐relevant measures of the climate are changing, especially in regions where in situ daily temperature and rainfall data are hard to come by. The annual and/or monthly indices have been interpolated on to a 1.875° × 1.25° longitude‐latitude grid for 1901–2018. We show changes in globally‐averaged time series of these indices in comparison with reanalysis products. Changes in temperature‐based indices are consistent with global scale warming, with days with Tmax > 30°C (TXge30) increasing virtually everywhere with potential impacts on crop fertility. At the other end of the scale, the number of days with Tmin < −2°C (TNltm2) are reducing, decreasing potential damage from frosts. Changes in heat wave characteristics show increases in the number, duration and intensity of these extreme events in most places. The gridded netCDF files and, where possible, the underlying station indices are available from https://www.metoffice.gov.uk/hadobs/hadex3 and https://www.climdex.org.

Validation of satellite water products based on HYPERNETS in situ data using a Match-up Database (MDB) file structure

A Match-up Database (MDB) file structure and tools were developed to ease the validation analysis of satellite water products and to improve the exchange and processing of match-up data from different sites, missions and atmospheric correction processors. In situ remote sensing reflectance (Rrs) measurements were available from the HYPSTAR® (HYperspectral Pointable System for Terrestrial and Aquatic Radiometry), a new automated hyperspectral radiometer. An MDB file is a NetCDF file containing all the potential match-ups between satellite and in situ data on a specific site and within a given time window. These files are generated and manipulated with three modules developed in Python to implement the validation protocols: extract satellite data, associate each extract with co-located in situ radiometry data, and then perform the validation analysis. This work provides details on the implementation of the open-source MDB file structure and tools. The approach is demonstrated by a multi-site matchup comparison based on satellite data from the Sentinel-2 MSI and Sentinel-3 OLCI sensors, and HYPSTAR® data acquired over six water sites with diverse optical regimes from February 2021 to March 2023.The analysis of Sentinel-3 OLCI matchups across the six sites shows consistency with previous comparisons based on AERONET-OC data over extended reflectance range. We evaluated Sentinel-2 MSI reflectance data corrected with two atmospheric correction processors (ACOLITE and C2RCC) over four sites with clear to highly turbid waters. Results showed that the performance of the processors depends on the optical regime of the sites. Overall, we proved the suitability of the open-source MDB-based approach to implement validation protocols and generate automated matchup analyses for different missions, processors and sites.

Exploring multi-decadal time series of temperature extremes in Australian coastal waters

Abstract. The intensity and frequency of extreme ocean temperature events, such as marine heatwaves (MHWs) and marine cold spells (MCSs), are expected to change as our oceans warm. Little is known about marine extremes in Australian coastal waters, particularly below the surface. Here we introduce a multi-decadal observational record of extreme ocean temperature events starting in the 1940s and 1950s between the surface and the bottom (50–100 m) at four long-term coastal sites around Australia: the Australian Multi-Decadal Ocean Time Series EXTreme (AMDOT-EXT) data products (https://doi.org/10.26198/wbc7-8h24, Hemming et al., 2024). The data products include indices indicating the timing of extreme warm and cold temperature events, their intensity and the corresponding temperature time series and climatology thresholds. We include MHWs, MCSs and shorter-duration heat spikes and cold spikes. For MHWs and MCSs, which are defined as anomalies above the daily varying 90th and 10th percentiles, respectively, and lasting more than 5 d, we also provide further event information, such as their category and onset and decline rates. The four data products are provided as CF-compliant NetCDF files, and it is our intention that they be updated periodically. It is advised that data users seek the latest data product version. Using these multi-decadal data products, we show the most intense and longest extreme temperature events at these sites, which have occurred below the surface. These data records highlight the value of long-term full water column ocean data for the identification of extreme temperature events below the surface.

Australian Ocean surface waves dataset from SAR

AbstractIn this article, a regional ocean surface waves dataset from Sentinel‐1 A and B Synthetic Aperture Radar (SAR) satellites has been described. The ocean wave data have been extracted from the Sentinel‐1 level‐2 OCN (ocean) product as provided by the European Space Agency and downloadable for this region from the Copernicus Australasia regional data hub. The source OCN data have been produced by evolving versions of Sentinel‐1 Instrument Processing Facility (IPF). The structure of the source OCN NetCDF files changes over time and presents a challenge in performing long duration, time series analyses, including the examination of potential inconsistencies in OCN wave data, due to employment of different IPF versions over the duration of the satellite missions. Here, the input OCN wave data have been homogenized to a single, easily usable standard format after applying a quality assurance and control procedure that removes various inconsistencies in variables, coordinates, dimensions and land flag, and through the addition of new auxiliary variables. The new format has the desirable properties of being compact in size, consistent in structure, and scalable in temporal and spatial coverage. It is also convenient to use and offers opportunities to perform fast, multi‐year regional processing and analysis for calibration and validation studies and scientific applications. No re‐processing of Sentinel‐1 level‐1 data has been carried out in this work.

ClimShift – A new toolbox for the detection of climate change

AbstractClimate change no longer involves and affects just a few people or communities. However, most of them need climate change detection studies to adapt to the current and future climate conditions efficiently. The present research aimed to detect climate changes by considering the shift in climate conditions from one region to another over different periods based on a similarity index in the Carpathians basin using the new ClimShift toolbox, specially created for this purpose. Developed in R, based on the cosine similarity index and using a set of 32 climate indices (temperature and precipitation), ClimShift uses NC raster format (NetCDF files) as input data. The application is compatible with Microsoft and Unix/Linux environments. The toolbox allows the detection of forward and backward climate shifts. The results can be employed as a Climate Service and are extremely helpful for an efficient process of adaption to climate changes at a local/regional scale. A user‐friendly interface and a tutorial on how to use the toolbox are also available. The toolbox was tested for four locations in the Carpathians Basin (Vienna, Bekes, Cluj‐Napoca and Kosice) using 1961–1990 as a base period and 1991–2021 as an analysis period for the forward climate shift analysis. For Cluj‐Napoca, the application was also tested for the backward climate shift, using 1991–2021 as the base period and 1961–1990 as the analysis period, identifying the region where present climate conditions were specific during the older period. The scientific results indicated a significant shift towards the east and northeast from the older period to the most recent one and a low percentage (6%–10%) in the overlapping area with highly similar conditions between the two periods.

Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output

Emulation through pattern scaling is a well-established method of rapidly producing climate fields (like temperature or precipitation) from existing Earth System Model (ESM) output that, while inaccurate, is often useful for a variety of downstream purposes. Conducting pattern scaling has historically been a laborious process, in large part due to the increasing volume of ESM output data that has often required downloading and storing locally to train on. Here we describe the Pangeo-Enabled ESM Pattern Scaling (PEEPS) dataset, a repository of trained annual and monthly patterns from CMIP6 outputs. This manuscript describes and validates these updated patterns so that users can save effort calculating and reporting error statistics in manuscripts focused on the use of patterns. The trained patterns are available as NetCDF files on Zenodo for ease of use in the impact community, and are reproducible with the code provided via GitHub in both Jupyter notebook and Python script formats. Because all training data for the PEEPS data set is cloud-based, users do not need to download and house the ESM output data to reproduce the patterns in the zenodo archive, should that be more efficient. Validating the PEEPS data set on the CMIP6 archive for annual and monthly temperature, precipitation, and near-surface relative humidity, pattern scaling performs well over a variety of future scenarios except for regions in which there are strong, potentially nonlinear climate feedbacks. Although pattern scaling is normally conducted on annual mean ESM output data, it works equally well on monthly mean ESM output data. We identify several downstream applications of the PEEPS data set, including impacts assessment and evaluating certain types of Earth system uncertainties.

METOCEAN SPECIFICATIONS FOR WIND DATA BASE ON THE BLACK SEA

In this paper we present the steps to establish a design of floating wind turbine in offshore area. In order to be able to design a floating wind turbine in the offshore area of the Black Sea, data about the wind potential, the potential of waves and sea currents are needed, as well as wave-wind cross-data and other atmospheric data. It is assumed that the location of the floating turbine is the area of the Galata platform, Varna, Bulgaria, located at 25 km south-east of the city of Varna in the South-West Black Sea area. The wind dataset can downloaded from a server in NetCDF file form. The spatial data are used to generate wind maps showing the distribution of the mean wind speed for the full-time distribution (i.e. total time) and for the four seasons, respectively.

Effect of Urban Heat Island on Growth Rate of the Convective Boundary Layer Over Baghdad City

Due to human activities and economic growth, in the last decades, urban expansion has increased, and it has a large role in the formation of urban heat island. Urban heat island effects natural physical properties of the air. In this study, data from satellite images included Landsat5 Landsat 9 and reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF) by ERA5 model are implemented. ERA5 archives NetCDF file data for many hourly atmospheric elements such as temperature, wind components, sensible heat flux, boundary layer height …etc. All the files were processed by MATLAB, while satellite images processed by ArcGIS to calculate the spectral index such as Normalized difference built up index (NDBI) and supervised classification. Results of composited images and supervised classification show that there is an increase in built up area of about 23% for the periods from July-1994 to July-2022. This change in LULC will create urban heat island for Baghdad and affect heat budget near the surface that will change structure of urban boundary layer, and convective boundary layer (CBL). Near the surface there is an increase in growth rate of convective boundary layer about 0.25m/s at 7AM for July-2022, relative to 0.136 m/s for July-1994. This difference in growth rate is due to urban heat island creation and will increase CBL to about 620m in 2022 relative to 1994 at 10AM.

CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model

Abstract. We present a versatile, powerful, and user-friendly chemical data assimilation toolkit for simultaneously optimizing emissions and concentrations of chemical species based on atmospheric observations from satellites or suborbital platforms. The CHemistry and Emissions REanalysis Interface with Observations (CHEEREIO) exploits the GEOS-Chem chemical transport model and a localized ensemble transform Kalman filter algorithm (LETKF) to determine the Bayesian optimal (posterior) emissions and/or concentrations of a set of species based on observations and prior information using an easy-to-modify configuration file with minimal changes to the GEOS-Chem or LETKF code base. The LETKF algorithm readily allows for nonlinear chemistry and produces flow-dependent posterior error covariances from the ensemble simulation spread. The object-oriented Python-based design of CHEEREIO allows users to easily add new observation operators such as for satellites. CHEEREIO takes advantage of the Harmonized Emissions Component (HEMCO) modular structure of input data management in GEOS-Chem to update emissions from the assimilation process independently from the GEOS-Chem code. It can seamlessly support GEOS-Chem version updates and is adaptable to other chemical transport models with similar modular input data structure. A post-processing suite combines ensemble output into consolidated NetCDF files and supports a wide variety of diagnostic data and visualizations. We demonstrate CHEEREIO's capabilities with an out-of-the-box application, assimilating global methane emissions and concentrations at weekly temporal resolution and 2∘ × 2.5∘ spatial resolution for 2019 using TROPOspheric Monitoring Instrument (TROPOMI) satellite observations. CHEEREIO achieves a 50-fold improvement in computational performance compared to the equivalent analytical inversion of TROPOMI observations.

Spatial Analysis of the PM2.5 Air Pollution in Iraq by using R Language

Abstract Remote sensing refers to the acquisition of data and information about a phenomenon and a territory, without a direct contact with it. It is alternative to in-situ observation. Due to its wide coverage, multi-temporal and multispectral capability, remote sensing satellites have been used for studying and monitoring the spatial distribution of the aerosol pollution (like PM2.5) in the atmosphere. PM2.5 refers to atmospheric particulate matter (PM) that has a diameter of less than 2.5 micrometers, which is about 3% the diameter of a human hair. The current study concentrates on the using of R programming language to process and analyze the retrieval PM2.5 dataset over Iraq for the period (January 1, 2020 to December 31, 2020), these data were downloaded from the European Union’s Earth observation Copernicus program through the Copernicus Atmosphere Monitoring Service (CAMs). The study presented output results of the PM2.5 concentration levels by developing an automatic tool that restructuring the netCDF file into excel file using R language. In addition, it produces different valuable charts that help in practical dataset interpretation and decision-making. The results of the study showed the variation in the spatial distribution of PM2.5 in the study area due to the diversity of pollution sources from one region to another.

Updated observations of clouds by MODIS for global model assessment

Abstract. This paper describes a new global dataset of cloud properties observed by MODIS relying on the current (collection 6.1) processing of MODIS data and produced to facilitate comparison with results from the MODIS observational proxy used in climate models. The dataset merges observations from the two MODIS instruments into a single netCDF file. Statistics (mean, standard deviation, and number of observations) are accumulated over daily and monthly timescales on an equal-angle grid for viewing and illumination geometry, cloud detection, cloud-top pressure, and cloud properties (optical thickness, effective particle size, and water path) partitioned by thermodynamic phase and an assessment as to whether the underlying observations come from fully or partly cloudy pixels. Similarly partitioned joint histograms are available for (1) optical thickness and cloud-top pressure, (2) optical thickness and particle size, and (3) cloud water path and particle size. Differences with standard data products, caveats for data use, and guidelines for comparison to the MODIS simulator are described. Data are available on daily (https://doi.org/10.5067/MODIS/MCD06COSP_D3_MODIS.062; NASA, 2022b) and monthly (https://doi.org/10.5067/MODIS/MCD06COSP_M3_MODIS.062; NASA, 2022a) timescales.

High-resolution calibrated and validated Synthetic Aperture Radar Ocean surface wind data around Australia

The dataset consists of ocean surface wind speed and direction at 10 m height and 1 km spatial resolution around the wider Australian coastal areas, spanning 4 years (2017 to 2021) of measurements from Sentinel-1 A and B imaging Synthetic Aperture Radar (SAR) platforms. The winds have been derived using a consistent SAR wind retrieval algorithm, processing the full Sentinel-1 archive in this region. The data are appropriately quality controlled, flagged, and archived as NetCDF files representing SAR wind field maps aligned with satellite along-track direction. The data have been calibrated against Metop-A/B Scatterometer buoy-calibrated, wind measurements and examined for potential changes in calibration over the duration of the data. The calibrated data are further validated by comparisons against independent Altimeter (Cryosat-2, Jason-2, Jason-3, and SARAL) wind speeds. Several methods for data access are also listed. The database is potentially useful for offshore industries (oil and gas, fisheries, shipping, offshore wind energy), public recreational activities (fishing, sailing, surfing), and protection and management of coasts and natural habitats.

Forest fire occurrence, distribution and risk mapping using geoinformation technology: A case study in the sub-tropical forest of the Meghalaya, India

The study focuses on Meghalaya, the North Eastern State of India, for forest fire occurrence, distribution, and risk mapping using the different geospatial techniques is validated using different decision-making techniques such as MCDM, Space Time Cube Analysis (STC), and Emerging Hot Spot (EHS) Analysis. The archive data of Moderate Resolution Imaging Spectroradiometer (MODIS) near real time (NRT) fire data has been considered for this study. Fire points were obtained from Fire Information for Resource Management System (FIRMS), a web service provided by NASA. Using two decadal data, a NetCDF file was created which can store four-dimensional data. Based on this NetCFD file, forest fire trend and future prospect has been foreseen. After analyzing these points, a relationship with other fire triggering factors such as Human settlement, Roads, Climate, Elevation, Slope, Aspect, Forest Type, Forest Canopy density, Soil Moisture, Wind Speed, and Forest Health has been established. Forest risk map with four classes (extreme, severe, moderate, and mild) is also generated. These results are very effective for the managers, planners, and other stakeholders for disaster management. The suggested methodology can be implemented for the preparedness of forest fire management. The policy makers must act quickly to protect the Himalayan region with the highest biodiversity by adopting focused interventions and the strategic allocation of scarce resources in effective areas.

Development of an Automated Algorithm to Detect and Track Developing and Mesoscale Convective Systems

An objective algorithm is developed which is capable of identifying convective cloud clusters (CCCs) at the point of initiation, tracking any selected of all identified CCCs to maturity (MCS) and then to the point of dissipation and thereby accumulating some physical, morphological and radiative properties. The technique is able to account for periods of growth, propagating, merging, splitting and decaying, which take place during the lifetime of an individual cluster. The algorithm was tested using brightness temperature from merged infrared images of at least eight reporting geostationary satellites or channels at a spatial resolution of 4 Km and temporal resolutions of 30 minutes. The model data used is Weather Research and Forecasting (WRF) with the Advanced Research WRF (ARW) core simulated using the same spatial and temporal resolution with the satellite data for uniform comparison. This was the first attempt of developing such algorithm taking into consideration the uniqueness of West African convective systems. The algorithm loads an input (text) file where necessary inputs as preferred by the user, takes input file of satellite data or model’s output in netcdf file format. The good graphical interface for interacting with user makes it very flexible and easy to use with little computer knowledge. Good results from the comparison and validation shows a good agreement between the model’s output and satellite data shows the consistency in the tracking algorithm. The algorithm can hence be used for comparison or evaluating the performances of different Physics parameterizations/schemes in same model with the observed. It can also be used for comparison or evaluating performances of different models in simulating convective systems of the West Africa sub-region. Keywords : convective systems, tracking, properties, propagation, growth and decaying. DOI: 10.7176/JNSR/13-18-06 Publication date: October 31 st 2022

Modflow-setup: Robust automation of groundwater model construction

In an age of both big data and increasing strain on water resources, sound management decisions often rely on numerical models. Numerical models provide a physics-based framework for assimilating and making sense of information that by itself only provides a limited description of the hydrologic system. Often, numerical models are the best option for quantifying even intuitively obvious connections between human activities and water resource impacts. However, despite many recent advances in model data assimilation and uncertainty quantification, the process of constructing numerical models remains laborious, expensive, and opaque, often precluding their use in decision making. Modflow-setup aims to provide rapid and consistent construction of MODFLOW groundwater models through robust and repeatable automation. Common model construction tasks are distilled in an open-source, online code base that is tested and extensible through collaborative version control. Input to Modflow-setup consists of a single configuration file that summarizes the workflow for building a model, including source data, construction options, and output packages. Source data providing model structure and parameter information including shapefiles, rasters, NetCDF files, tables, and other (geolocated) sources to MODFLOW models are read in and mapped to the model discretization, using Flopy and other general open-source scientific Python libraries. In a few minutes, an external array-based MODFLOW model amenable to parameter estimation and uncertainty quantification is produced. This paper describes the core functionality of Modflow-setup, including a worked example of a MODFLOW 6 model for evaluating pumping impacts to a lake in central Wisconsin, United States.

Contribution to the Study of Forest Fires in Semi-Arid Regions with the Use of Canadian Fire Weather Index Application in Greece

Forest fires are of critical importance in the Mediterranean region. Fire weather indices are meteorological indices that produce information about the impact as well as the characteristics of a fire event in an ecosystem and have been developed for that reason. This study explores the spatiotemporal patterns of the FWI system within a study area defined by the boundaries of the Greek state. The FWI has been calculated and studied for current and future periods using data from the CFSR reanalysis model from the National Centers for Environmental Protection (NCEP) as well as data from NASA satellite programs and the European Commission for Medium-Range Weather Forecasts (ECWMF) in the form of netCDF files. The calculation and processing of the results were conducted in the Python programming language, and additional drought- and fire-related indices were calculated, such as the standardized precipitation index (SPI), number of consecutive 50-day dry periods (Dry50), the Fosberg fire weather index (FFWI), the days where the FWI exceeds values of 40 and 50 days (FWI > 40) and (days FWI > 50). Similar patterns can easily be noted for all indices that seem to have their higher values concentrated in the southeast of the country owing to the higher temperatures and more frequent drought events that affect the indices’ behavior in both the current and future periods.

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.

Building Essential Biodiversity Variable netCDFs with the ebvcube R Package

The concept of Essential Biodiversity Variables (EBVs) was conceived to study, report, and manage biodiversity change. The EBV netCDF structure was developed in order to support publication and interoperability of biodiversity data. This standard is based on the Network Common Data Format (netCDF). Additionally, it follows the Climate and Forecast Conventions (CF, version 1.8) and the Attribute Convention for Data Discovery (ACDD, version 1.3). The standard allows several datacubes per netCDF file (see Fig. 1). These cubes have four dimensions: longitude, latitude, time and entity, whereby the last dimension can, for example, encompass different species or groups of species, ecosystem types or other aspects. The usage of hierarchical groups enables the coexistence of multiple EBV cubes (see Fig. 2). The first level (netCDF group) are scenarios, e.g., the modelling for different Shared Socioeconomic Pathways (SSP) scenarios. The second level (netCDF group) are metrics, e.g., the percentage of protected area per pixel and its proportional loss over a certain time span per pixel. All metrics are repeated per scenario, if any are present. The result is a rather complex raster dataset (see example dataset in Fig. 3). This is where the ebvcube R package comes into play. This R package enables scientists to create their own netCDFs in the EBV cube standard. Its functionality covers the creation, opening/reading and visualizing the EBV netCDFs. The ebvcube package is part of the overall EBV infrastructure and works together with the EBV Data Portal. Users can work with the downloaded EBV netCDFs or upload their own EBV netCDFs to the portal. Generally, the package aims to condense the output for the users and assist in the understanding of the file structure to overcome the complexity. The output is reduced to the necessary information, e.g., not displaying coordinate variables or any technical attributes. Moreover, functionality for a quick data exploration is implemented.

World Atlas of late Quaternary Foraminiferal Oxygen and Carbon Isotope Ratios

Abstract. We present a global atlas of downcore foraminiferal oxygen and carbon isotope ratios available at https://doi.org/10.1594/PANGAEA.936747 (Mulitza et al., 2021a). The database contains 2106 published and previously unpublished stable isotope downcore records with 361 949 stable isotope values of various planktic and benthic species of Foraminifera from 1265 sediment cores. Age constraints are provided by 6153 uncalibrated radiocarbon ages from 598 (47 %) of the cores. Each stable isotope and radiocarbon series is provided in a separate netCDF file containing fundamental metadata as attributes. The data set can be managed and explored with the free software tool PaleoDataView. The atlas will provide important data for paleoceanographic analyses and compilations, site surveys, or for teaching marine stratigraphy. The database can be updated with new records as they are generated, providing a live ongoing resource into the future.

The Collection of Hyperspectral Measurements on Snow and Ice Covers in Polar Regions (SISpec 2.0)

The data value of hyperspectral measurements on ice and snow cover is strongly impacted by the availability of data services, where spectral libraries are integrated to detailed descriptions of the observed surface cover. For snow and ice cover, we present an updated version of the Snow/Ice Spectral Archive (SISpec 2.0), which has been integrated into a web portal characterized by different functionalities. The adopted metadata scheme features basic geographic data, information about the acquisition setup, and parameters describing the different surface types. While the implementation of the IACS Classification of Seasonal Snow on the Ground is the core component for snow cover, ice cover is approached using different parameters associated with its surface roughness and location. The web portal is not only a visualization tool, but also supports interoperability functionalities, providing data in the NetCDF file format. The availability of these functionalities sets the foundation for sharing a novel platform with the community and is an interesting tool for calibrating and validating data and models.