Platform and Components
CityCLIM has developed the Generic City Climate Platform, which handles the backend and is open to the integration of further components and services. Components already developed are data processors that integrate and harmonise data sources with space, in-situ and airborne data, and engines that process this data for use in services. Let's find out.
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Generic City Climate Platform
A unified backend for city services
The Generic City Climate Platform manages the backend and is designed to accommodate additional components and services.
Generic City Climate Platform
The Generic City Climate Platform (GCCP) is a scalable multi-cloud solution and the central software part of the CityCLIM ecosystem that handles the orchestration and management of workflows required for the processing of various types of data (e.g., weather stations, mobile sensors, earth-observation, airborne, models, …) that form the foundation of the City Climate Services.
All services are provided via a modern SaaS-solution: the Generic City Climate Platform (GCCP). This platform collects all input data for the service provision, unifies and postprocesses them, and provides a variety of so-called “Engines” that generate the relevant output from the weather model and other service components.
However, the GCCP is not only a sheer data collection and processing tool, it also provides a user-centred frontend that allows its subscribers to access all services visually via an intuitive dashboard. Furthermore, the GCCP provides API endpoint for all participating cities, so that the output of some services (e.g., the Citizen Climate Knowledge Services) can be also accessed directly and each city can decide individually on how to display or integrate these services on their own public outlets for their citizen.
View on monitoring dashboard showing relevant information on processing tasks and communication within the platform.
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Generic City Climate Platform Prototype
Data Processors and Data
Data and Data Processors
In-situ Data Processor
The In-Situ Data Processor integrates (real-time) information from citizen science powered MeteoTrackers, Barani stationary sensors, and Valencia’s FIWARE sensor network.
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In-Situ Data Processor Prototype
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Citizen Science Framework SOP
Airborne Data Processor
The Airborne Data Processor leverages RAVEN’s high-resolution thermal infrared capabilities to bridge the gap between coarse satellite data and local in-situ measurements, converting airborne temperature readings into precisely geo-referenced datasets that fuel urban climate analysis, validate environmental simulations, and support diverse applications—from infrastructure inspections to vegetation health monitoring.
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Airborne Data Processor Prototype
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Airborne TIR camera RAVEN
Spaceborne Data Processor
The Spaceborne Data Processor seamlessly combines raw satellite imagery from Sentinel-2, Sentinel-3, and Landsat 8/9 into actionable climate insights across its three modules: the Spaceborne Data Preprocessor (which automates and harmonises data collection), the Thermal Sharpener (which produces high-resolution thermal data in an operational way using the ESTARFM and ATPRK sharpening methods by integrating both spatial and temporal variability into the model), and the Index Calculator (which generates critical heat indices as UHS, SUHI, UFTVI, UHIER or DI).
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Spaceborne Data Processor
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Thermal Sharpener
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Index Calculator
CityCLIM Data Samples
The following sample datasets illustrate CityCLIM’s diverse climate-related data utilized in this project. May they help the research community to better understand the impacts of climate change in urban areas:
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Surface Weather Observations – 10-minute meteorological measurements (temperature, humidity, wind) from Thessaloniki, provided as JSON via the Meteologix API.
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Valencia Airborne Campaign Data – Preliminary airborne data collected on 10/09/2024 and 11/09/2024, including calibrated raw data and derived land surface temperature.
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Modified Land-Cover Data – Georeference data based on ESA Worldcover 2021.
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Sharpened LST Scenes – Validated high-resolution land surface temperature data for Valencia, Luxembourg, Thessaloniki, and Karlsruhe, fused from Sentinel-3 and Landsat 8/9.
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Local Forecast Data (Thessaloniki) – Enhanced hourly predictions for temperature and dewpoint up to 72 hours.
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Bresser Weather Station Data – Example dataset from 21 August 2023 measuring wind, rainfall, temperature, UV, and light.
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Meteotracker Data Valencia – Mobile meteorological measurements from 10 October 2023, including temperature, humidity, pressure, and GPS tracking.
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SenseBox Data Valencia – Observations from 7 October 2023 capturing temperature, humidity, atmospheric pressure, and particle matter.
Data and
Data Processors
To integrate space, in-situ and airborne data, several developed data processors prepare raw data from connected data sources for further use within the CityCLIM ecosystem. They connect to the raw data sources, clean, harmonise and enrich the data for further processing within the CityCLIM ecosystem and are able to store the data in the data warehouse of the Generic City Climate Platform.
Citizen Science in Karlsruhe: Collecting In-Situ Data
Airborne Data Collection in Valencia
Engines
Engines
Engines are using pre-processed data from Data Processors, the UltraHD Weather Model and other intermediate data to calulate information needed by City Climate Services. They are the engines operating in the backend behind the the front-end of the services.
Engines
Forecast Engine
The City Climate Forecast Engine orchestrates the operational workflow of the UltraHD Weather Model by regularly providing the latest in-situ observations and static boundary conditions to initiate forecast runs. It continuously monitors model status, tracks simulation progress, and ensures all resulting UltraHD data sets are archived in the data warehouse for easy service access. Beyond its operational role, the Forecast Engine also enables visualisation by generating map overlays and time series from the data warehouse, granting stakeholders a detailed and up-to-date view of localised weather patterns and trends.
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D3.4 Optimized Prototype of Advanced Urban Weather Model
Simulation Engine
The City Climate Simulation Engine provides on-demand execution of the UltraHD Weather Model Processor by organising boundary conditions and other input data within the data warehouse into clearly defined scenarios. Working closely with the City Climate Services, it uses user-modified Inputs - such as land use changes, updated digital elevation models, or varying emissions - for scenario-based modelling. Leveraging these scenarios, the Engine then triggers UltraHD model runs, closely monitors their progress, and keeps users informed of the simulation’s status, ensuring that each forecast and climate assessment is both accurate and tailored to evolving urban configurations.
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D3.4 Optimized Prototype of Advanced Urban Weather Model
Diagnostics Engine
The City Climate Diagnostics Engine performs statistical analysis over selected operational runs of the Advanced Weather Model Processor. That includes calculation of minimum, maximum and mean values for different output parameters regarding environmental aspects in relation to heat, airflow, and pollution. The Diagnostic Engine is the main component that serves the Identification Services with on-demand analysis capabilities.
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D3.4 Optimized Prototype of Advanced Urban Weather Model
Citizen Weather Sensation Engine
The Citizen Sensation Engine for Personalised Climate & Weather Feeling acknowledges that individual comfort levels can vary significantly depending on factors such as health status, cultural background, and personal tolerance to temperature and humidity. By drawing on real-time data for variables like air temperature, relative humidity, and heat index, the engine generates a traffic light-coded city map—areas exceeding all comfort thresholds appear in red, those exceeding just one appear in yellow, and those remaining within the chosen ranges are marked green. Users can customise which weather parameters to include and see their own location highlighted according to their comfort zone. With this tailored approach, the engine helps citizens make informed decisions about where to go and what activities to plan, ensuring a more comfortable and health-conscious urban experience.
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D4.4 Optimised Full Prototype of Citizens Science Concepts for Urban Climate Monitoring
EO-based Heat Island Mitigation and Simulation Service Engine
The engine leverages standard Earth Observation (EO) data to estimate how urban modifications (e.g., adding vegetation or altering building configurations) affect local land surface temperatures. By applying a neural network trained on both typical and extreme summer-day datasets, the engine generates near-instantaneous heat impact scenarios, empowering city planners and the public to evaluate multiple mitigation strategies before they are implemented. Thanks to its reliance on readily available EO data, the engine remains both cost-effective and easily transferable to any urban area, making it a versatile tool for advancing climate resilience and informed urban planning.
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D5.4 Optimized Full Prototype of Data Processors for City Weather Model
You want to integrate own
City Climate Services?
The Developer Guide:
For more information on how developers and third party software providers can integrate their own City Climate Services and components into the CityCLIM ecosystem, please download the Developer Guide which provides comprehensive information for developers and third party software providers explaining how to integrate their own City Climate Services and components into the CityCLIM ecosystem.
Development & Integration: A Developer Guide