diff --git a/2023-07_Pilot-ER/sections/00-02-terms_and_definitions.adoc b/2023-07_Pilot-ER/sections/00-02-terms_and_definitions.adoc index c4eaa19..6645091 100644 --- a/2023-07_Pilot-ER/sections/00-02-terms_and_definitions.adoc +++ b/2023-07_Pilot-ER/sections/00-02-terms_and_definitions.adoc @@ -4,7 +4,7 @@ Carrying Capacity:: An area both suitable and available for human activity based CityGML:: is an open standardised data model and exchange format to store digital 3D models of cities and landscapes. Data Cube:: In computer programming contexts, a data cube (or datacube) is a multi-dimensional ("n-D") array of values. Typically, the term data cube is applied in contexts where these arrays are massively larger than the hosting computer's main memory; examples include multi-terabyte/petabyte data warehouses and time series of image data. FAIR Climate Service:: Climate resilience information system where the entire architecture is following FAIR principles. -FAIR principle:: Findability, Accessibility, Interoperability, and Reuse of digital assets. +FAIR principles footnote:[further reading: https://www.go-fair.org/fair-principles/]:: The approach of making digital assets Findable, Accessible, Interoperable, and Reusable. Resilience:: Ability of a system to compensate impacts. Sentinel (satellite mission):: A series of next-generation Earth observation missions developed by the European Space Agency (ESA), on behalf of the joint ESA/European Commission initiative Copernicus. @@ -60,7 +60,7 @@ ESDC:: Earth System Data Cube ESDL:: Earth System Data Laboratory ESIP:: Earth Science Information Partners EUMETSAT:: European Organisation for the Exploitation of Meteorological Satellites -FAIR:: Findability, Accessibility, Interoperability, and Reusability +FAIR:: Findable, Accessible, Interoperable, Reusable FAPAR:: Fraction of Absorbed Photosynthetically Active Radiation FME:: Feature Manipulation Engine FOSS4G:: Free and Open Source Software for Geospatial diff --git a/2023-07_Pilot-ER/sections/01-introduction.adoc b/2023-07_Pilot-ER/sections/01-introduction.adoc index 1f9c4cb..3748653 100644 --- a/2023-07_Pilot-ER/sections/01-introduction.adoc +++ b/2023-07_Pilot-ER/sections/01-introduction.adoc @@ -3,7 +3,9 @@ The OGC Climate Resilience Pilot represents the first phase of multiple long term climate activities aiming to evolve geospatial data, technologies, and other capabilities into valuable information for decision makers, scientists, policy makers, data providers, software developers, and service providers so we can make valuable, informed decisions to improve climate action. -=== Climate Resilience Information Systems +=== The role of the pilot + +The goal of this pilot was to enable everyone to take the relevant actions to address climate change and make well informed decisions for climate change adaptation. This includes scientists, decision makers, city managers, as well as politicians. the role of the pilot was to show how to use data from lots of organizations, available at different scales for large and small areas to be integrated with scientific processes, analytical models, and simulation environments. The role was to show the visualization and communication tools to shape the message in the right way for any client. Many challenges can be met through resources that adhere to FAIR principles. FAIR as in: Findable, Accessible, Interoperable, and Reusable. No single organization has all the data we need to understand the consequences of climate change. The OGC Climate Resilience Pilot identifies, discusses, and develops these resources. The goal was to help the location community develop more powerful visualization and communication tools to accurately address ongoing climate threats such as heat, drought, floods, fires as well as supporting the national determined contributions for greenhouse gas emission reduction. Climate resilience is often considered the use case of our lifetime, and the OGC community is uniquely positioned to accelerate solutions through collective problem solving with this initiative. @@ -11,37 +13,33 @@ The goal was to help the location community develop more powerful visualization .Value chain from raw data to climate information image::CCS_Pilot_Concept.png[ValueChain] -As illustrated, big, raw data from multiple sources requires further processing in order to be ready for analysis and climate change impact assessments. Applying data enhancement steps, such as bias adjustments, re-gridding, or calculation of climate indicators and essential variables, leads to “Decision Ready Indicators.” The spatial data infrastructures required for this integration should be designed with interoperable building blocks following FAIR data principles. Heterogeneous data from multiple sources can be enhanced, adjusted, refined, or quality controlled to provide Science Services data products for Climate Resilience. The OGC Climate Change Services Pilots also illustrated the graphical exploration of the Decision Ready Climate Data. It effectively demonstrated how to design FAIR climate services information systems. The OGC Pilot participants illustrated the necessary tools and the visualizations to address climate actions moving towards climate resilience. - -=== The Role of the Pilot - -The goal of this pilot was to enable everyone to take the relevant actions to address climate change and make well informed decisions for climate change adaptation. This includes scientists, decision makers, city managers, as well as politicians. So what do we need? We need data from lots of organizations, available at different scales for large and small areas to be integrated with scientific processes, analytical models, and simulation environments. We need data visualization and communication tools to shape the message in the right way for any client. Many challenges can be met through resources that adhere to FAIR principles. FAIR as in: Findable, Accessible, Interoperable, and Reusable. No single organization has all the data we need to understand the consequences of climate change. The OGC Climate Resilience Community identifies, discusses, and develops these resources. +As illustrated, big, raw data from multiple sources requires further processing in order to be ready for analysis and climate change impact assessments. Applying data enhancement steps, such as bias adjustments, re-gridding, or calculation of climate indicators and essential variables, leads to “Decision Ready Indicators.” The spatial data infrastructures required for this integration should be designed with interoperable application packages following FAIR data principles. Heterogeneous data from multiple sources can be enhanced, adjusted, refined, or quality controlled to provide Science Services data products for Climate Resilience. The OGC Climate resilience pilots also illustrated the graphical exploration of the Decision Ready Indicators. It effectively demonstrated how to design FAIR climate resilience information systems underpinning FAIR Climate Services. The OGC Pilot participants illustrated the necessary tools and the visualizations to address climate actions moving towards climate resilience. The OGC Climate Resilience Community has a vision to support efforts on climate actions and enable international partnerships (SDG 17), and move towards global interoperable open digital infrastructures providing climate resilience information on users demand. This pilot contributed to establishing an OGC climate resilience concept store for the community where all appropriate climate information to build climate resilience information systems as open infrastructures can be found in one place, be it Information about data services, tools, software, handbooks, or a place to discuss experiences and needs. It covers all phases of Climate Resilience, from initial hazards identification and mapping to vulnerability and risk analysis to options assessments, prioritization, and planning, and ends with implementation planning and monitoring capabilities. These major challenges can only be met through the combined efforts of many OGC members across government, industry, and academia. +=== Objectives + This Pilot has set the stage for a series of follow up activities. It therefore focused on use-case development, implementation, and exploration. It also answered the questions such as: - What use-cases can be realized with the current data, services, analytical functions, and visualization capabilities that we have? Current data services include for example the Copernicus Services, including Climate Data Store (CDS) https://cds.climate.copernicus.eu/ and Atmosphere Data Store (ADS) https://ads.atmosphere.copernicus.eu/. - How much effort is it to realize these use-cases? - What is missing, or needs to be improved, in order to transfer the use-cases developed in the pilot to other areas? -=== Objectives - -The pilot had three objectives. First, to better understand what is currently possible with the available data and technology. Second, what additional data and technology needs to be developed in future to better meet the needs of the Climate Resilience Community; and third, to capture Best Practices, and to allow the Climate Community to copy and transform as many use-cases as possible to other locations or framework conditions. +The pilot had. therefore three objectives. First, to better understand what is currently possible with the available data and technology. Second, what additional data and technology needs to be developed in future to better meet the needs of the Climate Resilience Community; and third, to capture Best Practices, and to allow the Climate Community to copy and transform as many use-cases as possible to other locations or framework conditions. === Background With growing local communities, an increase in climate-driven disasters, and an increasing risk of future natural hazards, the demand for National Resilience Frameworks and Climate Resilience Information Systems (CRIS) cannot be overstated. Climate Resilience Information Systems (CRIS) are enabling data-search, -fetch, -fusion, -processing and -visualization. They enable access, understanding, and use of federal data, facilitate integration of federal and state data with local data, and serve as local information hubs for climate resilience knowledge sharing. -CRIS are already existing and operational, like the Copernicus Climate Change Service with the Climate Data Store. CRIS architectures can be further enhanced by providing climate scientific methods and visualization capabilities as climate building blocks. Based on FAIR principles, these building blocks enable in particular the reusability of Climate Resilience Information Systems features and capabilities. Reusability is an essential component when goals, expertises, and resources are aligned from the national to the local level. Framework conditions differ across the country, but building blocks enable as much reuse of existing Best Practices, tools, data, and services as possible. +CRIS are already existing and operational, like the Copernicus Climate Change Service with the Climate Data Store. CRIS architectures can be further enhanced by providing climate scientific methods and visualization capabilities as climate application packages. Based on FAIR principles, these application packages enable in particular the reusability of CRIS features and capabilities. Reusability is an essential component when goals, expertises, and resources are aligned from the national to the local level. Framework conditions differ across the nations, but application packages enable as much reuse of existing Best Practices, tools, data, and services as possible. Goals and objectives of decision makers vary at different scales. At the municipal level, municipal leaders and citizens directly face climate-related hazards. Aspects thus come into focus such as reducing vulnerability and risk, building resilience through local measures, or enhancing emergency response. At the state level, the municipal efforts can be coordinated and supported by providing funding and enacting relevant policies. The national, federal, or international level provides funding, science data, and international coordination to enable the best analysis and decisions at the lower scales. -.Schematic synergies within different climate and science services due to FAIR and open Infrastructures +.Schematic synergies within different climate and science services FAIR and open infrastructures image::Interoperable_ScienceService.png[image] -Productivity and decision making are enhanced when climate building blocks are exchangeable across countries, organizations, or administrative levels (see Figure below). This OGC Climate Resilience Pilot is a contribution towards an open, multi-level infrastructure that integrates data spaces, open science, and local-to-international requirements and objectives. It contributes to the technology and governance stack that enables the integration of data including historical observations, real time sensing data, reanalyses, forecasts or future projections. It addresses data-to-decision pipelines, data analysis and representation, and bundles everything in climate resilience building blocks. These building blocks are complemented by Best Practices, guidelines, and cook-books that enable multi–stakeholder decision making for the good of society in a changing natural environment. +Productivity and decision making are enhanced when climate application packages are exchangeable across countries, organizations, or administrative levels (see Figure 2). This OGC Climate Resilience Pilot is a contribution towards an open, multi-level infrastructure that integrates data spaces, open science, and local-to-international requirements and objectives. It contributes to the technology and governance stack that enables the integration of data including historical observations, real time sensing data, reanalyses, forecasts or future projections. It addresses data-to-decision pipelines, data analysis and representation, and bundles everything in climate resilience application packages. These application packages are complemented by Best Practices, guidelines, and cook-books that enable multi–stakeholder decision making for the good of society in a changing natural environment. The OGC Innovation Program brings all groups together: The various members of the stakeholder group define use cases and requirements, the technologists and data providers experiment with new tools and data products in an agile development process. The scientific community provides results in appropriate formats and enables open science by providing applications that can be parameterized and executed on demand. @@ -50,9 +48,9 @@ image::Climate_Resilience_Pilot_Interaction.png[] This OGC Climate Resilience Pilot is part of the OGC Climate Community Collaborative Solution and Innovation process, an open community process that uses the OGC as the governing body for collaborative activities among all members. A spiral approach is applied to connect technology enhancements, new data products, and scientific research with community needs and framework conditions at different scales. The spiral approach defines real world use cases, identifies gaps, produces new technology and data, and tests these against the real world use cases before entering the next iteration. Evaluation and validation cycles alternate and continuously define new work tasks. These tasks include documentation and toolbox descriptions on the consumer side, and data and service offerings, interoperability, and system architecture developments on the producer side. It is emphasized that research and development is not constrained to the data provider or infrastructure side. Many tasks need to be executed on the data consumer side in parallel and then merged with advancements on the provider side in regular intervals. -Good experiences have been made using OGC API standards in the past. For example, the remote operations on climate simulations (roocs) use OGC API Processes for subsetting data sets to reduce the data volume being transported. Other systems use OGC STAC for metadata and data handling or OGC Earth Observation Exploitation Platform Best Practices for the deployment of climate building blocks or applications into CRIS architectures. Still data handling regarding higher complex climate impact assessments within FAIR and open infrastructures needs to be enhanced. There is no international recommendation or best practice on usage of existing API standards within individual CRIS. It is the goal of this pilot to contribute to the development of such a recommendation, respecting existing operational CRIS that are serving heterogen user groups +Good experiences have been made using OGC API standards in the past. For example, the remote operations on climate simulations (roocs) use OGC API Processes for subsetting data sets to reduce the data volume being transported. Other systems use OGC STAC for metadata and data handling or OGC Earth Observation Exploitation Platform Best Practices for the deployment of climate application packages into CRIS architectures. Still data handling regarding higher complex climate impact assessments within FAIR and open infrastructures needs to be enhanced. There is no international recommendation or best practice on usage of existing API standards within individual CRIS. It is the goal of this pilot to contribute to the development of such a recommendation, respecting existing operational CRIS that are serving heterogen user groups -.Schematic Architecture of a Climate Resilience Information System. By respecting FAIR principles for the Climate Building Blocks the architecture enables open infrastructures to produce and deliver information on demand of the users needs +.Schematic Architecture of a Climate Resilience Information System. By respecting FAIR principles for the climate application packages the architecture enables open infrastructures to produce and deliver information on demand of the users needs image::FAIR_Data_Spaces.png[] === Technical Challenges @@ -63,7 +61,7 @@ Realizing the delivery of Decision Ready Data on demand to achieve Climate Resil - Cross-Discipline Data Integration: Different disciplines such as Earth Observation, various social science, or climate modeling use different conceptual models in their data collection, production, and analytical processes. How can we map between these different models? What patterns have been used to transform conceptual models to logical models, and eventually physical models? The production of modern Decision-ready information needs the integration of several data sets, including census and demographics, further social science data, transportation infrastructure, hydrography, land use, topography and other data sets. This pilot cycle uses 'location' as the common denominator between these diverse data sets and works with several data providers and scientific disciplines. In terms of Data Exchange Formats the challenge is to know what data formats need to be supported at the various interfaces of the processing pipeline? What is the minimum constellation of required formats to cover the majority of use cases? What role do container formats play? Challenging on technical level is also the Data Provenance. Many archives include data from several production cycles, such as IPCC AR 5 and AR 6 models. In this context, long term support needs to be realized and full traceability from high level data products back to the original raw data. Especially in context of reliable data based policy, clear audit trails and accountability for the data to information evolution needs to be ensured. -- Building Blocks for processing pipelines: With a focus on Machine Learning and Artificial Intelligence which plays an increasing role in the context of data science and data integration. This focus area needs to evaluate the applicability of machine learning models in the context of the value-enhancing processing pipeline. What information needs to be provided to describe machine learning models and corresponding training data sufficiently to ensure proper usage at various steps of the pipeline? Upcoming options to deploy ML/AI within processing APIs to enhance climate services are rising challenges e.g. on how to initiate or ingest training models and the appropriate learning extensions for the production phase of ML/AI. Heterogeneity in data spaces can be bridged with Linked Data and Data Semantics. Proper and common use of shared semantics is essential to guarantee solid value-enhancement processes. At the same time, resolvable links to procedures, sampling & data process protocols, and used applications will ensure transparency and traceability of decisions and actions based on data products. What level is currently supported? What infrastructure is required to support shared semantics? What governance mechanisms need to be put in place? +- Application packages for processing pipelines: With a focus on Machine Learning and Artificial Intelligence which plays an increasing role in the context of data science and data integration. This focus area needs to evaluate the applicability of machine learning models in the context of the value-enhancing processing pipeline. What information needs to be provided to describe machine learning models and corresponding training data sufficiently to ensure proper usage at various steps of the pipeline? Upcoming options to deploy ML/AI within processing APIs to enhance climate services are rising challenges e.g. on how to initiate or ingest training models and the appropriate learning extensions for the production phase of ML/AI. Heterogeneity in data spaces can be bridged with Linked Data and Data Semantics. Proper and common use of shared semantics is essential to guarantee solid value-enhancement processes. At the same time, resolvable links to procedures, sampling & data process protocols, and used applications will ensure transparency and traceability of decisions and actions based on data products. What level is currently supported? What infrastructure is required to support shared semantics? What governance mechanisms need to be put in place? === Relevance to the Climate Resilience Domain Working Group