3.1.  Extreme Weather Event monitoring workflows applied to Typhoon, Flooding, Heat and Landslides

Prototype workflows that utilize OGC standards and standards-based APIs to deliver data-driven services—including modeling and analytical services—can now be developed and deployed rapidly. This advancement is demonstrated by Hartis’ review of typhoon and atmospheric models, GeoLab’s implementation of a landslide model, and additional examples currently in development.

Hartis’ Typhoons and Atmospherics Report outlines their approach to developing key components and workflows for typhoons, atmospheric rivers (ARs), and other extreme atmospheric phenomena. The report provides a comprehensive review, including:

Examining Tropical Cyclones (TCs) through extensive global simulations covering multiple decades necessitates the objective and automated identification and monitoring of these storms, a task achieved through specialized “TC trackers”. These trackers are algorithms designed to recognize cyclonic formations characterized by warm cores within gridded datasets and connect them into coherent trajectories (Bourding et al. 2022). Key parameters and modeling schemes according to Liu et al. (2023) can be identified as:

The work focused on analyzing different Numerical Weather Prediction (NWP) datasets and TC trackers used for predicting TC’s paths and intensities. A range of TC trackers have been tested for their performance including those detailed in the following reports: (Arthur et al. 2021; Bourdin et al. 2022; Kitamoto et al. 2024; Pérez-Alarcón et al. 2024). Their literature review demonstrates there is a large pool of observed TCs that all trackers detect: There were 3510 tracks in total in IBTrACS catalog and about 2400 of them are detected by almost all trackers. Notably, the detection skills of all trackers are identical and nearly perfect for the strongest tracks in a simulation. This means that no detected track beyond category 2 (included) is a false alarm, and TC observed in categories 4-5 are found regardless of the tracker used. This situation suggests the need for interoperable TC models which can be compared or used as an ensemble.

To progress this, workflows and tools for numerical weather prediction data fusion and interoperability are being examined by the Hartis team. This review of OGC Standards, libraries, and APIs focuses on the capabilities needed to support User-Defined Processes for utilizing OGC API Processes to allow users to define and deploy custom processing algorithms in the cloud. This is particularly useful for researchers and meteorologists to run custom models and simulations based on specific needs or experimental setups. This enables Machine Learning Models used in the TC trackers reported above (see Fig. below) to be integrated directly through an API endpoint. This enhances the capabilities of OGC API Processes and/or OGC Environmental Data Retrieval (EDR) API to support deploying and managing machine learning models that can predict typhoon behavior based on historical data.

The detailed report and Review of Typhoons and Atmospheric Models is published on Zenodo. The Flood Impact Analysis Workflow, which was developed for the realization of the calculation and analysis of areas that will be affected by a flood incident, is available on an Open Science Framework (OSF) node. It can be downloaded and used for further experimentation.

 

Figure 1 — Potential User-Defined Processes for Track Forecasts (red rectangle), utilizing OGC API Processes to allow users to define and deploy custom processing algorithms, for example by deploying machine learning in typhoon forecasts in different TC trackers (Liu et al. 2023)

Also focused on this challenge, Wuhan University’s Early Warning Flood Demonstrator deploys an overlapping set of tools to meet region-specific requirements in China. Due to complex geographic and climatic conditions in the region, flash floods exhibit characteristics of wide distribution, large quantity, strong suddenness, and high destructiveness. The extensive monitoring of floods poses challenges in organizing and managing geospatial observation data. The team proposed a new geospatial infrastructure called GeoCube Gao et al. (2022), which facilitates the management and analysis of massive Earth observation data. Compared to previous work on data cubes, GeoCube extends the capacity of data cubes to accommodate large vector and raster data volumes from multiple sources, making it suitable for complex flood warning analysis. Additionally, to address the system’s support for data stream processing, we also propose a real-time modeling observational stream computing model Shangguan et al. (2019). This model integrates sensor web with models in a streaming computing environment to provide timely decision support information.

Building on previous work, the team proposed a system architecture (see diagram below) supporting automated data retrieval and preprocessing workflow that fetches data from multiple sources and storage in different databases by type. The system uses the Spark distributed computing framework and a variety of third-party geographic analysis libraries to enrich operation. Operators and data are provided to the frontend via the OGC API, where users can interact with a visualization interface based on Cesium to obtain thematic map results as needed. This process uses OGC API-Features to provide web services for vector data, OGC API-Coverages for web services of raster data, and OGC API-Processes to provide web services for operators. With these services, users can call their own data and create their own workflows on the frontend of the system.

 

Figure 2 — Overview of the system developed by Wuhan University

The workflow established an interface for hourly data retrieval and preprocessing for the warning system, enabling the system to automatically update with the latest observation data, ensuring the timeliness of the alert system. It sets up a flood simulation workflow that implements several hydrological models based on a foundational geographic computing library and simulates floods by coupling these models together. This process enabled tracking changes in surface water accumulation during rainfall events.

On this basis, the team developed comprehensive flood warning indicators. For the indicators, ponding analysis was conducted using depressions of different scales. The analysis considered that during rainfall events, surface water first accumulates in local depressions and then gradually converges towards larger depressions across the entire area. This work allowed the simulation of the process of water accumulation from small to large depressions and calculated a weighted average of multi-scale ponding risks based on this process. Consequently, the system is capable of producing a comprehensive flood risk index for the entire area.

 

Figure 3 — Wuhan University’s Early Warning Flood Detection Model.

Hartis’s Early Warning Flood demonstrator highlights another path, building a workflow in ESRI’s software suite and publishing the final workflow as an OGC standards conformant service. This work illustrates use of regional higher resolution DEMs in automated workflows developed for flood modeling at a regional scale, which may be adapted and reused in new regions. To support reuse, their objective is to present end-users with an abstraction of the workflow, requiring only a few essential inputs to run in a python environment.

 

Figure 4 — Hartis’s early warning flood detection workflow.

This approach was paralleled by Safe Software. Their weather event workflow component took existing publicly available weather service data streams such as from the National Weather Service and enriched them with the context of local hazard information to increase the resolution and relevance of warning products for local residents and responders. This experimental application was implemented for North Manhattan in New York City as part of Safe’s contribution to the NYC GISMO led Extreme Heat project. This could also be readily deployed for other urban settings given the appropriate foundational data layers and services.

For example, temperature observations and forecasts were combined with urban heat index information so that areas more affected by urban heat island effects could receive localized warnings that take these temperature stresses into account. This workflow was authored on the FME platform. It accepted GeoRSS requests from client applications, consumed weather observations from the National Weather Service API on api .weather .gov, and queried the Urban Heat Index ARD component via OGC Feature API for locations of interest. Alert notifications were transmitted back to the GeoRSS client and also were available via OASIS Common Alerting Protocol (CAP) XML or HTML messages.

 

Figure 5 — Weather Event Service & ARD workflow to support urban heat scenarios.

Weather Event Service Workflow:

This localized urban weather notification tool added to the work Safe Software has contributed in previous OGC disaster and climate pilots related to heat, drought and flood modeling. Safe Software’s ARD to DRI contribution and component provides further details as to how data from this weather event service feeds decision indicators and notifications.

GeoLabs standardized approach for identifying potential landslide-affected areas utilizing satellite imagery from Sentinel 1 and Sentinel 2 in GeoDataCube format provides another example. They use sensor data on rainfall and displacement to predict future displacements and Sentinel 1 & 2 imagery to build a deep-learning model that predicts landslide-damage risk zones. The output is a thematic map for areas potentially affected by landslides. Utilizing data cubes makes data management easier and further helps in analysis.

Their work provides a regional implementation in Taiwan, which is highly prone to earthquakes, due to the intersection of three tectonic plates. The area is known as “Ring of Fire”, and is the most seismically active zone in the world. The region’s highly mountainous landscape magnifies ground shaking, leading to landslides.

The proposed prototype is a server implementation of a standardized framework built on Common Workflow Language (CWL) to support workflows for landslide susceptibility mapping and analyzing corresponding Decision Ready Indicators. The workflows are comprised of the following primary components supporting OGC API Processes — Part 2: Deploy, Replace, Undeploy.

 

Figure 6 — Schematic view of a landslide event.

Processes for landslide detection and prediction are implemented in a inference-as-a-service framework for passing inputs to the model for generating landslide detection maps and rainfall displacement.

 

Figure 7 — A conceptual workflow (left) and an implementation (right), showing the processes description for the services required to be developed for this landslide modeling task.

Two weather event modeling workflows consolidate the maturing of this toolset. WiTech GmbH developed a prototype capable of simulating carbon emissions from existing 3D building stock datasets that uses future weather profiles in real-time on the web, leveraging the SimStadt simulation engine integrated through OGC API: Processes and taking OGC API 3D GeoVolumes as an input data format.

On opening the web application, the client fetches default OGC API – 3D GeoVolumes endpoints, displaying available collections and their extents. Users can add custom 3D GeoVolumes endpoints. On selecting a 3D GeoVolume, the client retrieves the associated 3D city model from the server, centering the map on its spatial extent. Users can inspect building information in the 3D Tiles batch table. They can then initiate a simulation process for registered city models, defining process parameters and styling preferences. Configuration parameters are submitted via HTTP post request to the OGC API – Processes endpoint, where the process is executed server-side and results are returned to the client.

The overall benefit of this central standard interface is that it can help streamline multiple data operations into one online end-to-end web client which ultimately will allow even non-simulation experts such as architects or urban designers to make faster and better decisions about the effects of climate change on carbon emissions of building stocks, including for future climatic scenarios.