RHITA: a web tool for real-time detection of extreme weather events

Cazzaniga, Greta; Akakpo-Numado, Anastasia; Brockmann, Patrick; Burq, Adrien; Vrac, Mathieu; Faranda, Davide

doi:10.5194/egusphere-2026-1175

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https://doi.org/10.5194/egusphere-2026-1175

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18 Mar 2026

| 18 Mar 2026

Status: this preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).

RHITA: a web tool for real-time detection of extreme weather events

Greta Cazzaniga, Anastasia Akakpo-Numado, Patrick Brockmann, Adrien Burq, Mathieu Vrac, and Davide Faranda

Abstract. Extreme weather hazards are increasing and stakeholders need rapid, transparent information during unfolding events. We present RHITA (Real-time Hazard Identification and Tracking Algorithm), an open-source framework and web tool for near real-time detection and tracking of weather-related hazards over Europe. RHITA identifies grid cells exceeding local quantile thresholds, groups them into spatial clusters, and links clusters through time to reconstruct three-dimensional events in longitude, latitude, and time. For each event, RHITA provides intensity, extent and duration metrics and estimates rarity through return periods derived from a long historical record. RHITA is operated with ECMWF open forecasts for daily monitoring and ERA5 reanalysis for a consistent historical archive from 1950 to 2024. We target four hazards: heatwaves, cold spells, heavy precipitation and strong winds. Key spatial and temporal parameters are optimized against EM-DAT disaster records (2000 to 2023). Applying RHITA to ERA5 yields a European climatology of hazard events and reveals robust increases in heatwave frequency, intensity and affected area, a decline in cold spell frequency, and more heterogeneous signals for heavy precipitation and strong winds at the continental scale. RHITA provides open access data and an interactive interface to support rapid hazard characterization, event contextualization and downstream risk analysis.

Received: 02 Mar 2026 – Discussion started: 18 Mar 2026

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Greta Cazzaniga, Anastasia Akakpo-Numado, Patrick Brockmann, Adrien Burq, Mathieu Vrac, and Davide Faranda

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RC1:
'Comment on egusphere-2026-1175', Anonymous Referee #1, 08 Apr 2026 reply
Reviewer’s comments:
Recommendation: Subject to minor revision. If revised paper is resubmitted, it needs to be reconsidered and re-reviewed.
Comments: on egusphere-2026-1175: RHITA: a web tool for real-time detection of extreme weather events.
The manuscript entitled “RHITA: a web tool for real-time detection of extreme weather events” presents an open-source framework for near real-time detection and tracking of weather-related hazards across Europe using ECMWF forecasts and ERA5 reanalysis data (1950–2024). The study focuses on four hazard types: heatwaves, cold spells, heavy precipitation, and strong winds. The work addresses an important and timely topic, particularly in the context of climate change and the growing need for operational, user-friendly tools to support hazard monitoring and risk assessment. The integration of real-time capability with a historical climatology is a clear strength, and the web-based interface enhances accessibility and usability.
However, several methodological and conceptual aspects require clarification and strengthening before the manuscript can be considered for publication. It would be suitable for publication after addressing minor revisions. The following are some suggestions for improvement:
While making the revision, please highlight the corrections added to the manuscript, so it will be easier to track the changes.
General comments:
The study does not consider droughts, which are a major category of natural disasters and are included in the EM-DAT database. The authors should either: provide a clear justification for excluding droughts, or discuss the feasibility and limitations of incorporating drought detection within the RHITA framework.

The Discussion section would benefit from a dedicated paragraph addressing uncertainties associated with: ERA5 reanalysis and ECMWF forecasts, EM-DAT data biases, parameter selection in the algorithm. Additionally, the potential impact of these uncertainties on the results and conclusions should be explicitly discussed.

Please clarify whether any preprocessing steps were applied to ECMWF forecasts and ERA5 data.

The current evaluation relies primarily on sensitivity. Including additional performance metrics such as precision and F1-score would provide a more balanced and comprehensive assessment of the algorithm’s performance.

Specific comments:
Lines 22-24 : Please add the appropriate references to support the statements made in this section.

Line 66: It is recommended to include the date of data access/download for all datasets to ensure reproducibility and traceability.

Line 110 : Consider including a sensitivity analysis to evaluate how the results vary with different threshold choices. This would strengthen confidence in the robustness of the methodology.

Line 120 : The use of fixed quantile thresholds (e.g., 0.99 and 0.01) should be further justified. Please: provide references to previous studies that have used similar thresholds, and/or explain the rationale behind selecting these specific values.

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Citation: https://doi.org/10.5194/egusphere-2026-1175-RC1
RC2:
'Comment on egusphere-2026-1175', Milad Basirifard, 12 Apr 2026 reply
A key strength of the manuscript is the development of an open and operational multi-hazard framework. However, I have concerns regarding the robustness of the event climatology, given the sensitivity of the algorithm to thresholding and tracking parameters, the reliance on EM-DAT for calibration despite known reporting inhomogeneities, and the comparatively weak validation results for certain hazards, especially cold spells. Additional sensitivity analyses and clearer discussion of these limitations would strengthen the manuscript.
RHITA decides what an “event” is using chosen thresholds for extremeness, spatial grouping, minimum area, and temporal linking. Those choices are sensible, but they are still subjective and they strongly affect what gets counted as one event versus many. So the results are not purely “found” in the data; they are partly built by the algorithm design.

EM-DAT records disasters with societal impacts, but it has reporting gaps, inconsistencies, and country-level aggregation. The authors acknowledge this directly. That means RHITA is being tuned against a socially filtered disaster archive, not a perfect physical catalogue of meteorological extremes.

Validation sensitivity for cold spells is much lower than for heatwaves or heavy precipitation. That is a real weakness, not a minor caveat. The authors explain it as a mismatch between their symmetric physical threshold and EM-DAT’s broader reporting of cold events, but the practical outcome is still poorer detection for that hazard.

For strong winds, they reuse the heavy-precipitation parameter set because EM-DAT does not cleanly isolate wind-only disasters, and they do not report sensitivity metrics for wind. So one hazard class in the system is less well validated than the others.

The 0.25° data are good for broad, persistent systems, but not ideal for short-lived, localized, convective extremes. So RHITA is likely better at continent-scale heatwaves and large storms than at intense local downpours or small-scale severe weather.

The tool says how extreme the weather is, but not how exposed or vulnerable the affected population is. That means it cannot by itself explain disaster severity, even though users may be tempted to interpret it that way.

Some of the Europe-wide trend analyses may conceal important regional or seasonal differences, particularly for precipitation and wind, where the reported signals are more heterogeneous.

Its big contribution is building an operational framework and web tool. That is useful, but it means the scientific novelty is more methodological than conceptual. It does not revolutionize understanding of extremes; it operationalizes known ideas in a unified system.

Reply
Citation: https://doi.org/10.5194/egusphere-2026-1175-RC2

Greta Cazzaniga, Anastasia Akakpo-Numado, Patrick Brockmann, Adrien Burq, Mathieu Vrac, and Davide Faranda

Supplement

https://doi.org/10.5194/egusphere-2026-1175-supplement

Greta Cazzaniga, Anastasia Akakpo-Numado, Patrick Brockmann, Adrien Burq, Mathieu Vrac, and Davide Faranda

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Short summary

Extreme weather events are becoming more frequent and severe, creating a strong need for rapid and reliable information. We developed an open tool that automatically detects and tracks heatwaves, cold spells, heavy rain, and strong winds across Europe, both in real time and in past decades. By comparing current events with long historical records, the tool shows how unusual an event is and reveals clear increases in heatwaves, while other hazards show more mixed changes.


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