# ClimBurst: A Dynamic Visualization Tool to Display Climatological Anomalies over Time and Space

Guillaume Coulaud, Benoit Lange, Dennis Shasha, Audrey Brouillet, Reza Akbarinia, Florent Masseglia

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## To cite this version:

Guillaume Coulaud, Benoit Lange, Dennis Shasha, Audrey Brouillet, Reza Akbarinia, et al. ClimBurst: A Dynamic Visualization Tool to Display Climatological Anomalies over Time and Space. CIKM 2025 - 34th ACM International Conference on Information and Knowledge Management, ACM, Nov 2025, Seoul, South Korea. pp.6629-6633, 10.1145/3746252.3761466. hal-05379898

HAL Id: hal-05379898

https://inria.hal.science/hal-05379898v1

Submitted on 24 Nov 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# ClimBurst: A Dynamic Visualization Tool to Display Climatological Anomalies over Time and Space

Guillaume Coulaud

University of Montpellier, Inria,
CNRS, LIRMM

Montpellier, Occitanie, France
guillaume.coulaud@inria.fr

Benoit Lange

Inria, University of Montpellier,
CNRS, LIRMM

Montpellier, Occitanie, France
benoit.lange@inria.fr

Dennis Shasha

Courant Institute of Mathematical
Sciences, New York University

New York, NY, USA
shasha@cs.nyu.edu

Audrey Brouillet

ESPACE-DEV, Université Montpellier,
IRD, Université Guyane, Université

Réunion, Université Antilles

Montpellier, Occitanie, France
audrey.brouillet@ird.fr

Reza Akbarinia

Inria, University of Montpellier,
CNRS, LIRMM

Montpellier, Occitanie, France
reza.akbarinia@inria.fr

Florent Masseglia

Inria, University of Montpellier,
CNRS, LIRMM

Montpellier, Occitanie, France
florent.masseglia@inria.fr

## Abstract

Detecting abnormal climate events across temporal and spatial scales is crucial to the understanding of local and regional climate trends. This demonstration introduces ClimBurst, a dynamic tool to detect climate bursts, which are unusually high or low values of one or more climate variables over some time interval. ClimBurst detects bursts without prior assumptions about their temporal duration. The demonstration will allow users to interact directly with our system to see both a summary showing the presence/absence of bursts over a user-specified year and spatial range. The demonstration will also allow users to perform time-travel queries to see how bursts propagate over space and time.

## CCS Concepts

* Human-centered computing → Visualization systems and tools; * Information systems → Spatial-temporal systems; *
* Applied computing → *Earth and atmospheric sciences.*

## Keywords

Time Series, Anomaly detection, Visual analysis, Climate, Bursts

## ACM Reference Format:

Guillaume Coulaud, Benoit Lange, Dennis Shasha, Audrey Brouillet, Reza Akbarinia, and Florent Masseglia. 2025. ClimBurst: A Dynamic Visualization Tool to Display Climatological Anomalies over Time and Space. In *Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM '25), November 10–14, 2025, Seoul, Republic of Korea*. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3746252.3761466

# 1 Introduction

*Motivation:* Climate researchers have identified the detection of anomalous climate events as a goal meriting extensive research [7]. One reason is that such anomalous events can significantly affect

various sectors, particularly agriculture [1] and water availability [6, 8]. ClimBurst is an exploratory tool to find short and long-term anomalies (hereafter, *bursts*) and their propagation through space.

*Nature of Bursts:* A burst may vary in duration. For example, a single day's burst of heavy rain may constitute a high rain anomaly, as may several days in a row of moderate rain. Further, bursts may depend on the season. So, the same several moderate rain days that constitute a burst in a dry season may not constitute a burst in a rainy season. Spatially, a burst in one region may be followed by a burst in several neighboring regions, reflecting the propagation of a burst, such as an anomalous weather system, over space and time. Notice that while our use case is climatology, ClimBurst could be adapted to be used for any seasonal variable, e.g., electricity consumption, water reservoir levels, swimwear, etc.

*Novelty:* Unique among climate tools, ClimBurst finds bursts *without knowing their durations in advance*. It does this in an exhaustive way in order to guarantee not to miss any of these bursts. To achieve efficiency, it performs computations incrementally while taking full advantage of vector processors. Demonstration visitors will be able to interact with the system in real-time, with a few seconds response time over much of the Amazonian basin or the Mediterranean Sea.

Existing visualization tools for climate anomalies present deviations from a seasonal baseline, but for particular time periods. For example, MDV ClimEx [2] supports time-series exploration at individual locations, but considers anomalies at three specific time scales across timescales (i.e., daily, monthly, or seasonal). Voila [3] applies tensor-based methods for spatio-temporal anomaly detection, again for specific time points, leaving to the users the task of seeing whether these anomalies last. Similarly, the phenomena portal [5] displays events detected by convolutional neural networks on static map views, without temporal navigation or interaction.

In contrast, ClimBurst offers dynamic, interactive visualization of ERA5 gridded anomalies, defined as bursts, enabling users to observe how anomalies emerge and propagate across both space and time. Furthermore, ClimBurst's interactive design and rapid computation allow users to adjust parameters, such as selecting interval ranges, to explore and compare anomalies across multiple

<figure><img src="image_3.png" /></figure>
<figure><img src="image_4.png" /><figcaption>Figure 1: The left part (with blue circles) of the interface is dedicated to creating queries and allows the user to select: (1) the variable under study (e.g. temperature or precipitation), (2) the region (tile location, for example, Mediterranean Sea or Amazon), (3) the tile (multiple tiles can be used for the same region), (4) the years to analyze, (5) a map overview of the tile, (6) the z-score threshold to qualify as a burst and (7) the range of day lengths to consider. The right part (with green circle) of the interface is dedicated (1) to visualizing already run queries and (2) rescheduling them.</figcaption></figure>

time scales, for instance, long-term trends (8–12 months) or short-term fluctuations (7–14 days) in near real time.

*Audience:* The intended audience for this demonstration includes researchers and practitioners interested in rapid, scalable analysis of season data over many years. This might include those working with other seasonal time series, such as electricity consumption, crop yield in agriculture, disease surveillance, hydrological resource monitoring, etc.

# 2 ClimBurst Algorithm and Implementation

## 2.1 Definitions

A climate burst (or burst) is defined as a date interval starting in some year y whose accumulated values are unusually high or low for a given climate variable (e.g., +5°C temperature anomaly from July 3 to July 16, 2004) compared to the same date range in all years. Bursts may be short (e.g., a 3-day heat wave burst from April 13 to April 15 of some year) or long (e.g., a 15-day burst from December 26 of year y to January 10 of year y + 1).

Computationally, ClimBurst characterizes a climate burst by a z-score threshold, representing the number of standard deviations by which a data point (e.g., sum of degree-days) deviates from the mean of the distribution. The greater (in absolute value) the z-score value of a data point, the more anomalous it is.

As an illustrative climate example, the z-score of rainfall in Dakar over a certain time interval (e.g., July 8 to July 23) of a certain year (e.g. 2004) would be the z-score of the total rainfall over that period in Dakar based on the total rainfall in Dakar between July 8 to July 23 in each year on record.

Given that ClimBurst can identify climate bursts of any duration, some of these may be included in, or exhibit a high level of overlap, with, one another. This may lead to a considerable degree of

redundancy. We efficiently merge them into continuous sequences, forming a *Merged Burst Representation* (e.g., merging overlapping bursts from July 3–16 and July 10–19 into a single event spanning July 3–19).

## 2.2 Algorithm and Implementation

ClimBurst performs an exhaustive search over all user-selected grid cells, user-selected years, and all interval lengths within a year in order to find all climate bursts regardless of their sub-year durations. The algorithm calculates the sums, means, and variances for all subsequences of a time series within the range of lengths.

By taking advantage of the incremental nature of the sum, ClimBurst avoids redundant calculations and updates the sums of the subsequences with a single vectorized addition. Let T be a time series of observations over several years, $n_s$ the number of subsequences of length $l$ in T, and $A^l$ the vector containing the aggregated values computed for the subsequences of length $l$. To obtain the values for subsequences of length $l + 1$ we reuse the aggregated values obtained for the subsequences of length $l$ and add a single value to them. Formally, let $A_i^l$ be the aggregated value computed for the subsequence $T_{i,i+l}$. We compute the value of $A_i^{l+1}$ as follows for $i = 1, \dots, n_s$:

$$A_i^{l+1} = A_i^l + T_{i+l}$$

The mean and standard deviation are computed incrementally for all dates [9]. This enables the vectorization of the updates and improves the numerical stability of the calculations. The demo code is written in Python 3.12, compiled with Numba, and parallelized with Dask for grid evaluation.
<figure><img src="image_5.png" /><figcaption>Figure 2: Screenshot of the visualization interface for a use case of precipitation in the Amazonian basin. In all subfigures, red connotes high precipitation, blue connotes low, and other colors lie in between. (1) Heatmap of daily maximal z-scores over the years; (2) Histogram of the number of grid cells containing bursts (at least 3 standard deviations from the mean for interval lengths of 14 to 365 days) per year; (3) Histogram of the number of burst days over the course of the year for all grid cells of the region; (4) Raw precipitation data for the date 1956-03-25; (5) Date slider of the time travel; (6) Presence of bursts having z-scores of 3 or more above the mean (red) and below the mean (blue) at grid cells for the date 1956-03-25.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Figure 3: Screenshot of the time travel interface on six different frames. A high temperature event (red area) has been detected close to Cyprus, which propagates over the next four days and then dies out.</figcaption></figure>
## 3 Demonstration Scenarios

This paper demonstrates how the ClimBurst Viewer works and what kind of knowledge can be extracted from it. This demonstrator allows users to visualize and interact with meteorological datasets from anywhere in the world. Since representing the entire globe through multiple screenshots would be complex, the figures in this paper focus on two representative regions: the Amazon Basin and the Mediterranean Sea. For clarity, we also limit the figures to two meteorological variables—sea surface temperature and total precipitation—though the tool supports many other dimensions. A video of the demonstration is available here: <fnref n="1" />. Each dataset consists of gridded data from the ERA reanalysis of the Copernicus Climate Change Service [4]. The Mediterranean dataset includes approximately 4,000 time series, while the Amazon dataset contains around 24,000.

<figure><img src="image_7.png" /><figcaption>**Figure 4:** Sea Surface Temperature for the Mediterranean Sea from 1960 through 2020. Red represents warmth, and blue represents cold. (1) Heatmap of daily maximal z-scores over the years; (2) Histogram of the number of grid cells containing bursts (z-score > 3) per year; (3) Histogram of the number of burst days over the year for all grid cells.</figcaption></figure>

*Software Architecture.* The architecture of the ClimBurst Viewer comprises several blocks. A JavaScript application is used as a viewer, which communicates with a gateway. The gateway is responsible for scheduling a pipeline job for burst analyses and managing files and data. Pipeline tasks are split among worker threads of the server, allowing computations to run faster.

*Query User Interface.* Figure 1 shows the query creation interface on the left and the pipeline manager on the right. For the query creation, the user has to select the region in the world, the variable

(in our case, *sea surface temperature* or *total precipitation*), the analysis year range, the detection threshold, and the interval length range to run the pipeline. In the manager part, there is a summary of existing analyses with all the parameters. The user can delete, view, or rerun an analysis.

When a user clicks on the eye icon, the selected analysis is opened. The view is decomposed into two areas (see Figure 2). The left part is dedicated to statistical exploration, while the right part is dedicated to map exploration. The left part of the UI drives the maps. Users manipulate the year of the map by clicking on the dates. Figure 2 shows the visualization interface for precipitation in the Amazonian basin, while Figure 4 shows only the left part for surface temperature in the Mediterranean Sea.

*Heatmap Analysis.* ClimBurst computes a heatmap year-by-year over the entire region that shows anomalous date ranges above the mean in red and anomalous date ranges below the mean in blue (Figure 2.1 and Figure 4.1). The color saturation is proportional to the value of the z-score. On the selected data, ClimBurst computes the spatial average that provides region-wide information. For the regional analysis, ClimBurst provides daily absolute maximum z-scores. Because a single day can belong to multiple intervals of varying lengths with different z-score values, we assign to each day the most extreme z-score among those intervals. This heatmap analysis highlights the distribution of the highest z-score intensities both across years and within individual years on a day-to-day basis.

*Grid statistical insights.* In order to complement the visualizations, ClimBurst provides statistical insights into the detected bursts of the grid. In particular, it displays yearly histograms showing the number of grid cells where a burst (for the settings of the experiment) starts each year (see Figure 2.2 and Figure 4.2) and the aggregated number of burst days across the grid's time series (see Figure 2.3 and Figure 4.3).

*Time Travel.* A time travel video presents the evolution of the climatic parameter (e.g., sea surface temperature or total precipitation, see Figure 2.4 and 2.6) and bursts within a selected region over time as a video. Figure 3 shows a few frames of that video in which a high temperature burst starts near Cyprus and then spreads nearby.

This functionality allows users to observe how anomalies propagate over time and space within the specified region and time period. Users can slide through the video to focus on specific regions or events (see Figure 2.5). The user can set the number of frames per second for passive viewing.

*Conclusion.* ClimBurst enables an interactive study of climatological (or in general seasonal) time-varying variables over time and space. The ClimBurst interface allows users to upload regions and select years and the method parameters, after which the ClimBurst software will compute both summary statistics and produce time travel videos of bursts in the chosen region.

## Acknowledgments

This work is supported by the Région Occitanie, France.

<footnote n="1">¹https://seafile.lirmm.fr/f/244af984efc74f31b75b/?dl=1</footnote>
# GenAI Usage Disclosure

Some portions of the code used for this paper were written with assistance from Microsoft Copilot. The authors take full responsibility for the correctness and integrity of all code used in this work.

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