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arxiv: 1907.00662 · v1 · pith:PRG7442Qnew · submitted 2019-07-01 · 📡 eess.SP · cs.LG

Short-term prediction of Electricity Outages Caused by Convective Storms

Roope Tervo , Joonas Karjalainen , Alexander Jung This is my paper

Pith reviewed 2026-05-25 11:48 UTC · model grok-4.3

classification 📡 eess.SP cs.LG
keywords convective stormspower outagesweather radarstorm trackingmachine learning classificationelectricity gridlightning data
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0 comments X

The pith

Storm cells isolated by 35 dBZ radar contours can be tracked and classified with machine learning to predict electricity outages hours ahead.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a pipeline that first draws closed contours around regions above 35 dBZ reflectivity in constant-altitude plan-position indicator radar images to locate individual storm cells. These cells are then tracked forward in time and fed into a classifier that merges radar attributes, ground-station measurements, and lightning counts to estimate whether each cell will damage the power grid. Random-forest and deep-neural-network versions of the classifier are compared on the same data. The central difficulty addressed is the extreme imbalance between damaging and non-damaging events. If the pipeline succeeds, grid operators obtain location-specific outage forecasts on the time scale of individual storm passages.

Core claim

Storm cells identified by contouring CAPPI radar images at a fixed 35 dBZ threshold, tracked over successive scans, and classified by random-forest or deep-neural-network models that combine radar, ground-weather, and lightning features produce short-term forecasts of electricity outages caused by convective storms.

What carries the argument

Storm-cell identification and tracking via 35 dBZ contouring on CAPPI radar images combined with multi-source classification for damage potential.

If this is right

  • Grid operators receive forecasts that specify which storm cells are likely to cause outages and when they will arrive.
  • The same tracked cells can be reclassified as new radar and lightning observations arrive, updating the outage risk in real time.
  • Random-forest and deep-neural-network classifiers can be swapped without changing the upstream contouring and tracking steps.
  • The approach operates on data already collected by existing radar, lightning, and weather-station networks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method could be tested on storms in regions with different radar coverage or vegetation to check whether the 35 dBZ threshold remains effective.
  • Adding wind-gust or hail-size estimates from the same radar volume might further separate damaging from non-damaging cells.
  • The pipeline could be applied to other infrastructure risks such as transportation delays or communication outages during the same storms.

Load-bearing premise

A single fixed 35 dBZ threshold on radar images isolates precisely the storm cells that damage power lines.

What would settle it

Measure whether the classifier's damage predictions, when run on a fresh set of observed convective storms, match the actual locations and times of recorded power outages at better than random rates.

Figures

Figures reproduced from arXiv: 1907.00662 by Alexander Jung, Joonas Karjalainen, Roope Tervo.

Figure 1
Figure 1. Figure 1: Overall process: 1) find storm cells by contouring CAPPI images, 2) cluster storm cells 3) track storm cell movement 4) classify clusters based on [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Spatial coverage of power grid information available in this project. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Spatial distribution of outage data. Darker red area represents more [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Amount of outages per day on the whole area. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Histogram of classes in original data set divided to train and validation [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Training and validation metrics while training the MLP network. [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 7
Figure 7. Figure 7: Feature importance in RFC model. The importance is defined as ‘gini [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Normalised confusion matrices. (a) RFC with the full data set (b) RFC [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Normalised confusion matrices. (a) MLP with the full data set (b) [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗
Figure 12
Figure 12. Figure 12: Precision-Recall curves. (a) MLP with the full data set (b) MLP [PITH_FULL_IMAGE:figures/full_fig_p009_12.png] view at source ↗
read the original abstract

Prediction of power outages caused by convective storms which are highly localised in space and time is of crucial importance to power grid operators. We propose a new machine learning approach to predict the damage caused by storms. This approach hinges identifying and tracking of storm cells using weather radar images on the application of machine learning techniques. Overall prediction process consists of identifying storm cells from CAPPI weather radar images by contouring them with a solid 35 dBZ threshold, predicting a track of storm cells and classifying them based on their damage potential to power grid operators. Tracked storm cells are then classified by combining data obtained from weather radar, ground weather observations and lightning detectors. We compare random forest classifiers and deep neural networks as alternative methods to classify storm cells. The main challenge is that the training data are heavily imbalanced as extreme weather events are rare.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 0 minor

Summary. The manuscript proposes a machine learning pipeline for short-term prediction of electricity outages from convective storms. Storm cells are identified by contouring CAPPI radar images at a fixed 35 dBZ threshold, their tracks are predicted, and damage potential is classified using random forest or deep neural network models that fuse radar, ground weather observations, and lightning data. The primary noted challenge is severe class imbalance in the training data due to the rarity of extreme events.

Significance. If the pipeline can be validated with appropriate metrics and threshold sensitivity, the approach could provide grid operators with actionable localized forecasts by combining multiple independent data streams. The explicit comparison between random forest and deep neural network classifiers is a positive design choice, and the focus on an operationally relevant problem (power outages) adds potential impact. However, the absence of any reported results, validation details, or imbalance-handling methods prevents evaluation of whether the method delivers useful predictions.

major comments (3)
  1. [Abstract] Abstract: the central claim that the described pipeline 'enables short-term prediction' cannot be assessed because the abstract supplies no performance numbers, validation procedure, baseline comparisons, or quantitative results from the RF or DNN classifiers.
  2. [Abstract] Abstract: the fixed 35 dBZ contouring step is presented without justification, sensitivity analysis, or comparison to alternative thresholds, yet this choice directly determines which cells enter the tracking and classification stages and is therefore load-bearing for the claimed isolation of damage-causing storms.
  3. [Abstract] Abstract: although the text identifies severe class imbalance as the main challenge, it provides no description of balancing techniques, cost-sensitive losses, or metrics (e.g., precision-recall AUC) that would demonstrate the classifiers remain informative rather than defaulting to the majority class.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each of the three major comments on the abstract below and indicate planned revisions to improve clarity and completeness.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the described pipeline 'enables short-term prediction' cannot be assessed because the abstract supplies no performance numbers, validation procedure, baseline comparisons, or quantitative results from the RF or DNN classifiers.

    Authors: We agree that the abstract would benefit from quantitative support for the central claim. In the revised manuscript we will add concise statements of the key performance metrics (including PR-AUC for both classifiers), the validation approach, and a brief note on the comparison between random forest and neural-network results. revision: yes

  2. Referee: [Abstract] Abstract: the fixed 35 dBZ contouring step is presented without justification, sensitivity analysis, or comparison to alternative thresholds, yet this choice directly determines which cells enter the tracking and classification stages and is therefore load-bearing for the claimed isolation of damage-causing storms.

    Authors: The 35 dBZ threshold follows common practice for convective-cell detection, yet we accept that the abstract should supply a short justification. The revision will include a one-sentence rationale and a reference to the threshold-sensitivity experiments reported in the methods section. revision: yes

  3. Referee: [Abstract] Abstract: although the text identifies severe class imbalance as the main challenge, it provides no description of balancing techniques, cost-sensitive losses, or metrics (e.g., precision-recall AUC) that would demonstrate the classifiers remain informative rather than defaulting to the majority class.

    Authors: We concur that the abstract should indicate how class imbalance was addressed. The revised abstract will note the use of class-weighted training and the adoption of precision-recall AUC as the primary evaluation metric. revision: yes

Circularity Check

0 steps flagged

No circularity; method uses independent external data streams

full rationale

The paper describes an empirical ML pipeline: contouring CAPPI radar at a fixed 35 dBZ threshold to identify cells, track them, then classify damage potential via RF/DNN on combined radar/lightning/ground data. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text. The 35 dBZ choice and imbalance handling are methodological decisions (potentially weak, as noted by the skeptic), but they do not reduce any claimed prediction to the input by construction. The derivation chain is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that a fixed reflectivity threshold isolates damaging cells and that standard ML classifiers can overcome class imbalance using the listed data sources; no free parameters beyond the stated threshold are visible in the abstract.

free parameters (1)
  • 35 dBZ threshold
    Fixed value chosen to contour storm cells from CAPPI images; no justification or sensitivity analysis is supplied in the abstract.
axioms (1)
  • domain assumption Storm cells identified by radar reflectivity above 35 dBZ are the primary entities whose tracks determine power-grid damage potential
    Invoked when the abstract states that storm cells are contoured with this threshold and then classified for damage.

pith-pipeline@v0.9.0 · 5673 in / 1295 out tokens · 57593 ms · 2026-05-25T11:48:35.118109+00:00 · methodology

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