the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 May 2026
Mobile weather radar to provide intelligence on wildfire hazards
Abstract. Extreme wildfire behaviour is increasingly associated with deep pyro-convection and pyro-cumulonimbus (pyroCb) development, posing challenges for both scientific understanding and operational wildfire management. Existing wildfire intelligence relies on a combination of aerial observations, satellite remote sensing, numerical weather prediction, and in-situ measurements, which can be limited in resolving rapidly evolving plume dynamics and near-fire wind changes. Portable weather radars represent a promising observational approach by providing high-resolution measurements of plume structure, kinematics, and microphysical properties, together with conventional radar products such as precipitation and outflow detection. However, their role within wildfire intelligence frameworks remains relatively unexplored.
In this study, we review two decades of portable radar observations of wildfires and prescribed burns and present new analyses from a recent deployment of a mobile dual-polarization X-band radar during an extreme wildfire in Eastern Australia. Building on these observations, we introduce a set of radar-derived diagnostics relevant to wildfire intelligence, including plume depth evolution, signatures of pyro-convective dynamics, detection of low-level wind changes, and indicators associated with transitions from pyro-cumulus to deep convection. These diagnostics complement existing satellite and modelling approaches by providing information on processes that are not directly observable from current operational platforms.
Using a Technology Readiness Level (TRL) framework, we assess the maturity of portable radar applications for wildfire monitoring. While fundamental aspects of pyrometeor scattering remain at low readiness (TRL 1–3), repeated field deployments indicate increasing maturity of plume observations (TRL 4–5), with some diagnostic products approaching pre-operational capability. These findings suggest that further progress will depend not only on technical development but also on practical considerations such as deployment logistics, integration with existing systems, and operational evaluation. Overall, portable weather radar may provide an additional component of future multi-sensor wildfire intelligence systems, helping to bridge part of the observational gap between satellite remote sensing and fireground measurements while improving understanding of fire–atmosphere interactions.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Natural Hazards and Earth System Sciences.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
(13661 KB) - Metadata XML
-
Supplement
(696 KB) - BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on egusphere-2026-2256', Anonymous Referee #1, 10 Jul 2026
- AC1: 'Reply on RC1', Adrien Guyot, 08 Aug 2026
-
RC2: 'Comment on egusphere-2026-2256', Anonymous Referee #2, 30 Jul 2026
Review of the manuscript Mobile weather radar to provide intelligence on wildfire hazards by Guyot et al.
General comments
In this paper, the authors address the critical challenges of wildfire intelligence and operational management. They demonstrate the utility of mobile radar monitoring to resolve sub-km plume structure and detect rapid dynamical changes by listing observational requirements and operational constraints, radar-specific requirements and providing a comprehensive review of the main radar observations of smoke plumes and pyroCu/pyroCb convective clouds from prescribed burns and wildfires over the last two decades.
Then, the authors present a new analysis from a recent mobile X-band radar observation of a recent large wildfire in Australia to demonstrate the feasibility to produce portable radar-derived products that could be used by fire agencies. Most interestingly, they develop a road map from research to operations, assess the maturity of the technology for use in operational context to support wildfire management, and make recommendations for future work.
The manuscript is very clearly written and structured and comprehensively addresses challenges in wildfire observations with the stated objective of supporting operational management. Beyond the quasi-exhaustive review of the literature, the work is adequately supported by data, including Supplementary material from the Wallangara wildfire observations, and well suited to NHESS. I therefore recommend publication with minor corrections, as listed below.
Specific comments
135-140 Tsai et al (2009) first reported pulsed W-band radar RHI and PPI scans of a prescribed burn smoke plume revealing smoke plume reflectivities in the vicinity of -30 dBZ. Donnadieu et al (2026) recently measured a prescribed burn smoke plume in oblique fixed poiting using a miniBASTA FMCW cloud radar (95 GHz). Reflectivities ranged from -40 to -9 dBZ with Doppler +/-7m/s at ranges up to 6 km. Modify fig1 and Table1 accordingly
137 Doppler
148 the condensation level, the lidar attenuated
161 May et al. (2024) use
187 fully name AWS
273 (the so-called Doppler dilemma), making Doppler measurements
276-281 advantages of RHI and PPI scans are described and combined strategy promoted. However, wildfire observational conditions are often short and changing, pushing operators to chose ; based on their experience, the authors could give their opinion as to the optimum scan strategy in a general case or according to specific observation / smoke behaviour contexts, as done in the following sections on hardware architecture.
382 “range–height indicator (RHI) at 03:00 UTC”: please make timestamps consistent with those displayed in fig.3.
Fig.3b what does the vertical line at 04:38 stand for?
395 the “gradual buildup in low-level reflectivity, followed by a rapid vertical extension, consistent with convective-like burst dynamics”: not easily seen from fig.3; any way to show this important feature better (arrow pointing to data, zoomed inset showing different plotting)?
Fig.4 caption: “operational Doppler radar” ; NSW = ? (also line 425)
415 likewise horizontal wind components and vertical velocity, briefly explain how vertical vorticity shown in fig.4 is obtained
Fig.5 caption: “weather radar (Sydney (Terrey Hills) on the 21st of December 2019)” at 06:30 (upper panels) and 07:24 (lower panels).
509 Fig. 6 documents the evolution of one of the pyroconvective clouds
572 PCA ?
Fig.7 caption : indicate meaning of vertical dashed line
Fig.10 caption: Horizontal slice of…
Fig.11 This3D representation effort is valuable. To really take adavantage of it, more distinct colour scales for distinguishing better pyrometeors from hydrometeors would be useful, and some length scale for the atmospheric features
691 LES models ?
691-701 make reference to fig.12
Supplementary material
I was unable to find the “full temporal evolution is provided as an animated sequence” in the Supplementary Material: please provide it and make reference to a filename
Name/explain the original radar variables DBZH, VRADH, WRADH, ZDR, RHOHV
Please provide a reference for the Gaussian Mixture Model (GMM)
What does the “weighted PCA of the plume mask” stand for?
Fig.S1: subplot labels do not match those in caption: to be harmonized. what do the colours stand for in S1(a)?
Some contextualization and explanation of fig.S2 is needed in Suppl. Material; also label x-axis
Citation: https://doi.org/10.5194/egusphere-2026-2256-RC2 - AC2: 'Reply on RC2', Adrien Guyot, 08 Aug 2026
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 358 | 194 | 28 | 580 | 38 | 31 | 24 |
- HTML: 358
- PDF: 194
- XML: 28
- Total: 580
- Supplement: 38
- BibTeX: 31
- EndNote: 24
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
This is a timely and innovative manuscript addressing an important observational gap in wildfire science and operational fire intelligence. The paper makes a strong case for portable weather radar as a complementary observing platform capable of resolving plume structure, pyroconvective dynamics, pyrometeor transport, and low-level wind changes at spatial and temporal scales that are generally inaccessible to satellites, numerical models, and sparse surface observations. The integration of a literature synthesis, radar-derived diagnostics, a recent demonstration case, and a Technology Readiness Level framework is particularly valuable. The manuscript is scientifically interesting and contains several impressive observational examples. I particularly appreciated the effort to connect fundamental radar science with practical deployment constraints, fire-agency workflows, multisensor integration, and coupled fire–atmosphere modelling. The Wallangarra case study and the proposed operational roadmap provide a useful foundation for future research and pre-operational development.
I am positive about the manuscript and support publication following revision. The main improvements required concern the overall structure, methodological transparency, consistency between claims and evidence, clearer separation between review material and original analysis, and substantial language and presentation editing. At present, some sections read as a synthesis paper, others as a methods or case-study paper, and others as a perspective or operational concept article. Clarifying this identity would significantly strengthen the contribution.
Major comments:
- The manuscript combines four substantial components: i) a review of approximately two decades of portable radar observations, ii)a synthesis of radar-derived wildfire diagnostics, iii)original analysis of the Wallangarra case, and iv) a TRL assessment and operational roadmap. All four components are valuable, but their relative roles are not sufficiently clear. The title and abstract emphasize portable radar for wildfire intelligence, whereas large parts of the paper discuss operational fixed-site radar examples, previously published cases, model evaluation, and conceptual operational workflows. I suggest that the authors explicitly define the manuscript as either i) a review and perspective paper supported by a demonstration case, ii) or an original research paper presenting a diagnostic framework, with the review serving as context.
- Several examples are taken from earlier publications, while the Wallangarra analysis appears to contain the main new results. However, the distinction is sometimes difficult to follow. The authors should clearly identify for every figure and analysis whether it is reproduced or adapted from a previous publication, newly processed from an existing dataset, newly analysed for this manuscript, or presented only as a conceptual illustration.
- The Wallangarra case is central to the manuscript, but much of the methodological information is deferred to the Supplement. The main text should include sufficient detail to understand and evaluate the analysis independently. For example, how plume boundaries were identified, how the plume origin and principal axis were calculated, the radar volume and temporal sampling, quality-control and attenuation-correction procedures, treatment of ground clutter and precipitation contamination, uncertainty associated with the calculated plume volume and various metrics.
- Several interpretations appear stronger than can be supported directly by reflectivity and polarimetric observations alone. For example, increases in plume volume, reflectivity, or EPC fraction are interpreted as evidence of stronger updrafts, enhanced mass loading, aggregation, larger particles, firebrands, or increased fire intensity. These are physically plausible interpretations, but they are not always uniquely determined by the radar measurements. Thus, I would recommend more moderate casual and physical interpretations in some parts of the manuscript.
- The manuscript presents several promising diagnostics, including plume depth, pulsing, rotation, wind-change detection, pyroCu-to-pyroCb escalation, and firebrand-related signatures. However, the uncertainties and potential failure modes are not treated in sufficient depth. This would make the proposed operational pathway more credible.
- The TRL framework is a useful and original element, but its application is currently somewhat inconsistent (overlapping categories e.g., “5–6” and “6” category). In addition, the manuscript sometimes assigns different readiness levels to hardware, algorithms, deployment procedures, physical understanding, and operational integration. I would recommend either revising or adding a separate TRL assessment level into distinct capability components, for example, radar hardware and mobility, fireground deployment procedures, plume detection, pyrometeor classification, kinematic retrievals, operational integration.
- Although I fully understand the concept of “wildfire intelligence”, I would recommend the authors to clarify that and provide an explicit definition early in the manuscript.
Minor comments:
- The Figure 2 caption contains duplicated and apparently incorrect panel labels.
- In Section 4.2, Figure 3a is described in the text as 03:00 UTC, while the caption gives 04:12 UTC. Please correct.
- Figure 4 is hard to read in the current resolution and format.
- Section 4.1 would benefit from a schematic workflow.
- In Section 4.5, I think that Figure 6 is mistakenly referred to as Figure 4.
- Figure 8 uses different vertical and horizontal scales across panels. These differences should be emphasized to avoid misleading visual comparisons.
- Could the authors clarify what the blue contours in Figure 8 represent?
- Figure 9 would benefit from a geographic orientation arrow and clearer explanation of how plume origin was determined.