RAAVEN data processing for TORUS and TORUS-LItE

Houston, Adam L.; De Bruin, Mark; Gómez-Faulk, Céu; Argrow, Brian

doi:10.5194/essd-2026-134

Preprints

https://doi.org/10.5194/essd-2026-134

Preprints

20 Apr 2026

| 20 Apr 2026

Status: this preprint is currently under review for the journal ESSD.

RAAVEN data processing for TORUS and TORUS-LItE

Adam L. Houston, Mark De Bruin, Céu Gómez-Faulk, and Brian Argrow

Abstract. RAAVEN (Robust Autonomous Airborne Vehicle - Endurant and Nimble) uncrewed aircraft systems (UAS) were deployed in and around supercell thunderstorms during the Targeted Observation by Radars and UAS of Supercells (TORUS) and TORUS Left Flank Intensive Experiment (TORUS-LItE) field campaigns. On-board sensors measured temperature, humidity, pressure, and wind. Despite extensive predeployment testing, the demanding environments where data collection occurred presented numerous challenges to data quality. In this article, extensive quality control procedures adopted for these data are described. Many of these procedures aim to quantify data-quality uncertainty, in lieu of correcting questionable data. Procedures address the dependency of estimated wind on aircraft manoeuvring, periodically faulty sensors, questionable data induced by sensor wetting in rain, and sensor hysteresis and bias. Bulk data statistics are also presented, in part to assert data quality but also to highlight unique qualities of UAS data collected during TORUS and TORUS-LItE.

Received: 17 Feb 2026 – Discussion started: 20 Apr 2026

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.

Download & links

Adam L. Houston, Mark De Bruin, Céu Gómez-Faulk, and Brian Argrow

Status: open (until 10 Jun 2026)

Post a comment Subscribe to comment alert

CC1:
'Comment on essd-2026-134', Weiteng Qiu, 28 Apr 2026 reply
This manuscript presents a comprehensive description of data processing and quality control procedures for measurements collected by the RAAVEN uncrewed aircraft system during the TORUS and TORUS-LItE field campaigns. The dataset includes high-resolution in situ observations of temperature, humidity, pressure, and wind collected within and around supercell thunderstorms. This is a high-quality data paper. The dataset and quality control framework will be valuable for the community. I recommend this paper for minor revision.
Comments
The manuscript provides a thorough description of quality control procedures but offers limited discussion of the physical insights that can be derived from the dataset. While differences between flight regions are documented—such as stronger vertical motions in the left- and right-flank compared to the near-inflow region—these results are not sufficiently connected to underlying supercell dynamics. A brief interpretative section linking these observed patterns to processes such as convective updrafts, outflow boundaries, and mesoscale circulation (e.g., rear-flank downdrafts or boundary-induced vorticity) would substantially enhance the scientific value of the paper.

The manuscript applies a number of quality control thresholds (e.g., wind speeds exceeding 50 m s⁻¹, flow angles greater than 45°, or uncertainties above 30 m s⁻¹), but the rationale for selecting these values is not always clearly articulated. It would strengthen the methodological transparency to clarify whether these thresholds are derived from instrument limitations (e.g., sensor operating ranges), physical constraints (e.g., realistic bounds in supercell environments), or empirical tuning based on prior studies or dataset-specific characteristics. Providing this context—ideally with references or sensitivity tests—would help readers better assess the robustness and transferability of the QC procedures.

Reply
Citation: https://doi.org/10.5194/essd-2026-134-CC1
RC1:
'Comment on essd-2026-134', Anonymous Referee #1, 10 May 2026 reply
This manuscript presents the processing, quality control, uncertainty characterization, and documentation of RAAVEN uncrewed aircraft system observations collected during the TORUS and TORUS-LItE field campaigns. The dataset includes in situ measurements of temperature, humidity, pressure, and three-dimensional wind in and around supercell thunderstorms, including challenging near-storm regions such as the near-inflow, left-flank, and right-flank sectors. The study is valuable because UAS observations in severe convective environments remain rare, technically difficult, and scientifically important. The manuscript clearly recognizes that the main contribution is not a new physical interpretation of supercell dynamics, but rather a careful description of data processing and quality control procedures for a unique observational dataset. The dataset is substantial, including 88 RAAVEN flights and 57.5 flight hours, with 71 flights directly targeting supercells. The authors also make both the data and processing code available, which strengthens the reproducibility and long-term utility of the work.
In general, I find this to be a useful and publishable data paper. The manuscript addresses an important need for transparent documentation of UAS data processing in severe-storm environments. The authors provide detailed procedures for handling sensor wetting, sensor hysteresis, humidity bias, faulty sensors, aircraft-motion effects on wind retrievals, and wind uncertainty estimation. These are all important issues for users of the dataset. The paper is therefore suitable for publication after revision. However, several aspects require clarification, expansion, and improved presentation before the dataset can be used confidently by a broad community.

The authors correctly identify sensor wetting as a major challenge in thunderstorm UAS observations. The decision to flag questionable wetting periods rather than apply uncertain corrections is reasonable. However, the criteria for identifying wetting events remain partly qualitative. The manuscript states that typical wetting indicators include sudden temperature reductions, slower relaxation back to ambient conditions, increased relative humidity, and coincident radar-indicated precipitation. This is scientifically reasonable, but the exact thresholds or decision rules are not sufficiently formalized. The authors should clarify whether wetting identification was fully manual, semi-automatic, or automatic. If thresholds were used, they should be provided. If expert judgment was required, the manuscript should state how consistency was ensured across cases. Because wetting affects a substantial fraction of observations, especially in left-flank flights, this issue is important for downstream data users. Table 4 shows wet MHP occurrence of 18.4% for all storms and 30.7% for left-flank missions, and wet RSS-421 occurrence of 19.1% for all storms and 22.2% for left-flank missions.

The manuscript notes that the dedicated iPTH temperature thermistor functioned only 26.6% of the time and that a backup RH temperature sensor was used after hysteresis correction when the dedicated thermistor was unavailable. This is a reasonable solution, but the resulting variable may have heterogeneous response characteristics. The manuscript should more explicitly discuss the possible consequences of switching between the dedicated temperature sensor and the corrected backup RH temperature sensor.

The introduction could more clearly state why RAAVEN observations are complementary to mobile mesonet, radar, lidar, and balloon-borne observations. The manuscript mentions the broader TORUS observing system, but the unique role of UAS observations in filling near-surface and low-altitude sampling gaps should be emphasized more strongly. Table 1 is useful, but it would be helpful to add whether each sensor was exposed or shielded, and whether the sensor was used for final reported variables, backup variables, or only quality-control purposes. Table 2 is very large and difficult to read. Consider moving part of it to supplementary material and retaining a condensed summary table in the main manuscript.

The humidity correction equation is important, but the notation should be checked carefully. The text should clearly define all variables, units, valid ranges, and whether temperature is in Kelvin or Celsius. Since RH values are clipped at 99.9%, the authors should discuss whether this may affect analyses of near-saturated air, especially in storm flank or precipitation-adjacent environments.

The wind quality-control thresholds should be justified. For example, why were wind angles greater than 45 degrees, component magnitudes greater than 50 m s⁻¹, and uncertainties greater than 30 m s⁻¹ chosen as thresholds?

Reply
Citation: https://doi.org/10.5194/essd-2026-134-RC1

Adam L. Houston, Mark De Bruin, Céu Gómez-Faulk, and Brian Argrow

Data sets

RAAVEN TORUS-LItE data Adam L. Houston et al. https://doi.org/10.26023/E5PJ-5CN7-VQ0T

RAAVEN TORUS data Adam L. Houston et al. https://doi.org/10.26023/FJD8-VMV2-XW0Y

Model code and software

Python functions for smoothing, hysteresis correction, and iPTH RH correction Mark De Bruin https://github.com/markdebrstorms/UAS_Processing

Adam L. Houston, Mark De Bruin, Céu Gómez-Faulk, and Brian Argrow

Viewed

Total article views: 194 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	BibTeX	EndNote
145	33	16	194	13	12

HTML: 145
PDF: 33
XML: 16
Total: 194
BibTeX: 13
EndNote: 12

Views and downloads (calculated since 20 Apr 2026)

Month	HTML	PDF	XML	Total
Apr 2026	116	26	15	157
May 2026	29	7	1	37

Cumulative views and downloads (calculated since 20 Apr 2026)

Month	HTML	PDF	XML	Total
Apr 2026	116	26	15	157
May 2026	29	7	1	37

Viewed (geographical distribution)

Total article views: 194 (including HTML, PDF, and XML) Thereof 194 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 10 May 2026

Short summary

This article describes data processing performed for the RAAVEN uncrewed aircraft systems operated during the Targeted Observation by Radars and UAS of Supercells (TORUS) and TORUS Left Flank Intensive Experiment (TORUS-LItE) field campaigns. TORUS and TORUS-LItE focused data collection in and near supercell thunderstorms; an environment that presents numerous challenges to data quality. Sensors onboard the RAAVENs measured temperature, humidity, pressure, and the three components of wind.


Total:	0
HTML:	0
PDF:	0
XML:	0