the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 May 2025
Lagrangian Tracking of Moisture Sources for the Record-Breaking Rainfall of Storm Ianos
Abstract. This study utilizes a Lagrangian moisture tracking approach, supported by high-resolution weather simulations, to identify and quantify the sources of moisture contributing to precipitation associated with storm Ianos in the Mediterranean in September 2020. The findings reveal that the Ionian Basin and the southwestern Balkan Peninsula were the primary moisture contributors, closely aligned with the cyclone's trajectory. Secondary sources included regions in North Africa, such as Libya and Tunisia, the Tyrrhenian Basin, southern Italy, the Aegean, Marmara, and the Black Seas.
Moisture transport occurred along three dominant pathways. The first originated from the Black Sea, passing over the Marmara Sea and entering the region between Greece and the Dodecanese Islands. The second pathway traced particles from the Tyrrhenian Basin, across the Algerian Basin and Libya, before reaching the storm's core. The third route extended eastward from Northwest Africa, crossing the Gulf of Gabes. Among these, the Marmara-Black Sea region emerged as the most significant remote source, contributing moisture from the surface to approximately 850 hPa.
As Storm Ianos intensified, moisture flux from remote sources increased, with the final 24 hours before landfall marking the most significant period of moisture uptake. During this critical phase, in-situ evaporative processes over the Greek coastline and the Ionian Sea became dominant, with the last 36 hours contributing the majority of precipitation-related moisture.
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RC1: 'Comment on egusphere-2025-1775', Anonymous Referee #1, 10 Jun 2025
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Lagrangian Tracking of Moisture Sources for the Record-Breaking Rainfall of Storm Ianos
Authors: Coll-Hidalgo et al.
The paper identifies the moisture sources contributing to the precipitation associated with the very intense medicane Ianos. The objective is pursued using a Lagrangian moisture tracking approach, supported by high-resolution meteorological simulations. The main result is that the most significant moisture increases occur in the last 24 hours near the center of the cyclone, while remote sources are more evident at longer time intervals. The main source regions are also identified.
Although the topic is worthy of interest, I do not think that the paper can be published in the present version. Ianos has already been extensively studied in the scientific literature and the new elements added by the present study are not sufficient to justify a new paper.
I suggest completing the paper by extending the analysis to other medicanes and comparing the results. For example, Zorbas and Daniel are two medicanes that affected almost the same Ionian area, but showed a different evolution compared to Ianos. Therefore, starting from Ianos' analysis, a comparative study with these two medicanes would make the work more solid and attractive for potential readers.
MAJOR POINTS:
- After a description of the methodology for the calculation of the parameters relevant for the moisture analysis, the paper analyzes three aspects:
-Comparison of three simulations: the results show that the simulation with nudging above the PBL is the best. This is not surprising, considering that the absence of nudging or the presence of nudging at all levels prevents -for different reasons- the correct simulation of the event;
-Analysis of the dynamical mechanisms responsible for the development of Ianos: this has been already analyzed in several papers (that you mention);
-Analysis of the regional contributions to moisture uptake in different time intervals. This is the main contribution of the paper, but it covers less than 100 lines, in which the authors comment on 3-4 figures and the supplementary material.
I think the elements of novelty are limited to the analysis of these figures and not enough to justify a new paper. Moreover, the moisture uptake was discussed for medicane Qendresa in a previous paper by some of the authors (Coll-Hidalgo et al., 2023), sharing some points with the present paper; also, the paper has some analogies with the work done in Scherrman et al. (2023) and Miglietta et al. (2021), although they consider a different perspective for a similar purpose. A comparison with these works would probably highlight some peculiarities of this case and some analogies with the results in these studies. So, I think it is important you elaborate more on these aspects.
To make the paper interesting and acceptable for publication, I think some generalizations of your results to other cyclones is required, for example applying the same methodology (shortly) to other Ionian cyclones (e.g., Zorbas or Daniel) and drawing some general conclusions.
Coll-Hidalgo, P.; Pérez-Alarcón, A.; Nieto, R. Moisture Sources for the Precipitation of Tropical-like Cyclones in the Mediterranean Sea: A Case of Study. Atmosphere 2022, 13, 1327. https://doi.org/10.3390/atmos13081327
Scherrmann, A., Wernli, H., and Flaounas, E.: Origin of low-tropospheric potential vorticity in Mediterranean cyclones, Weather Clim. Dynam., 4, 157–173, https://doi.org/10.5194/wcd-4-157-2023, 2023.
Miglietta M.M., Carnevale D., Levizzani V., Rotunno R., Role of moist and dry air advection in the development of Mediterranean Tropical-Like Cyclones (Medicanes), Q. J. Roy. Meteor. Soc., 147, 876-899, 10.1002/qj.3951, 2021
- Are 6 hours enough to represent correctly the moisture uptake? A comparison with the results obtained using a finer temporal resolution (1 h?), at least for part of the trajectory, would convince me that the procedure is appropriate.
- please clarify the need of the additional parameterizations in FLEXPART: is not the vertical wind field already contained in the WRF model outputs? what is the need of activating the convection and turbulence schemes?
- The explanation of the results is somewhat obscure in some parts (see minor points).
MINOR POINTS
L47-48: Consider that the results in Zhang et al. (2020) are based on the relatively coarse ERA5 reanalysis, while the structure may change significantly for high-resolution runs.
L48-50: Note that convection mainly occurs in the extra-tropical phase.
L51: Lagouvardos not Lavaguardos.
L71: a short summary of the paper is missing here.
L113: what do you mean with “The selection of particle numbers ensured a balanced distribution across both grid points and vertical levels”?
L130-131: what do you mean with filter the centers in “The cyclone phase space method (Evans and Hart, 2003) was employed to filter the tracked low-pressure centres”?
L210-211: the values do not correspond with those shown in Fig. 3b.
L217: 1008 hPa? Or 998 hPa?
L217: what does it mean “which further corroborates the observed profiles”?
L251: the reasons for the development of the “low-PV values bubble” are not provided.
L288: how are the pathways of precipitating particles selected in Fig. 6?
Figure 7 top panels: x and y are not shown in the Figure. If the x-axis is horizontal, I would expect it represents longitude not latitude, as in the paper.
Figure 8: it is not immediate to identify the time each panel refers to. Please specify it. Also, please add “H” and “L” to identify high and low pressure you are referring to in the manuscript.
L353: what do you mean with “daily MSLP”?
L371, 372: southerly should be northerly.
Figure 9: only the top panel is commented on.
Citation: https://doi.org/10.5194/egusphere-2025-1775-RC1 -
RC2: 'Comment on egusphere-2025-1775', Anonymous Referee #2, 13 Jun 2025
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Review of “Lagrangian Tracking of Moisture Sources for the Record-Breaking Rainfall of Storm Ianos” by Patricia Coll-Hidalgo, Raquel Nieto, Alexandre Ramos, Patrick Ludwig, Luis Gimeno
Summary
The paper investigates moisture sources in the intense Medicane Ianos using FLEXPART Lagrangian trajectories based on WRF numerical simulations. Simulations lasting for several weeks and covering the whole Mediterranean basin are first compared in their representation of the cyclone between different levels of nudging toward the ERA5 reanalysis. The best simulation is then selected to discuss the large-scale dynamics, before identifying areas of moisture uptake during the cyclone evolution. The main moisture sources are found along the path of Ianos over the Ionian Sea, while remote sources such as North Africa and the Black Sea also contribute during different phases.
The paper is interesting and the identification of moisture sources is a new contribution to the well-studied Medicane Ianos. However, it tends to present too many contents and with insufficient depth. While the abstract clearly focuses on moisture sources, the actual scope is often blurred throughout the paper. Furthermore, the descriptions are often unclear or inaccurate (see long list of minor comments below), which does not help emphasizing the key results or understanding their relevance. In summary, it is unclear how the paper contributes to the existing literature about precipitation in medicanes or in Mediterranean cyclones. Substantial reorganization and additional work is needed before it can be considered for publication.
Major comments
- The scope of the paper is blurred: what is known or not known about moisture sources in medicanes and how do they compare with Mediterranean cyclones in general? It is very surprising that a former similar study by the first author about moisture sources in a medicane is not mentioned (https://doi.org/10.3390/atmos13081327). Also, why focus on Ianos? For instance, the more recent Medicane Daniel also produced (even more?) extreme precipitation, over both the Balkans and North Africa, while the recent Valencia floods were unrelated to a medicane. The Introduction must be strengthened to clarify the scope, while the results must be brought in the context of the existing literature.
- The methods are awkward in that they follow a systematic approach (automatic identification of all cyclones over a long time period and automatic adjustment of the cyclone extent) but are applied to a single case study. Furthermore, two sensitivity simulations are presented but not discussed despite surprising behaviour. Finally, the actual results related to moisture sources start in Section 4.2 only (wrongly labelled as 4.1; should actually be 3.2) and are not thoroughly developed and discussed. Altogether, this appears unbalanced and again questions the actual scope of the paper.
Minor comments
l. 24 Fig 1b
l. 34-37 It is unclear how this result compares with the above l. 33-34
l. 45 are there driving processes other than diabatic forcing and baroclinic instability?
l. 47 “They” = medicanes? Flaounas et al. 2018 refer to Mediterranean cyclones in general
l. 47–52 values would be helpful here: how close is close, near, or proximity? Also, ERA5 used in Zhang et al. 2020 is limited by its horizontal resolution (about 30 km)
l. 51 Lagouvardos
l. 52–54 this last sentence appears disconnected from the paragraph
l. 56 reference?
l. 69–71 more motivation for the study is needed here: in which sense was the precipitation associated with Ianos unprecedented, and why does it matter?
l. 71 a short summary is typically expected here (Section 2 shows this, Section 3 shows that, …)
l. 80 strictly speaking it is a 24-day period
l. 80 why the model spin-up? (without requiring the reader to dig into the references)
l. 83 the configuration sounds unusual: 18 km is quite close to the resolution of ERA5, while 6 km lies in the grey zone of deep convection; some discussion is needed here
l. 101 missing reference Beck et al; what is the resolution of ERA5 and MSWEP?
l. 111 Fig 1a; is the yellow box different from the blue box?
l. 115–120 without prior knowledge of FLEXPART it is not fully clear why the mentioned variables and processes are not taken from WRF
l. 122–133 the approach is certainly relevant for a systematic identification but as a single cyclone is investigated here the details of automatic filtering are not needed and the CyTRACK reference is sufficient
l. 133 ..
l. 140–158 repetitions between the text and the (long) figure caption
l. 175 Stohl and James
l. 177–179 similar to l. 115–120: why not use precipitation and cloud microphysics information from WRF?
l. 183–186 is FLEXPART-WRF or TROVA used here? Or both?
l. 187 Where is Section 3?
l. 189–193 the purpose of the sensitivity tests is not fully clear; for instance, it would be helpful to know more about the outcome of these papers and how they compare with yours
l. 194–197 this information would be useful in Section 2.1
l. 198–205 please specify which curve to look at (the simulation names are not very human-readable)
l. 207–208 in which sense is it in closest agreement? As shown below, ERA5 completely underestimates the observed intensity, which is actually well captured by WRF
l. 209–211 as stated in the introduction
l. 212 which time lag?
l. 213 the camel-shaped time evolution in WRF_full_ndg and WRF_deact_ndg deserves some comments (or should be removed)
l. 215–217 unclear
l. 219 simulation names in the supplementary are not consistent with the main paper and only two are shown
l. 220 “most accurate” compared to ERA5? And in which sense?
l. 221 the deep warm core structure of WRF_FUL deserves some comments (or should be removed)
l. 237 it is not clear what should be learned from Section B in the Supplementary Material, and only one simulation is shown (which name is not consistent with the main paper)
l. 238–239 more details are needed to support the claim that the wind field is accurate (when and where in the simulation and compared to which observations); moreover, the 6 h intervals are clearly not sufficient to depict the simulated wind footprint, which shows ‘jumps’ in Fig. 3
l. 240 which time step?
l. 243 a number would be helpful
l. 245–274 the dynamics of cyclone Ianos are largely described in the aforementioned references; it is not clear how their results compare with yours and what is new here
l. 256 missing reference Flaounas et al. 2021; and “major medicane” is not common terminology
l. 259–260 in the selected WRF simulation?
l. 266 maximum vertical extent?
l. 267 is convection shown somewhere?
l. 269–271 in the selected WRF simulation?
l. 275 Section 4.2
l. 276–281 the description suggests independent sources of moisture, which does not match the continuous area suggested by Fig. 6a; this mismatch may be due to the map rendering that hardly allows identifying coasts
l. 288–297 geographical labels (as in Fig. 1b) or numbering i) ii) iii) on Fig. 6 would be helpful to follow the discussion
l. 312–313 is this criterion different from the precipitation trajectories in Fig. 6?
l. 319–320 more details are needed on these satellite measurements of Ianos (what, where and when?)
l. 323–324 what type of satellite data?
l. 327 where and when? Missing reference Hourngir et al., 2021
l. 331 what is new compared to Fig. 6?
l. 332 uptake is a noun
l. 336–338 this information would be helpful in the introduction
l. 339 focussing the map on the Mediterranean would be more relevant (and readable)
l. 350–368 Fig. 8 is quite busy and the MSLP and VIMF fields are quite noisy, which make the discussion hard to follow
l. 351–352 this information would be helpful in the introduction
l. 354 acronym VIMF is not needed as not used
l. 366–367 where is the chanelling or low-level jet to be seen in Fig. 8?
l. 375–379 it is unclear what to learn from Fig. 9, as only panel (a) is referred to
l. 393–394, 426 “high resolution” is disputable for convective precipitation, which simulation typically requires horizontal grid spacing of O(1 km)
l. 396, 398, 402 it is not common practice to refer to specific figures in the conclusions, especially to the supplementary material
l. 400 what is an “improved” cyclonic organization?
Citation: https://doi.org/10.5194/egusphere-2025-1775-RC2
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