the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Jul 2025
Enhancing the Lagrangian approach for moisture source identification through sensitivity testing of assumptions using BTrIMS1.1
Abstract. Moisture is the fundamental basis for precipitation, and understanding the sources of moisture is crucial for comprehending changes in precipitation patterns. Lagrangian models have been employed for moisture tracking in both extreme weather events and climatological studies as a means to gain insight into driving physical processes. Lagrangian moisture tracking models follow independent air parcels based on a set of defined assumptions. Despite the existence of many Lagrangian models and studies applying them for moisture tracking, these assumptions are seldom thoroughly tested.
In this study, we use the Lagrangian model BTrIMS to demonstrate the impact of these assumptions on the results of moisture source identification. In particular, we test the method’s dependence on the number of air parcels released; the height that parcels are released; the vertical movement of air parcels; the vertical well-mixed assumptions that lead to different moisture identification methods along trajectories, the within-grid interpolation method and the back-trajectory time step. We find that releasing approximately 200 air parcels per day from each grid point is necessary to obtain accurate results for a region of 10 grid points or more (an area of ~9,000 km2 in this case). Additionally, the vertical movement of air parcels, their release height, and along-trajectory identification method of moisture substantially affect the identified moisture sources, whereas within-grid interpolation and back-trajectory time step within a reasonable range has a relatively minor role on the results. The mechanisms behind these assumptions involve heat exchange, precipitation formation height, vertical mixing of surface evapotranspiration, and numerical noise, all of which must be carefully considered for realistic results.
Based on the results of sensitivity tests and analysis of underlying mechanisms behind the assumptions, we improve the Lagrangian model BTrIMS1.0 to a new version (BTrIMS1.1) for broader applicability. The findings of this study provide critical information for improving Lagrangian moisture source identification methods in general and will benefit future research in this field, including studies examining changes in moisture sources due to climate change.
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Status: open (until 22 Sep 2025)
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RC1: 'Comment on egusphere-2025-2833', Anonymous Referee #1, 12 Aug 2025
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This study investigates the sensitivity of the Lagrangian estimation of moisture sources for precipitation to the choice of different assumptions and configurations, using the model BTrIMS. The authors explore several factors that could guide researchers in selecting appropriate settings for moisture tracking, such as the number of parcels released, the time step in the trajectory method, the initial vertical distribution of parcels, and the influence of different interpolation methods or mixing schemes. Although only one model is used, the results of some of the experiments are easily extrapolated to other models. Overall, the study is well-defined, clearly presented, and well written. The main area of improvement lies in the presentation of results, as in the absence of a general ground truth it is difficult to identify the optimal configuration from certain experiments. Furthermore, presenting the results for the three analyzed cases, together with a more rigorous exposition of the methodology, would help readers follow the conclusions more easily, as elaborated in the general and specific comments below.
General comments
- Lack of a reference configuration for comparison. Except for the test involving the number of air parcels released per grid point, there is not a reference configuration against which to compare the results. It would be beneficial to define a standard or “reference configuration” of BrTrIMS, which would be the most complex and realistic one, and then analyze how relaxing specific assumptions or modifying different components of the configuration impacts the results. For instance, the reference configuration could involve releasing 1000 air parcels per grid point, using the wind field for the vertical movement (kinetical scheme), using the ET-mixing method for moisture tracking, applying bicubic interpolation, and employing the smallest time step. Using this approach and clearly stating the reference setup in Sect. 2 would help the reader in following the analysis, as I find it difficult to determine the exact configuration that is being used in each subsection of Sect. 3. For example, what is the time step in Sect. 3.1? How many parcels per grid point are released in Sect. 3.6? Additionally, in cases where it is unclear which configuration is more realistic (such as choosing between the ET-mixing approach and WaterSip for moisture tracking) I would avoid making categorical statements about one setup underestimating results compared to the other, since there is not a “ground truth” for direct comparison in these cases.
- Focus on the Australian event. Although this study analyzes three different precipitation events (Australia, Pakistan and Scotland cases), the main manuscript presents results only for the Australian event. I believe that in most subsections it would be feasible and beneficial to include results for all three events, either by presenting them side by side or by averaging. For instance, in Sect. 3.1, the results shown in Fig. 1 could be averaged across the three events, which would provide the same information. In the case of Fig. 2 averaging may provide less valuable information, but it would still be possible to show the curves for all three events using a single value of pattern correlation, and move the analysis for different values of the pattern correlation to the supplement. Similarly, the subsequent maps could be easily displayed for all three events together, as there is minimal overlap between them.
Specific comments
L59-1: COSMO is a meteorological model used for numerical weather prediction and atmospheric research, not an Eulerian method for moisture tracking. In Winschall et al., (2014) they use an Eulerian tagging approach implemented in this model. It would be useful to clarify this information.
L59-2: The water vapor tracers implemented in the WRF model are most commonly abbreviated as WRF-WVTs. Also, the correct article to cite is Insua-Costa et al., (2018), where this tool is presented and validated in detail.
L60-61: Here it is asserted that Eulerian moisture tracking methods “are precise”. While this is true in general, the accuracy of these methods depends on how well the meteorological models in which they are implemented represent reality. It is possible to have a model simulation very deviated from reality, and then the moisture source calculation would be accurate in the model world, but not in reality. In this case, a moisture tracking method using reanalysis would be more accurate. Please, clarify that water vapor tagging methods depend on the accuracy of the underlying meteorological model.
L75: For accuracy and rigor, please use this more-explicit form of the trajectory equation dX/dt=u[X(t)], where the full dependence on time is highlighted. The velocity field u may also depend on time, not only on the 3-dimensional position. Furthermore, although it is useful to interpret dx and dt as the air parcel’s displacement in one time step and the time step, from a mathematical point of view it is not correct to state that, since dX/dt is just the derivative of X with respect to time.
L79-81: This may lead to misunderstanding, as it appears that Dirmeyer and Brubaker, (1999) do not use the wind fields at all. Please rephrase to indicate that wind fields are used in trajectory calculations to drive the horizontal movement of the parcel.
L84: FLEXPART also includes a detailed description of turbulence.
L97: Typo in “These two identification methods also differS in this”.
L100: “Due to limitations in Eulerian methods for moisture tracking”. It would be useful to expand on these limitations. In L61 the need to predefine water source regions is mentioned. What about the computational requirements of these methods?
L113: “there are three schemes based on different theories”. Please, refer to the section/subsection where these schemes are introduced and explained.
L136-137: “The air parcels are advected by wind” suggests a forward tracking of air parcels. Please rephrase for clarity. Also, here and in other parts of the manuscript, there is a reference to a “predefined large domain”, but it is not stated anywhere what this domain is (it may be deduced from Fig. 4). It would be useful to include a visualization of the domains in the appendix.
L173-L189: Although the description of the Australia event is very complete and detailed, I would move it to the appendix, as it may distract the reader from the main focus of the paper. I would only include a small summary with the most essential characteristics of the event, and perhaps also include a small summary of the other two cases.
L200-201: “total-precipitable-water-weighted height”. The total precipitable water is a two-dimensional field, calculated as the integral of all water components in the atmospheric column. Thus, this expression may lead to misunderstanding. If parcels are released randomly vertically following the humidity profile, please replace “total-precipitable-water” with “humidity”, otherwise explain how parcels are released in more detail.
L225-L238: I do not see the point of having this equation and all the involved parameters here. I would consider moving it to the appendix.
L276-278: Option 2 does not impose a threshold on initial relative humidity or require moisture uptake to occur within the PBL, arguing that subgrid processes can allow the lower troposphere to contribute to precipitation. This is true in general, but it also depends on the chosen time step. Since the time steps used in this study are short (less than 1 hour), I believe that the initial relative humidity threshold may have an important effect on the results. It would be useful to include these results in the appendix, even if the impact on the results is less important than expected.
L291-305: The only difference between the first set of equations and the second is in fracn1’ and fracn2’, as facq may be updated as facq(1- fracn1’). Considering this can help reduce the number of equations here and also to explain the differences between both sets of equations. If left as is, I would move the equations to the appendix and explain them in detail there.
L318: It would be useful to clarify here if parcels are also released from every grid point where precipitation occurs every time step, or if they are released with less frequency (for example, every 1 or 3 hours).
L321-322: I understand that pattern correlations are calculated by computing the Pearson correlation coefficient between two spatial distributions of moisture sources: the “true” one (1000 parcels) and the tested one (50, 100, 200 and 500 parcels). If this is the case, please explain it in more detail. Otherwise, explain how pattern correlation is calculated.
Furthermore, I think it could be better explained how the results in Fig. 1 are obtained. If I understand correctly, the Australian event involves a certain number of points (around 1000) where precipitation is larger than 1 mm, and then, for each given number of grid points, a smaller region of this size is being selected to calculate the pattern correlation. The smaller the number of grid points, the greater the number of regions that can be selected, and therefore the variability in pattern correlation decreases with the number of grid points.
L351: “Since we only selected four air parcels per grid point”. I understand that you are referring to the maximum number of air parcels per grid point (50, 100, 200 and 500). Thus, it would be more accurate to say “four different numbers of air parcels per grid point”.
L487: Shouldn’t it be “(e)” instead of “(f)”?
L540: Due to the absence of a reference for comparison, I would not use words such as “underestimate”. It is true that WaterSip gives more importance to local sources than other methods, but here there is not a reference methodology with which to compare the results, so it cannot be said whether the correct results are those of WaterSip or those of BrTrIMS.
L546: Shouldn’t it be “comes” instead of “coming”?
L548: What is the meaning here of “air column dividing”?
Citation: https://doi.org/10.5194/egusphere-2025-2833-RC1
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