the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Characterizing orographic clouds and precipitation in Qilian Mountains, northwestern China
Abstract. Orographic precipitation has a critical role in water resource and hydrologic cycle in many arid and semiarid regions of the world. The formation and characteristics for an orographic precipitation event on 16–17 August 2020 in Qilian Mountains of northwestern China are investigated based on observational data and high-resolution (up to 333 m) simulations of WRF model. The results show that the local mountain-valley wind circulation has a critical role in the formation of orographic clouds and precipitation, showing an obvious daily variation. In the afternoon, due to strong solar radiation heating, there is an obvious upslope wind on the sunny side of the mountain, and the windward slope of the mountain was blocked and lifted, and a strong terrain wave was excited, resulting in strong convective clouds and precipitation. In the evening, due to the strong long-wave radiation cooling effect of the mountains, the strong downslope wind generated converges and lifts at the valley bottom, which promotes the development of weak convective and stratiform clouds over the valley. In the early hours of the morning, the downslope wind reaches its strongest level, producing a strong downhill wind circulation (mountain wind), and the downslope wind produces a strong uplift effect at the bottom of the valley, forming a deep layered cloud and precipitation process. In the afternoon, the convective clouds are dominant. The microphysical process is mainly characterized by high content of graupel particles. The sources of rainwater are mainly from the warm rain process and the melting process of graupel particles, accounting for 30.3 % and 23.6 % respectively. In the evening and early morning, the weak convective clouds and stratiform clouds are dominant. The melting of snow is the main source of rainwater, accounting for 92.6 %; The precipitation conversion rate is basically consistent with the change trend of precipitation over time, and with the increase of terrain height, the precipitation conversion rate in this area also increases.
- Preprint
(2971 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on egusphere-2024-4116', Anonymous Referee #1, 14 Oct 2025
-
RC2: 'Reply on RC1', Anonymous Referee #2, 23 Nov 2025
Egusphere Paper Review on
Characterizing orographic clouds and precipitation in Qilian Mountains, northwestern China
General Comments:
Ren et al., in this manuscript, present, a detailed investigation of a stratocumulus precipitation event over the Qilian Mountains (northwestern China) on 16–17 August 2020, focusing on the dynamic role of local circulation and the resulting microphysical transformations. The study is highly relevant to understanding water resources and weather modification efforts in arid and semi-arid regions. The authors employ a robust, high-resolution WRF model setup (down to 333 m grid spacing) combined with valuable observational data, including in-situ aircraft measurements (LWC, CIP, PIP), weather radar (CINRAD/CD), and station precipitation data.
The paper’s primary strength lies in its detailed analysis of the diurnal variation of the mountain-valley wind circulation and its forcing mechanism on precipitation. The finding that strong valley wind circulation, exhibiting obvious diurnal characteristics, persists even under general cloud and rainfall conditions due to the complex and high terrain, is a significant contribution.
Furthermore, the quantitative microphysical analysis clearly demonstrates a transition from warm cloud process dominance (characterized by high graupel content and warm rain/graupel melting accounting for 53.9% of rainwater source in the afternoon) to cold cloud process dominance (characterized by snow melting accounting for 92.6% of rainwater source in the evening/early morning). The model is generally shown to accurately simulate the stratocumulus precipitation system and the changes in radar echo with elevation. This work provides important insights into the physical processes governing precipitation in this complex topographical region.
However, some central points related to dynamic forcing mechanisms remain ambiguously stated, and certain model-observation discrepancies warrant further discussion or sensitivity testing before publication.
- Under Section 3.2, the authors note a significant discrepancy in precipitation simulation: the simulated rainfall and rain belt range were larger than measured, with the simulated heavy precipitation center averaging 38.83 mm compared to the measured average of 25.32 mm. The authors attribute this to possible model error, complex terrain influence, or the selected physical scheme. A more detailed investigation or discussion on the sensitivity to the chosen physics (specifically the Thompson microphysics scheme) would strengthen the validation section, especially given the crucial role of microphysics in the subsequent analysis.
The discussion should more explicitly address how the lack of mass flux or updraft velocity measurements impacts the certainty of attributing cloud initiation solely to the local convergence axis versus broader wave dynamics (such as the high-amplitude wave response noted in Case 6).
- The aircraft microphysical data used for comparison was collected between 08:50 and 12:00 on August 16, with the specific vertical detection analyzed between 09:32 and 09:56. This time frame occurs before the peak heating/convective stage (13:00) analyzed dynamically. The authors classify the afternoon stage (convective, warm rain dominant) starting at 12:00. It would be beneficial to explicitly place the morning aircraft observations within the context of the diurnal cycle (e.g., as part of the transition phase or early stratiform phase) rather than contrasting them only with the general characteristics of the entire event.
- The simulated ice crystal number concentration (reaching 600 L) was generally higher than the observed CIP particle number concentration (highest near 40 L at 5.6 km and 5.9 km). Given that the cold cloud process is determined to be dominant during much of the precipitation cycle, this large overestimation of ice particle number concentration in the model needs reconciliation or a dedicated sensitivity test of the ice nucleation parameterization.
Below I provide specific comments and suggestions for improvement.
Specific Comments:
For organization and smooth flow, always define or expand abbreviations where they are first used (e.g., Ln 121: CINRAD/CD; Ln 130: CMORPH).
In all Table and Figure numberings, leave a colon (:) after the number.
Also, leave a space between values and SI units attached (e.g., Ln 161: 700hPa, Ln 230: 40L-1 etc.)
Always use the past tense form when talking about past studies. Change “is” to “was” in a few places like Lns 191, 197, etc.
It would be best to ensure units for model vs observation comparisons (e.g., stick to either g/kg or g m-3 for water content comparisons)
Remove parentheses in text references of Figure numbers with letters. For instance, Figure 9 (a) should be Figure 9a when referenced in the Figure discussion. Also, consider referencing specific subpanels in Figures when discussing them for easy comprehension.
Some Figure captions can be better written to make them intuitive at first glance (E.g., Figure 10, 15 and 16)
Ln 20: delete “and”
Ln 26: delete “the downslope wind produces”
Ln 30-31: This should read as “In the evening and early morning, weak convective and stratiform clouds are dominant.”
Ln 33: reword “change trend”
Ln 42: … China. This is also the ….
Ln 44. Here and elsewhere, delete the space before the period.
Ln 50: delete “heat”
Ln 53: airflow
Ln 64 – 66: This statement needs to be referenced.
Ln 66: Weather Research and Forecasting (WRF)
Ln 70: “showed”
Ln 71: Here and elsewhere, change “WRF mode” to WRF model
Ln 74: insert “of” after distributions
Ln 89: Here and elsewhere in the document, leave a space before “(”
Ln 107-108: Should read as: “This paper investigates the typical topographic cloud precipitation process over the Qilian Mountains using observational data …”
Ln 144: insert “this study” before discuss…
Ln 116: add “s” to reveal
Ln 122: Place coordinates after Station
Ln 125: delete “and”
134: Change “The Object of the measurement” to “Parameters Measured”
Ln 140: Add “respectively” after Figure 1
In Table 2, change “Mode top height” to “Model top height”
Ln 145: Use lower case for D03 and D04
Ln 149: changed “appeared” to “showed up”
Ln 153: change Figure 2 and Figure 3 to “Figures 2 and 3”
Ln 168: correct unit °C
Ln 181: Verify that “18:00” is correct or supposed to be “19:00”
Ln 183: I disagree with CTT being lower than -40 °C. This is where it is useful to maintain the box showing the study area in Figure 4. I think you actually meant higher than -40 °C (as in warmer). Actually, the blue box mentioned in the caption is missing from the plots
Ln 194: … and in a large range. It is…
Ln 200: Add “(2022)” to Zhang al.
Ln 202: What is this being compared with?
Ln 218: Change Figure 8 to Figure 7
Ln 230: delete “the”
In Figure 6, your height axis is not consistent with the corresponding unit.
Ln 298 – 302: Split these into 2 sentences
Ln 307: What does “large extent Condition” mean?
Ln 334: Change Figure 12a to Figure 11a
Ln 350: Delete “which is”
Ln 351: Change “caused by” to “as a result of”
Ln 367: Change “wind field and terrain height of 10 m on the ground” to “surface winds (10 m – winds and terrain height)”
Ln 380 – 383: To avoid redundancy, I suggest using something like: “Similar analysis is done at 02:00 on 17 August (Figure 13). During this time, ....”
In Figure 11, (b) and (c) are really quite hard to interpret, especially the reflectivity contours (supposed to be black solid lines). I recommend using color fill for reflectivity, solid and dashed lines for negative & positive temperature values respectively. Perturbation wind vectors can stay the same. Also, the AB line is not visible enough.
Ln 425: Change “topographical” to “orographic”
Ln 428: leave a space after “clouds”
In Figure 14, Panel or subplot labels are out of order
Ln 497: Remove space after the closed bracket.
Citation: https://doi.org/10.5194/egusphere-2024-4116-RC2
-
RC2: 'Reply on RC1', Anonymous Referee #2, 23 Nov 2025
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 699 | 85 | 20 | 804 | 16 | 25 |
- HTML: 699
- PDF: 85
- XML: 20
- Total: 804
- BibTeX: 16
- EndNote: 25
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
The preprint offers a valuable and accurate case study of orographic precipitation over the Qilian Mountains, combining aircraft microphysics and convection-permitting WRF. The main conclusion that diurnal mountain–valley circulation modulates microphysical pathways is plausible and relevant. However, the manuscript lacks quantitative verification (no bias/RMSE/POD/FAR/ETS/FSS), contains a configuration error in the physics table (BMJ listed as a boundary-layer scheme), and shows unit/notation inconsistencies (e.g., LWC g m⁻³ vs mixing ratio g kg⁻¹; unclear exponents in microphysics panels). The microphysical budget percentages are not reproducible as equations, terms, integration bounds, and code are not documented. I therefore recommend Major Revisions