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Article

Analysis of Precipitable Water Vapor, Liquid Water Path and Their Variations before Rainfall Event over Northeastern Tibetan Plateau

by
Mingxing Xue
1,2,3,
Qiong Li
4,
Zhen Qiao
1,2,3,
Xiaomei Zhu
1,2,3 and
Suonam Kealdrup Tysa
4,5,*
1
School of Civil Engineering and Water Resources, Qinghai University, Xining 810016, China
2
Laboratory of Ecological Protection and High Quality Development in the Upper Yellow River, Xining 810016, China
3
Key Laboratory of Water Ecology Remediation and Protection at Headwater Regions of Big Rivers, Ministry of Water Resources, Xining 810016, China
4
State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China
5
Greenhouse Gas and Carbon Neutral Key Laboratory of Qinghai Province, Xining 810001, China
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(8), 934; https://doi.org/10.3390/atmos15080934
Submission received: 12 July 2024 / Revised: 25 July 2024 / Accepted: 29 July 2024 / Published: 4 August 2024
(This article belongs to the Special Issue Advances in Rainfall-Induced Hazard Research)
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Abstract

:
A ground-based microwave radiometer (MWR) provides continuous atmospheric profiles under various weather conditions. The change in total precipitable water vapor (PWV) and liquid water path (LWP) before rainfall events is particularly important for the improvement in the rainfall forecast. However, the analysis of the PWV and LWP before rainfall event on the plateau is especially worth exploring. In this study, the MWR installed at Xining, a city located over the northeastern Tibetan Plateau, was employed during September 2021 to August 2022. The results reveal that the MWR-retrieved temperature and vapor density demonstrate reliable accuracy, when compared with radiosonde observations; PWV and LWP values during the summer account for over 70% of the annual totals in the Xining area; both PWV and LWP at the initiating time of rainfall events are higher during summer, especially after sunset (during 20-00 local solar time); and notably, PWV and LWP anomalies are enhanced abruptly 8 and 28 min prior to the initiating time, respectively. Furthermore, the mean of LWP anomaly rises after the turning time (the moment rises abruptly) to the initiating time; as the intensity of rainfall events increases, the occurrence of the turning time is delayed, especially for PWV anomalies; while the occurrence of the turning time is similar for both convective cloud and stratiform cloud rainfall events, the PWV and LWP anomalies jump more the initiating time; as the intensity of rainfall events increases, the occurrence of the turning time is delayed, especially for PWV anomalies; while the occurrence of the turning time is similar for both convective cloud and stratiform cloud rainfall events, the PWV and LWP anomalies jump more dramatically after the turning time in convective cloud events. This study aims are to analyze the temporal characteristics of PWV and LWP, and assess their potential in predicting rainfall event.

1. Introduction

Atmospheric water vapor, the dominant greenhouse gas, absorbs solar and infrared radiation and releases latent heat, playing a crucial role in global energy and hydrological cycles [1,2,3,4]. Under global warming, atmospheric water vapor generally increases at a rate of 6–7% K−1 [5,6]. The increase in atmospheric water vapor can lead to more extreme weather events [7]. Total precipitable water vapor (PWV) is a key measure of atmospheric water vapor, calculated by integrating water vapor from the surface to the top of the troposphere, and is a valuable predictor for weather forecasting [8,9]. Clouds, covering about two-thirds of the Earth’s surface, significantly affect the Earth’s radiation budget through shortwave albedo and longwave greenhouse radiation effects [10,11]. Liquid water path (LWP) is a critical parameter representing cloud microphysical properties and radiative characteristics [12,13], important for understanding rainfall occurrence and development [14,15]. LWP is the vertically integrated liquid water content in the atmosphere [16].
Understanding the temporal characteristics of PWV and LWP, and their changes before rainfall events, is vital for improving rainfall forecasts and the success rate of artificial rainfall [17,18,19,20]. Obtaining accurate PWV and LWP data with high spatial–temporal resolution is challenging. Radiosonde observation (RAOB) is a traditional direct measurement of vertical profiles of atmosphere parameters. It provides accurate vertical atmospheric structure but has limited spatial–temporal resolution [21]. Ground-based and satellite-based MWRs offer direct measurements of cloud emissivity and brightness temperature, which can be used to retrieve PWV and LWP data [22,23,24,25]. A satellite-based MWR has wide observation coverage but low temporal resolution and poor accuracy for atmospheric parameters at the bottom of the troposphere [26,27]. A ground-based MWR, which is designed to retrieve temperature, humidity and cloud profiles in the lower troposphere, continuously detects atmospheric profiles at minute intervals, making it suitable for capturing short-duration extreme precipitation events [28,29,30].
PWV and LWP have been successfully used for weather detection and nowcasting globally. For instance, Warner et al. (1988) demonstrated an accurate liquid water measurement by an MWR in a convective cloud [31]. Long-term characteristics of cloud liquid, water vapor, and cloud-base temperature detected by ground-based MWR were analyzed in the North Atlantic Ocean [32]. A ground-based MWR was used for nowcasting severe convective activity in southeast India [33] and also for exploring temperature and humidity parameters during two different types of strong thunderstorms. The results show that it has a good application in the analysis of medium-scales weather and of the continuous change in meteorological elements [34]. It also revealed consistency in meteorological data between a Lagrange mixed single-particle orbit model and a ground-based MWR during a rainstorm in the Jizhong Plain, China [35]. The Tibetan Plateau is the highest and largest geographic feature on the planet with a mean altitude over 4000 m. Precipitation events frequently occur over the Tibetan Plateau in summer due to the combined effects of the Asian monsoons, westerlies and orographic impacts [36,37]. However, the variations in PWV and LWP before a rainfall event in the northeastern Tibetan Plateau are not well understood due to a lack of in situ observations.
Retrieving PWV and LWP during precipitation is challenging, since the assumption of a non-scattering atmosphere becomes invalid for microwave frequencies less than 90 GHz when small cloud droplets transform into large raindrops [38,39]. Kostsov et al. found that retrieval errors for PWV and LWP increase before a rain event, and LWP retrieval quality post-rain is generally insufficient [40]. Drizzle cases from the cloud microphysical model shows no significant loss in the accuracy of LWP for microwave radiometer algorithms, while rain cases result in unreliable data when the total LWP exceeds 700 g/m2 (0.7 mm) [41]. The primary observation elements used are PWV and LWP, measured in millimeters.
In the study, observations from the ground-based MWR MP-3000A are used, which is installed in Xining, a city on the northeastern Tibetan Plateau. With the intensification of the water cycle driven by global warming, rainfall in Xining has significantly increased in recent years. Analyzing the evolutionary characteristics of PWV and LWP before a rainfall event helps predict severe convective weather. The purpose of this study is to analyze the temporal characteristics of PWV and LWP, and their variations before rainfall events, to improve rainfall forecast and the success rate of artificial rainfall. It should be noted that, in this study, the measurements made within the most interesting period of time just before rain events could contain uncontrolled errors, as well as the LWP and PWV retrievals between the turning time and the initiating time.

2. Materials and Methods

2.1. Study Region

The ground-based MWR and radiosonde observation station are located in the northern part of Xining, at coordinates 101°75 E and 36°73 N (Figure 1). Xining City, the capital city of the Qinghai province, China, is located on the northeastern Tibetan Plateau and along the middle reaches of the Huangshui River (the primary first-order tributaries of the Yellow River) valley basin. The city’s terrain is higher in the southwest and lower in the northeast, with an altitude of 2261 meters in the urban area. Xining experiences a continental plateau semi-arid climate, with an average temperature of 6 °C and annual precipitation of 370 mm, over 70% of which occurs in the summer.

2.2. Dataset

The ground-based MWR and RAOB from September 2021 to August 2022 are used in this study. The RAOB data are from the Xining sounding station. The ground-based MWR, placed at Qinghai University 1 km from the Xining sounding station, is an MP-3000A manufactured by Radiometrics Corporation. This 35-channel passive MWR includes 21K band (22–30 GHz) and 14V band (51–59 GHz) microwave channels. The MWR measures brightness temperatures, which, combined with ambient temperature, pressure, and relative humidity data from meteorological sensors, are used to retrieve atmospheric profiles, including temperature, vapor density, and relative humidity. The MP-3000A has a temporal resolution of 2 minutes (min), a detection range of 0–400 K, and a brightness temperature accuracy of 0.1 K. It observes atmospheric profiles across 58 height layers from 0 to 10 km, with vertical retrieval intervals of 50 m from the surface to 0.5 km, 100 m from 0.5 to 2.0 km, and 250 m from 2.0 to 10.0 km [42,43]. The PWV and LWP are retrieved using neural network algorithms based on the MWR atmospheric profile data, trained with historical radiosonde measurements from the Xining sounding station using the Stuttgart Neural Network Simulator.

2.3. Methods

2.3.1. Definition of Rainfall Classification

To detail the temporal patterns of PWV and LWP, the observation data are classified into rainy days and non-rainy days. Rainy days are defined as days when rainfall occurs. A rainfall event is identified if there is no additional rainfall 6 hours before or after its end. During the observation period, 65 rainfall cases were recorded (Table 1). Additionally, the variations in PWV and LWP within 180 min prior to the initiation of rainfall events were examined. Rainfall events are classified by intensity: light (less than 10 mm), medium (10–25 mm), and heavy (over 25 mm).
The diurnal variations in PWV and LWP across the four seasons, spring (March–May), summer (June–August), autumn (September–November), and winter (December–February), are analyzed. There were 4, 39, 22, and 0 rainfall events in spring, summer, autumn, and winter, respectively.

2.3.2. Definition in the Turning Time

The initiating time of rainfall events is determined by the rainfall signal recorded by the MWR. PWV and LWP anomalies are calculated by removing their means during the study period, that is, the one year of observation. To improve forecast accuracy, the variations in physical quantities that indicate and predict rainfall events are explored, focusing on the sudden intensification of PWV and LWP before rainfall initiation. Specifically, when the increments of PWV and LWP anomalies at two adjacent moments are positive before the initiating time of rainfall events, the timings are further obtained as the turning time in the study, respectively.

2.3.3. Process of Radiosonde Data

To verify the accuracy of microwave radiometer data, ground-based MWR data are compared with RAOB data. Due to differences in vertical spatial resolution between RAOB and MWR data, preprocessing is necessary. MWR data have a temporal resolution of 2 min, while RAOB provides data at 08:00 and 20:00 (local solar time or Beijing time, LST). MWR data from 07:30 to 08:30 and 19:30 to 20:30 are selected for comparison with RAOB, accounting for the typical duration of sounding balloons measurements. Spatially, RAOB data are interpolated to match the MWR’s height intervals. RAOB provides atmospheric temperature and relative humidity, and water vapor density is calculated using water vapor pressure and atmospheric temperature

2.3.4. Indices of Accuracy Evaluation

The main evaluation metrics in this article are the pearson correlation coefficient (r), relative deviation (BIAS), and root mean square error (RMSE). R reflects the degree of correlation between variables, BIAS indicates the degree of average deviation between MWR and RAOB data, and RMSE measures the difference between model predictions and observations, summarizing the prediction error size. The formula is as follows:
r = ∑ i = 1 n T R i − T ¯ R T S i − T ¯ S ∑ i = 1 n T R i − T ¯ R 2 ∑ i = 1 n T S i − T ¯ S 2
B I A S = ∑ ( T R − T S ) N
R M S E = ∑ ( T R − T S ) 2 N
where T R is the observed value of the microwave radiometer and T S is the RAOB value. T ¯ R is the sample mean of the MWR. T ¯ S is the sample mean of the RAOB.

3. Results and Discussion

3.1. Comparison of Variables Derived by MWR and RAOB

A statistically significant positive correlation exists between the variables provided by the MWR and RAOB, with an R value greater than 0.99, consistent with the conclusions drawn by Bai [44]. Both temperature and vapor density decrease with increasing height (Figure 2). Overall, the MWR values are slightly lower than those from the RAOB, with the difference varying across variables and heights. For instance, the discrepancy in temperature and water vapor density increases with height at 08 and 20 LST, respectively, while the difference in water vapor density fluctuates slightly at low altitudes (0–2 km). The inverse performance of MWR is better at a low altitude, leading to larger deviations at higher altitudes [45,46]. In general, the data derived by MWR show reliable accuracy over the southeast Tibetan Plateau, consistent with findings from the eastern and central Tibetan Plateau [47,48].

3.2. Temporal Patterns of Precipitable Water Vapor and Liquid Water Content

Firstly, the temporal patterns of PWV and LWP are elaborated during the rainy days and non-rainy days. A bimodal structure is exhibited in the seasonal variations in PWV and LWP, peaking in August and April, particularly during the summertime (Figure 3). The summer mean PWV is approximately 29.9 mm on rainy days and 20.0 mm on non-rainy days. Seasonal PWV and LWP distribution is very uneven in the Xining area, with summer accounting for over 70% of the annual total. Generally, mean PWV and LWP are higher on rainy days than non-rainy days, with the LWP values being more discrete compared to PWV.
In terms of diurnal variation, the summer PWV and LWP are more concentrated at night, particularly from 20 to 06 LST (Figure 4). Another notable peak occurs at 08 LST during rainy days in autumn. Meanwhile, the amplitude of diurnal variation in PWV and LWP is greater in autumn compared to summer (Figure 4a,e). The difference in PWV and LWP between non-rainy and rainy days is more pronounced in summer and autumn, with PWV reaching up to 20.82 mm at 23 LST in summer. In addition, diurnal variations during rainy days in spring and winter are not exhibited in Figure 4 since there are few and no events in the two seasons, respectively.

3.3. Temporal Patterns of Rainfall Events and Moisture Condition

During the study period, a total of 65 rainfall events occurred, with 60% and 33% in summer and autumn. Diurnal variation shows more events beginning between sunset and sunrise, ending before noon (Figure 5a). Nevertheless, the initiating time varies seasonally, occurring after sunsets in summer and before sunrise in autumn (Figure 5b,c). The PWV and LWP values at the initiating time of rainfall events are higher in summer, averaging 33.47 mm and 2.54 mm, respectively, which is 10% higher than the overall average (Table 2). Intriguingly, rainfall events during the diurnal peak period, corresponding to those after sunset, show enhanced PWV and LWP at the initiating time compared to other seasons (Figure 6).

3.4. Variations in Precipitable Water Vapor and Liquid Water Content before Rainfall Events

Figure 7 illustrates the temporal variations in PWV and LWP anomalies 360 min prior to rainfall event initiation. PWV anomalies increase obviously 180 min before the initiating time. The PWV anomalies are further enhanced abruptly 8 min before the initiating time, also showing a positive increment after this time, which is defined as the turning time (see details in Section 2). The rate of PWV anomalies enhances to 12.7 mm/2 min after the turning time. In contrast, LWP anomalies increase slightly 180 min prior to the initiating time. Then, they are enhanced abruptly after 28 min, reaching a rate of 0.09 mm/2 min from the turning time to the initiating time. However, since the retrieval errors can increase within a period of half an hour before a rain event [43], the LWP and PWV retrievals can contain uncontrolled errors between the turning time and the initiating time.
Furthermore, the temporal variations in PWV and LWP are calculated within 180 min prior to the initiating time of rainfall events with light, medium and heavy intensities, respectively (Figure 8). The turning time for anomalies is delayed with increasing rainfall event intensity, moving closer to the initiating time, especially for PWV. For instance, the turning time of a PWV anomaly appears 6–14 min prior to the initiating time, when the intensity of rainfall events is enhanced from light to heavy. For heavy rainfall events, PWV anomalies show a rate of 7.54 mm/2 min from the turning time to the initiating time, and LWP anomalies show a rate of 1.28 mm/2 min.
In both convective cloud and stratiform cloud rainfall events, the occurrence of the turning time of PWV and LWP anomalies is similar (Figure 9). Nevertheless, the anomalies jump more dramatically after the turning time, when the rainfall event is formed by convective cloud. The rates of PWV anomalies reach 19.46 mm/2 min and 18.2 mm/2 min from the turning time to the initiating time (Figure 9a) when the events are formed by a convective cloud and a stratiform cloud, respectively, and the rates of LWP anomalies reach 1.83 mm/2 min and 0.35 mm/2 min (Figure 9b).

4. Conclusions

  • The seasonal distributions of precipitable water vapor and liquid water content is highly uneven in the Xining area, with summer accounting for more than 70% of the annual total. Summer precipitable water vapor and liquid water content are concentrated at night (20-06 LST), another peak at 08 LST during rainy days in spring and autumn.
  • A total of 65 rainfall events occurred during the study period, primarily initiating between sunset and sunrise and ending before noon. In summer, events tend to start after sunset, while in autumn, they begin before sunrise. Although higher precipitable water vapor and liquid water content at the initiating time of rainfall events are observed in summer, they only occur during the diurnal peak, corresponding to after sunset.
  • Precipitable water vapor and liquid water content anomalies increases sharply 8 and 28 min before rainfall initiation, respectively, with rates of 12.7 mm/2 min and 0.09 mm/2 min. As the intensity of rainfall event is enhanced, the occurrence of the turning time for anomalies moves closer to the initiation time, especially for precipitable water vapor anomalies. The turning times are similar in convective cloud and stratiform cloud rainfall events, but convective cloud events exhibit more dramatic jumps in anomalies after reaching the turning time when compared with stratiform cloud events. Finally, we highlight again that the LWP and PWV retrievals could contain uncontrolled errors between the turning time and the initiating time in the study.

Author Contributions

M.X. and S.K.T. contributed to the central idea of the manuscript. S.K.T. designed this study. M.X. and Q.L. performed the calculations and wrote the main manuscript. Q.L. and S.K.T. revised the manuscript. Z.Q. and X.Z. maintained the operation of the equipment. All authors reviewed and approved the final manuscript.

Funding

This research is funded by the Qinghai Provincial Science and Technology Department Major Scientific and Technological Specialties (2021-SF-A6).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used in this manuscript are private.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The operation point site map of ground-based MWR (a) and its surrounding environment (b).
Figure 1. The operation point site map of ground-based MWR (a) and its surrounding environment (b).
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Figure 2. Mean temperature (a,b) and vapor density (c,d) of MWR (black curve) and RAOB (red curve) profiles at 08 (a–c) and 20 (b–d) LST. Units: °C and g·m−3, respectively.
Figure 2. Mean temperature (a,b) and vapor density (c,d) of MWR (black curve) and RAOB (red curve) profiles at 08 (a–c) and 20 (b–d) LST. Units: °C and g·m−3, respectively.
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Figure 3. The monthly mean PWV (a) and LWP (b) values during the rainy (blue) and non-rainy days (red), with the maximum (top) and minimum (bottom) value. Units: mm.
Figure 3. The monthly mean PWV (a) and LWP (b) values during the rainy (blue) and non-rainy days (red), with the maximum (top) and minimum (bottom) value. Units: mm.
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Figure 4. The diurnal variation in hourly mean PWV (a–d) and LWP (e–h) during the rainy (blue curve) and non-rainy days (red curve) from spring (a–e) to winter (d–h). Units: mm.
Figure 4. The diurnal variation in hourly mean PWV (a–d) and LWP (e–h) during the rainy (blue curve) and non-rainy days (red curve) from spring (a–e) to winter (d–h). Units: mm.
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Figure 5. Diurnal variations in the initiating (red) and ending (blue) time of the annual, summer and autumn (a–c) rainfall events during the study period.
Figure 5. Diurnal variations in the initiating (red) and ending (blue) time of the annual, summer and autumn (a–c) rainfall events during the study period.
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Figure 6. Diurnal variations in PWV (a) and LWP (b) anomalies at the initiating time of rainfall events in summer. Units: mm.
Figure 6. Diurnal variations in PWV (a) and LWP (b) anomalies at the initiating time of rainfall events in summer. Units: mm.
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Figure 7. The variations in PWV (left, red curve) and LWP (right, blue curve) anomalies within 360 min prior to the initiating time of rainfall events. The dot represents the moment of the turning time; units: mm. The time series is processed as three-point smoothing.
Figure 7. The variations in PWV (left, red curve) and LWP (right, blue curve) anomalies within 360 min prior to the initiating time of rainfall events. The dot represents the moment of the turning time; units: mm. The time series is processed as three-point smoothing.
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Figure 8. The variations in PWV (a) and LWP (b) anomalies within 180 min prior to the initiating time of rainfall events with light, medium and heavy intensities (blue, orange and red curve), respectively. The dot represents the moment of the turning time; units: mm. The time series is processed as three-point smoothing.
Figure 8. The variations in PWV (a) and LWP (b) anomalies within 180 min prior to the initiating time of rainfall events with light, medium and heavy intensities (blue, orange and red curve), respectively. The dot represents the moment of the turning time; units: mm. The time series is processed as three-point smoothing.
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Figure 9. The variations in PWV (a) and LWP (b) anomalies within 180 min prior to the initiating time of rainfall events, which are formed by a convective cloud (red curve) and a stratiform cloud (blue curve), respectively. The dot represents the moment of the turning time; units: mm. The time series is processed as three-point smoothing.
Figure 9. The variations in PWV (a) and LWP (b) anomalies within 180 min prior to the initiating time of rainfall events, which are formed by a convective cloud (red curve) and a stratiform cloud (blue curve), respectively. The dot represents the moment of the turning time; units: mm. The time series is processed as three-point smoothing.
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Table 1. A total of 65 rainfall cases in Xining from September 2021 to August 2022.
Table 1. A total of 65 rainfall cases in Xining from September 2021 to August 2022.
No.DateStart and Finish TimeNo.DateStart and Finish Time
12–3 September 202123:05–07:083420 June 202209:49–11:18
24–5 September 202125:50–11:043521 June 202220:10–21:59
36 September 202101:08–04:003625–26 June 202213:17–08:03
48 September 202117:04–17:38371 July 202201:59–06:49
513–14 September 202120:07–03:12383 July 202219:37–20:52
614–15 September 202122:52–07:36394 July 202209:38–14:46
716 September 202102:34–04:00408–9 July 202215:18–03:57
816–17 September 202123:53–06:38419 July 202221:13–21:44
918 September 202101:43–20:194211 July 202203:50–06:25
1022 September 202108:36–11:504311–12 July 202220:08–07:31
1124 September 202106:10–10:404415–16 July 202200:55–10:06
1226 September 202118:27–09:124517 July 202216:11–19:13
133 October 202118:11–23:084618–19 July 202214:40–04:43
145–6 October 202123:45–03:114719 July 202215:38–15:53
157–8 October 202120:08–06:034821 July 202205:03–16:06
169 October 202119:24–22:284926 July 202220:18–20:39
1713 October 202107:07–20:065031 July 202219:03–23:13
1819 October 202103:54–20:53513–4 August 202218:36–13:41
1920 October 202102:28–12:59529 August 202209:11–09:49
2022 October 202108:31–07:10539–10 August 202223:18–04:15
2126 October 202106:05–09:145413–14 August 202220:58–16:06
221 November 202123:05–08:255515 August 202205:53–10:18
2315 April 202207:41–09:005617–18 August 202223:34–02:11
2430 April 202200:27–06:005720 August 202221:34–22:57
251 May 202206:57–10:565821 August 202205:21–07:00
2629 May 202203:10–05:185921–22 August 202219:10–03:31
273 June 202203:10–12:086022–23 August 202220:30–06:28
285–6 June 202220:53–06:126124 August 202210:42–17:41
297 June 202200:58–03:516225 August 202201:54–03:40
308–9 June 202221:06–00:146327–28 August 202219:31–04:42
319 June 202217:24–20:016429 August 202206:30–10:16
3211 June 202216:05–17:336531 August 202203:45–09:22
3312 June 202214:31–15:39
Table 2. Mean PWV and LWP at the initiating time of all, summer and autumn rainfall events, with the standard deviation Units: mm.
Table 2. Mean PWV and LWP at the initiating time of all, summer and autumn rainfall events, with the standard deviation Units: mm.
PWVLWP
Annual30.90 (±10.43)2.17 (±2.12)
Summer33.47 (±10.54)2.54 (±2.39)
Autumn27.17 (±5.49)1.51 (±1.09)
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Xue, M.; Li, Q.; Qiao, Z.; Zhu, X.; Tysa, S.K. Analysis of Precipitable Water Vapor, Liquid Water Path and Their Variations before Rainfall Event over Northeastern Tibetan Plateau. Atmosphere 2024, 15, 934. https://doi.org/10.3390/atmos15080934

AMA Style

Xue M, Li Q, Qiao Z, Zhu X, Tysa SK. Analysis of Precipitable Water Vapor, Liquid Water Path and Their Variations before Rainfall Event over Northeastern Tibetan Plateau. Atmosphere. 2024; 15(8):934. https://doi.org/10.3390/atmos15080934

Chicago/Turabian Style

Xue, Mingxing, Qiong Li, Zhen Qiao, Xiaomei Zhu, and Suonam Kealdrup Tysa. 2024. "Analysis of Precipitable Water Vapor, Liquid Water Path and Their Variations before Rainfall Event over Northeastern Tibetan Plateau" Atmosphere 15, no. 8: 934. https://doi.org/10.3390/atmos15080934

APA Style

Xue, M., Li, Q., Qiao, Z., Zhu, X., & Tysa, S. K. (2024). Analysis of Precipitable Water Vapor, Liquid Water Path and Their Variations before Rainfall Event over Northeastern Tibetan Plateau. Atmosphere, 15(8), 934. https://doi.org/10.3390/atmos15080934

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