Influence of natural rainfall variability on the evaluation of artificial precipitation enhancement

Wu, Xianghua; Niu, Shengjie; Jin, Dezhen; Sun, Hua

doi:10.1007/s11430-015-5055-0

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…The mean relative change (MRC) reflects an annual average estimate of the impact of seeding over the 10-year period and is shown to be positive in all cases-varying from 5.6% at Al Malaiha gauge to 30.8% at Masafi gauge. An average increase of 22.8% is recorded over the target area using Equation (11), which is derived from the mean of target/control observations with the best fit (R 2 = 0.95). Figure 4a shows the inter-annual variability of the relative change ratio throughout the 2010-2019 seeding period at gauges within Target Area 1.…”

Section: Radar-based Evaluationmentioning

confidence: 99%

“…According to the most recent review on global precipitation enhancement activities conducted by the World Meteorological Organization (WMO) Expert Team on Weather Modification, cloud seeding from aircraft platforms is generally considered more effective compared to other techniques such as ground-based generators, customized rockets, and artillery shells [1]. Results from operational cloud seeding programs spanning several countries, including Australia [10], China [11], India [12], Israel [13], South Africa [14][15][16], Thailand [17], and the United States [18,19], record between 10-30% increases in precipitation amounts and cloud lifetime. Alternatively, several studies report on the limited efficacy of seeding experiments for drought relief [20], along with inconclusive results stemming from unreliable measurements and/or co-occurring microphysical and dynamical processes that are difficult to account for [21,22].…”

Section: Introductionmentioning

confidence: 99%

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The UAE Cloud Seeding Program: A Statistical and Physical Evaluation

Hosari¹,

Mandous²,

Wehbe³

et al. 2021

Atmosphere

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Operational cloud seeding programs have been increasingly deployed in several countries to augment natural rainfall amounts, particularly over water-scarce and arid regions. However, evaluating operational programs by quantifying seeding impacts remains a challenging task subject to complex uncertainties. In this study, we investigate seeding impacts using both long-term rain gauge records and event-based weather radar retrievals within the framework of the United Arab Emirates (UAE) National Center of Meteorology’s operational cloud seeding program. First, seasonal rain gauge records are inter-compared between unseeded (1981–2002) and seeded (2003–2019) periods, after which a posteriori target/control regression is developed to decouple natural and seeded rainfall time series. Next, trend analyses and change point detection are carried out over the July-October seeding periods using the modified Mann-Kendall (mMK) test and the Cumulative Sum (CUSUM) method, respectively. Results indicate an average increase of 23% in annual surface rainfall over the seeded target area, along with statistically significant change points detected during 2011 with decreasing/increasing rainfall trends for pre-/post-change point periods, respectively. Alternatively, rain gauge records over the control (non-seeded) area show non-significant change points. In line with the gauge-based statistical findings, a physical analysis using an archive of seeded (65) and unseeded (87) storms shows enhancements in radar-based storm properties within 15–25 min of seeding. The largest increases are recorded in storm volume (159%), area cover (72%), and lifetime (65%). The work provides new insights for assessing long-term seeding impacts and has significant implications for policy- and decision-making related to cloud seeding research and operational programs in arid regions.

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Section: Radar-based Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The UAE Cloud Seeding Program: A Statistical and Physical Evaluation

Hosari¹,

Mandous²,

Wehbe³

et al. 2021

Atmosphere

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“…In a statistical test, total N study units are usually divided into two categories (i.e., K seeded units and L unseeded units). The evaluation of a statistical test is influenced by values of these sample sizes together with the catalytic operation, test scheme, and data hierarchy (Manton et al, ; Wang, ; Wu et al, ). When we utilize only the historical unseeded data, K denotes the number of assumed seeded units.…”

Section: Evaluation Of Statistical Testmentioning

confidence: 99%

“…It compares the actual value with the estimated natural value with the assumption of no catalysis to establish the corresponding evaluation indicators to analyze the effect of cloud seeding (Breed et al, ; Gabriel, ). Most previous studies selected precipitation to conduct analysis, and the results showed that the relative effects of artificial precipitation enhancement operation are about 5%–45% (e.g., Gabriel & Gagin, ; Gabriel, ; Gagin & Gabriel, ; Griffith & Yorty, ; Jia et al, ; Koloskov et al, ; Li et al, ; Morrison et al, ; Solak et al, ; Xue, Hashimoto, et al, ; Xue, Tessendorf, et al, ; Woodley et al, , ; Wu et al, ; Wurtele, ; Zeng et al, ; Zhai, ; Zhai et al, ). Despite important progress, many critical issues or challenges remain to be solved, such as natural fluctuation of spatial and temporal distribution of precipitation, lacking complete and systematic understanding of the processes of artificial catalysis and physical mechanism of cloud seeding, and limitation of the application of statistical methods.…”

Section: Introductionmentioning

confidence: 99%

Advances in the Evaluation of Cloud Seeding: Statistical Evidence for the Enhancement of Precipitation

Yan

et al. 2018

Earth and Space Science

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Cloud seeding has been actively carried out across the globe in the past several decades due to the uneven spatial‐temporal distribution of precipitation. The catalytic effects of artificial precipitation enhancement experiments and operations caused widely academic debates in atmospheric physics. Both atmospheric physicists and statisticians have made much effort for exploratory and confirmatory studies on scientific evaluation of cloud seeding. Recent progresses and critical problems on how to evaluate cloud seeding and acquire statistical evidence for the enhancement of precipitation, including the design of statistical tests, the selection of target indicators and covariates, the evaluation of statistical methods, and the outlook of cloud seeding evaluation, are reviewed. We describe important issues, aiming to reduce systematic errors and uncertainties in statistical tests as well as increase the level of quantitative sounding and evaluation. First, a regularized set of design and operation in the steps should be established to optimize techniques of cloud seeding. Second, the latest achievements in atmospheric physics, physical sounding, and statistics need to be introduced to help improve the correctness and scientificity. Third, middle‐ and long‐term special research projects are expected to investigate the influence of ideal hypotheses of seeding schemes, statistical test plans, and statistical methods. These demands can update our knowledge and technology of weather modification and increase the cooperation of multidiscipline, such as logical integration of statistical tests, physical analysis, and numerical modeling.

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“…Direct observation of ice crystal formation and evolution is one of the most critical issues in supercooled stratus cloud seeding operations. It is still a challenge to distin-guishing between the impacts of cloud seeding and the natural variability of weather systems on the cloud-precipitation process [15], which requires multi-platform simultaneous observations [4,16]. For instance, only two of 36 experiments for seeding effects were confirmed by the research aircraft, radar, and surface instrumentation, mainly due to the natural fluctuations in ice crystal concentrations [17].…”

Section: Introductionmentioning

confidence: 99%

Cloud Seeding Evidenced by Coherent Doppler Wind Lidar

Yuan

Wei

et al. 2021

Remote Sensing

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Evaluation of the cloud seeding effect is a challenge due to lack of directly physical observational evidence. In this study, an approach for directly observing the cloud seeding effect is proposed using a 1548 nm coherent Doppler wind lidar (CDWL). Normalized skewness was employed to identify the components of the reflectivity spectrum. The spectrum detection capability of a CDWL was verified by a 24.23-GHz Micro Rain Radar (MRR) in Hefei, China (117°15′ E, 31°50′ N), and different types of lidar spectra were detected and separated, including aerosol, turbulence, cloud droplet, and precipitation. Spectrum analysis was applied as a field experiment performed in Inner Mongolia, China (112°39′ E, 42°21′ N ) to support the cloud seeding operation for the 70th anniversary of China’s national day. The CDWL can monitor the cloud motion and provide windshear and turbulence information ensuring operation safety. The cloud-precipitation process is detected by the CDWL, microwave radiometer (MWR) and Advanced Geosynchronous Radiation Imager (AGRI) in FY4A satellites. In particular, the spectrum width and skewness of seeded cloud show a two-layer structure, which reflects cloud component changes, and it is possibly related to cloud seeding effects. Multi-component spectra are separated into four clusters, which are well distinguished by spectrum width and vertical velocity. In general, our findings provide new evidence that the reflectivity spectrum of CDWL has potential for assessing cloud seeding effects.

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Influence of natural rainfall variability on the evaluation of artificial precipitation enhancement

Cited by 8 publications

References 12 publications

The UAE Cloud Seeding Program: A Statistical and Physical Evaluation

The UAE Cloud Seeding Program: A Statistical and Physical Evaluation

Advances in the Evaluation of Cloud Seeding: Statistical Evidence for the Enhancement of Precipitation

Cloud Seeding Evidenced by Coherent Doppler Wind Lidar

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