Study of Impact of Cloud-Seeding on Intensity-Duration-Frequency (IDF) Curves of Sharjah City, the United Arab Emirates

Despite the availability of some studies related to rainfall characteristics in Sharjah city and the UAE, very few of these studies have investigated any causal link between recent cloud-seeding missions and the increasing rainfall intensities and urban floods being experienced. This study has assessed the impact of cloud-seeding operations that started in 2010 on the IDF curves of Sharjah city, The UAE. Hourly rainfall data spanning between 2010 and 2020 available at three stations, namely Sharjah Airport, Al Dhaid, and Mleiha, and provided by the National Center of Meteorology were used. To allow comparison with the pre-cloudseeding (2010) era, these records were extended back to 1992 using the much longer data available at Dubai Airport with the aid of the Self-organizing map approach. The developed IDF curves showed an apparent increase in rainfall intensities after implementing the cloud-seeding missions. In addition, the estimated mean rainfall intensities for three regions of the city were also much higher for the cloudseeded years compared to the pre-cloudseeding period. The study suggests that, while cloud-seeding provides water security benefits, its impact on urban flooding should also be carefully considered in the context of urban development plans.

Investigating the Feasibility of Implementing a Cloud Seeding Project in Sistan and Baluchestan Province, South East of Iran

The water shortage is a challenge in many countries around the world. Today, the latest scientific and practical technologies are used to solve the problem of water shortage in arid and semi-arid regions. The optimal use of water resources as well as the use of novel methods of water extraction plays a significant role in alleviating the effects of this crisis. One of the methods used for increasing rainfall and water harvesting from the atmosphere is cloud seeding technology. The first step of this technique involves studying the target area and selecting the appropriate time and place for cloud seeding. The purpose of this study is to investigate the feasibility of cloud seeding in Sistan and Baluchestan province, south east of Iran, for rainmaking. Therefore, using the parameters of precipitation, minimum temperature, relative humidity and cloudy parameter, the status and feasibility for rainmaking in the province were evaluated and suitable months for cloud seeding were determined. Accordingly, December, January, February and March were found to provide suitable conditions for seeding. In order to select suitable places for cloud seeding, zoning maps of precipitation, temperature and relative humidity in selected months as well as the topographic map of the province were prepared by GIS After fuzzyization and integration of these maps, the zoning map of suitable areas for cloud seeding in Sistan and Baluchestan province was drawn to select the most susceptible areas. The area surrounding Khash synoptic station and the southern areas of the province were found to be suitable for cloud seeding.

Cloud Seeding Evidenced by Coherent Doppler Wind Lidar

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.

Evaluation of the First Negative Ion-Based Cloud Seeding and Rain Enhancement Trial in China

Negative ion-based cloud seeding has been shown to be an effective means in the laboratory. China’s first negative ion-based cloud seeding outfield trial was conducted in the northwestern interior. This paper briefly introduces the principle of the ion-based precipitation enhancement, and the trial location is described. The design of the ionization system and meteorological monitoring network are presented. The implementation plan of the outfield trial is explained. In addition, the evaluation of experimental effects is detailed in this paper. We designed various analytical methods to investigate both the overall precipitation variation of the experimental area and the precipitation variation within the experimental area. The overall precipitation of the experimental area was predicted using a neural network, and then the actual precipitation was compared with the predicted precipitation to evaluate the effectiveness of the experiment. The effectiveness of the experiment was also evaluated using historical precipitation data and the result of the randomized comparative trial. This paper also explores the effects of geographic location differences and wind direction differences on the precipitation differences within the trial area. The changes in the number of negative ions and clouds in the sky were also analyzed. From these analyses, we obtained quantitative assessment results. These results could indicate that the outfield trial basically met the expected requirements, which is to increase the rainfall of the trial area by 20%.

Cloud Seeding and Crops Yields: Evaluation of the North Dakota Cloud Modification Project

The North Dakota Cloud Modification Project was established in 1951 to reduce severe hail damage and increase precipitation in specific counties in North Dakota. Every year, participating counties receive cloud seeding treatment during the months of June, July, and August. Although some atmospheric studies have examined the efficacy of the treatment, few have used statistical procedures to determine how the program affected crop yields and crop losses. We use the panel nature of historical cloud seeding participation and crop data to estimate a two-way fixed effects regression with county-specific time trends to examine the effect of cloud seeding on wheat and barley yields. In addition, we use federal crop insurance data to estimate the effect of cloud seeding on losses for those same crops. Our evaluation indicates that the cloud seeding program had significant positive effects on crop yields and improved loss ratios.

The UAE Cloud Seeding Program: A Statistical and Physical Evaluation

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.