Precipitation change from a cumulonimbus cloud downwind of a seeded target area

Ćurić, Mladjen; Janc, D.; Vučković, Vladan

doi:10.1029/2007jd009483

Cited by 13 publications

(3 citation statements)

References 25 publications

(27 reference statements)

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“…Therefore, ground‐based MPLs can detect most of the low clouds with or without higher clouds above them [ Warren et al ., ], but may significantly underestimate upper multilayer clouds compared with an MMCR or spaceborne lidar or radar measurements [ Chang and Li , ]. Cloud models with good microphysics and high space and time resolutions, including both one‐dimensional models which can simulate the time evolution of cloud top and base heights [e.g., Ćurić and Janc , ] and three‐dimensional mesoscale cloud models [e.g., Ćurić et al ., ], can be reliably evaluated using cloud observations from the MPL and the MMCR.…”

Section: Introductionmentioning

confidence: 99%

A new cloud and aerosol layer detection method based on micropulse lidar measurements

Zhao

Wang

et al. 2014

JGR Atmospheres

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This paper introduces a new algorithm to detect aerosols and clouds based on micropulse lidar measurements. A semidiscretization processing technique is first used to inhibit the impact of increasing noise with distance. The value distribution equalization method which reduces the magnitude of signal variations with distance is then introduced. Combined with empirical threshold values, we determine if the signal waves indicate clouds or aerosols. This method can separate clouds and aerosols with high accuracy, although differentiation between aerosols and clouds are subject to more uncertainties depending on the thresholds selected. Compared with the existing Atmospheric Radiation Measurement program lidar-based cloud product, the new method appears more reliable and detects more clouds with high bases. The algorithm is applied to a year of observations at both the U.S. Southern Great Plains (SGP) and China Taihu sites. At the SGP site, the cloud frequency shows a clear seasonal variation with maximum values in winter and spring and shows bimodal vertical distributions with maximum occurrences at around 3-6 km and 8-12 km. The annual averaged cloud frequency is about 50%. The dominant clouds are stratiform in winter and convective in summer. By contrast, the cloud frequency at the Taihu site shows no clear seasonal variation and the maximum occurrence is at around 1 km. The annual averaged cloud frequency is about 15% higher than that at the SGP site. A seasonal analysis of cloud base occurrence frequency suggests that stratiform clouds dominate at the Taihu site.

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Section: Introductionmentioning

confidence: 99%

A new cloud and aerosol layer detection method based on micropulse lidar measurements

Zhao

Wang

et al. 2014

JGR Atmospheres

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“…In Figure 9e,f,i,j, cloud spectra extracted from the upper layer have largely normalized skewness and spectrum width, which indicates that multiple components exist. It is probable that cloud seeding generates a large number of ice crystals [70], resulting in big cloud droplet or that raindrops coexist below the melting layer [71]. Considering that cluster D falls slower than raindrops, it also could be that cloud seeding intensified the process of cloud-precipitation and generated graupel or small hail [59].…”

Section: Application In An Artificial Precipitation Operationmentioning

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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“…In contrast to the inadvertent cloud seeding by background atmospheric aerosols, the artificial seeding with AgI is a deliberate modification of clouds and precipitation by additionally injecting appropriate amount of AgI particles into a supercooled cloud in specific time and location, for the purpose of enhancing the formation of ice crystals and stimulating precipitation by ice particle growth so that the cloud seeding can be artificially controlled. Previous studies have suggested that AgI seeding had a large effect on the microphysical and dynamical properties of convective clouds and found that cloud microphysical changes induced by seeding played a very important role in determining storm development and precipitation [e.g., Orville and Chen , ; Rosenfeld and Woodley , ; Farley et al ., ; Ćurić et al ., , ; Chen and Xiao , ; Xue et al ., ]. Modeling results of Farley et al .…”

Section: Introductionmentioning

confidence: 99%

Can we modify stratospheric water vapor by deliberate cloud seeding?

Chen

Yin

2014

JGR Atmospheres

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Stratospheric water vapor has an important effect on Earth's climate. Considering the significance of overshooting deep convection in modulating the water vapor content (WVC) of the lower stratosphere (LS), we use a three-dimensional convective cloud model to simulate the effects of various silver iodide (AgI) seeding scenarios on tropical overshooting deep convection that occurred on 30 November 2005 in Darwin, Australia. The primary motivation for this study is to investigate whether the WVC in the LS can be artificially modified by deliberate cloud seeding. It is found that AgI seeding done at the early stages of clouds produces significant effects on cloud microphysical and dynamical properties, and that further affects the WVC in the LS, while seeding at the mature stages of clouds has only a slight impact. The response of stratospheric water vapor to changes in the amount of seeding agent is nonlinear. The seeding with a small (large) amount of AgI increases (decreases) the WVC in the LS, due to enhanced (reduced) production and vertical transport of cloud ice from the troposphere and subsequent sublimation in the stratosphere. The results show that stratospheric water vapor can be artificially altered by deliberate cloud seeding with proper amount of seeding agent. This study also shows an important role of graupel in regulating cloud microphysics and dynamics and in modifying the WVC in the LS.

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Precipitation change from a cumulonimbus cloud downwind of a seeded target area

Cited by 13 publications

References 25 publications

A new cloud and aerosol layer detection method based on micropulse lidar measurements

A new cloud and aerosol layer detection method based on micropulse lidar measurements

Cloud Seeding Evidenced by Coherent Doppler Wind Lidar

Can we modify stratospheric water vapor by deliberate cloud seeding?

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