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Daniel Rosenfeld, Xing Yu, and Jin Dai

Abstract

NOAA Advanced Very High Resolution Radiometer (AVHRR) images revealed conspicuous tracks of glaciated cloud in thick supercooled layer clouds over central China. These tracks were identified as being artificially produced by cloud-seeding operations at the −10°C isotherm, less than 1 km below cloud tops, aimed at precipitation enhancement, by means of AgI acetone generators. The cloud composition was deduced by retrieving the cloud-top effective radius (re) and analyzing its spatial relations with cloud-top temperatures and with the visible reflectance. Cloud-top temperature varied between −13° and −17°C. The glaciation became apparent at cloud tops about 22 min after seeding. The glaciated tops sank and formed a channel in the supercooled layer cloud. The rate of sinking of about 40 cm s−1 is compatible with the fall velocity of ice crystals that are likely to form at these conditions. A thin line of new water clouds formed in the middle of the channel of the seeded track between 38 and 63 min after seeding, probably as a result of rising motions induced by the released latent heat of freezing. These clouds disappeared in the more mature segments of the seeded track, which continued to expand throughout the observation period of more than 80 min. Eventually the seeding tracks started to dissipate by expansion of the ambient cloud tops inward from the sides. Using the brightness temperature difference between 10.8 and 12.0 μm allowed for observation of the seeding signature deep in the clouds, even when it was obscured under thin supercooled layer clouds. This is the third and most detailed report of effects of advertent cloud seeding for precipitation enhancement being detected and analyzed based on satellite observations. It opens new possibilities of using satellites for directing and monitoring weather modification experiments and operations.

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Guiting Song, Robert Huva, Yu Xing, and Xiaohui Zhong

Abstract

For most locations on Earth the ability of a Numerical Weather Prediction (NWP) model to accurately simulate surface irradiance relies heavily on the NWP model being able to resolve cloud coverage and thickness. At horizontal resolutions at or below a few kilometres NWP models begin to explicitly resolve convection and the clouds that arise from convective processes. However, even at high resolutions, biases may remain in the model and result in under- or over-prediction of surface irradiance. In this study we explore the correction of such systematic biases using a moisture adjustment method in tandem with the Weather Research and Forecasting model (WRF) for a location in Xinjiang, China. After extensive optimisation of the configuration of the WRF model we show that systematic biases still exist—in particular for wintertime in Xinjiang. We then demonstrate the moisture adjustment method with cloudy days for January 2019. Adjusting the relative humidity by 12% through the vertical led to a Root Mean Square Error (RMSE) improvement of 57.8% and a 90.5% reduction in bias for surface irradiance.

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Xing Yu, Jin Dai, Daniel Rosenfeld, Hengchi Lei, Xiaohong Xu, Peng Fan, and Zhengqi Chen

Abstract

From 0615 to 0749 UTC 14 March 2000, an operation of cloud seeding for precipitation enhancement by aircraft was carried out in the middle part of Shaanxi Province, China. National Oceanic and Atmospheric Administration (NOAA)-14 satellite imagery was received at 0735 UTC for the study region. A vivid cloud track appeared on the satellite imagery; its length was about 350 km, and its average width and width maximum were 9 and 14 km, respectively. Through application of a three-dimensional numerical model of the transport and diffusion of the seeding material, the simulated plume shape, the turning points, and the width and length of seeding lines agree with that of the cloud pattern indicated by the satellite imagery. The track is consistent with the transport and diffusion of the seeding line. All of these factors suggest that the cloud track that is detected by satellite imaging is the direct physical evidence of cloud seeding near the cloud top, with the cloud responding to the transport and diffusion of the seeding material and/or the propagation of the glaciation by secondary effects. The track is indeed caused by the cloud seeding, and the model can predict the evolution of the response zone of cloud seeding. For this seeding case, the duration for segments of the seeding line varies between 20 and 80 min, and the time period for each segment of the seeding line diffusing to the maximum width is about from 40 to 70 min. One hour after cloud seeding, the dispersion rate of the cloud track is 7.0 km h−1, and the predicted expansion rates of the seeding material concentrations of 1 and 4 L−1 are 7.6 and 4.6 km h−1, respectively. The comparison demonstrates that the numerical model of transport and diffusion can predict the main characteristics of transport and diffusion of the seeding effect, and the simulation results are reasonable.

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Zhiguo Yue, Daniel Rosenfeld, Guihua Liu, Jin Dai, Xing Yu, Yannian Zhu, Eyal Hashimshoni, Xiaohong Xu, Ying Hui, and Oliver Lauer

Abstract

The advent of the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi NPP (SNPP) satellite made it possible to retrieve a new class of convective cloud properties and the aerosols that they ingest. An automated mapping system of retrieval of some properties of convective cloud fields over large areas at the scale of satellite coverage was developed and is presented here. The system is named Automated Mapping of Convective Clouds (AMCC). The input is level-1 VIIRS data and meteorological gridded data. AMCC identifies the cloudy pixels of convective elements; retrieves for each pixel its temperature T and cloud drop effective radius r e; calculates cloud-base temperature T b based on the warmest cloudy pixels; calculates cloud-base height H b and pressure P b based on T b and meteorological data; calculates cloud-base updraft W b based on H b; calculates cloud-base adiabatic cloud drop concentrations N d,a based on the T–r e relationship, T b, and P b; calculates cloud-base maximum vapor supersaturation S based on N d,a and W b; and defines N d,a/1.3 as the cloud condensation nuclei (CCN) concentration N CCN at that S. The results are gridded 36 km × 36 km data points at nadir, which are sufficiently large to capture the properties of a field of convective clouds and also sufficiently small to capture aerosol and dynamic perturbations at this scale, such as urban and land-use features. The results of AMCC are instrumental in observing spatial covariability in clouds and CCN properties and for obtaining insights from such observations for natural and man-made causes. AMCC-generated maps are also useful for applications from numerical weather forecasting to climate models.

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Xu Dao, Yu-Chi Lin, Fang Cao, Shi-Ying Di, Yihang Hong, Guanhua Xing, Jianjun Li, Pingqing Fu, and Yan-Lin Zhang
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Xu Dao, Yu-Chi Lin, Fang Cao, Shi-Ying Di, Yihang Hong, Guanhua Xing, Jianjun Li, Pingqing Fu, and Yan-Lin Zhang

Abstract

The North China Plain (NCP) is becoming one of the most polluted areas characterized by a high frequency of haze pollution. However, the spatial and temporal evolutions of aerosol chemical compositions in such a highly polluted region are not well understood due to the lack of a long-term and comprehensive observation-based network. China’s National Aerosol Composition Monitoring Network (NACMON) has conducted comprehensive offline and online measurements of compositions and optical properties of airborne aerosols in order to systematically investigate the formation process, source apportionments of haze, and interactions between haze pollution and climate change. The objective of the observations is to provide information for policy makers to make strategies for the alleviation of haze occurrence. In this paper, we present instrumentations and methodologies as well as the preliminary results of the offline observations in NACMON stations over the NCP region. The implications and future perspectives of the network are also summarized. Benefiting from simultaneous observations from this network, we found that secondary aerosols were the dominant component in haze pollution. High anthropogenic emissions, low wind speed, and high relative humidity (RH) facilitated gas-to-particle transformation and resulted in high PM2.5 formation (PM2.5 is particulate matter that is smaller than 2.5 μm in diameter). Sulfate-dominant or nitrate-dominant aerosols during the haze period were driven by ambient RH. Moreover, the contributions of coal combustion and biomass burning to PM2.5 revealed downward trends, whereas secondary aerosols showed upward trends over the last decade. Thus, we highlighted that strict control of anthropogenic emissions of precursor gases, such as NOx, NH3, and volatile organic compounds (VOCs), will be an important way to decrease PM2.5 pollution in the NCP region.

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Xin-Zhong Liang, Min Xu, Xing Yuan, Tiejun Ling, Hyun I. Choi, Feng Zhang, Ligang Chen, Shuyan Liu, Shenjian Su, Fengxue Qiao, Yuxiang He, Julian X. L. Wang, Kenneth E. Kunkel, Wei Gao, Everette Joseph, Vernon Morris, Tsann-Wang Yu, Jimy Dudhia, and John Michalakes

The CWRF is developed as a climate extension of the Weather Research and Forecasting model (WRF) by incorporating numerous improvements in the representation of physical processes and integration of external (top, surface, lateral) forcings that are crucial to climate scales, including interactions between land, atmosphere, and ocean; convection and microphysics; and cloud, aerosol, and radiation; and system consistency throughout all process modules. This extension inherits all WRF functionalities for numerical weather prediction while enhancing the capability for climate modeling. As such, CWRF can be applied seamlessly to weather forecast and climate prediction. The CWRF is built with a comprehensive ensemble of alternative parameterization schemes for each of the key physical processes, including surface (land, ocean), planetary boundary layer, cumulus (deep, shallow), microphysics, cloud, aerosol, and radiation, and their interactions. This facilitates the use of an optimized physics ensemble approach to improve weather or climate prediction along with a reliable uncertainty estimate. The CWRF also emphasizes the societal service capability to provide impactrelevant information by coupling with detailed models of terrestrial hydrology, coastal ocean, crop growth, air quality, and a recently expanded interactive water quality and ecosystem model.

This study provides a general CWRF description and basic skill evaluation based on a continuous integration for the period 1979– 2009 as compared with that of WRF, using a 30-km grid spacing over a domain that includes the contiguous United States plus southern Canada and northern Mexico. In addition to advantages of greater application capability, CWRF improves performance in radiation and terrestrial hydrology over WRF and other regional models. Precipitation simulation, however, remains a challenge for all of the tested models.

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