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Boniface O. Fosu, S.-Y. Simon Wang, and Jin-Ho Yoon
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S.-Y. Simon Wang, Robert R. Gillies, Boniface Fosu, and Pratibha M. Singh
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Q.-S. Ge, J.-Y. Zheng, Z.-X. Hao, P.-Y. Zhang, and W.-C. Wang

Chinese historical documents that contain descriptions of weather conditions can be used for studying climate of the past hundreds or even thousands of years. In this study, the progress of reconstructing a 273-station quantitative precipitation dataset for 1736–1911—a period when records of the depth of rain infiltration (into the ground) and snow depth (above the surface) were kept in the Yu–Xue–Fen–Cun (which is part of memos routinely sent to the emperors during the Qing Dynasty) is reported. To facilitate the rainfall reconstruction, a field program of 29 sites covering different climate regimes and soil characteristics was designed for the purpose of establishing the transfer function between the rain infiltration depth and rainfall amount, while the relation between the snow depth and snowfall is obtained using instrumental measurements of recent decades. The results of the first site at Shijiazhuang (near Beijing) are reported here. The reconstruction shows that the summer and winter precipitation during 1736–1911 were generally greater than their respective 1961–90 means. Two years with extreme summer precipitation are identified—112 mm in 1792 and 1167 mm in 1801; the latter is larger than the 998 mm in 1996, which has been the most severe one of recent decades. The long-term high-resolution quantitative data can be used to study climate variability as well as to evaluate historical climate model simulations.

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P. Bechtold, S. K. Krueger, W. S. Lewellen, E. van Meijgaard, C.-H. Moeng, D. A. Randall, A. van Ulden, and S. Wang

Several one-dimensional (ID) cloud/turbulence ensemble modeling results of an idealized nighttime marine stratocumulus case are compared to large eddy simulation (LES). This type of model intercomparison was one of the objects of the first Global Energy and Water Cycle Experiment Cloud System Study boundary layer modeling workshop held at the National Center for Atmospheric Research on 16–18 August 1994.

Presented are results obtained with different 1D models, ranging from bulk models (including only one or two vertical layers) to various types (first order to third order) of multilayer turbulence closure models. The ID results fall within the scatter of the LES results. It is shown that ID models can reasonably represent the main features (cloud water content, cloud fraction, and some turbulence statistics) of a well-mixed stratocumulus-topped boundary layer.

Also addressed is the question of what model complexity is necessary and can be afforded for a reasonable representation of stratocumulus clouds in mesoscale or global-scale operational models. Bulk models seem to be more appropriate for climate studies, whereas a multilayer turbulence scheme is best suited in mesoscale models having at least 100- to 200-m vertical resolution inside the boundary layer.

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Q. Duan, Z. Di, J. Quan, C. Wang, W. Gong, Y. Gan, A. Ye, C. Miao, S. Miao, X. Liang, and S. Fan

Abstract

Weather forecasting skill has been improved over recent years owing to advances in the representation of physical processes by numerical weather prediction (NWP) models, observational systems, data assimilation and postprocessing, new computational capability, and effective communications and training. There is an area that has received less attention so far but can bring significant improvement to weather forecasting—the calibration of NWP models, a process in which model parameters are tuned using certain mathematical methods to minimize the difference between predictions and observations. Model calibration of the NWP models is difficult because 1) there are a formidable number of model parameters and meteorological variables to tune, and 2) a typical NWP model is very expensive to run, and conventional model calibration methods require many model runs (up to tens of thousands) or cannot handle the high dimensionality of NWP models. This study demonstrates that a newly developed automatic model calibration platform can overcome these difficulties and improve weather forecasting through parameter optimization. We illustrate how this is done with a case study involving 5-day weather forecasting during the summer monsoon in the greater Beijing region using the Weather Research and Forecasting Model. The keys to automatic model calibration are to use global sensitivity analysis to screen out the most important parameters influencing model performance and to employ surrogate models to reduce the need for a large number of model runs. Through several optimization and validation studies, we have shown that automatic model calibration can improve precipitation and temperature forecasting significantly according to a number of performance measures.

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Pandora Hope, Mitchell T. Black, Eun-Pa Lim, Andrew Dowdy, Guomin Wang, Robert J. B. Fawcett, and Acacia S. Pepler
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Jin-Ho Yoon, Ben Kravitz, Philip J. Rasch, S.-Y. Simon Wang, Robert R. Gillies, and Lawrence Hipps
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H. J. S. Fernando, I. Gultepe, C. Dorman, E. Pardyjak, Q. Wang, S. W Hoch, D. Richter, E. Creegan, S. Gaberšek, T. Bullock, C. Hocut, R. Chang, D. Alappattu, R. Dimitrova, D. Flagg, A. Grachev, R. Krishnamurthy, D. K. Singh, I. Lozovatsky, B. Nagare, A. Sharma, S. Wagh, C. Wainwright, M. Wroblewski, R. Yamaguchi, S. Bardoel, R. S. Coppersmith, N. Chisholm, E. Gonzalez, N. Gunawardena, O. Hyde, T. Morrison, A. Olson, A. Perelet, W. Perrie, S. Wang, and B. Wauer

Abstract

C-FOG is a comprehensive bi-national project dealing with the formation, persistence, and dissipation (life cycle) of fog in coastal areas (coastal fog) controlled by land, marine, and atmospheric processes. Given its inherent complexity, coastal-fog literature has mainly focused on case studies, and there is a continuing need for research that integrates across processes (e.g., air–sea–land interactions, environmental flow, aerosol transport, and chemistry), dynamics (two-phase flow and turbulence), microphysics (nucleation, droplet characterization), and thermodynamics (heat transfer and phase changes) through field observations and modeling. Central to C-FOG was a field campaign in eastern Canada from 1 September to 8 October 2018, covering four land sites in Newfoundland and Nova Scotia and an adjacent coastal strip transected by the Research Vessel Hugh R. Sharp. An array of in situ, path-integrating, and remote sensing instruments gathered data across a swath of space–time scales relevant to fog life cycle. Satellite and reanalysis products, routine meteorological observations, numerical weather prediction model (WRF and COAMPS) outputs, large-eddy simulations, and phenomenological modeling underpin the interpretation of field observations in a multiscale and multiplatform framework that helps identify and remedy numerical model deficiencies. An overview of the C-FOG field campaign and some preliminary analysis/findings are presented in this paper.

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X. Y. Zhang, Y. Q. Wang, W. L. Lin, Y. M. Zhang, X. C. Zhang, S. Gong, P. Zhao, Y. Q. Yang, J. Z. Wang, Q. Hou, X. L. Zhang, H. Z. Che, J. P. Guo, and Y. Li

Before and during the 2008 Beijing Olympics from June to September, ground-based and satellite monitoring were carried out over Beijing and its vicinity (BIV) in a campaign to quantify the outcomes of various emission control measures. These include hourly surface PM10 and PM2.5 and their fraction of black carbon (BC), organics, nitrate, sulfate, ammonium, and daily aerosol optical depth (AOD), together with hourly reactive gases, surface ozone, and daily columnar NO2 from satellite. The analyses, excluding the estimates from weather contributions, demonstrate that after the control measures, including banning ~300,000 “yellow-tag” vehicles from roads, the even–odd turn of motor vehicles on the roads, and emission reduction aiming at coal combustion, were implemented, air quality in Beijing improved substantially. The levels of NO, NO2, NOx, CO, SO2, BC, organics, and nitrate dropped by about 30%–60% and the ozone moderately increased by ~40% while the sulfate and ammonium exhibited different patterns during various control stages. Weather conditions have a great impact on the summertime secondary aerosol (~80% of total PM) and O3 formations over BIV. During the Olympic Game period, various atmospheric components decreased dramatically at Beijing compared to the same period in the previous years. This decrease was related not only to the implementation of rigorous control measures, but also to the favorable weather processes. The subtropical high was located to the south so that Beijing's weather was dominated by the interaction between a frequently eastward shifting trough in the westerlies and a cold continental high with clear to cloudy days or showery weather.

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David M. L. Sills, Gregory A. Kopp, Lesley Elliott, Aaron L. Jaffe, Liz Sutherland, Connell S. Miller, Joanne M. Kunkel, Emilio Hong, Sarah A. Stevenson, and William Wang

Abstract

Canada is a vast country with most of its population located along its southern border. Large areas are sparsely populated and/or heavily forested, and severe weather reports are rare when thunderstorms occur there. Thus, it has been difficult to accurately assess the true tornado climatology and risk. It is also important to establish a reliable baseline for tornado-related climate change studies. The Northern Tornadoes Project (NTP), led by Western University, is an ambitious multidisciplinary initiative aimed at detecting and documenting every tornado that occurs across Canada. A team of meteorologists and wind engineers collects research-quality data during each damage investigation via thorough ground surveys and high-resolution satellite, aircraft, and drone imaging. Crowdsourcing through social media is also key to tracking down events. In addition, NTP conducts research to improve our ability to detect and accurately assess tornadoes that affect forests, cropland, and grassland. An open data website allows sharing of resulting datasets and analyses. Pilot investigations were carried out during the warm seasons of 2017 and 2018, with the scope expanding from the detection of any tornadoes in heavily forested regions of central Canada in 2017 to the detection of all EF1+ tornadoes in Ontario plus all significant events outside of Ontario in 2018. The 2019 season was the first full campaign, systematically collecting research-quality tornado data across the entire country. To date, the project has found 89 tornadoes that otherwise would not have been identified, and increased the national tornado count in 2019 by 78%.

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