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Yu-Kun Hou, Hua Chen, Chong-Yu Xu, Jie Chen, and Sheng-Lian Guo

Abstract

Statistical downscaling is useful for managing scale and resolution problems in outputs from global climate models (GCMs) for climate change impact studies. To improve downscaling of precipitation occurrence, this study proposes a revised regression-based statistical downscaling method that couples a support vector classifier (SVC) and first-order two-state Markov chain to generate the occurrence and a support vector regression (SVR) to simulate the amount. The proposed method is compared to the Statistical Downscaling Model (SDSM) for reproducing the temporal and quantitative distribution of observed precipitation using 10 meteorological indicators. Two types of calibration and validation methods were compared. The first method used sequential split sampling of calibration and validation periods, while the second used odd years for calibration and even years for validation. The proposed coupled approach outperformed the other methods in downscaling daily precipitation in all study periods using both calibration methods. Using odd years for calibration and even years for validation can reduce the influence of possible climate change–induced nonstationary data series. The study shows that it is necessary to combine different types of precipitation state classifiers with a method of regression or distribution to improve the performance of traditional statistical downscaling. These methods were applied to simulate future precipitation change from 2031 to 2100 with the CMIP5 climate variables. The results indicated increasing tendencies in both mean and maximum future precipitation predicted using all the downscaling methods evaluated. However, the proposed method is an at-site statistical downscaling method, and therefore this method will need to be modified for extension into a multisite domain.

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Yuxiao Chen, Jing Chen, Dehui Chen, Zhizhen Xu, Jie Sheng, and Fajing Chen

Abstract

The simulated radar reflectivity used by current mesoscale numerical weather prediction models can reflect the grid precipitation but cannot reflect the subgrid precipitation generated by a cumulus parameterization scheme. To solve this problem, this study developed a new simulated radar reflectivity calculation method to obtain the new radar reflectivity corresponding to the subgrid-scale and grid-scale precipitation based on the mesoscale Global/Regional Assimilation and Prediction System (GRAPES) model of the China Meteorological Administration. Based on this new method, two 15-day forecast experiments were carried out for two different time periods (11–25 April 2019 and 1–15 August 2019), and the radar reflectivity products obtained by the new method and previous method were compared. The results show that the radar reflectivity obtained by the new simulated radar reflectivity calculation method gives a clear indication of the subgrid-scale precipitation in the model. Verification results show that the threat scores of the improved experiments are better than those of the control experiments in general and that the reliability of the simulated radar reflectivity for the indication of precipitation is improved. It is concluded that the new simulated radar reflectivity calculation method is effective and significantly improves the reflectivity products. This method has good prospects for providing more information about forecasting precipitation and convective activity in operational models.

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Sara Lance, Jie Zhang, James J. Schwab, Paul Casson, Richard E. Brandt, David R. Fitzjarrald, Margaret J. Schwab, John Sicker, Cheng-Hsuan Lu, Sheng-Po Chen, Jeongran Yun, Jeffrey M. Freedman, Bhupal Shrestha, Qilong Min, Mark Beauharnois, Brian Crandall, Everette Joseph, Matthew J. Brewer, Justin R. Minder, Daniel Orlowski, Amy Christiansen, Annmarie G. Carlton, and Mary C. Barth

Abstract

Aqueous chemical processing within cloud and fog water is thought to be a key process in the production and transformation of secondary organic aerosol mass, found abundantly and ubiquitously throughout the troposphere. Yet, significant uncertainty remains regarding the organic chemical reactions taking place within clouds and the conditions under which those reactions occur, owing to the wide variety of organic compounds and their evolution under highly variable conditions when cycled through clouds. Continuous observations from a fixed remote site like Whiteface Mountain (WFM) in New York State and other mountaintop sites have been used to unravel complex multiphase interactions in the past, particularly the conversion of gas-phase emissions of SO2 to sulfuric acid within cloud droplets in the presence of sunlight. These scientific insights led to successful control strategies that reduced aerosol sulfate and cloud water acidity substantially over the following decades. This paper provides an overview of observations obtained during a pilot study that took place at WFM in August 2017 aimed at obtaining a better understanding of Chemical Processing of Organic Compounds within Clouds (CPOC). During the CPOC pilot study, aerosol cloud activation efficiency, particle size distribution, and chemical composition measurements were obtained below-cloud for comparison to routine observations at WFM, including cloud water composition and reactive trace gases. Additional instruments deployed for the CPOC pilot study included a Doppler lidar, sun photometer, and radiosondes to assist in evaluating the meteorological context for the below-cloud and summit observations.

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