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Wonbae Bang, GyuWon Lee, Alexander Ryzhkov, Terry Schuur, and Kyo-Sun Sunny Lim

1. Introduction Different climatology and regional differences of the atmospheric environment can significantly affect the microphysical characteristics of precipitation ( Bringi et al. 2003 ). When compared with Oklahoma (OKL), United States, the atmospheric environment of the southern Korean Peninsula (KOR) is characterized by abundant lower-level moisture caused by an ample supply of water vapor from the sea during the summer, which leads to deficient ice crystals at upper levels ( Sohn et

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Graham A. Sexstone, Colin A. Penn, Glen E. Liston, Kelly E. Gleason, C. David Moeser, and David W. Clow

. Itkin , J. King , I. Merkouriadi , and J. Haapala , 2018 : A distributed snow-evolution model for sea-ice applications (SnowModel) . J. Geophys. Res. Oceans , 123 , 3786 – 3810 , https://doi.org/10.1002/2017JC013706 . 10.1002/2017JC013706 López-Moreno , J. I. , S. R. Fassnacht , S. Beguería , and J. B. P. Latron , 2011 : Variability of snow depth at the plot scale: Implications for mean depth estimation and sampling strategies . Cryosphere , 5 , 617 – 629 , https

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Lingjing Zhu, Jiming Jin, and Yimin Liu

warming over the TP in recent years (0.36°C decade −1 for the period of 1957–2007) ( Wang et al. 2008 ), most TP lakes have also undergone pronounced changes in terms of surface areas, water level/storage, and ice processes ( Huang et al. 2017 ; Phan et al. 2012 ; Song et al. 2013 ; Zhang et al. 2011 ). For example, the average annual temperature for Serling Co (the second largest lake in the TP) increased at a rate of 0.49°C decade −1 for the period of 1979–2017, and local precipitation enhanced

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Erin Dougherty and Kristen L. Rasmussen

temperature, sea level pressure, and sea ice. This results in a future warming over the CONUS of approximately +3°–6°C and increase in water vapor mixing ratio of ~20%–40%, consistent with Clausius–Clapeyron theory. The Liu et al. (2017) simulations provide a useful framework for understanding how flood-producing storms might change in a future warmer climate over the CONUS. In the CTRL simulations, there is high fidelity in the depiction of precipitation characteristics, as Beck et al. (2019) found

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Xinxuan Zhang, Emmanouil N. Anagnostou, Maria Frediani, Stavros Solomos, and George Kallos

and the Massif Central mountain range that exhibit frequent heavy precipitation and floods. A recent study ( Mehta and Yang 2008 ) about the Mediterranean basin indicates that heavy precipitation has peak frequency and accumulation over the mountainous regions according to satellite measurements. The areas along the Alpine foothills and the southern flanks of the Massif Central mountains are particularly under the influence of extreme rain accumulations because the air from the Mediterranean Sea

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Anna-Maria Tilg, Flemming Vejen, Charlotte Bay Hasager, and Morten Nielsen

, the underlying N ( D ) is different and causes the seasonal variation of the mean 10-min values of N ( D ) and KE( D ). The causes for annual variation are numerous and may be related also for example to the current sea ice extent or teleconnections (e.g., North Atlantic Oscillation). The strong deviation of the winter values in 2013 from the seasonal mean might also be influenced by the high percentage of snow in that winter (see also Con3 in Table 3 ). The most unexpected result in this

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Doug Richardson, Amanda S. Black, Didier P. Monselesan, Thomas S. Moore II, James S. Risbey, Andrew Schepen, Dougal T. Squire, and Carly R. Tozer

-matching archetype by minimizing the sum of squared differences (SSD) between the field and each archetype definition. For the observed time series, the fields are from the JRA55 reanalysis, on the same grid and over the same period as for the archetype generation. For the hindcast dataset, we use the ECMWF ensemble prediction system from the S2S prediction project (ECMWF-S2S; Vitart et al. 2017 ). This is a coupled atmosphere–ocean–sea ice model with a lead time of 46 days. The horizontal atmospheric

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Erin Dougherty and Kristen L. Rasmussen

− CMIP 5 1976 − 2005 . The perturbed fields in the PGW simulations include horizontal wind, geopotential, temperature, specific humidity, sea surface temperature, soil temperature, sea level pressure, and sea ice. Over the CONUS, this PGW approach results in a +3°–6°C warming and an increase in water vapor mixing ratio of ~20%–40%, consistent with the Clausius–Clapeyron theory ( Trenberth et al. 2003 ; Liu et al. 2017 ). This PGW approach has been similarly utilized in future regional climate change

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Junxia Li, Xueping Bai, Yuting Jin, Fangbo Song, Zhenju Chen, Lixin Cai, Fenghua Zou, Mengzhu Jiang, Ruixin Yun, and Zhaoyang Lv

as well as the whole basin. The models’ performance was examined by the split-sample calibration–verification method. The drought–pluvial years and periods were defined, and the dominant cycles of the reconstructed runoff were identified. Correlation analysis of the reconstructed/measured runoffs with sea surface temperature (SST) was undertaken to investigate the relationship between runoff and the El Niño–Southern Oscillation (ENSO). The running variance of reconstruction was calculated to

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Jian Zhang, Lin Tang, Stephen Cocks, Pengfei Zhang, Alexander Ryzhkov, Kenneth Howard, Carrie Langston, and Brian Kaney

QPE (“Q3DP” in short for the third generation of MRMS radar QPE) calculates R based on a combination of A , K DP , and Z . While A has the aforementioned advantages for rain rate estimation, it is not applicable in radar observations that contain ice. Therefore, Q3DP applies R ( A ) relationship in areas where radar is observing pure rain and R ( K DP ) in areas with potential presence of hail. The area of pure rain was defined as where the radar observations were below the ML bottom and Z

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