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Ervin Zsótér, Florian Pappenberger, Paul Smith, Rebecca Elizabeth Emerton, Emanuel Dutra, Fredrik Wetterhall, David Richardson, Konrad Bogner, and Gianpaolo Balsamo

TIGGE forecast experiment (at least 80% of days available from August 2008 to May 2010) evaluation. The yellow points provide enough observation only for the reanalysis while the red points have enough data available only for the TIGGE forecasts. f. Forecasting system setup To produce runoff from the TIGGE atmospheric ensemble variables (see section 2c ), HTESSEL experiments were run with 6-hourly forcing frequency and hourly model time step. For the instantaneous variables (such as 2-m temperature

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Chia-Chi Wang, Huang-Hsiung Hsu, and Ying-Ting Chen

frequency increased significantly over the central to eastern Pacific because of subtropical high expansion. Catto et al. (2015) and Blázquez and Solman (2018) evaluated front simulation performance and associated precipitation in current state-of-the-art climate models by using CMIP5 simulations. In both studies, front frequency was adequately reproduced, but the ratio of frontal to total precipitation was overestimated. Front frequency with precipitation contributed to model bias in most frontal

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Russell L. Elsberry, Tara D. B. Lambert, and Mark A. Boothe

demonstrated because DSHIPS predicted 12 of the 18 rapid decay episodes whereas SHIPS predicted only four episodes ( Table 4 ). The dynamical models GFDI and GFNI predicted 8 and 10 of the 18 rapid decay episodes. The NHC had the best performance in predicting 13 of the 18 rapid decay episodes within ±12 h of the actual time. The surprising result from this evaluation is the large number of false alarms by SHIPS, DSHIPS, GFDI, and GFNI with 17, 28, 24, and 23 episodes, respectively. Using this guidance

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Ming Liu, Jason E. Nachamkin, and Douglas L. Westphal

measurements at the ARM SGP site and against the standard COAMPS Harshvardhan radiation parameterization. Fu–Liou outperforms the standard model for both shortwave and longwave radiative fluxes, exhibiting bias and RMS scores 40%–50% of the standard model. The Fu–Liou also demonstrates stable performance over a 5-day forecast and significantly surpasses the standard model in the verification. The new model is then evaluated in a cloudy case consisting of a 15-day period at the ARM SGP site. Nine

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Md. Rafsan Nahian, Amir Nazem, Manoj K. Nambiar, Ryan Byerlay, Shohel Mahmud, Alison M. Seguin, Françoise R. Robe, James Ravenhill, and Amir A. Aliabadi

topography, boundary conditions, terrain, vegetation characteristics, and grid configuration in both horizontal and vertical directions ( Awan et al. 2011 ). Although several studies have been conducted to evaluate the performance of the WRF Model by using high-resolution topography and land-use datasets, there has been a lack of research involving the use of WRF for meteorological predictions over open-pit mining facilities. In particular, there is a need to better understand WRF, its uncertainties, and

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Hyun Il Choi, Xin-Zhong Liang, and Praveen Kumar

( Liang et al. 2005a , b , c , d , 2006 , 2012 ) with numerous crucial updates and improvements for land processes ( Choi 2006 ; Choi et al. 2007 ; Choi and Liang 2010 ; Yuan and Liang 2011 ). The original Community Land Model (CLM) and CoLM have been extensively evaluated for good performance against field measurements in a stand-alone mode as driven by the observational forcings ( Dai et al. 2003 ; Niu and Yang 2003 ; Niu et al. 2005 ; Maxwell and Miller 2005 ; Qian et al. 2006 ; Niu and

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Erik Noble, Leonard M. Druyan, and Matthew Fulakeza

eliminating the presence of the Guinea Highlands prevents the AEW from developing into a tropical disturbance. Both studies note difficulties with WRF evolving and propagating AEWs for simulations beyond 5 days. An evaluation of RCM performance must consider alternative model configurations. WRF has become host to many alternative sophisticated parameterizations of physical processes, such as radiation transfer, surface hydrology, boundary layer turbulence, and cumulus convection. The optimum WRF

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Hua Song, Wuyin Lin, Yanluan Lin, Audrey B. Wolf, Leo J. Donner, Anthony D. Del Genio, Roel Neggers, Satoshi Endo, and Yangang Liu

1. Introduction Clouds are a major source of uncertainty in future climate change projections (e.g., Cess et al. 1996 ; Solomon et al. 2007 ; Boucher et al. 2014 ). This motivates the quantitative evaluations of clouds in climate models against in situ and remote sensing observational data. A key question is how to use the increasingly available observational data to efficiently evaluate the performance of climate models in representing clouds. Testing the treatment of cloud-related physical

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Wen Li Zhao, Guo Yu Qiu, Yu Jiu Xiong, Kyaw Tha Paw U, Pierre Gentine, and Bao Yu Chen

; Yao et al. 2017b ; Mallick et al. 2018a , b ; Gerhards et al. 2019 ). Therefore, the objectives of this study are threefold: 1) to evaluate the uncertainties caused by resistances in ET estimates by employing one- and two-source PM models with different parameterizations and complexities, 2) to identify the sources of uncertainty in resistance parameterizations, and 3) to discuss possible solutions for reducing such uncertainties. The remainder of this paper is organized as follows. We first

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CLIVAR MADDEN–JULIAN OSCILLATION WORKING GROUP:

insightful, assessment of model performance regarding the MJO. Over the course of the following year, this working group developed these diagnostics via teleconferences and meetings during the U.S. CLIVAR Summit in July 2006 as well as informally at a number of scientific meetings and workshops. The group considered issues such as ease of use versus level of physical insight, potential pitfalls/ambiguity in the interpretation of the model-data comparisons, observation quality, availability, and

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