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Barry H. Lynn, Guy Kelman, and Gary Ellrod

of simulated radar fields during the assimilation period compared to the control (without). Moreover, the improvement carried over quite prominently to the subsequent forecast hours. Also, Fierro et al. (2014) compared the same lightning assimilation scheme with a three-dimensional variational data assimilation (3DVAR) technique that assimilated radar reflectivity and radial velocity data. For the case of a severe continental derecho mesoscale convective system (MCS), they found that

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Prakash Pithani, Sachin D. Ghude, R. K. Jenamani, Mrinal Biswas, C. V. Naidu, Sreyashi Debnath, Rachana Kulkarni, Narendra G. Dhangar, Chinmay Jena, Anupam Hazra, R. Phani, P. Mukhopadhyay, Thara Prabhakaran, Ravi S. Nanjundiah, and M. Rajeevan

Tropical Meteorology (IITM), Pune, India. The funding institute for this research is MoES. REFERENCES Bhowmik , S. K. R. , A. M. Sud , and C. Singh , 2004 : Forecasting fog over Delhi—An objective method . Mausam , 55 , 313 – 322 . Biswadip , G. , 2014 : IRS-P6 AWiFS derived gridded land use/land cover data compatible to mesoscale models (MM5 and WRF) over Indian region. NRSC Tech. Doc. NRSC-ECSA-ACSG-OCT-2014-TR-651, 17 pp . Collins , W. D. , 2001a : Effects of enhanced shortwave

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Inger-Lise Frogner, Ulf Andrae, Jelena Bojarova, Alfons Callado, Pau Escribà, Henrik Feddersen, Alan Hally, Janne Kauhanen, Roger Randriamampianina, Andrew Singleton, Geert Smet, Sibbo van der Veen, and Ole Vignes

 assimilation ALADIN Aire Limitée Adaptation Dynamique  Développement International Alaro ALADIN–AROME AROME Applications of Research to Operations  at Mesoscale ARPÉGE Action de Recherche Petite Echelle  Grande Echelle BRAND B matrix randomization COMEPS Continuously Updated Mesoscale Ensemble Prediction System DA Data assimilation ECMWF European Centre for Medium-Range  Weather Forecasts EDA Ensemble data assimilation EnKF Ensemble Kalman filtering EnSRF Ensemble square root filter EPS Ensemble prediction

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Kuan-Jen Lin, Shu-Chih Yang, and Shuyi S. Chen

Tenerelli , J. E. , and S. S. Chen , 2001 : High-resolution simulations of Hurricane Floyd using MM5 with vortex-following mesh refinement. 18th Conf. on Weather Analysis and Forecasting/14th Conf. on Numerical Weather Prediction/9th Conf. on Mesoscale Processes , Fort Lauderdale, FL, Amer. Meteor. Soc., JP1.11, https://ams.confex.com/ams/pdfpapers/23165.pdf . Torn , R. D. , and G. J. Hakim , 2009 : Ensemble data assimilation applied to RAINEX observations of Hurricane Katrina (2005) . Mon

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Barry H. Lynn, Seth Cohen, Leonard Druyan, Adam S. Phillips, Dennis Shea, Haim-Zvi Krugliak, and Alexander P. Khain

; Colle and Charles 2011 ; Greybush et al. 2017 ). Reducing model grid spacing could possibly lead to improved snowfall forecasts. For instance, forecasts described as “convection-allowing” (grid scale of 3 or 4 km) or “cloud-allowing 1 ” (grid scale of 1 or 2 km) do not (generally) use a cumulus parameterization, but explicitly simulate clouds and precipitation using model microphysical schemes. For instance, Zhang et al. (2002) used a mesoscale model with 3.3-km grid spacing to investigate the

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Burkely T. Gallo, Adam J. Clark, Israel Jirak, John S. Kain, Steven J. Weiss, Michael Coniglio, Kent Knopfmeier, James Correia Jr., Christopher J. Melick, Christopher D. Karstens, Eswar Iyer, Andrew R. Dean, Ming Xue, Fanyou Kong, Youngsun Jung, Feifei Shen, Kevin W. Thomas, Keith Brewster, Derek Stratman, Gregory W. Carbin, William Line, Rebecca Adams-Selin, and Steve Willington

Mesoscale Forecast System (NAM) model analysis on a 12-km grid was used as a background for the analysis, and NAM forecasts provided boundary conditions. Perturbed members applied initial condition and boundary condition perturbations drawn from the SREF to the control analyses and forecasts. The CAPS forecasts were run with 3-km grid spacing and extended to 60 h, supporting day 2 forecasts. Table 2. SSEF ensemble specifications. All members use RRTMG radiation schemes. Microphysics schemes used

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David E. Jahn and William A. Gallus Jr.

anticyclonic flow. Forecasting LLJ location and strength is thus important for forecasting convective precipitation in the Great Plains. Challenges remain, however, in the use of mesoscale models for LLJ forecasting. LLJ evolution is influenced by the turbulent mixing of the boundary layer ( Hu et al. 2013 ; Klein et al. 2016 ), the parameterizations and effects of which differ among planetary boundary layer (PBL) schemes. Local PBL schemes, such as the Mellor–Yamada–Nakanishi–Niino (MYNN) PBL scheme

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Brice E. Coffer, Matthew D. Parker, Richard L. Thompson, Bryan T. Smith, and Ryan E. Jewell

ramifications to forecasters. Since very few severe weather events have optimal observed proximity soundings, forecasters rely on these analyses for operational awareness of the mesoscale environment, especially above the surface where observations are particularly scarce. Although the RUC appears to struggle to represent the wind profile in the PBL (at least for these cases), the goal of the SPC’s SFCOA scheme, which was applied to the all the proximity soundings discussed in sections 3 and 4 , is to

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Phillipa Cookson-Hills, Daniel J. Kirshbaum, Madalina Surcel, Jonathan G. Doyle, Luc Fillion, Dominik Jacques, and Seung-Jong Baek

placement of precipitation over the Olympics as the corresponding stage IV analysis, except for a southward shift of the maximum, smaller accumulations, and less mesoscale detail ( Fig. 4b ). 3) Kriging with external drift (KED) Given that the stage IV analysis is restricted to the United States, and the CaPA analysis is based on uncertain model forecasts, we seek a third gridded product that relies purely on gauge and radar data over the entire PNW. In an evaluation of various radar–gauge merging

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Erik R. Nielsen, Gregory R. Herman, Robert C. Tournay, John M. Peters, and Russ S. Schumacher

. , Thompson R. L. , and Weisman M. L. , 2000 : Predicting supercell motion using a new hodograph technique . Wea. Forecasting , 15 , 61 – 79 , doi: 10.1175/1520-0434(2000)015<0061:PSMUAN>2.0.CO;2 . Corfidi, S. F. , 2003 : Cold pools and MCS propagation: Forecasting the motion of downwind-developing MCSs . Wea. Forecasting , 18 , 997 – 1017 , doi: 10.1175/1520-0434(2003)018<0997:CPAMPF>2.0.CO;2 . Corfidi, S. F. , Merritt J. , and Fritsch J. , 1996 : Predicting the movement of mesoscale

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