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Kathy Pegion and Arun Kumar

conditional to average. 3. ENSO as a predictor of perfect-model forecast skill We evaluate ENSO as a predictor of variations in forecast skill by exploring the linear relationship between two ENSO indices and perfect-model forecast skill of 2-m temperature and precipitation over the United States. The first index is Niño-3.4, a commonly used index of ENSO ( Trenberth 1997 ). The index used by CPC in preparing operational seasonal forecasts, the oceanic Niño index, is a 3-month running mean of Niño-3

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Leonard W. Snellman

The impact of increasing automation of meteorological guidance on manual preparation of operational weather forecasts is discussed. There appears to be some evidence that forecasters are abdicating their meteorological input into the final forecast as automated guidance increases in quantity and quality. Results of a National Weather Service Western Region program to stimulate more forecaster input into the final forecast are given. Suggestions are made regarding “interactive” forecasting using on-station computers in the upcoming Automation of Field Operations and Services (AFOS) era.

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Burkely T. Gallo, Jamie K. Wolff, Adam J. Clark, Israel Jirak, Lindsay R. Blank, Brett Roberts, Yunheng Wang, Chunxi Zhang, Ming Xue, Tim Supinie, Lucas Harris, Linjiong Zhou, and Curtis Alexander

1. Introduction Convection-allowing models (CAMs) are becoming more widely available and play an increasingly important role in the forecast process, particularly since the operationalization of the High-Resolution Rapid Refresh (HRRR; Benjamin et al. 2016 ; Alexander et al. 2017 ) model in 2014 and the High Resolution Ensemble Forecast system in November of 2017 (HREF; Roberts et al. 2019 ). CAMs can be particularly helpful in forecasting severe convective weather, since the small grid

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Suranjana Saha, Shrinivas Moorthi, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, David Behringer, Yu-Tai Hou, Hui-ya Chuang, Mark Iredell, Michael Ek, Jesse Meng, Rongqian Yang, Malaquías Peña Mendez, Huug van den Dool, Qin Zhang, Wanqiu Wang, Mingyue Chen, and Emily Becker

assimilation system (GODAS) operational at NCEP in 2003 ( Behringer 2007 ) that provided the ocean initial states, NCEP’s Global Forecast System (GFS) operational in 2003 that was the atmospheric model run at a lower resolution of T62L64, and the Modular Ocean Model, version 3 (MOM3), from the Geophysical Fluid Dynamics Laboratory (GFDL). The CFSv1 system worked well enough that it became difficult to terminate it, as it was used by many in the community, even after the CFSv2 was implemented into

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Jonathan J. Gourley and Humberto Vergara

1. Introduction The study of Herman and Schumacher (2018 , hereafter HS18 ) used radar-based quantitative precipitation estimates (QPEs) from different algorithms and compared exceedance thresholds to flash flood reports (FFRs) and flash flood warnings (FFWs). One objective of HS18 was to provide the best practices for using the tools in an operational flash flood monitoring and forecasting environment. The commentary presented herein is motivated by the following points: QPE algorithms in

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Lori Drake

potential directions in which adjustments to the existing information could be usefully made, with the ultimate objective of curtailing misunderstanding and ensuring that the full value of the TC forecast is realized for the benefit of the society at large. While forecasters are able to interpret and give meaning to their meteorological observations through the context of the collected knowledge of modern science, and the operational models based on it, to make their forecasts, members of the public do

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Paul Gregory, Frederic Vitart, Rabi Rivett, Andrew Brown, and Yuriy Kuleshov

cyclone forecasts in 2012 ( Vitart et al. 2012 ). Forecast skill was assessed using a research version of the 32-day IFS with model version Cy36r4 featuring 15 ensemble members. This assessment used forecasts initialized on the first day of every month from 1989 to 2008. BSS showed skill out to 25 days, with improved skill in the Southern Hemisphere. The ECMWF remains the only center to issue operational subseasonal cyclone forecasts based on a dynamical model. A recent review by Camargo et al. (2019

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Jung-Hoon Kim, Hye-Yeong Chun, Robert D. Sharman, and Teddie L. Keller

), and output from the GTG system is currently available operationally (online at ). The approach used for upper-level turbulence forecasting in the GTG system is as follows. First, the GTG system calculates several turbulence diagnostics representing large-scale forcings such as frontogenesis, tropopause proximity, and ageostrophic flow. Second, the calculated diagnostics are optimally combined by weighting scores that are based on the forecasting performance of the

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John A. Knaff, Charles R. Sampson, and Kate D. Musgrave

1. Introduction Forecasters at the Joint Typhoon Warning Center (JTWC) have to predict the future movement, intensity, and the extent of the wind field for tropical cyclones (TCs). To aid in the forecast process, progress has been made in developing forecast guidance tools. As guidance tools improve, so do the TC forecasts produced by operational centers. Illustrating this point, JTWC’s operational track forecasts have steadily improved since the 1990s due primarily to the improvement in global

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Pieter De Meutter, Luc Gerard, Geert Smet, Karim Hamid, Rafiq Hamdi, Daan Degrauwe, and Piet Termonia

-Aire Limitée Adaptation Dynamique Développement International (ARPEGE-ALADIN) operational limited area model with a revised and modular structure of the physical parameterizations] of the Royal Meteorological Institute (RMI) of Belgium that was running with a horizontal grid spacing of 4 km operationally that time. However, the operationally forecasted precipitation rates were an order of magnitude smaller than the observed values. Inspecting the values of the subgrid downdraft velocities, it was found

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