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Brice E. Coffer, Mateusz Taszarek, and Matthew D. Parker

–1-h forecasts and SPC mesoscale analyses using VORTEX2 soundings . Wea. Forecasting , 27 , 667 – 683 , https://doi.org/10.1175/WAF-D-11-00096.1 . 10.1175/WAF-D-11-00096.1 Coniglio , M. C. , and M. D. Parker , 2020 : Insights into supercells and their environments from three decades of targeted radiosonde observations . Mon. Wea. Rev. , 148 , 4893 – 4915 , https://doi.org/10.1175/MWR-D-20-0105.1 . 10.1175/MWR-D-20-0105.1 Coniglio , M. C. , J. Correia Jr ., P. T. Marsh , and

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Jason C. Knievel, Yubao Liu, Thomas M. Hopson, Justin S. Shaw, Scott F. Halvorson, Henry H. Fisher, Gregory Roux, Rong-Shyang Sheu, Linlin Pan, Wanli Wu, Joshua P. Hacker, Erik Vernon, Frank W. Gallagher III, and John C. Pace

). In 2014, E-4DWX was extended to three more government test sites in the Great Basin of the United States: White Sands Missile Range (WSMR), New Mexico; Yuma Proving Ground (YPG), Arizona; and Electronic Proving Ground (EPG), Arizona. For brevity, this paper focuses on just DPG’s experience with E-4DWX. b. Testing and forecasting at DPG One of DPG’s primary missions is to test equipment that detects chemical and biological hazards. Such tests are very sensitive to mesoscale and microscale weather

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Brian J. Squitieri and William A. Gallus Jr.

1. Introduction Mesoscale convective systems (MCSs) are the primary source of precipitation across the Great Plains and Midwest during the summer months ( Fritsch et al. 1986 ; Stensrud 1996 ; Ashley et al. 2003 ; Jirak and Cotton 2007 ; Coniglio et al. 2010 ) and provide the rainfall needed for agricultural purposes; thus, better forecasts of nocturnal convection benefit farmers ( Jirak et al. 2003 ). Nocturnal MCS development and sustenance is often tied to the occurrence of the Great

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William R. Ryerson and Joshua P. Hacker

and coastal regions compared to raw mesoscale ensemble forecasts, with modest skill increases after sunrise. The challenge in adding skill to the ensemble by statistically adjusting zero or near-zero q c predictions from the members is in knowing whether fog is likely. The strategy is intentionally conservative such that the fog prediction is taken directly from the NWP model when fog is predicted. The probability of light fog can only be increased from zero, reducing complexity while adding

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John Lawson and William A. Gallus Jr.

-decreasing grid spacings, there will always be a scale below which wavelengths are truncated, and chaotic, nonlinear processes are implicitly resolved, or parameterized. Parameterization is used in operational NWP models, such as the North American Mesoscale (NAM) model and the Global Forecast System (GFS), to capture the planetary boundary layer (PBL), cloud microphysics, and other subgrid-scale processes. The “spread” of parameterization schemes, each with their own set of biases and random errors

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Claudia Roeger, Roland Stull, David McClung, Joshua Hacker, Xingxiu Deng, and Henryk Modzelewski

forecast agrees with the measurement ( Roeger et al. 2001 ). A forecast of good quality may also show skill, which is the degree of correctness above some reference baseline, such as a climatological average. Thus, by determining the accuracy and skill of a forecast, one can improve it and use it with confidence in the future. Although these theoretical ideas about weather forecast verification are well known, not many verification results are actually published or are easily accessible for mesoscale

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Philip A. Lutzak

temporally limited in coverage due to their source from 1- or 3-hourly model output with spatial grid limitations that are too large to represent a mesoscale undular bore event. Thus, it is only these data taken together with all recommended forecast parameters that provide a cumulative indication that such an event is likely to occur.

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John V. Cortinas Jr. and David J. Stensrud

716 WEATHER AND FORECASTING VOLUME 10The Importance of Understanding Mesosca~e lV~odel Parameterizafion Schemes for Weather Forecasting JOHN V. CORTINAS JR.NOYDl /National Severe Storms Laboratory and Cooperative Institute for Mesoscale Meteorological Studies, Norman, Oklahoma DAVID J. STENSRUDNOAA /National Severe Storms Laboratory

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Craig S. Schwartz

Range, but the observed rainfall far exceeded forecasters’ expectations. This underprediction may have been related to reliance on operational numerical weather prediction (NWP) model guidance with parameterized convection, including the Global Forecast System (GFS), North American Mesoscale Forecast System (NAM), and Rapid Refresh (RR) models, which also severely underpredicted the storm-total precipitation (discussed in section 3 ). However, numerous studies have demonstrated that high

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Shu-Chih Yang, Eugenia Kalnay, and Takemasa Miyoshi

period. However, during the spinup period, the choice of the initial conditions for the ensemble affects not only the accuracy of the analyses and forecasts but also the length of the spinup period ( Kalnay and Yang 2010 , hereafter KY10 ). All data assimilation methods suffer from spinup problems; however, the spinup problems tend to be more serious for EnKFs than for variational methods because both the mean and the ensemble perturbations require initial conditions. In mesoscale NWP, spinup occurs

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