Search Results

You are looking at 1 - 10 of 4,716 items for :

  • Mesoscale forecasting x
  • Monthly Weather Review x
  • All content x
Clear All
Aaron J. Hill, Christopher C. Weiss, and Brian C. Ancell

predictability in forecasting convection because of such initial condition sensitivities. Previously, Zhang et al. (2003) illustrated that moist processes (e.g., through convective and microphysical parameterizations) create a limitation to mesoscale predictability, enhancing the Melhauser and Zhang (2012) findings. Furthermore, Martin and Xue (2006) utilized a large ensemble to carry out perturbation experiments on water vapor mixing ratio, soil moisture, and meridional wind near the surface to

Full access
T. N. Krishnamurti, S. Pattnaik, and D. V. Bhaskar Rao

experience gained in physical initialization with large-scale models ( Krishnamurti et al. 1991 , 1993 , 2001 ), it is possible to formulate a simplified version of rain-rate initialization for mesoscale models. That is the goal of this paper. We hope to see the extent to which the observed estimates of rain rate from satellites can be incorporated within a mesoscale model. We also wish to ask how far into the future we can demonstrate a positive impact on the forecast skills. In our study we

Full access
Edward R. Mansell, Conrad L. Ziegler, and Donald R. MacGorman

1. Introduction Recent studies have shown that forecasts can be improved by incorporating the effects of deep convection during the initialization period of mesoscale forecast models. For example, based on model experiments that used subjective analyses to improve initial conditions, Stensrud and Fritsch (1994a) suggested that forecast skill could be enhanced by using data assimilation procedures that include “the effects of parameterized convection, as indicated by radar or satellite during

Full access
Luke E. Madaus, Gregory J. Hakim, and Clifford F. Mass

1. Introduction Short-term numerical weather forecasts continue to suffer from poor definition and prediction of mesoscale weather features (e.g., Roebber et al. 2004 ). This is particularly true for small-scale, but potentially high-impact features such as the timing and structure of frontal passages ( Colle et al. 2001 ) or convective development and evolution ( Melhauser and Zhang 2012 ; Hanley et al. 2013 ). Not only can these features present significant hazards to public safety, but

Full access
Frédéric Fabry

evaluated either in specific case studies (e.g., Gao et al. 1999 ; Montmerle et al. 2002 ; Sun 2005 ) or in simulations of the usefulness of observing systems (e.g., Sokolovskiy et al. 2005 ; Tong and Xue 2005 ). But to the author’s knowledge, nothing has been done to systematically study the occurrence of a measurable and usable signal in the data itself, at least in the context of the mesoscale forecasting of convection. Both measurability and usability are important. Measurability refers

Full access
Nathan Snook, Ming Xue, and Youngsun Jung

) use an idealized two-dimensional model to investigate the predictability of ensemble forecasts of mesoscale convective systems (MCSs), while Aksoy et al. (2010) used the Weather Research and Forecasting Model (WRF) to assimilate Doppler radar observations and perform idealized ensemble predictions of storms with both supercellular and linear convective modes. Stensrud and Gao (2010) assimilate radar data using 3DVAR and perform short-range ensemble forecasts of a supercell case. All three of

Full access
Frédéric Fabry and Juanzhen Sun

paradigm. In the other case, data are available to constrain only some of the variables, and the remaining ones are constrained using the model’s ability to simulate them to be compatible with the time evolution of available observations. Methods such as 4D-Var (e.g., Talagrand 1997 ) are designed to handle such situations. For mesoscale forecasting, the second scenario generally applies: balloon soundings and other measurements of all model fields throughout the atmosphere are sparse in space and

Full access
Russ S. Schumacher and Adam J. Clark

convective processes may not be analyzed properly by ensemble-based assimilation systems, nor predicted adequately by high-resolution ensembles, if this model uncertainty is not represented in the assimilation and forecast system. However, diversity in physical parameterizations can also complicate the interpretation of the forecasts, and can complicate quantitative methods such as ensemble-based synoptic and mesoscale analysis (e.g., Hakim and Torn 2008 ), as those methods assume that all ensemble

Full access
John M. Peters and Paul J. Roebber

-Dynamic Meteorology and Weather Analysis and Forecasting, Meteor. Monogr., No. 33, Amer. Meteor. Soc., 5 – 34 . Bryan , G. H. , J. C. Wyngaard , and M. Fritsch , 2003 : Resolution requirements for the simulation of deep moist convection . Mon. Wea. Rev. , 131 , 2394 – 2416 , doi: 10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2 . Davis , C. , B. Brown , and R. Bullock , 2006a : Object-based verification of precipitation forecasts. Part I: Methodology and application to mesoscale rain areas

Full access
Matthew J. Carrier, Xiaolei Zou, and William M. Lapenta

. Another application of AIRS data that takes advantage of its high spectral resolution is the direct use of AIRS radiance observations at different channels for mesoscale forecast verification, which is presented in this study. Comparisons between simulated radiances and observed values are routinely done at various operational centers prior to assimilating the radiance data into their respective forecast models ( McNally et al. 2006 ; Le Marshall et al. 2006 ). This is done for several reasons: 1) to

Full access