Search Results

You are looking at 1 - 2 of 2 items for :

  • Author or Editor: P. Moll x
  • Refine by Access: All Content x
Clear All Modify Search
Alan R. Moller, Charles A. Doswell III, Michael P. Foster, and Gary R. Woodall


Supercell thunderstorm forecasting and detection is discussed, in light of the disastrous weather events that often accompany supercells. The emphasis is placed on using a scientific approach to evaluate supercell potential and to recognize their presence rather than the more empirical methodologies (e.g., “rules of thumb”) that have been used in the past. Operational forecasters in the National Weather Service (NWS) can employ conceptual models of the supercell, and of the meteorological environments that produce supercells, to make operational decisions scientifically.

The presence of a mesocyclone is common to all supercells, but operational recognition of supercells is clouded by the various radar and visual characteristics they exhibit. The notion of a supercell spectrum is introduced in an effort to guide improved operational detection of supercells. An important part of recognition is the anticipation of what potential exists for supercells in the prestorm environment. Current scientific understanding suggests that cyclonic updraft rotation originates from streamwise vorticity (in the storm's reference frame) within its environment. A discussion of how storm-relative helicity can be used to evaluate supercell potential is given. An actual supercell event is employed to illustrate the usefulness of conceptual model visualization when issuing statements and warnings for supercell storms. Finally, supercell detection strategies using the advanced datasets from the modernized and restructured NWS are described.

Full access
C. Faccani, F. Rabier, N. Fourrié, A. Agusti-Panareda, F. Karbou, P. Moll, J.-P. Lafore, M. Nuret, F. Hdidou, and O. Bock


The high vertical density soundings recorded during the 2006 African Monsoon Multidisciplinary Analysis (AMMA) campaign are assimilated into the French numerical weather prediction Action de Recherche Petite Echelle Grande Echelle (ARPEGE) four-dimensional variational data assimilation (4DVAR) system, with and without a bias correction for relative humidity. Four different experiments are carried out to assess the impacts of the added observations. The analyses and forecasts from these different scenarios are evaluated over western Africa. For the full experiment using all data together with a bias correction, the humidity analysis is in better agreement with surface observations and independent GPS observations than it was for the other experiments. AMMA data also improve the African easterly jet (AEJ) on its southeasterly side, and when they are used with an appropriate bias correction, the daily and monthly averaged precipitation results are in relatively good agreement with the satellite-based precipitation estimates. Forecast scores are computed with respect to surface observations, radiosondes, and analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF). The positive impacts of additional radiosonde observations (with a relevant bias correction) are found to propagate downstream with a positive impact over Europe at the 2–3-day forecast range.

Full access