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Ronald Gelaro, Rolf H. Langland, Simon Pellerin, and Ricardo Todling

Greenland. The results differ in this respect compared with NOGAPS and GEOS-5, and may indicate a deficiency in the use of AMSU-A radiances in GDPS. There are also common areas of nonbeneficial impact from AMSU-A radiances in all forecast systems, which occur over parts of India and north-central Canada near Hudson Bay. This could be caused by land- or ice-surface contamination of the processed radiance observations, and demonstrates the utility of the adjoint method for isolating possible problems

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E. A. Irvine, S. L. Gray, J. Methven, and I. A. Renfrew

targeted observation coverage and number using a two-dimensional sampling pattern. Targeted observations were spaced according to the horizontal correlation length scales assumed by the European Centre for Medium-Range Weather Forecasts (ECMWF) four-dimensional variational data assimilation (4D-Var) scheme, and the number of observations and size of target region were varied. Taking targeted observations over a larger area was found to be more effective. The proximity of the targeted observations to

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Benoît Vié, Olivier Nuissier, and Véronique Ducrocq

). While large amounts of precipitation can accumulate over several days when frontal systems are slowed down and strengthened by coastal mountains, some of these Mediterranean HPEs can be attributed to back-building quasi-stationary mesoscale convective systems (MCSs) staying over the same area for several hours, producing large rainfall totals in a very short time (typically over 200 mm in 6–24 h). Using nonhydrostatic cloud-resolving models (CRMs) improves the realism of simulated precipitating

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John E. Janowiak, Peter Bauer, Wanqiu Wang, Phillip A. Arkin, and Jon Gottschalck

resolution satellite-derived estimates of precipitation that have been accumulated to daily totals and interpolated to 2.5° latitude–longitude grid form to match the model forecasts, along with a coarser heritage dataset that contains rain gauge data (over land only) as well as satellite-derived precipitation estimates. In addition to operational model forecasts, we examine the precipitation predictions from NCEP’s Climate Forecast System (CFS) and ECMWF’s Re-Analysis Interim (ERA-Interim) to

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Sharanya J. Majumdar, Kathryn J. Sellwood, Daniel Hodyss, Zoltan Toth, and Yucheng Song

1. Introduction The global atmospheric observational network has traditionally comprised land-based rawinsonde balloons and satellite-borne sensors. Yet, as stated by Lorenz and Emanuel (1998) , “ … despite this wealth of data—more, in fact, than we know how to use to full advantage—large gaps remain in our picture of the global weather pattern, particularly over the less frequently visited areas of the oceans.” In an effort to fill these gaps, several field campaigns have taken place over the

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Thomas M. Hamill, Jeffrey S. Whitaker, Michael Fiorino, and Stanley G. Benjamin

://collaboration.cmc.ec.gc.ca/science/rpn/gef_html_public/DOCUMENTATION/GENERAL/general.html and in Charron et al. (2010) .] The CMC ensemble system used a horizontal computational grid of 400 × 200 grid points, or approximately 0.9°, and 28 vertical levels. The ensemble was initialized using an EnKF, following Charron et al. (2010) and Houtekamer et al. (2009) and references therein. The 20 forecast ensemble members used a variety of perturbed physics; changing gravity wave drag parameters, land surface process type, condensation scheme type, convection scheme type, shallow

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