The German Weather Service (Deutscher Wetterdienst) has recently developed a new operational global numerical weather prediction model, named GME, based on an almost uniform icosahedral-hexagonal grid. The GME gridpoint approach avoids the disadvantages of spectral techniques as well as the pole problem in latitudelongitude grids and provides a data structure extremely well suited to high efficiency on distributed memory parallel computers. The formulation of the discrete operators for this grid is described and evaluations that demonstrate their second-order accuracy are provided. These operators are derived for local basis functions that are orthogonal and conform perfectly to the spherical surface. The local basis functions, unique for each grid point, are the latitude and longitude of a spherical coordinate system whose equator and zero meridian intersect at the grid point. The prognostic equations for horizontal velocities, temperature, and surface pressure are solved using a semi-implicit Eulerian approach and for two moisture fields using a semi-Lagrangian scheme to ensure monotonicity and positivity. In the vertical direction, finite differences are applied in a hybrid (sigma pressure) coordinate system to all prognostic variables. The semi-implicit treatment of gravity waves presented here leads to a 3D Helmholtz equation that is diagonalized into a set of 2D Helmholtz equations that are solved by successive relaxation. Most of the same physical parameterizations used in the authors' previous operational regional model, named EM, are employed in GME. Some results from the verification process for GME are provided and GME performance statistics on a Cray T3E1200 as well as on the ECMWF Fujitsu VPP5000 systems are summarized. For the case of the severe Christmas 1999 storm over France and Germany the pronounced sensitivity of the model with respect to the initial state is discussed. Finally, a test case is shown where it is currently possible, though not yet operationally practical, to run GME at 15-km resolution on the VPP5000.

The AD/VI-Aeo/us mission will provide global wind profile observationswith the aim to demonstrate improvement in atmospheric wind analyses for the benefit of numerical weather prediction and climate studies.

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Even as operational numerical weather prediction models become more accurate, improvements in rain forecasts are hard to come by. O f all the weather elements for which forecasts are provided to the public, rainfall is perhaps of the greatest interest. While most people simply want to know whether they will need an umbrella that day, there is growing demand from industry, agriculture, government, and many other sectors for more detailed rainfall predictions. Unfortunately, rainfall is certainly among the most difficult weather elements to predict correctly. Rainfall has greater spatial and temporal variability than most other meteorological quantities of interest. Many processes can lead to rain, including large-scale ascent of moist air, convection caused by heating of moist air near the surface, con-AMERJCAN METEOROLOGICAL SOCIETY vergence of moist air in a baroclinic zone, and orographic lifting. These processes must all be represented in numerical weather prediction (NWP) models, whose output forms the basis for most rainfall forecasts. It is of great interest to assess how well we can meet the need for timely and accurate rainfall forecasts using operational NWP models.In the mid-1990s, the Working Group on Numerical Experimentation (WGNE), established under the World Meteorological Organisation's World Climate Research Programme (WCRP) and Commission for Atmospheric Sciences (CAS), turned its attention to quantitative precipitation forecasts (QPFs). Since accurate prediction of rainfall depends critically on the accurate prediction of atmospheric motion and moisture content, it is reasonable to expect that a good forecast of rainfall over a large domain indicates a good forecast overall. Indeed, many operational centers use QPF skill as a critical measure of model health. Accumulated precipitation can be verified (albeit imperfectly, given its highly variable nature) using rain gauge networks. Knowledge of a model's QPF behavior not only helps model developers but also users of the QPFs to understand the reliability of the model output.At the 10th annual WGNE meeting it was recommended that QPFs from several operational NWP models be evaluated in different areas of the globe

SUMMARYIn data assimilation for numerical weather prediction, measurements of various observation systems are combined with background data to define initial states for the forecasts. Current and future observation systems, in particular satellite instruments, produce large numbers of measurements with high spatial and temporal density. Such datasets significantly increase the computational costs of the assimilation and, moreover, can violate the assumption of spatially independent observation errors. To ameliorate these problems, we propose two greedy thinning algorithms, which reduce the number of assimilated observations while retaining the essential information content of the data. In the first method, the number of points in the output set is increased iteratively. We use a clustering method with a distance metric that combines spatial distance with difference in observation values. In a second scheme, we iteratively estimate the redundancy of the current observation set and remove the most redundant data points. We evaluate the proposed methods with respect to a geometric error measure and compare them with a uniform sampling scheme. We obtain good representations of the original data with thinnings retaining only a small portion of observations. We also evaluate our thinnings of ATOVS satellite data using the assimilation system of the Deutscher Wetterdienst. Impact of the thinning on the analysed fields and on the subsequent forecasts is discussed.