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Miodrag Rančić, R. James Purser, Dušan Jović, Ratko Vasic, and Thomas Black


The rapid expansion of contemporary computers is expected to enable operational integrations of global models of the atmosphere at resolutions close to 1 km, using tens of thousands of processors in the foreseeable future. Consequently, the algorithmic approach to global modeling of the atmosphere will need to change in order to better adjust to the new computing environment. One simple and convenient solution is to use low-order finite-differencing models, which generally require only local exchange of messages between processing elements, and thus are more compatible with the new computing environment. These models have already been tested with physics and are well established at high resolutions over regional domains. A global nonhydrostatic model, the Nonhydrostatic Multiscale Model on the B grid (NMMB), developed at the Environmental Modeling Center of the National Centers for Environmental Prediction during the first decade of this century is one such model. A drawback of the original version of global NMMB is that it is discretized on the standard longitude–latitude grid and requires application of Fourier polar filtering, which is relatively inefficient on massively parallel computers. This paper describes a reformulation of the NMMB on the grid geometry of a novel cubed sphere featuring a uniform Jacobian of the horizontal mapping, which provides a uniform resolution close to that of the equiangular gnomonic cubed sphere, but with a smooth transition of coordinates across the edges. The modeling approach and encountered challenges are discussed and several results are shown that demonstrate the viability of the approach.

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Fedor Mesinger, Geoff DiMego, Eugenia Kalnay, Kenneth Mitchell, Perry C. Shafran, Wesley Ebisuzaki, Dušan Jović, Jack Woollen, Eric Rogers, Ernesto H. Berbery, Michael B. Ek, Yun Fan, Robert Grumbine, Wayne Higgins, Hong Li, Ying Lin, Geoff Manikin, David Parrish, and Wei Shi

In 1997, during the late stages of production of NCEP–NCAR Global Reanalysis (GR), exploration of a regional reanalysis project was suggested by the GR project's Advisory Committee, “particularly if the RDAS [Regional Data Assimilation System] is significantly better than the global reanalysis at capturing the regional hydrological cycle, the diurnal cycle and other important features of weather and climate variability.” Following a 6-yr development and production effort, NCEP's North American Regional Reanalysis (NARR) project was completed in 2004, and data are now available to the scientific community. Along with the use of the NCEP Eta model and its Data Assimilation System (at 32-km–45-layer resolution with 3-hourly output), the hallmarks of the NARR are the incorporation of hourly assimilation of precipitation, which leverages a comprehensive precipitation analysis effort, the use of a recent version of the Noah land surface model, and the use of numerous other datasets that are additional or improved compared to the GR. Following the practice applied to NCEP's GR, the 25-yr NARR retrospective production period (1979–2003) is augmented by the construction and daily execution of a system for near-real-time continuation of the NARR, known as the Regional Climate Data Assimilation System (R-CDAS). Highlights of the NARR results are presented: precipitation over the continental United States (CONUS), which is seen to be very near the ingested analyzed precipitation; fits of tropospheric temperatures and winds to rawinsonde observations; and fits of 2-m temperatures and 10-m winds to surface station observations. The aforementioned fits are compared to those of the NCEP–Department of Energy (DOE) Global Reanalysis (GR2). Not only have the expectations cited above been fully met, but very substantial improvements in the accuracy of temperatures and winds compared to that of GR2 are achieved throughout the troposphere. Finally, the numerous datasets produced are outlined and information is provided on the data archiving and present data availability.

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