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1. Introduction There are many ways to learn from the confrontation of an atmosphere model with observations, in service of model improvement. The study of initial tendencies [or errors in one-time-step forecasts, Klinker and Sardeshmukh (1992) ] is appealing because the effect of a model process error is localized. However, initialization shock may dominate the results, making interpretation subtle (e.g., Judd et al. 2008 ). At the other extreme of time scale, the biases of unconstrained
1. Introduction There are many ways to learn from the confrontation of an atmosphere model with observations, in service of model improvement. The study of initial tendencies [or errors in one-time-step forecasts, Klinker and Sardeshmukh (1992) ] is appealing because the effect of a model process error is localized. However, initialization shock may dominate the results, making interpretation subtle (e.g., Judd et al. 2008 ). At the other extreme of time scale, the biases of unconstrained
Bourras et al. (2002) . Finally, sea surface turbulent fluxes can be derived from global model results that have been constrained by surface and rawinsonde observations and satellite measurements. Such products are called reanalyses and are produced by some of the major modeling centers, such as the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency
Bourras et al. (2002) . Finally, sea surface turbulent fluxes can be derived from global model results that have been constrained by surface and rawinsonde observations and satellite measurements. Such products are called reanalyses and are produced by some of the major modeling centers, such as the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency
has been done to derive forcing using constrained variational analysis from observations during intensive observation periods (IOPs) at the Department of Energy (DOE) Atmospheric Radiation Measurement Program (ARM) sites ( Zhang and Lin 1997 ; Zhang et al. 2001 ). More recently, Xie et al. (2003) evaluated the forcing datasets derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) during three IOPs at the ARM Southern Great Plains (SGP) site. They found that although the
has been done to derive forcing using constrained variational analysis from observations during intensive observation periods (IOPs) at the Department of Energy (DOE) Atmospheric Radiation Measurement Program (ARM) sites ( Zhang and Lin 1997 ; Zhang et al. 2001 ). More recently, Xie et al. (2003) evaluated the forcing datasets derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) during three IOPs at the ARM Southern Great Plains (SGP) site. They found that although the
–atmosphere–sea ice) model to derive the best estimate of the state of the atmosphere and land surface. The National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC)’s Global Modeling and Assimilation Office (GMAO) are three major centers that have recently produced “second generation” reanalysis datasets. While they are the best approximation of the state of
–atmosphere–sea ice) model to derive the best estimate of the state of the atmosphere and land surface. The National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC)’s Global Modeling and Assimilation Office (GMAO) are three major centers that have recently produced “second generation” reanalysis datasets. While they are the best approximation of the state of
associated with the presence of deep convection, and it has already been used to evaluate cloud structures in the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis and the 40-yr ECMWF Re-Analysis (ERA-40; Xu 2009 ). The benefit of an object-based analysis is the ability to study coherent structures. The wealth of information contained in the probability density function (PDF) of cloud properties provides the opportunity to examine how the details of deep convection change
associated with the presence of deep convection, and it has already been used to evaluate cloud structures in the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis and the 40-yr ECMWF Re-Analysis (ERA-40; Xu 2009 ). The benefit of an object-based analysis is the ability to study coherent structures. The wealth of information contained in the probability density function (PDF) of cloud properties provides the opportunity to examine how the details of deep convection change
evidence of two time scales within the easterly wave regime. One of the two subregimes, appearing on the 6–9-day time scale with a wavelength of about 6000 km, was attributed to oscillations within subtropical high belts. In the more extensive follow-up study by the same team ( Diedhiou et al. 1999 ), the spectral analysis and wave tracks were computed from both NCEP and European Centre for Medium-Range Weather Forecasts (ECMWF) daily reanalyses, confirming those results and providing more robust
evidence of two time scales within the easterly wave regime. One of the two subregimes, appearing on the 6–9-day time scale with a wavelength of about 6000 km, was attributed to oscillations within subtropical high belts. In the more extensive follow-up study by the same team ( Diedhiou et al. 1999 ), the spectral analysis and wave tracks were computed from both NCEP and European Centre for Medium-Range Weather Forecasts (ECMWF) daily reanalyses, confirming those results and providing more robust
1. Introduction The boreal summer extratropical circulation lacks the strong jets and large-amplitude stationary waves that typify the boreal winter climate. This, together with the presence of pervasive tropical easterlies that inhibit remote forcing from the tropics, tends to limit boreal summer middle-latitude variability to more local/regional processes, with mesoscale convective weather systems and land–atmosphere coupling playing important roles (e.g., Parker and Johnson 2000 ; Koster
1. Introduction The boreal summer extratropical circulation lacks the strong jets and large-amplitude stationary waves that typify the boreal winter climate. This, together with the presence of pervasive tropical easterlies that inhibit remote forcing from the tropics, tends to limit boreal summer middle-latitude variability to more local/regional processes, with mesoscale convective weather systems and land–atmosphere coupling playing important roles (e.g., Parker and Johnson 2000 ; Koster
