Search Results

You are looking at 1 - 7 of 7 items for :

  • Middle atmosphere x
  • Intercomparisons of 4D-Variational Assimilation and the Ensemble Kalman Filter x
  • All content x
Clear All
Mark Buehner, P. L. Houtekamer, Cecilien Charette, Herschel L. Mitchell, and Bin He

t is defined as either for 3D-FGAT, for 4D-Var, or for En-4D-Var, where 𝗠 t is the tangent linear model that maps perturbations from the beginning of the assimilation window to time t and 𝗕 1/2 is the square root of the background-error covariance matrix valid either at the beginning (4D-Var) or middle (3D-FGAT) of the assimilation time window or the 4D ensemble covariances valid for 5 time levels over the entire (0 ≤ t ≤ T ) window (En-4D

Full access
Takemasa Miyoshi, Yoshiaki Sato, and Takashi Kadowaki

qualitative difference between multiplicative and additive inflation methods, Fig. 7 shows ensemble spread at three different levels at 1200 UTC 1 August, sufficiently prior to the date of the abnormal termination. Similarly to Fig. 6 , multiplicative inflation more clearly shows patterns based upon observing density. Since fewer observations exist in the upper atmosphere, MUL shows larger spread at the upper levels. We also find similarities between ADD and MUL. Generally, spread in the tropics is

Full access
Mark Buehner, P. L. Houtekamer, Cecilien Charette, Herschel L. Mitchell, and Bin He

only for day 5. In the tropical region (middle panels of Fig. 2 ), a more systematic positive impact is obtained for the zonal wind (exceeding 0.5 m s −1 ). The corresponding panels in Fig. 3 show that the impact on zonal wind is significant and that significant (small) positive impacts also occur for temperature and geopotential height for the early portion of the forecasts. In the southern extratropics (right panels of Fig. 2 ) positive impacts are seen for all three variables with the

Full access
José A. Aravéquia, Istvan Szunyogh, Elana J. Fertig, Eugenia Kalnay, David Kuhl, and Eric J. Kostelich

1. Introduction Although ensemble-based Kalman filter (EnKF) data assimilation schemes were first proposed more than a decade ago ( Evensen 1994 ; Burgers et al. 1998 ; Houtekamer and Mitchell 1998 ) and several successful attempts at assimilating observations of the atmosphere have been reported in the last few years (e.g., Houtekamer et al. 2005 ; Whitaker et al. 2004 , 2008 ; Szunyogh et al. 2008 ; Miyoshi and Sato 2007 ; Miyoshi and Yamane 2007 ; Torn and Hakim 2008 ; Bonavita et

Full access
Loïk Berre and Gérald Desroziers

illustrated in the middle panel of Fig. 10 , where values have been averaged over a radius of 500 km for each observation point. In this case, some large-scale contrasts can be identified more clearly, such as relatively large values over the central Pacific compared to relatively small values over the southern Atlantic. This filtered innovation-based variance map was also compared to an ensemble-based variance map. For this purpose, the background field of each ensemble member was projected to

Full access
Monika Krysta, Eric Blayo, Emmanuel Cosme, and Jacques Verron

1. Introduction The successful integration of data and model information has become an increasing challenge for atmosphere and ocean sciences with the emergence of efficient numerical models and the rapidly increasing availability of remote-sensed and in situ measurements. The data assimilation methodologies offer a mathematical and, in principle, optimal framework for responding to this challenge. Two main approaches, variational and sequential data assimilation, coexist in geophysical

Full access
Marc Bocquet, Carlos A. Pires, and Lin Wu

. Ballabrera , 2007 : 4D-Var or ensemble Kalman filter. Tellus , 59A , 758 – 773 . Kitagawa , G. , 1987 : Non-Gaussian state-space modeling of nonstationary time series. J. Amer. Stat. Assoc. , 82 , 1032 – 1063 . Kleeman , R. , 2002 : Measuring dynamical prediction utility using relative entropy. J. Atmos. Sci. , 59 , 2057 – 2072 . Kleeman , R. , 2007 : Statitical predictibility in the atmosphere and other dynamical systems. Physica D , 230 , 65 – 71 . Krüger , J. , 1993

Full access