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(GTS), profiler and U.S. radar-derived winds, Special Sensor Microwave Imager (SSM/I) oceanic winds and total cloud water (TCW) retrievals, and satellite wind data from the National Environmental Satellite Data and Information Service (NESDIS). We noticed that the NCEP conventional observations do not include surface mesonet data over land during our study period. Surface mesonet observations are currently not commonly assimilated into NCEP operational global and regional models. Prior to Katrina
(GTS), profiler and U.S. radar-derived winds, Special Sensor Microwave Imager (SSM/I) oceanic winds and total cloud water (TCW) retrievals, and satellite wind data from the National Environmental Satellite Data and Information Service (NESDIS). We noticed that the NCEP conventional observations do not include surface mesonet data over land during our study period. Surface mesonet observations are currently not commonly assimilated into NCEP operational global and regional models. Prior to Katrina
also valid with our results: The Advanced Microwave Sounding Unit A (AMSU-A) contributes most positively followed by aircraft, radiosondes and the Infrared Atmospheric Sounding Interferometer (IASI). Ozone contributes slightly negatively. All satellite radiance observations [especially the Microwave Humidity Sounder (MHS)] and piloted balloon (PIBAL) exhibit smaller impacts with the dry norm than with the moist norm. Fig . 1. Comparison of EFSO impacts from each observation type evaluated for
also valid with our results: The Advanced Microwave Sounding Unit A (AMSU-A) contributes most positively followed by aircraft, radiosondes and the Infrared Atmospheric Sounding Interferometer (IASI). Ozone contributes slightly negatively. All satellite radiance observations [especially the Microwave Humidity Sounder (MHS)] and piloted balloon (PIBAL) exhibit smaller impacts with the dry norm than with the moist norm. Fig . 1. Comparison of EFSO impacts from each observation type evaluated for
1. Introduction Assessment of the impact of observations on reducing ocean model forecast error from data assimilation is a fundamental aspect of any ocean analysis and forecasting system. The purpose of assimilation is to reduce the model initial condition error. Improved initial conditions should lead to an improved forecast. However, it is likely that not all observations assimilated have equal value in reducing forecasting error. Estimation of which observations are best and the
1. Introduction Assessment of the impact of observations on reducing ocean model forecast error from data assimilation is a fundamental aspect of any ocean analysis and forecasting system. The purpose of assimilation is to reduce the model initial condition error. Improved initial conditions should lead to an improved forecast. However, it is likely that not all observations assimilated have equal value in reducing forecasting error. Estimation of which observations are best and the
predictors used in the bias model was slightly modified in the new system to reduce the inconsistency between microwave and infrared observation bias estimates ( Table 2 ). Also, to increase the influence of radiosonde observations on the estimated bias, the bias model coefficients are now estimated over the last seven days only from the assimilation windows centered at 0000 and 1200 UTC instead of using all four analysis times per day. However, it was recently realized that using data from only these
predictors used in the bias model was slightly modified in the new system to reduce the inconsistency between microwave and infrared observation bias estimates ( Table 2 ). Also, to increase the influence of radiosonde observations on the estimated bias, the bias model coefficients are now estimated over the last seven days only from the assimilation windows centered at 0000 and 1200 UTC instead of using all four analysis times per day. However, it was recently realized that using data from only these
at NCEP and the National Aeronautics and Space Administration (NASA) have simulated observations that were operationally available in 2005, including radiosonde, surface, aircraft, satellite-derived atmospheric motion vectors, wind profiler, ship and buoy, and scatterometer-based surface winds. Additionally, satellite microwave and infrared brightness temperature temperatures were simulated [e.g., High Resolution Infrared Radiation Sounder (HIRS), Advanced Microwave Sounding Unit A (AMSU-A), AMSU
at NCEP and the National Aeronautics and Space Administration (NASA) have simulated observations that were operationally available in 2005, including radiosonde, surface, aircraft, satellite-derived atmospheric motion vectors, wind profiler, ship and buoy, and scatterometer-based surface winds. Additionally, satellite microwave and infrared brightness temperature temperatures were simulated [e.g., High Resolution Infrared Radiation Sounder (HIRS), Advanced Microwave Sounding Unit A (AMSU-A), AMSU
core depth varies from 225 m to 75 m. No such in situ current measurements occur in the NECC. In contrast to the very small number of direct current observations, currents generated with an ocean general circulation model (OGCM) constrained by observations provide an exceedingly large number of virtual current meters, which invites exploration of their utility in studies of ocean circulation. The tenet of faith that an OGCM constrained by observations would yield a significantly different
core depth varies from 225 m to 75 m. No such in situ current measurements occur in the NECC. In contrast to the very small number of direct current observations, currents generated with an ocean general circulation model (OGCM) constrained by observations provide an exceedingly large number of virtual current meters, which invites exploration of their utility in studies of ocean circulation. The tenet of faith that an OGCM constrained by observations would yield a significantly different
1. Introduction Forecast errors from numerical weather prediction (NWP) models arise in part from imperfect initial conditions, as a result of the lack of sufficient observations as well as their suboptimal use. Different data assimilation systems (DASs) have been developed since the objective analysis of meteorological fields was introduced in the midtwentieth century; for example, Cressman (1959) developed the empirical successive corrections method and Gandin (1963) introduced optimal
1. Introduction Forecast errors from numerical weather prediction (NWP) models arise in part from imperfect initial conditions, as a result of the lack of sufficient observations as well as their suboptimal use. Different data assimilation systems (DASs) have been developed since the objective analysis of meteorological fields was introduced in the midtwentieth century; for example, Cressman (1959) developed the empirical successive corrections method and Gandin (1963) introduced optimal
data assimilation, especially satellite data assimilation, where observations are unevenly spaced and in regions of tight gradients (i.e., tropical cyclones). 5 Additional data include QuikSCAT and Advanced Scatterometer (ASCAT) imagery and Cooperative Institute for Research in the Atmosphere (CIRA) Advanced Microwave Sounding Unit (AMSU) objective estimates were used to recalculate the wind radii (D. Herndon, CIMSS, 2010, personal communication). Flight-level winds reduced to the surface together
data assimilation, especially satellite data assimilation, where observations are unevenly spaced and in regions of tight gradients (i.e., tropical cyclones). 5 Additional data include QuikSCAT and Advanced Scatterometer (ASCAT) imagery and Cooperative Institute for Research in the Atmosphere (CIRA) Advanced Microwave Sounding Unit (AMSU) objective estimates were used to recalculate the wind radii (D. Herndon, CIMSS, 2010, personal communication). Flight-level winds reduced to the surface together
1. Introduction The ensemble Kalman filter (EnKF) is a popular method for doing data assimilation (DA) in the geosciences. This study is concerned with the treatment of model noise in the EnKF forecast step. a. Relevance and scope While uncertainty quantification is an important end product of any estimation procedure, it is paramount in DA because of the sequentiality and the need to correctly weight the observations at the next time step. The two main sources of uncertainty in a forecast are
1. Introduction The ensemble Kalman filter (EnKF) is a popular method for doing data assimilation (DA) in the geosciences. This study is concerned with the treatment of model noise in the EnKF forecast step. a. Relevance and scope While uncertainty quantification is an important end product of any estimation procedure, it is paramount in DA because of the sequentiality and the need to correctly weight the observations at the next time step. The two main sources of uncertainty in a forecast are
. Oceanogr. , 70 , 45 – 62 , doi: 10.1007/s10872-013-0211-7 . Kurapov , A. L. , D. Foley , P. T. Strub , G. D. Egbert , and J. S. Allen , 2011 : Variational assimilation of satellite observations in a coastal ocean model off Oregon . J. Geophys. Res. , 116 , C05006 , doi: 10.1029/2010JC006909 . Kurihara , Y. , T. Sakurai , and T. Kuragano , 2000 : Global daily sea surface temperature analysis using data from satellite microwave radiometer, satellite infrared radiometer and in
. Oceanogr. , 70 , 45 – 62 , doi: 10.1007/s10872-013-0211-7 . Kurapov , A. L. , D. Foley , P. T. Strub , G. D. Egbert , and J. S. Allen , 2011 : Variational assimilation of satellite observations in a coastal ocean model off Oregon . J. Geophys. Res. , 116 , C05006 , doi: 10.1029/2010JC006909 . Kurihara , Y. , T. Sakurai , and T. Kuragano , 2000 : Global daily sea surface temperature analysis using data from satellite microwave radiometer, satellite infrared radiometer and in
