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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
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
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
-based observations have been made, recent satellite microwave imager (MWI) and radar data have enabled us to obtain global precipitation information ( Kummerow et al. 1998 ). MWI brightness temperatures (TBs) and radar reflectivity are nonlinear and flow-dependent functions of various atmospheric and surface variables. Some studies ( Lorenc 2003 ; Zupanski 2005 ; Zupanski et al. 2008 ; AE ) proposed ensemble-based variational assimilation (EnVar) in order to address these problems. AE pointed out that
-based observations have been made, recent satellite microwave imager (MWI) and radar data have enabled us to obtain global precipitation information ( Kummerow et al. 1998 ). MWI brightness temperatures (TBs) and radar reflectivity are nonlinear and flow-dependent functions of various atmospheric and surface variables. Some studies ( Lorenc 2003 ; Zupanski 2005 ; Zupanski et al. 2008 ; AE ) proposed ensemble-based variational assimilation (EnVar) in order to address these problems. AE pointed out that
methodology was revisited and modified to be used in a consistent way with observations having correlated errors. This novel formulation of the iterative—and “nonsequential” [i.e., without the sequential updating of the forecast error covariance matrix discussed in Rodgers (1996) ]—selection method was then used to select the most effective IASI channels for the estimation of atmospheric water vapor profiles both in clear-sky and overcast conditions. To this end, an ensemble-based estimate of forecast
methodology was revisited and modified to be used in a consistent way with observations having correlated errors. This novel formulation of the iterative—and “nonsequential” [i.e., without the sequential updating of the forecast error covariance matrix discussed in Rodgers (1996) ]—selection method was then used to select the most effective IASI channels for the estimation of atmospheric water vapor profiles both in clear-sky and overcast conditions. To this end, an ensemble-based estimate of forecast
