Search Results
configurations are summarized in Table 4 . It is important to notice that the radiative transfer models are different, as the radiative transfer model (RTTOV-8) contains a significantly different surface emissivity model from RTTOV-7. Therefore the satellite radiances computed from the first guess for the channels sensitive to surface emissivity would be subject to bias between LETKF and 4D-Var. To avoid this problem, additional experiments using both LETKF and 4D-Var are performed without satellite
configurations are summarized in Table 4 . It is important to notice that the radiative transfer models are different, as the radiative transfer model (RTTOV-8) contains a significantly different surface emissivity model from RTTOV-7. Therefore the satellite radiances computed from the first guess for the channels sensitive to surface emissivity would be subject to bias between LETKF and 4D-Var. To avoid this problem, additional experiments using both LETKF and 4D-Var are performed without satellite
observations we assimilate are the Advanced Microwave Sounding Unit-A (AMSU-A) level 1B brightness temperature data from an instrument flown on the Earth Observing System (EOS) Aqua spacecraft ( Olsen 2007 ). Hereafter, we refer to brightness temperature and radiance observations collectively as radiance observations, as the assimilation of both of these types of data requires the use of a radiative transfer model. The performance of the LETKF in assimilating radiance observations is assessed by
observations we assimilate are the Advanced Microwave Sounding Unit-A (AMSU-A) level 1B brightness temperature data from an instrument flown on the Earth Observing System (EOS) Aqua spacecraft ( Olsen 2007 ). Hereafter, we refer to brightness temperature and radiance observations collectively as radiance observations, as the assimilation of both of these types of data requires the use of a radiative transfer model. The performance of the LETKF in assimilating radiance observations is assessed by
. These observations are typically related to temperature and/or humidity over many vertical levels as modeled by a radiative transfer model. Without covariance localization and assuming the observation is linearly related to the analysis vector, the analysis increment from using the EnKF to assimilate a single observation is proportional to 𝗕 ens h T , where 𝗕 ens is the sample covariance matrix computed from the EnKF ensemble and h is a row vector corresponding to the observation operator
. These observations are typically related to temperature and/or humidity over many vertical levels as modeled by a radiative transfer model. Without covariance localization and assuming the observation is linearly related to the analysis vector, the analysis increment from using the EnKF to assimilate a single observation is proportional to 𝗕 ens h T , where 𝗕 ens is the sample covariance matrix computed from the EnKF ensemble and h is a row vector corresponding to the observation operator
radiances from AIRS, SSM/I, and geostationary satellites. Another difference between the systems is that in the operational 4D-Var system, radiosonde humidity observations up to 70 hPa were assimilated, whereas in the EnKF they were only assimilated up to 200 hPa. In the 2008 operational 4D-Var system, radiance observations were assimilated using version 8 of the Radiative Transfer for (A)TOVS (RTTOV) model ( Saunders et al. 2006 ) and a vertical interpolation algorithm that ensures all relevant
radiances from AIRS, SSM/I, and geostationary satellites. Another difference between the systems is that in the operational 4D-Var system, radiosonde humidity observations up to 70 hPa were assimilated, whereas in the EnKF they were only assimilated up to 200 hPa. In the 2008 operational 4D-Var system, radiance observations were assimilated using version 8 of the Radiative Transfer for (A)TOVS (RTTOV) model ( Saunders et al. 2006 ) and a vertical interpolation algorithm that ensures all relevant
represent subgrid-scale processes in Eulerian models, or when stochastic particles are simulated to represent dispersion in Lagrangian models. The uncertainty could also come from the observations in the form of representativeness or instrumental errors, or indirectly from the models and algorithms used to filter these observations through quality control. Finally, in the case of remote sensing, it could stem from the joint use of a model (a radiative transfer model for instance) and an algorithm that
represent subgrid-scale processes in Eulerian models, or when stochastic particles are simulated to represent dispersion in Lagrangian models. The uncertainty could also come from the observations in the form of representativeness or instrumental errors, or indirectly from the models and algorithms used to filter these observations through quality control. Finally, in the case of remote sensing, it could stem from the joint use of a model (a radiative transfer model for instance) and an algorithm that
