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Abstract
Following the successful launch of the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) satellites in April 2006, NCEP’s Environmental Modeling Center (EMC) is planning to use the COSMIC data in its next-generation Global Data Assimilation System. In preparation for the assimilation of GPS radio occultation (RO) data from COSMIC and other missions, NCEP/EMC has developed the infrastructure necessary to use profiles of refractivity and bending angle in an operational framework. In both forward operators, horizontal gradients of refractivity have been neglected and each operator has been tuned with its corresponding quality control checks and error characterization. In this paper, the benefits of the assimilation of profiles of GPS RO on top of the current observations being regularly used in operations are analyzed. In addition, differences between the assimilation of bending angle and refractivity are discussed. To avoid unrealistic increments within the higher model layers, experiments not using GPS RO observations above 30 km are also performed. This stratospheric data assimilation problem was present in earlier experiments with GPS RO data at NCEP/EMC and impacted the forecast in the lower-atmospheric levels as well as the stratosphere. Some characteristics of the assimilation of profiles of bending angle are also discussed. Data from the Challenging Minisatellite Payload (CHAMP) satellite are available in non–real time at NOAA and have been used to perform the experiments examined herein.
Abstract
Following the successful launch of the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) satellites in April 2006, NCEP’s Environmental Modeling Center (EMC) is planning to use the COSMIC data in its next-generation Global Data Assimilation System. In preparation for the assimilation of GPS radio occultation (RO) data from COSMIC and other missions, NCEP/EMC has developed the infrastructure necessary to use profiles of refractivity and bending angle in an operational framework. In both forward operators, horizontal gradients of refractivity have been neglected and each operator has been tuned with its corresponding quality control checks and error characterization. In this paper, the benefits of the assimilation of profiles of GPS RO on top of the current observations being regularly used in operations are analyzed. In addition, differences between the assimilation of bending angle and refractivity are discussed. To avoid unrealistic increments within the higher model layers, experiments not using GPS RO observations above 30 km are also performed. This stratospheric data assimilation problem was present in earlier experiments with GPS RO data at NCEP/EMC and impacted the forecast in the lower-atmospheric levels as well as the stratosphere. Some characteristics of the assimilation of profiles of bending angle are also discussed. Data from the Challenging Minisatellite Payload (CHAMP) satellite are available in non–real time at NOAA and have been used to perform the experiments examined herein.
Abstract
Variational four-dimensional (4D) data assimilation is performed using an adiabatic version of the National Meteorological Center (NMC) baroclinic spectral primitive equation model with operationally analyzed fields as well as simulated datasets. Two limited-memory quasi-Newton minimization techniques were used to iteratively find the minimum of a cost function, with the NMC forecast as a constraint. The cost function consists of a weighted square sum of the differences between the model forecast and observations over a time interval. In all the experiments described in this paper, observations are available for all degrees of freedom of the model. The derivation of the adjoint of the discretized adiabatic NMC spectral model is presented. The creation of this adjoint model allows the gradient of the cost function with respect to the initial conditions to be computed using a single backward-in-time integration of the adjoint equations.
As an initial evaluation of the variational data-assimilation procedure, an assimilation system with a low-resolution version of the NMC spectral model was tested using fields from a Rossby-Haurwitz-wave solution as observations. The results were encouraging, with a significant reduction in the magnitudes of both the cost function and the norm of its gradient during the minimization process. In particular, the high-frequency noise exhibited in the rms of the divergence field, produced by random perturbation in the initial conditions, is largely eliminated after the variational data assimilation.
The performance of the assimilation scheme was examined in a more realistic configuration using the adiabatic NMC spectral model truncated at T40. Both operationally analyzed observations, consisting of vorticity, divergence, temperature, surface pressure and moisture fields (distributed at two time levels separated by a 6-h time interval), and model-generated data were variationally assimilated. The effect of the number of observation fields in time on the convergence rate of the minimization and the impacts due to the inclusion of the horizontal diffusion and the surface drag in the model and its adjoint on the convergence rate and the accuracy of the retrieval are addressed.
Abstract
Variational four-dimensional (4D) data assimilation is performed using an adiabatic version of the National Meteorological Center (NMC) baroclinic spectral primitive equation model with operationally analyzed fields as well as simulated datasets. Two limited-memory quasi-Newton minimization techniques were used to iteratively find the minimum of a cost function, with the NMC forecast as a constraint. The cost function consists of a weighted square sum of the differences between the model forecast and observations over a time interval. In all the experiments described in this paper, observations are available for all degrees of freedom of the model. The derivation of the adjoint of the discretized adiabatic NMC spectral model is presented. The creation of this adjoint model allows the gradient of the cost function with respect to the initial conditions to be computed using a single backward-in-time integration of the adjoint equations.
As an initial evaluation of the variational data-assimilation procedure, an assimilation system with a low-resolution version of the NMC spectral model was tested using fields from a Rossby-Haurwitz-wave solution as observations. The results were encouraging, with a significant reduction in the magnitudes of both the cost function and the norm of its gradient during the minimization process. In particular, the high-frequency noise exhibited in the rms of the divergence field, produced by random perturbation in the initial conditions, is largely eliminated after the variational data assimilation.
The performance of the assimilation scheme was examined in a more realistic configuration using the adiabatic NMC spectral model truncated at T40. Both operationally analyzed observations, consisting of vorticity, divergence, temperature, surface pressure and moisture fields (distributed at two time levels separated by a 6-h time interval), and model-generated data were variationally assimilated. The effect of the number of observation fields in time on the convergence rate of the minimization and the impacts due to the inclusion of the horizontal diffusion and the surface drag in the model and its adjoint on the convergence rate and the accuracy of the retrieval are addressed.
Abstract
The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission launched six small satellites in April 2006, each carrying a GPS radio occultation (RO) receiver. At final orbit, COSMIC will provide ∼2500–3000 RO soundings per day uniformly distributed around the globe in near–real time. In preparation for the assimilation of COSMIC data in an operational framework, the NCEP/Environmental Modeling Center (EMC) has successfully developed the capability of assimilating profiles of refractivity and bending angle. Each forward operator has been implemented with its own quality control and error characterization. In this paper, the infrastructure developed at NCEP/EMC to assimilate GPS RO observations, including forward models, observational and representativeness errors, and quality control procedures, is described. The advantages of using a forward operator for bending angle versus refractivity are discussed and some preliminary results on the benefits of the GPS RO in weather analysis and forecasts are presented. The different strategies adopted at NCEP/EMC to assimilate GPS RO data are aimed to select the most appropriate forward operator in the operational data assimilation system when COSMIC products are stable and routinely available to the Numerical Weather Centers. In the meantime, data from the Challenging Minisatellite Payload (CHAMP) satellite is available in non–real time and has been used in the assimilation tests to examine the potential benefits of the GPS RO–derived products. In the preliminary results presented in this study, the use of GPS RO observations slightly improves anomaly correlation scores for temperature (by ∼0.01–0.03) in the Southern Hemisphere and Tropics throughout the depth of the atmosphere while a slight degradation is found in the upper troposphere and stratosphere in the Northern Hemisphere. However, significant reduction of the temperature and humidity biases is found for all latitudes. The benefits from assimilating GPS RO data also extend to other fields, such as 500-hPa geopotential heights and tropical winds, demonstrating the potential use of GPS RO data in operational forecasting.
Abstract
The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission launched six small satellites in April 2006, each carrying a GPS radio occultation (RO) receiver. At final orbit, COSMIC will provide ∼2500–3000 RO soundings per day uniformly distributed around the globe in near–real time. In preparation for the assimilation of COSMIC data in an operational framework, the NCEP/Environmental Modeling Center (EMC) has successfully developed the capability of assimilating profiles of refractivity and bending angle. Each forward operator has been implemented with its own quality control and error characterization. In this paper, the infrastructure developed at NCEP/EMC to assimilate GPS RO observations, including forward models, observational and representativeness errors, and quality control procedures, is described. The advantages of using a forward operator for bending angle versus refractivity are discussed and some preliminary results on the benefits of the GPS RO in weather analysis and forecasts are presented. The different strategies adopted at NCEP/EMC to assimilate GPS RO data are aimed to select the most appropriate forward operator in the operational data assimilation system when COSMIC products are stable and routinely available to the Numerical Weather Centers. In the meantime, data from the Challenging Minisatellite Payload (CHAMP) satellite is available in non–real time and has been used in the assimilation tests to examine the potential benefits of the GPS RO–derived products. In the preliminary results presented in this study, the use of GPS RO observations slightly improves anomaly correlation scores for temperature (by ∼0.01–0.03) in the Southern Hemisphere and Tropics throughout the depth of the atmosphere while a slight degradation is found in the upper troposphere and stratosphere in the Northern Hemisphere. However, significant reduction of the temperature and humidity biases is found for all latitudes. The benefits from assimilating GPS RO data also extend to other fields, such as 500-hPa geopotential heights and tropical winds, demonstrating the potential use of GPS RO data in operational forecasting.
