Results 1  10
of
16
Ensemble Kalman Filter Assimilation of Doppler Radar Data with a Compressible Nonhydrostatic Model: OSS Experiments
, 2004
"... A Doppler radar data assimilation system is developed based on ensemble Kalman filter (EnKF) method and tested with simulated radar data from a supercell storm. As a first implementation, we assume the forward models are perfect and radar data are sampled at the analysis grid points. A general pur ..."
Abstract

Cited by 127 (78 self)
 Add to MetaCart
A Doppler radar data assimilation system is developed based on ensemble Kalman filter (EnKF) method and tested with simulated radar data from a supercell storm. As a first implementation, we assume the forward models are perfect and radar data are sampled at the analysis grid points. A general purpose nonhydrostatic compressible model is used with the inclusion of complex multiclass ice microphysics. New aspects compared to previous studies include the demonstration of the ability of EnKF method in retrieving multiple microphysical species associated with a multiclass ice microphysics scheme, and in accurately retrieving the wind and thermodynamic variables. Also new are the inclusion of reflectivity observations and the determination of the relative role of radial velocity and reflectivity data as well as their spatial coverage in recovering the full flow and cloud fields. In general, the system is able to reestablish the model storm extremely well after a number of assimilation cycles, and best results are obtained when both radial velocity and reflectivity data, including reflectivity information outside precipitation regions, are used. Significant positive impact of the reflectivity assimilation
2002: Numerical simulations of radar rainfall error propagation
 Water Resour. Res
"... [1] The primary advantage of radar observations of precipitation compared with traditional rain gauge measurements is their high spatial and temporal resolution and large areal coverage. Unfortunately, radar data require vigorous quality control before being converted into precipitation products tha ..."
Abstract

Cited by 11 (3 self)
 Add to MetaCart
(Show Context)
[1] The primary advantage of radar observations of precipitation compared with traditional rain gauge measurements is their high spatial and temporal resolution and large areal coverage. Unfortunately, radar data require vigorous quality control before being converted into precipitation products that can be used as input to hydrologic models. In this study we coupled a physically based atmospheric model of convective rainfall with an active microwave radiative transfer model to simulate radar observation of thunderstorms. We used the atmospheric model to simulate a welldocumented tornadic supercell storm that occurred near Del City, Oklahoma, on 20 May 1977. We then generated radar observations of that storm and used them to evaluate the propagation of radar rainfall errors through distributed hydrologic simulations. This physically based methodology allows us to directly examine the impact of radar rainfall estimation errors on landsurface hydrologic predictions and to avoid the limitations imposed by the use of rain gauge data. Results indicate that the geometry of the radar beam and coordinate transformations, due to radarwatershedstorm orientation, have an effect on radar rainfall estimation and runoff prediction errors. In addition to uncertainty in the radar reflectivity versus rainfall intensity
2006: A comparison of the radar ray path equation and approximations for use in radar data assimilation, adv
 Adv. Atmos. Sci
"... The radar ray path equations are used to determine the physical location of each radar measurement. These equations are necessary for mapping radar data to computational grids for diagnosis, display and numerical weather prediction (NWP). They are also used to determine the forward operators for ass ..."
Abstract

Cited by 11 (8 self)
 Add to MetaCart
(Show Context)
The radar ray path equations are used to determine the physical location of each radar measurement. These equations are necessary for mapping radar data to computational grids for diagnosis, display and numerical weather prediction (NWP). They are also used to determine the forward operators for assimilation of radar data into forecast models. In this paper, a stepwise ray tracing method is developed. The influence of the atmospheric refractive index on the ray path equations at different locations related to an intense cold front is examined against the ray path derived from the new tracing method. It is shown that the radar ray path is not very sensitive to sharp vertical gradients of refractive index caused by the strong temperature inversion and large moisture gradient in this case. In the paper, the errors caused by using the simplified straight ray path equations are also examined. It is found that there will be significant errors in the physical location of radar measurements if the earth’s curvature is not considered, especially at lower elevation angles. A reduced form of the equation for beam height calculation is derived using Taylor series expansion. It is computationally more efficient and also avoids the need to use double precision variables to mitigate the small difference between two large terms in the original form. The accuracy of this reduced form is found to be sufficient for modeling use. Key words: Doppler radar, ray path equations, refractivity index, data assimilation
2007: An efficient dualresolution approach for ensemble data assimilation and tests with assimilated Doppler radar data
"... A new efficient dualresolution (DR) data assimilation algorithm is developed based on the ensemble Kalman filter (EnKF) method and tested using simulated radar radial velocity data for a supercell storm. Radar observations are assimilated on both highresolution and lowerresolution grids using the ..."
Abstract

Cited by 7 (5 self)
 Add to MetaCart
(Show Context)
A new efficient dualresolution (DR) data assimilation algorithm is developed based on the ensemble Kalman filter (EnKF) method and tested using simulated radar radial velocity data for a supercell storm. Radar observations are assimilated on both highresolution and lowerresolution grids using the EnKF algorithm with flowdependent background error covariances estimated from the lowerresolution ensemble. It is shown that the flowdependent and dynamically evolved background error covariances thus estimated are effective in producing quality analyses on the highresolution grid. The DR method has the advantage of being able to significantly reduce the computational cost of the EnKF analysis. In the system, the lowerresolution ensemble provides the flowdependent background error covariance, while the singlehighresolution forecast and analysis provides the benefit of higher resolution, which is important for resolving the internal structures of thunderstorms. The relative smoothness of the covariance obtained from the lower 4kmresolution ensemble does not appear to significantly degrade the quality of analysis. This is because the cross covariance among different variables is of firstorder importance for “retrieving ” unobserved variables from the radar radial velocity data. For the DR analysis, an ensemble size of 40 appears to be a reasonable choice with the use of a 4km horizontal resolution in the ensemble and a 1km resolution in the highresolution analysis. Several sensitivity tests show that the DR EnKF system is quite robust to different observation errors. A 4km thinned data resolution is a compromise that is acceptable under the constraint of realtime applications. A data density of 8 km leads to a significant degradation in the analysis. 1.
2005: Development of an adjoint for a complex atmospheric model, the ARPS, using TAF. Automatic Differentiation: Applications
"... Adjoint models have been used in meteorology for applications such as fourdimensional variational analysis (4DVAR) and sensitivity studies for over two ..."
Abstract

Cited by 7 (4 self)
 Add to MetaCart
(Show Context)
Adjoint models have been used in meteorology for applications such as fourdimensional variational analysis (4DVAR) and sensitivity studies for over two
Impacts of Beam Broadening and Earth Curvature on Stormscale 3D Variational Data Assimilation of Radial Velocity with Two Doppler Radars
"... The radar ray path and beam broadening equations are important for assimilation of radar data into Numerical Weather Prediction (NWP) models. They can be used to determine the physical location of each radar measurement, and for properly mapping the atmospheric state variables from the model grid to ..."
Abstract

Cited by 5 (3 self)
 Add to MetaCart
(Show Context)
The radar ray path and beam broadening equations are important for assimilation of radar data into Numerical Weather Prediction (NWP) models. They can be used to determine the physical location of each radar measurement, and for properly mapping the atmospheric state variables from the model grid to the radar measurement space as part of the forward observation operators. Historically, different degrees of approximations have been made with these equations but no systematic evaluation of their impact exists, at least in the context of variational data assimilation. This study examines the effects of simplifying ray path and ray broadening calculations on the radar data assimilation in a 3D variational (3DVAR) system. Several groups of Observational System Simulation Experiments (OSSEs) are performed to test the impact of these equations to radar data assimilation with an idealized tornadic thunderstorm case. Our study shows that the errors caused by simplifications vary with the distance between the analyzed storm and the radar. For single time level wind analysis, as the surface range increases, the impact of beam broadening on analyzed wind field becomes evident and can cause relatively large error for distances beyond 150 km. The impact of earth’s curvature is more significant even
Variation of Radio Refractivity with Respect to Moisture and Temperature and Influence on Radar Ray Path
, 2007
"... In this study, the variation of radio refractivity with respect to temperature and moisture is analyzed. Also, the effects of vertical gradients in temperature and moisture on the propagation paths of electromagnetic waves of weather radar are examined for several sites across the United States usin ..."
Abstract
 Add to MetaCart
(Show Context)
In this study, the variation of radio refractivity with respect to temperature and moisture is analyzed. Also, the effects of vertical gradients in temperature and moisture on the propagation paths of electromagnetic waves of weather radar are examined for several sites across the United States using several years of sounding data from the National Weather Service. The ray path is important for identifying storm characteristics and for properly using the radar data in initializing numerical weather prediction models. It is found that during the warm season the radio refractivity gradient is more sensitive to moisture gradients than to temperature gradients. Ray paths from the commonly accepted vertical ray path model are compared to a ray path computed from a stepwise ray tracing algorithm using data from actual soundings. For the sample of about 16 000 soundings examined, we find that only a small fraction of the ray paths diverge significantly from those calculated using a ray path model based on the US Standard Atmosphere. While the problem of ray ducting in the presence of a temperature inversion is fairly well known, we identify the presence of a strong vertical moisture gradient as the culprit in the majority of the cases where significant deviations occurred. Key words: radio refractivity, radar ray path
Corresponding author address:
, 2013
"... A hybrid parallel scheme for the ensemble square root filter (EnSRF) suitable for parallel assimilation of multiscale observations including those from dense observational networks such as those of radar is developed based on the domain decomposition strategy. The scheme handles internode communic ..."
Abstract
 Add to MetaCart
(Show Context)
A hybrid parallel scheme for the ensemble square root filter (EnSRF) suitable for parallel assimilation of multiscale observations including those from dense observational networks such as those of radar is developed based on the domain decomposition strategy. The scheme handles internode communication through message passing interface (MPI), and the communication within sharedmemory nodes via Open MultiProcessing (OpenMP) threads; it also supports pure MPI and pure OpenMP modes. The parallel framework can accommodate highvolume remotesensed radar (or satellite) observations as well as conventional observations that usually have larger covariance localization radii. The performance of the parallel algorithm has been tested with simulated and real radar data. The parallel program shows good scalability in pure MPI and hybrid MPI/OpenMP modes, while pure OpenMP runs exhibit limited scalability on a symmetric sharedmemory system. It is found that in MPI mode, better parallel performance is achieved with domain decomposition configurations in which the leading dimension of the state variable arrays is larger, because this configuration allows for more efficient memory access. Given a fixed amount of computing
Submitted to Advance in Atmospheric Sciences
, 2005
"... The radar ray path equations are used to determine the physical location of each radar measurement. These equations are necessary for mapping radar data to computational grids for diagnosis, display and numerical weather prediction (NWP). They are also used to determine the forward operators for ass ..."
Abstract
 Add to MetaCart
(Show Context)
The radar ray path equations are used to determine the physical location of each radar measurement. These equations are necessary for mapping radar data to computational grids for diagnosis, display and numerical weather prediction (NWP). They are also used to determine the forward operators for assimilation of radar data into forecast models. In this paper, a stepwise ray tracing method is developed. The influence of atmospheric refractive index to ray path equations at different locations related to an intense cold front is examined against the ray path derived from the new tracing method. It is shown that the radar ray path is not very sensitive to sharp vertical gradients of refractive index caused by strong temperature inversion and large moisture gradient in this case. In the paper, the errors caused by using the simplified straight ray path equations are also examined. It is found that there will be significant errors in the physical location of radar measurements if the earth’s curvature is not considered, especially at lower elevation angles. A reduced form of the equation for beam height calculation is derived using Taylor series expansion. It is computationally more efficient and also avoids the need for using double precision due to a small difference between two large terms in the original form. The accuracy of this reduced form is found to be sufficient for modeling use.