scholarly journals Using Radar Wind Observations to Improve Mesoscale Numerical Weather Prediction

2006 ◽  
Vol 21 (4) ◽  
pp. 502-522 ◽  
Author(s):  
Qingyun Zhao ◽  
John Cook ◽  
Qin Xu ◽  
Paul R. Harasti
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Radar Data ◽  
The Impact ◽  
Data Assimilation System ◽  
Radar Data Assimilation ◽  
Assimilation System

Abstract A high-resolution radar data assimilation system is presented for high-resolution numerical weather prediction models. The system is under development at the Naval Research Laboratory for the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System. A variational approach is used to retrieve three-dimensional dynamical fields of atmospheric conditions from multiple-Doppler radar observations of radial velocity within a limited area. The methodology is described along with a preliminary evaluation of the impact of assimilated radar data on model forecasts using a case study of a squall line that occurred along the east coast of the United States on 9 May 2003. Results from the experiments show a significant impact from the assimilated radar radial velocity data on the model forecast of not just dynamical but also hydrological fields at all model levels for the duration of the storm. A verification system has also been developed to assess the radar data assimilation impact, and the results show improvements in the three-dimensional wind forecasts but relatively small changes in the prediction of storm locations. This study highlights the need to develop a continuous radar data assimilation system to maximize the impact of the data.

A Velocity Dealiasing Technique Using Rapidly Updated Analysis from a Four-Dimensional Variational Doppler Radar Data Assimilation System

2010 ◽  
Vol 27 (7) ◽  
pp. 1140-1152 ◽  
Author(s):  
Eunha Lim ◽  
Juanzhen Sun
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Radar Data ◽  
Objective Analysis ◽  
Doppler Velocity ◽  
Horizontal Shear ◽  
Data Assimilation System ◽  
Radar Data Assimilation ◽  
Assimilation System

Abstract A Doppler velocity dealiasing algorithm is developed within the storm-scale four-dimensional radar data assimilation system known as the Variational Doppler Radar Analysis System (VDRAS). The innovative aspect of the algorithm is that it dealiases Doppler velocity at each grid point independently by using three-dimensional wind fields obtained either from an objective analysis using conventional observations and mesoscale model output or from a rapidly updated analysis of VDRAS that assimilates radar data. This algorithm consists of three steps: preserving horizontal shear, global dealiasing using reference wind from the objective analysis or the VDRAS analysis, and local dealiasing. It is automated and intended to be used operationally for radar data assimilation using numerical weather prediction models. The algorithm was tested with 384 volumes of radar data observed from the Next Generation Weather Radar (NEXRAD) for a severe thunderstorm that occurred during 15 June 2002. It showed that the algorithm was effective in dealiasing large areas of aliased velocities when the wind from the objective analysis was used as the reference and that more accurate dealiasing was achieved by using the continuously cycled VDRAS analysis.

scholarly journals Efficient Regional Hybrid Ensemble-Variational Data Assimilation using the Global-Ensemble-Model-Augmented Error Covariance for Numerical Weather Prediction over Eastern China

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 365
Author(s):  
Yuanbing Wang ◽  
Yaodeng Chen ◽  
Jinzhong Min
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Eastern China ◽  
Ensemble Model ◽  
Numerical Weather ◽  
Error Covariance ◽  
Data Assimilation System ◽  
Assimilation System

An efficient regional hybrid ensemble-variational (EnVar) data assimilation method using the global-ensemble-model-augmented error covariance is proposed and preliminarily tested in this study. This method uses the global ensemble error covariance as the complementary low-resolution regional ensemble error covariance. The high-resolution dynamic ensemble mean is used as the first guess in hybrid EnVar and then re-centered to the updated high-resolution dynamic ensemble perturbations after minimization analysis. In this study, the proposed method is implemented into the Weather Research and Forecasting Model’s (WRF) data assimilation system coupled with the ensemble transform Kalman filter (ETKF) and preliminarily tested for numerical weather prediction during the Mei-Yu season over eastern China. It is found that the experiment containing fewer regional dynamic ensemble members but augmented with global ensemble error covariance obtains similar results to the experiment containing many more regional dynamic ensemble members. However, the former experiment only takes up one third of the latter experiment’s computational cost. The method proposed in this study also outperforms the 3DVar, hybrid EnVar using the pure global ensemble error covariance, as well as the hybrid EnVar using regional ETKF ensemble with a smaller size. The method proposed in this paper effectively combines the contributions of the ensemble error covariance from both the global and the regional models to produce better initial conditions for the regional WRF data assimilation system.

scholarly journals Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast

2020 ◽  
Vol 10 (16) ◽  
pp. 5493 ◽  
Author(s):  
Jingnan Wang ◽  
Lifeng Zhang ◽  
Jiping Guan ◽  
Mingyang Zhang
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Rainfall Forecast ◽  
Skill Scores ◽  
The Individual ◽  
The Impact ◽  
Radar Data Assimilation

Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.

scholarly journals SINGV‐DA: A data assimilation system for convective‐scale numerical weather prediction over Singapore

2020 ◽  
Vol 146 (729) ◽  
pp. 1923-1938 ◽  
Author(s):  
B. C. Peter Heng ◽  
Robert Tubbs ◽  
Xiang‐Yu Huang ◽  
Bruce Macpherson ◽  
Dale M. Barker ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Convective Scale ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals CIRA/CSU Four-Dimensional Variational Data Assimilation System

2005 ◽  
Vol 133 (4) ◽  
pp. 829-843 ◽  
Author(s):  
Milija Zupanski ◽  
Dusanka Zupanski ◽  
Tomislava Vukicevic ◽  
Kenneth Eis ◽  
Thomas Vonder Haar
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Control Variable ◽  
Three Dimensional ◽  
Atmospheric Modeling ◽  
Variational Data Assimilation ◽  
Regional Atmospheric Modeling ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

A new four-dimensional variational data assimilation (4DVAR) system is developed at the Cooperative Institute for Research in the Atmosphere (CIRA)/Colorado State University (CSU). The system is also called the Regional Atmospheric Modeling Data Assimilation System (RAMDAS). In its present form, the 4DVAR system is employing the CSU/Regional Atmospheric Modeling System (RAMS) nonhydrostatic primitive equation model. The Weather Research and Forecasting (WRF) observation operator is used to access the observations, adopted from the WRF three-dimensional variational data assimilation (3DVAR) algorithm. In addition to the initial conditions adjustment, the RAMDAS includes the adjustment of model error (bias) and lateral boundary conditions through an augmented control variable definition. Also, the control variable is defined in terms of the velocity potential and streamfunction instead of the horizontal winds. The RAMDAS is developed after the National Centers for Environmental Prediction (NCEP) Eta 4DVAR system, however with added improvements addressing its use in a research environment. Preliminary results with RAMDAS are presented, focusing on the minimization performance and the impact of vertical correlations in error covariance modeling. A three-dimensional formulation of the background error correlation is introduced and evaluated. The Hessian preconditioning is revisited, and an alternate algebraic formulation is presented. The results indicate a robust minimization performance.

scholarly journals A Comparison of the Impacts of Radiosonde and AMSU Radiance Observations in GSI Based 3DEnsVar and 3DVar Data Assimilation Systems for NCEP GFS

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Govindan Kutty ◽  
Xuguang Wang
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Winter Season ◽  
Data Quality Control ◽  
Hybrid Data Assimilation ◽  
Environmental Prediction ◽  
Hemisphere Winter ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

The impact of observations can be dependent on many factors in a data assimilation (DA) system including data quality control, preprocessing, skill of the model, and the DA algorithm. The present study focuses on comparing the impacts of observations assimilated by two different DA algorithms. A three-dimensional ensemble-variational (3DEnsVar) hybrid data assimilation system was recently developed based on the Gridpoint Statistical Interpolation (GSI) data assimilation system and was implemented operationally for the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS). One question to address is, how the impacts of observations on GFS forecasts differ when assimilated by the traditional GSI-three dimensional variational (3DVar) and the new 3DEnsVar. Experiments were conducted over a 6-week period during Northern Hemisphere winter season at a reduced resolution. For both the control and data denial experiments, the forecasts produced by 3DEnsVar were more accurate than GSI3DVar experiments. The results suggested that the observations were better and more effectively exploited to increment the background forecast in 3DEnsVar. On the other hand, in GSI3DVar, where the observation will be making mostly local, isotropic increments without proper flow dependent extrapolation is more sensitive to the number and types observations assimilated.

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