scholarly journals A Radar Reflectivity Operator with Ice-Phase Hydrometeors for Variational Data Assimilation (RadZIceVarv1.0) and Its Evaluation with Real Radar Data

2019 ◽  
Author(s):  
Shizhang Wang ◽  
Zhiquan Liu
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Radar Data ◽  
Poor Quality ◽  
Variational Data Assimilation ◽  
Radar Reflectivity ◽  
3D Analysis ◽  
Background Error ◽  
Ice Phase

Abstract. A reflectivity forward operator and its associated tangent linear and adjoint operators (together named RadZIceVar) were developed for variational data assimilation (DA). RadZIceVar can analyze both rainwater and ice-phase species (snow and graupel) by directly assimilating radar reflectivity observations. The results of three-dimensional variational (3DVAR) DA experiments with a 3 km grid mesh setting of the Weather Research and Forecasting (WRF) model showed that RadZIceVar was effective at producing an analysis of reflectivity pattern and intensity similar to the observed data. Two to three outer loops with 50–100 iterations in each loop were needed to obtain a converged 3D analysis of rainwater, snow, and graupel, including the melting layers with mixed-phase hydrometeors. The deficiencies in the analysis using this operator could be caused by the poor quality of the background fields and the use of the static background error covariance, and these issues can be partially resolved by using radar-retrieved hydrometeors in a preprocessing step and tuning the spatial correlation length scales of the background errors. The direct radar reflectivity assimilation using RadZIceVar also improved the short-term (2 h–5 h) precipitation forecasts compared to those of the experiment without DA.

scholarly journals A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data

2019 ◽  
Vol 12 (9) ◽  
pp. 4031-4051 ◽  
Author(s):  
Shizhang Wang ◽  
Zhiquan Liu
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Radar Data ◽  
Poor Quality ◽  
Variational Data Assimilation ◽  
Radar Reflectivity ◽  
Background Error ◽  
Background Fields ◽  
Ice Phase

Abstract. A reflectivity forward operator and its associated tangent linear and adjoint operators (together named RadarVar) were developed for variational data assimilation (DA). RadarVar can analyze both rainwater and ice-phase species (snow and graupel) by directly assimilating radar reflectivity observations. The results of three-dimensional variational (3D-Var) DA experiments with a 3 km grid mesh setting of the Weather Research and Forecasting (WRF) model showed that RadarVar was effective at producing an analysis of reflectivity pattern and intensity similar to the observed data. Two to three outer loops with 50–100 iterations in each loop were needed to obtain a converged 3-D analysis of reflectivity, rainwater, snow, and graupel, including the melting layers with mixed-phase hydrometeors. It is shown that the deficiencies in the analysis using this operator, caused by the poor quality of the background fields and the use of the static background error covariance, can be partially resolved by using radar-retrieved hydrometeors in a preprocessing step and tuning the spatial correlation length scales of the background errors. The direct radar reflectivity assimilation using RadarVar also improved the short-term (2–5 h) precipitation forecasts compared to those of the experiment without DA.

scholarly journals An Examination of the Incremental Balance in a Global Ensemble-Based 3D-Var Data Assimilation System

2010 ◽  
Vol 138 (10) ◽  
pp. 3946-3966 ◽  
Author(s):  
Jean-François Caron ◽  
Luc Fillion
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Three Dimensional ◽  
Vertical Motion ◽  
Variational Data Assimilation ◽  
Spatial Covariance ◽  
Background Error ◽  
Localization Technique ◽  
Vertical Localization

Abstract This study examines the modification to the balance properties of the analysis increments in a global three-dimensional variational data assimilation scheme when using flow-dependent background-error covariances derived from an operational ensemble Kalman filter instead of static homogenous and isotropic background-error covariances based on lagged forecast differences. It is shown that the degree of balance in the analysis increments is degraded when the former method is used. This change can be attributed in part to the reduced degree of rotational balance found in short-term ensemble Kalman filter perturbations as compared to lagged forecast differences based on longer-range forecasts. However, the use of a horizontal and vertical localization technique to increase the rank of the ensemble-based covariances are found to have a significant deleterious effect on the rotational balance with the largest detrimental impact coming from the vertical localization and affecting particularly the upper levels. The examination of the vertical motion part of the analysis increments revealed that the spatial covariance localization technique also produces unrealistic vertical structure of vertical motion increments with abnormally large increments near the surface. A comparison between the analysis increments from the ensemble Kalman filter and from the ensemble-based three-dimensional variational data assimilation (3D-Var) scheme showed that the balance characteristics of the analysis increments resulting from the two systems are very similar.

scholarly journals Comparison of 3DVar and EnSRF Data Assimilation Using Radar Observations for the Analysis and Prediction of an MCS

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Shibo Gao ◽  
Jinzhong Min
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Mesoscale Convective System ◽  
Final Analysis ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Convective System ◽  
Wind Fields ◽  
Radar Observations

Using radar observations, the performances of the ensemble square root filter (EnSRF) and an indirect three-dimensional variational (3DVar) data assimilation method were compared for a mesoscale convective system (MCS) that occurred in the Front Range of the Rocky Mountains, Colorado (USA). The results showed that the root mean square innovations (RMSIs) of EnSRF were lower than 3DVar for radar reflectivity and radial velocity and that the spread of EnSRF was generally consistent with its RMSIs. EnSRF substantially improved the analysis of the MCS compared with an experiment without radar data assimilation, and it produced a slight but noticeable improvement over 3DVar in terms of both coverage and intensity. Forecast results initiated from the final analysis revealed that EnSRF generally produced the best prediction of the MCS, with improved quantitative reflectivity and precipitation forecast skills. EnSRF also demonstrated better performance than 3DVar in the prediction of neighborhood probability for reflectivity at thresholds of 20 and 35 dBZ, which better matched the observed radar reflectivity in terms of both shape and extension. Additionally, the humidity, temperature, and wind fields were also improved by EnSRF; the largest error reduction was found in the water vapor field near the surface and at upper levels.

Four-Dimensional Variational Data Assimilation for WRF: Formulation and Preliminary Results

2009 ◽  
Vol 137 (1) ◽  
pp. 299-314 ◽  
Author(s):  
Xiang-Yu Huang ◽  
Qingnong Xiao ◽  
Dale M. Barker ◽  
Xin Zhang ◽  
John Michalakes ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Dynamic Balance ◽  
Wrf Model ◽  
Real Data ◽  
Weather Conditions ◽  
Research Community ◽  
Variational Data Assimilation ◽  
Background Error ◽  
Preliminary Results ◽  
Increasing Demand

Abstract The Weather Research and Forecasting (WRF) model–based variational data assimilation system (WRF-Var) has been extended from three- to four-dimensional variational data assimilation (WRF 4D-Var) to meet the increasing demand for improving initial model states in multiscale numerical simulations and forecasts. The initial goals of this development include operational applications and support to the research community. The formulation of WRF 4D-Var is described in this paper. WRF 4D-Var uses the WRF model as a constraint to impose a dynamic balance on the assimilation. It is shown to implicitly evolve the background error covariance and to produce the flow-dependent nature of the analysis increments. Preliminary results from real-data 4D-Var experiments in a quasi-operational setting are presented and the potential of WRF 4D-Var in research and operational applications are demonstrated. A wider distribution of the system to the research community will further develop its capabilities and to encourage testing under different weather conditions and model configurations.

An Approach of Radar Reflectivity Data Assimilation and Its Assessment with the Inland QPF of Typhoon Rusa (2002) at Landfall

2007 ◽  
Vol 46 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Qingnong Xiao ◽  
Ying-Hwa Kuo ◽  
Juanzhen Sun ◽  
Wen-Chau Lee ◽  
Dale M. Barker ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Mesoscale Model ◽  
Control Variable ◽  
Three Dimensional ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Mixing Ratio ◽  
Warm Rain Process ◽  
Reflectivity Data

Abstract A radar reflectivity data assimilation scheme was developed within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) three-dimensional variational data assimilation (3DVAR) system. The model total water mixing ratio was used as a control variable. A warm-rain process, its linear, and its adjoint were incorporated into the system to partition the moisture and hydrometeor increments. The observation operator for radar reflectivity was developed and incorporated into the 3DVAR. With a single reflectivity observation, the multivariate structures of the analysis increments that included cloud water and rainwater mixing ratio increments were examined. Using the onshore Doppler radar data from Jindo, South Korea, the capability of the radar reflectivity assimilation for the landfalling Typhoon Rusa (2002) was assessed. Verifications of inland quantitative precipitation forecasting (QPF) of Typhoon Rusa (2002) showed positive impacts of assimilating radar reflectivity data on the short-range QPF.

scholarly journals The Impact of Assimilating Atmospheric Infrared Sounder Observation on the Forecast of Typhoon Tracks

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Chien-Ben Chou ◽  
Huei-Ping Huang
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Atmospheric Infrared Sounder ◽  
Background Error ◽  
Forecasting System ◽  
Typhoon Tracks ◽  
The Impact ◽  
Decorrelation Scale

This work assesses the effects of assimilating atmospheric infrared sounder (AIRS) observations on typhoon prediction using the three-dimensional variational data assimilation (3DVAR) and forecasting system of the weather research and forecasting (WRF) model. Two major parameters in the data assimilation scheme, the spatial decorrelation scale and the magnitude of the covariance matrix of the background error, are varied in forecast experiments for the track of typhoon Sinlaku over the Western Pacific. The results show that within a wide parameter range, the inclusion of the AIRS observation improves the prediction. Outside this range, notably when the decorrelation scale of the background error is set to a large value, forcing the assimilation of AIRS data leads to degradation of the forecast. This illustrates how the impact of satellite data on the forecast depends on the adjustable parameters for data assimilation. The parameter-sweeping framework is potentially useful for improving operational typhoon prediction.

scholarly journals Operational Implementation of the 1D+3D-Var Assimilation Method of Radar Reflectivity Data in the AROME Model

2014 ◽  
Vol 142 (5) ◽  
pp. 1852-1873 ◽  
Author(s):  
Eric Wattrelot ◽  
Olivier Caumont ◽  
Jean-François Mahfouf
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Three Dimensional ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Short Term ◽  
Background Error ◽  
Reflectivity Data

AbstractThis paper presents results from radar reflectivity data assimilation experiments with the nonhydrostatic limited-area model Application of Research to Operations at Mesoscale (AROME) in an operational context. A one-dimensional (1D) Bayesian retrieval of relative humidity profiles followed by a three-dimensional variational data assimilation (3D-Var) technique is adopted. Several preprocessing procedures of raw reflectivity data are presented and the use of the nonrainy signal in the assimilation is widely discussed and illustrated. This two-step methodology allows the authors to build up a screening procedure that takes into account the evaluation of the results from the 1D Bayesian retrieval. In particular, the 1D retrieval is checked by comparing a pseudoanalyzed reflectivity to the observed reflectivity. Additionally, a physical consistency between the reflectivity innovations and the 1D relative humidity increments is imposed before assimilating relative humidity pseudo-observations with other observations. This allows the authors to counteract the difficulty of the current 3D-Var system to correct strong differences between model and observed clouds from the crude specification of background-error covariances. Assimilation experiments of radar reflectivity data in a preoperational configuration are first performed over a 1-month period. Positive impacts on short-term precipitation forecast scores are systematically found. The evaluation shows improvements on the analysis and also on objective conventional forecast scores, in particular for the model wind field up to 12 h. A case study for a specific precipitating system demonstrates the capacity of the method for improving significantly short-term forecasts of organized convection.

scholarly journals An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part III: Antarctic Applications with Advanced Research WRF Using Real Data

2013 ◽  
Vol 141 (8) ◽  
pp. 2721-2739 ◽  
Author(s):  
Chengsi Liu ◽  
Qingnong Xiao
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Real Data ◽  
Variational Data Assimilation ◽  
Ensemble Forecasts ◽  
Background Error ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Short Period ◽  
Inflation Factor

Abstract A four-dimensional ensemble-based variational data assimilation (4DEnVar) algorithm proposed in Part I of the 4DEnVar series (denoted En4DVar in Part I, but here we refer to it as 4DEnVar according to WMO conference recommendation to differentiate it from En4DVar algorithm using adjoint model) uses a flow-dependent background error covariance calculated from ensemble forecasts and performs 4DVar optimization based on an incremental approach and a preconditioning algorithm. In Part II, the authors evaluated 4DEnVar with observing system simulation experiments (OSSEs) using the Advanced Research Weather Research and Forecasting Model (ARW-WRF, hereafter WRF). The current study extends the 4DEnVar to assimilate real observations for a cyclone in the Antarctic and the Southern Ocean in October 2007. The authors performed an intercomparison of four different WRF variational approaches for the case, including three-dimensional variational data assimilation (3DVar), first guess at the appropriate time (FGAT), and ensemble-based three-dimensional (En3DVar) and four-dimensional (4DEnVar) variational data assimilations. It is found that all data assimilation approaches produce positive impacts in this case. Applying the flow-dependent background error covariance in En3DVar and 4DEnVar yields forecast skills superior to those with the homogeneous and isotropic background error covariance in 3DVar and FGAT. In addition, the authors carried out FGAT and 4DEnVar 3-day cycling and 72-h forecasts. The results show that 4DEnVar produces a better performance in the cyclone prediction. The inflation factor on 4DEnVar can effectively improve the 4DEnVar analysis. The authors also conducted a short period (10-day lifetime of the cyclone in the domain) of analysis/forecast intercomparison experiments using 4DEnVar, FGAT, and 3DVar. The 4DEnVar scheme demonstrates overall superior and robust performance.

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