scholarly journals Analysis and Prediction of a Squall Line Observed during IHOP Using Multiple WSR-88D Observations

2008 ◽  
Vol 136 (7) ◽  
pp. 2364-2388 ◽  
Author(s):  
Juanzhen Sun ◽  
Ying Zhang
Keyword(s):  
Data Assimilation ◽  
Radar Data ◽  
Squall Line ◽  
Short Term ◽  
Accurate Analysis ◽  
The Individual ◽  
Convective Cells ◽  
The Impact ◽  
Radar Data Assimilation

Abstract This paper presents a case study on the assimilation of observations from multiple Doppler radars of the Next Generation Weather Radar (NEXRAD) network. A squall-line case documented during the International H2O Project (IHOP_2002) is used for the study. Radar radial velocity and reflectivity observations from four NEXRADs are assimilated into a convection-permitting model using a four-dimensional variational data assimilation (4DVAR) scheme. A mesoscale analysis using a supplementary sounding, velocity–azimuth display (VAD) profiles, and surface observations from Meteorological Aerodrome Reports (METAR) are produced and used to provide a background and boundary conditions for the 4DVAR radar data assimilation. Impact of the radar data assimilation is assessed by verifying the skill of the subsequent very short-term (5 h) forecasts. Assimilation and forecasting experiments are conducted to examine the impact of radar data assimilation on the subsequent precipitation forecasts. It is found that the 4DVAR radar data assimilation significantly reduces the model spinup required in the experiments without radar data assimilation, resulting in significantly improved 5-h forecasts. Additional experiments are conducted to study the sensitivity of the precipitation forecasts with respect to 4DVAR cycling configurations. Results from these experiments suggest that the forecasts with three 4DVAR cycles are improved over those with cold start, but the cycling impact seems to diminish with more cycles. The impact of observations from each of the individual radars is also examined by conducting a set of experiments in which data from each radar are alternately excluded. It is found that the accurate analysis of the environmental wind surrounding the convective cells is important in successfully predicting the squall line.

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 Multiple-Radar Data Assimilation and Short-Range Quantitative Precipitation Forecasting of a Squall Line Observed during IHOP_2002

2007 ◽  
Vol 135 (10) ◽  
pp. 3381-3404 ◽  
Author(s):  
Qingnong Xiao ◽  
Juanzhen Sun
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Doppler Radar ◽  
Radar Data ◽  
Squall Line ◽  
Convective System ◽  
Precipitation Forecasting ◽  
Doppler Radar Data ◽  
The Impact ◽  
Radar Data Assimilation

Abstract The impact of multiple–Doppler radar data assimilation on quantitative precipitation forecasting (QPF) is examined in this study. The newly developed Weather Research and Forecasting (WRF) model Advanced Research WRF (ARW) and its three-dimensional variational data assimilation system (WRF 3DVAR) are used. In this study, multiple–Doppler radar data assimilation is applied in WRF 3DVAR cycling mode to initialize a squall-line convective system on 13 June 2002 during the International H2O Project (IHOP_2002) and the ARW QPF skills are evaluated for the case. Numerical experiments demonstrate that WRF 3DVAR can successfully assimilate Doppler radial velocity and reflectivity from multiple radar sites and extract useful information from the radar data to initiate the squall-line convective system. Assimilation of both radial velocity and reflectivity results in sound analyses that show adjustments in both the dynamical and thermodynamical fields that are consistent with the WRF 3DVAR balance constraint and background error correlation. The cycling of the Doppler radar data from the 12 radar sites at 2100 UTC 12 June and 0000 UTC 13 June produces a more detailed mesoscale structure of the squall-line convection in the model initial conditions at 0000 UTC 13 June. Evaluations of the ARW QPF skills with initialization via Doppler radar data assimilation demonstrate that the more radar data in the temporal and spatial dimensions are assimilated, the more positive is the impact on the QPF skill. Assimilation of both radial velocity and reflectivity has more positive impact on the QPF skill than does assimilation of either radial velocity or reflectivity only. The improvement of the QPF skill with multiple-radar data assimilation is more clearly observed in heavy rainfall than in light rainfall. In addition to the improvement of the QPF skill, the simulated structure of the squall line is also enhanced by the multiple–Doppler radar data assimilation in the WRF 3DVAR cycling experiment. The vertical airflow pattern shows typical characteristics of squall-line convection. The cold pool and its related squall-line convection triggering process are better initiated in the WRF 3DVAR analysis and simulated in the ARW forecast when multiple–Doppler radar data are assimilated.

Impact of radar data assimilation on simulations of precipitable water with the Harmonie model: A case study over Cyprus

2021 ◽  
Vol 253 ◽  
pp. 105473
Author(s):  
Serguei Ivanov ◽  
Silas Michaelides ◽  
Igor Ruban ◽  
Demetris Charalambous ◽  
Filippos Tymvios
Keyword(s):  
Data Assimilation ◽  
Precipitable Water ◽  
Radar Data ◽  
Radar Data Assimilation

scholarly journals Predictability of Deep Convection in Idealized and Operational Forecasts: Effects of Radar Data Assimilation, Orography, and Synoptic Weather Regime

2019 ◽  
Vol 148 (1) ◽  
pp. 63-81 ◽  
Author(s):  
Kevin Bachmann ◽  
Christian Keil ◽  
George C. Craig ◽  
Martin Weissmann ◽  
Christian A. Welzbacher
Keyword(s):  
Data Assimilation ◽  
Deep Convection ◽  
Radar Data ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Model Errors ◽  
Ensemble Prediction System ◽  
Beneficial Impact ◽  
The Impact ◽  
Radar Data Assimilation

Abstract We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.

scholarly journals The Impact of Data Assimilation on WRF Simulation using Surface Data and Radar Data: Case Study

Atmosphere ◽  
2013 ◽  
Vol 23 (2) ◽  
pp. 143-160 ◽  
Author(s):  
Won Choi ◽  
Jae Gyoo Lee ◽  
Yu-Jin Kim
Keyword(s):  
Data Assimilation ◽  
Radar Data ◽  
Surface Data ◽  
The Impact

scholarly journals The Impacts of Representing the Correlation of Errors in Radar Data Assimilation. Part I: Experiments with Simulated Background and Observation Estimates

2014 ◽  
Vol 142 (11) ◽  
pp. 3998-4016 ◽  
Author(s):  
Dominik Jacques ◽  
Isztar Zawadzki
Keyword(s):  
Data Assimilation ◽  
Cost Function ◽  
Radar Data ◽  
Spatial Correlations ◽  
Radar Observations ◽  
Lack Of Information ◽  
Data Thinning ◽  
The Cost ◽  
The Impact ◽  
Radar Data Assimilation

Abstract In radar data assimilation, statistically optimal analyses are sought by minimizing a cost function in which the variance and covariance of background and observation errors are correctly represented. Radar observations are particular in that they are often available at spatial resolution comparable to that of background estimates. Because of computational constraints and lack of information, it is impossible to perfectly represent the correlation of errors. In this study, the authors characterize the impact of such misrepresentations in an idealized framework where the spatial correlations of background and observation errors are each described by a homogeneous and isotropic exponential decay. Analyses obtained with perfect representation of correlations are compared to others obtained by neglecting correlations altogether. These two sets of analyses are examined from a theoretical and an experimental perspective. The authors show that if the spatial correlations of background and observation errors are similar, then neglecting the correlation of errors has a small impact on the quality of analyses. They suggest that the sampling noise, related to the precision with which analysis errors may be estimated, could be used as a criterion for determining when the correlations of errors may be omitted. Neglecting correlations altogether also yields better analyses than representing correlations for only one term in the cost function or through the use of data thinning. These results suggest that the computational costs of data assimilation could be reduced by neglecting the correlations of errors in areas where dense radar observations are available.

scholarly journals A 3.5-Dimensional Variational Method for Doppler Radar Data Assimilation and Its Application to Phased-Array Radar Observations

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Qin Xu ◽  
Li Wei ◽  
Wei Gu ◽  
Jiandong Gong ◽  
Qingyun Zhao
Keyword(s):  
Data Assimilation ◽  
Variational Method ◽  
Radial Velocity ◽  
Phased Array ◽  
Doppler Radar ◽  
Radar Data ◽  
Velocity Fields ◽  
Squall Line ◽  
Phased Array Radar ◽  
Radar Data Assimilation

A 3.5-dimensional variational method is developed for Doppler radar data assimilation. In this method, incremental analyses are performed in three steps to update the model state upon the background state provided by the model prediction. First, radar radial-velocity observations from three consecutive volume scans are analyzed on the model grid. The analyzed radial-velocity fields are then used in step 2 to produce incremental analyses for the vector velocity fields at two time levels between the three volume scans. The analyzed vector velocity fields are used in step 3 to produce incremental analyses for the thermodynamic fields at the central time level accompanied by the adjustments in water vapor and hydrometeor mixing ratios based on radar reflectivity observations. The finite element B-spline representations and recursive filter are used to reduce the dimension of the analysis space and enhance the computational efficiency. The method is applied to a squall line case observed by the phased-array radar with rapid volume scans at the National Weather Radar Testbed and is shown to be effective in assimilating the phased-array radar observations and improve the prediction of the subsequent evolution of the squall line.

A Case Study of Weather Radar Data Assimilation into the Harmonie Numerical Weather Prediction System

2020 ◽  
Author(s):  
Silas Michaelides ◽  
Serguei Ivanov ◽  
Igor Ruban ◽  
Demetris Charalambous ◽  
Filippos Tymvios
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Frontal Zone ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Radar Data ◽  
Severe Weather ◽  
Numerical Weather ◽  
Radar Data Assimilation

<p>Quantitative Precipitation Forecasting (QPF) is among the most central challenges of atmospheric prediction systems. The primary aim of such a task is the generation of accurate estimates of heavy precipitation events associated with severe weather, atmospheric fronts and heavy convective rainfalls. QPF is still among the most intricate challenges of Numerical Weather Prediction. The efforts in this direction are mainly concentrated on improving model formulations for microphysics and convective process and remote sensing data assimilation.</p><p>This paper describes the first results with the regional radar signal processing chain that provides the radar data assimilation (RDA) in the Harmonie convection permitting numerical model. This task is performed for a case study focusing on a wintertime frontal cyclone over the island of Cyprus. Reflectivity measurements from two weather radars, at Larnaka and Paphos, are exploited for simulations of severe weather conditions associated with this synoptic-scale system. Through the variational assimilation procedure, the model takes into account the atmospheric processes occurring in the upstream flow which can be outside the area of radar measurements. The focus is on the precipitable water vapor content and its changes during the cyclone evolution, as well as on the impact of the radar data assimilation on precipitation estimates.</p><p>The results show that the numerical experiments exhibit, in general, a suitable simulation of precipitable water at different stages of the cyclone. In particular, the bulk of the rainfall volume exhibits three stages: intensive rain on the cyclone's frontal zone, weaker precipitation immediately behind the front, and the secondary enhancement of rainfall. The largest corrections due to RDA are of up to 5 mm and occur during the approach of the cyclone frontal zone in a form of enhanced rainfall over the whole area, but more prominently in weak precipitation locations.</p>

scholarly journals An Ice-Phase Microphysics Forward Model and Preliminary Results of Polarimetric Radar Data Assimilation

2017 ◽  
Vol 145 (2) ◽  
pp. 683-708 ◽  
Author(s):  
Xuanli Li ◽  
John R. Mecikalski ◽  
Derek Posselt
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Weather Prediction ◽  
Radar Data ◽  
Forward Model ◽  
Precipitation Forecast ◽  
Upper Troposphere ◽  
The Impact ◽  
Radar Data Assimilation ◽  
Ice Phase

In this study, an ice-phase microphysics forward model has been developed for the Weather Research and Forecasting (WRF) Model three-dimensional variational data assimilation (WRF 3D-Var) system. Radar forward operators for reflectivity and the polarimetric variable, specific differential phase ( KDP), have been built into the ice-phase WRF 3D-Var package to allow modifications in liquid (cloud water and rain) and solid water (cloud ice and snow) fields through data assimilation. Experiments have been conducted to assimilate reflectivity and radial velocity observations collected by the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Hytop, Alabama, for a mesoscale convective system (MCS) on 15 March 2008. Numerical results have been examined to assess the impact of the WSR-88D data using the ice-phase WRF 3D-Var radar data assimilation package. The main goals are to first demonstrate radar data assimilation with an ice-phase microphysics forward model and second to improve understanding on how to enhance the utilization of radar data in numerical weather prediction. Results showed that the assimilation of reflectivity and radial velocity data using the ice-phase system provided significant improvement especially in the mid- to upper troposphere. The improved initial conditions led to apparent improvement in the short-term precipitation forecast of the MCS. An additional experiment has been conducted to explore the assimilation of KDP data collected by the Advanced Radar for Meteorological and Operational Research (ARMOR). Results showed that KDP data have been successfully assimilated using the ice-phase 3D-Var package. A positive impact of the KDP data has been found on rainwater in the lower troposphere and snow in the mid- to upper troposphere.

scholarly journals Impact of a Diagnostic Pressure Equation Constraint on Tornadic Supercell Thunderstorm Forecasts Initialized Using 3DVAR Radar Data Assimilation

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Guoqing Ge ◽  
Jidong Gao ◽  
Ming Xue
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Three Dimensional ◽  
Radar Data ◽  
Pressure Equation ◽  
Supercell Thunderstorm ◽  
Weighting Coefficients ◽  
The Impact ◽  
Radar Data Assimilation ◽  
Equation Constraint

A diagnostic pressure equation constraint has been incorporated into a storm-scale three-dimensional variational (3DVAR) data assimilation system. This diagnostic pressure equation constraint (DPEC) is aimed to improve dynamic consistency among different model variables so as to produce better data assimilation results and improve the subsequent forecasts. Ge et al. (2012) described the development of DPEC and testing of it with idealized experiments. DPEC was also applied to a real supercell case, but only radial velocity was assimilated. In this paper, DPEC is further applied to two real tornadic supercell thunderstorm cases, where both radial velocity and radar reflectivity data are assimilated. The impact of DPEC on radar data assimilation is examined mainly based on the storm forecasts. It is found that the experiments using DPEC generally predict higher low-level vertical vorticity than the experiments not using DPEC near the time of observed tornadoes. Therefore, it is concluded that the use of DPEC improves the forecast of mesocyclone rotation within supercell thunderstorms. The experiments using different weighting coefficients generate similar results. This suggests that DPEC is not very sensitive to the weighting coefficients.

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