Radar Data Assimilation System Research Articles

Investigating the mechanisms of an intense coastal rainfall event during TAHOPE/PRECIP-IOP3 using a multiscale radar ensemble data assimilation system

Abstract The joint Taiwan-Area Heavy Rain Observation and Prediction Experiment (TAHOPE)/Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) field campaign between Taiwan and the United States took place from late May to mid-August in 2022. The field campaign aimed to understand the dynamics, thermodynamics, and predictability of heavy rainfall events in the Taiwan area. This study investigated the mechanisms of a heavy rainfall event that occurred on 6–7 June during the intensive observation period-3 (IOP3) of the field campaign. Heavy rainfall occurs on Taiwan’s western coast when a Meiyu front hovers in northern Taiwan. A multiscale radar ensemble data assimilation system based on the successive covariance localization (SCL) method is used to derive a high-resolution analysis for forecasts. Two numerical experiments are conducted with the use of convective-scale (RDA) or multiscale (MRDA) corrections in the assimilation of the radial velocity from operational radars at Chigu and Wufen, and the additional S-Pol radar deployed at Hsinchu during the field campaign. Compared with RDA, MRDA results in large-area wind corrections, which help reshape and relocate a low-level mesoscale vortex, a key element of this heavy rainfall event, offshore of western central Taiwan and enhances the front intensity offshore of northwestern Taiwan. Consequently, MRDA improves the 6-h heavy rainfall prediction over the coast of western Taiwan and better represents the elongated rainband in northern Taiwan during the 3- to 6-h forecast. Sensitivity experiments demonstrate the importance of assimilating winds from Chigu and S-Pol radar in establishing low-level mesoscale vortex and convergence zones.

Comparison of Cold Pool Characteristics of Two Distinct Gust Fronts over Bohai Sea Bay in China

Previous studies have demonstrated that cold pools play a pivotal role in the initiation and organization of convection, yet their influence on the evolution of gust fronts (GFs) remains inadequately understood. A destructive wind event associated with a rearward gust front (RGF; 8 grade gale after passing GF) and a prior gust front (PGF; 10 grade gale before passing GF) over the north coast of China on 10 June 2016 was analyzed. Using multiple forms of observation data, as well as the four-dimensional Variational Doppler Radar Data Assimilation System (VDRAS), we found that the depth and intensity of the cold pool in RGF are relatively shallower and weaker, leading to a correspondingly reduced strength in both outflow and convergence. In contrast, the enhanced vertical shear and boundary northeaster inflow of PGF generate intensified and more organized downdrafts, resulting in a deeper cold pool, robust outflow, and convergence. Two schematic models were proposed to explain the discrepancy between GFs and associated cold pools. We further show that there is an internal correlation between meso-γ-scale vortices (MVs) and cold pools, the collision of MVs strengthened low-level convergence and updraft between these two GFs. Moreover, the consolidation of the two cold pools exacerbates low-layer instability and rotation, generating an intense horizontal vorticity that leads to rapid convective storm intensification. These findings offer novel insights into the diversity of GFs and associated cold pools.

An hourly updated WRF-3DVar weather radar data assimilation system and its application for rainfall-runoff prediction in North China

An hourly updated WRF-3DVar weather radar data assimilation system and its application for rainfall-runoff prediction in North China

Detailed Evolution Characteristics of an Inclined Structure Hailstorm Observed by Polarimetric Radar over the South China Coast

A hailstorm with an inclined structure occurred in the western part of the South China coast on 27 March 2020. This study investigates the detailed evolution characteristics of this inclined structure using the Doppler radar data assimilation system (VDRAS) and the improved fuzzy logic hydrometeor classification algorithm (HCA). Obvious differential reflectivity (often referred to as ZDR) arc characteristics, ZDR column characteristics, and the specific differential phase (often referred to as KDP) of the column are observed using dual-polarization radar prior to hailfall. Both the ZDR column and KDP column reached their strongest intensities during the hailfall phase, with their heights exceeding the height of the −20 °C layer (7.997 km above ground level), displaying a cross-correlation coefficient (CC) valley during this phase. Meanwhile, two centers of strong reflectivity were found, with one (C1) being located at 2–4 km, and the other (C2) being located at 6–8 km. The maximum horizontal distance between the two centers is 8 km, suggesting a strongly inclined structure. This inclined structure was closely related to the interaction between upper-level divergent outflows and ambient horizontal winds. The updraft on the front edge of the hailstorm continued to increase, keeping C2 at the upper level. At the same time, large raindrops at the lower part of C2 are continuously lifted, leading to ice formation. These ice particles then fell obliquely from their high altitude, merging with C1.

Including observation error correlation for ensemble radar radial wind assimilation and its impact on heavy rainfall prediction

AbstractAn assumption of uncorrelated observation errors is commonly adopted in conventional data assimilation. For this reason, high‐resolution data are resampled with strategies such as superobbing or data thinning. These strategies diminish the advantages of high temporal and spatial resolutions that can provide essential details in convection development. However, assimilating high‐resolution data, such as radar radial wind, without considering observation error correlations can lead to overfitting and thus degrade the performance of data assimilation and forecasts. This study uses a radar ensemble data assimilation system that combines the Weather Research and Forecasting model and Local Ensemble Transform Kalman Filter (WRF‐LETKF) to assimilate radar radial wind and reflectivity data. We present a strategy to include the error correlation of the Doppler radar radial wind in the WRF‐LETKF radar assimilation system and examine its impact on the accuracy of short‐term precipitation predictions based on a heavy rainfall event on June 2, 2017 in Taiwan.For radial wind, the horizontal error correlation scale is approximately 25 km according to the innovation statistics. The introduction of observation error correlation for radar radial wind assimilation produces more small‐scale features in wind analysis corrections compared to the experiment using an independent observation assumption. Consequently, the modification of wind corrections leads to stronger convergence accompanied by higher water vapor content, which enhances local convections. This results in more accurate simulations of reflectivity and short‐term precipitation. In particular, this advantage is identified for extreme heavy rainfall thresholds at small scales according to probability quantitative precipitation forecasts and fractions skill score.

Initiation of an Elevated Mesoscale Convective System With the Influence of Complex Terrain During Meiyu Season

AbstractA mesoscale convective system (MCS) was initiated on the lee side of the Wuling Mountain in central‐east China in the late afternoon (1700 LT) on June 27, 2016, and led to an early morning precipitation peak. We investigated the convective initiation (CI) mechanism of this MCS using radars and automatic weather station observations, WRF model simulation, and analysis from the radar data assimilation system Variational Doppler Radar Analysis System. It was found that the convection initiation was elevated as indicated by an elevated convergence zone and updraft located above 1,625 m from the ground. A zero convective inhibition layer was located at 850 hPa, over a stable boundary layer (BL) resulting from the daytime rainfall in the plain region, and provided a favorable condition for the elevated CI. Our study suggested that the transition of mountain‐plain thermal wind near sunset was the trigger for the convective initiation. An early afternoon mountain‐plain thermal circulation was disrupted when the east‐facing lee side slope was in the shadow of sunshine after 4 p.m. As a result, a local‐scale northwest downslope wind accelerated and led to a downstream convergence above the stable BL, where the elevated convection was initiated near 1,625 m above ground level. By combining the model simulation, the analysis from data assimilation, and the observations, this study is the first to provide a detailed analysis of CI mechanism of elevated convection in a complex topography region during the Meiyu season in China.

Impact of assimilating radar data using a hybrid 4DEnVar approach on prediction of convective events

This study developed a hybrid four-dimensional (4D) ensemble–variational (4DEnVar) radar data assimilation (DA) system for the Weather Research and Forecasting model. The 4DEnVar approach incorporated ensemble covariances at multiple time levels to assimilate observations distributed in the assimilation windows. By approximating the evolution of the background error using 4D ensemble covariance, use of the tangent linear and adjoint models was avoided. The impact of 4DEnVar radar DA on convective-scale analyses and forecasts was examined through comparison with 3DVar and 3DEnVar methods for the case of a squall line that occurred over southeastern China. In comparison with the other methods, 4DEnVar produced both smaller root mean square innovations for radar reflectivity and radial velocity and better analysis of the vertical structure of reflectivity. The corresponding relative humidity and vertical wind in convective regions were strengthened. Ultimately, 4DEnVar produced a substantially improved forecast, including improved quantitative precipitation and reflectivity forecast skill, and better representation of the squall line in terms of both areal coverage and intensity. In contrast, 3DEnVar improved the analysis and forecast modestly in comparison with 3DVar. Furthermore, sensitivity experiments indicated that a moderate assimilation window and a stronger ensemble weighting factor used in 4DEnVar could produce superior forecast results. The wind, temperature and water vapor were also improved by 4DEnVar, with the largest bias reduction for water vapor at low and middle levels. The improvements of 4DEnVar were further verified and shown effective using a mesocale convective system case and a local convection case.

Convective-Scale Sampling Error and Its Impact on the Ensemble Radar Data Assimilation System: A Case Study of a Heavy Rainfall Event on 16 June 2008 in Taiwan

ABSTRACT Sampling error stems from the use of ensemble-based data assimilation (EDA) with a limited ensemble size and can result in spurious background error covariances, leading to false analysis corrections. The WRF-LETKF radar assimilation system (WLRAS) is performed separately with 256 and 40 members to investigate the characteristics of convective-scale sampling errors in the EDA and its impact on precipitation prediction based on a heavy rainfall event on 16 June 2008. The results suggest that the sampling errors for this event are sensitive to the relationships between the simulated observations and model variables, the intensity of reflectivity, and how the prevailing wind projects to the radial wind in the areas that the radar cannot resolve U or V wind. The sampling errors lead to an underprediction of heavy rainfall when the horizontal localization radius is inadequately large, but this can be mitigated when a more accurate moisture condition is provided. In addition, being able to use a larger vertical localization plays an important role in providing necessary adjustments for representing the vertical thermodynamical structure of convection, which further improves precipitation prediction. A strategy mitigating the impact of sampling errors associated with the limitation of radial wind measurement by inflating the observation error over sensitive areas can bring benefits to precipitation prediction.

An Investigation of the Sensitivity of Predicting a Severe Rainfall Event in Northern Taiwan to the Upstream Condition with a WRF-based Radar Data Assimilation System

An Investigation of the Sensitivity of Predicting a Severe Rainfall Event in Northern Taiwan to the Upstream Condition with a WRF-based Radar Data Assimilation System

The development of a hybrid EnSRF-En3DVar system for convective-scale data assimilation

A coupled hybrid ensemble square root filter and three-dimensional ensemble-variational (EnSRF-En3DVar) radar data assimilation system, developed for the Weather Research and Forecasting model, was applied to a mesoscale convective system (MCS) that occurred over southeastern China on 5 June 2009. This hybrid system uses the extended control variable method to combine the static and ensemble flow-dependent forecast error covariance. The potential of the hybrid EnSRF-En3DVar system was first explored by comparing the derived results with those obtained using 3DVar and EnSRF approaches alone. Assimilation results showed the hybrid EnSRF-En3DVar system reduces the root mean square innovations for reflectivity and radial velocity, and better represents the pattern of the MCS than either 3DVar or EnSRF. This successful analysis improved quantitative reflectivity and precipitation forecasting skills, and helped forecast the MCS more realistically with regard to location, structure and intensity. Moreover, the root mean square error of the forecast wind, temperature and water vapor mixing ratio were found reduced by the hybrid EnSRF-En3DVar system when compared with the other methods. Diagnoses of the forecast fields showed the hybrid EnSRF-En3DVar system increases low-level cooling and mid-level warming within the convective area. Furthermore, sensitivity experiments testing the ensemble covariance weighting factor effect in the hybrid EnSRF-En3DVar method suggested that a stronger ensemble covariance weighting value led to improved precipitation forecast results.

Assimilating surface observations in a four-dimensional variational Doppler radar data assimilation system to improve the analysis and forecast of a squall line case

AbstractThis paper examines how assimilating surface observations can improve the analysis and forecast ability of a fourdimensional Variational Doppler Radar Analysis System (VDRAS). Observed surface temperature and winds are assimilated together with radar radial velocity and reflectivity into a convection-permitting model using the VDRAS four-dimensional variational (4DVAR) data assimilation system. A squall-line case observed during a field campaign is selected to investigate the performance of the technique. A single observation experiment shows that assimilating surface observations can influence the analyzed fields in both the horizontal and vertical directions. The surface-based cold pool, divergence and gust front of the squall line are all strengthened through the assimilation of the single surface observation. Three experiments—assimilating radar data only, assimilating radar data with surface data blended in a mesoscale background, and assimilating both radar and surface observations with a 4DVAR cost function—are conducted to examine the impact of the surface data assimilation. Independent surface and wind profiler observations are used for verification. The result shows that the analysis and forecast are improved when surface observations are assimilated in addition to radar observations. It is also shown that the additional surface data can help improve the analysis and forecast at low levels. Surface and low-level features of the squall line—including the surface warm inflow, cold pool, gust front, and low-level wind—are much closer to the observations after assimilating the surface data in VDRAS.

Constraining a 3DVAR Radar Data Assimilation System with Large-Scale Analysis to Improve Short-Range Precipitation Forecasts

AbstractIt is known from previous studies that radar data assimilation can improve short-range forecasts of precipitation, mainly when radial wind and reflectivity are available. However, from the authors’ experience radar data assimilation, when using the three-dimensional variational data assimilation (3DVAR) technique, can produce spurious precipitation results and large errors in the position and amount of precipitation. One possible reason for the problem is attributed to the lack of proper balance in the dynamical and microphysical fields. This work attempts to minimize this problem by adding a large-scale analysis constraint in the cost function. The large-scale analysis constraint is defined by the departure of the high-resolution 3DVAR analysis from a coarser-resolution large-scale analysis. It is found that this constraint is able to guide the assimilation process in such a way that the final result still maintains the large-scale pattern, while adding the convective characteristics where radar data are available. As a result, the 3DVAR analysis with the constraint is more accurate when verified against an independent dataset. The performance of this new constraint on improving precipitation forecasts is tested using six convective cases and verified against radar-derived precipitation by employing four skill indices. All of the skill indices show improved forecasts when using the methodology presented in this paper.

The Implementation of the Ice-Phase Microphysical Process into a Four-Dimensional Variational Doppler Radar Analysis System (VDRAS) and Its Impact on Parameter Retrieval and Quantitative Precipitation Nowcasting

Abstract The microphysical process of a cloud-scale model used by a four-dimensional Variational Doppler Radar Analysis System (VDRAS) is extended from its original warm rain parameterization scheme to a cold rain process containing ice and snow. The development of the adjoint equations for the additional control variables related to ice physics is accomplished by utilizing the existing four-dimensional variational (4DVar) minimization framework employed by VDRAS. Experiments are conducted to examine the accuracy of the new 4DVar system with the ice physics scheme implemented and to explore the impact of the ice-phase process on numerical simulations, parameter retrievals, and the model’s quantitative precipitation nowcasting (QPN) capability. It is shown that the ice-phase microphysical process can significantly alter the kinematic and thermodynamic structure of deep convection and provide a better description of the contents of the hydrometeors. During the 4DVar minimization, using the VDRAS-predicted freezing level after the previous assimilation cycle to replace the true but unknown 0°C line is found to be a feasible approach for separating the rain and snow and, at the same time, allowing the 4DVar minimization algorithm to converge to an optimal solution. A real case study from intensive observation period 8 of the 2008 Southwest Monsoon Experiment shows that, with the added ice-phase process, VDRAS is more capable of capturing the actual evolution of the reflectivity field than the original scheme. The model’s QPN skill is also improved significantly. Thus, the benefits of adding the ice-phase process into a 4DVar radar data assimilation system on the convective-scale weather analysis and forecast are demonstrated.

Improving quantitative precipitation nowcasting with a local ensemble transform Kalman filter radar data assimilation system: observing system simulation experiments

This study develops a Doppler radar data assimilation system, which couples the local ensemble transform Kalman filter with the Weather Research and Forecasting model. The benefits of this system to quantitative precipitation nowcasting (QPN) are evaluated with observing system simulation experiments on Typhoon Morakot (2009), which brought record-breaking rainfall and extensive damage to central and southern Taiwan. The results indicate that the assimilation of radial velocity and reflectivity observations improves the three-dimensional winds and rain-mixing ratio most significantly because of the direct relations in the observation operator. The patterns of spiral rainbands become more consistent between different ensemble members after radar data assimilation. The rainfall intensity and distribution during the 6-hour deterministic nowcast are also improved, especially for the first 3 hours. The nowcasts with and without radar data assimilation have similar evolution trends driven by synoptic-scale conditions. Furthermore, we carry out a series of sensitivity experiments to develop proper assimilation strategies, in which a mixed localisation method is proposed for the first time and found to give further QPN improvement in this typhoon case.

The influence of erroneous background, beam-blocking and microphysical non-linearity on the application of a four-dimensional variational Doppler radar data assimilation system for quantitative precipitation forecasts

A series of observation system simulation experiments (OSSEs) and a real case study are conducted to investigate the application of the Doppler radar data assimilation technique for numerical model quantitative precipitation forecasts (QPFs). A four-dimensional variational Doppler radar analysis system (VDRAS) is adopted for all experiments. The first set of OSSEs demonstrates that when the background field contains the imperfect information predicted from a mesoscale model, the incorrect convective-scale perturbations in the background can result in spurious scattered precipitation. However, a smoothing procedure can be used to remove the fine structures from the primitive model output in order to avoid this over-prediction. Results from the second set of OSSEs indicate that the lack of low-elevation data owing to radar scan and/or beam blockage could significantly alter the retrieved low-level thermal and dynamical structures when a different number of data assimilation cycles is applied. These impacts could lower the rainfall forecast capability of the model. The third set of OSSEs shows that, when the rainwater is assimilated over a long assimilation window, the non-linearity embedded in the microphysical process could lead the minimization algorithm in a wrong direction, causing a further degradation of the rainfall prediction. However, using multiple short assimilation cycles produces better minimization and forecast results than those obtained with a single long cycle. A real case experiment based on data collected during Intensive Operation Period (IOP) #8 of the 2008 Southwest Monsoon Experiment (SoWMEX) is conducted to provide a verification of the conclusions obtained from OSSEs under a realistic framework.

Radar Data Assimilation with WRF 4D-Var. Part I: System Development and Preliminary Testing

Abstract The major goal of this two-part study is to assimilate radar data into the high-resolution Advanced Research Weather Research and Forecasting Model (ARW-WRF) for the improvement of short-term quantitative precipitation forecasting (QPF) using a four-dimensional variational data assimilation (4D-Var) technique. In Part I the development of a radar data assimilation scheme within the WRF 4D-Var system (WRF 4D-Var) and the preliminary testing of the scheme are described. In Part II the performance of the enhanced WRF 4D-Var system is examined by comparing it with the three-dimensional variational data assimilation system (WRF 3D-Var) for a convective system over the U.S. Great Plains. The WRF 4D-Var radar data assimilation system has been developed with the existing framework of an incremental formulation. The new development for radar data assimilation includes the tangent-linear and adjoint models of a Kessler warm-rain microphysics scheme and the new control variables of cloud water, rainwater, and vertical velocity and their error statistics. An ensemble forecast with 80 members is used to produce background error covariance. The preliminary testing presented in this paper includes single-observation experiments as well as real data assimilation experiments on a squall line with assimilation windows of 5, 15, and 30 min. The results indicate that the system is able to obtain anisotropic multivariate analyses at the convective scale and improve precipitation forecasts. The results also suggest that the incremental approach with successive basic-state updates works well at the convection-permitting scale for radar data assimilation with the selected assimilation windows.

WRF-ARW Variational Storm-Scale Data Assimilation: Current Capabilities and Future Developments

The variational radar data assimilation system has been developed and tested for the Advanced Research Weather Research and Forecasting (WRF-ARW) model since 2005. Initial efforts focused on the assimilation of the radar observations in the 3-dimensional variational framework, and recently the efforts have been extended to the 4-dimensional system. This article provides a review of the basics of the system and various studies that have been conducted to evaluate and improve the performance of the system. Future activities that are required to further improve the system and to make it operational are also discussed.

Diagnostic Pressure Equation as a Weak Constraint in a Storm-Scale Three-Dimensional Variational Radar Data Assimilation System

Abstract A diagnostic pressure equation is incorporated into a storm-scale three-dimensional variational data assimilation (3DVAR) system in the form of a weak constraint in addition to a mass continuity equation constraint (MCEC). The goal of this diagnostic pressure equation constraint (DPEC) is to couple different model variables to help build a more dynamic consistent analysis, and therefore improve the data assimilation results and subsequent forecasts. Observational System Simulation Experiments (OSSEs) are first performed to examine the impact of the pressure equation constraint on storm-scale radar data assimilation using an idealized tornadic thunderstorm simulation. The impact of MCEC is also investigated relative to that of DPEC. It is shown that DPEC can improve the data assimilation results slightly after a given period of data assimilation. Including both DPEC and MCEC yields the best data assimilation results. Sensitivity tests show that MCEC is not very sensitive to the choice of its weighting coefficients in the cost function, while DPEC is more sensitive and its weight should be carefully chosen. The updated 3DVAR system with DPEC is further applied to the 5 May 2007 Greensburg, Kansas, tornadic supercell storm case assimilating real radar data. It is shown that the use of DPEC can speed up the spinup of precipitation during the intermittent data assimilation process and also improve the follow-on forecast in terms of the general evolution of storm cells and mesocyclone rotation near the time of observed tornado.

Assimilation of HF radar-derived surface currents on tidal-timescales

A surface current observation system based on high-frequency (HF) radar has been developed for Raritan Bay and the coastal waters of New York and New Jersey. An HF radar network provides synoptic surface current maps in near-real-time that can be optimally combined with ocean circulation models using data assimilation (DA) framework to obtain the best possible estimate of a three-dimensional ocean state. A nudging or Newtonian damping scheme has been developed to assimilate HF radar data into an estuarine and coastal ocean circulation model. This model, with an extensive embedded real-time observational network, is called the New York Harbour Observing and Prediction System (NYHOPS). A nudging parameter is introduced into the equations of motion which affects the model dynamics. The data is imparted to neighbouring (three-dimensional) grid points via model dynamics. The impact of HF radar DA is analysed by computing the DA skill score (DAskill) based on the mean-square-error (mse). The DAskill is computed by comparing non-assimilated and assimilated model solutions with in-situ observations of three-dimensional currents, temperature and salinity, which have not been included in the assimilation. A positive DAskill (0∼1) represents an improvement in the model performance by assimilation. HF radar data covering Raritan Bay and the New York Bight (NYB) Apex were assimilated into the NYHOPS model in the model hindcast cycle (–24h to 0h) on a daily forecast basis for a period of 40 days. The DAskill is assessed with respect to the NYHOPS model hindcasts (daily model solutions from –24h to 0h) as well as the first day forecasts (daily model solutions from 0h to 24h). HF radar DA improved the NYHOPS model performance during both the hindcast and forecast periods. The model skill metrics for the near-surface layers in the inner-NJ shelf region shows a hindcast DAskill of 24% (14%) and forecast DAskill of 18% (7%) for horizontal velocities u: east-west component (v: north-south component), and a hindcast DAskill of 33% (38%) and forecast DAskill of 25% (30%) for temperature (salinity). The nudging scheme is robust and efficient for the HF radar DA into the NYHOPS operational forecast model. The NYHOPS-HF radar DA system is capable of importing in the observations and produce useful hindcasts/forecasts with minimum computational expense.

PREDICTABILITY OF THE HEAVY RAINFALL DISASTER AT KANI CITY IN JULY 2010 BASED ON MODEL ENSEMBLE FORECASTS

The predictability of the heavy rainfall disaster at Kani City, Japan in July 2010 is investigated using a model ensemble approach which relies on the combination of 32 member ensembles obtained from multiple parameterizations in the mesoscale meteorological model PSU/NCAR MM5. The model is initialized using the 20-km resolution global analyses JMA GSM-GPV with and without the radar-data assimilation system developed by Yoshino et al. (2009). It is found that the multi-parameterization ensemble forecasts averaged by 32 members during 24 hours are very skillful in indicating the area of severe local storm activity, while each member deviates largely from the ensemble mean. Furthermore, the radar-assimilated ensemble forecasts enable us to more precisely predict the area of greatest rainfall potential, because the initial conditions used here realistically represent 3-dimensional distributions of cloud microphysical properties in the atmosphere.