Doppler Radar Data Assimilation Research Articles

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.

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.

Roles of Multi‐Scale Orography in Triggering Nocturnal Convection at a Summer Rainfall Hotspot Over the South China Coast: A Case Study

AbstractThe northern mountainous area of the Pearl River Delta (PRD) is a center of convection and heavy rainfall of coastal southern China during the summer monsoon season. Although there are frequent nighttime convective activities and rainfall over the region, the key dynamical and physical processes responsible for the nocturnal convection initiation (CI) are complex and not yet well documented. In this study, high‐resolution reanalyses from the Variational Doppler Radar Assimilation System (VDRAS) were used to explore the triggering mechanisms of a representative nocturnal convection event that occurred over the northern PRD on August 14, 2014. Results showed that the enhanced prevailing low‐level southerly winds, deflected easterly winds caused by the blocking of multi‐scale orography, and northern downslope winds associated with an orographic cooling effect were crucial for generating low‐level convergence and strong updrafts before CI. The low‐level convergence and moisture flux in the boundary layer started to increase at 00:20 local solar time (LST), and by the CI time (01:00 LST), the convergence intensity more than doubled. The intensification of low‐level convergence and moisture transport was strongest before 00:40 LST and was dominated by a zonal component that was closely related to the deflected easterly winds. Using Doppler radar data assimilation, this is the first study to reveal how the deflected flows and the thermally induced downslope winds combine with prevailing monsoonal winds to affect a nocturnal CI over the complex mountainous region of the South China coast.

Combined Assimilation of Doppler Wind Lidar and Tail Doppler Radar Data over a Hurricane Inner Core for Improved Hurricane Prediction with the NCEP Regional HWRF System

Accurate specification of hurricane inner-core structure is critical to predicting the evolution of a hurricane. However, observations over hurricane inner cores are generally lacking. Previous studies have emphasized Tail Doppler radar (TDR) data assimilation to improve hurricane inner-core representation. Recently, Doppler wind lidar (DWL) has been used as an observing system to sample hurricane inner-core and environmental conditions. The NOAA P3 Hurricane Hunter aircraft has DWL installed and can obtain wind data over a hurricane’s inner core when the aircraft passes through the hurricane. In this study, we examine the impact of assimilating DWL winds and TDR radial winds on the prediction of Hurricane Earl (2016) with the NCEP operational Hurricane Weather Research and Forecasting (HWRF) system. A series of data assimilation experiments are conducted with the Gridpoint Statistical Interpolation (GSI)-based ensemble-3DVAR hybrid system to identify the best way to assimilate TDR and DWL data into the HWRF forecast system. The results show a positive impact of DWL data on hurricane analysis and prediction. Compared with the assimilation of u and v components, assimilation of DWL wind speed provides better hurricane track and intensity forecasts. Proper choices of data thinning distances (e.g., 5 km horizontal thinning and 70 hPa vertical thinning for DWL) can help achieve better analysis in terms of hurricane vortex representation and forecasts. In the analysis and forecast cycles, the combined TDR and DWL assimilation (DWL wind speed and TDR radial wind, along with other conventional data, e.g., NCEP Automated Data Processing (ADP) data) offsets the downgrade analysis from the absence of DWL observations in an analysis cycle and outperforms assimilation of a single type of data (either TDR or DWL) and leads to improved forecasts of hurricane track, intensity, and structure. Overall, assimilation of DWL observations has been beneficial for analysis and forecasts in most cases. The outcomes from this study demonstrate the great potential of including DWL wind profiles in the operational HWRF system for hurricane forecast improvement.

Impact of Combined Assimilation of Wind Profiler and Doppler Radar Data on a Convective-Scale Cycling Forecasting System

Abstract The two types of wind observations, profiler and radar radial velocity, have been successfully assimilated into numerical weather prediction (NWP) systems. However, the added value of profiler data, especially from a densely deployed profiler network, is unknown when assimilated together with Doppler radar radial velocity. In this article, two combined assimilation strategies of profilers along with radar radial winds are compared within a convective-scale data assimilation (DA) framework. In strategy I, the profiler data are assimilated with conventional observations to generate an intermediate analysis that acts as a prior for radar data assimilation. In strategy II, both profiler and radar data are considered as storm-scale and assimilated within the same pass. Single- and dual-observation assimilation experiments indicate that for strategy I, the profiler DA improvement can be partly canceled by the potentially negative impact of the assimilation of single-radar radial velocity afterward, particularly when the radial wind is nearly orthogonal to the prevailing wind. For strategy II, important complements are provided when profilers are assimilated within the same pass along with radial winds. The diagnostics for a low-level jet case demonstrate that both strategies facilitate improved analyses and forecasts. But strategy II may bring more moderate analysis increments, which indicate mutual constraints of the profiler and radial winds when assimilated within the same pass. The results obtained in 1-month, retrospective cycling experiments also show that the strategy II outperforms the strategy I with slightly better wind and precipitation forecasts. Significance Statement Due to the high spatial–temporal wind information provided by profiler and radar radial velocity measurements, their combined assimilation would be expected to improve wind analysis. To fully utilize dense profiler data and radar radial wind in future operational applications, this study proposes a suitable assimilation strategy. If the profilers are defined as synoptic-scale observations, the profiler and Doppler radar data must be assimilated in different passes to adopt different length and variance scales. Whereas it is more reasonable to use a small background correlation length consistent with the radial velocity and, therefore, assimilate in the same pass if the profiler data are considered to better sample storm-scale features. Single- and dual-observation experiments indicate that profiler data provide important complements, while the assimilation of single-radar radial wind may yield analyzed wind results that do not depict the ground truth. A low-level jet case and a 1-month impact study further show that the combined assimilation strategy of assimilating both profiler and Doppler radar using smaller background correlation lengths enhances the analysis and forecasting of wind, resulting in more accurate accumulated precipitation forecasts.

Assimilation of Doppler radar radial wind data in the GRAPES mesoscale model with observation error covariances tuning

AbstractThe accurate estimation of observation error statistics for high‐density remote‐sensing data is a fundamental step toward optimally utilizing such data, including radar data, for operational numerical weather prediction. Based on the high‐resolution (3 × 3 km) GRAPES model together with the three‐dimensional variational assimilation system (GRAPES_3DVAR) developed by the China Meteorological Administration, a set of assimilation processes for radar radial wind data is designed. First, a scheme is proposed to guarantee the quality of radar radial wind data by correcting the velocity ambiguity for the data. The observation error covariances of the radar data in the GRAPES_3DVAR assimilation system are re‐estimated by using a diagnostic method based on analysis residuals, showing that the re‐estimated radial wind observation error covariances increase with altitude. An optimal regularization parameter is selected by the L‐curve method to constrain the radar observations that were assimilated using inaccurate observation error covariances. Furthermore, to verify the assimilation effects of the refined radial velocity observation error covariances and the constrained observations by the regularization parameter on the analysis and forecasting, a numerical study is carried out on typhoonYunque, which occurred on 3 August 2018. The results show that the modified Doppler radar wind data assimilation using the re‐estimated radial velocity observation error covariances enhances the wind analysis field and the water vapour transport, and accordingly obtains better short‐term precipitation estimates. The radar wind data assimilation results of using the constrained observations also indicate an enhanced wind analysis field and water vapour transport, which leads to better short‐term precipitation estimates.

Typhoon rainstorm simulations with radar data assimilation on the southeast coast of China

Abstract. As an effective technique to improve the rainfall forecast, data assimilation plays an important role in meteorology and hydrology. The aim of this study is to explore the reasonable use of Doppler radar data assimilation to correct the initial and lateral boundary conditions of the numerical weather prediction (NWP) systems. The Weather Research and Forecasting (WRF) model is applied to simulate three typhoon storm events on the southeast coast of China. Radar data from a Doppler radar station in Changle, China, are assimilated with three-dimensional variational data assimilation (3-DVar) model. Nine assimilation modes are designed by three kinds of radar data and at three assimilation time intervals. The rainfall simulations in a medium-scale catchment, Meixi, are evaluated by three indices, including relative error (RE), critical success index (CSI), and root mean square error (RMSE). Assimilating radial velocity at a time interval of 1 h can significantly improve the rainfall simulations, and it outperforms the other modes for all the three storm events. Shortening the assimilation time interval can improve the rainfall simulations in most cases, while assimilating radar reflectivity always leads to worse simulations as the time interval shortens. The rainfall simulations can be improved by data assimilation as a whole, especially for the heavy rainfall with strong convection. The findings provide references for improving the typhoon rainfall forecasts at catchment scale and have great significance on typhoon rainstorm warning.

Doppler weather radar data assimilation at convective-allowing grid spacing for predicting an extreme weather event in Southern India

ABSTRACT Thunderstorms and heavy rainfall are the most devastating and dangerous weather phenomena over the tropical region. Southern Peninsula India in general and regions close to Western Ghats in particular are very prone to such severe weather activity. To improve the short-range forecasting of such extreme weather hazards, tremendous efforts are made to develop Doppler Weather Radar (DWR) network over Indian region for detection and continuous monitoring of weather activities. In this study, the Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-Var) data assimilation system are used to evaluate the importance of the frequently available DWR observations at convective-allowing grid spacing for predicting an extreme weather event. Various assimilation experiments are performed using background error covariance statistics generated from the National Meteorological Center (NMC) method and Ensemble method, quality control in radial winds, and with/without Digital Filter Initialization (DFI) schemes for DWR data assimilation. Results suggest that with the assimilation of DWR observations, the WRF model is able to predict selected rainfall events with less temporal, spatial and intensity error. Moreover, synoptic conditions are more favourable after DWR assimilation. Due to lack of precise initial conditions, control experiments are not able to capture this extreme weather activity. Results suggest that better skill is seen when the DFI method is used in combination with ensemble background error covariance as compared to other experiments. The maximum rainfall prediction skill is enhanced with DWR data assimilation experiment with DFI method and ensemble background error covariance statistics.

Assimilation of Doppler Radar Data with an Ensemble 3DEnVar Approach to Improve Convective Forecasting

An ensemble three-dimensional ensemble-variational (3DEnVar) data assimilation (E3DA) system was developed within the Weather Research and Forecasting model’s 3DVar framework to assimilate radar data to improve convective forecasting. In this system, ensemble perturbations are updated by an ensemble of 3DEnVar and the ensemble forecasts are used to generate the flow-dependent background error covariance. The performance of the E3DA system was first evaluated against one experiment without radar DA and one radar DA experiment with 3DVar, using a severe storm case over southeastern China on 5 June 2009. Results indicated that E3DA improved the quantitative forecast skills of reflectivity and precipitation, as well as their spatial distributions in terms of both intensity and coverage over 3DVar. The root-mean-square error of radial velocity from 3DVar was reduced by E3DA, with stronger low-level wind closer to observation. It was also found that E3DA improved the wind, temperature and water vapor mixing ratio, with the lowest errors at the surface and upper levels. 3DVar showed moderate improvements in comparison with forecasts without radar DA. A diagnosis of the analysis revealed that E3DA increased vertical velocity, temperature, and humidity corresponding to the added reflectivity, while 3DVar failed to produce these adjustments, because of the lack of reasonable cross-variable correlations. The performance of E3DA was further verified using two convective cases over southern and southeastern China, and the reflectivity forecast skill was also improved over 3DVar.

Application of a Physical Ensemble Method in the POD-4DEnVar

Abstract The sensitivity of the proper orthogonal decomposition (POD)-based ensemble four-dimensional variational assimilation (4DVar) method (referred to as POD-4DEnVar) to cumulus and microphysics schemes was investigated using the Weather Research and Forecasting (WRF) Model for heavy rainfall in South China. Results show that the choice of the cumulus and microphysics schemes for ensemble samples significantly impacts precipitation prediction and that Doppler radar data assimilation using POD-4DEnVar is sensitive to the parameterization schemes used for the ensemble samples. The cumulus and microphysics schemes primarily affect the vertical velocity and rainwater mixing ratio of the ensemble forecasts. Variations in the ensemble samples caused by different parameterization schemes are introduced into the four-dimensional ensemble variational assimilation by the radar data observation operator. These variations affect the analysis fields and result in variations in precipitation prediction. To obtain the optimal result (smallest forecast error), three methods are designed based on the physical ensemble technique, which can filter out the effects of different parameterization schemes for the ensemble samples through averaging. The results show that the precipitation forecasts from the three assimilation experiments are improved compared with a control experiment, but each physical ensemble method leads to a unique precipitation forecast.

Characteristic Features of the Evolution of a Meiyu Frontal Rainstorm with Doppler Radar Data Assimilation

During the Meiyu period, floods are prone to occur in the middle and lower reaches of the Yangtze River because of the highly concentrated and heavy rainfall, which caused huge life and economic losses. Based on numerical simulation by assimilating Doppler radar, radiosonde, and surface meteorological observations, the evolution mechanism for the initiation, development, and decaying of a Meiyu frontal rainstorm that occurred from 4th to 5th July 2014 is analyzed. Results show that the numerical experiment can well reproduce the temporal variability of heavy precipitation and successfully simulate accumulative precipitation and its evolution over the key rainstorm area. The simulated “rainbelt training” is consistent with observed “echo training” on both spatial structure and temporal evolution. The convective cells in the mesoscale convective belt propagated from southwest to northeast across the key rainstorm area, leading to large accumulative precipitation in this area. There existed convective instability in lower levels above the key rainstorm area, while strong ascending motion developed during the rainstorm process. Combined with abundant water vapor supply, the above condition was favorable for the formation and development of heavy rainstorm. The low-level jet (LLJ) provided sufficient energy for the rainstorm system, and the low-level convergence intensified, which was important for the maintenance of precipitation system and its eventual intensification to rainstorm. At its mature stage, the rainstorm system demonstrated vertically tilted structure with strong ascending motion in the key rainstorm area, which was favorable for the occurrence of rainstorm. In the decaying stage, unstable energy decreased and the rainstorm no longer had sufficient energy to sustain. The rapid weakening of LLJ resulted in smaller energy supply to the convective system, and the stratification tended to be stable in the middle and lower levels. The ascending motion weakened correspondingly, which made it hard for the convective system to maintain.

Evaluation of Doppler radar and GTS data assimilation for NWP rainfall prediction of an extreme summer storm in northern China: from the hydrological perspective

Abstract. Data assimilation is an effective tool in improving high-resolution rainfall of the numerical weather prediction (NWP) systems which always fails in providing satisfactory rainfall products for hydrological use. The aim of this study is to explore the potential effects of assimilating different sources of observations, i.e., the Doppler weather radar and the Global Telecommunication System (GTS) data, in improving the mesoscale NWP rainfall products. A 24 h summer storm occurring over the Beijing–Tianjin–Hebei region of northern China on 21 July 2012 is selected as a case study. The Weather Research and Forecasting (WRF) Model is used to obtain 3 km rainfall forecasts, and the observations are assimilated using the three-dimensional variational (3DVar) data assimilation method. Eleven data assimilation modes are designed for assimilating different combinations of observations in the two nested domains of the WRF model. Both the rainfall accumulative amount and its distribution in space and time are examined for the forecasting results with and without data assimilation. The results show that data assimilation can effectively help improve the WRF rainfall forecasts, which is of great importance for hydrologic applications through the rainfall–runoff transformation process. Both the radar reflectivity and the GTS data are good choices for assimilation in improving the rainfall products, whereas special attention should be paid to assimilating radial velocity where unsatisfactory results are always found. The assimilation of the GTS data in the coarser domain has positive effects on the radar data assimilation in the finer domain, which can make the rainfall forecasts more accurate than assimilating the radar data alone. It is also found that the assimilation of more observations cannot guarantee further improvement of the rainfall products, whereas the effective information contained in the assimilated data is of more importance than the data quantity. Potential improvements of data assimilation in improving the NWP rainfall products are discussed and suggestions are further made.

Assimilation of Doppler Radar Data and Its Impact on Prediction of a Heavy Meiyu Frontal Rainfall Event

Operational Doppler radar observations have potential advantages over other above-surface observations when it comes to assimilation for mesoscale model simulations with high spatial and temporal resolution. To improve the forecast of a heavy frontal rainfall event that occurred in the Yangtze-Huaihe River Basin from 4 July to 5 July 2014 in China, operational radar observations are assimilated by the Local Analysis and Prediction System (LAPS). Radar reflectivity data are used primarily in the LAPS cloud analysis procedure, which retrieves the number of hydrometeors and adjusts the moisture and cloud fields. Radial velocity data are analyzed through the LAPS wind analysis-based successive correction method. A new correction method is developed to correct three-dimensional radar reflectivity data based on hourly surface rain gauge observations. The performance of the correction method is demonstrated by assimilating radar reflectivity observations into LAPS. Experiments with different radar data assimilation are examined. Results show that the assimilation of radar data can effectively correct the background errors and improve the heavy rainfall forecast. The simulated intensity, pattern, and temporal evolution of the heavy rainfall event are better improved with radar reflectivity assimilation, especially when the correction method is implemented to correct radar observations.

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.

Assimilation of Chinese Doppler Radar and Lightning Data Using WRF-GSI: A Case Study of Mesoscale Convective System

The radar-enhanced GSI (version 3.1) system and the WRF-ARW (version 3.4.1) model were modified to assimilate radar/lightning-proxy reflectivity. First, cloud-to-ground lightning data were converted to reflectivity using a simple assumed relationship between flash density and reflectivity. Next, the reflectivity was used in the cloud analysis of GSI to adjust the cloud/hydrometeors and moisture. Additionally, the radar/lightning-proxy reflectivity was simultaneously converted to a 3D temperature tendency. Finally, the model-calculated temperature tendencies from the explicit microphysics scheme, as well as cumulus parameterization at 3D grid points at which the radar temperature tendency is available, were updated in a forward full-physics step of diabatic digital filter initialization in the WRF-ARW. The WRF-GSI system was tested using a mesoscale convective system that occurred on June 5, 2009, and by assimilating Doppler radar and lightning data, respectively. The forecasted reflectivity with assimilation corresponded more closely to the observed reflectivity than that of the parallel experiment without assimilation, particularly during the first 6 h. After assimilation, the short-range precipitation prediction improved, although the precipitation intensity was stronger than the observed one. In addition, the improvements obtained by assimilating lightning data were worse than those from assimilating radar reflectivity over the first 3 h but improved thereafter.

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.

A comparison between the Local Ensemble Transform Kalman Filter and the Ensemble Square Root Filter for the assimilation of radar data in convective‐scale models

Two ensemble data assimilation methods are used to assimilate Doppler radar observations into a convection‐allowing model. The analyses and subsequent forecasts from the two systems are compared. The Local Ensemble Transform Kalman Filter (LETKF) simultaneously assimilates all observations that can impact the model state at a given location. It is compared to the Ensemble Square Root Filter (EnSRF), which assimilates observations sequentially and has commonly been used for convective‐scale Doppler radar data assimilation. While the filters should behave the same for ideal systems, a comparison between the serial and simultaneous filters has not previously been explored at the convective scale where significant nonlinear effects are present. Observing System Simulation Experiments (OSSEs) are first used to compare the assimilation systems for the analysis and forecast of a supercell thunderstorm. Both the EnSRF and LETKF produce reasonable analyses from the Doppler velocity and reflectivity observations of the true supercell. Small improvements in analysis errors and system noise from the LETKF simultaneous update do not significantly impact the subsequent forecasts. This result is consistent across a range of localization length‐scales and is independent of the manner in which localization is applied. Tests comparing the EnSRF and LETKF for a real‐data case also have small differences. The magnitudes of these differences are similar to those that arise from the sampling variability associated with a finite ensemble. Overall, the results suggest the EnSRF and LETKF approaches are equally capable methods for radar data assimilation at convective scales.

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.

Assimilation of Doppler radar observations with an ensemble square root filter: A squall line case study

The effectiveness of using an Ensemble Square Root Filter (EnSRF) to assimilate real Doppler radar observations on convective scale is investigated by applying the technique to a case of squall line on 12 July 2005 in midwest Shandong Province using the Weather Research and Forecasting (WRF) model. The experimental results show that: (1) The EnSRF system has the potential to initiate a squall line accurately by assimilation of real Doppler radar data. The convective-scale information has been added into the WRF model through radar data assimilation and thus the analyzed fields are improved noticeably. The model spin-up time has been shortened, and the precipitation forecast is improved accordingly. (2) Compared with the control run, the deterministic forecast initiated with the ensemble mean analysis of EnSRF produces more accurate prediction of microphysical fields. The predicted wind and thermal fields are reasonable and in accordance with the characteristics of convective storms. (3) The propagation direction of the squall line from the ensemble mean analysis is consistent with that of the observation, but the propagation speed is larger than the observed. The effective forecast period for this squall line is about 5–6 h, probably because of the nonlinear development of the convective storm.

The Effect of Topographic Variability on Initial Condition Sensitivity of Low-Level Wind Forecasts. Part II: Experiments Using Real Terrain and Observations

Abstract A study by Bieringer et al., which is Part I of this two-part study, demonstrated analytically using the shallow-water equations and numerically in controlled experiments that the presence of terrain can result in an enhancement of sensitivities to initial condition adjustments. The increased impact of adjustments to initial conditions corresponds with gradients in the flow field induced by the presence of the terrain obstacle. In cross-barrier flow situations the impact of the initial condition adjustments on the final forecast increases linearly as the height of the terrain obstacle increases. While this property associated with initial condition perturbations may be present in an analytic and controlled numerical environment, it is often difficult to realize these benefits in a more operationally realistic setting. This study extends the prior work to a situation with actual terrain, Doppler radar wind observations over the terrain, and observations from a surface mesonet for model verification. The results indicate that the downstream surface wind forecast was improved more when the initial conditions adjusted through the assimilation of Doppler radar data originated from areas with terrain gradients than from regions where the terrain was relatively flat. This result is consistent with the findings presented in Part I and suggests that when varying terrain elevation is present upstream of a target forecast area, a greater benefit (in terms of forecast accuracy) can be made by targeting additional observations in the regions containing variable terrain than regions where the terrain is relatively flat.