Variational Doppler Radar Analysis System Research Articles

The Development of a Terrain-Resolving Scheme for the Forward Model and Its Adjoint in the Four-Dimensional Variational Doppler Radar Analysis System (VDRAS)

Abstract The four-dimensional Variational Doppler Radar Analysis System (VDRAS) developed at the National Center for Atmospheric Research (NCAR) is significantly improved by implementing a terrain-resolving scheme to its forward model and adjoint based on the ghost cell immersed boundary method (GCIBM), which allows the topographic effects to be considered without the necessity to rebuild the model on a terrain-following coordinate system. The new system, called IBM_VDRAS, is able to perform forward forecast and backward adjoint model integration over nonflat lower boundaries, ranging from mountains with smooth slopes to buildings with sharp surfaces. To evaluate the performance of the forward model over complex terrain, idealized numerical experiments of a two-dimensional linear mountain wave and three-dimensional leeside vortices are first conducted, followed by a comparison with a simulation by the Weather Research and Forecasting (WRF) Model. An observing system simulation experiment is also conducted with the assimilation of simulated radar data to examine the ability of IBM_VDRAS in analyzing orographically forced moist convection. It is shown that the IBM_VDRAS can retrieve terrain-influenced three-dimensional meteorological fields including winds, thermodynamic, and microphysical parameters with reasonable accuracy. The new system, with the advanced radar data assimilation capability and the GCIBM terrain scheme, has the potential to be used for studying the evolution of convective weather systems under the influence of terrain.

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.

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.

Raindrop Size Distribution and Rain Characteristics during the 2013 Great Colorado Flood

Abstract Drop size distributions observed by four Particle Size Velocity (PARSIVEL) disdrometers during the 2013 Great Colorado Flood are used to diagnose rain characteristics during intensive rainfall episodes. The analysis focuses on 30 h of intense rainfall in the vicinity of Boulder, Colorado, from 2200 UTC 11 September to 0400 UTC 13 September 2013. Rainfall rates R, median volume diameters D0, reflectivity Z, drop size distributions (DSDs), and gamma DSD parameters were derived and compared between the foothills and adjacent plains locations. Rainfall throughout the entire event was characterized by a large number of small- to medium-sized raindrops (diameters smaller than 1.5 mm) resulting in small values of Z (<40 dBZ), differential reflectivity Zdr (<1.3 dB), specific differential phase Kdp (<1° km−1), and D0 (<1 mm). In addition, high liquid water content was present throughout the entire event. Raindrops observed in the plains were generally larger than those in the foothills. DSDs observed in the foothills were characterized by a large concentration of small-sized drops (d < 1 mm). Heavy rainfall rates with slightly larger drops were observed during the first intense rainfall episode (0000–0800 UTC 12 September) and were associated with areas of enhanced low-level convergence and vertical velocity according to the wind fields derived from the Variational Doppler Radar Analysis System. The disdrometer-derived Z–R relationships reflect how unusual the DSDs were during the 2013 Great Colorado Flood. As a result, Z–R relations commonly used by the operational NEXRAD strongly underestimated rainfall rates by up to 43%.

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 II: Comparison with 3D-Var for a Squall Line over the U.S. Great Plains

Abstract The Weather Research and Forecasting Model (WRF) four-dimensional variational data assimilation (4D-Var) system described in Part I of this study is compared with its corresponding three-dimensional variational data assimilation (3D-Var) system using a Great Plains squall line observed during the International H2O Project. Two 3D-Var schemes are used in the comparison: a standard 3D-Var radar data assimilation (DA) that is the same as the 4D-Var except for the exclusion of the constraining dynamical model and an enhanced 3D-Var that includes a scheme to assimilate an estimated in-cloud humidity field. The comparison is made by verifying their skills in 0–6-h quantitative precipitation forecast (QPF) against stage-IV analysis, as well as in wind forecasts against radial velocity observations. The relative impacts of assimilating radial velocity and reflectivity on QPF are also compared between the 4D-Var and 3D-Var by conducting data-denial experiments. The results indicate that 4D-Var substantially improves the QPF skill over the standard 3D-Var for the entire 6-h forecast range and over the enhanced 3D-Var for most forecast hours. Radial velocity has a larger impact relative to reflectivity in 4D-Var than in 3D-Var in the first 3 h because of a quicker precipitation spinup. The analyses and forecasts from the 4D-Var and 3D-Var schemes are further compared by examining the meridional wind, horizontal convergence, low-level cold pool, and midlevel temperature perturbation, using analyses from the Variational Doppler Radar Analysis System (VDRAS) as references. The diagnoses of these fields suggest that the 4D-Var analyzes the low-level cold pool, its leading edge convergence, and midlevel latent heating in closer resemblance to the VDRAS analyses than the 3D-Var schemes.

Precipitation Forecasting Using Doppler Radar Data, a Cloud Model with Adjoint, and the Weather Research and Forecasting Model: Real Case Studies during SoWMEX in Taiwan

Abstract The quantitative precipitation forecast (QPF) capability of the Variational Doppler Radar Analysis System (VDRAS) is investigated in the Taiwan area, where the complex topography and surrounding oceans pose great challenges to accurate rainfall prediction. Two real cases observed during intensive operation periods (IOPs) 4 and 8 of the 2008 Southwest Monsoon Experiment (SoWMEX) are selected for this study. Experiments are first carried out to explore the sensitivity of the retrieved fields and model forecasts with respect to different background fields. All results after assimilation of the Doppler radar data indicate that the principal kinematic and thermodynamic features recovered by the VDRAS four-dimensional variational data assimilation (4DVAR) technique are rather reasonable. Starting from a background field generated by blending ground-based in situ measurements (radiosonde and surface mesonet station) and reanalysis data over the oceans, VDRAS is capable of capturing the evolution of the major precipitation systems after 2 h of simulation. The model QPF capability is generally comparable to or better than that obtained using only in situ observations or reanalysis data to prepare the background fields. In a second set of experiments, it is proposed to merge the VDRAS analysis field with the Weather Research and Forecasting Model (WRF), and let the latter continue with the following model integration. The results indicate that through this combination, the performance of the model QPF can be further improved. The accuracy of the predicted 2-h accumulated rainfall turns out to be significantly higher than that generated by using VDRAS or WRF alone. This can be attributed to the assimilation of meso- and convective-scale information, embedded in the radar data, into VDRAS, and to better treatment of the topographic effects by the WRF simulation. The results illustrated in this study demonstrate a feasible extension for the application of VDRAS in other regions with similar geographic conditions and observational limitations.

A Frequent-Updating Analysis System Based on Radar, Surface, and Mesoscale Model Data for the Beijing 2008 Forecast Demonstration Project

Abstract The Variational Doppler Radar Analysis System (VDRAS) was implemented in Beijing, China, and contributed to the Beijing 2008 Forecast Demonstration Project (B08FDP) in support of the Beijing Summer Olympics. VDRAS is a four-dimensional variational data assimilation system that produces frequently updated analyses using Doppler radar radial velocities and reflectivities, surface observations, and mesoscale model data. The system was tested in real time during the B08FDP pretrials in the summers of 2006 and 2007 and run during the Olympics to assist the 0–6-h convective weather nowcasting. This paper provides a description of the upgraded system and its Beijing implementation, an evaluation of the system performance using data collected during the pretrials, and its utility on convective weather nowcasting through two case studies. Verification of VDRAS wind against a wind profiler shows that the analyzed wind is reasonably accurate with a smaller RMS difference for 2006 than for 2007 due to better radar data coverage in 2006. The analyzed cold pools in three convective episodes are compared with surface observations at selected stations. The result shows good agreement between the analysis and the observations. The two case studies demonstrate the role that VDRAS could play in nowcasting convective initiation.

Convective scale structure and evolution of a squall line observed by C-band dual Doppler radar in an arid region of northwestern China

A long-lived and loosely organized squall line moved rapidly across Urumqi, the capital city of Xinjiang Uygur Autonomous Region of China on 26 June 2005, generating hail and strong winds. The squall line was observed by a dual Doppler radar system in a field experiment conducted in 2004 and 2005 by the Chinese Academy of Meteorological Sciences and the local meteorological bureau in northwestern China. The 3D wind fields within the squall line were retrieved through dual Doppler analyses and a variational Doppler radar analysis system (VDRAS). The formation and structure of the squall line as well as the genesis and evolution of embedded convective cells were investigated. During its life period, the squall line consisted of six storm cells extending about 100 km in length, and produced hail of about 25 mm in diameter and strong surface winds up to 11 m s−1. Radar observations revealed a broad region of stratiform rain in a meso-β cyclone, with the squall line located to the west of this. Two meso-γ scale vortices were found within the squall line. Compared to typical squall lines in moist regions, such as Guangdong Province and Shanghai, which tend to be around 300–400 km in length, have echo tops of 17–19 km, and produce maximum surface winds of about 25 m s−1 and temperature variations of about 8°C this squall line system had weaker maximum reflectivity (55 dBZ), a lower echo top (13 km) and smaller extension (about 100 km), relatively little stratiform rainfall preceding the convective line, and a similar moving speed and temperature variation at the surface.

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

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

Improving Parameterization of Rain Microphysics with Disdrometer and Radar Observations

Abstract Disdrometer observations indicate that the raindrop size distribution (DSD) can be represented by a constrained-gamma (CG) distribution model. The model is used to retrieve DSDs from polarization radar measurements of reflectivity and differential reflectivity and to characterize rain microphysics and physical processes such as evaporation, accretion, and precipitation. The CG model parameterization is simplified to a single parameter for application in single-moment numerical models. This simplified parameterization is applied in the Variational Doppler Radar Analysis System (VDRAS) using Kessler-type parameterizations for model initialization and forecasting. Results are compared to those for the Marshall–Palmer (MP) DSD model. It is found that the simplified CG model parameterization better preserves the stratiform rain and produces better forecasts than the MP model parameterization.

Real-Time Low-Level Wind and Temperature Analysis Using Single WSR-88D Data

A four-dimensional variational Doppler radar analysis system (VDRAS) has been developed and implemented at a weather forecast office to produce real-time boundary layer wind and temperature analyses using WSR88D radar data. This paper describes significant changes made to convert VDRAS from a research tool to a real-time analysis system and presents results of low-level wind and temperature analysis using operational radar data. In order to produce continuous analyses with time, VDRAS was implemented with a cycling procedure, in which the analysis from the previous cycle is used as a first guess and background for the next cycle. Other enhancements in this real-time system include direct assimilation of data on constant elevation angle levels, addition of mesonet observations, inclusion of an analysis background term, and continuous updating of lateral boundary conditions. An observed case of a line of storms and strong outflow is used to examine the performance of the real-time analysis system and its sensitivity to various system changes. The quality of the analysis for this case is examined by comparing the subsequent 90-min forecast with the observed radial velocity. It is shown that the forecast initialized using the VDRAS analysis outperforms persistence and a forecast using a mesoscale analysis. The accuracy of the retrieved wind in six convective cases is also verified against automated weather reports from commercial aircraft data. The verification shows an average difference of 3.3 m s21 over these six cases.

Dynamical and Microphysical Retrieval from Doppler Radar Observations Using a Cloud Model and Its Adjoint. Part II: Retrieval Experiments of an Observed Florida Convective Storm

Abstract The variational Doppler radar analysis system developed in part I of this study is tested on a Florida airmass storm observed during the Convection and Precipitation/ Electrification Experiment. The 3D wind, temperature, and microphysical structure of this storm are obtained by minimizing the difference between the radar-observed radial velocities and rainwater mixing ratios (derived from reflectivity) and their model predictions. Retrieval experiments are carried out to assimilate information from one or two radars. The retrieved fields are compared with measurements of two aircraft penetrating the storm at different heights. The retrieved wind, thermodynamical, and microphysical fields indicate that the minimization converges to a solution consistent with the input velocity and rainwater fields. The primary difference between using single-Doppler and dual-Doppler information is the reduction of the peak strength of the storm on the order of 10% when information from only one radar is provided. ...