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

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.

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

Monthly Weather Review ◽

10.1175/mwr3471.1 ◽

2007 ◽

Vol 135 (10) ◽

pp. 3381-3404 ◽

Cited By ~ 104

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.

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Radar Data Assimilation with WRF 4D-Var. Part II: Comparison with 3D-Var for a Squall Line over the U.S. Great Plains

Monthly Weather Review ◽

10.1175/mwr-d-12-00169.1 ◽

2013 ◽

Vol 141 (7) ◽

pp. 2245-2264 ◽

Cited By ~ 57

Author(s):

Juanzhen Sun ◽

Hongli Wang

Keyword(s):

Data Assimilation ◽

Radial Velocity ◽

Great Plains ◽

Radar Data ◽

Variational Data Assimilation ◽

Precipitation Forecast ◽

Squall Line ◽

Cold Pool ◽

Low Level ◽

Radar Data Assimilation

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.

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Typhoon rainstorm simulation with radar data assimilation in southeast coast of China

10.5194/nhess-2020-146 ◽

2020 ◽

Author(s):

Jiyang Tian ◽

Ronghua Liu ◽

Liuqian Ding ◽

Liang Guo ◽

Bingyu Zhang

Keyword(s):

Data Assimilation ◽

Radial Velocity ◽

Doppler Radar ◽

Radar Data ◽

Radar Reflectivity ◽

Time Interval ◽

Storm Events ◽

Southeast Coast ◽

Assimilation Time ◽

Radar Data Assimilation

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 in southeast coast of China. Radar data from Changle Doppler radar station are assimilated with three-dimensional variational data assimilation (3-DVar) model. Nine assimilation modes are designed by three kinds of radar data (radar reflectivity, radial velocity, radar reflectivity and radial velocity) and three assimilation time intervals (1 h, 3 h and 6 h). 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 with time interval of 1 h can significantly improve the rainfall simulations and 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 simulation as the time interval shortens. The rainfall simulation 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 in catchment scale and have great significance on typhoon rainstorm warning.

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

Advances in Atmospheric Sciences ◽

10.1007/s00376-016-5290-0 ◽

2016 ◽

Vol 33 (10) ◽

pp. 1106-1119 ◽

Cited By ~ 8

Author(s):

Xingchao Chen ◽

Kun Zhao ◽

Juanzhen Sun ◽

Bowen Zhou ◽

Wen-Chau Lee

Keyword(s):

Data Assimilation ◽

Doppler Radar ◽

Radar Data ◽

Squall Line ◽

Doppler Radar Data ◽

Data Assimilation System ◽

Radar Data Assimilation ◽

Surface Observations ◽

Assimilation System

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Doppler Radar Data Assimilation for Mesoscale Numerical Rainfall Prediction

10.29007/h6dv ◽

2018 ◽

Author(s):

Jiyang Tian ◽

Jia Liu ◽

Chuanzhe Li ◽

Fuliang Yu

Keyword(s):

Data Assimilation ◽

Radial Velocity ◽

Doppler Radar ◽

Weather Prediction ◽

Wrf Model ◽

Radar Data ◽

Radar Reflectivity ◽

Rainfall Prediction ◽

Doppler Radar Data ◽

Radar Data Assimilation

Hydrological prediction needs high-resolution and accurate rainfall information, which can be provided by mesoscale Numerical Weather Prediction (NWP) models. However, the predicted rainfall is not always satisfactory for hydrological use. The assimilation of Doppler radar observations is found to be an effective method through correcting the initial and lateral boundary conditions of the NWP model. The aim of this study is to explore an efficient way of Doppler radar data assimilation from different height layers for mesoscale numerical rainfall prediction. The Weather Research and Forecasting (WRF) model is applied to the Zijingguan catchment located in semi-humid and semi-arid area of Northern China. Three-dimensional variational data assimilation (3-DVar) technique is adopted to assimilate the Doppler radar data. Radar reflectivity and radial velocity are assimilated separately and jointly. Each type of radar data are divided into seven data sets according to the observation heights: (1) <500m; (2) <1000m; (3) <2000m; (4) 500~1000m; (5) 1000~2000m; (6) >2000m; (7) all heights. Results show that the assimilation of radar reflectivity leads to better results than radial velocity. The accuracy of the predicted rainfall deteriorates as the rise of the observation height of the assimilated radar data. Conclusions of this study provide a reference for efficient utilisation of the Doppler radar data in numerical rainfall prediction for hydrological use.

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

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2010jtecha1300.1 ◽

2010 ◽

Vol 27 (7) ◽

pp. 1140-1152 ◽

Cited By ~ 16

Author(s):

Eunha Lim ◽

Juanzhen Sun

Keyword(s):

Data Assimilation ◽

Doppler Radar ◽

Weather Prediction ◽

Radar Data ◽

Objective Analysis ◽

Doppler Velocity ◽

Horizontal Shear ◽

Data Assimilation System ◽

Radar Data Assimilation ◽

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.

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