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.

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Assimilating Doppler radar observations with an ensemble Kalman filter for convection-permitting prediction of convective development in a heavy rainfall event during the pre-summer rainy season of south China

Science China Earth Sciences ◽

10.1007/s11430-017-9076-9 ◽

2017 ◽

Vol 60 (10) ◽

pp. 1866-1885 ◽

Cited By ~ 7

Author(s):

XingHua Bao ◽

YaLi Luo ◽

JiaXiang Sun ◽

ZhiYong Meng ◽

Jian Yue

Keyword(s):

Kalman Filter ◽

South China ◽

Ensemble Kalman Filter ◽

Rainy Season ◽

Heavy Rainfall ◽

Doppler Radar ◽

Rainfall Event ◽

Heavy Rainfall Event ◽

Radar Observations

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A Cloud-Resolving 4DVAR Assimilation Experiment for a Local Heavy Rainfall Event in the Tokyo Metropolitan Area

Monthly Weather Review ◽

10.1175/2011mwr3428.1 ◽

2011 ◽

Vol 139 (6) ◽

pp. 1911-1931 ◽

Cited By ~ 44

Author(s):

Takuya Kawabata ◽

Tohru Kuroda ◽

Hiromu Seko ◽

Kazuo Saito

Keyword(s):

Data Assimilation ◽

Metropolitan Area ◽

Heavy Rainfall ◽

Rainfall Event ◽

Radar Reflectivity ◽

Heavy Rainfall Event ◽

Convective System ◽

Single Observation ◽

Assimilation Experiment ◽

Reflectivity Data

Abstract A cloud-resolving nonhydrostatic four-dimensional variational data assimilation system (NHM-4DVAR) was modified to directly assimilate radar reflectivity and applied to a data assimilation experiment using actual observations of a heavy rainfall event. Modifications included development of an adjoint model of the warm rain process, extension of control variables, and development of an observation operator for radar reflectivity. The responses of the modified NHM-4DVAR were confirmed by single-observation assimilation experiments for an isolated deep convection, using pseudo-observations of rainwater at the initial and end times of the data assimilation window. The results showed that the intensity of convection could be adjusted by assimilating appropriate observations of rainwater near the convection and that undesirable convection could be suppressed by assimilating small or no reflectivity. An assimilation experiment using actual observations of a local heavy rainfall in the Tokyo, Japan, metropolitan area was conducted with a horizontal resolution of 2 km. Precipitable water vapor derived from global positioning system data was assimilated at 5-min intervals within 30-min assimilation windows, and surface and wind profiler data were assimilated at 10-min intervals. Doppler radial wind and radar-reflectivity data below the elevation angle of 5.4° were assimilated at 1-min intervals. The 4DVAR assimilation reproduced a line-shaped rainband with a shape and intensity consistent with the observation. Assimilation of radar-reflectivity data intensified the rainband and suppressed false convection. The simulated rainband lasted for 1 h in the extended forecast and then gradually decayed. Sustaining the low-level convergence produced by northerly winds in the western part of the rainband was key to prolonging the predictability of the convective system.

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An Approach of Radar Reflectivity Data Assimilation and Its Assessment with the Inland QPF of Typhoon Rusa (2002) at Landfall

Journal of Applied Meteorology and Climatology ◽

10.1175/jam2439.1 ◽

2007 ◽

Vol 46 (1) ◽

pp. 14-22 ◽

Cited By ~ 99

Author(s):

Qingnong Xiao ◽

Ying-Hwa Kuo ◽

Juanzhen Sun ◽

Wen-Chau Lee ◽

Dale M. Barker ◽

...

Keyword(s):

Data Assimilation ◽

Doppler Radar ◽

Mesoscale Model ◽

Control Variable ◽

Three Dimensional ◽

Radar Data ◽

Radar Reflectivity ◽

Mixing Ratio ◽

Warm Rain Process ◽

Reflectivity Data

Abstract A radar reflectivity data assimilation scheme was developed within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) three-dimensional variational data assimilation (3DVAR) system. The model total water mixing ratio was used as a control variable. A warm-rain process, its linear, and its adjoint were incorporated into the system to partition the moisture and hydrometeor increments. The observation operator for radar reflectivity was developed and incorporated into the 3DVAR. With a single reflectivity observation, the multivariate structures of the analysis increments that included cloud water and rainwater mixing ratio increments were examined. Using the onshore Doppler radar data from Jindo, South Korea, the capability of the radar reflectivity assimilation for the landfalling Typhoon Rusa (2002) was assessed. Verifications of inland quantitative precipitation forecasting (QPF) of Typhoon Rusa (2002) showed positive impacts of assimilating radar reflectivity data on the short-range QPF.

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Evaluation of the Polarimetric-Radar Quantitative Precipitation Estimates of an Extremely Heavy Rainfall Event and Nine Common Rainfall Events in Guangzhou

Atmosphere ◽

10.3390/atmos9090330 ◽

2018 ◽

Vol 9 (9) ◽

pp. 330 ◽

Cited By ~ 7

Author(s):

Yang Zhang ◽

Liping Liu ◽

Hao Wen ◽

Chong Wu ◽

Yonghua Zhang

Keyword(s):

Heavy Rainfall ◽

Doppler Radar ◽

Heavy Rain ◽

Rainfall Event ◽

Retrieval Algorithm ◽

Heavy Rainfall Event ◽

Polarimetric Radar ◽

Rainfall Events ◽

Factors Affecting ◽

Hourly Rainfall

The development and application of operational polarimetric radar (PR) in China is still in its infancy. In this study, an operational PR quantitative precipitation estimation (QPE) algorithm is suggested based on data for PR hydrometeor classification and local drop size distribution (DSD). Even though this algorithm performs well for conventional rainfall events, in which hourly rainfall accumulations are less than 50 mm, the capability of a PR to estimate extremely heavy rainfall remains unclear. The proposed algorithm is used for nine different types of rainfall events that occurred in Guangzhou, China, in 2016 and for an extremely heavy rainfall event that occurred in Guangzhou on 6 May 2017. It performs well for all data of these nine rainfall events and for light-to-moderate rain (hourly accumulation <50 mm) in this extremely heavy rainfall event. However, it severely underestimated heavy rain (>50 mm) and the extremely heavy rain at stations where total rainfall exceeded 300 mm within 5 h in this extremely heavy rainfall event. To analyze the reasons for underestimation, a rain microphysics retrieval algorithm is presented to retrieve Dm and Nw from the PR measurements. The DSD characteristics and the factors affecting QPE are analyzed based on Dm and Nw. The results indicate that compared with statistical DSD data in Yangjiang (estimators are derived from these data), the average raindrop diameter during this rainfall event occurred on 6 May 2017 was much smaller and the number concentration was higher. The algorithm underestimated the precipitation with small and midsize particles, but overestimated the precipitation with midsize and large particles. Underestimations occurred when Dm and Nw are both very large, and the severe underestimations for heavy rain are mainly due to these particles. It is verified that some of these particles are associated with melting hail. Owing to the big differences in DSD characteristics, R(KDP, ZDR) underestimates most heavy rain. Therefore, R(AH), which is least sensitive to DSD variations, replaces R(KDP, ZDR) to estimate precipitation. This improved algorithm performs well even for extremely heavy rain. These results are important for evaluating S-band Doppler radar polarization updates in China.

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Impact of Radar Data Assimilation on the Simulation of a Heavy Rainfall Event Over Manaus in the Central Amazon

Pure and Applied Geophysics ◽

10.1007/s00024-021-02901-0 ◽

2021 ◽

Author(s):

Paulo Maurício Moura de Souza ◽

Eder Paulo Vendrasco ◽

Ivan Saraiva ◽

Maximiliano Trindade ◽

Maria Betânia Leal de Oliveira ◽

...

Keyword(s):

Data Assimilation ◽

Heavy Rainfall ◽

Radar Data ◽

Rainfall Event ◽

Heavy Rainfall Event ◽

Central Amazon ◽

Radar Data Assimilation

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Rain microstructure retrievals using 2-D video disdrometer and C-band polarimetric radar

Advances in Geosciences ◽

10.5194/adgeo-20-13-2009 ◽

2009 ◽

Vol 20 ◽

pp. 13-18 ◽

Cited By ~ 21

Author(s):

M. Thurai ◽

V. N. Bringi ◽

W. A. Petersen

Keyword(s):

Correlation Coefficient ◽

Radar Data ◽

Correction Method ◽

Heavy Rainfall Event ◽

Polarimetric Radar ◽

Individual Drop ◽

The Individual ◽

Reflectivity Data ◽

Differential Reflectivity ◽

Polar Correlation

Abstract. Measurements using the 2-D video disdrometer (2DVD) taken during a heavy rainfall event in Huntsville, Alabama, are analysed. The 2DVD images were processed to derive the rain microstructure parameters for each individual drop, which in turn were used as input to the T-matrix method to compute the forward and back scatter amplitudes of each drop at C-band. The polarimetric radar variables were then calculated from the individual drop contribution over a finite time period, e.g., 1 min. The calculated co-polar reflectivity, differential reflectivity, specific differential propagation phase and the co-polar correlation coefficient were compared with measurements from a C-band polarimetric radar located 15 km away. An attenuation-correction method based on the specific differential propagation phase was applied to the co-polar and differential reflectivity data from the C-band radar, after ensuring accurate radar calibration. Time series comparisons of the parameters derived from the 2DVD and C-band radar data show very good agreement for all four quantities, the agreement being sometimes better than the computations using the 1-min drop size distribution and bulk assumptions on rain microstructure (such as mean shapes and model-based assumptions for drop orientation). The agreement is particularly improved in the case of co-polar correlation coefficient since this parameter is very sensitive to variation of shapes as well as orientation angles. The calculations mark the first attempt at utilizing experimentally derived "drop- by-drop" rain microstructure information to compute the radar polarimetric parameters and to demonstrate the value of utilizing the 2-D video disdrometer for studying rain microstructure under various precipitation conditions. Histograms of drop orientation angles as well as the most probable drop shapes and the corresponding variations were also derived and compared with prior results from the 80 m fall "artificial rain" experiment.

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