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

2017 ◽  
Vol 60 (10) ◽  
pp. 1866-1885 ◽  
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

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

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Hongli Li ◽  
Xiangde Xu ◽  
Yang Hu ◽  
Yanjiao Xiao ◽  
Zhibin Wang
Keyword(s):  
Heavy Rainfall ◽  
Doppler Radar ◽  
Radar Data ◽  
Correction Method ◽  
Rainfall Event ◽  
Radar Reflectivity ◽  
Local Analysis ◽  
Heavy Rainfall Event ◽  
Radar Observations ◽  
Reflectivity Data

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.

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

Atmosphere ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 330 ◽  
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.

Assimilation of Simulated Doppler Radar Observations with an Ensemble Kalman Filter*

2003 ◽  
Vol 131 (8) ◽  
pp. 1663-1677 ◽  
Author(s):  
Chris Snyder ◽  
Fuqing Zhang
Keyword(s):  
Kalman Filter ◽  
Radial Velocity ◽  
Ensemble Kalman Filter ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Forecast Model ◽  
Forecast Errors ◽  
Radar Observations ◽  
Cloud Models ◽  
Reference Simulation

Abstract Assimilation of Doppler radar data into cloud models is an important obstacle to routine numerical weather prediction for convective-scale motions; the difficulty lies in initializing fields of wind, temperature, moisture, and condensate given only observations of radial velocity and reflectivity from the radar. This paper investigates the potential of the ensemble Kalman filter (EnKF), which estimates the covariances between observed variables and the state through an ensemble of forecasts, to assimilate radar observations at convective scales. In the basic experiment, simulated observations are extracted from a reference simulation of a splitting supercell and assimilated using the EnKF and the same numerical model that produced the reference simulation. The EnKF produces accurate analyses, including the unobserved variables, after roughly 30 min (or six scans) of radial velocity observations. Additional experiments, in which forecasts are made from the ensemble-mean analysis, reveal that forecast errors grow significantly in this simple system, so that the ability of the EnKF to track the reference solution is not simply because of stable system dynamics. It is also found that the covariances between radial velocity and temperature, moisture, and condensate are important to the quality of the analyses, as is the initialization chosen for the ensemble members prior to assimilating the first observations. These results are promising, especially given the ease of implementing the EnKF. A number of important issues remain, however, including the initialization of the ensemble prior to the first observation, the treatment of uncertainty in the environmental sounding, the role of error in the forecast model (particularly the microphysical parameterizations), and the treatment of lateral boundary conditions.

Cloud-Resolving Hurricane Initialization and Prediction through Assimilation of Doppler Radar Observations with an Ensemble Kalman Filter

2009 ◽  
Vol 137 (7) ◽  
pp. 2105-2125 ◽  
Author(s):  
Fuqing Zhang ◽  
Yonghui Weng ◽  
Jason A. Sippel ◽  
Zhiyong Meng ◽  
Craig H. Bishop
Keyword(s):  
Kalman Filter ◽  
Radial Velocity ◽  
Ensemble Kalman Filter ◽  
Doppler Radar ◽  
Gulf Coast ◽  
Radar Data ◽  
The United States ◽  
Radar Observations ◽  
Probabilistic Forecasts ◽  
Rapid Formation

This study explores the assimilation of Doppler radar radial velocity observations for cloud-resolving hurricane analysis, initialization, and prediction with an ensemble Kalman filter (EnKF). The case studied is Hurricane Humberto (2007), the first landfalling hurricane in the United States since the end of the 2005 hurricane season and the most rapidly intensifying near-landfall storm in U.S. history. The storm caused extensive damage along the southeast Texas coast but was poorly predicted by operational models and forecasters. It is found that the EnKF analysis, after assimilating radial velocity observations from three Weather Surveillance Radars-1988 Doppler (WSR-88Ds) along the Gulf coast, closely represents the best-track position and intensity of Humberto. Deterministic forecasts initialized from the EnKF analysis, despite displaying considerable variability with different lead times, are also capable of predicting the rapid formation and intensification of the hurricane. These forecasts are also superior to simulations without radar data assimilation or with a three-dimensional variational scheme assimilating the same radar observations. Moreover, nearly all members from the ensemble forecasts initialized with EnKF analysis perturbations predict rapid formation and intensification of the storm. However, the large ensemble spread of peak intensity, which ranges from a tropical storm to a category 2 hurricane, echoes limited predictability in deterministic forecasts of the storm and the potential of using ensembles for probabilistic forecasts of hurricanes.

scholarly journals Determining Key Model Parameters of Rapidly Intensifying Hurricane Guillermo (1997) Using the Ensemble Kalman Filter

2012 ◽  
Vol 69 (11) ◽  
pp. 3147-3171 ◽  
Author(s):  
Humberto C. Godinez ◽  
Jon M. Reisner ◽  
Alexandre O. Fierro ◽  
Stephen R. Guimond ◽  
Jim Kao
Keyword(s):  
Kalman Filter ◽  
Latent Heat ◽  
Ensemble Kalman Filter ◽  
Wind Shear ◽  
Doppler Radar ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Radar Observations ◽  
The Impact ◽  
Dual Doppler Radar

Abstract In this work the authors determine key model parameters for rapidly intensifying Hurricane Guillermo (1997) using the ensemble Kalman filter (EnKF). The approach is to utilize the EnKF as a tool only to estimate the parameter values of the model for a particular dataset. The assimilation is performed using dual-Doppler radar observations obtained during the period of rapid intensification of Hurricane Guillermo. A unique aspect of Guillermo was that during the period of radar observations strong convective bursts, attributable to wind shear, formed primarily within the eastern semicircle of the eyewall. To reproduce this observed structure within a hurricane model, background wind shear of some magnitude must be specified and turbulence and surface parameters appropriately specified so that the impact of the shear on the simulated hurricane vortex can be realized. To identify the complex nonlinear interactions induced by changes in these parameters, an ensemble of model simulations have been conducted in which individual members were formulated by sampling the parameters within a certain range via a Latin hypercube approach. The ensemble and the data, derived latent heat and horizontal winds from the dual-Doppler radar observations, are utilized in the EnKF to obtain varying estimates of the model parameters. The parameters are estimated at each time instance, and a final parameter value is obtained by computing the average over time. Individual simulations were conducted using the estimates, with the simulation using latent heat parameter estimates producing the lowest overall model forecast error.

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