A Three-Dimensional Variational Radar Data Assimilation Scheme Developed for Convective Scale NWP

2016 ◽  
pp. 285-326
Author(s):  
Jidong Gao
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Radar Data ◽  
Data Assimilation Scheme ◽  
Convective Scale ◽  
Assimilation Scheme ◽  
Radar Data Assimilation

scholarly journals Modeling of the Melting Layer. Part IV: Brightband Bulk Parameterization

2008 ◽  
Vol 65 (6) ◽  
pp. 1991-2001 ◽  
Author(s):  
Catherine Heyraud ◽  
Wanda Szyrmer ◽  
Stéphane Laroche ◽  
Isztar Zawadzki
Keyword(s):  
Data Assimilation ◽  
Radar Data ◽  
Melting Layer ◽  
Observation Operator ◽  
Data Assimilation Scheme ◽  
Bulk Model ◽  
Assimilation Scheme ◽  
Bulk Parameterization ◽  
Good Agreement ◽  
Extract Information

Abstract In this paper a simplified UHF-band backscattering parameterization for individual melting snowflakes is proposed. This parameterization is a function of the density, shape, and melted fraction, and is used here in a brightband bulk modeling study. A 1D bulk model is developed where aggregation and breakup are neglected. Model results are in good agreement with detailed bin-model results and simulate the radar brightband observations well. It is shown the model can be seen as an observation operator that could be introduced into a data assimilation scheme to extract information contained in the radar data measurements.

The Met. Office global three-dimensional variational data assimilation scheme

2000 ◽  
Vol 126 (570) ◽  
pp. 2991-3012 ◽  
Author(s):  
A. C. Lorenc ◽  
S. P. Ballard ◽  
R. S. Bell ◽  
N. B. Ingleby ◽  
P. L. F. Andrews ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

A Parallel Three-dimensional Variational Data Assimilation Scheme

2011 ◽  
Author(s):  
L. D’Amore ◽  
R. Arcucci ◽  
L. Marcellino ◽  
A. Murli ◽  
Theodore E. Simos ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

scholarly journals Improving the Model Convective Storm Quantitative Precipitation Nowcasting by Assimilating State Variables Retrieved from Multiple-Doppler Radar Observations

2014 ◽  
Vol 142 (11) ◽  
pp. 4017-4035 ◽  
Author(s):  
Yu-Chieng Liou ◽  
Jian-Luen Chiou ◽  
Wei-Hao Chen ◽  
Hsin-Yu Yu
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Scale Up ◽  
Three Dimensional ◽  
Radar Data ◽  
State Variables ◽  
Weather System ◽  
Additional Information ◽  
Convective Scale ◽  
Fine Resolution

Abstract This research combines an advanced multiple-Doppler radar synthesis technique with the thermodynamic retrieval method, originally proposed by Gal-Chen, and a moisture/temperature adjustment scheme, and formulates a sequential procedure. The focus is on applying this procedure to improve the model quantitative precipitation nowcasting (QPN) skill in the convective scale up to 3 hours. A series of (observing system simulation experiment) OSSE-type tests and a real case study are conducted to investigate the performance of this algorithm under different conditions. It is shown that by using the retrieved three-dimensional wind, thermodynamic, and microphysical parameters to reinitialize a fine-resolution numerical model, its QPN skill can be significantly improved. Since the Gal-Chen method requires the horizontal average properties of the weather system at each altitude, utilization of in situ radiosonde(s) to obtain this additional information for the retrieval is tested. When sounding data are not available, it is demonstrated that using the model results to replace the role played by observing devices is also a feasible choice. The moisture field is obtained through a simple, but effective, adjusting scheme and is found to be beneficial to the rainfall forecast within the first hour after the reinitialization of the model. Since this algorithm retrieves the unobserved state variables instantaneously from the wind measurements and directly uses them to reinitialize the model, fewer radar data and a shorter model spinup time are needed to correct the rainfall forecasts, in comparison with other data assimilation techniques such as four-dimensional variational data assimilation (4DVAR) or ensemble Kalman filter (EnKF) methods.

scholarly journals Impact of a Diagnostic Pressure Equation Constraint on Tornadic Supercell Thunderstorm Forecasts Initialized Using 3DVAR Radar Data Assimilation

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Guoqing Ge ◽  
Jidong Gao ◽  
Ming Xue
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Three Dimensional ◽  
Radar Data ◽  
Pressure Equation ◽  
Supercell Thunderstorm ◽  
Weighting Coefficients ◽  
The Impact ◽  
Radar Data Assimilation ◽  
Equation Constraint

A diagnostic pressure equation constraint has been incorporated into a storm-scale three-dimensional variational (3DVAR) data assimilation system. This diagnostic pressure equation constraint (DPEC) is aimed to improve dynamic consistency among different model variables so as to produce better data assimilation results and improve the subsequent forecasts. Ge et al. (2012) described the development of DPEC and testing of it with idealized experiments. DPEC was also applied to a real supercell case, but only radial velocity was assimilated. In this paper, DPEC is further applied to two real tornadic supercell thunderstorm cases, where both radial velocity and radar reflectivity data are assimilated. The impact of DPEC on radar data assimilation is examined mainly based on the storm forecasts. It is found that the experiments using DPEC generally predict higher low-level vertical vorticity than the experiments not using DPEC near the time of observed tornadoes. Therefore, it is concluded that the use of DPEC improves the forecast of mesocyclone rotation within supercell thunderstorms. The experiments using different weighting coefficients generate similar results. This suggests that DPEC is not very sensitive to the weighting coefficients.

scholarly journals Constraining a 3DVAR Radar Data Assimilation System with Large-Scale Analysis to Improve Short-Range Precipitation Forecasts

2016 ◽  
Vol 55 (3) ◽  
pp. 673-690 ◽  
Author(s):  
Eder Paulo Vendrasco ◽  
Juanzhen Sun ◽  
Dirceu Luis Herdies ◽  
Carlos Frederico de Angelis
Keyword(s):  
Data Assimilation ◽  
Short Range ◽  
Large Scale ◽  
Three Dimensional ◽  
Radar Data ◽  
Scale Analysis ◽  
Large Scale Analysis ◽  
Assimilation Process ◽  
The Cost ◽  
Radar Data Assimilation

AbstractIt is known from previous studies that radar data assimilation can improve short-range forecasts of precipitation, mainly when radial wind and reflectivity are available. However, from the authors’ experience radar data assimilation, when using the three-dimensional variational data assimilation (3DVAR) technique, can produce spurious precipitation results and large errors in the position and amount of precipitation. One possible reason for the problem is attributed to the lack of proper balance in the dynamical and microphysical fields. This work attempts to minimize this problem by adding a large-scale analysis constraint in the cost function. The large-scale analysis constraint is defined by the departure of the high-resolution 3DVAR analysis from a coarser-resolution large-scale analysis. It is found that this constraint is able to guide the assimilation process in such a way that the final result still maintains the large-scale pattern, while adding the convective characteristics where radar data are available. As a result, the 3DVAR analysis with the constraint is more accurate when verified against an independent dataset. The performance of this new constraint on improving precipitation forecasts is tested using six convective cases and verified against radar-derived precipitation by employing four skill indices. All of the skill indices show improved forecasts when using the methodology presented in this paper.

Cloud-Resolving Model Applied to Nowcasting: An Evaluation of Radar Data Assimilation and Microphysics Parameterization

2020 ◽  
Vol 35 (6) ◽  
pp. 2345-2365
Author(s):  
Eder P. Vendrasco ◽  
Luiz A. T. Machado ◽  
Bruno Z. Ribeiro ◽  
Edmilson D. Freitas ◽  
Rute C. Ferreira ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Convective Cloud ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Southeastern Brazil ◽  
Convective Clouds ◽  
Cloud Resolving Model ◽  
Cycling Frequency ◽  
Radar Data Assimilation

AbstractThis research explores the benefits of radar data assimilation for short-range weather forecasts in southeastern Brazil using the Weather Research and Forecasting (WRF) Model’s three-dimensional variational data assimilation (3DVAR) system. Different data assimilation options are explored, including the cycling frequency, the number of outer loops, and the use of null-echo assimilation. Initially, four microphysics parameterizations are evaluated (Thompson, Morrison, WSM6, and WDM6). The Thompson parameterization produces the best results, while the other parameterizations generally overestimate the precipitation forecast, especially WDSM6. Additionally, the Thompson scheme tends to overestimate snow, while the Morrison scheme overestimates graupel. Regarding the data assimilation options, the results deteriorate and more spurious convection occurs when using a higher cycling frequency (i.e., 30 min instead of 60 min). The use of two outer loops produces worse precipitation forecasts than the use of one outer loop, and the null-echo assimilation is shown to be an effective way to suppress spurious convection. However, in some cases, the null-echo assimilation also removes convective clouds that are not observed by the radar and/or are still not producing rain, but have the potential to grow into an intense convective cloud with heavy rainfall. Finally, a cloud convective mask was implemented using ancillary satellite data to prevent null-echo assimilation from removing potential convective clouds. The mask was demonstrated to be beneficial in some circumstances, but it needs to be carefully evaluated in more cases to have a more robust conclusion regarding its use.

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