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

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.

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Diagnostic Pressure Equation as a Weak Constraint in a Storm-Scale Three-Dimensional Variational Radar Data Assimilation System

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-11-00201.1 ◽

2012 ◽

Vol 29 (8) ◽

pp. 1075-1092 ◽

Cited By ~ 21

Author(s):

Guoqing Ge ◽

Jidong Gao ◽

Ming Xue

Keyword(s):

Data Assimilation ◽

Three Dimensional ◽

Radar Data ◽

Pressure Equation ◽

Storm Cells ◽

The Cost ◽

Weighting Coefficients ◽

The Impact ◽

Radar Data Assimilation ◽

Equation Constraint

Abstract A diagnostic pressure equation is incorporated into a storm-scale three-dimensional variational data assimilation (3DVAR) system in the form of a weak constraint in addition to a mass continuity equation constraint (MCEC). The goal of this diagnostic pressure equation constraint (DPEC) is to couple different model variables to help build a more dynamic consistent analysis, and therefore improve the data assimilation results and subsequent forecasts. Observational System Simulation Experiments (OSSEs) are first performed to examine the impact of the pressure equation constraint on storm-scale radar data assimilation using an idealized tornadic thunderstorm simulation. The impact of MCEC is also investigated relative to that of DPEC. It is shown that DPEC can improve the data assimilation results slightly after a given period of data assimilation. Including both DPEC and MCEC yields the best data assimilation results. Sensitivity tests show that MCEC is not very sensitive to the choice of its weighting coefficients in the cost function, while DPEC is more sensitive and its weight should be carefully chosen. The updated 3DVAR system with DPEC is further applied to the 5 May 2007 Greensburg, Kansas, tornadic supercell storm case assimilating real radar data. It is shown that the use of DPEC can speed up the spinup of precipitation during the intermittent data assimilation process and also improve the follow-on forecast in terms of the general evolution of storm cells and mesocyclone rotation near the time of observed tornado.

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The Impacts of Representing the Correlation of Errors in Radar Data Assimilation. Part I: Experiments with Simulated Background and Observation Estimates

Monthly Weather Review ◽

10.1175/mwr-d-14-00104.1 ◽

2014 ◽

Vol 142 (11) ◽

pp. 3998-4016 ◽

Cited By ~ 7

Author(s):

Dominik Jacques ◽

Isztar Zawadzki

Keyword(s):

Data Assimilation ◽

Cost Function ◽

Radar Data ◽

Spatial Correlations ◽

Radar Observations ◽

Lack Of Information ◽

Data Thinning ◽

The Cost ◽

The Impact ◽

Radar Data Assimilation

Abstract In radar data assimilation, statistically optimal analyses are sought by minimizing a cost function in which the variance and covariance of background and observation errors are correctly represented. Radar observations are particular in that they are often available at spatial resolution comparable to that of background estimates. Because of computational constraints and lack of information, it is impossible to perfectly represent the correlation of errors. In this study, the authors characterize the impact of such misrepresentations in an idealized framework where the spatial correlations of background and observation errors are each described by a homogeneous and isotropic exponential decay. Analyses obtained with perfect representation of correlations are compared to others obtained by neglecting correlations altogether. These two sets of analyses are examined from a theoretical and an experimental perspective. The authors show that if the spatial correlations of background and observation errors are similar, then neglecting the correlation of errors has a small impact on the quality of analyses. They suggest that the sampling noise, related to the precision with which analysis errors may be estimated, could be used as a criterion for determining when the correlations of errors may be omitted. Neglecting correlations altogether also yields better analyses than representing correlations for only one term in the cost function or through the use of data thinning. These results suggest that the computational costs of data assimilation could be reduced by neglecting the correlations of errors in areas where dense radar observations are available.

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

Advances in Meteorology ◽

10.1155/2013/947874 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 1

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.

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

Weather and Forecasting ◽

10.1175/waf-d-20-0017.1 ◽

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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Using Radar Wind Observations to Improve Mesoscale Numerical Weather Prediction

Weather and Forecasting ◽

10.1175/waf936.1 ◽

2006 ◽

Vol 21 (4) ◽

pp. 502-522 ◽

Cited By ~ 27

Author(s):

Qingyun Zhao ◽

John Cook ◽

Qin Xu ◽

Paul R. Harasti

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Three Dimensional ◽

Radar Data ◽

The Impact ◽

Data Assimilation System ◽

Radar Data Assimilation ◽

Assimilation System

Abstract A high-resolution radar data assimilation system is presented for high-resolution numerical weather prediction models. The system is under development at the Naval Research Laboratory for the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System. A variational approach is used to retrieve three-dimensional dynamical fields of atmospheric conditions from multiple-Doppler radar observations of radial velocity within a limited area. The methodology is described along with a preliminary evaluation of the impact of assimilated radar data on model forecasts using a case study of a squall line that occurred along the east coast of the United States on 9 May 2003. Results from the experiments show a significant impact from the assimilated radar radial velocity data on the model forecast of not just dynamical but also hydrological fields at all model levels for the duration of the storm. A verification system has also been developed to assess the radar data assimilation impact, and the results show improvements in the three-dimensional wind forecasts but relatively small changes in the prediction of storm locations. This study highlights the need to develop a continuous radar data assimilation system to maximize the impact of the data.

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The Case-to-Case Variability of the Predictability of Precipitation by a Storm-Scale Ensemble Forecasting System

Monthly Weather Review ◽

10.1175/mwr-d-15-0232.1 ◽

2015 ◽

Vol 144 (1) ◽

pp. 193-212 ◽

Cited By ~ 14

Author(s):

Madalina Surcel ◽

Isztar Zawadzki ◽

M. K. Yau

Keyword(s):

Data Assimilation ◽

Large Scale ◽

Radar Data ◽

Ensemble Forecasting ◽

Precipitation Forecast ◽

Quantitative Precipitation Forecast ◽

Spatial Coverage ◽

Forecasting System ◽

Forecast Time ◽

Radar Data Assimilation

Abstract This paper analyzes the case-to-case variability of the predictability of precipitation by a storm-scale ensemble forecasting (SSEF) system. Relationships are sought between ensemble spread and quantitative precipitation forecast (QPF) skill, and the characteristics of an event, such as the strength of the quasigeostrophic forcing for ascent, the presence of convective equilibrium, and the spatial extent of the precipitation system. It is found that most of the case-to-case variability of predictability is explained by the spatial coverage of the system. The relationship between convection and large-scale forcing seems to affect predictability mostly during the afternoon hours. While the relationships are weak for the entire dataset, two distinct types of cases are identified: widespread and diurnally forced cases. The loss of predictability at small scales, the effect of the radar data assimilation, and the comparison between forecasts from the SSEF and Lagrangian persistence forecasts are analyzed separately for these two types of cases. Despite overall predictability being better than average for the widespread cases, the loss of predictability with forecast time and spatial scale is just as rapid as for the other cases. For the diurnally forced cases, the radar data assimilation causes larger differences between the precipitation fields corresponding to the assimilating and nonassimilating members than for the widespread cases. However, the effect of radar data assimilation on QPF skill is similar for both types of cases. Also, for the diurnal cases, the models with radar data assimilation outperform very rapidly (after 2 h) the Lagrangian persistence forecasts.

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