Impacts of the radar data assimilation frequency and large-scale constraint on the short-term precipitation forecast of a severe convection case

2021 ◽  
pp. 105590
Author(s):  
Erliang Lin ◽  
Yi Yang ◽  
Xiaobin Qiu ◽  
Qian Xie ◽  
Ruhui Gan ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Short Term ◽  
Severe Convection ◽  
Radar Data Assimilation

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

2015 ◽  
Vol 144 (1) ◽  
pp. 193-212 ◽  
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.

scholarly journals Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast

2020 ◽  
Vol 10 (16) ◽  
pp. 5493 ◽  
Author(s):  
Jingnan Wang ◽  
Lifeng Zhang ◽  
Jiping Guan ◽  
Mingyang Zhang
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Rainfall Forecast ◽  
Skill Scores ◽  
The Individual ◽  
The Impact ◽  
Radar Data Assimilation

Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.

scholarly journals Sensitivity of 0–12-h Warm-Season Precipitation Forecasts over the Central United States to Model Initialization

2012 ◽  
Vol 27 (4) ◽  
pp. 832-855 ◽  
Author(s):  
Juanzhen Sun ◽  
Stanley B. Trier ◽  
Qingnong Xiao ◽  
Morris L. Weisman ◽  
Hongli Wang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Case Studies ◽  
Large Scale ◽  
Initial Conditions ◽  
Warm Season ◽  
Radar Data ◽  
Short Wave ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Scale Model

Abstract Sensitivity of 0–12-h warm-season precipitation forecasts to atmospheric initial conditions, including those from different large-scale model analyses and from rapid cycled (RC) three-dimensional variational data assimilations (3DVAR) with and without radar data, is investigated for a 6-day period during the International H2O Project. Neighborhood-based precipitation verification is used to compare forecasts made with the Advanced Research core of the Weather Research and Forecasting Model (ARW-WRF). Three significant convective episodes are examined by comparing the precipitation patterns and locations from different forecast experiments. From two of these three case studies, causes for the success and failure of the RC data assimilation in improving forecast skill are shown. Results indicate that the use of higher-resolution analysis in the initialization, rapid update cycling via WRF 3DVAR data assimilation, and the additional assimilation of radar observations each play a role in shortening the period of the initial precipitation spinup as well as in placing storms closer to observations, thus improving precipitation forecast skill by up to 8–9 h. Impacts of data assimilation differ for forecasts initialized at 0000 and 1200 UTC. The case studies show that the pattern and location of the forecasted precipitation were noticeably improved with radar data assimilation for the two late afternoon cases that featured lines of convection driven by surface-based cold pools. In contrast, the RC 3DVAR, both with and without radar data, had negative impacts on convective forecasts for a case of morning elevated convection associated with a midlatitude short-wave trough.

scholarly journals An Ice-Phase Microphysics Forward Model and Preliminary Results of Polarimetric Radar Data Assimilation

2017 ◽  
Vol 145 (2) ◽  
pp. 683-708 ◽  
Author(s):  
Xuanli Li ◽  
John R. Mecikalski ◽  
Derek Posselt
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Weather Prediction ◽  
Radar Data ◽  
Forward Model ◽  
Precipitation Forecast ◽  
Upper Troposphere ◽  
The Impact ◽  
Radar Data Assimilation ◽  
Ice Phase

In this study, an ice-phase microphysics forward model has been developed for the Weather Research and Forecasting (WRF) Model three-dimensional variational data assimilation (WRF 3D-Var) system. Radar forward operators for reflectivity and the polarimetric variable, specific differential phase ( KDP), have been built into the ice-phase WRF 3D-Var package to allow modifications in liquid (cloud water and rain) and solid water (cloud ice and snow) fields through data assimilation. Experiments have been conducted to assimilate reflectivity and radial velocity observations collected by the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Hytop, Alabama, for a mesoscale convective system (MCS) on 15 March 2008. Numerical results have been examined to assess the impact of the WSR-88D data using the ice-phase WRF 3D-Var radar data assimilation package. The main goals are to first demonstrate radar data assimilation with an ice-phase microphysics forward model and second to improve understanding on how to enhance the utilization of radar data in numerical weather prediction. Results showed that the assimilation of reflectivity and radial velocity data using the ice-phase system provided significant improvement especially in the mid- to upper troposphere. The improved initial conditions led to apparent improvement in the short-term precipitation forecast of the MCS. An additional experiment has been conducted to explore the assimilation of KDP data collected by the Advanced Radar for Meteorological and Operational Research (ARMOR). Results showed that KDP data have been successfully assimilated using the ice-phase 3D-Var package. A positive impact of the KDP data has been found on rainwater in the lower troposphere and snow in the mid- to upper troposphere.

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

2013 ◽  
Vol 141 (7) ◽  
pp. 2245-2264 ◽  
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.

scholarly journals Precipitation Forecast Characteristics of Radar Data Assimilation Based on Precipitation Types

2021 ◽  
Author(s):  
Jeong-Ho Bae ◽  
Ki-Hong Min
Keyword(s):  
Numerical Model ◽  
Data Assimilation ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Convective System ◽  
Observation Data ◽  
Microphysics Scheme ◽  
Assimilation Experiment ◽  
Mesoscale Convective ◽  
Radar Data Assimilation

Radar observation data with high temporal and spatial resolution are used in the data assimilation experiment to improve precipitation forecast of a numerical model. The numerical model considered in this study is Weather Research and Forecasting (WRF) model with double-moment 6-class microphysics scheme (WDM6). We calculated radar equivalent reflectivity factor using higher resolution WRF and compared with radar observations in South Korea. To compare the precipitation forecast characteristics of three-dimensional variational (3D-Var) assimilation of radar data, four experiments are performed based on different precipitation types. Comparisons of the 24-h accumulated rainfall with Automatic Weather Station (AWS) data, Contoured Frequency by Altitude Diagram (CFAD), Time Height Cross Sections (THCS), and vertical hydrometeor profiles are used to evaluate and compare the accuracy. The model simulations are performed with and with-out 3D-VAR radar reflectivity, radial velocity and AWS assimilation for two mesoscale convective cases and two synoptic scale cases. The radar data assimilation experiment improved the location of precipitation area and rainfall intensity compared to the control run. Especially, for the two convective cases, simulating mesoscale convective system was greatly improved.

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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