Lightning Data Assimilation Scheme in a 4DVAR System and its Impact on Very-Short-Term Convective Forecasting

2020 ◽  
Author(s):  
Xian Xiao ◽  
Juanzhen Sun ◽  
Xiushu Qie ◽  
Zhuming Ying ◽  
Lei Ji ◽  
...  
Keyword(s):  
Vertical Velocity ◽  
Doppler Radar ◽  
Analysis Data ◽  
Radar Data ◽  
Lightning Flash ◽  
Short Term ◽  
Distance Weighted ◽  
Dynamical Consistency ◽  
Analysis System ◽  
Total Lightning

AbstractA proof-of-concept method for the assimilation of total lightning observations in the 4DVAR framework is proposed and implemented into the Variational Doppler Radar Analysis System (VDRAS). Its performance is evaluated for the very-short-term precipitation forecasts of a localized convective event over northeastern China. The lightning DA scheme assimilated pseudo observations for vertical velocity fields derived from observed total lightning rates and statistically computed vertical velocity profile from VDRAS analysis data. To reduce representative errors of the derived vertical velocity, a distance-weighted horizontal interpolation is applied to the input data prior to the DA. The case study reveals that although 0–2 hour precipitation nowcasts are improved by assimilating lightning data alone compared to CTRL (no radar or lightning) and RAD (radar only), better results are obtained when the lightning data are assimilated with radar data simultaneously. The assimilation of both data sources results in improved dynamical consistency with enhanced updraft and latent heat as well as improved moisture distributions. Additional experiments are conducted to evaluate the sensitivity of the combined DA scheme to varied vertical velocity profiles, radii of horizontal interpolation, binning time intervals, and relationships used to estimate the maximum vertical velocity from lightning flash rates. It is shown that the scheme is robust to these variations with both radar and lightning assimilated data.

scholarly journals Relationships between Deep Convection Updraft Characteristics and Satellite-Based Super Rapid Scan Mesoscale Atmospheric Motion Vector–Derived Flow

2018 ◽  
Vol 146 (10) ◽  
pp. 3461-3480 ◽  
Author(s):  
Jason M. Apke ◽  
John R. Mecikalski ◽  
Kristopher Bedka ◽  
Eugene W. McCaul ◽  
Cameron R. Homeyer ◽  
...  
Keyword(s):  
Doppler Radar ◽  
Deep Convection ◽  
Motion Vector ◽  
Three Dimensional ◽  
Radar Data ◽  
Severe Weather ◽  
Lightning Flash ◽  
Rapid Scan ◽  
Total Lightning ◽  
The Relationship

Abstract Rapid acceleration of cloud-top outflow near vigorous storm updrafts can be readily observed in Geostationary Operational Environmental Satellite-14 (GOES-14) super rapid scan (SRS; 60 s) mode data. Conventional wisdom implies that this outflow is related to the intensity of updrafts and the formation of severe weather. However, from an SRS satellite perspective, the pairing of observed expansion and updraft intensity has not been objectively derived and documented. The goal of this study is to relate GOES-14 SRS-derived cloud-top horizontal divergence (CTD) over deep convection to internal updraft characteristics, and document evolution for severe and nonsevere thunderstorms. A new SRS flow derivation system is presented here to estimate storm-scale (<20 km) CTD. This CTD field is coupled with other proxies for storm updraft location and intensity such as overshooting tops (OTs), total lightning flash rates, and three-dimensional flow fields derived from dual-Doppler radar data. Objectively identified OTs with (without) matching CTD maxima were more (less) likely to be associated with radar-observed deep convection and severe weather reports at the ground, suggesting that some OTs were incorrectly identified. The correlation between CTD magnitude, maximum updraft speed, and total lightning was strongly positive for a nonsupercell pulse storm, and weakly positive for a supercell with multiple updraft pulses present. The relationship for the supercell was nonlinear, though larger flash rates are found during periods of larger CTD. Analysis here suggests that combining CTD with OTs and total lightning could have severe weather nowcasting value.

scholarly journals Evaluation of Fengyun-4A Lightning Mapping Imager (LMI) Performance during Multiple Convective Episodes over Beijing

2021 ◽  
Vol 13 (9) ◽  
pp. 1746
Author(s):  
Zhixiong Chen ◽  
Xiushu Qie ◽  
Juanzhen Sun ◽  
Xian Xiao ◽  
Yuxin Zhang ◽  
...  
Keyword(s):  
Doppler Radar ◽  
Detection Efficiency ◽  
Ground Truth ◽  
Radar Data ◽  
Detection Capability ◽  
Cloud Properties ◽  
Order Of Magnitude ◽  
Total Lightning ◽  
Matching Criteria ◽  
Event Based

This study investigates the characteristics of space-borne Lightning Mapping Imager (LMI) lightning products and their relationships with cloud properties using ground-based total lightning observations from the Beijing Broadband Lightning Network (BLNET) and cloud information from S-band Doppler radar data. LMI showed generally consistent lightning spatial distributions with those of BLNET, and yielded a considerable lightning detection capability over regions with complex terrain. The ratios between the LMI events, groups and flashes were approximately 9:3:1, and the number of LMI-detected flashes was roughly one order of magnitude smaller than the number of BLNET-detected flashes. However, in different convective episodes, the LMI detection capability was likely to be affected by cloud properties, especially in strongly electrified convective episodes associated with frequent lightning discharging and thick cloud depth. As a result, LMI tended to detect lightning flashes located in weaker and shallower cloud portions associated with fewer cloud shielding effects. With reference to the BLNET total lightning data as the ground truth of observation (both intra-cloud lightning and cloud-to-ground lightning flashes), the LMI event-based detection efficiency (DE) was estimated to reach 28% under rational spatiotemporal matching criteria (1.5 s and 65 km) over Beijing. In terms of LMI flash-based DE, it was much reduced compared with event-based DE. The LMI flash-based ranged between 1.5% and 3.5% with 1.5 s and 35–65 km matching scales. For 330 ms and 35 km, the spatiotemporal matching criteria used to evaluate Geostationary Lightning Mapper (GLM), the LMI flash-based DE was smaller (<1%).

Assimilation of Doppler Radar Observations with a Regional 3DVAR System: Impact of Doppler Velocities on Forecasts of a Heavy Rainfall Case

2005 ◽  
Vol 44 (6) ◽  
pp. 768-788 ◽  
Author(s):  
Qingnong Xiao ◽  
Ying-Hwa Kuo ◽  
Juanzhen Sun ◽  
Wen-Chau Lee ◽  
Eunha Lim ◽  
...  
Keyword(s):  
Radial Velocity ◽  
Vertical Velocity ◽  
Short Range ◽  
Heavy Rainfall ◽  
Doppler Radar ◽  
Radar Data ◽  
Radial Velocities ◽  
Doppler Velocity ◽  
Rainfall Forecast ◽  
Background Fields

Abstract In this paper, the impact of Doppler radar radial velocity on the prediction of a heavy rainfall event is examined. The three-dimensional variational data assimilation (3DVAR) system for use with the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) is further developed to enable the assimilation of radial velocity observations. Doppler velocities from the Korean Jindo radar are assimilated into MM5 using the 3DVAR system for a heavy rainfall case that occurred on 10 June 2002. The results show that the assimilation of Doppler velocities has a positive impact on the short-range prediction of heavy rainfall. The dynamic balance between atmospheric wind and thermodynamic fields, based on the Richardson equation, is introduced to the 3DVAR system. Vertical velocity (w) increments are included in the 3DVAR system to enable the assimilation of the vertical velocity component of the Doppler radial velocity observation. The forecast of the hydrometeor variables of cloud water (qc) and rainwater (qr) is used in the 3DVAR background fields. The observation operator for Doppler radial velocity is developed and implemented within the 3DVAR system. A series of experiments, assimilating the Korean Jindo radar data for the 10 June 2002 heavy rainfall case, indicates that the scheme for Doppler velocity assimilation is stable and robust in a cycling mode making use of high-frequency radar data. The 3DVAR with assimilation of Doppler radial velocities is shown to improve the prediction of the rainband movement and intensity change. As a result, an improved skill for the short-range heavy rainfall forecast is obtained. The forecasts of other quantities, for example, winds, are also improved. Continuous assimilation with 3-h update cycles is important in producing an improved heavy rainfall forecast. Assimilation of Doppler radar radial velocities using the 3DVAR background fields from a cycling procedure produces skillful rainfall forecasts when verified against observations.

scholarly journals Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI EnKF for the Analysis and Short-Term Forecast of a Mesoscale Convective System

2020 ◽  
Vol 148 (5) ◽  
pp. 2111-2133 ◽  
Author(s):  
Rong Kong ◽  
Ming Xue ◽  
Alexandre O. Fierro ◽  
Youngsun Jung ◽  
Chengsi Liu ◽  
...  
Keyword(s):  
Mesoscale Convective System ◽  
Convective System ◽  
Lightning Flash ◽  
Moist Convection ◽  
State Variables ◽  
Short Term ◽  
Pixel Resolution ◽  
Cross Covariance ◽  
Mesoscale Convective ◽  
Total Lightning

Abstract The recently launched Geostationary Operational Environmental Satellite “R-series” (GOES-R) satellites carry the Geostationary Lightning Mapper (GLM) that measures from space the total lightning rate in convective storms at high spatial and temporal frequencies. This study assimilates, for the first time, real GLM total lightning data in an ensemble Kalman filter (EnKF) framework. The lightning flash extent density (FED) products at 10-km pixel resolution are assimilated. The capabilities to assimilate GLM FED data are first implemented into the GSI-based EnKF data assimilation (DA) system and tested with a mesoscale convective system (MCS). FED observation operators based on graupel mass or graupel volume are used. The operators are first tuned through sensitivity experiments to determine an optimal multiplying factor to the operator, before being used in FED DA experiments FEDM and FEDV that use the graupel-mass or graupel-volume-based operator, respectively. Their results are compared to a control experiment (CTRL) that does not assimilate any FED data. Overall, both DA experiments outperform CTRL in terms of the analyses and short-term forecasts of FED and composite/3D reflectivity. The assimilation of FED is primarily effective in regions of deep moist convection, which helps improve short-term forecasts of convective threats, including heavy precipitation and lightning. Direct adjustments to graupel mass via observation operator as well as adjustments to other model state variables through flow-dependent ensemble cross covariance within EnKF are shown to work together to generate model-consistent analyses and overall improved forecasts.

scholarly journals Evaluating the Performance of Lightning Data Assimilation from BLNET Observations in a 4DVAR-Based Weather Nowcasting Model for a High-Impact Weather over Beijing

2021 ◽  
Vol 13 (11) ◽  
pp. 2084
Author(s):  
Xian Xiao ◽  
Xiushu Qie ◽  
Zhixiong Chen ◽  
Jingyu Lu ◽  
Lei Ji ◽  
...  
Keyword(s):  
Vertical Velocity ◽  
Convective System ◽  
Weighted Interpolation ◽  
Forecast Performance ◽  
Horizontal Length ◽  
Distance Weighted ◽  
Vertical Velocity Profile ◽  
Set Up ◽  
Total Lightning ◽  
The Impact

The Beijing Broadband Lightning Network (BLNET) was successfully set up in North China and had yielded a considerable detection capability of total lightning (intracloud and cloud to ground) over the regions with complex underlying (plains, mountains, and oceans). This study set up a basic framework for the operational application of assimilating total lightning activities from BLNET and assesses the potential benefits in cloud-scale, very short-term forecast (nowcasting) by modulating the vertical velocity using the 4DVar technique. Nowcast statistics aggregated over 11 cycles show that the nowcasting performances with the assimilation of BLNET lightning datasets outperform RAD and the assimilation of GLD360 (Global Lightning) datasets. The assimilation of BLNET data improves the model's dynamical states in the analysis by enhancing the convergence and updraft in and near the convective system. To better implement of assimilating real-time lightning data, this study also conducts sensitivity experiments to investigate the impact of the horizontal length scale of a distance-weighted interpolation, binning time intervals, and different vertical profile or distance weights prior to the DA. The results indicate that the best forecast performance for assimilating BLNET lightning datasets is obtained in a 4DVar cycle when the lightning accumulation interval is 3 min, the radius of horizontal interpolation is 5 × 5, and the statistically vertical velocity profile and the distance weights obtained from cumulus cloud.

Random Forest Model to Assess Predictor Importance and Nowcast Severe Storms using High-Resolution Radar–GOES Satellite–Lightning Observations

2021 ◽  
Author(s):  
John R. Mecikalski ◽  
Thea N. Sandmæl ◽  
Elisa M. Murillo ◽  
Cameron R. Homeyer ◽  
Kristopher M. Bedka ◽  
...  
Keyword(s):  
Great Plains ◽  
Random Forests ◽  
Motion Vector ◽  
Radar Data ◽  
Lightning Flash ◽  
Severe Storms ◽  
Tornado Warnings ◽  
Predictor Importance ◽  
Scan Data ◽  
Total Lightning

AbstractFew studies have assessed combined satellite, lightning, and radar databases to diagnose severe storm potential. The research goal here is to evaluate next-generation, 60-second update frequency geostationary satellite and lightning information with ground-based radar to isolate which variables, when used in concert, provide skillful discriminatory information for identifying severe (hail ≥2.5 cm in diameter, winds ≥25 m s–1, tornadoes) versus non-severe storms. The focus of this study is predicting severe thunderstorm and tornado warnings. A total of 2,004 storms in 2014–2015 were objectively tracked with 49 potential predictor fields related to May, daytime Great Plains convective storms. All storms occurred when 1-min Geostationary Operational Environmental Satellite (GOES)–14 “super rapid scan” data were available. The study used three importance methods to assess predictor importance related to severe warnings, and random forests to provide a model and skill evaluation measuring the ability to predict severe storms. Three predictor importance methods show that GOES mesoscale atmospheric motion vector derived cloud-top divergence and above anvil cirrus plume presence provide the most satellite-based discriminatory power for diagnosing severe warnings. Other important fields include Earth Networks Total Lightning flash density, GOES estimated cloud-top vorticity, and overshooting-top presence. Severe warning predictions are significantly improved at the 95% confidence level when a few important satellite and lightning fields are combined with radar fields, versus when only radar data are used in the random forests model. This study provides a basis for including satellite and lightning fields within machine-learning models to help forecast severe weather.

Evaluation of the impact of assimilating spaceborne (GLM) total lightning data and radar data on short-term forecasts of convective events in the 3DVAR framework

2021 ◽  
Author(s):  
Alexandre Fierro ◽  
Junjun Hu ◽  
Yunheng Wang ◽  
Jidong Gao ◽  
Edward Mansell
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Hybrid Methods ◽  
Radar Data ◽  
Western Hemisphere ◽  
Added Value ◽  
Short Term ◽  
Background Error ◽  
Total Lightning ◽  
The Impact

&lt;p&gt;The GLM instruments aboard the GOES-16 and 17 satellites provides nearly uniform spatiotemporal coverage of total lightning over the Americas and adjacent vast oceanic regions of the western hemisphere. This work summarizes recent efforts from our group at CIMMS/NSSL geared towards the evaluation of the potential added value of assimilating GLM-observed total lightning data on short-term, convection-allowing scale (dx = 2-3 km) forecasts for higher impact weather events. Results using data assimilation (DA) approaches ranging from single deterministic three-dimensional variational (3DVAR) methods applied in real time to experimental ensemble-based VAR hybrid methods (3DEnVAR) will be highlighted.&amp;#160;&lt;br&gt;The lightning data assimilation (DA) scheme in these frameworks follow the same core philosophy wherein background water vapor mass mixing ratio is adjusted (increased) locally at or around observed lightning locations, either throughout the entire atmospheric column or within a fixed, confined layer above the lifted condensation level. Toward a more systematic assimilation of real GLM data, emphasis will be directed toward: (i) sensitivity tests with deterministic 3DVAR experiments aimed at evaluating the impact of the horizontal decorrelation length scale, DA cycling frequency as well the length of the accumulation window for the lightning data, (ii) aggregate statistics from real time CONUS-scale experiments over the Spring 2020 and (iii) preliminary results employing ensemble of 3DEnVARs with hybrid (static + flow dependent) background error covariances.&amp;#160;&lt;br&gt;Aggregate statistical results from all deterministic 3DVAR exercises in (i) and (ii) revealed that the assimilation of either radar (radial wind and reflectivity factor) or total lightning (GLM) resulted in overall notably more skillful, shorter term (0-3 h) forecast of composite reflectivity fields, accumulated rainfall, as well as individual storm tracks &amp;#8211; with optimal skill obtained when both radar and lightning data were assimilated. In (iii) forecast impacts related to the following will be summarized: (1) the respective weights assigned to the flow-dependent component and static components of the background error covariances, (2) the inclusion of three time-level sampling for each member during each cycle and (3) the usage of Gaussian noise coupled with a fixed 3 to 12 h spin-up period prior to the beginning of the cycled 3DVAR.&lt;/p&gt;

Impact of Assimilating GOES-R Geostationary Lightning Mapper Flash Extent Density Data on Severe Convection Forecast in a Warn-on-Forecast System

2021 ◽  
Author(s):  
Yaping Wang ◽  
Nusrat Yussouf ◽  
Edward R. Mansell ◽  
Brian C. Matilla ◽  
Rong Kong ◽  
...  
Keyword(s):  
Detection Efficiency ◽  
Radar Data ◽  
Forecast Skill ◽  
The Other ◽  
Storm Events ◽  
Forecast System ◽  
Short Term ◽  
Isolated Cells ◽  
Total Lightning ◽  
The Impact

AbstractThe Geostationary Operational Environmental Satellite-R (GOES-R) Geostationary Lightning Mapper (GLM) instrument detects total lightning rate at high temporal and spatial resolution over the Americas and adjacent oceanic regions. The GLM observations provide detection and monitoring of deep electrified convection. This study explores the impact of assimilating the GLM derived flash extent density (FED) on the analyses and short-term forecasts of two severe weather events into an experimental Warn-on-Forecast system (WoFS) using the Ensemble Kalman Filter data assimilation technique. Sensitivity experiments are conducted using two tornadic severe storm events, one with a line of individual supercells and the other one with both isolated cells and a severe convective line. The control experiment (CTRL) assimilates conventional surface observations and geostationary satellite cloud water path into WoFS. Additional experiments also assimilate either GLM FED or radar data (RAD), or a combination of both (RAD+GLM). It is found that assimilating GLM data in the absence of radar data into the WoFS improves the short-term forecast skill over CTRL in one case, while in the other case degrades the forecast skill by generating weaker cold pools and overly suppressing convection, mainly owing to assimilating zero FED values in the trailing stratiform regions. Assimilating unexpectedly low FED values in some regions due to low GLM detection efficiency also accounts for the poorer forecasts. Although RAD provides superior forecasts over GLM, the combination RAD+GLM shows further gains in both cases. Additional observation operators should consider different storm types and GLM detection efficiency.

scholarly journals The Development of a Terrain-Resolving Scheme for the Forward Model and Its Adjoint in the Four-Dimensional Variational Doppler Radar Analysis System (VDRAS)

2017 ◽  
Vol 145 (1) ◽  
pp. 289-306 ◽  
Author(s):  
Sheng-Lun Tai ◽  
Yu-Chieng Liou ◽  
Juanzhen Sun ◽  
Shao-Fan Chang
Keyword(s):  
Doppler Radar ◽  
Three Dimensional ◽  
Wrf Model ◽  
Radar Data ◽  
Forward Model ◽  
Immersed Boundary ◽  
Topographic Effects ◽  
Moist Convection ◽  
Analysis System ◽  
New System

Abstract The four-dimensional Variational Doppler Radar Analysis System (VDRAS) developed at the National Center for Atmospheric Research (NCAR) is significantly improved by implementing a terrain-resolving scheme to its forward model and adjoint based on the ghost cell immersed boundary method (GCIBM), which allows the topographic effects to be considered without the necessity to rebuild the model on a terrain-following coordinate system. The new system, called IBM_VDRAS, is able to perform forward forecast and backward adjoint model integration over nonflat lower boundaries, ranging from mountains with smooth slopes to buildings with sharp surfaces. To evaluate the performance of the forward model over complex terrain, idealized numerical experiments of a two-dimensional linear mountain wave and three-dimensional leeside vortices are first conducted, followed by a comparison with a simulation by the Weather Research and Forecasting (WRF) Model. An observing system simulation experiment is also conducted with the assimilation of simulated radar data to examine the ability of IBM_VDRAS in analyzing orographically forced moist convection. It is shown that the IBM_VDRAS can retrieve terrain-influenced three-dimensional meteorological fields including winds, thermodynamic, and microphysical parameters with reasonable accuracy. The new system, with the advanced radar data assimilation capability and the GCIBM terrain scheme, has the potential to be used for studying the evolution of convective weather systems under the influence of terrain.

scholarly journals Evaluation of Different Storm Parameters as the Proxies for Gridded Total Lightning Flash Rates: A Convection-Allowing Model Study

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 95
Author(s):  
Xinyao Qian ◽  
Haoliang Wang
Keyword(s):  
Data Assimilation ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Skill Score ◽  
Convective System ◽  
Test Case ◽  
Lightning Flash ◽  
Simulation Accuracy ◽  
Mesoscale Convective ◽  
Total Lightning

Lightning simulation is important for a variety of applications, including lightning forecast, atmospheric chemical simulation, and lightning data assimilation. In this study, the potential of five storm parameters (graupel volume, precipitation ice mass, radar echo volume, maximum updraft, and updraft volume) to be used as the proxy for the diagnosis of gridded total lightning flash rates has been investigated in a convection-allowing model. A mesoscale convective system occurred in the Guangdong province of China was selected as the test case. Radar data assimilation was used to improve the simulation accuracy of the convective clouds, hence providing strong instantaneous correlations between observed and simulated storm signatures. The areal coverage and magnitude of the simulated lightning flash rates were evaluated by comparing to those of the total lightning observations. Subjective and the Fractions Skill Score (FSS) evaluations suggest that all the five proxies tested in this study are useful to indicate general tendencies for the occurrence, region, and time of lightning at convection-allowing scale (FSS statistics for the threshold of 1 flash per 9 km2 per hour were around 0.7 for each scheme). The FSS values were decreasing as the lightning flash rate thresholds used for FSS computation increased for all the lightning diagnostic schemes with different proxies. For thresholds from 1 to 3 and 16 to 20 flashes per 9 km2 per hour, the graupel contents related schemes achieved higher FSS values compared to the other three schemes. For thresholds from 5 to 15 flashes per 9 km2 per hour, the updraft volume related scheme yielded the largest FSS. When the thresholds of lightning flash rates were greater than 13 flashes per 9 km2 per hour, the FSS values were below 0.5 for all the lightning diagnostic schemes with different proxies.

Sign in / Sign up

Export Citation Format

Share Document