scholarly journals Evaluation of a Cloud-Scale Lightning Data Assimilation Technique and a 3DVAR Method for the Analysis and Short-Term Forecast of the 29 June 2012 Derecho Event

2014 ◽  
Vol 142 (1) ◽  
pp. 183-202 ◽  
Author(s):  
Alexandre O. Fierro ◽  
Jidong Gao ◽  
Conrad L. Ziegler ◽  
Edward R. Mansell ◽  
Donald R. MacGorman ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Computational Cost ◽  
Mesoscale Convective System ◽  
Radar Reflectivity ◽  
Convective System ◽  
Variational Technique ◽  
Short Term ◽  
Short Term Forecast ◽  
Term Forecast ◽  
Mesoscale Convective

Abstract This work evaluates the short-term forecast (≤6 h) of the 29–30 June 2012 derecho event from the Advanced Research core of the Weather Research and Forecasting Model (WRF-ARW) when using two distinct data assimilation techniques at cloud-resolving scales (3-km horizontal grid). The first technique assimilates total lightning data using a smooth nudging function. The second method is a three-dimensional variational technique (3DVAR) that assimilates radar reflectivity and radial velocity data. A suite of sensitivity experiments revealed that the lightning assimilation was better able to capture the placement and intensity of the derecho up to 6 h of the forecast. All the simulations employing 3DVAR, however, best represented the storm’s radar reflectivity structure at the analysis time. Detailed analysis revealed that a small feature in the velocity field from one of the six selected radars in the original 3DVAR experiment led to the development of spurious convection ahead of the parent mesoscale convective system, which significantly degraded the forecast. Thus, the relatively simple nudging scheme using lightning data complements the more complex variational technique. The much lower computational cost of the lightning scheme may permit its use alongside variational techniques in improving severe weather forecasts on days favorable for the development of outflow-dominated mesoscale convective systems.

scholarly journals The impact of lightning and radar data assimilation on the performance of very short term rainfall forecast for two case studies in Italy

2018 ◽  
Author(s):  
Stefano Federico ◽  
Rosa Claudia Torcasio ◽  
Elenio Avolio ◽  
Olivier Caumont ◽  
Mario Montopoli ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Case Studies ◽  
Radar Reflectivity ◽  
Short Term ◽  
Short Term Forecast ◽  
Term Forecast ◽  
First Case ◽  
Factor Data ◽  
The Impact ◽  
Reflectivity Factor

Abstract. In this paper, we study the impact of lightning and radar reflectivity factor data assimilation on the precipitation VSF (Very Short-term Forecast, 3 hours in this study) for two relevant case studies occurred over Italy. The first case refers to a moderate localised rainfall over Central Italy happened on 16 September 2017. The second case, occurred on 09 and 10 September 2017, was very intense and caused damages in several parts of Italy, while nine people died around Livorno, in Tuscany. The first case study was missed by most operational forecasts over Italy, including that performed by the model used in this paper, while the Livorno case was partially predicted by operational models. We use the RAMS@ISAC model (Regional Atmospheric Modelling System at Institute for Atmospheric Sciences and Climate of the Italian National Research Council), whose 3D-Var extension to the assimilation of RADAR reflectivity factor is shown in this paper. Results for the two cases show that the assimilation of lightning and radar reflectivity factor, especially when used together, have a significant and positive impact on the precipitation forecast. The improvement compared to the control model, not assimilating lightning and radar reflectivity factor, is systematic because occurs for all the Very Short-term Forecast (VSF, 3h) of the events considered. For specific time intervals, the data assimilation is of practical importance for Civil Protection purposes because it transforms a missed forecast of intense precipitation (> 40 mm/3h) in a correct forecast. While there is an improvement of the rainfall VSF thanks to the lightning and radar reflectivity factor data assimilation, its impact is reduced by the increase of the false alarms in the forecast assimilating both types of data.

Ensemble Probabilistic Forecasts of a Tornadic Mesoscale Convective System from Ensemble Kalman Filter Analyses Using WSR-88D and CASA Radar Data

2012 ◽  
Vol 140 (7) ◽  
pp. 2126-2146 ◽  
Author(s):  
Nathan Snook ◽  
Ming Xue ◽  
Youngsun Jung
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Convective System ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective

Abstract This study examines the ability of a storm-scale numerical weather prediction (NWP) model to predict precipitation and mesovortices within a tornadic mesoscale convective system that occurred over Oklahoma on 8–9 May 2007, when the model is initialized from ensemble Kalman filter (EnKF) analyses including data from four Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) X-band and five Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars. Ensemble forecasts are performed and probabilistic forecast products generated, focusing on prediction of radar reflectivity (a proxy of quantitative precipitation) and mesovortices (an indication of tornado potential). Assimilating data from both the CASA and WSR-88D radars for the ensemble and using a mixed-microphysics ensemble during data assimilation produces the best probabilistic mesovortex forecast. The use of multiple microphysics schemes within the ensemble aims to address at least partially the model physics uncertainty and effectively plays a role of flow-dependent inflation (in precipitation regions) during EnKF data assimilation. The ensemble predicts with high probability (approximately 0.65) the near-surface mesovortex associated with the first of three reported tornadoes. Though a bias toward stronger precipitation is noted in the ensemble forecasts, all experiments produce skillful probabilistic forecasts of radar reflectivity on a 0–3-h time scale as evaluated by multiple probabilistic verification metrics. These results suggest that both the inclusion of CASA radar data and use of a mixed-microphysics ensemble during EnKF data assimilation positively impact the skill of 2–3-h ensemble forecasts of mesovortices, despite having little impact on the quality of precipitation forecasts (analyzed in terms of predicted radar reflectivity), and are important steps toward an operational EnKF-based ensemble analysis and probabilistic forecast system to support convective-scale warn-on-forecast operations.

scholarly journals Evaluating Smartphone Pressure Observations for Mesoscale Analyses and Forecasts

2017 ◽  
Vol 32 (2) ◽  
pp. 511-531 ◽  
Author(s):  
Luke E. Madaus ◽  
Clifford F. Mass
Keyword(s):  
United States ◽  
Rural Areas ◽  
Mesoscale Convective System ◽  
Convective System ◽  
Active Period ◽  
Eastern United States ◽  
Short Term ◽  
Mesoscale Convective ◽  
Dynamic Structures ◽  
Further Development

Abstract Smartphone pressure observations have the potential to greatly increase surface observation density on convection-resolving scales. Currently available smartphone pressure observations are tested through assimilation in a mesoscale ensemble for a 3-day, convectively active period in the eastern United States. Both raw pressure (altimeter) observations and 1-h pressure (altimeter) tendency observations are considered. The available observation density closely follows population density, but observations are also available in rural areas. The smartphone observations are found to contain significant noise, which can limit their effectiveness. The assimilated smartphone observations contribute to small improvements in 1-h forecasts of surface pressure and 10-m wind, but produce larger errors in 2-m temperature forecasts. Short-term (0–4 h) precipitation forecasts are improved when smartphone pressure and pressure tendency observations are assimilated as compared with an ensemble that assimilates no observations. However, these improvements are limited to broad, mesoscale features with minimal skill provided at convective scales using the current smartphone observation density. A specific mesoscale convective system (MCS) is examined in detail, and smartphone pressure observations captured the expected dynamic structures associated with this feature. Possibilities for further development of smartphone observations are discussed.

scholarly journals Comparison of 3DVar and EnSRF Data Assimilation Using Radar Observations for the Analysis and Prediction of an MCS

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Shibo Gao ◽  
Jinzhong Min
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Mesoscale Convective System ◽  
Final Analysis ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Convective System ◽  
Wind Fields ◽  
Radar Observations

Using radar observations, the performances of the ensemble square root filter (EnSRF) and an indirect three-dimensional variational (3DVar) data assimilation method were compared for a mesoscale convective system (MCS) that occurred in the Front Range of the Rocky Mountains, Colorado (USA). The results showed that the root mean square innovations (RMSIs) of EnSRF were lower than 3DVar for radar reflectivity and radial velocity and that the spread of EnSRF was generally consistent with its RMSIs. EnSRF substantially improved the analysis of the MCS compared with an experiment without radar data assimilation, and it produced a slight but noticeable improvement over 3DVar in terms of both coverage and intensity. Forecast results initiated from the final analysis revealed that EnSRF generally produced the best prediction of the MCS, with improved quantitative reflectivity and precipitation forecast skills. EnSRF also demonstrated better performance than 3DVar in the prediction of neighborhood probability for reflectivity at thresholds of 20 and 35 dBZ, which better matched the observed radar reflectivity in terms of both shape and extension. Additionally, the humidity, temperature, and wind fields were also improved by EnSRF; the largest error reduction was found in the water vapor field near the surface and at upper levels.

scholarly journals Comparison between Low-Flash and Non-Lightning-Producing Convective Areas within a Mature Mesoscale Convective System

2011 ◽  
Vol 26 (4) ◽  
pp. 468-486 ◽  
Author(s):  
Jennifer L. Palucki ◽  
Michael I. Biggerstaff ◽  
Donald R. MacGorman ◽  
Terry Schuur
Keyword(s):  
Vertical Motion ◽  
Mesoscale Convective System ◽  
Phase Region ◽  
Life Cycles ◽  
Radar Reflectivity ◽  
Mixed Phase ◽  
Convective System ◽  
Lightning Flash ◽  
Mesoscale Convective ◽  
Mixed Phase Region

Abstract Two small multicellular convective areas within a larger mesoscale convective system that occurred on 20 June 2004 were examined to assess vertical motion, radar reflectivity, and dual-polarimetric signatures between flash and non-flash-producing convection. Both of the convective areas had similar life cycles and general structures. Yet, one case produced two flashes, one of which may have been a cloud-to-ground flash, while the other convective area produced no flashes. The non-lightning-producing case had a higher peak reflectivity up to 6 km. Hence, if a reflectivity-based threshold were used as a precursor to lightning, it would have yielded misleading results. The peak upward motion in the mixed-phase region for both cases was 8 m s−1 or less. However, the lightning-producing storm contained a wider region where the updraft exceeded 5 m s−1. Consistent with the broader updraft region, the lightning-producing case exhibited a distinct graupel signature over a broader region than the non-lightning-producing convection. Slight differences in vertical velocity affected the quantity of graupel present in the mixed-phase region, thereby providing the subtle differences in polarimetric signatures that were associated with lightning activity. If the results here are generally applicable, then graupel volume may be a better precursor to a lightning flash than radar reflectivity. With the dual-polarimetric upgrade to the national observing radar network, it should be possible to better distinguish between lightning- and non-lightning-producing areas in weak convective systems that pose a potential safety hazard to the public.

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 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.

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