scholarly journals Comparison of Thunderstorm Simulations from WRF-NMM and WRF-ARW Models over East Indian Region

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
A. J. Litta ◽  
Sumam Mary Ididcula ◽  
U. C. Mohanty ◽  
S. Kiran Prasad
Keyword(s):  
Indian Region ◽  
Squall Line ◽  
Severe Thunderstorm ◽  
East Indian ◽  
Weather Events ◽  
Arw Model ◽  
High Impact Weather ◽  
Activity Comparison ◽  
Cloud Top Temperature ◽  
Better Than

The thunderstorms are typical mesoscale systems dominated by intense convection. Mesoscale models are essential for the accurate prediction of such high-impact weather events. In the present study, an attempt has been made to compare the simulated results of three thunderstorm events using NMM and ARW model core of WRF system and validated the model results with observations. Both models performed well in capturing stability indices which are indicators of severe convective activity. Comparison of model-simulated radar reflectivity imageries with observations revealed that NMM model has simulated well the propagation of the squall line, while the squall line movement was slow in ARW. From the model-simulated spatial plots of cloud top temperature, we can see that NMM model has better captured the genesis, intensification, and propagation of thunder squall than ARW model. The statistical analysis of rainfall indicates the better performance of NMM than ARW. Comparison of model-simulated thunderstorm affected parameters with that of the observed showed that NMM has performed better than ARW in capturing the sharp rise in humidity and drop in temperature. This suggests that NMM model has the potential to provide unique and valuable information for severe thunderstorm forecasters over east Indian region.

scholarly journals Visualizing Long-Range Severe Thunderstorm Environment Guidance from CFSv2

2016 ◽  
Vol 97 (6) ◽  
pp. 1021-1031 ◽  
Author(s):  
Gregory W. Carbin ◽  
Michael K. Tippett ◽  
Samuel P. Lillo ◽  
Harold E. Brooks
Keyword(s):  
Weather Prediction ◽  
Severe Weather ◽  
Time Averaging ◽  
Forecast System ◽  
Climate Forecast System ◽  
Severe Thunderstorm ◽  
Weather Events ◽  
Ensemble Mean ◽  
Composite Parameter ◽  
High Impact Weather

Abstract Two novel approaches to extending the range of prediction for environments conducive to severe thunderstorm events are described. One approach charts Climate Forecast System, version 2 (CFSv2), run-to-run consistency of the areal extent of severe thunderstorm environments using grid counts of the supercell composite parameter (SCP). Visualization of these environments is charted for each 45-day CFSv2 run initialized at 0000 UTC. CFSv2 ensemble-mean forecast maps of SCP coverage over the contiguous United States are also produced for those forecasts meeting certain criteria for high-impact weather. The applicability of this approach to the severe weather prediction challenge is illustrated using CFSv2 output for a series of severe weather episodes occurring in March and April 2014. Another approach, possibly extending severe weather predictability from CFSv2, utilizes a run-cumulative time-averaging technique of SCP grid counts. This process is described and subjectively verified with severe weather events from early 2014.

Environmental Compliance in River Valley Projects of North East Indian Region

Pleione ◽  
2017 ◽  
Vol 11 (2) ◽  
pp. 455
Author(s):  
V. Saio ◽  
H. Tynsong ◽  
Shahida P. Quazi ◽  
V. P. Upadhyay ◽  
S. K. Aggarwal
Keyword(s):  
River Valley ◽  
Indian Region ◽  
Environmental Compliance ◽  
East Indian ◽  
North East

scholarly journals A methodology to conduct wind damage field surveys for high-impact weather events of convective origin

2020 ◽  
Vol 20 (5) ◽  
pp. 1513-1531 ◽  
Author(s):  
Oriol Rodríguez ◽  
Joan Bech ◽  
Juan de Dios Soriano ◽  
Delia Gutiérrez ◽  
Salvador Castán
Keyword(s):  
Field Studies ◽  
High Impact ◽  
Strong Wind ◽  
Field Surveys ◽  
Weather Events ◽  
Straight Line ◽  
High Impact Weather ◽  
Strong Winds ◽  
Wind Events ◽  
The One

Abstract. Post-event damage assessments are of paramount importance to document the effects of high-impact weather-related events such as floods or strong wind events. Moreover, evaluating the damage and characterizing its extent and intensity can be essential for further analysis such as completing a diagnostic meteorological case study. This paper presents a methodology to perform field surveys of damage caused by strong winds of convective origin (i.e. tornado, downburst and straight-line winds). It is based on previous studies and also on 136 field studies performed by the authors in Spain between 2004 and 2018. The methodology includes the collection of pictures and records of damage to human-made structures and on vegetation during the in situ visit to the affected area, as well as of available automatic weather station data, witness reports and images of the phenomenon, such as funnel cloud pictures, taken by casual observers. To synthesize the gathered data, three final deliverables are proposed: (i) a standardized text report of the analysed event, (ii) a table consisting of detailed geolocated information about each damage point and other relevant data and (iii) a map or a KML (Keyhole Markup Language) file containing the previous information ready for graphical display and further analysis. This methodology has been applied by the authors in the past, sometimes only a few hours after the event occurrence and, on many occasions, when the type of convective phenomenon was uncertain. In those uncertain cases, the information resulting from this methodology contributed effectively to discern the phenomenon type thanks to the damage pattern analysis, particularly if no witness reports were available. The application of methodologies such as the one presented here is necessary in order to build homogeneous and robust databases of severe weather cases and high-impact weather events.

scholarly journals A Total Lightning Trending Algorithm to Identify Severe Thunderstorms

2010 ◽  
Vol 27 (1) ◽  
pp. 3-22 ◽  
Author(s):  
Patrick N. Gatlin ◽  
Steven J. Goodman
Keyword(s):  
Severe Weather ◽  
Lightning Flash ◽  
Severe Thunderstorm ◽  
Critical Success ◽  
Severe Thunderstorms ◽  
Weather Events ◽  
Total Lightning ◽  
Jump Algorithm ◽  
Early Indication ◽  
Critical Success Index

Abstract An algorithm that provides an early indication of impending severe weather from observed trends in thunderstorm total lightning flash rates has been developed. The algorithm framework has been tested on 20 thunderstorms, including 1 nonsevere storm, which occurred over the course of six separate days during the spring months of 2002 and 2003. The identified surges in lightning rate (or jumps) are compared against 110 documented severe weather events produced by these thunderstorms as they moved across portions of northern Alabama and southern Tennessee. Lightning jumps precede 90% of these severe weather events, with as much as a 27-min advance notification of impending severe weather on the ground. However, 37% of lightning jumps are not followed by severe weather reports. Various configurations of the algorithm are tested, and the highest critical success index attained is 0.49. Results suggest that this lightning jump algorithm may be a useful operational diagnostic tool for severe thunderstorm potential.

A role of high impact weather events in waterborne disease outbreaks in Canada, 1975 – 2001

2006 ◽  
Vol 16 (3) ◽  
pp. 167-180 ◽  
Author(s):  
Kate M. Thomas ◽  
Dominique F. Charron ◽  
David Waltner-Toews ◽  
Corinne Schuster ◽  
Abdel R. Maarouf ◽  
...  
Keyword(s):  
Disease Outbreaks ◽  
High Impact ◽  
Waterborne Disease ◽  
Weather Events ◽  
High Impact Weather

scholarly journals Long-term trends of instability and associated parameters over the Indian region obtained using radiosonde network

2018 ◽  
Author(s):  
Rohit Chakraborty ◽  
Madineni Venkat Ratnam ◽  
Ghouse Basha
Keyword(s):  
Convective Available Potential Energy ◽  
Lower Troposphere ◽  
Central India ◽  
Indian Region ◽  
Coastal Regions ◽  
Severe Thunderstorm ◽  
Equilibrium Level ◽  
Long Term Trends ◽  
Rainfall Occurrences

Abstract. Long-term trends of the parameters related to convection and instability obtained from 27 radiosonde stations across 6 sub-divisions over Indian region during the period 1980–2016 is presented. A total of 16 parcel and instability parameters along with moisture content, wind shear, and thunderstorm and rainfall frequencies have been utilized for this purpose. Robust fit regression analysis is employed on the regional average time series to calculate the long-term trends on both seasonal and yearly basis. The Level of Free Convection (LFC) and Equilibrium Level (EL) height is found to ascend significantly in all Indian sub-divisions. Consequently, the coastal regions (particularly the western coasts) experience strengthening in Severe Thunderstorm (TSS) and Severe Rainfall Frequencies (SRF) in the pre-monsoon while the inland regions (especially central India) experience an increase in Ordinary Thunderstorm (TSO) and Weak Rain Frequency (WRF) during the monsoon and post-monsoon. The 16–20 year periodicity is found to dominate the long-term trends significantly compared to other periodicities and the increase in TSS, SRF and CAPE is found more severe after the year 1999. The enhancement in moisture transport and associated cooling at 100 hPa along with dispersion of boundary layer pollutants is found to be the main cause for the increase in Convective Available Potential Energy (CAPE) which leads to more convective severity in the coastal regions. However, in inland regions moisture-laden winds are absent and the presence of strong capping effect of pollutants on instability in the lower troposphere has resulted in more Convective Inhibition Energy (CINE). Hence, TSO and weak rainfall occurrences have increased particularly in these regions.

Diagnosing factors in parameterised and resolved convection with physical tendency output in AROME-Arctic during a Cold-Air Outbreak event

2020 ◽  
Author(s):  
Marvin Kähnert ◽  
Teresa M. Valkonen ◽  
Harald Sodemann
Keyword(s):  
Weather Prediction ◽  
Cloud Microphysics ◽  
High Impact ◽  
Surface Fluxes ◽  
The Arctic ◽  
Shallow Convection ◽  
Time Step ◽  
Cold Air ◽  
Weather Events ◽  
High Impact Weather

<p>Numerical weather prediction (NWP) models generally display comparatively low predictive skill in the Arctic. Particularly, the large impact of sub-grid scale, parameterised processes, such as surface fluxes, radiation or cloud microphysics during high-latitude weather events pose a substantial challenge for numerical modelling. Such processes are most influential during mesoscale weather events, such as polar lows, often embedded in cold air outbreaks (CAO), some of which cause high impact weather. Uncertainty in Arctic weather forecasts is thus critically dependent on parameterised processes. The strong influence from several parameterised processes also makes model forecasts particularly susceptible to compensation of errors from different parameterisations, which potentially limits model improvement.<br>Here we analyse model output of individual parameterised tendencies of wind, temperature and humidity during Arctic high-impact weather in AROME-Arctic, the operational NWP model used by the Norwegian Meteorological Institute Norway for the European Arctic. Individual tendencies describe the contribution of each applied physical parameterisation to a respective variable per model time step. We study a CAO-event taking place during 24 - 27 December 2015. This intense and widespread CAO event, reaching from the Fram Straight to Norway and affecting a particularly large portion of the Nordic seas at a time, was characterised by strong heat fluxes along the sea ice edge. <br>Model intern definitions for boundary layer type become apparent as a decisive factor in tendency contributions. Especially the interplay between the dual mass flux and the turbulence scheme is of essence here. Furthermore, sensitivity experiments, featuring a run without shallow convection and a run with a new statistical cloud scheme, show how a physically similar result is obtained by substantially different tendencies in the model.</p>

The Impact of Initial Condition and Warm Conveyor Belt Forecast Uncertainty on Variability in the Downstream Waveguide in an ECWMF Case Study

2019 ◽  
Vol 147 (11) ◽  
pp. 4071-4089 ◽  
Author(s):  
Jeremy D. Berman ◽  
Ryan D. Torn
Keyword(s):  
Conveyor Belt ◽  
Forecast Uncertainty ◽  
Ensemble Forecasts ◽  
Rossby Wave Breaking ◽  
Weather Forecasts ◽  
The North ◽  
Weather Events ◽  
High Impact Weather ◽  
Sensitivity Method ◽  
The Impact

Abstract Perturbations to the potential vorticity (PV) waveguide, which can result from latent heat release within the warm conveyor belt (WCB) of midlatitude cyclones, can lead to the downstream radiation of Rossby waves, and in turn high-impact weather events. Previous studies have hypothesized that forecast uncertainty associated with diabatic heating in WCBs can result in large downstream forecast variability; however, these studies have not established a direct connection between the two. This study evaluates the potential impact of latent heating variability in the WCB on subsequent downstream forecasts by applying the ensemble-based sensitivity method to European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts of a cyclogenesis event over the North Atlantic. For this case, ensemble members with a more amplified ridge are associated with greater negative PV advection by the irrotational wind, which is associated with stronger lower-tropospheric southerly moisture transport east of the upstream cyclone in the WCB. This transport is sensitive to the pressure trough to the south of the cyclone along the cold front, which in turn is modulated by earlier differences in the motion of the air masses on either side of the front. The position of the cold air behind the front is modulated by upstream tropopause-based PV anomalies, such that a deeper pressure trough is associated with a more progressive flow pattern, originating from Rossby wave breaking over the North Pacific. Overall, these results suggest that more accurate forecasts of upstream PV anomalies and WCBs may reduce forecast uncertainty in the downstream waveguide.

scholarly journals Towards the operational use of tweets data in high impact weather scenarios: data mining and analytics in Basque Country.

2021 ◽  
Author(s):  
Santiago Gaztelumendi
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Real Time ◽  
Basque Country ◽  
Local Level ◽  
High Impact ◽  
Severe Weather ◽  
Weather Events ◽  
The World ◽  
High Impact Weather

<p>Although social media industry is now a very congested Marketplace, Twitter continues to maintain its status as a popular social media platform. There are 330 million monthly active users and 145 million daily active users on Twitter sending more than 6,000 tweets every second in the world. In Spain case 85% population are social media users, with around 5 million tweeter profiles for a population around 47 million. In the autonomous community of Basque country (2.17 million inhabitants) around 20% of citizens use Twitter.</p><p>Twitter is a social tool that enables users to post messages (tweets) of up to 280 characters supporting a wide variety of social communication practices including photo and video attach. The Basque Meteorology Agency @Euskalmet with more than 115,3 K followers is one of the most popular accounts in Basque Country. Twitter is not only an opportunity to instantaneous spread messages to people without intermediaries, but also as a potential platform for valuable data acquisition using tweeter API capabilities. In this contribution, we present a study of different aspects related to the operational use of Twitter data in the context of high impact weather scenarios at local level.</p><p>The most important activity in Euskalmet are actions in severe weather events. Before the event, mainly centered in forecast and communication, during the event in nowcast, surveillance and impact monitoring and after the event in post-event analysis. During all these complex processes real time tweets posted by local users offer a huge amount of data that conveniently processed could be useful for different purposes. For operational staff, working at office during severe weather episodes, is critical to understand the local effects that an adverse phenomenon is causing and the correct perception of the extent of impact and social alarm. For this purposes, among others, different information associated with posted tweets can be extracted and exploited conveniently. In this work, we present some results that demonstrate how different data mining and advances analytics techniques can be used in order to include social media data information for different tasks and particularly during high impact weather events.</p><p>In this paper we summarize our experience during a proof of concept project for automatic real time tweeter analysis and the development of an operational tool for tweeter API data exploitation in the Basque Country. We present the main challenges and problems that we have had to face, including how to deal with the lack of geolocation information, since in the case of the Basque country, as in other parts of the world, tweets containing geotags are the exception, not the rule.</p>

scholarly journals Identifikasi Fenomena Mesoscale Convective System (MCC) di Selat Karimata

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Estri Diniyati ◽  
Yosafat Donni Haryanto
Keyword(s):  
Mesoscale Convective System ◽  
Equatorial Region ◽  
Extreme Weather Events ◽  
Convective System ◽  
Physical Conditions ◽  
Weather Events ◽  
Mesoscale Convective ◽  
Extreme Rain ◽  
Cloud Top Temperature ◽  
Cloud Core

Abstract—Indonesia located in the equatorial region which has potential to have a major impact on atmospheric physical conditions during extreme weather events such as the Mesoscale Convective Complex (MCC). MCC is a phenomenon that was first discovered by (Maddox, 1980) where this phenomenon is characterized by the presence of a quasi-circular (almost circular) cloud shield with an eccentricity of 0.7 with a cloud cover area of 100,000 km², the cloud core area covers 50,000 km² and cloud top temperature IR1 -52 ℃. These cloud conditions last for a minimum of 6 hours and cause severe weather and extreme rain. This study aims to identify the MCC phenomenon in the Karimata Strait on 19-20 September 2020 which caused heavy rains in parts of the West coast of Kalimantan and Bangka Island using Himawari-8 Satellite imagery data and the MATLAB application. The results showed that on September 19, MCC was identified at 09.00-19.00 UTC, then on September 20, MCC was identified at 16.00-23.00 UTC. At the time of the MCC event, Bangka and Pontianak regions experienced extreme rains recorded on AWS Digi Stamet Pontianak with rainfall reaching 43.4 mm/hour and ARG Lubuk Besar Bangka Tengah with rainfall reaching 16.8 mm/hour. Keywords: mesoscale convective complex (MCC), himawari-8, MATLAB Abstrak—Indonesia merupakan negara yang terletak diwilayah ekuator dimana berpotensi memiliki dampak besar terhadap kondisi fisik atmosfer saat terjadi cuaca ekstrem seperti Mesoscale Convective Complex (MCC). MCC merupakan fenomena yang pertama kali ditemukan oleh (Maddox, 1980) dimana fenomena ini dicirikan dengan adanya perisai awan yang berbentuk quasi circular (hampir lingkaran) dengan eksentrisitas ≥ 0,7 dengan luas area selimut awan ≥ 100.000 km² , luas area inti awan mencakup ≥ 50.000 km² serta suhu puncak awan IR1 ≤ -52 ℃. Kondisi awan tersebut bertahan minimun selama 6 jam dan menyebabkan cuaca buruk dan hujan ekstrem. Penelitian ini bertujuan untuk mengidentifikasi fenomena MCC di Selat Karimata pada Tanggal 19-20 September 2020 yang menyebabkan hujan lebat di sebagian wilayah Kalimantan bagian pesisir Barat dan Pulau Bangka menggunakan data citra Satelit Himawari-8 dan aplikasi MATLAB. Hasil penelitian menunjukkan pada tanggal 19 September, MCC teridentifikasi pada pukul 09.00-19.00 UTC selanjutnya tanggal 20 September 2020 MCC teridentifikasi pada pukul 16.00-23.00 UTC. Pada saat peristiwa MCC, wilayah Bangka dan Pontianak mengalami hujan ekstrem yang tercatat pada AWS Digi Stasiun Meteorologi Pontianak dengan curah hujan mencapai 43,4 mm/jam dan ARG Lubuk Besar Bangka Tengah dengan curah hujan mencapai 16,8 mm/jam. Kata kunci: mesoscale convective complex (MCC), himawari-8, MATLAB

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