Storm Tracking Using Geostationary Lightning Observation Videos

2019 ◽  
pp. 391-402
Author(s):  
Nora Elizabeth Joby ◽  
Nimisha Susan George ◽  
M. N. Geethasree ◽  
B. NimmiKrishna ◽  
Noora Rahman Thayyil ◽  
...  
Keyword(s):  
Storm Tracking

scholarly journals Recent wind driven high sea ice export in the Fram Strait contributes to Arctic sea ice decline

2011 ◽  
Vol 5 (3) ◽  
pp. 1311-1334 ◽  
Author(s):  
L. H. Smedsrud ◽  
A. Sirevaag ◽  
K. Kloster ◽  
A. Sorteberg ◽  
S. Sandven
Keyword(s):  
Sea Ice ◽  
Geostrophic Wind ◽  
Reanalysis Data ◽  
Arctic Sea Ice ◽  
Fram Strait ◽  
Storm Tracking ◽  
Steady Rate ◽  
Ice Drift ◽  
Arctic Sea ◽  
Radar Satellite

Abstract. Arctic sea ice area decrease has been visible for two decades, and continues at a steady rate. Apart from melting, the southward drift through Fram Strait is the main loss. We present high resolution sea ice drift across 79&deg N from 2004 to 2010. The ice drift is based on radar satellite data and correspond well with variability in local geostrophic wind. The underlying current contributes with a constant southward speed close to 5 cm s−1, and drives about 33 % of the ice export. We use geostrophic winds derived from reanalysis data to calculate the Fram Strait ice area export back to 1957, finding that the sea ice area export recently is about 25 % larger than during the 1960's. The increase in ice export occurred mostly during winter and is directly connected to higher southward ice drift velocities, due to stronger geostrophic winds. The increase in ice drift is large enough to counteract a decrease in ice concentration of the exported sea ice. Using storm tracking we link changes in geostrophic winds to more intense Nordic Sea low pressure systems. Annual sea ice export likely has a significant influence on the summer sea ice variability and we find low values in the 60's, the late 80's and 90's, and particularly high values during 2005–2008. The study highlight the possible role of variability in ice export as an explanatory factor for understanding the dramatic loss of Arctic sea ice the last decades.

Determining requisite revisit time for storm tracking

2011 ◽  
Author(s):  
Benjamin Root ◽  
Mark Yeary ◽  
Tian-You Yu
Keyword(s):  
Storm Tracking

Simulated hailstorms over Switzerland in May 2018 in current and future climate conditions

2020 ◽  
Author(s):  
Andrey Martynov ◽  
Timothy Raupach ◽  
Olivia Martius
Keyword(s):  
Climate Change ◽  
21St Century ◽  
Model Output ◽  
Cmip5 Model ◽  
Model Data ◽  
Climate Conditions ◽  
Cell Identification ◽  
Current Climate ◽  
Storm Tracking ◽  
1D Model

<p>Several remarkable hailstorms have occurred on the territory of Switzerland during the month<br>of May, 2018.<br>This period has been simulated, using the WRF4.0 model at a convection-permitting<br>resolution (1.5 km), using different microphysical schemes (Thompson, Morrison, P3).<br>The surrogate climate change approach has been used for imitating the climate conditions,<br>corresponding to the end of the 21st century (CMIP5 model data, RCP8.5 scenario).<br>The HAILCAST-1D model output has been used as a measure of simulated hail size and 5-<br>minute 3-D radar reflectivity field has been used for cell identification and tracking.<br>Hailstorms produced in the current climate and in surrogate climate change simulations have<br>been examined using neighborhood methods and a storm-tracking algorithm. Current-climate<br>simulated hailstorms were compared with the ground observations and MeteoSwiss radar<br>data.<br>The influence of microphysical schemes to the characteristics of simulated hailstorms has<br>been studied. </p>

Storm Tracking and Ensemble Prediction

2007 ◽  
pp. 101-119
Author(s):  
Lizzie S.R. Froude ◽  
Lennart Bengtsson ◽  
Kevin I. Hodges
Keyword(s):  
Ensemble Prediction ◽  
Storm Tracking

Kernel-based storm tracking in radar data

2008 ◽  
Author(s):  
C. Picus ◽  
C. Beleznai ◽  
C. Nowak ◽  
H. Ramoser ◽  
S. Mitterhuber
Keyword(s):  
Radar Data ◽  
Storm Tracking

scholarly journals Effects of City Size on Thunderstorm Evolution Revealed through a Multiradar Climatology of the Central United States

2018 ◽  
Vol 57 (2) ◽  
pp. 295-317 ◽  
Author(s):  
Darrel M. Kingfield ◽  
Kristin M. Calhoun ◽  
Kirsten M. de Beurs ◽  
Geoffrey M. Henebry
Keyword(s):  
Oklahoma City ◽  
City Size ◽  
Fort Worth ◽  
Mean Values ◽  
Late Afternoon ◽  
Maximum Value ◽  
Storm Tracking ◽  
Central United States ◽  
Favorable Conditions

AbstractFive years of 0.01° latitude × 0.01° longitude multiradar multisensor grids of composite reflectivity and vertically integrated signals from the maximum expected size of hail (MESH) and vertically integrated liquid (VIL) were created to examine the role of city size on thunderstorm occurrence and strength around four cities: Dallas–Fort Worth, Texas; Minneapolis–St. Paul, Minnesota; Oklahoma City, Oklahoma; and Omaha, Nebraska. A storm-tracking algorithm identified thunderstorm areas every minute and connected them together to form tracks. These tracks defined the upwind and downwind regions around each city on a storm-by-storm basis and were analyzed in two ways: 1) by sampling the maximum value every 10 min and 2) by accumulating the spatial footprint over its lifetime. Beyond examining all events, a subset of events corresponding to favorable conditions for urban modification was explored. This urban favorable (UF) subset consisted of nonsupercells occurring in the late afternoon/evening in the meteorological summer on weak synoptically forced days. When examining all thunderstorm events, regions at variable ranges upwind of all four cities generally had higher areal mean values of reflectivity, MESH, and VIL relative to downwind areas. In the UF subset, the larger cities (Dallas–Fort Worth and Minneapolis–St. Paul) had a 24%–50% increase in the number of downwind thunderstorms, resulting in a higher areal mean reflectivity, MESH, and VIL in this region. The smaller cities (Oklahoma City and Omaha) did not show such a downwind enhancement in thunderstorm occurrence and strength for the radar variables examined. This pattern suggests that larger cities could increase thunderstorm occurrence and intensity downwind of the prevailing flow under unique environmental conditions.

scholarly journals A Bayesian Framework for Storm Tracking Using a Hidden-State Representation

2010 ◽  
Vol 138 (6) ◽  
pp. 2132-2148 ◽  
Author(s):  
Lucas Scharenbroich ◽  
Gudrun Magnusdottir ◽  
Padhraic Smyth ◽  
Hal Stern ◽  
Chia-chi Wang
Keyword(s):  
Feature Detection ◽  
Analysis Data ◽  
Relative Vorticity ◽  
Storm Tracks ◽  
Posterior Distributions ◽  
Unknown Parameters ◽  
Storm Tracking ◽  
Tracking Model ◽  
Probabilistic Tracking ◽  
Environmental Prediction

Abstract A probabilistic tracking model is introduced that identifies storm tracks from feature vectors that are extracted from meteorological analysis data. The model assumes that the genesis and lysis times of each track are unknown and estimates their values along with the track’s position and storm intensity over time. A hidden-state dynamics model (Kalman filter) characterizes the temporal evolution of the storms. The model uses a Bayesian methodology for estimating the unknown lifetimes (genesis–lysis pairs) and tracks of the storms. Prior distributions are placed over the unknown parameters and their posterior distributions are estimated using a Markov Chain Monte Carlo (MCMC) sampling algorithm. The posterior distributions are used to identify and report the most likely storm tracks in the data. This approach provides a unified probabilistic framework that accounts for uncertainty in storm timing (genesis and lysis), storm location and intensity, and the feature detection process. Thus, issues such as missing observations can be accommodated in a statistical manner without human intervention. The model is applied to the field of relative vorticity at the 975-hPa level of analysis from the National Centers for Environmental Prediction Global Forecast System during May–October 2000–02, in the tropical east Pacific. Storm tracks in the National Hurricane Center best-track data (HURDAT) for the same period are used to assess the performance of the storm identification and tracking model.

Storm tracking using rain gauge data

1987 ◽  
Vol 93 (1-2) ◽  
pp. 135-152 ◽  
Author(s):  
Janusz Niemczynowicz
Keyword(s):  
Rain Gauge ◽  
Rain Gauge Data ◽  
Storm Tracking ◽  
Gauge Data

scholarly journals In the eye of the STORM: Tracking the myosin-binding protein C N terminus in heart muscle

2021 ◽  
Vol 153 (3) ◽  
Author(s):  
Brett A. Colson
Keyword(s):  
Cardiac Muscle ◽  
Heart Muscle ◽  
Protein C ◽  
Binding Protein ◽  
Myosin Binding Protein ◽  
Myosin Binding Protein C ◽  
Storm Tracking ◽  
N Terminus ◽  
Myosin Binding

Colson discusses a recent investigation of the localization of N-terminal myosin-binding protein C in cardiac muscle.

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