scholarly journals Opposite anomalous synoptic patterns for potential California large wildfire spread and extinguishing in 2018 cases

2021 ◽  
pp. 105804
Author(s):  
Weihong Qian ◽  
Yang Ai ◽  
Jin-Yi Yu ◽  
Jun Du
2008 ◽  
Vol 136 (6) ◽  
pp. 2006-2022 ◽  
Author(s):  
Cheng-Shang Lee ◽  
Kevin K. W. Cheung ◽  
Jenny S. N. Hui ◽  
Russell L. Elsberry

Abstract The mesoscale features of 124 tropical cyclone formations in the western North Pacific Ocean during 1999–2004 are investigated through large-scale analyses, satellite infrared brightness temperature (TB), and Quick Scatterometer (QuikSCAT) oceanic wind data. Based on low-level wind flow and surge direction, the formation cases are classified into six synoptic patterns: easterly wave (EW), northeasterly flow (NE), coexistence of northeasterly and southwesterly flow (NE–SW), southwesterly flow (SW), monsoon confluence (MC), and monsoon shear (MS). Then the general convection characteristics and mesoscale convective system (MCS) activities associated with these formation cases are studied under this classification scheme. Convection processes in the EW cases are distinguished from the monsoon-related formations in that the convection is less deep and closer to the formation center. Five characteristic temporal evolutions of the deep convection are identified: (i) single convection event, (ii) two convection events, (iii) three convection events, (iv) gradual decrease in TB, and (v) fluctuating TB, or a slight increase in TB before formation. Although no dominant temporal evolution differentiates cases in the six synoptic patterns, evolutions ii and iii seem to be the common routes taken by the monsoon-related formations. The overall percentage of cases with MCS activity at multiple times is 63%, and in 35% of cases more than one MCS coexisted. Most of the MC and MS cases develop multiple MCSs that lead to several episodes of deep convection. These two patterns have the highest percentage of coexisting MCSs such that potential interaction between these systems may play a role in the formation process. The MCSs in the monsoon-related formations are distributed around the center, except in the NE–SW cases in which clustering of MCSs is found about 100–200 km east of the center during the 12 h before formation. On average only one MCS occurs during an EW formation, whereas the mean value is around two for the other monsoon-related patterns. Both the mean lifetime and time of first appearance of MCS in EW are much shorter than those developed in other synoptic patterns, which indicates that the overall formation evolution in the EW case is faster. Moreover, this MCS is most likely to be found within 100 km east of the center 12 h before formation. The implications of these results to internal mechanisms of tropical cyclone formation are discussed in light of other recent mesoscale studies.


2021 ◽  
Vol 11 (15) ◽  
pp. 7046
Author(s):  
Jorge Francisco Ciprián-Sánchez ◽  
Gilberto Ochoa-Ruiz ◽  
Lucile Rossi ◽  
Frédéric Morandini

Wildfires stand as one of the most relevant natural disasters worldwide, particularly more so due to the effect of climate change and its impact on various societal and environmental levels. In this regard, a significant amount of research has been done in order to address this issue, deploying a wide variety of technologies and following a multi-disciplinary approach. Notably, computer vision has played a fundamental role in this regard. It can be used to extract and combine information from several imaging modalities in regard to fire detection, characterization and wildfire spread forecasting. In recent years, there has been work pertaining to Deep Learning (DL)-based fire segmentation, showing very promising results. However, it is currently unclear whether the architecture of a model, its loss function, or the image type employed (visible, infrared, or fused) has the most impact on the fire segmentation results. In the present work, we evaluate different combinations of state-of-the-art (SOTA) DL architectures, loss functions, and types of images to identify the parameters most relevant to improve the segmentation results. We benchmark them to identify the top-performing ones and compare them to traditional fire segmentation techniques. Finally, we evaluate if the addition of attention modules on the best performing architecture can further improve the segmentation results. To the best of our knowledge, this is the first work that evaluates the impact of the architecture, loss function, and image type in the performance of DL-based wildfire segmentation models.


2007 ◽  
Vol 83 (2-4) ◽  
pp. 185-200 ◽  
Author(s):  
Koji Nishiyama ◽  
Shinichi Endo ◽  
Kenji Jinno ◽  
Cintia Bertacchi Uvo ◽  
Jonas Olsson ◽  
...  

2021 ◽  
Author(s):  
Martín Senande-Rivera ◽  
Gonzalo Miguez-Macho

<p>Extreme wildfire events associated with strong pyroconvection have gained the attention of the scientific community and the society in recent years. Strong convection in the fire plume can influence fire behaviour, as downdrafts can cause abrupt variations in surface wind direction and speed that can result in bursts of unexpected fire propagation. Climate change is expected to increase the length of the fire season and the extreme wildfire potential, so the risk of pyroconvection occurence might be also altered. Here, we analyse atmospheric stability and near-surface fire weather conditions in the Iberian Peninsula at the end of the 21st century to assess the projected changes in pyroconvective risk during favourable weather conditions for wildfire spread.  </p>


2002 ◽  
Vol 17 (1) ◽  
pp. 83-98 ◽  
Author(s):  
Todd P. Lericos ◽  
Henry E. Fuelberg ◽  
Andrew I. Watson ◽  
Ronald L. Holle

2018 ◽  
Vol 127 (2) ◽  
Author(s):  
Bromand Salahi ◽  
Ahmad Nohegar ◽  
Mahmoud Behrouzi ◽  
Mehdi Aalijahan
Keyword(s):  

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