Extremes of Severe Storm Environments under a Changing Climate

One of the more critical issues in a changing climate is the behavior of extreme weather events, such as severe tornadic storms as seen recently in Moore and El Reno, Oklahoma. It is generally thought that such events would increase under a changing climate. How to evaluate this extreme behavior is a topic currently under much debate and investigation. One approach is to look at the behavior of large scale indicators of severe weather. The use of the generalized extreme value distribution for annual maxima is explored for a combination product of convective available potential energy and wind shear. Results from this initial study show successful modeling and high quantile prediction using extreme value methods. Predicted large scale values are consistent across different extreme value modeling frameworks, and a general increase over time in predicted values is indicated. A case study utilizing this methodology considers the large scale atmospheric indicators for the region of Moore, Oklahoma for Class EF5 tornadoes on May 3, 1999 and more recently on May 20, 2013, and for the class EF5 storm in El Reno, Oklahoma on May 31, 2013.

Testing Geographical Methodology and Tools for the Study of Territories Damaged by Earthquakes. The Case of L’Aquila and Other Localities Three Years after the April 6th 2009 Event

In this paper, we discuss a geographical methodology supported by specific geo-technologies which we are testing for the study of territories damaged by the L’Aquila earthquake of 6 April 2009 and which can be used in similar situations. Subsequently, we provide an overview of the current situation and make a comparison between some aerial photographs obtained from an overflight in March 2012 and some photos made during our first field study in February 2010, in order to show the work undertaken or not during this period and to substantiate any considerations regarding the choices adopted and the necessary future planning. Moreover, we provide an example of the added value provided by the analysis of aerial photographs in both visible and thermal light for recognizing the provisional non-painted metal roofing of buildings in a post-earthquake urban area. In fact this technique can be useful for the rapid identification of damaged buildings and zones with provisional covering. In the present paper, we focus attention on L’Aquila town centre which provides a significant example of a “City of Stone” almost “minus” the presence of people.

Resilience Building and Collaborative Governance for Climate Change Adaptation in Response to a New State of More Frequent and Intense Extreme Weather Events

The weather conditions of the summer of 2022 were very unusual,particularly in Eastern Asia,Europe,and North America.The devasting impact of climate change has come to our attention,with much hotter and drier conditions,and with more frequent and intense flooding events.Some extreme events have reached a dangerous level,increasingly threatening human lives.The interconnected risks caused by these extreme disaster events are triggering a chain effect,forcing us to respond to these crises through changes in our living environment,which affect the atmosphere,the biosphere,the economy including the availability of energy,our cities,and our global society.Moreover,we have to confront the abnormal consequences of untypical,rapid changes of extreme events and fast switches between extreme states,such as from severe drought to devastating flooding.Recognizing this new situation,it is crucial to improve the adaptation capacity of our societies in order to reduce the risks associated with climate change,and to develop smarter strategies for climate governance.High-quality development must be science-based,balanced,safe,sustainable,and climate-resilient,supported by the collaborative governance of climate mitigation and adaptation.This article provides some recommendations and suggestions for resilience building and collaborative governance with respect to climate adaptation in response to a new planetary state that is characterized by more frequent and severe extreme weather events.

Recommendations for improved tropical cyclone formation and position probabilistic Forecast products

Prediction of the potentially devastating impact of landfalling tropical cyclones(TCs)relies substantially on numerical prediction systems.Due to the limited predictability of TCs and the need to express forecast confidence and possible scenarios,it is vital to exploit the benefits of dynamic ensemble forecasts in operational TC forecasts and warnings.RSMCs,TCWCs,and other forecast centers value probabilistic guidance for TCs,but the International Workshop on Tropical Cyclones(IWTC-9)found that the“pull-through”of probabilistic information to operational warnings using those forecasts is slow.IWTC-9 recommendations led to the formation of the WMO/WWRP Tropical Cyclone-Probabilistic Forecast Products(TC-PFP)project,which is also endorsed as a WMO Seamless GDPFS Pilot Project.The main goal of TC-PFP is to coordinate across forecast centers to help identify best practice guidance for probabilistic TC forecasts.TC-PFP is being implemented in 3 phases:Phase 1(TC formation and position);Phase 2(TC intensity and structure);and Phase 3(TC related rainfall and storm surge).This article provides a summary of Phase 1 and reviews the current state of the science of probabilistic forecasting of TC formation and position.There is considerable variability in the nature and interpretation of forecast products based on ensemble information,making it challenging to transfer knowledge of best practices across forecast centers.Communication among forecast centers regarding the effectiveness of different approaches would be helpful for conveying best practices.Close collaboration with experts experienced in communicating complex probabilistic TC information and sharing of best practices between centers would help to ensure effective decisions can be made based on TC forecasts.Finally,forecast centers need timely access to ensemble information that has consistent,user-friendly ensemble information.Greater consistency across forecast centers in data accessibility,probabilistic forecast products,and warnings and their communication to users will produce more reliable information and support improved outcomes.

Evaluation and comparison of various fast fluid dynamics modeling methods for predicting airflow around buildings

Computational fluid dynamics(CFD)methods are being increasingly used for predicting airflow fields around buildings,but personal computers can still take tens of hours to create a single design using traditional computing models.Considering both accuracy and efficiency,this study compared the performances of the conventional algorithm PIMPLE,fast fluid dynamics(FFD),semi-Lagrangian PISO(SLPISO),and implicit fast fluid dynamics(IFFD)in OpenFOAM for simulating wind flow around buildings.The effects of calculation parameters,including grid resolution,discrete-time step,and calculation time for these methods are analyzed.The results of the simulations are compared with wind tunnel tests.It is found that IFFD and FFD have the fastest calculation speeds,but also have the largest discrepancies with test data.The PIMPLE algorithm has the highest accuracy,but with the slowest calculation speed.The calculation speeds of the FFD,SLPISO,and IFFD models are 6.3,3 and 13.3 times faster than the PIMPLE model,respectively.The calculation accuracy and speed of the SLPISO model are in between those of the IFFD,FFD and PIMPLE models.An appropriate algorithm for a project may be chosen based on the requirements of the project.