scholarly journals A multivariate semiparametric Bayesian spatial modeling framework for hurricane surface wind fields

2007 ◽  
Vol 1 (1) ◽  
pp. 249-264 ◽  
Author(s):  
Brian J. Reich ◽  
Montserrat Fuentes
1997 ◽  
Vol 102 (D12) ◽  
pp. 13907-13921 ◽  
Author(s):  
Gareth J. Marshall ◽  
John Turner
Keyword(s):  

2015 ◽  
Vol 15 (8) ◽  
pp. 1695-1709 ◽  
Author(s):  
J. P. Sierra ◽  
M. Casas-Prat ◽  
M. Virgili ◽  
C. Mösso ◽  
A. Sánchez-Arcilla

Abstract. The objective of the present work is to analyse how changes in wave patterns due to the effect of climate change can affect harbour agitation (oscillations within the port due to wind waves). The study focuses on 13 harbours located on the Catalan coast (NW Mediterranean) using a methodology with general applicability. To obtain the patterns of agitation, a Boussinesq-type model is used, which is forced at the boundaries by present/future offshore wave conditions extracted from recently developed high-resolution wave projections in the NW Mediterranean. These wave projections were obtained with the SWAN model forced by present/future surface wind fields projected, respectively, by five different combinations of global and regional circulation models (GCMs and RCMs) for the A1B scenario. The results show a general slight reduction in the annual average agitation for most of the ports, except for the northernmost and southernmost areas of the region, where a slight increase is obtained. A seasonal analysis reveals that the tendency to decrease is accentuated in winter. However, the inter-model variability is large for both the winter and the annual analysis. Conversely, a general increase with a larger agreement among models is found during summer, which is the period with greater activity in most of the studied ports (marinas). A qualitative assessment of the factors of variability seems to indicate that the choice of GCM tends to affect the spatial pattern, whereas the choice of RCM induces a more homogeneous bias over the regional domain.


2003 ◽  
Vol 56 (1-3) ◽  
pp. 32-40 ◽  
Author(s):  
Duncan C. Thomas ◽  
Daniel O. Stram ◽  
David Conti ◽  
John Molitor ◽  
Paul Marjoram

2008 ◽  
Vol 137 (2) ◽  
pp. 438-453 ◽  
Author(s):  
Raymond A. Webster ◽  
Kenneth H. Pollock ◽  
Sujit K. Ghosh ◽  
David G. Hankin

Ecography ◽  
2010 ◽  
Vol 33 (6) ◽  
pp. 1093-1096
Author(s):  
Norbert Solymosi ◽  
Sara E. Wagner ◽  
Ákos Maróti-Agóts ◽  
Alberto Allepuz

2018 ◽  
Vol 27 (4) ◽  
pp. 257 ◽  
Author(s):  
O. Rios ◽  
W. Jahn ◽  
E. Pastor ◽  
M. M. Valero ◽  
E. Planas

Local wind fields that account for topographic interaction are a key element for any wildfire spread simulator. Currently available tools to generate near-surface winds with acceptable accuracy do not meet the tight time constraints required for data-driven applications. This article presents the specific problem of data-driven wildfire spread simulation (with a strategy based on using observed data to improve results), for which wind diagnostic models must be run iteratively during an optimisation loop. An interpolation framework is proposed as a feasible alternative to keep a positive lead time while minimising the loss of accuracy. The proposed methodology was compared with the WindNinja solver in eight different topographic scenarios with multiple resolutions and reference – pre-run– wind map sets. Results showed a major reduction in computation time (~100 times once the reference fields are available) with average deviations of 3% in wind speed and 3° in direction. This indicates that high-resolution wind fields can be interpolated from a finite set of base maps previously computed. Finally, wildfire spread simulations using original and interpolated maps were compared showing minimal deviations in the fire shape evolution. This methodology may have an important effect on data assimilation frameworks and probabilistic risk assessment where high-resolution wind fields must be computed for multiple weather scenarios.


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