Weather-based estimation of wildfire risk

Catastrophic wildfires in California have become more frequent in past decades, while insured losses per event have been rising substantially. On average, California ranks the highest among states in the U.S. in the number of fires as well as the number of acres burned each year. The study of catastrophic wildfire models plays an important role in the prevention and mitigation of such disasters. Accurate forecasts of potential large fires assist fire managers in preparing resources and strategic planning for fire suppression. Furthermore, fire forecasting can a priori inform insurers on potential financial losses due to large fires. This paper describes a probabilistic model for predicting wildland fire risks using the two-stage Heckman procedure. Using 37 years of spatial and temporal information on weather and fire records in Southern California, this model measures the probability of a fire occurring and estimates the expected size of the fire on a given day and location, offering a technique to predict and forecast wildfire occurrences based on weather information that is readily available at low cost.biased sampling, forest fires, fire occurrence probabilities, fire weather

Object-oriented construction of a multigrid electronic-structure code with Fortran 90

We describe the object-oriented implementation of a higher-order finite-difference density-functional code in Fortran 90. Object-oriented models of grid and related objects are constructed and employed for the implementation of an efficient one-way multigrid method we have recently proposed for the density-functional electronic-structure calculations. Detailed analysis of performance and strategy of the one-way multigrid scheme will be presented.Comment: 24 pages, 6 figures, to appear in Comput. Phys. Com

Static Properties of a Simulated Supercooled Polymer Melt: Structure Factors, Monomer Distributions Relative to the Center of Mass, and Triple Correlation Functions

We analyze structural and conformational properties in a simulated bead-spring model of a non-entangled, supercooled polymer melt. We explore the statics of the model via various structure factors, involving not only the monomers, but also the center of mass (CM). We find that the conformation of the chains and the CM-CM structure factor, which is well described by a recently proposed approximation [Krakoviack et al., Europhys. Lett. 58, 53 (2002)], remain essentially unchanged on cooling toward the critical glass transition temperature of mode-coupling theory. Spatial correlations between monomers on different chains, however, depend on temperature, albeit smoothly. This implies that the glassy behavior of our model cannot result from static intra-chain or CM-CM correlations. It must be related to inter-chain correlations at the monomer level. Additionally, we study the dependence of inter-chain correlation functions on the position of the monomer along the chain backbone. We find that this site-dependence can be well accounted for by a theory based on the polymer reference interaction site model (PRISM). We also analyze triple correlations by means of the three-monomer structure factors for the melt and for the chains. These structure factors are compared with the convolution approximation that factorizes them into a product of two-monomer structure factors. For the chains this factorization works very well, indicating that chain connectivity does not introduce special triple correlations in our model. For the melt deviations are more pronounced, particularly at wave vectors close to the maximum of the static structure factor.Comment: REVTeX4, 16 pages, 16 figures, accepted for publication in Physical Review

Butterfly Factorization

The paper introduces the butterfly factorization as a data-sparse approximation for the matrices that satisfy a complementary low-rank property. The factorization can be constructed efficiently if either fast algorithms for applying the matrix and its adjoint are available or the entries of the matrix can be sampled individually. For an $N \times N$ matrix, the resulting factorization is a product of $O(\log N)$ sparse matrices, each with $O(N)$ non-zero entries. Hence, it can be applied rapidly in $O(N\log N)$ operations. Numerical results are provided to demonstrate the effectiveness of the butterfly factorization and its construction algorithms