This paper introduces the boomerang sampler as a novel class of continuous-time non-reversible Markov chain Monte Carlo algorithms. The methodology begins by representing the target density as a density, $e^{-U}$, with respect to a prescribed (usually) Gaussian measure and constructs a continuous trajectory consisting of a piecewise circular path. The method moves from one circular orbit to another according to a rate function which can be written in terms of $U$. We demonstrate that the method is easy to implement and demonstrate empirically that it can out-perform existing benchmark piecewise deterministic Markov processes such as the bouncy particle sampler and the Zig-Zag. In the Bayesian statistics context, these competitor algorithms are of substantial interest in the large data context due to the fact that they can adopt data subsampling techniques which are exact (ie induce no error in the stationary distribution). We demonstrate theoretically and empirically that we can also construct a control-variate subsampling boomerang sampler which is also exact, and which possesses remarkable scaling properties in the large data limit. We furthermore illustrate a factorised version on the simulation of diffusion bridges.

Infrared (IR) remote sensing is increasingly used in studies of vegetation fire behavior, and high spatiotemporal resolution investigations often require data to be collected from airborne platforms, for example, standard helicopters. This paper aims to extend the range of conditions under which low-cost “handheld” thermal imaging cameras can be employed in such studies, particularly by enabling the effective and efficient geometric correction of thermal imagery collected from such devices, even when viewing far off-nadir (e.g., out of a side door or window). The approach is based on the automated detection of a set of fixed thermal “ground control points,” coupled with the use of a linear transformation matrix for warping the raw IR imagery to a fixed coordinate system. The output set of geometrically corrected brightness temperature and radiance images can be used to derive fire radiative power (FRP) and flame front rate of spread (ROS). We demonstrate and test our IR image processing methods on a series of case study fires, ranging from a small-scale laboratory to a 945-m2outdoor experimental burn. We compare mapped information on FRP obtained from simultaneous nadir and off-nadir views, where we find differences that are in part controlled by flame structure and/or view angle. In the large open fire case, we compare the mapped fire radiative energy and ROS to simultaneously acquired aerial photography that provides the position of fuel and flames in high detail, and we demonstrate how these data sets can be used to explore various aspects of fire behavior.

An analysis of one year of METEOSAT Spinning Enhanced Visible and Infrared Imager (SEVIRI) active fire data is presented. Fire Radiative Power (FRP) observations, acquired at a 15-minute time interval and 3 km spatial resolution, are used to estimate the total amount of African biomass combustion. African biomass combustion between February 2004 and January 2005 is estimated to be at least 855 million tonnes. FRP-derived biomass combustion estimates at a 1-degree spatial scale are found to be less than 2 kg/m2. In savanna grasslands the FRP-derived median fuel consumption estimate of 300 g/m2is in good agreement with literature values. Analysis of fire pixel temporal persistence indicates that the majority of African fires are detected only once in consecutive 15 minute images. Fire pixel duration is longest during the peak biomass burning season with a greater proportion of pixels detected only once during the wet season. An investigation of the variability of the diurnal fire cycle is carried out with respect to land cover type, and whilst differences are noted between land covers, the diurnal characteristics for a given land cover type are similar in both African hemispheres.

Africa is the single largest continental source of biomass burning emissions and one where emission source strengths are characterized by strong diurnal and seasonal cycles. This paper describes the development of a fire detection and characterization algorithm for generating high temporal resolution African pyrogenic emission data sets using data from the geostationary spinning enhanced visible and infrared imager (SEVIRI). The algorithm builds on a prototype approach tested previously with preoperational SEVIRI data and utilizes both spatial and spectral detection methods whose thresholds adapt contextually within and between imaging slots. Algorithm validation is carried out via comparison to data from ~800 temporally coincident moderate resolution imaging spectroradiometer (MODIS) scenes, and performance is significantly improved over the prior algorithm version, particularly in terms of detecting low fire radiative power (FRP) signals. On a per-fire basis, SEVIRI shows a good agreement with MODIS in terms of FRP measurement, with a small (3.7 MW) bias. In comparison to regional-scale total FRP derived from MODIS, SEVIRI underestimates this by, on average, 40% to 50% due to the nondetection of many low-intensity fire pixels (FRP < 50 MW). Frequency-magnitude analysis can be used to adjust fire radiative energy estimates for this effect, and taking this and other adjustments into account, SEVIRI-derived fuel consumption estimates for southern Africa from July to October 2004 are 259-339 Tg, with emission intensity peaking after midday and reducing by more than an order of magnitude each night.

Annual sequences of the first reprocessed albedo, bidirectional reflectance distribution function (BRDF), and nadir BRDF-adjusted surface reflectance (NBAR) products are being evaluated. BRDF model parameters (or weights) are used to compute black sky albedo at local solar noon and white sky albedo and to compute surface reflectance at a common nadir geometry. In addition to these standard resolution albedo, BRDF, and NBAR products, which are provided in the integerized sinusoidal grid projection, the BRDF parameters, black sky albedos (at local solar noon), and white sky albedos are now also being operationally produced in a global geographic projection known as the Climate Modeling Grid (CMG). These are currently available at a 0.25 degree spatial resolution, although there is community interest in a 0.05 degree resolution. In addition to the operational CMGs, coarser 0.5 degree and 1 degree resolution versions of the reprocessed albedo data have also been produced for the ISLSCP-II initiative. The global distribution of these albedos (as they vary throughout the year) are presented, as well as discussions of the most recent evaluations of the quality of the standard products.