ALBERT

Description: No abstract available

Description: This paper derives an efficient procedure for using the three-dimensional (3D) vector radiative transfer equation (VRTE) to adjust atmosphere and surface properties and improve their fit with multi-angle/multi-pixel radiometric and polarimetric measurements of scattered sunlight. The proposed adjoint method uses the 3D VRTE to compute the measurement misfit function and the adjoint 3D VRTE to compute its gradient with respect to all unknown parameters. In the remote sensing problems of interest, the scalar-valued misfit function quantifies agreement with data as a function of atmosphere and surface properties, and its gradient guides the search through this parameter space. Remote sensing of the atmosphere and surface in a three-dimensional region may require thousands of unknown parameters and millions of data points. Many approaches would require calls to the 3D VRTE solver in proportion to the number of unknown parameters or measurements. To avoid this issue of scale, we focus on computing the gradient of the misfit function as an alternative to the Jacobian of the measurement operator. The resulting adjoint method provides a way to adjust 3D atmosphere and surface properties with only two calls to the 3D VRTE solver for each spectral channel, regardless of the number of retrieval parameters, measurement view angles or pixels. This gives a procedure for adjusting atmosphere and surface parameters that will scale to the large problems of 3D remote sensing. For certain types of multi-angle/multi-pixel polarimetric measurements, this encourages the development of a new class of three-dimensional retrieval algorithms with more flexible parametrizations of spatial heterogeneity, less reliance on data screening procedures, and improved coverage in terms of the resolved physical processes in the Earths atmosphere.

Description: We describe a method for accelerating a 3D Monte Carlo forward radiative transfer model to the point where it can be used in a new kind of Bayesian retrieval framework. The remote sensing challenge is to detect and quantify a chemical effluent of a known absorbing gas produced by an industrial facility in a deep valley. The available data is a single low resolution noisy image of the scene in the near IR at an absorbing wavelength for the gas of interest. The detected sunlight has been multiply reflected by the variable terrain and/or scattered by an aerosol that is assumed partially known and partially unknown. We thus introduce a new class of remote sensing algorithms best described as "multi-pixel" techniques that call necessarily for a 3D radaitive transfer model (but demonstrated here in 2D); they can be added to conventional ones that exploit typically multi- or hyper-spectral data, sometimes with multi-angle capability, with or without information about polarization. The novel Bayesian inference methodology uses adaptively, with efficiency in mind, the fact that a Monte Carlo forward model has a known and controllable uncertainty depending on the number of sun-to-detector paths used.

Description: No abstract available

Description: No abstract available

Description: This manual represents a review of the potential sources and methods to be applied when providing prior information to Bayesian stock assessments and marine risk analysis. The manual is compiled as a product of the EC Framework 7 ECOKNOWS project (www.ecoknows.eu). The manual begins by introducing the basic concepts of Bayesian inference and the role of prior information in the inference. Bayesian analysis is a mathematical formalization of a sequential learning process in a probabilistic rationale. Prior information (also called ”prior knowledge”, ”prior belief”, or simply a ”prior”) refers to any existing relevant knowledge available before the analysis of the newest observations (data) and the information included in them. Prior information is input to a Bayesian statistical analysis in the form of a probability distribution (a prior distribution) that summarizes beliefs about the parameter concerned in terms of relative support for different values. Apart from specifying probable parameter values, prior information also defines how the data are related to the phenomenon being studied, i.e. the model structure. Prior information should reflect the different degrees of knowledge about different parameters and the interrelationships among them. Different sources of prior information are described as well as the particularities important for their successful utilization. The sources of prior information are classified into four main categories: (i) primary data, (ii) literature, (iii) online databases, and (iv) experts. This categorization is somewhat synthetic, but is useful for structuring the process of deriving a prior and for acknowledging different aspects of it. A hierarchy is proposed in which sources of prior information are ranked according to their proximity to the primary observations, so that use of raw data is preferred where possible. This hierarchy is reflected in the types of methods that might be suitable – for example, hierarchical analysis and meta-analysis approaches are powerful, but typically require larger numbers of observations than other methods. In establishing an informative prior distribution for a variable or parameter from ancillary raw data, several steps should be followed. These include the choice of the frequency distribution of observations which also determines the shape of prior distribution, the choice of the way in which a dataset is used to construct a prior, and the consideration related to whether one or several datasets are used. Explicitly modelling correlations between parameters in a hierarchical model can allow more effective use of the available information or more knowledge with the same data. Checking the literature is advised as the next approach. Stock assessment would gain much from the inclusion of prior information derived from the literature and from literature compilers such as FishBase (www.fishbase.org), especially in data-limited situations. The reader is guided through the process of obtaining priors for length–weight, growth, and mortality parameters from FishBase. Expert opinion lends itself to data-limited situations and can be used even in cases where observations are not available. Several expert elicitation tools are introduced for guiding experts through the process of expressing their beliefs and for extracting numerical priors about variables of interest, such as stock–recruitment dynamics, natural mortality, maturation, and the selectivity of fishing gears. Elicitation of parameter values is not the only task where experts play an important role; they also can describe the process to be modelled as a whole. Information sources and methods are not mutually exclusive, so some combination may be used in deriving a prior distribution. Whichever source(s) and method(s) are chosen, it is important to remember that the same data should not be used twice. If the 2 | ICES Cooperative Research Report No. 328 plan is to use the data in the analysis for which the prior distribution is needed, then the same data cannot be used in formulating the prior. The techniques studied and proposed in this manual can be further elaborated and fine-tuned. New developments in technology can potentially be explored to find novel ways of forming prior distributions from different sources of information. Future research efforts should also be targeted at the philosophy and practices of model building based on existing prior information. Stock assessments that explicitly account for model uncertainty are still rare, and improving the methodology in this direction is an important avenue for future research. More research is also needed to make Bayesian analysis of non-parametric models more accessible in practice. Since Bayesian stock assessment models (like all other assessment models) are made from existing knowledge held by human beings, prior distributions for parameters and model structures may play a key role in the processes of collectively building and reviewing those models with stakeholders. Research on the theory and practice of these processes will be needed in the future.