Global sensitivity measures from given data

[1] A new data mode and new analysis methods are used to detect Terrestrial Gamma-ray Flashes (TGFs) with the Fermi Gamma-ray Burst Monitor (GBM) 10 times more frequently than previously. In 1037 h of observations at times and over regions for which TGFs are expected, 384 new TGFs were found in addition to the 39 TGFs and two Terrestrial Electron Beam events already detected without the new data mode and methodology. Cosmic ray showers were found to be an important background; they show characteristic signatures in the data of both GBM and the Fermi Large Area Telescope Calorimeter that enable their removal, leaving a sample estimated to consist of ≈98% TGFs. The sample includes shorter TGFs than previously found with GBM. The true duration distribution likely contains additional short TGFs because their detection by GBM is limited by detector dead time. One-third of this sample has matches with locations from the World Wide Lightning Location Network (WWLLN)—maps of these locations show the geographic and meteorological features more clearly than maps of spacecraft locations. The intrinsic TGF rate is evaluated using the lightning rate maps of the Lightning Imaging Sensor, accounting for the detection efficiency of GBM as a function of spacecraft-source offset, from which we estimate a global TGF rate of ≈400,000 per year. With continuous production of data in the new mode we estimate that GBM will detect ≈850 TGFs per year.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgra.50205

Terrestrial gamma-ray flashes in the Fermi era: Improved observations and analysis methods

SaSAT (Sampling and Sensitivity Analysis Tools) is a user-friendly software package for applying uncertainty and sensitivity analyses to mathematical and computational models of arbitrary complexity and context. The toolbox is built in Matlab®, a numerical mathematical software package, and utilises algorithms contained in the Matlab® Statistics Toolbox. However, Matlab® is not required to use SaSAT as the software package is provided as an executable file with all the necessary supplementary files. The SaSAT package is also designed to work seamlessly with Microsoft Excel but no functionality is forfeited if that software is not available. A comprehensive suite of tools is provided to enable the following tasks to be easily performed: efficient and equitable sampling of parameter space by various methodologies; calculation of correlation coefficients; regression analysis; factor prioritisation; and graphical output of results, including response surfaces, tornado plots, and scatterplots. Use of SaSAT is exemplified by application to a simple epidemic model. To our knowledge, a number of the methods available in SaSAT for performing sensitivity analyses have not previously been used in epidemiological modelling and their usefulness in this context is demonstrated.

/pdf/sampling-and-sensitivity-analyses-tools-sasat-for-5fcsqvt4iv.pdf

Sampling and sensitivity analyses tools (SaSAT) for computational modelling.

Since industrialization global CO2 emissions have increased, and as a consequence oceanic pH is predicted to drop by 0.3–0.4 units before the end of the century – a process coined ‘ocean acidification’. Consequently, there is significant interest in how pH changes will affect the ocean's biota and integral processes. We investigated marine picoplankton (0.2–2 µm diameter) community response to predicted end of century CO2 concentrations, via a ‘high-CO2’ (∼ 750 ppm) large-volume (11 000 l) contained seawater mesocosm approach. We found little evidence of changes occurring in bacterial abundance or community composition due to elevated CO2 under both phytoplankton pre-bloom/bloom and post-bloom conditions. In contrast, significant differences were observed between treatments for a number of key picoeukaryote community members. These data suggested a key outcome of ocean acidification is a more rapid exploitation of elevated CO2 levels by photosynthetic picoeukaryotes. Thus, our study indicates the need for a more thorough understanding of picoeukaryote-mediated carbon flow within ocean acidification experiments, both in relation to picoplankton carbon sources, sinks and transfer to higher trophic levels.

/pdf/the-response-of-marine-picoplankton-to-ocean-acidification-59d234a1j3.pdf

The response of marine picoplankton to ocean acidification

While often confused, the Kolmogorov–Smirnov test and the Smirnov test are actually distinct. Specifically, the Kolmogorov–Smirnov test is used to test the goodness of fit of a given set of data to a theoretical distribution, making this a one-sample test. In contrast, the Smirnov test is a two-sample test, used to determine if two samples appear to follow the same distribution. The intuition behind the two tests is the same, however, in that both compare cumulative distribution functions, either two empirical cumulative distribution functions for the two-sample Smirnov test, or one empirical cumulative distribution function and one known cumulative distribution function for the one-sample Kolmogorov–Smirnov test.


Keywords:

distribution function;
exact test;
nonparametric test;
ranks

Andrei M. Nikiforov

Papers

Exact Smirnov Two-sample Tests for Arbitrary Distributions