Development of Fog Detection Algorithm Using GK2A/AMI and Ground Data

Fog affects transportation due to low visibility and also aggravates air pollutants. Thus, accurate detection and forecasting of fog are important for the safety of transportation. In this study, we developed a decision tree type fog detection algorithm (hereinafter GK2A_FDA) using the GK2A/AMI and auxiliary data. Because of the responses of the various channels depending on the time of day and the underlying surface characteristics, several versions of the algorithm were created to account for these differences according to the solar zenith angle (day/dawn/night) and location (land/sea/coast). Numerical model data were used to distinguish the fog from low clouds. To test the detection skill of GK2A_FDA, we selected 23 fog cases that occurred in South Korea and used them to determine the threshold values (12 cases) and validate GK2A_FDA (11 cases). Fog detection results were validated using the visibility data from 280 stations in South Korea. For quantitative validation, statistical indices, such as the probability of detection (POD), false alarm ratio (FAR), bias ratio (Bias), and equitable threat score (ETS), were used. The total average POD, FAR, Bias, and ETS for training cases (validation cases) were 0.80 (0.82), 0.37 (0.29), 1.28 (1.16), and 0.52 (0.59), respectively. In general, validation results showed that GK2A_FDA effectively detected the fog irrespective of time and geographic location, in terms of accuracy and stability. However, its detection skill and stability were slightly dependent on geographic location and time. In general, the detection skill and stability of GK2A_FDA were found to be better on land than on coast at all times, and at night than day at any location.

Composite Estimators for Forest Growth Derived from Symmetric, Varying-Length Observation Intervals

Estimates of growth or change in a forest population parameter for a specific length of time, such as cubic meters of wood per hectare per year, are often made from sample observation intervals of different lengths of time. For instance, a basic building block of growth estimators in forest inventory systems is often the annual mean of the first differences of all observations for a particular year, regardless of observation interval length. The aggregate differences between successive observations on re-measured forest sample plots can be viewed as a linear combination, while forest growth is usually assumed to be non-linear. Bias can be assumed to exist whenever a linear combination is used to estimate a specific segment of an underlying non-linear trend. The amount of bias will depend upon the relationship of the intended estimation interval relative to the set of observation intervals. Here, three specific segments, relative to each year of interest, form the bases for a standard set of three estimands. Bias-ratio-adjusted composite estimators for use with observations made on alternative sets of symmetric interval lengths are compared in a simulation against this standard set of estimands. The first estimand has a one-year basis, the second has a five-year mid-interval basis, and the third has a five-year end-of-period basis. For the first and second bases, the initial results clearly show a logical ordering of bias and mean-squared error by observation interval length relative to the target interval length. As expected, some deviance from these clear trends are shown for the end-of-period basis. In the presence of three simple distributions of symmetric measurement intervals, the bias-ratio adjustments and subsequent composite estimators are shown to usually be effective in reducing bias and mean-squared error, while being most obviously effective for the most disparate distribution of intervals and for the end-of-period basis.

A Prototype Quantitative Precipitation Estimation Algorithm for Operational S-Band Polarimetric Radar Utilizing Specific Attenuation and Specific Differential Phase. Part II: Performance Verification and Case Study Analysis

The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.

What affects power to estimate speciation rate shifts?

The development of methods to estimate rates of speciation and extinction from time-calibrated phylogenies has revolutionized evolutionary biology by allowing researchers to correlate diversification rate shifts with causal factors. A growing number of researchers are interested in testing whether the evolution of a trait or a trait variant has influenced speciation rate, and three modelling methods—BiSSE, MEDUSA and BAMM—have been widely used in such studies. We simulated phylogenies with a single speciation rate shift each, and evaluated the power of the three methods to detect these shifts. We varied the degree of increase in speciation rate (speciation rate asymmetry), the number of tips, the tip-ratio bias (ratio of number of tips with each character state) and the relative age in relation to overall tree age when the rate shift occurred. All methods had good power to detect rate shifts when the rate asymmetry was strong and the sizes of the two lineages with the distinct speciation rates were large. Even when lineage size was small, power was good when rate asymmetry was high. In our simulated scenarios, small lineage sizes appear to affect BAMM most strongly. Tip-ratio influenced the accuracy of speciation rate estimation but did not have a strong effect on power to detect rate shifts. Based on our results, we provide suggestions to users of these methods.

Minimax Shrunken Technique for Estimate Burr X Distribution Shape Parameter

The present paper concern with minimax shrinkage estimator technique in order to estimate Burr X distribution shape parameter, when prior information about the real shape obtainable as original estimate while known scale parameter. Derivation for Bias Ratio, Mean squared error and the Relative Efficiency equations. Numerical results and conclusions for the expressions mentioned above were displayed. Comparisons for proposed estimator with most recent works were made.

What affects power to estimate speciation rate shifts?

The development of methods to estimate rates of speciation and extinction from time- calibrated phylogenies has revolutionized evolutionary biology by allowing researchers to correlate diversification rate shifts with causal ecological factors. A growing number of researchers are interested in testing whether the evolution of a trait or a trait variant has influenced speciation rates, and three modelling methods – BiSSE, MEDUSA and BAMM – have been widely used in such studies. We simulated phylogenies with a single speciation rate shift each, and evaluated the power of the three methods to detect these shifts. We varied the degree of increase in speciation rate (rate asymmetry), the number of tips, the tip-ratio bias (ratio of number of tips with each character state) and the relative age in relation to overall tree age when the rate shift occurred. All methods had good power to detect rate shifts when the rate asymmetry was strong and the sizes of the two lineages with the distinct speciation rates were large. Even when lineage size was small, power was good when rate asymmetry was high. In our simulated scenarios, small lineage sizes appear to affect BAMM most strongly. Tip-ratio influenced the accuracy of speciation rate estimation but did not have a strong effect on power to detect rate shifts. Based on our results, we provide some suggestions to users of these methods.

What affects power to estimate speciation rate shifts?

The development of methods to estimate rates of speciation and extinction from time- calibrated phylogenies has revolutionized evolutionary biology by allowing researchers to correlate diversification rate shifts with causal ecological factors. A growing number of researchers are interested in testing whether the evolution of a trait or a trait variant has influenced speciation rates, and three modelling methods – BiSSE, MEDUSA and BAMM – have been widely used in such studies. We simulated phylogenies with a single speciation rate shift each, and evaluated the power of the three methods to detect these shifts. We varied the degree of increase in speciation rate (rate asymmetry), the number of tips, the tip-ratio bias (ratio of number of tips with each character state) and the relative age in relation to overall tree age when the rate shift occurred. All methods had good power to detect rate shifts when the rate asymmetry was strong and the sizes of the two lineages with the distinct speciation rates were large. Even when lineage size was small, power was good when rate asymmetry was high. In our simulated scenarios, small lineage sizes appear to affect BAMM most strongly. Tip-ratio influenced the accuracy of speciation rate estimation but did not have a strong effect on power to detect rate shifts. Based on our results, we provide some suggestions to users of these methods.

Combining data from bottom-trawl and acoustic-trawl surveys to estimate an index of abundance for semipelagic species

Fishery-independent surveys are useful for estimating abundance of fish populations and their spatial distribution. It is necessary in the case of semipelagic species to perform acoustic-trawl (AT) and bottom-trawl (BT) surveys to ensure that sampling encompasses both midwater and demersal components of the population. Abundance estimates from both survey types are negatively biased because of the blind zones associated with fish vertical distribution. These biases can vary spatially and temporally, resulting in confounded trends and additional variation in abundance estimates. To improve abundance estimates for semipelagic species we propose a new method for combining BT and AT survey data using environmental variables to predict the vertical overlap. Using walleye pollock (Gadus chalcogrammus) AT and BT surveys in the eastern Bering Sea as an example, we show that combined estimates provide more reliable whole water column and spatial distribution estimates than either survey can by itself. Although the combined estimates are still relative, they account for the uncertainty in the bias ratio between the two survey methods and the uncertainty associated with the extent of the water column sampled by both surveys. Our method of combining BT and AT data can be extended to other semipelagic species.

Multispecies acoustic dead-zone correction and bias ratio estimates between acoustic and bottom-trawl data

In this study, we extended the original work of Kotwicki et al. (2013. Combining bottom trawl and acoustic data to model acoustic dead zone correction and bottom trawl efficiency parameters for semipelagic species. Canadian Journal of Fisheries and Aquatic Sciences 70: 208–219) to jointly estimate the acoustic dead-zone correction, the bias ratio, and the gear efficiency for multiple species by using simultaneously collected acoustic and bottom-trawl data. The model was applied to cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) in the Barents Sea and demonstrated a better or similar performance compared with a single species approach. The vertical distribution of cod and haddock was highly variable and was influenced by light level, water temperature, salinity, and depth. Temperature and sunlight were the most influential factors in this study. Increase in temperature resulted in decreasing catch and fish density in the acoustic dead zone (ADZ), while increasing sun altitude (surrogate for light level) increased the catch and fish density in the ADZ. The catch and density of haddock in the ADZ also increased at the lowest sun altitude level (shortly after midnight). Generally, the density of cod and haddock changed more rapidly in the ADZ than in the catch (from bottom to the effective fishing height) indicating the importance of modelling fish density in the ADZ. Finally, the uncorrelated variability in the annual residual variance of cod and haddock further strengthen the conclusion that species vertical distribution changes frequently and that there are probably many other unobserved environmental variables that affect them independently.

Flagging potential fraudulent investment activity

Purpose This paper aims to investigate the use of the bias ratio as a possible early indicator of financial fraud – specifically in the reporting of hedge fund returns. In the wake of the 2008-2009 financial crisis, numerous hedge funds were liquidated and several cases of financial fraud exposed. Design/methodology/approach Risk-adjusted return metrics such as the Sharpe ratio and Value at Risk were used to raise suspicion for fraud. These metrics, however, assume distributional normality and thus have had limited success with hedge fund returns (a characteristic of which is highly skewed, non-normal return distributions). Findings Results indicate that potential fraud would have been detected in the early stages of the scheme’s life. Having demonstrated the credibility of the bias ratio, it was then applied to several indices and (anonymous) South African hedge funds. The results were used to demonstrate the ratio’s scope and robustness and draw attention to other metrics which could be used in conjunction with it. Results from these multiple sources could be used to justify further investigation. Research limitations/implications The traditional metrics for performance evaluation (such as the Sharpe ratio), assume distributional normality and thus have had limited success with hedge fund returns (a characteristic of which is highly skewed, non-normal return distributions). The bias ratio, which does not rely on normally distributed returns, was applied to a known fraud case (Madoff’s Ponzi scheme). Practical implications The effectiveness of the bias ratio in demonstrating potential suspicious financial activity has been demonstrated. Originality/value The financial market has come under heightened scrutiny in the past decade (2005 – 2015) as a result of the fragile and uncertain economic milieu that still (2015) persists. Numerous risk and return measures have been used to evaluate hedge funds’ risk-adjusted performance, but many fail to account for non-normal return distributions exhibited by hedge funds. The bias ratio, however, has been demonstrated to effectively flag potentially fraudulent funds.