Preliminary analysis on the noise characteristics of MWISP data

Noise is a significant part within a millimeter-wave molecular line datacube. Analyzing the noise improves our understanding of noise characteristics, and further contributes to scientific discoveries. We measure the noise level of a single datacube from MWISP and perform statistical analyses. We identified major factors which increase the noise level of a single datacube, including bad channels, edge effects, baseline distortion and line contamination. Cleaning algorithms are applied to remove or reduce these noise components. As a result, we obtained the cleaned datacube in which noise follows a positively skewed normal distribution. We further analyzed the noise structure distribution of a 3D mosaicked datacube in the range l = 40 ⋅ ° 7 to 43 ⋅ ° 3 and b = − 2 ⋅ ° 3 to 0 ⋅ ° 3 and found that noise in the final mosaicked datacube is mainly characterized by noise fluctuation among the cells.

Feature Comparison of Two Mesoscale Eddy Datasets Based on Satellite Altimeter Data

Although a variety of ocean mesoscale eddy datasets are available for researchers to study eddy properties throughout the global ocean, subtle differences in how these datasets are produced often lead to large differences between one another. This study compares the Global Ocean Mesoscale Eddy Atmospheric-Oceanic-Biological interaction Observational Dataset (GOMEAD) with the well-recognized Mesoscale Eddy Trajectory Atlas in four regions with strong eddy activity: the Northwest Pacific Subtropical Front (SF), Kuroshio Extension (KE), South China Sea (SCS), and California Coastal Current (CC), and assesses the relative advantages and disadvantages of each. It was identified that while there is a slight difference in the total number of eddies detected in each dataset, the frequency distribution of eddy radii presents a right-skewed normal distribution, tending towards larger radii eddies, and there are more short- than long-lived eddies. Interestingly, the total number of GOMEAD eddies is 8% smaller than in the META dataset and this is most likely caused by the GOMEAD dataset’s underestimation of total eddy numbers and lifespans due to their presence near islands, and the tendency to eliminate eddies from its database if their radii are too small to be adequately detected. By contrast, the META dataset, due to tracking jumps in detecting eddies, may misidentify two eddies as a single eddy, reducing total number of eddies detected. Additionally, because the META dataset is reliant on satellite observations of sea surface level anomalies (SLAs), when SLAs are weak, the META dataset struggles to detect eddies. The GOMEAD dataset, by contrast, is reliant on applying vector geometry to detect and track eddies, and thus, is largely insulated from this problem. Thus, although both datasets are excellent in detecting and characterizing eddies, users should use the GOMEAD dataset when the region of interest is far from islands or when SLAs are weak but use the META dataset if the region of interest is populated by islands, or if SLAs are intense.

Rolling window VaR: an EVT approach

In this study, a method of estimating value-at-risk is proposed. This method combines elements of extreme value theory (EVT), the APARCH model (Ding et al. 1993) and the rolling window method. The research was conducted using 20 stock market indexes worldwide during 2006-2019. Value-at-risk was estimated via 12 competing models which were evaluated using 5 tests. The back testing results indicate that the best model was the one which takes into consideration the asymmetric character of financial data (APARCH with skewed normal distribution), the Generalized Pareto Distribution for modeling the tail of the financial returns distribution and the rolling window approach. The methodologies discussed in this paper could provide a useful tool for both financial entities and regulatory authorities.

Using Pre-Determined Patterns to Analyze the Common Behavior of Compressed Data and Their Compressibility Apeal

This paper studies the behavior of compressed/uncompressed data on predetermined binary patterns. These patterns were generated according to specific criteria to ensure that they represent binary files. Each pattern is structurally unique. This study shows that all compressed data behave almost similarly when analyzing predetermined patterns. They all follow a curve similar to that of a skewed normal distribution. The uncompressed data, on the other hand, behave differently. Each file of uncompressed data plots its own curve without a specific shape. The paper confirms the side effect of these patterns, and the fact that they can be used to measure the compressibility appeal of compressed data.