Tracking Nonlinear Correlation for Complex Dynamic Systems Using a Windowed Error Reduction Ratio Method

Studying complex dynamic systems is usually very challenging due to limited prior knowledge and high complexity of relationships between interconnected components. Current methods either are like a “black box” that is difficult to understand and relate back to the underlying system or have limited universality and applicability due to too many assumptions. This paper proposes a time-varying Nonlinear Finite Impulse Response model to estimate the multiple features of correlation among measurements including direction, strength, significance, latency, correlation type, and nonlinearity. The dynamic behaviours of correlation are tracked through a sliding window approach based on the Blackman window rather than the simple truncation by a Rectangular window. This method is particularly useful for a system that has very little prior knowledge and the interaction between measurements is nonlinear, time-varying, rapidly changing, or of short duration. Simulation results suggest that the proposed tracking approach significantly reduces the sensitivity of correlation estimation against the window size. Such a method will improve the applicability and robustness of correlation analysis for complex systems. A real application to environmental changing data demonstrates the potential of the proposed method by revealing and characterising hidden information contained within measurements, which is usually “invisible” for conventional methods

Methodology for designing accelerated aging tests for predicting life of photovoltaic arrays

A methodology for designing aging tests in which life prediction was paramount was developed. The methodology builds upon experience with regard to aging behavior in those material classes which are expected to be utilized as encapsulant elements, viz., glasses and polymers, and upon experience with the design of aging tests. The experiences were reviewed, and results are discussed in detail

Estimating summer sea ice extent in the Weddell Sea during the early 19th century

Over the past 3 decades, discordant trends in sea ice extent have been observed between the Arctic and Antarctic regions. Arctic sea ice extent has been characterised by a rapid decline, whereas Antarctic sea ice extent, while highly variable interannually, has tended to increase. Climate models have so far failed to capture these trends. Coupled with the limited pre-1970 sea ice dataset, this poses a significant challenge to quantifying the mechanisms responsible for driving such trends. However, historical records from early Antarctic expeditions contain a wealth of information regarding the nature and concentration of sea ice. Such records have been underutilised, and their analysis may enhance our understanding of recent Antarctic sea ice variability. For the purpose of this study, nine records from eight Antarctic expeditions have been examined. Summer sea ice positions recorded during 1820–1843 have been compared to satellite observations from 1987–2017, as well as historical data for the period 1897–1917. Through analysis of these three time series, estimates for the northern limits of summer sea ice in the Weddell Sea during the early 19th century have been produced. The key findings of this study indicate a 19th century average core summer northernmost sea ice latitude in much of the Weddell Sea that was further north than during the modern era, with 19th century February having significantly more sea ice by all measures. However, late summer sea ice was most extensive in the early years of the 20th century.</p

Meteorological effects of the solar eclipse of 20 March 2015: analysis of UK Met Office automatic weather station data and comparison with automatic weather station data from the Faroes and Iceland.

Here, we analyse high-frequency (1 min) surface air temperature, mean sea-level pressure (MSLP), wind speed and direction and cloud-cover data acquired during the solar eclipse of 20 March 2015 from 76 UK Met Office weather stations, and compare the results with those from 30 weather stations in the Faroe Islands and 148 stations in Iceland. There was a statistically significant mean UK temperature drop of 0.83±0.63°C, which occurred over 39 min on average, and the minimum temperature lagged the peak of the eclipse by about 10 min. For a subset of 14 (16) relatively clear (cloudy) stations, the mean temperature drop was 0.91±0.78 (0.31±0.40)°C but the mean temperature drops for relatively calm and windy stations were almost identical. Mean wind speed dropped significantly by 9% on average during the first half of the eclipse. There was no discernible effect of the eclipse on the wind-direction or MSLP time series, and therefore we can discount any localized eclipse cyclone effect over Britain during this event. Similar changes in air temperature and wind speed are observed for Iceland, where conditions were generally clearer, but here too there was no evidence of an eclipse cyclone; in the Faroes, there was a much more muted meteorological signature.This article is part of the themed issue 'Atmospheric effects of solar eclipses stimulated by the 2015 UK eclipse'

A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland

Icebergs have long been a threat to shipping in the NW Atlantic and the iceberg season of February to late summer is monitored closely by the International Ice Patrol. However, reliable predictions of the severity of a season several months in advance would be useful for planning monitoring strategies and also for shipping companies in designing optimal routes across the North Atlantic for specific years. A seasonal forecast model of the build-up of seasonal iceberg numbers has recently become available, beginning to enable this longer-term planning of marine operations. Here we discuss extension of this control systems model to include more recent years within the trial ensemble sample set and also increasing the number of measures of the iceberg season that are considered within the forecast. These new measures include the seasonal iceberg total, the rate of change of the seasonal increase, the number of peaks in iceberg numbers experienced within a given season, and the timing of the peak(s). They are predicted by a range of machine learning tools. The skill levels of the new measures are tested, as is the impact of the extensions to the existing seasonal forecast model. We present a forecast for the 2021 iceberg season, predicting a medium iceberg year

The Atlantic Ocean at the last glacial maximum: 1. Objective mapping of the GLAMAP sea-surface conditions

Recent efforts of the German paleoceanographic community have resulted in a unique data set of reconstructed sea-surface temperature for the Atlantic Ocean during the Last Glacial Maximum, plus estimates for the extents of glacial sea ice. Unlike prior attempts, the contributing research groups based their data on a common definition of the Last Glacial Maximum chronozone and used the same modern reference data for calibrating the different transfer techniques. Furthermore, the number of processed sediment cores was vastly increased. Thus the new data is a significant advance not only with respect to quality, but also to quantity. We integrate these new data and provide monthly data sets of global sea-surface temperature and ice cover, objectively interpolated onto a regular 1°x1° grid, suitable for forcing or validating numerical ocean and atmosphere models. This set is compared to an existing subjective interpolation of the same base data, in part by employing an ocean circulation model. For the latter purpose, we reconstruct sea surface salinity from the new temperature data and the available oxygen isotope measurements

Data/model integration for vertical mixing in the stable Arctic boundary layer

This is the final report of a short Laboratory Directed Research and Development (LDRD) project at Los Alamos National Laboratory (LANL). Data on atmospheric trace constituents and the vertical structure of stratus clouds from a 1996 expedition to the central Arctic reveal mechanisms of vertical mixing that have not been observed in mid-latitudes. Time series of the altitude and thickness of summer arctic stratus have been observed using an elastic backscatter lidar aboard an icebreaker. With the ship moored to the pack ice during 14 data collection stations and the lidar staring vertically, the time series represent advected cloud fields. The lidar data reveal a significant amount of vertical undulation in the clouds, strongly suggestive of traveling waves in the buoyantly damped atmosphere that predominates in the high Arctic. Concurrent observations of trace gases associated with the natural sulfur cycle (dimethyl sulfide, SO{sub 2}, NH{sub 3}, H{sub 2}O{sub 2}) and aerosols show evidence of vertical mixing events that coincide with a characteristic signature in the cloud field that may be called dropout or lift out. A segment of a cloud deck appears to be relocated from the otherwise quasicontinuous layer to another altitude a few hundred meters lower or higher. Atmospheric models have been applied to identify the mechanism that cause the dropout phenomenon and connect it dynamically to the surface layer mixing