Characterizing the Northern Hemisphere Circumpolar Vortex Through Space and Time

This hemispheric-scale, steering atmospheric circulation represented by the circumpolar vortices (CPVs) are the middle- and upper-tropospheric wind belts circumnavigating the poles. Variability in the CPV area, shape, and position are important topics in geoenvironmental sciences because of the many links to environmental features. However, a means of characterizing the CPV has remained elusive. The goal of this research is to (i) identify the Northern Hemisphere CPV (NHCPV) and its morphometric characteristics, (ii) understand the daily characteristics of NHCPV area and circularity over time, (iii) identify and analyze spatiotemporal variability in the NHCPV’s centroid, and (iv) analyze how CPV features relate to the air-sea teleconnections that are known to explain important variability in weather/climate. Daily data (1979─2017) were collected from the National Centers for Environmental Prediction at the 500-hPa geopotential height level, and processed and analyzed in Python, MATLAB, R, and ArcGIS Desktop platform. Results suggest that the innovative method improves the calculation of NHCPV area and circularity, proven with the significant correlations between the NHCPV and teleconnection indices. At a daily scale, both correlations and principal components analysis reveal that the NHCPV is closely related to some air-sea teleconnections. The NHCPV area has expanded linearly over the 1979─2017 period and within its four subperiods, likely because of the weakened gradient of atmospheric mass over time. On the other hand, the NHCPV has alternating periods of increasing and decreasing circularity, suggesting that it may have become more unstable in its delivery of west-to-east flow. Spectrum analysis shows distinct annual and semiannual cycles for the area and circularity over all periods. While the NHCPV centroid shifts annually and intra- annually throughout the time series, probably because of the seasonality and teleconnection linkage, the linear trend analysis shows that the day-to-day distance moved by the NHCPV centroid decreased significantly, suggesting stability in the centroid positions. Emerging hot spot analysis reveals that new and oscillating hot spots have been emerged over time. This research can be extended to understand the current and projected relationship between the full 4-D (x-y-z-t) feature-based CPV structure, ocean-air teleconnections, sea-ice forcing, and natural hazard impacts

Earth resources: A continuing bibliography with indexes (issue 55)

This bibliography lists 368 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1987. Emphasis is placed on the use of remote sensing and geographical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Applications of unsupervised machine learning in climate research

The use of machine learning in climate science is expanding rapidly after its success in other fields. The more powerful data driven models of machine learning show promise to give us more accurate predictions of and potentially more insights into the physical climate system than conventional statistical models. Many new applications for machine learning in climate science have been proposed in recent years, although it is uncertain which of them will prove to be fruitful lines of research. In this thesis, we explore two well-established problems in statistical climate science and approach them using unsupervised machine learning techniques, namely climate mode extraction and the bias correction of simulations. In the first part of this thesis, we develop a framework to test methods of mode extraction on climate-like data. We generate imitation global climate fields, which include as much of the expected complexity of real data as possible. We find that the newer mode extraction methods, which brand themselves as machine learning rather than conventional statistics, outperform the conventional approaches by more accurately extracting the known modes in the data. When applied to reanalysis and model surface temperature data, the newer methods extract a well-constrained ENSO signal and warming trend than the classical methods. However, no method can safeguard against generating false modes in all circumstances. We show the consequences of false mode extraction with examples and suggest how incorrectly extracted modes could lead to false proposed modal mechanisms. In the second part, we explore the use of unsupervised deep neural networks for bias correcting large, simulated climate fields of multiple variables. We show that the structure of neural networks allows for more faithful bias correction of cross-variable and spatial correlations when used to map between simulations and reanalysis data. A particular advantage of these methods is that they can shift misplaced climate features. We also show the limitation of these networks and how the lack of constraints in optimising them can lead to unexpected bias correction results. By exploring these uses of unsupervised machine learning in climate science we hope that can be developed further and be utilised more by researchers

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Earth resources: A continuing bibliography with indexes (issue 51)

This bibliography lists 382 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1986. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Novel Approaches in Landslide Monitoring and Data Analysis

Significant progress has been made in the last few years that has expanded the knowledge of landslide processes. It is, therefore, necessary to summarize, share and disseminate the latest knowledge and expertise. This Special Issue brings together novel research focused on landslide monitoring, modelling and data analysis

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

Multiresolution analysis for separating closely spaced frequencies with an application to Indian monsoon rainfall data

In this paper we make use of the multiresolution properties of discrete wavelets, including their ability to remove interference, to reveal closely spaced spectral peaks. We propose a procedure which we first verify on two test signals, and then apply it to the time series of homogeneous Indian monsoon rainfall annual data. We show that, compared to empirical mode decomposition, discrete wavelet analysis is more effective in identifying closely spaced frequencies if used in combination with classical power spectral analysis of wavelet-based partially reconstructed time series. An effective criterion based on better localization of specific frequency components and accurate estimation of their amplitudes is used to select an appropriate wavelet. It is shown here that the discrete Meyer wavelet has the best frequency properties among the wavelet families considered (Haar, Daubechies, Coiflet and Symlet). In rainfall data, the present analysis reveals two additional spectral peaks besides the fifteen found by classical spectral analysis. Moreover, these two new peaks have been found to be statistically significant, although a detailed discussion of testing for significance is being presented elsewhere

Earth resources: A continuing bibliography with indexes (issue 60)

This bibliography lists 485 reports, articles, and other documents introduced into the NASA scientific and technical information system between October 1 and December 31, 1988. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, oceanography and marine resources, hydrology and water management, data processing and distribution systems, and instrumentation and sensors