Analyzing Tropical Waves Using the Parallel Ensemble Empirical Model Decomposition Method: Preliminary Results from Hurricane Sandy

In this study, we discuss the performance of the parallel ensemble empirical mode decomposition (EMD) in the analysis of tropical waves that are associated with tropical cyclone (TC) formation. To efficiently analyze high-resolution, global, multiple-dimensional data sets, we first implement multilevel parallelism into the ensemble EMD (EEMD) and obtain a parallel speedup of 720 using 200 eight-core processors. We then apply the parallel EEMD (PEEMD) to extract the intrinsic mode functions (IMFs) from preselected data sets that represent (1) idealized tropical waves and (2) large-scale environmental flows associated with Hurricane Sandy (2012). Results indicate that the PEEMD is efficient and effective in revealing the major wave characteristics of the data, such as wavelengths and periods, by sifting out the dominant (wave) components. This approach has a potential for hurricane climate study by examining the statistical relationship between tropical waves and TC formation

Identification of Sea Level Rise and Land Subsidence Based on Sentinel 1 Data in the Coastal City of Pekalongan, Central Java, Indonesia

Sea level rise is a pure impact of climate change. However, the process of studying sea level rise must include local factors that influence such as land subsidence. This study focuses on sea level rise using the CEEMDAN method and land subsidence using the DInSAR method. The location of this research is Pekalongan, Central Java, Indonesia. Tidal data used in this study was for five years, from 2016 to 2020, obtained from the Geospatial Information Agency (BIG). Then the data used to study land subsidence in this study uses Sentinel-1 Synthetic Aperture Radar (SAR) data in 2015, 2016, 2017, and 2020. Pekalongan is an area with mixed diurnal tidal types with Formzahl number 1.7. The sea level rise in Pekalongan is relatively high, at 10.6 mm/year. Then the land subsidence that occurred in Pekalongan is the phenomenon that has the most influence on the occurrence of coastal flooding in the region. The average land subsidence on the coast of Pekalongan is 5.37 cm/year. In addition, the sampling results in 6 areas showed that the most significant decrease was in Area 2, with a decrease of -7.91 cm/year. Based on this research, land subsidence is the most considerable influence on flooding in Pekalongan compared to sea level rise

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Deep Learning Techniques in Extreme Weather Events: A Review

Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecasting and understanding the dynamics of extreme weather events. This review aims to provide a comprehensive overview of the state-of-the-art deep learning in the field. We explore the utilization of deep learning architectures, across various aspects of weather prediction such as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and tropical cyclones. We highlight the potential of deep learning, such as its ability to capture complex patterns and non-linear relationships. Additionally, we discuss the limitations of current approaches and highlight future directions for advancements in the field of meteorology. The insights gained from this systematic review are crucial for the scientific community to make informed decisions and mitigate the impacts of extreme weather events

Effective denoising and classification of hyperspectral images using curvelet transform and singular spectrum analysis

Hyperspectral imaging (HSI) classification has become a popular research topic in recent years, and effective feature extraction is an important step before the classification task. Traditionally, spectral feature extraction techniques are applied to the HSI data cube directly. This paper presents a novel algorithm for HSI feature extraction by exploiting the curvelet transformed domain via a relatively new spectral feature processing technique – singular spectrum analysis (SSA). Although the wavelet transform has been widely applied for HSI data analysis, the curvelet transform is employed in this paper since it is able to separate image geometric details and background noise effectively. Using the support vector machine (SVM) classifier, experimental results have shown that features extracted by SSA on curvelet coefficients have better performance in terms of classification accuracies over features extracted on wavelet coefficients. Since the proposed approach mainly relies on SSA for feature extraction on the spectral dimension, it actually belongs to the spectral feature extraction category. Therefore, the proposed method has also been compared with some state-of-the-art spectral feature extraction techniques to show its efficacy. In addition, it has been proven that the proposed method is able to remove the undesirable artefacts introduced during the data acquisition process as well. By adding an extra spatial post-processing step to the classified map achieved using the proposed approach, we have shown that the classification performance is comparable with several recent spectral-spatial classification methods

River landform dynamics detection and responses to morphology change in the rivers of North Luzon, the Philippines

River morphology detection has been improved considerably with the application of remote sensing and developments in computer science. However, applications that extract landforms within the active river channel remain limited, and there is a lack of studies from tropical regions. This thesis developed and then applied a workflow employing Sentinel-2 imagery for seasonal and annual river landform classification. Image downscaling approaches were investigated, and the performance of object-based image segmentation was assessed. The area to point regression kriging (ATPRK) approach was chosen to downscale coarser 20 m resolution Sentinel-2 bands to finer 10 m resolution bands. All features were set or processed at 10 m resolution before applying support vector machine (SVM) classification. To improve machine learning classification accuracy, Sentinel-2 acquisitions across one year, which incorporates multiple seasons, should be used. For rivers with different hydrological or geology settings, the thesis considered collecting river specific ground truth data to build a training model to avoid underfitting of models from other hydrological/geological settings. Applying the workflow, three landforms (water, unvegetated bars and vegetated bars) were classified within the active channel of the Bislak, Laoag, Abra and Cagayan Rivers, north Luzon, the Philippines, between 2016 to 2021, respectively. The spatial-temporal river landform datasets enabled the quantitative analysis of the river morphology changes. Water and unvegetated bars showed clear seasonal dynamics in all four rivers, whilst vegetated bars only showed seasonality in the rivers located in the northwest Luzon (the Bislak, Laoag and Abra Rivers). This thesis employed correlated coefficients to investigate the longitudinal correlation between river landforms and active width. It was found that vegetated bar areas always have strong significant correlations (≥0.67) with the active widths in all four rivers, whilst correlation coefficients between vegetated bar areas and active widths in the wet season are higher than that in the dry season. Ensemble empirical mode decomposition (EEMD) was applied to detect landform periodicity; this method indicated that water and vegetated bars commonly showed synchronised fluctuations with precipitation, while unvegetated bars had an anti-phase oscillation with precipitation. In the case of EEMD, deviations from periodic consistency in river pattern may reflect the influence of extreme events and/or human disturbance. Coefficient of variation (COV) was then used to evaluate the stability of the landforms; results suggested that the interplay of faults, elevation, confinement and tributary locations impacted landform stability. Finally, tributary inflow impacts on the mainstem river were investigated for eight tributaries of the lowland Cagayan River, also on Luzon Island. Longitudinal variations in channel morphology and stability, and temporal changes in landform frequency, using Simpson’s diversity index and COV, showed downstream widening associated with tributaries that was controlled by water discharge, with a secondary sediment flux effect. Overall, this thesis provided a novel example of combining remote sensing and GIS science, computing science, statistical science, and river morphology science to study the earth surface processes synthetically and quantitatively within river active channels in the tropical north Luzon, the Philippines. This work demonstrated how the fusion of techniques from these disciplines can be used to detect and analyse river landform changes, with potential applications for river management and restoration

Hand in Hand Tropical Cyclones and Climate Change: Investigating the Response of Tropical Cyclones to the Warming World

What are the primary factors governing Tropical Cyclone Potential Intensity (TCPI) and how does the TCPI vary with the change in CO2 concentration are the two fundamental questions we investigated here. In the first part, a strong spatial correlation between the TCPI and the ocean temperature underneath was used to develop a statistical model to quantify the TCPI over the remote regions where the tropical cyclone related observations are difficult to acquire. The model revealed an overall increase in the TCPI when the atmospheric CO2 concentration was doubled. Finally, the study examines the TCPI’s sensitivity on the ocean temperature (at the spatial scales). Two independent models (HADCM3 from Met Office, UK and GFDL-CM3 from GFDL, NOAA, USA) on an average reveals an increase in the TCPI between 8 to 10 m/s per unit increase in the ocean temperature (in degree C). The key finding to emerge from this study is that the increase in the TCPI responds comparatively weakly to the increasing ocean temperature when CO2 amount is increased. We call this observation as, “the sensitivity saturation effect”. According to our findings, the TCPI responds weakly (become less sensitive) to the ocean temperature on doubling the CO2 concentration. This effect was observed in all the ocean basins and in both the considered climate models. Though the TCPI show a rise in increasing the CO2 concentration but, its response to the SST decreases. This observation leads to a set of next level questions for instance, will there be a sensitivity saturation effect, analogous to the well-known “Band Saturation effect”, on increasing the CO2 levels and if it does, will the TCPI’s sensitivity plateau? If it plateaus, at what cut-off CO2 levels would that happen? These emerging questions open up a new area of investigation for the climatologists and the enthusiasts in the related fields. In this manner, this part of the research provides a framework for the future exploration of the subject.UKIERI Fellowshi