Related papers: Modelling ocean temperatures from bio-probes under…
We propose a nonlinear ocean forecasting technique based on a combination of genetic algorithms and empirical orthogonal function (EOF) analysis. The method is used to forecast the space-time variability of the sea surface temperature (SST)…
The TRAPPIST-1 system is home to at least seven terrestrial planets and is a target of interest for future James Webb Space Telescope (JWST) observations. Additionally, these planets will be of interest to future missions making…
The impact of statistical methodologies on studying groundwater has been significant in the last several decades, due to cheaper computational abilities and presence of technologies that enable us to extract and measure more and more data.…
Oil slicks are widely distributed in the ocean today, as a kind of coverage on sea surface, they became a part of ocean environment and affect their surroundings. A stochastic-dynamic theoretical model proposed in this work to illustrate…
There is increasing evidence that global warming manifests itself in more frequent warm days and that heat waves will become more frequent. Presently, a formal definition of a heat wave is not agreed upon in the literature. To avoid this…
Modern Earth observation relies on satellites to capture detailed surface properties. Yet, many phenomena that affect humans and ecosystems unfold in the atmosphere close to the surface. Near-ground sensors provide accurate measurements of…
Physiological and behavioral data collected from wearable or mobile sensors have been used to estimate self-reported stress levels. Since the stress annotation usually relies on self-reports during the study, a limited amount of labeled…
With the rapid advances of data acquisition techniques, spatio-temporal data are becoming increasingly abundant in a diverse array of disciplines. Here we develop spatio-temporal regression methodology for analyzing large amounts of…
Accurate prediction of global sea surface temperature at sub-seasonal to seasonal (S2S) timescale is critical for drought and flood forecasting, as well as for improving disaster preparedness in human society. Government departments or…
Earth observation (EO) foundation models have emerged as an effective approach to derive latent representations of the Earth system from various remote sensing sensors. These models produce embeddings that can be used as analysis-ready…
This paper examines the challenges and advancements in recognizing seals within their natural habitats using conventional photography, underscored by the emergence of machine learning technologies. We used the leopard seal, \emph{Hydrurga…
As the hunt for an Earth-like exoplanets has intensified in recent years, so has the effort to characterise and model the stellar signals that can hide or mimic small planetary signals. Stellar variability arises from a number of sources,…
The effect caused by the presence of a number of distinct time scales in a simple stochastic model for the Earth's atmosphere temperature fluctuations is studied. The model is described by a dissipative dynamics consisting of a set of…
Coral bleaching is a major concern for marine ecosystems; more than half of the world's coral reefs have either bleached or died over the past three decades. Increasing sea surface temperatures, along with various spatiotemporal…
In the stably stratified ocean, small-scale turbulence is important for vertical exchange and hence for the mixing of water masses and suspended matter. To observationally study turbulent motions and the buoyancy- and shear-generators…
This study investigates how conditional normalizing flows can be applied to remote sensing data products in climate science for spatio-temporal prediction. The method is chosen due to its desired properties such as exact likelihood…
Recently developed spatial capture-recapture (SCR) models represent a major advance over traditional capture-recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area.…
Remote sensing data are increasingly available and frequently used to produce forest attributes maps. The sampling strategy of the calibration plots may directly affect predictions and map qualities. The aim of this manuscript is to…
Model-assisted, two-stage forest survey sampling designs provide a means to combine airborne remote sensing data, collected in a sampling mode, with field plot data to increase the precision of national forest inventory estimates, while…
Accurate estimation of thermospheric density is critical for precise modeling of satellite drag forces in low Earth orbit (LEO). Improving this estimation is crucial to tasks such as state estimation, collision avoidance, and re-entry…