The paper proposed a theoretical basis that use Gaussian elimination method and special cases of two-variables functions to detect the maxima points by Potential Field Data. Thence, a built computer program to test on models.The obtained results shown that this new approach has more maxima points than the traditional approach. Moreover, the application of new approach to gravitational data in the East Vietnam Sea demonstrated that existent a gravity trench undetectable in the traditional method.

For more than a decade, the climate has attracted stochastic dynamists with its unpredictable and complex phenomena. Our attention was attracted by the results of studies on the possibility of oceanic thermohaline circulation failure. We set the task to analyze the stability of the circulation current on-state and to predetermine what extreme events can unbalance it leading to attenuation. We also suggested possible scenarios for the resuscitation of the circulation in the event of its fading.

In this paper, we introduce a new tool for data-driven discovery of early warning signs of critical transitions in ice shelves, from remote sensing data. Our approach adopts principles of directed spectral clustering methodology considering an asymmetric affinity matrix and the associated directed graph Laplacian. We applied our approach generally to reprocess the ice velocity data and remote sensing satellite images of the Larsen C ice shelf.

The North Atlantic Oscillation (NAO) phenomenon has a significant impact on the global climate. In this paper, we perform the identification of optimal precursors for NAO to investigate the predictability problem of NAO events. We select proper simulation duration and find out the structure of OPRs for two types of NAO events. Besides, the method applied in this work can be generalized to numerical models that do not have an adjoint model, and its efficiency has been further enhanced.

Fluid parcels that are transported in complicated flows often contain sub-sets of particles that stay close to each other over a finite time interval. We here propose a new method to detect such finite-time coherent sets based on the density-based clustering technique OPTICS. Different from previous methods, our method has an intrinsic notion of coherent sets at different spatial scales. OPTICS is readily implemented in the SciPy sklearn package, making our method easy to use for everybody.

We propose an enhanced correlation identification (ECI) algorithm to attenuate the background noise. The cross-correlation matching method is helpful for the extraction of useful components of the raw SSIP data and suppression of background noise. Experiments on both synthetic and real SSIP data show that the ECI algorithm is proposed to preserves the valid information of the raw SSIP data to display the actual location and shape of adjacent high resistivity anomalies.

Recently, various models have been developed, including the fractional Brownian motion (fBm), to analyse the stochastic properties of geodetic time series, together with the estimated geophysical signals. The noise spectrum of these time series is generally modelled as a mixed spectrum, with a sum of white and coloured noise. Here, we are interested in modelling the residual time series, after deterministically subtracting geophysical signals from the observations.

To infer information of an unknown quantity that helps to understand an associated system better and to predict future outcomes, observations and a physical model that connects the data points to the unknown parameter are typically used as information sources. Yet this problem is often very challenging due to the fact that the unknown is generally high dimensional, the data is sparse and the model can be nonlinear. We propose a novel approach to address these challenges.

An unprecedented amount of rainfall data is nowadays available, such as ensemble model output, weather radar estimates and in-situ observations from networks of both traditional and opportunistic sensors. Nevertheless, the exact amount of precipitation, to some extent, eludes our knowledge. The objective of our study is the precipitation reconstruction through the combination of numerical model output with observations from multiple data sources.