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Forecasting the height of new snow is essential for avalanche hazard survey, road and ski resort management, tourism attractiveness, etc. Meteo-France operates a probabilistic forecasting system using a Numerical Weather Prediction system, and a snowpack model. It provides better forecasts than direct diagnostics but exhibits significant biases. Post-processing methods can be applied to provide automatic forecasting products from this system.

Effective use of observations with numerical weather prediction models, a.k.a. data assimilation, is a key part of weather forecasting systems. For precise prediction at the scales of thunderstorms, fast nonlinear processes bring a grand challenge because most data assimilation systems are based on linear processes and normal-distribution errors. We investigate how every 30-seconds weather radar observations can help reduce the effect of nonlinear processes and non-normal distributions.

In weather forecasting, observations are incorporated into a model of the atmosphere through a process called data assimilation. Sometimes observations in one location may impact the weather forecast in another faraway location in undesirable ways. The impact of distant observations on the forecast is mitigated through a process called localization. We propose a new method for localization when a model has multiple length scales, as in a model spanning both the ocean and the atmosphere.

The same power-law we used to quantify growth of tree roots in time describes equally the assemblage of river networks in time. Even the basic length scale of both networks is the same. The one difference is that the basic time scale is 10 times shorter for drainage networks than for tree roots, since the relevant flow rate is ten times faster. This result overturns the understanding of drainage networks and forms a basis to organize thoughts about surface and subsurface hydrology.

The skewness of a time series is commonly used to quantify the extent to which positive (negative) deviations from the mean are larger than negative (positive) ones. However, in some cases, traditional skewness may not provide reliable information about time series skewness, motivating the development of a waveform skewness index in this paper. The waveform skewness index is used to show that changes in the relationship strength between climate time series could arise from changes in skewness.

In non-equilibrium statistical mechanics, which describes forced-dissipative systems, as air-sea interaction, there is no universal probability density function (pdf). Some such systems have recently been demonstrated to exhibit a symmetry called a fluctuation theorem (FT), which strongly constrains the shape of the pdf. Using satellite data, the mechanical power-input to the ocean by air-sea interaction is questioned to follow or not a FT. A FT is found to apply over specific ocean regions.

Earthquake precursor and earthquake monitoring are always important in the earthquake research field, even if there is still debatable about the existence of earthquake precursor. The existence of precursory signal is confirmed by our results. We then attempt to capture and quantitative precursors before rock fracture, by which we establish a link between the rock experiments and natural earthquakes. We try to make a different type of analysis by comparing their similar characteristics.

Large amplitude waves in the interior of the ocean internal waves in the ocean propagate in a dynamic, highly variable environment with changes in background current, local depth, and stratification. These waves have a well known mathematical theory that, despite considerable progress, has some gaps. In particular, waves have been observed in situations that preclude an application of the mathematical theory. We present numerical simulations of the spontaneous generation of such waves.

This paper investigates the potential of a type of deep generative neural network to produce realistic weather situations when trained from the climate of a general circulation model. The generator represents the climate in a compact latent space. It is able to reproduce many aspect of the targeted multivariate distribution. Some properties of our method open new perspectives such as the exploration of the extremes close to a given state or how to connect two realistic weather states.

A general methodology is introduced to capture regime transitions of the Atlantic meridional overturning circulation (AMOC). The assimilation models with different parameters simulate different paths for AMOC to switch between equilibrium states. Constraining model parameters with observations can significantly mitigate the model deviations, thus capturing AMOC regime transitions. This simple model study serves as a guideline for improving coupled general circulation models.

In geophysics large samples of extended fields need to be analysed and we therefore often work with high-dimensional data. Fortunately, high dimensionality can be a blessing and we show how this often allows analytical results to be derived. These results include estimates of correlations between sample members and how the sample mean depends on the sample size. We show that the properties of high dimensionality with success can be applied to climate fields, e.g., those from ensemble modelling.