Related papers: Inferring long memory processes in the climate net…
This letter adopts long short-term memory(LSTM) to predict sea surface temperature(SST), which is the first attempt, to our knowledge, to use recurrent neural network to solve the problem of SST prediction, and to make one week and one…
Quantitative forecasting of average rainfall into the next season remains highly challenging, but in some favourable isolated cases may be possible with a series of relatively simple steps. We chose to explore predictions of austral…
Aspects of operational modeling for climate, weather, and space weather forecasts are contrasted, with a particular focus on the somewhat conflicting demands of 'operational stability' versus 'dynamic development' of the involved models.…
We formulate seasonal rainfall prediction as a reduced-order nonlinear forecasting problem, embedding coupled Indian-Pacific Ocean variability into a low-dimensional state space and projecting it forward using deep neural networks.…
This paper gives an overview on how to develop a dense and deep neural network for making a time series prediction. First, the history and cornerstones in Artificial Intelligence and Machine Learning will be presented. After a short…
The apparantly irregular (unpredictable) space-time fluctuations in atmospheric flows ranging from climate (thousands of kilometers - years) to turbulence (millimeters - seconds) exhibit the universal symmetry of self-similarity.…
It is important to calculate and analyze temperature and humidity prediction accuracies among quantitative meteorological forecasting. This study manipulates the extant neural network methods to foster the predictive accuracy. To achieve…
We suggest a way of rationalizing an intra-seasonal oscillations (IOs) of the Earth atmospheric flow as four meteorological relevant triads of interacting planetary waves, isolated from the system of all the rest planetary waves. Our model…
The temporal fluctuations in global mean surface temperature is an example of a geophysical quantity which can be described using the notions of long-range persistence and scale invariance/scaling, but this description has suffered from…
We introduce and test a general machine-learning-based technique for the inference of short term causal dependence between state variables of an unknown dynamical system from time series measurements of its state variables. Our technique…
Climate change is affecting and will increasingly affect astronomical observations. In this paper, we investigated the role some key weather parameters play in the quality of astronomical observations, and analysed their long-term trends…
Radio occultations are commonly used to assess remotely the thermodynamic properties of planets or satellites' atmospheres within the solar system. The data processing usually involves the so-called Abel inversion method or the numerical…
We use continuous wavelet tools to characterize the dynamics of climate change across time and frequencies. This approach allows us to capture the changing patterns in the relationship between global mean temperature anomalies and climate…
One of the defining features of both recent and historical cases of global climate change is Arctic Amplification (AA). This is the more rapid change in the surface air temperature (SAT) in the Arctic compared to some wider reference…
Near-surface air temperature is a key physical property of the Earth's surface. Although weather stations offer continuous monitoring and satellites provide broad spatial coverage, no single data source offers seamless data in a…
Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller…
The temperatures in different zones in the world do not show significant changes due to El-Nino except when measured in a restricted area in the Pacific Ocean. We find, in contrast, that the dynamics of a climate network based on the same…
We show that the climate phenomena of El Nino and La Nina arise naturally as states of macro-variables when our recent causal feature learning framework (Chalupka 2015, Chalupka 2016) is applied to micro-level measures of zonal wind (ZW)…
``Online" data assimilation (DA) is used to generate a new seasonal-resolution reanalysis dataset over the last millennium by combining forecasts from an ocean--atmosphere--sea-ice coupled linear inverse model with climate proxy records.…
We propose a method for causal inference using satellite image time series, in order to determine the treatment effects of interventions which impact climate change, such as deforestation. Simply put, the aim is to quantify the 'before…