Future of Earth

Summary

The biological and geological future of Earth can be extrapolated based on the estimated effects of several long-term influences. These include the chemistry at Earth's surface, the cooling rate of the planet's interior, the gravitational interactions with other objects in the Solar System, and a steady increase in the Sun's luminosity. An uncertain factor is the pervasive influence of technology introduced by humans, such as climate engineering, which could cause significant changes to the planet. For example, the current Holocene extinction is being caused by technology, and the effects may last for up to five million years. In turn, technology may result in the extinction of humanity, leaving the planet to gradually return to a slower evolutionary pace resulting solely from long-term natural processes. Over time intervals of hundreds of millions of year

Official source

https://en.wikipedia.org/wiki/Future_of_Earth

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A radar-based regional extreme rainfall analysis to derive the thresholds for a novel automatic alert system in Switzerland

Alexis Berne, Marco Gabella, Luca Panziera

This paper presents a regional extreme rainfall analysis based on 10 years of radar data for the 159 regions adopted for official natural hazard warnings in Switzerland. Moreover, a nowcasting tool aimed at issuing heavy precipitation regional alerts is introduced. The two topics are closely related, since the extreme rainfall analysis provides the thresholds used by the nowcasting system for the alerts. Warm and cold seasons' monthly maxima of several statistical quantities describing regional rainfall are fitted to a generalized extreme value distribution in order to derive the precipitation amounts corresponding to sub-annual return periods for durations of 1, 3, 6, 12, 24 and 48 h. It is shown that regional return levels exhibit a large spatial variability in Switzerland, and that their spatial distribution strongly depends on the duration of the aggregation period: for accumulations of 3 h and shorter, the largest return levels are found over the northerly alpine slopes, whereas for longer durations the southern Alps exhibit the largest values. The inner alpine chain shows the lowest values, in agreement with previous rainfall climatologies. The nowcasting system presented here is aimed to issue heavy rainfall alerts for a large variety of end users, who are interested in different precipitation characteristics and regions, such as, for example, small urban areas, remote alpine catchments or administrative districts. The alerts are issued not only if the rainfall measured in the immediate past or forecast in the near future exceeds some predefined thresholds but also as soon as the sum of past and forecast precipitation is larger than threshold values. This precipitation total, in fact, has primary importance in applications for which antecedent rainfall is as important as predicted one, such as urban floods early warning systems. The rainfall fields, the statistical quantity representing regional rainfall and the frequency of alerts issued in case of continuous threshold exceedance are some of the configurable parameters of the tool. The analysis of the urban flood which occurred in the city of Schaffhausen in May 2013 suggests that this alert tool might have complementary skill with respect to radar-based thunderstorm nowcasting systems for storms which do not show a clear convective signature.

Copernicus GmbH2016

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We use the latest Coordinated Regional Dynamical Experiment regional climate models over South Asia at 0.22 degrees grid spacing (WAS-22) under two Representative Concentration Pathways scenarios (RCP 2.6 and RCP 8.5) to evaluate their performance against their predecessor at 0.44 degrees grid spacing (WAS-44) and investigate the impact of climate change on future wind resources in the Indian sub-continent. The comparison of WAS-22 with the previous WAS-44 regional climate models looks at their ability to reproduce annual and seasonal wind speed climatology and trends with respect reanalysis datasets and finds that WAS-22 models achieve the best results. Hence, we use WAS-22 models as a source of climate information, correlate them to local station measurements and wind farm operation data at three sites in the study domain. We then estimate future annual and seasonal wind trends in the Indian sub-continent. Our findings on future trends suggest increasing annual wind speeds in the north-west (+ 0.04 m/s per decade (+/- 8%)), the center (+ 0.03 m/s per decade (+/- 11%)) and the east (+ 0.02 m/s per decade (+/- 19%)) of the Indian sub-continent under the RCP 8.5 scenario at the end of the century, with changes occurring more substantially during the last 30 years of the century. The results project future perturbations during the pre-monsoon season occurring throughout India and trends with the highest magnitude concentrated during the monsoon season. An additional contribution of this investigation is a decision-making tool enabling the quantification of the spatio-temporal consistency of wind trends using a structure-function.

ELSEVIER2022

Generating Smooth Pose Sequences for Diverse Human Motion Prediction

Wei Mao, Mathieu Salzmann

Recent progress in stochastic motion prediction, i.e., predicting multiple possible future human motions given a single past pose sequence, has led to producing truly diverse future motions and even providing control over the motion of some body parts. However, to achieve this, the state-of-the-art method requires learning several mappings for diversity and a dedicated model for controllable motion prediction. In this paper, we introduce a unified deep generative network for both diverse and controllable motion prediction. To this end, we leverage the intuition that realistic human motions consist of smooth sequences of valid poses, and that, given limited data, learning a pose prior is much more tractable than a motion one. We therefore design a generator that predicts the motion of different body parts sequentially, and introduce a normalizing flow based pose prior, together with a joint angle loss, to achieve motion realism. Our experiments on two standard benchmark datasets, Human3.6M and HumanEva-I, demonstrate that our approach outperforms the state-of-the-art baselines in terms of both sample diversity and accuracy. The code is available at https://github.com/wei-mao-2019/gsps

IEEE2021

A radar-based regional extreme rainfall analysis to derive the thresholds for a novel automatic alert system in Switzerland

Alexis Berne, Marco Gabella, Luca Panziera

Copernicus GmbH2016