One of the most pressing questions in climate science is that of the effect of anthropogenic aerosol on the Earth's energy balance. Aerosols provide the `seeds' on which cloud droplets form, and changes in the amount of aerosol available to a cloud can change its brightness and other physical properties such as optical thickness and spatial extent. Clouds play a critical role in moderating global temperatures and small perturbations can lead to significant amounts of cooling or warming. Uncertainty in this effect is so large it is not currently known if it is negligible, or provides a large enough cooling to largely negate present-day warming by CO2. This work uses deep convolutional neural networks to look for two particular perturbations in clouds due to anthropogenic aerosol and assess their properties and prevalence, providing valuable insights into their climatic effects.
"I tried to stay with some of the things that I know will be happening because we have some control of them," said Mr. Vogels. On Wednesday, he shared eight predictions based on customer-behavior patterns and technology investments by the company. The cloud will be everywhere. Next year will see more devices and more organizations powered by the cloud. Mr. Vogel, whose expertise in scalable systems led him to Amazon.com in 2004, predicts that the cloud in 2021 will continue to move beyond the traditional notion of a centralized system, with troves of data moving back and forth between customers and massive data centers in real time.
One of the greatest sources of uncertainty in future climate projections comes from limitations in modelling clouds and in understanding how different cloud types interact with the climate system. A key first step in reducing this uncertainty is to accurately classify cloud types at high spatial and temporal resolution. In this paper, we introduce Cumulo, a benchmark dataset for training and evaluating global cloud classification models. It consists of one year of 1km resolution MODIS hyperspectral imagery merged with pixel-width 'tracks' of CloudSat cloud labels. Bringing these complementary datasets together is a crucial first step, enabling the Machine-Learning community to develop innovative new techniques which could greatly benefit the Climate community. To showcase Cumulo, we provide baseline performance analysis using an invertible flow generative model (IResNet), which further allows us to discover new sub-classes for a given cloud class by exploring the latent space. To compare methods, we introduce a set of evaluation criteria, to identify models that are not only accurate, but also physically-realistic.
SpaceX successfully launches NASA astronauts from Kennedy Space Center into space. With an "above-normal" Atlantic hurricane season underway, researchers at NASA have partnered with the National Oceanic and Atmospheric Administration (NOAA) and other organizations to provide critical forecast data that could potentially save lives. While astronauts on board the International Space Station are able to observe and take photos from an average altitude of approximately 250 miles, georeferencing the images for use by hazard teams on the ground, new technology has advanced efforts to support weather prediction and disaster relief. In August 2020, the agency utilized satellite data to monitor Hurricane Laura, which made landfall along the Louisiana and Texas coastline, bringing winds at speeds of up to 150 mph. NASA's Earth Applied Sciences Disasters Program processed and analyzed that data before, during and after the hurricane made landfall in order to create flood maps, locate damaged areas and assess coastal erosion.
The sustainability of urban environments is an increasingly relevant problem. Air pollution plays a key role in the degradation of the environment as well as the health of the citizens exposed to it. In this chapter we provide a review of the methods available to model air pollution, focusing on the application of machine-learning methods. In fact, machine-learning methods have proved to importantly increase the accuracy of traditional air-pollution approaches while limiting the development cost of the models. Machine-learning tools have opened new approaches to study air pollution, such as flow-dynamics modelling or remote-sensing methodologies.