The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatio-temporal climate data. Like other neural networks, ESNs are non-interpretable black-box models, which poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatio-temporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we select a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which we use as a proxy for an artificial stratospheric aerosol injection. Using the proposed approach, we are able to characterize relationships between pathway variables associated with this event.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Last week's White House report reiterated President Biden's employer mandate that businesses with 100 or more employees require every worker to be fully vaccinated for COVID-19 or tested weekly. Jeffrey Zients, the White House COVID-19 response coordinator, summarized in last week's press briefing that, "We are on track to quadruple the supply of rapid, at-home tests available to Americans by December to more than 200 million a month and to increase the number of places Americans can access free testing in the United States to 30,000 community-based locations." He emphasized the president's staunch commitment in adding $1 billion of extra funding already to the recent $2 billion investment to increase supply.

A highway is closed due to snow and ice in Houston, Texas on Feb. 15, 2021. Up to 2.5 million ... [ ] customers were without power as the state's power generation capacity was impacted by an ongoing winter storm brought by Arctic blast. A new weather tech startup says it has created a new artificial intelligence (AI)-powered weather radar and satellite network to take on big weather. Climavision, which has $100 million in private equity funding, has created a high-resolution weather radar and satellite network that combines lower altitude, proprietary data with machine learning and AI technology. Chris Goode, CEO of Climavision, says the new sensing network will fill the coverage gaps in the existing NOAA and NWS systems across the US.

I wrote a blog post inspired by Jamie Goode's book "Wine Science: The Application of Science in Winemaking". In this book, Goode argued that reductionistic approach cannot explain relationship between chemical ingredients and taste of wine. Indeed, we know not all high (alcohol) wines are excellent, although in general high wines are believed to be good. Usually taste of wine is affected by a complicated balance of many components such as sweetness, acid, tannin, density or others that are given by corresponding chemical entities. However, I think (and probably many other data science experts agree) that it is not a limitation of reductionistic approach, but a limitation of univariate modeling. To illustrate it, I performed a series of multivariate modeling with random forest or other models on "Wine Quality" dataset of UCI Machine Learning repository.