The snakes behind the blockbuster are megafauna throwbacks. Breakthroughs, discoveries, and DIY tips sent every weekday. At roughly the length of a small school bus, anacondas are famously some of the world's largest snakes. Now fossil evidence proves that these enormous reptiles are also glimpses of an ancient world. According to a study published on December 1st in the, anacondas reached their maximum length around 12.4 million years ago--and have remained giants ever since.

Gabriela Cibils is on a mission - to help turn Paraguay into the Silicon Valley of South America. When she was growing up in the landlocked country, nestled between Brazil and Argentina, she says the nation wasn't super tech focused. But it was different for Ms Cibils, as her parents worked in the technology sector. And she was inspired to study in the US, where she got a degree in computing and neuroscience from the University of California, Berkeley. After graduating she spent eight years working in Silicon Valley, near San Francisco, with roles at various American start-ups.

In recent years, great progress has been made in the field of forecasting meteorological variables. Recently, deep learning architectures have made a major breakthrough in forecasting the daily average temperature over a ten-day horizon. However, advances in forecasting events related to the maximum temperature over short horizons remain a challenge for the community. A problem that is even more complex consists in making predictions of the maximum daily temperatures in the short, medium, and long term. In this work, we focus on forecasting events related to the maximum daily temperature over medium-term periods (90 days). Therefore, instead of addressing the problem from a meteorological point of view, this article tackles it from a climatological point of view. Due to the complexity of this problem, a common approach is to frame the study as a temporal classification problem with the classes: maximum temperature "above normal", "normal" or "below normal". From a practical point of view, we created a large historical dataset (from 1981 to 2018) collecting information from weather stations located in South America. In addition, we also integrated exogenous information from the Pacific, Atlantic, and Indian Ocean basins. We applied the AutoGluonTS platform to solve the above-mentioned problem. This AutoML tool shows competitive forecasting performance with respect to large operational platforms dedicated to tackling this climatological problem; but with a "relatively" low computational cost in terms of time and resources.

In this research, renewable energy expansion in South America up to 2050 is predicted based on machine learning models that are trained on past energy data. The research employs gradient boosting regression and Prophet time series forecasting to make predictions of future generation capacities for solar, wind, hydroelectric, geothermal, biomass, and other renewable sources in South American nations. Model output analysis indicates staggering future expansion in the generation of renewable energy, with solar and wind energy registering the highest expansion rates. Geospatial visualization methods were applied to illustrate regional disparities in the utilization of renewable energy. The results forecast South America to record nearly 3-fold growth in the generation of renewable energy by the year 2050, with Brazil and Chile spearheading regional development. Such projections help design energy policy, investment strategy, and climate change mitigation throughout the region, in helping the developing economies to transition to sustainable energy.

Climate change is increasing the frequency of extreme precipitation events, making weather disasters such as flooding and landslides more likely. The ability to accurately nowcast precipitation is therefore becoming more critical for safeguarding society by providing immediate, accurate information to decision makers. Motivated by the recent success of generative models at precipitation nowcasting, this paper: extends the DYffusion framework to this task and evaluates its performance at forecasting IMERG satellite precipitation data up to a 4-hour horizon; modifies the DYffusion framework to improve its ability to model rainfall data; and introduces a novel loss function that combines MSE, MAE and the LPIPS perceptual score. In a quantitative evaluation of forecasts up to a 4-hour horizon, the modified DYffusion framework trained with the novel loss outperforms four competitor models. It has the highest CSI scores for weak, moderate, and heavy rain thresholds and retains an LPIPS score $<$ 0.2 for the entire roll-out, degrading the least as lead-time increases. The proposed nowcasting model demonstrates visually stable and sharp forecasts up to a 2-hour horizon on a heavy rain case study. Code is available at https://github.com/Dseal95/DYffcast.

Real-time detection and prediction of extreme weather protect human lives and infrastructure. Traditional methods rely on numerical threshold setting and manual interpretation of weather heatmaps with Geographic Information Systems (GIS), which can be slow and error-prone. Our research redefines Extreme Weather Events Detection (EWED) by framing it as a Visual Question Answering (VQA) problem, thereby introducing a more precise and automated solution. Leveraging Vision-Language Models (VLM) to simultaneously process visual and textual data, we offer an effective aid to enhance the analysis process of weather heatmaps. Our initial assessment of general-purpose VLMs (e.g., GPT-4-Vision) on EWED revealed poor performance, characterized by low accuracy and frequent hallucinations due to inadequate color differentiation and insufficient meteorological knowledge. To address these challenges, we introduce ClimateIQA, the first meteorological VQA dataset, which includes 8,760 wind gust heatmaps and 254,040 question-answer pairs covering four question types, both generated from the latest climate reanalysis data. We also propose Sparse Position and Outline Tracking (SPOT), an innovative technique that leverages OpenCV and K-Means clustering to capture and depict color contours in heatmaps, providing ClimateIQA with more accurate color spatial location information. Finally, we present Climate-Zoo, the first meteorological VLM collection, which adapts VLMs to meteorological applications using the ClimateIQA dataset. Experiment results demonstrate that models from Climate-Zoo substantially outperform state-of-the-art general VLMs, achieving an accuracy increase from 0% to over 90% in EWED verification. The datasets and models in this study are publicly available for future climate science research: https://github.com/AlexJJJChen/Climate-Zoo.

One key task in environmental science is to map environmental variables continuously in space or even in space and time. Machine learning algorithms are frequently used to learn from local field observations to make spatial predictions by estimating the value of the variable of interest in places where it has not been measured. However, the application of machine learning strategies for spatial mapping involves additional challenges compared to "non-spatial" prediction tasks that often originate from spatial autocorrelation and from training data that are not independent and identically distributed. In the past few years, we developed a number of methods to support the application of machine learning for spatial data which involves the development of suitable cross-validation strategies for performance assessment and model selection, spatial feature selection, and methods to assess the area of applicability of the trained models. The intention of the CAST package is to support the application of machine learning strategies for predictive mapping by implementing such methods and making them available for easy integration into modelling workflows. Here we introduce the CAST package and its core functionalities. At the case study of mapping plant species richness, we will go through the different steps of the modelling workflow and show how CAST can be used to support more reliable spatial predictions.

Many important real-world problems in different The combination of domain knowledge and centralized scientific fields can be naturally expressed as MaxSAT control is an effective solution to a broad class of optimization [6]: routing and scheduling problems in industrial engineering, problems. However, in the case of complex adaptive systems, software and hardware debugging in computer science and the system's control tends to be distributed and it is often computer engineering, different problems of bioinformatics unclear what the most appropriate trajectory is and even the in biological sciences, just to name a few. It was previously form of the optimal solution may simply be unknown. This is mentioned [7] that the initial weights of the HN network in the case for many kinds of biological systems, but also social an optimization framework represent a weighted-Max-2-SAT systems, that tend to be capable of giving rise to creative problem, but it was never actually shown how one would start solutions even under novel circumstances. Such a complex from a SAT problem in question and use the SO model to solve adaptive system cannot necessarily rely on the availability it (an analogous model to that of SO was used before to solve of error or reward signals to improve its behavior, which a concrete problem [8], but not in the form of a SAT problem raises the intriguing question of what other, more minimal on which we expand subsequently). This poses an obstacle for mechanisms could be available.

In this paper we present the development of a dataset consisting of 91 Multi-band Cloud and Moisture Product Full-Disk (MCMIPF) from the Advanced Baseline Imager (ABI) on board GOES-16 geostationary satellite with 91 temporally and spatially corresponding CLDCLASS products from the CloudSat polar satellite. The products are diurnal, corresponding to the months of January and February 2019 and were chosen such that the products from both satellites can be co-located over South America. The CLDCLASS product provides the cloud type observed for each of the orbit's steps and the GOES-16 multiband images contain pixels that can be co-located with these data. We develop an algorithm that returns a product in the form of a table that provides pixels from multiband images labelled with the type of cloud observed in them. These labelled data conformed in this particular structure are very useful to perform supervised learning. This was corroborated by training a simple linear artificial neural network based on the work of Gorooh et al. (2020), which gave good results, especially for the classification of deep convective clouds.

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