Seamless forecasting that produces warning information at continuum timescales based on only one system is a long-standing pursuit for weather-climate service. While the rapid advancement of deep learning has induced revolutionary changes in classical forecasting field, current efforts are still focused on building separate AI models for weather and climate forecasts. To explore the seamless forecasting ability based on one AI model, we propose FengWu-Weather to Subseasonal (FengWu-W2S), which builds on the FengWu global weather forecast model and incorporates an ocean-atmosphere-land coupling structure along with a diverse perturbation strategy. FengWu-W2S can generate 6-hourly atmosphere forecasts extending up to 42 days through an autoregressive and seamless manner. Our hindcast results demonstrate that FengWu-W2S reliably predicts atmospheric conditions out to 3-6 weeks ahead, enhancing predictive capabilities for global surface air temperature, precipitation, geopotential height and intraseasonal signals such as the Madden-Julian Oscillation (MJO) and North Atlantic Oscillation (NAO). Moreover, our ablation experiments on forecast error growth from daily to seasonal timescales reveal potential pathways for developing AI-based integrated system for seamless weather-climate forecasting in the future.

Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these extreme events remains challenging, as their complex interactions with large-scale atmospheric and climatic variables are difficult to capture with traditional statistical and dynamical models. This work presents a general method for driver identification in extreme climate events. A novel framework (STCO-FS) is proposed to identify key immediate (short-term) HW drivers by combining clustering algorithms with an ensemble evolutionary algorithm. The framework analyzes spatio-temporal data, reduces dimensionality by grouping similar geographical nodes for each variable, and develops driver selection in spatial and temporal domains, identifying the best time lags between predictive variables and HW occurrences. The proposed method has been applied to analyze HWs in the Adda river basin in Italy. The approach effectively identifies significant variables influencing HWs in this region. This research can potentially enhance our understanding of HW drivers and predictability.

In recent years, the global and Indian government efforts in monitoring and collecting data related to the fisheries industry have witnessed significant advancements. Despite this wealth of data, there exists an untapped potential for leveraging artificial intelligence based technological systems to benefit Indian fishermen in coastal areas. To fill this void in the Indian technology ecosystem, the authors introduce Jal Anveshak. This is an application framework written in Dart and Flutter that uses a Llama 2 based Large Language Model fine-tuned on pre-processed and augmented government data related to fishing yield and availability. Its main purpose is to help Indian fishermen safely get the maximum yield of fish from coastal areas and to resolve their fishing related queries in multilingual and multimodal ways.

US warships came under sustained missile and drone attack from Houthi fighters as they sailed off the coast of Yemen, the Pentagon has confirmed, with the armed group claiming it attacked the US aircraft carrier Abraham Lincoln and two US destroyers. Pentagon spokesperson Air Force Major General Patrick Ryder said on Tuesday that the United States military's Central Command (CENTCOM) forces "successfully repelled multiple Iranian backed Houthi attacks during a transit of the Bab al-Mandeb strait", which connects the Red Sea to the Gulf of Aden. Ryder told reporters at a news conference that two US-guided missile destroyers – the USS Stockdale and USS Spruance – were attacked by at least eight one-way attack drones, five antiship ballistic missiles and three antiship cruise missiles. All the Houthi drones and missiles "were successfully engaged and defeated", and neither of the US Navy ships were damaged or personnel hurt, he said. Ryder added that he was not aware of any attacks against the aircraft carrier USS Abraham Lincoln.

The wreckage of the USS Edsall, an American warship that was sunk during a battle with Japanese forces in World War II, has been discovered more than 80 years after it was lost at the bottom of the sea, U.S. and Australian officials announced Monday. The final resting place of the USS Edsall, a Clemson-class destroyer, was discovered late last year at the bottom of the Indian Ocean, according to the U.S. Navy and Royal Australian Navy. "Working in collaboration with the U.S. Navy, the Royal Australian Navy used advanced robotic and autonomous systems, normally used for hydrographic survey capabilities, to locate USS Edsall on the sea-bed," Chief of Royal Australian Navy, Vice Admiral Mark Hammond, said in a statement. The warship was sunk on March 1, 1942, three months after the attack on Pearl Harbor, during an encounter with Japanese battleships and dive bombers. The USS Edsall was a Clemson-class destroyer, measuring 314 feet in length and capable of 35 knots.

Some one million eligible voters in the Indian Ocean Mauritius will head out to vote on Sunday amid an explosive scandal that has implicated government figures in a covert wiretapping operation. Since independence from Britain in 1968, the southeast African country has maintained a strong, vibrant parliamentary democracy. This will be its 12th national election. Elections are usually deemed free and fair and turnout is normally high, at close to 80 percent. This time, however, the unusual drama caused by the leaked recordings has sparked national agitation and dominated the campaign season.

Many important phenomena in scientific fields such as climate, neuroscience, and epidemiology are naturally represented as spatiotemporal gridded data with complex interactions. For example, in climate science, researchers aim to uncover how large-scale events, such as the North Atlantic Oscillation (NAO) and the Antarctic Oscillation (AAO), influence other global processes. Inferring causal relationships from these data is a challenging problem compounded by the high dimensionality of such data and the correlations between spatially proximate points. We present SPACY (SPAtiotemporal Causal discoverY), a novel framework based on variational inference, designed to explicitly model latent time-series and their causal relationships from spatially confined modes in the data. Our method uses an end-to-end training process that maximizes an evidence-lower bound (ELBO) for the data likelihood. Theoretically, we show that, under some conditions, the latent variables are identifiable up to transformation by an invertible matrix. Empirically, we show that SPACY outperforms state-of-the-art baselines on synthetic data, remains scalable for large grids, and identifies key known phenomena from real-world climate data.

The wreck was like a bug on the wall, a jumbly shape splayed on the abyssal plain. It was noticed by a team of autonomous-underwater-vehicle operators on board a subsea exploration vessel, working at an undisclosed location in the Atlantic Ocean, about a thousand miles from the nearest shore. The analysts belonged to a small private company that specializes in deep-sea search operations; I have been asked not to name it. They were looking for something else. In the past decade, the company has helped to transform the exploration of the seabed by deploying fleets of A.U.V.s--underwater drones--which cruise in formation, mapping large areas of the ocean floor with high-definition imagery.

Israel launched its first-ever strikes against Houthi rebels in Yemen just days after Jerusalem vowed revenge for a drone strike on Tel Aviv. Russia has been aiding the Houthis' assault on Western shipping lanes in the Red Sea by providing them targeting data. As the Houthis ramped up their strikes on the U.S. and other nations' postures in the region after the Oct. 7 attack on Israel, Russians offered satellite data allowing them to expand their strikes, take out multimillion-dollar U.S. drones and hit ships sailing through the Red Sea and the Suez Canal, through which 12% of global trade passes, according to a Wall Street Journal report. Each munition used to intercept a Houthi strike costs the U.S. upwards of between 1 million and 4 million. The data passed through Iran's Revolutionary Guard Corps (IRGC).

Soft robots offer adaptability and safe interaction with complex environments. Rapid prototyping kits that allow soft robots to be assembled easily will allow different geometries to be explored quickly to suit different environments or to mimic the motion of biological organisms. We introduce SoftSnap modules: snap-together components that enable the rapid assembly of a class of untethered soft robots. Each SoftSnap module includes embedded computation, motor-driven string actuation, and a flexible thermoplastic polyurethane (TPU) printed structure capable of deforming into various shapes based on the string configuration. These modules can be easily connected with other SoftSnap modules or customizable connectors. We demonstrate the versatility of the SoftSnap system through four configurations: a starfish-like robot, a brittle star robot, a snake robot, a 3D gripper, and a ring-shaped robot. These configurations highlight the ease of assembly, adaptability, and functional diversity of the SoftSnap modules. The SoftSnap modular system offers a scalable, snap-together approach to simplifying soft robot prototyping, making it easier for researchers to explore untethered soft robotic systems rapidly.