A Ukrainian strike destroyed a missile complex in Russian-occupied Crimea on Wednesday, August 23, Ukraine's military intelligence agency said. Russia and Ukraine launched drone strikes against each other Wednesday morning, each looking to score a major win in a fight that continues to drag on with little progress or end in sight. Ukrainian intelligence claimed to have destroyed a Russian S-400 surface-to-air missile defense system in Crimea, while Russia struck grain facilities in Odesa overnight Tuesday. The S-400 system shows another instance of Ukraine's plan to strike at Russian assets, even behind the front line. Ukraine's intelligence agency GUR claimed on its Telegram channel that Russia has a "limited number" of sophisticated systems left and that this loss strikes a "painful blow" to their forces.

General Oleksandr Tarnavskyi, the deputy commander of Ukrainian forces in the south, said Ukraine's troops had gained a footing in the southeastern village of Robotyne and were organising the evacuation of civilians. Oleksandr Prokudin, the governor of Ukraine's Kherson region, said an elderly woman was killed and a 55-year-old man injured in Russian air attacks. A drone raid was reported in Moscow, forcing a temporary halt to air traffic at Vnukovo, Sheremetyevo and Domodedovo airports. City Mayor Sergei Sobyanin said Russian air defence systems shot down the two drones west of the capital and blamed Ukraine. Russia's Air Force said it scrambled two jets against two drones flying near the Crimean Peninsula, which it annexed in 2014.

Russian air defence systems have brought down two combat drones west of the Russian capital, Moscow mayor Sergei Sobyanin said. The drones were downed early on Tuesday over the Moscow region's towns of Krasnogorsk and the settlement of Chastsy, Sobyanin said. One in the Krasnogorsk area, the other in the Chastsy area," he Sobyanin said on the Telegram messaging app, adding that emergency services were responding. The Moscow mayor did not give details on damage or casualties in what is the latest attempted drone raid on the Russian capital. Air traffic at Moscow's Vnukovo, Sheremetyevo and Domodedovo airports was briefly halted, Russia's state news agency TASS reported, quoting an aviation service source as saying. "Glass damage was recorded on several floors" in a multi-storey residential building in Krasnogorsk," the news agency said, without specifying whether it was the result of a drone strike.

Data-driven, deep-learning modeling frameworks have been recently developed for forecasting time series data. Such machine learning models may be useful in multiple domains including the atmospheric and oceanic ones, and in general, the larger fluids community. The present work investigates the possible effectiveness of such deep neural operator models for reproducing and predicting classic fluid flows and simulations of realistic ocean dynamics. We first briefly evaluate the capabilities of such deep neural operator models when trained on a simulated two-dimensional fluid flow past a cylinder. We then investigate their application to forecasting ocean surface circulation in the Middle Atlantic Bight and Massachusetts Bay, learning from high-resolution data-assimilative simulations employed for real sea experiments. We confirm that trained deep neural operator models are capable of predicting idealized periodic eddy shedding. For realistic ocean surface flows and our preliminary study, they can predict several of the features and show some skill, providing potential for future research and applications.

Global warming's impact on high temperatures in various parts of the world has raised concerns. This study investigates long-term temperature trends in four major Ghanaian cities representing distinct climatic zones. Using NASA's Prediction of Worldwide Energy Resource (POWER) data, statistical analyses assess local climate warming and its implications. Linear regression trend analysis and eXtreme Gradient Boosting (XGBoost) machine learning predict temperature variations. Land Surface Temperature (LST) profile maps generated from the RSLab platform enhance accuracy. Results reveal local warming trends, particularly in industrialized Accra. Demographic factors aren't significant. XGBoost model's low Root Mean Square Error (RMSE) scores demonstrate effectiveness in capturing temperature patterns. Wa unexpectedly has the highest mean temperature. Estimated mean temperatures for mid-2023 are: Accra 27.86{\deg}C, Kumasi 27.15{\deg}C, Kete-Krachi 29.39{\deg}C, and Wa 30.76{\deg}C. These findings improve understanding of local climate warming for policymakers and communities, aiding climate change strategies.

A Ukrainian military drone has damaged a building in central Moscow, causing an explosion that was heard across the city's business district in the latest attack on the Russian capital by unmanned aerial vehicles. Moscow Mayor Sergei Sobyanin said in a statement on the Telegram messaging app that air defence systems had shot down a drone early on Friday morning and debris had fallen on the city's Expo Center. The Expo Center – a large event space used for major exhibitions – is located less than 5km (3.1 miles) from the Kremlin. A video published by Russian media outlets showed thick smoke rising next to skyscrapers in the city. The Russian defence ministry said that Ukraine launched the drone attack at about 4am local time (01:00 GMT) "using an unmanned aerial vehicle against objects located in Moscow and the Moscow region".

Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecasting and understanding the dynamics of extreme weather events. This review aims to provide a comprehensive overview of the state-of-the-art deep learning in the field. We explore the utilization of deep learning architectures, across various aspects of weather prediction such as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and tropical cyclones. We highlight the potential of deep learning, such as its ability to capture complex patterns and non-linear relationships. Additionally, we discuss the limitations of current approaches and highlight future directions for advancements in the field of meteorology. The insights gained from this systematic review are crucial for the scientific community to make informed decisions and mitigate the impacts of extreme weather events.

Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world's freshwater resources have inadequate monitoring of critical environmental variables needed for management. Yet, the need to have widespread predictions of hydrological variables such as river flow and water quality has become increasingly urgent due to climate and land use change over the past decades, and their associated impacts on water resources. Modern machine learning methods increasingly outperform their process-based and empirical model counterparts for hydrologic time series prediction with their ability to extract information from large, diverse data sets. We review relevant state-of-the art applications of machine learning for streamflow, water quality, and other water resources prediction and discuss opportunities to improve the use of machine learning with emerging methods for incorporating watershed characteristics into deep learning models, transfer learning, and incorporating process knowledge into machine learning models. The analysis here suggests most prior efforts have been focused on deep learning learning frameworks built on many sites for predictions at daily time scales in the United States, but that comparisons between different classes of machine learning methods are few and inadequate. We identify several open questions for time series predictions in unmonitored sites that include incorporating dynamic inputs and site characteristics, mechanistic understanding and spatial context, and explainable AI techniques in modern machine learning frameworks.

President Zelensky has described seaborne drones as Ukraine's "eyes and protection on the frontline", with claims of a series of successful strikes against Russian ships in the Black Sea and on a key bridge to Crimea. These remote-controlled devices are playing an increasingly prominent role, with both sides ramping up their use for attacks and reconnaissance. The BBC's Security Correspondent Frank Gardner and BBC Verify examine their influence on the conflict.

Here is the situation on Thursday, August 17, 2023. Ukraine said Russia carried out a series of drone attacks on grain silos and warehouses at a Danube River port near the border with Romania. Kyiv said its forces liberated the settlement of Urozhaine in the southeast, but top general Oleksandr Syrskyi warned the situation around Kupiansk on the northeastern front was deteriorating amid Russian counterattacks. Video obtained by Al Jazeera suggests a controversial unit of Chechen troops has been policing the town of Enerhodar near the Russian-occupied Zaporizhzhia Nuclear Power Plant. Russia's Ministry of Defence said it shot down three Ukrainian drones southwest of Moscow and one over Crimea.