Artificial intelligence plays an important role in climate forecasting as it is developing rapidly and has the potential to revolutionize these forecasting methods.
AI algorithms analyze large amounts of historical weather data and can identify patterns that are difficult or impossible for humans to detect, meaning more accurate and faster forecasting capabilities than traditional models.
Machine learning and deep learning
Machine learning techniques push the capabilities of artificial intelligence weather forecasting to greater heights by training its algorithms on historical weather data to learn how to predict future weather conditions. These algorithms can determine the complex relationships between various meteorological variables such as temperature, pressure and humidity.
Another important technology is “deep learning,” algorithms that can analyze large amounts of data without human intervention. Deep learning algorithms have been shown to be particularly effective in forecasting complex weather events such as hurricanes and storms.
A successful prediction story
In 2023, artificial intelligence models were able to predict the path of Hurricane Lee a week before its occurrence and with greater accuracy than traditional forecasting models.
Hurricane Lee formed in the Atlantic Ocean on September 20, 2023, and the storm quickly intensified to a Category 4. The hurricane then turned north and began approaching the New England coast.
On September 25, the National Hurricane Center (NHC) issued a hurricane warning for coastal areas of Massachusetts and Rhode Island. The center expected Lee to make landfall near Nantucket, Massachusetts on September 26. Later that day, Lee turned slightly eastward and made landfall in Nova Scotia, Canada as a Category 2 hurricane, causing extensive damage without major loss of life.
This is thanks to Google AI’s “Grabcast” model, a “deep learning” model that is trained on large data sets of satellite images and other weather data. The model can identify patterns in this data that are difficult for humans to detect.
In the case of Hurricane Lee, GraffCast was able to identify a pattern in the storm’s motion as it moved northward and approached the coast of New England. This pattern is not immediately apparent to humans, but GraphCast has detected it and used it to predict the storm’s path with great accuracy.
“Stock – weather” model
In addition to Google AI’s graph cast model, Huawei Cloud is developing another AI-powered weather model. The model can predict up to a week in advance, just as accurately as traditional forecasting methods, but much faster.
“Stock – Weather” uses a “deep learning” algorithm to learn the relationships between various weather variables such as temperature, pressure and wind speed. It can identify patterns in data that are too complex for traditional forecasting methods to detect.
Modeling – can generate highly accurate forecasts of weather for specific locations, which can be used to make informed decisions about things like agriculture, transportation and disaster preparedness. It is also used by the European Center for Meteorological Forecasting (ECMWF) to improve its forecasts.
In 2022, researchers at the University of California, Berkeley developed an artificial intelligence model that could predict thunderstorms six hours in advance with 80 percent accuracy. This model is called the Thunderstorm Forecast System (TFS). It was trained on the National Oceanic and Atmospheric Administration’s (NOAA) dataset of more than 10 million thunderstorm observations.
TFS works by analyzing a variety of data sources, including satellite imagery, radar data, and ground-based weather stations. It uses this data to identify patterns and trends associated with thunderstorm formation. Once TFS detects these patterns and trends, it can use them to predict the formation, intensity, and duration of thunderstorms in a given location. TFS is still under development, but it will be a valuable tool for forecasting thunderstorms and improving the accuracy and timeliness of thunderstorm warnings.
The application of artificial intelligence to weather forecasting benefits a wide range of fields. It helps farmers determine the best time to plant and harvest their crops.
This helps airlines plan their flights more efficiently and avoid dangerous weather conditions. Energy companies can generate and distribute electricity more efficiently based on artificial intelligence advice. Insurance companies can assess risks and set premiums more precisely.
Weather forecasting using AI is still in its infancy, but it has already made significant progress. While AI-based predictive systems are currently being used, they are expected to play a greater role in the future.
Central banks around the world continue to demand… Gold In 2023, gold trends for the third quarter of the current year 2023 as per the reports of the World Gold Council show that the demand for gold by banks has increased.
Central banks added 337 tonnes in the third quarter of 2023
The third largest buying level in the quarter reached by central banks
In the third quarter of 2022, banks bought a large amount of 459 tonnes of gold..
Since the beginning of 2023, demand by central banks has increased by more than 14%.
Total bank purchases of gold since the beginning of 2023 have reached a record high of 800 tonnes of gold.
Gold reserves reported by global central banks rose by a net 77 tonnes in September.
Central bank’s gold sale is only 1 ton.
– Fund outflows from gold investment funds continued in October, $2 billion
Since the beginning of the year, the funds’ investments have fallen 6%.
– Total cash outflows from gold-backed global investment funds have hit $13 billion since the start of the year
Oil prices settled up more than 2% – yesterday, Friday – after a volatile trading week as the market anxiously watched the latest round of OPEC Plus production cuts and a slowdown in global production activity.
Brent crude futures for February delivery were down 2.45% at $78.88 a barrel, while US West Texas Intermediate crude futures were down 1.9% at $74.07.
For the week, Brent posted a decline of about 2.1%, while the West Texas Intermediate posted a decline of more than 1.9%.
On Thursday, oil-producing countries in the OPEC Plus alliance – which includes members of the Organization of the Petroleum Exporting Countries (OPEC) and other countries including Russia – agreed to cut global oil production by about 2.2 million barrels on the world market. per day in the first quarter of next year, including… extending current voluntary cuts by 1.3 million barrels per day from Saudi Arabia and Russia.
The OPEC Plus alliance – which accounts for more than 40% of the world’s oil – is focused on cutting production, with prices falling from around $98 a barrel in late September, amid fears of weaker economic growth in 2024.
A survey showed that the US manufacturing sector is still weak, with the factory employment rate falling last November.
On Friday, talks to extend a week-long ceasefire between Israel and the Palestinian Islamist movement (Hamas) collapsed, leading to renewed fighting in Gaza that could disrupt global oil supplies, Reuters reported.
If this is a “golden moment” for private lending, where will things go? What are the risks? Higher interest rates and turmoil in regional banks earlier this year have boosted confidence in the recovery of private credit. According to data provider Preqin, the market is expected to grow from $1.6 trillion to $2.8 trillion this year. BlackRock takes a more optimistic view, predicting the market will grow to $3.2 trillion.
Mark Rowan, CEO of private equity firm Apollo, sees “de-banking” in its early stages, while John Gray, chairman of BlackRock, coined the phrase “golden moment” to describe conditions in private capital at the start of the year. .
If the new banking rules under Federal Reserve regulations are considered a catalyst, capital requirements for the commercial banking industry in the US are likely to increase by up to 35%, according to Oliver Wyman, the world’s leading management consultancy. company — and no wonder Jamie Dimon said. , head of JP Morgan, said private lenders would be “very happy.”
How things develop in the market will be a key issue not only for large firms and banks in the private market, but also for traditional asset managers who have begun to use the capabilities of the private market to avoid the extreme rise of passive asset management. . This coincides with at least 26 traditional asset managers buying or launching new private credit units in the past two years.
This shift confirms the extent to which the structure of the financial market has changed. 20 years ago, when I was working at Morgan Stanley, I noted in a research paper that investor flows would split into barbells. On the one hand, investors would flock to passive, exchange-traded funds to get record returns. They are cheap and convenient. On the other hand, investors looking for higher returns will use asset allocation with specialist fund managers who invest in private equity, hedge funds and real estate. For traditional “major” fund managers, caught between the two, they will be pressured to make their investment machines more specialized or merge to increase their size, which has already been achieved.
According to ETFGI, ETFs have grown from $218 billion in 2003 to $10.3 trillion last October, but what’s surprising is how unbalanced the situation has become in terms of returns, with management fees likely to account for half of the investment sector. to alternative asset managers in 2023 from 28% in 2003.
Central banks are now scaling back their quantitative easing, which was implemented to support economies and markets, which has traditionally supported corporate profits. Without these tailwinds, the pressures on fund managers become more severe. So, how will the transition to private lending proceed?
Currently, Preqin estimates that just 10 companies have received 40% of private credit resources in the last 24 months. There are three reasons why private credit growth has disproportionately favored these large firms.
First, a good amount of growth is expected from the sale of investment portfolios by regional banks, which have to reduce their debt and are forced to sell good assets. The central bank’s new rules signal an inability for big banks to step up. In light of the large portfolio sizes and the speed required for transactions, the acquisition of these assets is a specialized venture that is in the interest of large companies that can underwrite the risks.
Second, a growing number of deals require more money, and August saw a new record for the largest loan, reaching $4.8 billion for fintech firm Finastra. The third and most important reason is that banks prefer to enter into partnerships so as not to lose access to customers. Even though tougher rules mean they have to divest assets, banks want to continue lending and partnering to help manage deal flows, which could benefit larger firms.
Several major banks have already closed deals and more are expected to follow. Citi is the latest bank to report its intention to launch a new unit in 2024.
A changing interest rate regime will mean loan losses rise as funding costs normalize and exposed weak balance sheets, which will be a source of challenges for private lenders. It may be unwise for new companies to try to exploit the growth. This requires a strong focus on the risks and rewards of selection and contracts, and teams that specialize in reconciliation, which many of the major players in the market have.
Of course, there will be key opportunities, such as hard credit or energy infrastructure credit, that are places that efficient companies can tap into, but they may not be on the scale that traditional companies need to maximize opportunities.
In general, a complete and comprehensive shift in capital allocation awaits us, requiring a major shift towards private credit, as Howard Marks recently argued, but the coming tide will not smooth all boats.