Bureau of Meteorology, Australia ABSTRACT: Artificial Intelligence (AI) weather models are now reaching operational-grade performance for some variables, but like traditional Numerical Weather Prediction (NWP) models, they exhibit systematic biases and reliability issues. We test the application of the Bureau of Meteorology's existing statistical post-processing system, IMPROVER, to ECMWF's deterministic Artificial Intelligence Forecasting System (AIFS), and compare results against post-processed outputs from the ECMWF HRES and ENS models. Without any modification to processing workflows, post-processing yields comparable accuracy improvements for AIFS as for traditional NWP forecasts, in both expected value and probabilistic outputs. We show that blending AIFS with NWP models improves overall forecast skill, even when AIFS alone is not the most accurate component. These findings show that statistical post-processing methods developed for NWP are directly applicable to AI models, enabling national meteorological centres to incorporate AI forecasts into existing workflows in a low-risk, incremental fashion. Notice This Work has been accepted by Artificial Intelligence for the Earth Systems. The AMS does not guarantee that the copy provided here is an accurate copy of the Version of Record (VoR).

The Madden-Julian Oscillation (MJO) is the dominant mode of tropical atmospheric variability on intraseasonal timescales, and reliable MJO predictions are essential for protecting lives and mitigating impacts on societal assets. However, numerical models still fall short of achieving the theoretical predictability limit for the MJO due to inherent constraints. In an effort to extend the skillful prediction window for the MJO, machine learning (ML) techniques have gained increasing attention. This study examines the MJO prediction performance of the FuXi subseasonal-to-seasonal (S2S) ML model during boreal winter, comparing it with the European Centre for Medium- Range Weather Forecasts S2S model. Results indicate that for the initial strong MJO phase 3, the FuXi-S2S model demonstrates reduced biases in intraseasonal outgoing longwave radiation anomalies averaged over the tropical western Pacific (WP) region during days 15-20, with the convective center located over this area. Analysis of multiscale interactions related to moisture transport suggests that improvements could be attributed to the FuXi-S2S model's more accurate prediction of the area-averaged meridional gradient of low-frequency background moisture over the tropical WP. These findings not only explain the enhanced predictive capability of the FuXi-S2S model but also highlight the potential of ML approaches in advancing the MJO forecasting.

For instance, according to the Centers for Medicare & Medicaid Services, in December 2021, it was reported that the total national health expenditure in the U.S. increased to USD 4.1 trillion in 2020, which was a growth of 9.7% as compared to the previous year. Thus, a significant number of insurance providers are relying on the service providers to negotiate the drug price with retail pharmacy units and lower the price of the listed drugs in the insurance coverage. Furthermore, increasing initiatives, such as extending mail order delivery services and strengthening distribution network in remote areas, were responsible for the growing adoption of these services. Hence, these initiatives by the major players coupled with increasing demand for specialty drugs boosted the pharmacy benefit management market growth during the COVID-19 pandemic. Request a Free sample to learn more about this report.

Ever since the adoption of Smartphones and further mobile applications have flooded the market, global industries are on their way to offering their services through the adoption of mobile app development trends. One of the prominent and visible growth has been in the healthcare industry, which has gone many miles ahead of where it stood a decade ago. However, that can't be done with just a snap of a finger. You, as a healthcare organization entering into the mHealth service market, must follow healthcare app development trends, which we have discussed over here, to lead your app concept to success. In addition, to know healthcare app development trends, the market study is the need of the hour!!

As wearables like glasses and watches become more popular among consumers and become more relevant to fashion trends, the market is expected to increase dramatically. In fact, it was found by SkyQuest in one of the research that more than 441 million smart wearables were supplied globally in 2021. Additionally, it is estimated that increasing usage of technologies like sophisticated cloud technology, IoT, and machine learning would increase demand for AI-based products, which will in turn propel market expansion. The wearable AI device market in North America region is predicted to gain the largest market share by growing at a significant CAGR during the forecast period. The growth of the market can be ascribed to the increasing adoption of cutting-edge technology, the presence of significant smart watch manufacturing businesses, and increasing consumer disposable income. Additionally, it is predicted that rising demand for smart timepieces/smartwatches in wealthy countries like the United States will spur market expansion. SkyQuest noted that in 2019, more than 20 million smartwatch pieces were sold in the USA and this is estimated to increase during the forecast period owing to growing urbanization and millennials' rising interest in smart gadgets.

The Global Machine Learning as a Service (MLaaS) Market 2032 Industry Report is a professional and in-depth study on the current state of the Machine Learning as a Service (MLaaS) Market by QMI. The Machine Learning as a Service (MLaaS) Market is supposed to demonstrate a considerable growth during the forecast period of 2023 – 2032. The company profiles of all the key players and brands that are dominating the market have been given in this report. Their moves like product launches, joint ventures, mergers and acquisitions and the respective effect on the sales, import, export, revenue and CAGR values have been studied completely in the report. The scope of this Machine Learning as a Service (MLaaS) Market report can be expanded from market scenarios to comparative pricing between major players.

All segmentation provided above in this report is represented at country level and can be customized according to needs. All products covered in the market, product volume and average selling prices will be included as customizable options which may incur no or minimal additional cost (depends on customization).

Forecasts from Persistence Market Research indicate that by the end of the forecast period in 2027, the worldwide deep learning market would be worth US$ 261,113.0 This indicates a 49.0% compound annual growth rate that was seen over the anticipated period. This development can be ascribed to the demand for improved processing hardware, an increase in global R&D activity in particular industries, and the quick global adoption of cloud-based technologies. A recent research from Persistence Market Research offers a complete review of the worldwide deep learning market. In-depth analysis of the deep learning concept and the performance of the global deep learning market across significant end-use industry sectors throughout seven significant geographies are provided in this study.

The global artificial intelligence (AI) in energy market size was valued at $4.0 billion in 2021, and is projected to reach $19.8 billion by 2031, growing at a CAGR of 17.4% from 2022 to 2031. The developed and developing nations are adopting artificial intelligence in energy sector and are highly contributing towards the growth of the market during the forecast period. Measuring greenhouse gas emissions with AI enabled drones, accelerating energy transition and optimizing geothermal energy are the strategies being adopted by major players that are operating in the artificial intelligence in the energy market which may enhance the market position and boost the growth of the market over the forthcoming years. Artificial intelligence (AI) is an extent of computer technology that emphasizes the formation of intelligent machines which works and reacts such as humans. In addition, considering advanced technologies, it creates significant changes and effects of AI technology in the energy and utility market that can be seen in their applications such as smart automated grid and energy distribution system.

PORTLAND, OR, USA, UNITED STATES, November 9, 2022 / / -- Increase in investment by companies in & machine learning and rise in preference for personalized insurance services boost the growth of the global market. Allied Market Research published a report, titled, 'AI in Insurance Market by Offering (Hardware, Software, Service), by Deployment Model (On-premise, Cloud), by Technology (Machine Learning, Natural Language Processing, Computer Vision, Others), by Enterprise Size (Large Enterprises, SMEs), by End-user (Life and Health Insurance, Property and Casualty Insurance), by Application (Fraud Detection and Credit Analysis, Customer Profiling and Segmentation, Product and Policy Design, Underwriting and Claims Assessment): Global Opportunity Analysis and Industry Forecast, 2021-2031'. According to the report, the global AI in insurance industry generated $2.74 billion in 2021, and is anticipated to generate $45.74 billion by 2031, witnessing a CAGR of 32.5% from 2022 to 2031. Increase in investment by insurance companies in AI & machine learning, surge in collaboration between insurance companies and AI & machine learning solution companies, and rise in preference for personalized insurance services boost the growth of the global AI in insurance market. However, high deployment cost of AI & advanced machine learning and lack of skilled labor hamper the market growth. On the contrary, increase in government initiatives and rise in investments to leverage the AI technology are expected to offer remunerative opportunities for expansion of the market during the forecast period.