godiva.com case study

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Godiva Marketing Strategy 2024: A Case Study

In the competitive world of luxury chocolate brands, Godiva Chocolatier has established itself as a frontrunner in the market. With its impeccable marketing tactics and digital strategies, Godiva continues to captivate the taste buds of chocolate enthusiasts worldwide.

Godiva’s marketing strategy revolves around effective brand positioning and a deep understanding of its target audience. By conducting thorough competitive analysis, the company ensures it remains ahead of the curve and stands out in an overcrowded market.

Through meticulously crafted marketing campaigns , Godiva creates an aura of indulgence and sophistication, appealing to its discerning customer base. The company’s commitment to excellence is evident in its high-quality products and elegant experiences.

Key Takeaways:

• Godiva Chocolatier is a leading luxury chocolate brand with a strong marketing strategy .
• The company focuses on effective brand positioning and understands its target audience.
• Thorough competitive analysis is conducted to ensure Godiva stays ahead of the competition.
• Godiva’s marketing campaigns create an aura of indulgence and sophistication.
• The company’s commitment to excellence is reflected in its high-quality products and elegant experiences.

Advantages of the Ülker Group and Godiva Chocolatier Acquisition

The acquisition of Godiva by the Ülker Group brings several advantages that contribute to the growth and success of both companies.

Increased Market Share

By joining forces, both Ülker Group and Godiva Chocolatier can expand their market share within the highly competitive chocolate industry. This acquisition strategically positions the companies for greater market dominance and enables them to reach a wider customer base.

Meeting Stakeholder Expectations

The acquisition aligns with stakeholder expectations as it combines the strengths and competitive advantages of both Ülker Group and Godiva Chocolatier. Stakeholders, including shareholders and employees, benefit from the enhanced capabilities and resources that result from the merger.

Reduced Entry Barriers

For Ülker Group, the acquisition of Godiva Chocolatier provides a valuable opportunity to enter the premium-luxury chocolate industry at a global scale. The acquisition grants Ülker Group immediate access to a well-established brand with a strong reputation, lowering the barriers to entry into this competitive market segment .

Risk Reduction

Introducing a new product to a new market poses inherent risks. However, by leveraging Godiva’s existing reputation and brand value, Ülker Group mitigates many of these risks. The acquisition allows for a smoother market entry with a reduced level of uncertainty, increasing the chances of success.

Disadvantages of the Ülker Group and Godiva Chocolatier Acquisition

While the acquisition of Godiva Chocolatier by Ülker Group offers numerous advantages, it also comes with some disadvantages that need to be considered. These drawbacks include the potential for culture clashes, product duplication, poorly matched businesses, and a high cost.

Culture Clashes

Due to the different backgrounds and organizational cultures of Ülker Group and Godiva Chocolatier, there is a possibility of culture clashes arising from the acquisition. Employees from both companies may have different ways of working, communication styles, and decision-making processes, which can lead to conflicts and hinder the integration process.

Product Duplication

Since Ülker Group and Godiva Chocolatier operate in the same industry, there is a risk of product duplication. This means that both companies might offer similar products, resulting in overlaps and increased competition within their own portfolio. It can also lead to confusion in the market and cannibalization of sales.

Poorly Matched Businesses

The businesses of Ülker Group and Godiva Chocolatier may not be well matched in terms of culture or strategy. Although both companies are in the food industry, their operations, target markets , and brand positioning may differ significantly. This misalignment can create challenges when integrating processes, systems, and teams, potentially impacting the overall business performance.

The acquisition of Godiva Chocolatier by Ülker Group comes with a high cost of $850 million. This significant investment can put financial strain on Ülker Group and require careful management to ensure a return on investment. It also increases the pressure for the successful integration and performance of Godiva within the group.

While these disadvantages exist, they should be evaluated alongside the advantages to gain a holistic understanding of the acquisition’s impact on Ülker Group and Godiva Chocolatier. Managing these challenges effectively and leveraging the strengths of both companies can lead to a successful integration and improved business outcomes.

Segmentation Strategy of Godiva Chocolatier

When it comes to reaching the right audience, Godiva Chocolatier understands the importance of segmentation. The brand has successfully positioned itself as a luxurious and high-end chocolate maker, catering to a specific group of consumers who appreciate premium chocolates. By targeting a high-income group, Godiva ensures that its products align with the preferences and purchasing power of its target audience.

The segmentation strategy focuses on appealing to individuals who are willing to indulge in the finer things in life, including premium chocolates. These consumers value quality and are more likely to splurge on luxury items, making them ideal customers for Godiva.

Godiva’s brand positioning as a provider of premium chocolates helps them differentiate themselves in the market . The brand has built a reputation for producing high-quality handmade chocolates, enticing consumers with their exquisite taste and elegant packaging.

By catering to a niche market of high-income individuals, Godiva Chocolatier maintains a strong brand identity and ensures that its products remain synonymous with luxury and indulgence.

Targeting Strategy of Godiva Chocolatier

Godiva Chocolatier has a comprehensive targeting strategy focused on capturing the global market of luxury chocolate consumers, particularly affluent individuals seeking indulgence in high-quality and upscale chocolates. The brand specifically caters to individuals with disposable incomes, who possess a preference for luxury goods.

By targeting the global market , Godiva aims to reach chocolate enthusiasts worldwide who appreciate the finer things in life. The brand’s meticulous attention to detail and commitment to crafting exquisite chocolates resonates with discerning consumers who prioritize taste, craftsmanship, and an exceptional sensory experience.

Godiva positions itself as the go-to chocolatier for individuals seeking a touch of luxury and sophistication. With a range of delectable flavors, meticulously designed packaging, and a reputation for excellence, Godiva stands tall as a leading provider of premium chocolates in the global market.

Positioning Strategy of Godiva Chocolatier

Godiva Chocolatier has established itself as a luxurious brand that prioritizes elegance and premium quality. The brand’s positioning strategy revolves around creating a sense of indulgence and sophistication for its customers. One of the key elements that contribute to this positioning is Godiva’s iconic gold packaging, which plays a significant role in enhancing the overall luxury experience.

The gold packaging is instantly recognizable and has become synonymous with the brand. It conveys a sense of opulence and exclusivity, elevating the perceived value of Godiva’s chocolates. The elegant gold color adds a touch of sophistication and allure, capturing the attention of consumers and making the brand stand out on shelves.

Godiva understands that packaging is more than just a means to protect the chocolates; it is an opportunity to create a memorable experience for customers. The gold packaging entices the senses and creates anticipation, heightening the overall enjoyment of the chocolate. Opening a box of Godiva chocolates becomes a moment of luxury and elegance.

By placing emphasis on packaging design, Godiva ensures that every aspect of the brand embodies its commitment to providing elegant experiences. From the moment the customer sees the gold packaging to the moment they savor the decadent chocolate inside, Godiva aims to deliver a seamless and memorable journey that reflects the brand’s positioning as a luxurious and indulgent choice.

This image represents the captivating gold packaging that contributes to Godiva’s luxurious positioning.

Godiva’s Packaging Design Strategy

Godiva Chocolatier understands the significance of packaging design in creating a luxurious and captivating experience for its customers. With the help of its dedicated global design team, Godiva ensures brand consistency and delivers visually appealing packaging that reflects the brand’s essence.

Heading this global design team are three talented individuals: Maria Williams, Azita Shahidi, and Lisa Russell. Together, they bring their expertise and creativity to maintain the highest standards in packaging design.

The team firmly believes that the product experience begins the moment a customer receives the shiny gold box. The distinctive gold packaging serves as a symbol of luxury and preciousness, setting the stage for what awaits inside.

Every aspect of the packaging is meticulously crafted to showcase the quality and allure of Godiva chocolates. The team incorporates elegant designs, meticulous finishes, and premium materials to create a truly remarkable unboxing experience for the customers.

With a keen eye for beauty and attention to detail, the global design team continually strives to create packaging that exemplifies the brand’s identity and captures the hearts of customers worldwide.

Godiva Packaging Design Showcase

Importance of content access and management.

The global design team at Godiva understands the significance of content accessibility and management in ensuring brand consistency and facilitating global brand development . To achieve these goals, the team relies on a central depository called Canto, which serves as a reliable platform for storing and sharing brand assets.

Canto’s central depository provides the design team with easy access to previous packaging designs, ensuring consistency and preventing duplication. With a centralized library, team members can quickly search and retrieve specific assets, saving time and effort in the design process. This accessibility is crucial in maintaining a unified visual identity for Godiva’s packaging design.

In addition to content accessibility, Canto enables effective communication and collaboration among team members who may be working in different locations. By having a centralized platform, the global design team can seamlessly share information, ideas, and feedback, fostering efficient workflows and supporting global brand development initiatives.

The use of Canto as a central depository also reinforces the importance of consistency in packaging design. It ensures that each team member has access to the most up-to-date assets and guidelines, minimizing the risk of producing inconsistent or disparate designs. By adhering to brand standards and guidelines, Godiva can consistently deliver the desired brand experience to consumers worldwide.

The central depository provided by Canto serves as a digital hub for the global design team, empowering them to create compelling packaging designs that align with Godiva’s brand identity. Through efficient content access and management, Godiva can maintain its global brand presence and deliver visually captivating packaging designs that resonate with customers.

Impact of Canto on Brand Coherence

Canto, as a digital asset management system, has played a significant role in achieving brand coherence at Godiva. The marketing and creative teams utilize Canto to ensure consistency in their vision across different regions and cultures. Cultural nuances and regional differences require careful consideration, and Canto allows for easy sharing and adaptation of content. By leveraging Canto’s features, the marketing and creative teams can maintain a strong brand identity while catering to specific cultural contexts.

Table: Benefits of Canto on Brand Coherence

Benefits of Canto for Godiva’s Design Team

Canto, the digital asset management system, has proven to be highly advantageous for Godiva’s design team. Notably, it enables the team to easily manage and access their valuable assets, contributing to a more streamlined design process and enhanced productivity. Let’s explore the key benefits that Canto brings to the table:

• Asset Management: One of the primary advantages of using Canto is the effective management of digital assets. Godiva’s design team can effortlessly organize and retrieve previous packaging designs, facilitating efficient research and reference for upcoming projects.
• Research and Data-Driven Decisions: With Canto’s robust reporting features, Godiva’s design team gains valuable insights into content usage. By tracking the popularity of assets, they can make data-driven decisions, ensuring their designs align with customer preferences and current trends.
• Consistency in Brand Identity: The centralized asset management system offered by Canto plays a pivotal role in maintaining brand consistency. Designers have quick access to approved brand assets, ensuring that their work aligns with Godiva’s established visual identity and brand guidelines.
• Facilitating New Team Members: When new designers join the team, Canto serves as a valuable resource for them to familiarize themselves with Godiva’s brand identity. By exploring previous designs and assets, new team members can gain valuable insights, contributing to a seamless integration into the design team.
• Excellent Customer Support: Canto’s readily available customer support has proven to be invaluable for Godiva’s design team. Whenever assistance is required, they can rely on Canto’s dedicated support team to promptly address their queries and provide solutions.

By leveraging the features and capabilities of Canto, Godiva’s design team enjoys a range of benefits that enhance their creative processes, align their designs with brand guidelines, and ultimately contribute to the consistent visual identity of the Godiva brand.

In conclusion, the success of Godiva Chocolatier’s marketing strategy lies in its focused approach to brand positioning, targeting a high-income group of consumers, and creating luxurious experiences through packaging design. By positioning itself as a luxurious brand with elegant and sophisticated products, Godiva has captured the attention of discerning consumers who value premium chocolates.

The acquisition by Ülker Group has presented both advantages and disadvantages, reflecting the challenges of merging two companies. While there may be culture clashes and product duplication, the acquisition has allowed Ülker Group to expand its market share and meet the expectations of stakeholders. Additionally, it has reduced entry barriers and risks for Ülker Group in the premium-luxury chocolate industry.

To enhance brand coherence and maintain a consistent brand identity across global markets, Godiva has implemented a digital asset management system like Canto. This system has streamlined content management, ensuring that the global design team can easily access and share brand assets, including previous packaging designs. With Canto’s support, Godiva can effectively manage its content, maintain consistency, and continue delivering a luxurious and indulgent experience to its target audience.

What is the objective of the Ülker Group and Godiva Chocolatier acquisition?

What are the advantages of the ülker group and godiva chocolatier acquisition, what are the disadvantages of the ülker group and godiva chocolatier acquisition, who is the target audience for godiva chocolatier, what is the segmentation strategy of godiva chocolatier, what is the targeting strategy of godiva chocolatier, how does godiva chocolatier position itself as a brand, how does godiva chocolatier approach packaging design, why is content accessibility and management important for godiva chocolatier, how does canto impact brand coherence at godiva, what are the benefits of using canto for godiva’s design team, related posts.

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Gojek marketing strategy 2024: a case study, golden goose marketing strategy 2024: a case study.

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Godiva Japan: Think Local, Scale Global

By: Rohit Deshpande, Esel Cekin, Akiko Kanno

This case tracks Jerome Chouchan's strategies and execution for a successful turn around of Godiva Japan's operations which was experiencing a decline in sales when he became the managing director of…

• Length: 13 page(s)
• Publication Date: Jan 2, 2017
• Discipline: Marketing
• Product #: 517056-PDF-ENG

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This case tracks Jerome Chouchan's strategies and execution for a successful turn around of Godiva Japan's operations which was experiencing a decline in sales when he became the managing director of the company in 2010. Through various initiatives and innovations, Godiva Japan had targeted a variety of demographic segments in different sales points, acquired new customers and created a moment of luxurious consumption for all ages. Accordingly, within Godiva's global enterprise, Godiva Japan had become number two in terms of worldwide sales and number one in terms of profits. It exported made-in Japan products and concepts to Godiva's other markets. How could Chouchan keep the momentum and sustain Godiva Japan's top-line and bottom-line growth going forward? Would he be able to keep the balance between aspirational and accessible? How much of the success in Japan might contribute to the growth of Godiva's global sales?

Learning Objectives

This case provides an opportunity for students to learn about branding strategies in a highly competitive market, focusing on product innovations, as well as the importance of occasion segmentation where each channel and product variation offers new occasions for people to shop.

Jan 2, 2017 (Revised: Feb 12, 2018)

Discipline:

Geographies:

Europe, Japan, Turkey, United States

Industries:

Beverage industry, Food industry, Retail trade

Harvard Business School

517056-PDF-ENG

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Increased Engagements by 97% YOY & 5.76% Engagement Rate

Godiva is a luxury Belgian chocolate brand with over 600 boutiques and present in more than 100 countries.

Client Goals:

Build brand awareness after the account had been inactive for some time.

Drive more people to their key location stores.

What We Did

Since their best-performing content was UGC, our creative team produced more of it, as well as partnered with content creators.

We also saw that Reels performed very well for them, so we decided to make our creatives very video-heavy.

Because Godiva has stores in key locations, we made sure we were location-tagging every post, as well as using localised hashtags.

Increased engagements by 97% YOY.

Reached 94% more users than the previous year.

Increased the account’s engagement to 5.76% purely for organic content.

Achieved 72k views on organic Reels from November 2021 to March 2022.

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Gift Guides That Convert

Customer Story: Godiva

The Challenge: Limited ability to create and launch experiential content with robust functionality.

Godiva’s ecommerce team aspires to deliver conversational and lifestyle-driven digital experiences that engage their customers as a part of the brand experience. However, with a small team, Godiva has been limited in their ability to create and launch experiential content with robust digital functionality.

With a growing customer demand to learn more about Godiva’s heritage, product quality, and production processes, as well as interactive content such as recipes and entertaining tips; Godiva struggled to regularly meet the scope of digital content. Intending to further expand their content, Godiva needed a scalable solution to generate rich content for an elevated site experience.

As content-driven marketing has gained strategic importance at Godiva, they knew their current model simply would not scale. They needed to get more content up faster and make it easy for any member of their team—and even others at the company—to design and publish amazing experiences without any need to code.

The Solution: Simplified content creation and publishing, without coding.

Upon seeing Fastr Frontend in action, the Godiva team realized that Fastr Frontend could solve many of their immediate needs. Its drag-and-drop functionality reduced the amount of coding needed. Fastr Frontend also has a Salesforce Commerce Cloud cartridge, meaning it integrates with their eCommerce platform for easy “shop the look” experiences, and it simplified publishing rich content pages. The tool was easy for non-technical staff to learn, including the marketing team, allowing for updates at any time.

Having just hand-coded a time-consuming new gift guide for Christmas, the Godiva team was eager to compare that process with their inaugural Fastr Frontend campaign, Valentine’s Day 2016 — one of Godiva’s biggest campaigns of the year. Celebrity spokesperson Eva Longoria anchored the campaign and Godiva used Fastr Frontend to build an online lookbook showcasing Eva’s top six gift picks. As compared to historical online celebrity appearances, this campaign generated a 300% conversion rate lift year over year.

“This campaign helped to bring excitement to our website in a way we could not offer in the past,” said Christine O’Brien, site merchandise manager who manages Godiva’s dig ital campaigns. For the first time, the team has the creative freedom to focus on building an interactive digital experience without worrying about the back-end coding.

The Results: Increased output and productivity, and delivering better experiences that convert.

In the first few months of using Fastr Frontend, Godiva has already realized a 380% increase in productivity and creative output—and they expect that number will only increase as more colleagues are brought onto the team and utilize the platform.

O’Brien notes that Fastr Frontend enables her to be at least 50% more productive. And beyond building more experiences, she sees the platform as helping the team deliver better experiences. 

Moving to Fastr Frontend has fundamentally changed how the team works. They are now able to spend more time thinking about their content strategy. They have more bandwidth to dig into metrics to find out what’s working and what’s not, and they can reflect site changes on demand. After only a few months of use, Godiva is already planning how to utilize more of Fastr Frontend's capabilities to drive even better customer experiences and higher ROI.

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Godiva Case

Godiva Case Godiva is segmented into the luxury Market and has invested a lot in order to position its brand in the US market including a marketing campaign with the renowed jeweler H. Stern.

Its president of North America is wondering about the decision on expand its distribution into the major retail companies in the US and with that increase sales, this idea came up because Godiva lost its position in the US to Lindt, which its growth can be explained by the strategy of increase its distribution through national retail chains.The president of Godiva in the US is concerned that this decision can risk Godiva brand as a luxury brand. “Dunkin knew that one of the biggest difficulties facing all luxury marketers expanding their distribution is the control and maintenance of their brand image” “Godiva’s brand mark was unmistakable, but consumption needed to be increased” “Lindt & Sprungli surpassed Godiva in gourmet chocolate sales in the United States, but Godiva still remained the most recognized chocolate marketer”The decision on increase the sales through the national retail chains and discount stores can be a bad strategy because this means that: Godiva can risk their premium brand image, decrease its profit margin, harm its luxury stores sales through cannibalization, competition of new players. Godiva chocolate is very much used to present people.With this in mind, the company has invested a lot in marketing campaigns, in packaging and in its own luxury retail stores to sell its premium chocolates and also publicize its brand. They have also achieved 6% of sales in the e-commerce in the direct market and it would be very damaging for them to lose all this equity.

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In addition the fact that Lindt increased its sales in the US taking the 1st position in sales from Godiva it doesn’t mean that the company has been more profitable than Godiva.In fact the chocolate luxury products has showed to be more profitable than the regular chocolate. It is known that the discount stores work a lot in shrinking the suppliers costs in order to offer their customers lower prices. Moreover, Godiva has invested a lot in its own boutiques and therefore the idea of selling in the retail chains could take customers from these stores to buy in the supermarkets. As pposed to the regular chocolate market, the luxury chocolate segment shows a very high increase through the years to come. It was also disclosed that new players are trying to enter in the US market and therefore it would be important to Godiva to defend its position in the US market instead as the best luxury brand instead of fight for sales.

In the same time the increase of sales could be achieved through the entrance into new markets such as the emerging countries.

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• Godiva Japan: Think Local, Scale Global
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At EMBA PRO , we provide corporate level professional case study solution. Godiva Japan: Think Local, Scale Global case study is a Harvard Business School (HBR) case study written by Rohit Deshpande, Esel Cekin, Akiko Kanno. The Godiva Japan: Think Local, Scale Global (referred as “Godiva Godiva's” from here on) case study provides evaluation & decision scenario in field of Sales & Marketing. It also touches upon business topics such as - Value proposition, Change management, Customers, Product development, Strategy execution, Talent management. Our immersive learning methodology from – case study discussions to simulations tools help MBA and EMBA professionals to - gain new insight, deepen their knowledge of the Sales & Marketing field, and broaden their skill set.

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Case Description of Godiva Japan: Think Local, Scale Global Case Study

This case tracks Jerome Chouchan's strategies and execution for a successful turn around of Godiva Japan's operations which was experiencing a decline in sales when he became the managing director of the company in 2010. Through various initiatives and innovations, Godiva Japan had targeted a variety of demographic segments in different sales points, acquired new customers and created a moment of luxurious consumption for all ages. Accordingly, within Godiva's global enterprise, Godiva Japan had become number two in terms of worldwide sales and number one in terms of profits. It exported made-in Japan products and concepts to Godiva's other markets. How could Chouchan keep the momentum and sustain Godiva Japan's top-line and bottom-line growth going forward? Would he be able to keep the balance between aspirational and accessible? How much of the success in Japan might contribute to the growth of Godiva's global sales?

Case Authors : Rohit Deshpande, Esel Cekin, Akiko Kanno

Topic : sales & marketing, related areas : change management, customers, product development, strategy execution, talent management, what is the case study method how can you use it to write case solution for godiva japan: think local, scale global case study.

Almost all of the case studies contain well defined situations. MBA and EMBA professional can take advantage of these situations to - apply theoretical framework, recommend new processes, and use quantitative methods to suggest course of action. Awareness of the common situations can help MBA & EMBA professionals read the case study more efficiently, discuss it more effectively among the team members, narrow down the options, and write cogently.

Case Study Solution Approaches

Three Step Approach to Godiva Japan: Think Local, Scale Global Case Study Solution

The three step case study solution approach comprises – Conclusions – MBA & EMBA professionals should state their conclusions at the very start. It helps in communicating the points directly and the direction one took. Reasons – At the second stage provide the reasons for the conclusions. Why you choose one course of action over the other. For example why the change effort failed in the case and what can be done to rectify it. Or how the marketing budget can be better spent using social media rather than traditional media. Evidences – Finally you should provide evidences to support your reasons. It has to come from the data provided within the case study rather than data from outside world. Evidences should be both compelling and consistent. In case study method there is ‘no right’ answer, just how effectively you analyzed the situation based on incomplete information and multiple scenarios.

Case Study Solution of Godiva Japan: Think Local, Scale Global

We write Godiva Japan: Think Local, Scale Global case study solution using Harvard Business Review case writing framework & HBR Sales & Marketing learning notes. We try to cover all the bases in the field of Sales & Marketing, Change management, Customers, Product development, Strategy execution, Talent management and other related areas.

Objectives of using various frameworks in Godiva Japan: Think Local, Scale Global case study solution

By using the above frameworks for Godiva Japan: Think Local, Scale Global case study solutions, you can clearly draw conclusions on the following areas – What are the strength and weaknesses of Godiva Godiva's (SWOT Analysis) What are external factors that are impacting the business environment (PESTEL Analysis) Should Godiva Godiva's enter new market or launch new product (Opportunities & Threats from SWOT Analysis) What will be the expected profitability of the new products or services (Porter Five Forces Analysis) How it can improve the profitability in a given industry (Porter Value Chain Analysis) What are the resources needed to increase profitability (VRIO Analysis) Finally which business to continue, where to invest further and from which to get out (BCG Growth Share Analysis)

SWOT Analysis of Godiva Japan: Think Local, Scale Global

SWOT analysis stands for – Strengths, Weaknesses, Opportunities and Threats. Strengths and Weaknesses are result of Godiva Godiva's internal factors, while opportunities and threats arise from developments in external environment in which Godiva Godiva's operates. SWOT analysis will help us in not only getting a better insight into Godiva Godiva's present competitive advantage but also help us in how things have to evolve to maintain and consolidate the competitive advantage.

- Streamlined processes and efficient operation management – Godiva Godiva's is one of the most efficient firms in its segment. The credit for the performance goes to successful execution and efficient operations management.

- Experienced and successful leadership team – Godiva Godiva's management team has been a success over last decade by successfully predicting trends in the industry.

- Little experience of international market – Even though it is a major player in local market, Godiva Godiva's has little experience in international market. According to Rohit Deshpande, Esel Cekin, Akiko Kanno , Godiva Godiva's needs international talent to penetrate into developing markets.

- Godiva Godiva's business model can be easily replicated by competitors – According to Rohit Deshpande, Esel Cekin, Akiko Kanno , the business model of Godiva Godiva's can be easily replicated by players in the industry.

Opportunities

- Lucrative Opportunities in International Markets – Globalization has led to opportunities in the international market. Godiva Godiva's is in prime position to tap on those opportunities and grow the market share.

- Increase in Consumer Disposable Income – Godiva Godiva's can use the increasing disposable income to build a new business model where customers start paying progressively for using its products. According to Rohit Deshpande, Esel Cekin, Akiko Kanno of Godiva Japan: Think Local, Scale Global case study, Godiva Godiva's can use this trend to expand in adjacent areas Change management, Customers, Product development, Strategy execution, Talent management.

- Home market marketing technique won’t work in new markets such as India and China where scale is prized over profitability.

- Age and life-cycle segmentation of Godiva Godiva's shows that the company still hasn’t able to penetrate the millennial market.

Once all the factors mentioned in the Godiva Japan: Think Local, Scale Global case study are organized based on SWOT analysis, just remove the non essential factors. This will help you in building a weighted SWOT analysis which reflects the real importance of factors rather than just tabulation of all the factors mentioned in the case.

What is PESTEL Analysis

PESTEL /PEST / STEP Analysis of Godiva Japan: Think Local, Scale Global Case Study

PESTEL stands for – Political, Economic, Social, Technological, Environmental, and Legal factors that impact the macro environment in which Godiva Godiva's operates in. Rohit Deshpande, Esel Cekin, Akiko Kanno provides extensive information about PESTEL factors in Godiva Japan: Think Local, Scale Global case study.

Political Factors

- Political and Legal Structure – The political system seems stable and there is consistency in both economic policies and foreign policies.

- Little dangers of armed conflict – Based on the research done by international foreign policy institutions, it is safe to conclude that there is very little probability of country entering into an armed conflict with another state.

Economic Factors

- Inflation rate is one of the key criteria to consider for Godiva Godiva's before entering into a new market.

- According to Rohit Deshpande, Esel Cekin, Akiko Kanno . Godiva Godiva's should closely monitor consumer disposable income level, household debt level, and level of efficiency of local financial markets.

Social Factors

- Leisure activities, social attitudes & power structures in society - are needed to be analyzed by Godiva Godiva's before launching any new products as they will impact the demand of the products.

- Consumer buying behavior and consumer buying process – Godiva Godiva's should closely follow the dynamics of why and how the consumers are buying the products both in existing categories and in segments that Godiva Godiva's wants to enter.

Technological Factors

- 5G has potential to transform the business environment especially in terms of marketing and promotion for Godiva Godiva's.

- Proliferation of mobile phones has created a generation whose primary tool of entertainment and information consumption is mobile phone. Godiva Godiva's needs to adjust its marketing strategy accordingly.

Environmental Factors

- Consumer activism is significantly impacting Godiva Godiva's branding, marketing and corporate social responsibility (CSR) initiatives.

- Environmental regulations can impact the cost structure of Godiva Godiva's. It can further impact the cost of doing business in certain markets.

Legal Factors

- Property rights are also an area of concern for Godiva Godiva's as it needs to make significant Change management, Customers, Product development, Strategy execution, Talent management infrastructure investment just to enter new market.

- Intellectual property rights are one area where Godiva Godiva's can face legal threats in some of the markets it is operating in.

What are Porter Five Forces

Porter Five Forces Analysis of Godiva Japan: Think Local, Scale Global

Competition among existing players, bargaining power of suppliers, bargaining power of buyers, threat of new entrants, and threat of substitutes.

What is VRIO Analysis

VRIO Analysis of Godiva Japan: Think Local, Scale Global

VRIO stands for – Value of the resource that Godiva Godiva's possess, Rareness of those resource, Imitation Risk that competitors pose, and Organizational Competence of Godiva Godiva's. VRIO and VRIN analysis can help the firm.

What is Porter Value Chain

Porter Value Chain Analysis of Godiva Japan: Think Local, Scale Global

As the name suggests Value Chain framework is developed by Michael Porter in 1980’s and it is primarily used for analyzing Godiva Godiva's relative cost and value structure. Managers can use Porter Value Chain framework to disaggregate various processes and their relative costs in the Godiva Godiva's. This will help in answering – the related costs and various sources of competitive advantages of Godiva Godiva's in the markets it operates in. The process can also be done to competitors to understand their competitive advantages and competitive strategies. According to Michael Porter – Competitive Advantage is a relative term and has to be understood in the context of rivalry within an industry. So Value Chain competitive benchmarking should be done based on industry structure and bottlenecks.

What is BCG Growth Share Matrix

BCG Growth Share Matrix of Godiva Japan: Think Local, Scale Global

BCG Growth Share Matrix is very valuable tool to analyze Godiva Godiva's strategic positioning in various sectors that it operates in and strategic options that are available to it. Product Market segmentation in BCG Growth Share matrix should be done with great care as there can be a scenario where Godiva Godiva's can be market leader in the industry without being a dominant player or segment leader in any of the segment. BCG analysis should comprise not only growth share of industry & Godiva Godiva's business unit but also Godiva Godiva's - overall profitability, level of debt, debt paying capacity, growth potential, expansion expertise, dividend requirements from shareholders, and overall competitive strength. Two key considerations while using BCG Growth Share Matrix for Godiva Japan: Think Local, Scale Global case study solution - How to calculate Weighted Average Market Share using BCG Growth Share Matrix Relative Weighted Average Market Share Vs Largest Competitor

5C Marketing Analysis of Godiva Japan: Think Local, Scale Global

4p marketing analysis of godiva japan: think local, scale global, porter five forces analysis and solution of godiva japan: think local, scale global, porter value chain analysis and solution of godiva japan: think local, scale global, case memo & recommendation memo of godiva japan: think local, scale global, blue ocean analysis and solution of godiva japan: think local, scale global, marketing strategy and analysis godiva japan: think local, scale global, vrio /vrin analysis & solution of godiva japan: think local, scale global, pestel / step / pest analysis of godiva japan: think local, scale global, swot analysis and solution of godiva japan: think local, scale global, references & further readings.

Rohit Deshpande, Esel Cekin, Akiko Kanno (2018) , "Godiva Japan: Think Local, Scale Global Harvard Business Review Case Study. Published by HBR Publications.

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Pro-Palestinian campus protests and Baltimore fights Dali ship owners: Morning Rundown

A judge considers whether Donald Trump violated a gag order by calling key figures in his hush money trial ‘’sleaze bags.’’ Arrests escalate tensions at campus Gaza protests. And a study shows how the definition of “old age” is shifting.

  Here’s what to know today.

Judge will weigh if Trump violated gag order before witness testimony continues

Former President Donald Trump’s trial to determine whether he falsified business records is set to pick back up with more testimony from former American Media chief David Pecker. But first, Judge Juan Merchan will consider whether Trump should be held in contempt over a series of posts on Truth Social calling Michael Cohen and Stormy Daniels ‘’sleaze bags’’ that prosecutors argue violated a gag order. 

The hearing to consider whether Trump violated rules follows a day of opening statements from both sides. It marked the first time time New York prosecutors have laid out their case against Trump. They told a story of a “criminal scheme to corrupt the 2016 presidential election.” And that “he covered up that criminal scheme by lying in his New York business records over and over and over again,” said Michael Colangelo, a lawyer with Manhattan District Attorney Alvin Bragg’s office.

Colangelo also quoted from the widely publicized “Access Hollywood” tape, which caught Trump on a hot mic saying he could grope women without their consent because “when you’re a star, they let you do it.”

This is Morning Rundown, a weekday newsletter to start your morning. Sign up here to get it in your inbox.

Trump attorney Todd Blanche went straight to trying to tear apart the prosecution’s story. He said there was “nothing wrong with trying to influence an election.” He added, “It’s called democracy.”

Blanche also alleged adult film star Stormy Daniels was trying to “extort” money from Trump, and that ex-Trump lawyer Michael Cohen was waging a vendetta against him. Here’s what else happened on the fifth day of the trial.

Trump was seen with his eyes closed as Merchan began the day by running through rote procedural matters. He began to focus more intently on key players in the room when opening statements began, and became even more animated when Pecker, the prosecution’s first witness, took the stand.

As senior politics reporter Jonathan Allen wrote in an analysis: Trump’s “entire demeanor changed over the course of a day, hinting that he plans to be an active participant in this trial going forward.” Read the full analysis here.  

Senate holds key test vote on Ukraine aid

The Senate is expected to vote to advance the House-passed package that includes $60 billion in aid to Ukraine and a provision that could lead to a nationwide ban on TikTok. The package also includes $26 billion in aid to Israel and humanitarian relief in Gaza, in addition to $8 billion for security in Taiwan and the Indo-Pacific. 

This afternoon’s vote isn’t on final passage of the bill, but is instead about whether senators can reach a deal to quickly vote to pass the bill the same day, or whether they will have to wait until tomorrow. The outcome of the vote is nonetheless a key indicator of whether the legislation has enough support to head to President Joe Biden’s desk. If it passes, TikTok’s China-based parent company will have nine months — or the president could extend it to a year — to sell the popular social media platform or be banned in the U.S. Here’s what else to know.

Arrests at NYU, Yale as campus protests escalate

New York police said they were “ready” to take Gaza war protesters into custody again at the request of New York University if demonstrators refused to leave. The statement came hours after several arrests were made last night. The number of people arrested, as well as the charges against them, were not available.

The tensions at NYU coincide with escalating campus protests across the U.S. over the war in Gaza. Forty-seven protesters at Yale University were arrested earlier yesterday. Classes at Columbia University will be hybrid for the rest of the spring semester, the school’s provost said, in light of recent protests. Here’s what else happened.

More on the tensions at college campuses:  

• Some faculty members at Morehouse College  have raised concerns  about plans for President Joe Biden to deliver a commencement address there next month.

A study considers the question: When does ‘old age’ begin?

People’s definition of “old age” seems to be skewing later and later, according to a new study, which asked people about their perceptions of age over the course of 25 years. The study found that people in their mid-60s believe old age starts at 75. But the older people got, the later they thought old age began. The generation in which participants were born also influenced their perception.

Experts say there is one important caveat to the research, which was published in the American Psychological Association’s Psychology and Aging journal. The study’s participants lived in Germany, where life expectancy has risen in the past few decades. But U.S. life expectancy declined from 79 in 2019 to 76 in 2021, which could skew the study’s results elsewhere.

Still, experts say the study offers insight into how people’s perspectives on aging change as they get older.

Baltimore battles Dali ship owners in bridge collapse

The City of Baltimore is seeking a jury trial in its effort to hold the owners of the Dali cargo ship liable for the collapse of the Francis Scott Key Bridge. In new court filings, Mayor Brandon Scott and the Baltimore City Council argued that Grace Ocean Private Ltd. and Synergy Marine Pte Ltd. “put a clearly unseaworthy vessel into the water.” The city also said the companies’ effort to limit responsibility for the vessel and the cargo’s value at $43.6 million is “substantially less than the amount that will be claimed for losses and damages” from the March 26 crash that killed six construction workers. The Port of Baltimore generated over $70 billion last year alone, the city said. 

The city alleges that the alarms on the Dali sounded “even before leaving the port,” but the ship left anyway. The court filings also take aim at crew members , accusing the companies of staffing the ship with people who lacked proper skill or training. 

Politics in Brief

Pennsylvania primaries: A key contest in the fight for control of the Senate, a moderate Republican put to the test and a district where the war in Gaza is front and center. Here’s what to watch for in the Pennsylvania primaries. 

EMILY’s List: As Trump moves closer to selecting his running mate, a major Democratic abortion rights advocacy group is focusing its annual “On Notice” list on “extremist” candidates. 

RFK Jr.’s influence: Trump has recently stepped up his efforts to brand third-party presidential candidate Robert F. Kennedy Jr. as left-wing and Biden’s problem. It’s a sign that he and his allies are worried that Trump may have more to lose from Kennedy’s presence on the ballot.

Supreme Court: Conservative justices appeared skeptical about a challenge to an Oregon city’s ordinances that punish homeless people for camping on public property when they have nowhere else to go.

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Staff Pick: Pastor vows to fight Satan’s influence in Metropolis

As I drove toward the city of Metropolis, Illinois, earlier this month, a billboard invited me to visit the “Home of Superman.” The small town, which shares a name with the city from DC Comics, is known for welcoming outsiders. But that reputation has been tested in recent months, as a local Christian pastor has rallied his congregation to stand against “evil” forces that he says have been encroaching on their community, via the public library.

The conflict — what some are calling “a battle for the soul” of Metropolis — follows a national pattern of Christian conservatives waging what they view as a spiritual battle inside libraries, as they seek to restrict access to LGBTQ-affirming books. But unlike comic books and the Bible,  the story I found in Metropolis  did not break along obvious ideological lines or black-and-white depictions of good and evil.  —  Mike Hixenbaugh,  senior investigative reporter

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• Published: 22 April 2024

Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán

• Andrew J. Charlton-Perez   ORCID: orcid.org/0000-0001-8179-6220 1 ,
• Helen F. Dacre   ORCID: orcid.org/0000-0003-4328-9126 1 ,
• Simon Driscoll 1 , 2 ,
• Suzanne L. Gray   ORCID: orcid.org/0000-0001-8658-362X 1 ,
• Ben Harvey   ORCID: orcid.org/0000-0002-6510-8181 1 , 3 ,
• Natalie J. Harvey 1 ,
• Kieran M. R. Hunt   ORCID: orcid.org/0000-0003-1480-3755 1 , 3 ,
• Robert W. Lee   ORCID: orcid.org/0000-0002-1946-5559 1 ,
• Ranjini Swaminathan 1 , 2 ,
• Remy Vandaele 1 , 2 &
• Ambrogio Volonté   ORCID: orcid.org/0000-0003-0278-952X 1 , 3  

npj Climate and Atmospheric Science volume  7 , Article number:  93 ( 2024 ) Cite this article

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There has been huge recent interest in the potential of making operational weather forecasts using machine learning techniques. As they become a part of the weather forecasting toolbox, there is a pressing need to understand how well current machine learning models can simulate high-impact weather events. We compare short to medium-range forecasts of Storm Ciarán, a European windstorm that caused sixteen deaths and extensive damage in Northern Europe, made by machine learning and numerical weather prediction models. The four machine learning models considered (FourCastNet, Pangu-Weather, GraphCast and FourCastNet-v2) produce forecasts that accurately capture the synoptic-scale structure of the cyclone including the position of the cloud head, shape of the warm sector and location of the warm conveyor belt jet, and the large-scale dynamical drivers important for the rapid storm development such as the position of the storm relative to the upper-level jet exit. However, their ability to resolve the more detailed structures important for issuing weather warnings is more mixed. All of the machine learning models underestimate the peak amplitude of winds associated with the storm, only some machine learning models resolve the warm core seclusion and none of the machine learning models capture the sharp bent-back warm frontal gradient. Our study shows there is a great deal about the performance and properties of machine learning weather forecasts that can be derived from case studies of high-impact weather events such as Storm Ciarán.

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Introduction

During the 20th century and the first two decades of the 21st century, numerical weather prediction (NWP) transformed atmospheric science 1 . The combination of physical and mathematical understanding, the availability of high-performance computing and the expansion of the network of Earth system observation led to remarkable and continued progress in the skill and availability of weather forecasts. Numerical weather predictions are a ubiquitous part of modern life, with applications on many different timescales and in sectors as diverse as transport, agriculture, healthcare and recreation.

Over the last two years, machine learning (ML) techniques, a subset of the rapidly developing field of artificial intelligence (AI), have begun to be applied to the weather prediction problem in earnest. Whilst ML has had applications in climate science for many decades 2 , 3 , 4 , with these communities aware of its potential 5 , and is increasingly used for post-processing weather forecasts 6 , 7 , recent advances in ML and advancements in GPUs (Graphics Processing Units), have enabled the beginning of a ‘new dawn’ in the application of ML and AI techniques to weather and climate prediction 8 .

The publication of the WeatherBench dataset 9 and the 10-year roadmap for ML use by the European Centre for Medium-Range Weather Forecasts (ECMWF) 10 , amongst other developments, stimulated interest and investment in the development of ML models for weather forecasting. During 2022 and 2023, four ML models were developed by major technology companies to address the short to medium-range (0–10 day) forecasting problem. These models have all been shown to produce skillful 0–10 day forecasts of the 500 hPa geopotential height field, based on the widely used Anomaly Correlation Coefficient metric 11 . All four models use an encode-process-decode framework but with differing architectures:

FourCastNet 12 , developed by NVIDIA and based on Fourier Neural Operators (FNO) with a vision transformer architecture;

FourCastNet version 2 13 , which builds on FourCastNet by using spherical FNOs;

Pangu-Weather 14 , developed by Huawei and based on a three-dimensional Earth-specific transformer and hierarchical temporal aggregation; and

GraphCast 15 , developed by Google DeepMind and based on graph neural networks.

Similar techniques have been used to develop models for other forecast tasks (e.g., MetNet-3 for 12-h precipitation forecasts in the contiguous United States and 27 European countries 16 ). At the present time, ML models primarily produce deterministic forecasts, but rapid progress is being made in producing fully probabilistic forecasts 17 , 18 , 19 . All four ML models are extremely efficient when run on GPU or TPU (tensor processing unit) devices, typically producing 10-day forecasts in a few minutes.

Given the infancy of ML model weather prediction, to the author’s knowledge, there are no prior studies that compare how the four ML-models and NWP models capture individual, impactful weather events. Examination of individual weather events available from the papers that describe the ML models is limited to qualitative comparisons of the simulation of tropical cyclones and atmospheric rivers by FourCastNet 12 and quantitative assessment of the tracking error of tropical cyclones by Pangu-Weather 14 and GraphCast 15 . There are no published studies that examine ML model forecasts of extratropical windstorms 20 , despite their potential to cause multi-billion dollar damages 21 and increasing severity under climate and population change 22 .

In this study, we, therefore, seek to advance knowledge of the comparative performance of ML and NWP models by comparing their forecasts of Storm Ciarán, which affected several European countries during November 2023. This is a valuable out-of-sample test for the ML models because their training datasets all end before the beginning of 2023. We compare the ability of the models to capture the detailed physical structure of the storm and its impacts at two lead times over which operational weather forecasters were actively engaged in issuing weather warnings to the public. An accurate description of the physical structure of this, or any other, storm is a key component of forecasting its compound impact 23 and in constructing plausible storylines for end-users 24 .

Storm Ciarán and its associated impacts

Storm Ciarán was first seen as a low-pressure weather system south of Newfoundland at about 00 UTC on 31 October 2023. Based on surface analysis charts issued by the UK Met Office, it then tracked quickly across the North Atlantic, undergoing explosive deepening from 988 hPa at 00 UTC on 1 November to 954 hPa at 00 UTC on 2 November at which time it was located to the northeast of France. This deepening rate, 34 hPa in 24 h means that Ciarán was an extratropical cyclone “bomb” 25 . The lowest pressure recorded, 953 hPa at 06 UTC on 2 November, is a record low pressure for a November storm observed in England 26 . Figure 1 shows surface observations of the 10-m wind speed, cloud cover and mean sea level pressure (MSLP). The cyclonic circulation around the storm centre (with the lowest MSLP observed on the English south coast near the Isle of Wight) has a maximum wind speed of 65 knots on the Normandy coast in France.

The observations are shown as simplified station circles using conventional notation 54 . Circle shading indicates cloud cover in octas, wind barbs and feathers indicate wind speed in knots with the wind direction towards the circle, and numbers are the last three digits, including a decimal place, of the MSLP (in hPa) e.g., 543 equates to 954.3 hPa. Some thinning of observations has been performed for clarity and note that two ships both reported at 51.1 o N, 1.7 o E with different wind speeds and directions.

Although Storm Ciaran was not a classic Shapiro-Keyser cyclone 27 , clear banding in the vicinity of the tip of the cloud as it encircles the storm centre to the poleward side (called the cloud head) could be seen in satellite imagery before it made landfall in northern France. This banding suggests that a sting jet may have been present in Storm Ciarán 28 , however its identification requires methodologies beyond the scope of this study. Gusts of over 100 knots (51 m s -1 ) were reported in several locations in Brittany 29 , with a maximum of 111.7 knots (57.5 m s -1 ) recorded at Pointe du Raz at approximately 0200 UTC on 2 November 30 .

Across Northern Europe, at least 16 people were killed 31 . All flights were cancelled from Amsterdam Schiphol Airport and there were numerous cancellations from Spanish airports. An estimated 1.2 million households in northern France were left without electricity 32 and more than 1 million residents were cut off from the mobile telephone network. Brest and Quimper Airports were also shut and there was disruption to Eurostar operations 33 .

Approximately 10,000 homes in Cornwall were left without power, hundreds of schools were closed and many train services were disrupted by fallen trees. Gusts in Channel Islands ranged from 70-90 knots (36-46 m s -1 ) 32 with a maximum gust of 90 knots (46 ms -1 ) recorded in Alderney at approximately 08 UTC on 2 November 34 . Jersey also experienced a T6 tornado with estimated winds in the region of 161-186 mph (71–83 m s -1 ). Its 8-km track left a trail of destruction and tens of people needed to leave their homes. It is likely that this is the strongest tornado reported in the British Isles since the Gunnersbury tornado in December 1954 35 .

The 10-m wind speed and MSLP structure of Ciarán are shown in Fig. 2a, b at the times when it impacted the land: 00 and 06 UTC 2 November 2023. State-of-the-art model analyses, such as the IFS analysis used in this figure, represent the best three-dimensional estimates of the actual atmospheric state. The low-pressure centre of the storm tracked along the southern UK coast and the strongest winds (turning cyclonically) occurred in an arc in the southwest quadrant of the storm when the strong winds impacted Brittany and later more directly to the south of the low centre when they impacted the Channel Islands. The 10-m winds weakened significantly over land due to surface friction, no longer reaching the threshold for shading in the figure. They also weakened between the two times shown, with the peak winds falling by about 6 ms -1 , likely due to a combination of the storm making landfall and having already reached its mature stage. The observed wind speeds, shown by the overplotted colour-filled circles, are consistent with the analysed fields, away from the coastlines but exceed those analysed in some locations, notably some coastal locations and at the narrowest point of the English channel. The winds in these locations will be influenced by local mesoscale processes and so these exceedances are not unexpected given the resolution of the IFS model. The track of the storm, defined as the locations of its minimum MSLP according to IFS analyses, is shown by the black symbols joined by lines in panel (c). Ciarán had its genesis in the western North Atlantic around the time of the first track point shown (06 UTC 31 October) and travelled rapidly eastwards across the North Atlantic. The contours shown illustrate the MSLP and 250-hPa wind speed (i.e., the upper-level jet) at the start, middle and end times of the tracks and show how Ciarán evolved from a weak disturbance (with central MSLP exceeding 995 hPa) to a record-breaking deep storm as it crossed from the equatorward to the poleward side of the jet at about 06 UTC 1 November.

a , b Maps of 10-m wind speed (shading) and MSLP (contours) at a 00 UTC and b 06 UTC 2 November 2023 from the IFS analysis. Synoptic wind observations above 20 m s –1 are shown as coloured dots. c Six-hourly track points from the IFS analysis (blacks squares joined by lines) and the IFS HRES forecasts and AI models (coloured symbols as in Fig. 3a , b ) from 06 UTC 31 October to 06 UTC 06 UTC 2nd November 2023 (left to right) together with partial MSLP (grey contours in hPa) and 250-hPa wind speed (colour-filled contours) from the IFS analysis at 06 UTC on 31 October, 1 November and 2 November (left to right). The locations of the jet maxima at the longitude points of the MSLP minima at each time are indicated by the cyan squares connected by lines.

Track, intensification and wind impacts of Storm Ciarán

Ciarán’s track was well forecast by both the IFS HRES and ML-models (I. 2(c)) initialised at 00UTC on 31 October, although small differences in the location of the storm centre, and associated wind field, were critical for the accurate predictions of weather warnings along the southern English coast. Two days before Ciarán began to impact land and well before the start of its fast intensification, the spread in the position of the storm in ML models and NWP models is similar.

The evolution of the minimum mean sea pressure (MSLP) at the centre of the developing storm and its associated maximum 10-m wind speed are shown in Fig. 3 for the IFS analysis, IFS HRES forecast and ML model forecasts in panels (a, c), and for the ERA5 reanalysis, forecasts based on the ERA5 system, and the control (unperturbed) ensemble members of four NWP models in panels (b, d). Considering first the minimum (MSLP) evolution, all the forecasts closely follow both analysis products, capturing both the rapid deepening phase of the storm and its maximum intensity depth. The minimum MSLP at the end of the forecast (06 UTC 2 November) is 954 hPa in both the IFS analysis and ERA5. This value varies between 951 and 955 hPa for the ML models and between 950 and 953 hPa for the six NWP models (including the IFS HRES). In contrast, the spread in the maximum wind speed evolution is far greater. At the time of peak wind speed in both analyses, 00 UTC 2 November (48-h lead time), the value in the IFS analysis is 34 m s -1 . The IFS HRES forecast predicts this well (36 m s -1 ), while the other, generally slightly coarser resolution, NWP models mostly forecast slightly weaker winds (30-37 m s -1 ) with the NCEP model being a clear outlier, predicting winds of 40 m s -1 . The wind speeds forecast by the ML models are far too weak (25–26 m s -1 ), even in comparison with the analysis from ERA5 (30 m s -1 ). The ML models failed to capture the rapid intensification of the winds after about 06 UTC on 1 November (30-h lead time). Forecasts made using the ERA5 analysis system do not suffer from this low wind bias and so the underestimation is unlikely to be the result of training the ML models on the ERA5 data. The economic loss resulting from strong surface winds is often assumed to scale as the cube of normalised wind gust speed over a threshold (such as the 98 th percentile value) 36 , so even a small underestimation in predicted wind speed can be significant in terms of the subsequent losses.

a , c minimum MSLP and b , d maximum 10-m wind speed. a , b IFS analysis, IFS HRES forecast and forecasts from the four ML models. a , d ERA5, forecasts made from the ERA5 system and forecasts from control members of the ensemble forecasts from IFS (IFS ens_cntl), the Met Office (UKMO ens_cntl), the Japan Meteorological Agency (JMA ens_cntl) and National Centers for Environmental Prediction (NCEP ens_cntl). Note that the ECMWF HRES and IFS control members use the same model and resolution but are not bit-identical for technical computational reasons.

The differences in maximum 10-m wind speed are explored further in Fig. 4 which shows maps of the 10-m wind speed and MSLP for ERA5, the IFS HRES forecast and the four ML-models valid at 00 UTC 2 November, the time of peak wind speed in both analyses and when the strong winds made landfall in France. All the forecasts were initialised 48 h prior to this time (as for the data shown in Fig. 3 ). These maps can be compared directly with the IFS analysis fields shown in Fig. 2a . The region of strong winds is located in an arc in the region of the tight MSLP gradient in the southwest quadrant of the Ciarán in all seven maps. However, the ML models fail to predict the strongest winds in a band following the isobars (contours of constant MSLP) in the region of the tightest MSLP gradient, as is seen in the IFS HRES forecast, ERA5 and the IFS analysis. It is notable that, despite all the ML models being trained on ERA5, they fail to capture the structure and magnitude of the winds in ERA5 (including in forecasts made using the ERA5 system, as shown in Supp. Fig. 1a ) for this storm, implying that the far weaker winds found for the ML models compared to the NWP forecasts and IFS analysis are not simply a consequence of them being trained on a coarser resolution dataset. Note the NWP models used in Fig. 2 have a similar resolution to ERA5 (equivalent grid spacings of ERA5 ~ 31 km, Met Office ~20 km, JMA ~ 27 km, NCEP ~ 25 km) with the exception of the IFS ( ~ 9 km).

Maps of 10-m wind speed (shading) and MSLP (contours) from a ERA5 and b – f forecasts, initialised at 00 UTC 31 October 2023, from the b IFS HRES model and c – f ML models, as labelled.

Dynamical structure of Storm Ciarán

In this section, we evaluate the dynamics of Storm Ciarán during the final stage of its rapid development with a focus on the formation of strong winds at low altitudes. We compare the predictive capability of the ML models by comparing with the IFS HRES forecast and ERA5. The ML models are all trained on ERA5 and have the same resolution as the output provided for ERA5, allowing a fair comparison of model performance. The forecasts are all initialised at 00 UTC on 1 November, during the onset of Ciarán’s rapid intensification phase (Fig. 3a ). They were evaluated 18 h later (Fig. 5 ) and 24 h later (Fig. 6 ), when Storm Ciarán’s peak wind speeds were observed. By shifting the focus to these short lead times from the previous section, the aim is to highlight both the similarities in and differences between, the NWP and ML forecasts on timescales relevant for refining hazard warnings. To aid the reader, some key parts of the storm structure are labelled in Fig. 5a .

Maps show wind speed at 850 hPa (shading), wind speed at 250 hPa (65 ms –1 , cyan contour with high values in the bottom left of the panels), the wet-bulb potential temperature at 850 hPa (dark blue, light blue, light red and dark red contours indicating values increasing every 2.5 K from 280 K to 287.5 K), MSLP (thin grey contours), relative humidity with respect to water at 700 hPa (grey shading encircling regions above 80%, not shown for FourCastNet ( e ) as not available), the vertical component of relative vorticity at 850 hPa (light-to-dark green shading, from 3 × 10 −4 s –1 and then every 2 × 10 −4 s –1 ). a shows the structure in ERA5 while b – f show the structure from forecasts initialised at 00 UTC 1 November 2023. Note that the range of the wind speed colour bars in Figs. 5 and 6 is different to that in Figs. 2 and 4 . The main features of the cyclone described in the text are annotated in ( a ).

Contours of wind speed at 250 hPa and some of the contours of wet-bulb potential temperature at 850 hPa are not present as the associated values are not reached in the maps shown.

On 1 November 2023, Ciarán underwent significant intensification beneath the left exit region of an upper-level jet streak (Fig. 2c ). All the ML models captured the position and extent of the upper-level jet streak accurately with the minimum MSLP associated with Ciarán beneath the left exit region at 18 UTC (Fig. 5a–f ), a critical aspect of Storm Ciarán’s dynamics.

There is also consensus among the ML models concerning the general shape of the cyclone. Figure 5a–f shows the position of the selected moist isentropes, chosen to indicate the frontal locations and, by their separation, the frontal strengths. The position of the warm sector, identified as the region inside the 285 K moist isentrope, is characterised as a hooked feature in ERA5. The shape of the warm sector is well captured by all the ML models. The cloud head, represented by 700-hPa relative humidity above 80% (grey shading), is seen wrapping around the poleward side of the cyclone centre in ERA5. The IFS HRES, Graphcast and PanguWeather forecasts accurately depict the shape of the cloud head; however, the cloud head in FourCastNet v2 appears less curved than the other forecasts. FourCastNet forecast does not output a humidity variable at 700 hPa.

Despite capturing the general shape of Storm Ciarán, there are noticeable differences in the strength of frontal structures, indicated by the gradient in wet-bulb potential temperature (how close together moist isentropes are). This is true for the cold front, denoted by the 285-K and 287.5-K moist isentropes to the southeast of the cyclone centre and also for the “bent-back front”, i.e., the gradient between the 282.5-K and 285-K moist isentropes that wrap around the cyclone centre on its northwestern side. To the southwest of the low centre the moist isentropes indicate the bent-back front diverge, and this is known as the frontal-fracture region. While all the ML forecasts include a frontal-fracture region, they struggle to resolve the sharp across-front temperature gradient to the west and southwest of the low centre.

Cross-frontal wind shear is another indicator of frontal strength, and the values of the vertical component of 850-hPa relative vorticity (green shading in Fig. 5a–f ) near the bent-back front provide further evidence of the difficulties that the ML models have in simulating the frontal structures in the region. The hook-shaped narrow strip of high relative vorticity aligned with the bent-back warm front that is present in both IFS HRES and ERA5 (with maximum values up to 9 x 10 -4  s -1 and 7 x 10 -4  s -1 , respectively) becomes broader and weaker in the ML models. The discrepancy between the ERA5 (and also the forecasts based on the ERA5 system, see Supp. Fig. 1b ) and ML models in representing the sharpness of the bent-back front indicates that this shortcoming of the ML models is not solely due to model resolution.

This difference in frontal strength is particularly significant since it directly relates to the environment that can be conducive to the descent of a sting jet. Sting jets are coherent air flows that descend over a few hours from inside the tip of the cloud head at mid-tropospheric levels leading to a distinct mesoscale (perhaps 50–100 km across) region of near-surface stronger winds, and particularly gusts 37 . Among the models presented in this study, only the IFS forecasts and analysis have the necessary resolution to resolve a sting jet. It is crucial to recognise that the ML models, trained on coarse resolution data, are not equipped to discern features such as the presence of mesoscale jets. This limitation highlights the importance of using models with adequate resolution when predicting high-impact weather phenomena occurring at small spatial scales. However, our analysis suggests that the ML models struggle to represent frontal structures conducive to mesoscale high-impact features even when compared against NWP models with similar resolution, such as that used to generate ERA5.

The lack of sharpness of the bent-back warm front and cold front in the ML models impacts the strength of the wind speed maxima, as can be seen by turning the focus of evaluation to the region of strong winds. Figure 5a–f shows the 850-hPa wind speed (filled contours) to give an indication of the lower-tropospheric storm structure that is less influenced by the presence of land below than the 10-m winds shown previously; consequently, winds at this pressure level, roughly a km above the ground, are normally stronger than those nearer the surface. All the ML models consistently identify two regions of strong winds: one in the frontal-fracture region and another in the warm sector. The strong winds situated in the frontal-fracture region are associated with the tight pressure gradient near the tip of the bent-back warm front, the associated descent and acceleration where the moist isentropes spread out, and the alignment with the direction of propagation of the storm (with a possible local enhancement due to sting-jet descent in the IFS HRES, see the small-scale areas above 46 ms -1 ). The strong winds mostly in the core of the warm sector (enclosed by the 287.5-K moist isentropes) are associated with a broad jet, known as the warm conveyor belt jet, which ascends through the depth of the atmosphere from the top of the atmospheric boundary layer. The ability of the ML models to identify both the frontal-fracture and warm conveyor belt wind maxima (albeit with differences in the spatial structure and intensity of the latter) underscores their ability to accurately capture the general structure of extratropical cyclones. However, maximum wind speeds are weaker in the ML models than in ERA5, where they exceed 46 m s -1 (and even 48 m s -1 in the forecast based on the ERA5 system) in a broad region at the entrance of the frontal fracture. While Graphcast and FourCastNet display a small deficit of around 2 m s -1 , PanguWeather and FourCastNetv2 are roughly 4 m s -1 and 6 m s -1 lower, respectively.

We now turn our attention to the forecasts valid 6 h later, at 00UTC on 2 November 2023 (Fig. 6a–f ), the time of peak ERA5 wind speeds. At this stage of Ciarán’s evolution, analysis of the frontal-fracture region and the warm sector reveals several interesting features. ERA5 exhibits a region of warm air at the centre of the storm, which is separated from the main warm sector, a process known as warm core seclusion. Warm core seclusion occurs during the mature stage of extratropical cyclone development when cold air wraps around the low centre and cuts it off from the warm subtropical airmass. Relative vorticity starts decreasing along the bent-back front and generally increases near the cyclone centre as the front wraps around it. While the general evolution is captured by all models, the degree of clarity in the presence of a well-defined warm seclusion varies noticeably among the ML-models.

Focusing on the maximum winds in the frontal-fracture region, now compounded by the arrival of the cold conveyor belt (the main low-level jet in the cold sector, behind the cold front, of an extratropical cyclone), reveals that the ERA5, the forecast based on the ERA5 system (Supp. Fig. 1c ) and the IFS forecast all have peak 850-hPa wind speeds between 48-50 m s -1 . GraphCast and FourCastNet exhibit peak wind speeds 4–6 m s -1 lower. PanguWeather and FourCastNet-v2 have a larger weak bias, with wind speeds underestimated by 6-8 m s -1 . Wind maxima are consistently underestimated in the ML models when compared to the benchmarks provided by the ERA5 (and its forecast) and the IFS forecast. This discrepancy in predicting wind maxima at the time of peak winds and as they approach land is crucial for assessing the potential impact of Storm Ciaran’s surface winds and associated gusts.

By inspection, the structures of the MSLP fields in Figs. 5 and 6 are similar for the different models despite the differences in the wind speed structure and magnitude. This raises a further interesting question, is the discrepancy between the wind maxima in the conventional NWP and ML because the ML models do not reproduce the dynamical balances between the wind and pressure fields inherent in the conventional NWP models? This question is examined in detail in the Supplementary Material (and included Supp. Figs. 2 – 8 ) and a short summary is included here.

While the calculation of the geostrophic wind field (resulting from the balance of the pressure gradient and Coriolis forces) is relatively straightforward, calculating the more accurate gradient wind field (with the further inclusion of the centrifugal force associated with the curvature of a parcel trajectory) is more complex. Since both calculations require the evaluation of horizontal gradients in the geopotential field, an unphysical lack of smoothness on the smallest scales in all of the ML models becomes easily apparent and should be further investigated. Note that while the gradient wind should provide a better approximation to the frictionless large-scale flow (where that flow is curved) than the geostrophic wind, the flow can still differ from gradient wind balance due to unbalanced motions which may be physically realistic, particularly in high-resolution model output (and reality).

Smoothed geostrophic and gradient wind fields have physically plausible structures in both NWP and ML model outputs. While the strongest full winds are found in the NWP model outputs even after smoothing, the strongest gradient winds are not clearly different between the ML and NWP models. The differences between the smoothed full and gradient wind fields for the NWP and ML models have similar characteristic structures and magnitudes in strong wind regions of the storm. Within the limitations of the accuracy of our calculations, we cannot conclude that the weak winds in the ML model forecasts are the result of an inability to resolve the proper dynamical balances, but are likely to instead be related to inadequacies in the geopotential field, i.e., in the gradient and curvature of the geopotential contours.

In summary, the ML models represent the large-scale dynamical drivers key to the development of Storm Ciarán well, including the position of the storm relative to the upper-level jet exit. They also accurately capture the larger synoptic-scale structure of the cyclone such as the position of the cloud head, the shape of the warm sector and the location of the warm conveyor belt jet. The ability of the ML models to resolve the more detailed structure of the storm is more mixed. Only some ML models correctly resolve the warm core seclusion and none of them capture the sharp bent-back warm frontal gradient. ML models underestimate the magnitude of the strongest winds at the surface and in the free atmosphere (above the boundary layer), particularly in the frontal-fracture region near the end of the bent-back front. Note that this underestimation of the strongest wind speeds is not a consequence of the resolution of the output of the ML models or their training data, since it also applies when comparing against the ERA5 (and forecasts based on the ERA5 system) and NWP models with resolution similar to ERA5.

The contrasting ability of the four ML models considered to accurately forecast the large-scale dynamical properties of Storm Ciarán and its damaging winds serve to highlight the need for a more comprehensive assessment of this new and potentially transformative forecasting tool. More than 48 h before Storm Ciarán affected communities surrounding the English Channel, forecasts of the rapid MSLP deepening and track of the storms produced by the ML models were essentially indistinguishable from forecasts from an ensemble of conventional NWP models. Our analysis shows that the ML models were able to reproduce the upper-level flow that steered the developing storm into the left exit region of the jet and led to its rapid intensification. Many of the important dynamical features of the storm including the position of the cloud head, the shape of the warm sector and the location of cold and warm conveyor belt jets were also well captured by the ML models. The ML models do not seem to have been limited by the fact that a storm of comparable central pressure has never previously been observed over England during November. However, even in the relatively short ERA5 record, storms developing in a similar way with similar dynamical drivers (such as the upper-level jet) are common throughout winter and the ability of ML models to forecast more dynamically unusual storms, such as small-scale storms that develop rapidly from waves on pre-existing fronts, is an open question.

In contrast, when considering the damaging winds associated with Storm Ciarán in detail, forecasts from the ML models had significant errors and poorer performance than conventional NWP models. All four ML models failed to produce the narrow band of very strong winds at the surface that led to the most severe impacts, The ML models also failed to represent the strength of the cross-front thermal gradient in the bent-back front (a feature also dynamically linked to strong winds) and had variable success in producing the warm seclusion of air that formed in the centre of the storm in its mature stage.

Much further work, considering other storms, is needed to assess if the biases apparent in the simulation of Storm Ciarán are a systematic feature of this first generation of ML models. Increased scrutiny of the models is likely to lead to the identification of target areas for model improvement, as it has done for NWP models. Since the ML models are available to all through public repositories, this scrutiny is likely to enable rapid model improvement. Detailed documentation of the performance of ML models will also be critical to weather forecasters seeking to make greater use of the ML models as part of the forecasting process. Forecasting centres like ECMWF are already beginning to develop and test alpha versions of ML models that complement their existing capabilities 38 .

Based on a single case study, it would be premature to draw conclusions about the relative abilities of the four different approaches to ML weather forecasting exemplified by the different models. In particular, given we only had access to the ‘small’ version of the FourCastNet-v2 model it might be expected that this model would have a limited ability to produce the detailed properties of Storm Ciarán. Nonetheless, studies like ours are useful for identifying knowledge gaps in ML model development for forecasting, particularly in their ability to capture the structure of extreme weather patterns. This can be direct, such as the inclusion of necessary output variables (a 700-hPa humidity variable to identify the shape of the cloud head missing from the FourCastNet model), or through the formulation of more nuanced hypotheses for investigation. For instance, PanguWeather’s ability to capture the vertical component of the 850-hPa relative vorticity could be due to the model integrating height information across levels. Similarly, GraphCast’s ability to simulate the warm core seclusion better may be due to its multi-mesh representation rather than the spatial mixing used in the other models. All the ML models failed to capture the intense winds at the surface associated with Ciarán. As shown in the supplementary figures, wind errors in the lower troposphere are smaller. This may indicate that future models could benefit from including variables and the relationships across these variables that better characterise the planetary boundary layer in their training datasets. Most importantly, our analysis makes a strong case for a more robust evaluation of the forecasts from the ML models across all relevant spatio-temporal features of the physical phenomenon considered instead of isolated error metrics on individual variables.

The rapid acceleration of the forecasting capabilities of ML models as exemplified by our study of Storm Ciarán poses many new challenges and opportunities for atmospheric science 39 . Explainable AI (xAI) techniques 40 , 41 could be powerfully combined with the ML models we have considered to develop a deeper understanding of the reasons that they were able to produce skillful forecasts of Storm Ciarán in line with other attempts to unify ML and causal discovery methods 42 . The development of general-purpose, foundational models 43 could add further to the set of tools available to both forecast and understand high-impact weather events.

In this study, we compare forecasts produced by four different models based on machine learning methods. All are initialised from the same operational ECMWF analysis allowing a direct comparison with the current operational forecast of the ECMWF high-resolution model (CY48R1). The ML model forecasts are produced using the ai-models toolbox developed by ECMWF ( https://github.com/ecmwf-lab/ai-models ).

All four models considered are data-driven Deep Learning models and originally trained on a few ( ~ 4) decades worth of atmospheric and surface variables from the ERA5 dataset 44 at a resolution of 0.25°x 0.25° ( ~ 30 km resolution at the equator), which translates to 720 x 1440 grid cells. The ML models are all autoregressive, which means model output from a given time step can be used to predict output at the next time step. Model differences arise chiefly from the individual architectures, the selection of variables, parameterisations and training schemes briefly summarised in the technical details below:

FourCastNet 12 , uses the vision transformer (ViT) architecture with an Adaptive Fourier Neural Operator (AFNO) 45 . The AFNO enables dependencies across spatial and channel dimensions to be modelled efficiently at high resolutions where spatial token (feature) mixing occurs as a global convolution in the Fourier domain with FFTs. The model has a pre-training step where the AFNO is trained ahead on the ERA5 data with 20 different surface and atmospheric variables and then used for inference. The pre-training step learns mappings between X ( t ) and X ( t  + Δ t ) where t is a time step, Δ t is a time increment (set to 6 h) and X is a tensor of features called patches. In the second fine-tuning or inference step, the pre-trained model is used to produce inferences from a defined state X ( t ), first for X ( t + Δ t ) and this output from the models is itself then used to generate X ( t  + 2* Δ t ) or the output for the second time step. Thus, while the training of the model is resource intensive, it is a one-time cost and the inference step is very fast.

FourCastNet v2 13 is a development of the original FourCastNet model that uses Spherical Harmonics Neural Operators for modelling non-linear chaotic and dynamical systems on a sphere as opposed to flat Euclidean spaces. The model is trained with ERA5 data in a two-step process similar to FourCastNet—a single autoregressive step followed by fine-tuning. By learning global convolutions in computationally efficient manners, Fourier Neural Operators (such as those used in FourCastNet) are capable of accurately simulating long-range dependencies in spatio-temporal data. However, the Discrete Fourier Transform that FNOs rely on assumes a flat geometry, resulting in dissipation together with visual and spectral artefacts. The Spectral Fourier Neural Operators (SFNO), forming the basis for the FourCastNet v2 model architecture in its update from the FourCastNet model, in addition to having the desirable properties of FNOs also have translational or rotational equivariance. FourCastNet v2 is trained on a 73-channel subset of the ERA5 reanalysis dataset on single levels and pressure levels.

Pangu-Weather 14 consists of four deep neural networks with different lead times (time between input and output) of 1 h, 3 h, 6 h and 24 h. 5 upper atmosphere and 4 surface variables at 13 different pressure levels were used to train the model with a combined total of 256 million parameters. The overall deep network architecture is called 3DEST or 3D-Earth-specific Transformer that integrates height information into a new dimension thus capturing relationships between atmospheric variables across pressure levels, unlike similar transformer-based models such as FourCastNet. Data is fed into the neural network and a process called patch embedding is used to downsample the input data from individual grid cells into a 3D cube. This cube is then put through an encoder-decoder based on a ViT called the Swin transformer 25 with 16 blocks. The positional bias in the Swin transformer is replaced with an Earth-specific positional bias to reflect the fact that in a 2D projection of a sphere, distances between neighbouring points are not the same across all latitudes. The decoder is symmetric to the encoder. Although the transformer-based neural network has a large training time similar to FourCastNet, this is partially improved in Pangu-Weather by the use of a hierarchical temporal aggregation scheme that reduces cumulative forecast errors and also the forecast generation time. This is done by employing the neural network with the largest lead time iteratively for a forecast so that neural networks with shorter lead times are used closer to the forecast. The height integration and aggregated forecast schemes are also considered improvements over other transformer-based architectures.

GraphCast 15 is based on Graph Neural Networks (GNNs) 46 with around 36.7 million parameters. The model is trained with 5 surface and 6 atmospheric variables at 37 pressure levels resulting in 227 variables for every data point or grid cell. In the first step, the Encoder maps information from individual grid cells to nodes in a multi-mesh representation. The multi-mesh is derived as icosahedral meshes of increasing resolution from coarse (12 nodes) to fine (40,962 nodes). The second step has Processors using 16 GNN layers to propagate local and long-range information across the nodes on the multi-mesh through message passing. Finally, the decoder uses a single GNN layer to map the final processor layer’s multi-mesh representation back to the grid cells. GraphCast thus avoids the use of transformers and the associated scaling issues with higher resolutions that could result in large training times.

Numerical weather prediction model forecasts and analysis products

The ML model forecasts are compared to a set of forecasts from conventional numerical weather prediction (NWP) models to assess both systematic differences in the capabilities of the NWP and ML models and how the spread in the forecasts from the two architectures compare. Forecasts from the IFS HRES forecast and forecasts based on the ERA5 system (see below for a description of ERA5) were obtained from ECMWF and control (unperturbed) members of the ensemble forecasts for four models (the IFS 47 , the Met Office 48 , the Japan Meteorological Agency (JMA) 49 , and the National Centres for Environmental Prediction (NCEP) 50 ) were downloaded from the TIGGE archive 51 of operational global ensemble weather forecasts out to the medium range. The models chosen differ in their design and the resolution of the numerical model grid. Cycle 48r1 of the IFS was operational at the time of Storm Ciarán. Following the upgrade to this cycle in June 2023, the HRES and ensemble forecasts have the same resolution, equivalent to 9 km grid spacing. The Met Office, JMA and NCEP ensembles have grid spacings of approximately 20 km, 27 km and 25 km, respectively. The data for the four control ensemble members were all obtained regridded to a regular latitude-longitude grid of 0.5 degrees.

Analysis products

Both sets of models are compared to two analysis products (optimal blends of short-range forecasts and observations): the operational IFS analysis and ERA5 44 . The operational IFS analysis is produced using the IFS HRES forecast and has a resolution equivalent to 9 km grid spacing, ERA5 has a resolution equivalent to 31 km grid spacing. The IFS analysis and ERA5 were regridded to a regular latitude-longitude grid of 0.25 degrees.

ERA5 is used as an additional measure of forecasts ‘truth’ because the ML models all used ERA5 as their training data. Hence comparison with ERA5 indicates the skilfulness of these models relative to the best possible forecast given their training data. It is to be expected that the IFS analysis will include smaller-scale and higher amplitude weather features than ERA5 due to the use of a higher resolution model, despite being regridded to the same grid. It is also expected that the IFS analysis will be closer to the “truth” due to the use of higher resolution and an upgraded modelling system.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.

Code availability

Apart from the Python packages referenced in the Acknowledgements, the underlying code for this study is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author.

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Acknowledgements

We thank the ECMWF labs team for building the publicly available ai-models library which enabled us to produce and compare forecasts from the four ML models. This library can be accessed at https://github.com/ecmwf-lab/ai-models . We also thank the modelling groups who made the code for the ML models publicly available through the following repositories: • FourCastNet https://github.com/NVlabs/FourCastNet . • PanguWeather https://github.com/198808xc/Pangu-Weather . • GraphCast https://github.com/google-deepmind/graphcast . This work is partly based on TIGGE data. TIGGE (The International Grand Global Ensemble) is an initiative of the World Weather Research Programme (WWRP). We are also grateful to ECMWF for providing access to operational analysis products to members of the research team through national research accounts held through the Met Office. The work is partly funded by the UKRI Natural Environment Research Council (UKRI-NERC) through several grants held by contributors and by the Schmidt Futures Foundation. BH is funded by the UKRI-NERC CANARI programme (NE/W004984/1). NJH is funded by UKRI-NERC UMBRELLA (NE/X018555/1). KMRH is funded by a UKRI-NERC Independent Research Fellowship (MITRE; NE/W007924/1). RS is funded by UKRI-NERC TerraFIRMA (NE/W004895/1). AV is funded by UKRI-NERC Arctic Summer-time cyclones (NE/T006773/1). SD is funded and supported by the Schmidt Futures Foundation.

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Andrew J. Charlton-Perez, Helen F. Dacre, Simon Driscoll, Suzanne L. Gray, Ben Harvey, Natalie J. Harvey, Kieran M. R. Hunt, Robert W. Lee, Ranjini Swaminathan, Remy Vandaele & Ambrogio Volonté

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Simon Driscoll, Ranjini Swaminathan & Remy Vandaele

National Centre for Atmospheric Science, Univ. of Reading, Reading, UK

Ben Harvey, Kieran M. R. Hunt & Ambrogio Volonté

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Contributions

A.J.C.P. conceived the analysis and produced ML forecasts and initial characterisation of their performance. S.D., K.M.R.H., R.S. and R.V. contributed to the analysis and description of the ML models. N.J.H. led analysis of the properties of Storm Ciarán and its impacts. H.D., S.G., B.H. and A.V. produced the detailed dynamical analysis of the properties of Storm Ciarán in the forecasts including collecting NWP data and producing all figures. All authors contributed to discussions, writing, proofreading and editing the manuscript. All authors should be considered co-first authors as the work was completed collaboratively. The funders played no role in the study design, data collection, analysis and interpretation of data, or the writing of this manuscript.

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Correspondence to Andrew J. Charlton-Perez .

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Charlton-Perez, A.J., Dacre, H.F., Driscoll, S. et al. Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán. npj Clim Atmos Sci 7 , 93 (2024). https://doi.org/10.1038/s41612-024-00638-w

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Received : 02 December 2023

Accepted : 31 March 2024

Published : 22 April 2024

DOI : https://doi.org/10.1038/s41612-024-00638-w

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