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Jua raises $2.5m for advanced weather modelling platform

© Shutterstock / KittyflyPost Thumbnail

Swiss start-up Jua has raised $2.5 million in pre-seed funding to launch its Accuracy Engine, which helps businesses, government bodies and researchers to create high-resolution weather models. 

  • Businesses of any size can use Jua’s Accuracy Engine to predict and manage the impacts of extreme weather events. 
  • Severe climate conditions are costing the global economy hundreds of billions of dollars, but companies are poorly prepared to manage future climate risks. 
  • The demand for accessible climate risk technologies will continue to rise, leading to further investment in emerging start-ups such as Jua. 

Zurich-based Jua aims to provide authorities, academics and businesses of any size with more accurate and accessible weather and climate modelling capabilities. The start-up has established bases in Zurich, Berlin and Capetown, and has secured $2.5 million in a pre-seed funding round led by Promus Ventures. 

The investment coincides with Jua’s launch of its Accuracy Engine platform, which will now be offered to a select range of customers before its wider release in early 2023. 

Jua’s co-founder and chief executive Andreas Brenner said: “We are going to change perceptions of weather prediction forever. In terms of geospatial resolution, temporal resolution and update frequency, our first model already outperforms all existing numerical models by several orders of magnitude. We now enable everyone from small startups to large companies to get access to much better weather data”. 

Jua’s Accuracy Engine 

The platform reportedly comprises one of the largest weather and geospatial data sets created to date, with its weather prediction model providing a global one-km squared worth of coverage every five minutes. The model predicts over 15 different weather-based parameters, including air pressure, precipitation, wind speed and wind direction. 

In addition to its frequent updates and small-scale pattern prediction, Jua claims that its technology offers up to 25 times higher spatial resolution and 10 times higher temporal resolution than existing alternatives. It says it has achieved this improvement by combining artificial intelligence with tens of millions of sensors, rather than the hundreds of thousands used by conventional models. 

A range of training materials is also available, enabling non-technical users of the platform to customise their models with proprietary data. Jua says that its customers can create purpose-built weather models within a matter of hours, enabling them to predict and manage extreme weather events before they occur. 

Jua has said that, previously, tailored weather models were only available to a small number of large organisations. It has designed its Accuracy Engine for use by start-ups and small to medium-sized enterprises (SMEs), as well as by larger companies, government institutions or academic researchers, to make weather modelling capabilities more accessible. 

As the company’s chief technology officer Marvin Gabler explains: ““Weather impacts almost all areas in business. Our technology enables SMEs to use highly accurate weather data in their decision making, increasing the SME’s resource efficiency and capital output. Furthermore, our technology enables SMEs to adapt to the impact of changing weather patterns as climate change accelerates.””

The rising costs of extreme weather 

According to the Intergovernmental Panel on Climate Change’s (IPCC) sixth assessment report, global greenhouse gas emissions have contributed to an increase in both the frequency and intensity of weather and climate-related extremes since pre-industrial times. It goes on to say that these trends are likely to continue as global average temperatures increase further. 

Such extremes pose a major threat to the global economy. Indeed, AON (NYSE:AON) estimates that weather and climate-related events cost around $329 billion in 2021 alone, while Munich Re has said that natural disasters caused overall losses of $65 billion in the first half of 2022. 

These disasters are occurring worldwide, with Munich Re’s study referencing disasters including extreme tornadoes in the US, drastic flooding in Australia and the extreme heat, wildfires and drought experienced in Southern Europe. 

Extreme weather events can destroy business assets, but can also contribute to interconnected risks such as supply chain disruptions or losses in worker productivity.

This highlights how the cascading risks of climate change can intensify over time and continue contributing to business costs in multiple areas. 

Small businesses are not immune to the costs of extreme weather 

Research suggests that small and medium-sized enterprises are more vulnerable to losses from extreme weather than larger firms. This is primarily due to financial constraints, which limit SMEs’ ability to invest in risk management strategies and leave them with a greater need to access bank credit. 

SMEs are also disproportionately affected by wider economic downturns, as they typically have less-established supply chain management procedures, higher exposure to financial fragility and weaker liquidity. 

With climate-related events becoming a crucial factor in global economic prospects, these disproportionate effects will place SMEs in an extremely vulnerable position. 

This is evidenced by research from multinational insurance group AXA (XPAR:AXA) and the United Nations Environment Programme Finance Initiative (UNEP FI). Their survey of 2,600 SME executives and managers found that 79% consider climate change to be a long-term risk to their business, but that only 27% feel well-prepared to manage its impacts. 

Businesses are poorly prepared for future climate risks 

A 2022 report by the Grantham Research Institute on Climate Change and the Environment suggests that the majority of SMEs are poorly prepared for future climate risks. Its analysis found that low levels of awareness, limited ability to make assessments, and poor understanding of associated costs was leading SMEs to make decisions that would ‘lock in’ future risks to their business. 

Although almost half of the UK SMEs it studied were concerned about possible climate risks, they had not yet been able to assess them. This lack of assessment had led businesses to make decisions relating to their location, infrastructure, supply chain networks or revenue models that would expose them to substantial risks in coming years. 

Larger businesses are also struggling to prepare. Despite their increasing awareness of the need to model climate risk, survey analysis by Bloomberg concluded that only 5% of its respondents were in the advanced stage of having comprehensive data and multi-scenario analysis based on a variety of climate variables like carbon emissions, geolocation data, and extreme weather events. 

Technological innovation can help companies manage climate risks 

Emerging technologies such as Jua’s could help companies to manage climate risks by helping them to identify possible events and develop appropriate strategies to lessen their impacts.  

A greater understanding of climate-related vulnerabilities can also enable companies to educate their stakeholders, serving as justification for changes in strategic direction and demonstrating a well-managed risk profile that could support their access to lower cost capital or insurance premiums. 

Bloomberg estimates that only 19% of established corporations are currently using digital technologies to gain insights into their climate risks, while research suggests that SMEs tend to adopt digital tools and technologies at around half the rate of larger firms. With SMEs accounting for around 99% of global businesses, there is cause for concern around how the economy could be affected if they are left behind. 

As such, there is an increasing demand for accessible technologies that support businesses of all size in managing their future climate risks. Jua’s successful fundraising therefore provides an early example of what is likely to be a growing investment trend.


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