Climate risk analytics has become essential for businesses navigating an unpredictable environmental landscape. As extreme weather events intensify and climate patterns shift, organizations across industries require sophisticated forecasting tools to protect operations and identify opportunities. This comprehensive overview examines two pioneering firms AccuWeather and ClimateAi that are revolutionizing climate risk modeling through advanced technology and artificial intelligence. Discover how these industry leaders leverage proprietary systems, extensive data networks, and innovative patents to deliver actionable climate intelligence for informed decision-making.

Accuweather

Headquarters: State College, Pennsylvania, United States | Founder: Joel Myers | Founded: 1962| Number of Employees: 450+| Website: https://www.accuweather.com/

Accuweather combine weather data, technology, and human insight to improve lives and businesses.

Company forecast for every longitude and latitude point on Earth with Superior Accuracy™

AccuWeather Forecast Engine

• Step 1: Largest and best collection of real-time data.
• Step 2: Expert data analysis
• Step 3: Forecasts with proven Superior Accuracy.
• Step 4: Partner, prepare and protect with greater confidence.

Leading the Charge in Climate Adaptation

• 190+ Forecast models used
• 3.5m Locations forecasted
• 100+ Expert meteorologists on staff

Industries Served

Manufacturing | Retail | Financial Services | Energy | Insurance | Healthcare | Transportation | Forensic Meteorology

AccuWeather Evolution

• 1962 AccuWeather was founded by Dr. Joel N. Myers in State College, Pennsylvania.
• 1967 First Snow Warning Service client served.
• 1975 AccuWeather introduces the industry's first 7-day local forecast for television.
• 1983 AccuWeather launches worldwide forecast services and begins transmitting weather data and text directly to newspapers typesetting computers.
• 1986 AccuWeather pioneers the electronic delivery of complete weather pages for newspapers and air-ready satellite, radar, and weather graphics for television.
• 2002 AccuWeather begins services to PDAs and cell phones, and the one billionth free page is accessed on AccuWeather.com.
• 2005 The 24/7 AccuWeather Channel™ is introduced and the ABC Television Group becomes the first customer to sign up for the service.
• 2013 AccuWeather launches first-of-its-kind 45-day forecast. (AccuWeather was also first to introduce detailed 5-day, 7-day, 10-day, 15-day, 25-day, 30-day, and 90-day forecasts.)
• 2021 AccuWeather acquires Plume Labs, the leading authority on air pollution data.

Services

SkyGuard® Severe Weather Warnings

Protect people and production with 24/7 weather warnings and 1:1 consulting from our expert meteorologists

Forecasting

Understand the expected impact of weather events on business operations

Forensic Meteorology

Expert witness testimony and past weather event verification from our PhD and Certified Consulting Meteorologists

Data & Analytics

Maximize real-time weather-driven business opportunities based on data from current and past weather events

AccuWeather – Canadian Patent

The solution to the problem is Given by Radich Rosemary Yielding; Akers Jennifer Bowers; Loftus Timothy; Root Michael R

Overview

The invention discloses an economic forecast system that analyzes weather metrics that are divided into groups (based on the multicollinearity of the weather metrics in each group) and generates a statistical model using the one or more most statistically significant weather metrics from each group.

Problem

When incorporating past weather data, conventional systems generate overfitted and/or underfitted models because of the high multicollinearity of weather metrics.

Solution

In contrast to the underfitted or overfitted models generated using conventional methods, analyzing weather metrics that are divided into groups based on the multicollinearity of those weather metrics causes the disclosed system to efficiently identify the weather metrics that are most predictive of the future economic trends, even using a large number of weather metrics that are computationally expensive to test.

The Solution is Invented by “AccuWeather Inc”

Solution

The solution provided by Accuweather discloses a system for forecasting an economic performance metric of interest.

The system includes:

• A historical economic performance database that stores one or more geo-located and time-indexed historical economic performance metrics including the economic performance metric of interest.
• A historical weather database that stores geo-located and time-indexed historical weather metrics, wherein the historical weather metrics are separated into groups such that the historical weather metrics with high multicollinearity are grouped together.
• A weather forecast database that stores geo-located and time-indexed forecasted weather metrics.

An economic forecast engine that:

• Performs a correlation analysis to identify the correlation and statistical significance of each of the historical weather metrics with respect to the economic performance metric of interest.
• Selects up to a predetermined number of historical weather metrics from each group with the highest correlation with respect to the economic performance metric of interest and a statistical significance meeting or exceeding a predetermined threshold.
• Generates a statistical model to forecast the economic performance metric of interest using the selected historical weather metrics from all of the groups.
• Forecasts the economic performance metric of interest using the statistical model and the forecasted weather metrics; and outputs the forecasted economic performance metric of interest for display to a user.

ClimateAi

Headquarters: 388 Beale St, San Francisco, California, 94105, United States | Co-Founder: Himanshu Gupta and Max Evans | Founded: 2017 | Number of Employees: 51-200 | Website: https://climate.ai/

ClimateAi has built the first climate resilience platform, pioneering the application of artificial intelligence to mitigate the impact of climate change and uncover new opportunities that may arise as a result.

By applying AI to climate risk modeling, we provide short and long-term insights into weather and climate impact, helping businesses identify the actions needed today to adapt to the climate change disruptions of tomorrow.

Industry Leaders Working with ClimateAI to Build Their Climate Resilience

Leading the Charge in Climate Adaptation

• 887K CO2e Metric Tons Avoided
• 35+ Countries
• 5 Patents

Industries Served

Agriculture | Food & Beverage |Finance | Apparel, CPG, & Retail | Manufacturing | Energy | Government & NGOs

ClimateAi’s Evolution

• 2017 Co-founders Himanshu Gupta and Max Evans start ClimateAi out of a dorm room at Stanford University while completing their MBAs
• 2019 ClimateAi receives $4 million in seed funding from investors such as Blackhorn Ventures, NeoTribe Ventures, and Yahoo co-founder Jerry Yang to build the world’s first climate adaptation platform
• 2019 ClimateAi signs its first major food & ag customer, starting the company's journey to becoming the leading climate resilience platform for food and beverage
• 2021 ClimateAi secures $12 Million in an oversubscribed Series A funding led by Radical Ventures, with participation from Finistere Ventures and Robert Downey Jr.’s FootPrint Coalition Ventures
• 2021 ClimateAi joins the joint White House and United Arab Emirates-backed global climate change and food security initiative, AIM for Climate
• 2022 ClimateAi begins working with its first governmental partner, supplying insights around food and water security.
• 2022 With 30+ customers, operating in 35 countries, and 5 patents, ClimateAi wins Best Invention in Sustainability from TIME and gets profiled on CBS Sunday Morning

Products

ClimateLens-Monitor

AI and machine learning-powered platform enables day-to-day and season-ahead operational planning decisions that help  business adapt to weather-related challenges.

ClimateLens-Adapt

AI and machine learning-powered platform gives you long-term visibility into climate risks and opportunities so you can start adapting business to tomorrow’s climate challenges today.

ClimateLens-Assess

AI and machine learning-powered platform allows to see  business’s climate risks and opportunities within a single tool. Build the right impact models for your unique business context–then our client success team will support you in translating the insights into disclosure language.

ClimateLens-Enterprise

AI and machine learning-powered platform gives you both short and long-term visibility into climate risks and opportunities so entire business start adapting to tomorrow’s climate challenges today.

ClimateAi Patent - Climate forecasting using artificial neural networks

Overview

Methods and systems for climate forecasting using an artificial neural network-based forecasting model.

The CLIMATEAI climate forecasting system employs a deep learning network that is capable of extracting spatial-temporal features as well as functional dependencies and correlations among different GCM simulation datasets to predict future climate conditions.

Problem

• Climate forecasts have been produced using computationally intensive dynamical models, or statistical models that make limiting assumptions such as linearity between predictors and predictands.
• There is an unsolved need to develop a low-cost, fast, and robust climate forecasting system that can project climate trends and predict climate events with high accuracy while providing insights into complex underlying mechanisms

Solution

• Accurate climate forecasting enables the anticipation and mitigation of extreme or disruptive climate events, and are of huge human and economic values to climate-sensitive sectors such as agriculture, energy, water resource management, and urban planning.
• Dynamical models rely on fundamental physical principles and use mathematical equations to represent physical, chemical, and biological mechanisms that influence global climate, taking into account of climate system components such as atmospheric circulation, land coverage, ocean current circulation and biogeochemistry, atmosphere-ocean interactions including air-sea heat and water exchanges, and many external forcing factors.

Comment

Methods, systems, and apparatuses are provided for climate forecasting using an artificial neural network-based climate forecasting model trained on global climate simulation data and fine-tuned on observational historical climate data.

The Solution is Invented by “ClimateAI”

Solution

• The recommendation provided by Matias Castillo Tocornal, Brent Donald Lunghino, Maximilian Cody Evans, Carlos Felipe Gaitan Ospina relates to a neural network (NN)-based climate forecasting model.
• Method for generating a neural network (NN)-based climate forecasting model for a target climate forecast application
• NN-based climate forecasting model comprises a Convolutional Recurrent Neural Network (CRNN) having at least one Long Short-Term Memory (LSTM) layer.
• In some embodiments, the NN-based climate forecasting model comprises a Spherical Convolutional Neural Network (S2-CNN).
• The plurality of global climate simulation models comprises at least one of CNRM-CM5 model, MPI-ESM-LR model, GISS-E2-H Model, NorESM1-M model, HadGEM2-ES model, and GFDL-ESM2G model.
• The method further comprises tuning the NN-based climate forecasting model on reanalysis data, the tuning of the NN-based climate forecasting model comprises the steps of freezing one or more layers of the NN; and training the NN-based climate forecasting model on the reanalysis data.
• Neural network (NN)-based climate forecasting model, comprising at least one processor, and a non-transitory physical storage medium for storing program code and accessible by the processor

Application in Climate forecasting

• Method for generating a neural network (NN)-based climate forecasting model for a target climate forecast application, comprising:
• The steps of generating a multi-model ensemble of global climate simulation data by combining simulation data from at least two of a plurality of global climate simulation models (GCMs);
• Pre-processing the multi-model ensemble of global climate simulation data; training the NN-based climate forecasting model on the pre-processed multi-model ensemble of global climate simulation data, wherein the NN-based climate forecasting model comprises a predictive neural network, and wherein each input and corresponding desired output used in the training are selected from the multi-model ensemble of global climate simulation data;
• Validating the NN-based climate forecasting model using a first set of observational historical climate data.

Advantages

• Accurate climate forecasting enables the anticipation and mitigation of extreme or disruptive climate events, and are of huge human and economic values to climate-sensitive sectors such as agriculture, energy, water resource management, and urban planning.
• Combine results from multiple forecasting runs to reduce forecast uncertainty.

Conclusion

AccuWeather and ClimateAi represent the forefront of climate risk analytics, each bringing unique strengths to the market. Their innovative approaches combining meteorological expertise, AI-powered modeling, and comprehensive data analysis empower businesses to adapt proactively to climate challenges, minimize disruptions, and capitalize on emerging opportunities in an evolving environmental landscape.

About Effectual Services

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Related Resources:

Climate Risk Analytic Models: Overview

Climate Risk Analytic Models: Competitive Landscape Analysis