Big Data. Big Decisions
InformationWeek
Special Coverage Series


Big Data Tackles Classic Question: What's The Weather Forecast?

Startup takes 60+ years of historical weather data, crunches it with 82 billion calculations to make more accurate long-term forecasts.

Predicting the weather more than 10 days in advance has long been more art than science. But a San Diego-based startup says it can predict extreme weather conditions, such as severe winter cold spells and searing summer heat, up to 40 days in advance with a 70% accuracy rate.

EarthRisk Technologies, a two-year-old software firm with 11 full-time employees, says its TempRisk application uses statistical methods to study decades of weather data to uncover key patterns, and then applies these patterns to current atmospheric conditions.

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

"There are dramatic developments in predictive analytics and big data, and they're being used in very obvious ways--and some not-so-obvious ways," said John Plavan, EarthRisk Technologies CEO, in a phone interview with InformationWeek. "I think we've hit on one of those not-so-obvious ways: Really improved weather forecasting."

Conventional weather modeling techniques tend to break down after 10 days, and have proven ineffective at predicting weather 20 to 40 days in advance, Plavan said. TempRisk's algorithmic approach to long-term forecasting includes daily processing of 1.3 billion calculations and 200 weather patterns across the globe.

"The methods we use are traditionally called analog forecasting methods, which have been used for years and years," said Plavan. "A forecaster will say, 'I think I see the atmosphere setting up like the winter of 1985. And the winter of 1985 was generally a pretty cold winter, so I'm going to use an analog method to (predict) that this winter will be pretty cold too.'"

[ Gartner sees big business for big data. See Big Data Drives Big IT Spending. ]

TempRisk's statistical methodology takes weather forecasting into what the company claims is a new--and more accurate--direction.

TempRisk was created by a software development team led by EarthRisk Technologies' co-founder and president Stephen Bennett, an energy meteorologist who's worked previously at Enron, Citadel Investment Group, and Scripps Institution of Oceanography at the University of California, San Diego.

Working in conjunction with energy traders, who helped manage the project to make sure it had real-world applications, Bennett's team compiled a 6,000-page catalog of data of weather patterns and occurrences from around the world, over the past 60-plus years.

"They ended up with this catalog of relationships that they found really promising," said Plavan. For example, "when the atmosphere looks like a certain combination of patterns, 30 days later there's an elevated possibility of an extreme cold event in the eastern U.S."

Plavan, whose varied entrepreneurial background includes stints in the venture capital, real estate, and petroleum industries, co-founded EarthRisk Technologies with Bennett in 2010.

"We built the prototype software interface product around the research output, and tested it in the winter of 2010-11 with three energy trading development partners," said Plavan.

TempRisk's current users include energy traders in four countries, as well as some electric power generation utilities.

"Our primary client is the energy trader, somebody who's looking at the long term--and long term to us is defined as anything past the traditional forecast model," Plavan said. "Anything past about 10 days, they're using our product to inform energy trades, whether they're for power or natural gas."

Plavan believes TempRisk has potential uses beyond energy trading, however.

"There's a need for storm prediction--seeing flood and drought risks," he said. "And our power-generation customers are looking for better insight into long lead times for wind and solar, which are having a better impact on the pricing for energy."

Plavan added: "Our initial market is energy traders because, frankly, there's a huge need for that, and there's lots of dollar value at stake there. And they can fund our development."

TempRisk is available for 10 geographical areas: eight regions in the United States, and one each for Europe and Asia Pacific.

At this hands-on Wall Street & Technology Virtual Event, Big Data On Wall Street, experts and solution providers will offer detailed insight into how risk management, financial reporting, trading analytics and financial modeling, along with a host of other opportunities, can all benefit from applying big data techniques and technologies to business processes. When you register, you will gain access to live and on-demand webcast presentations, as well as virtual booths packed with free resources. It happens Nov. 1.



Related Reading




Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

BYTE encourages readers to engage in spirited, healthy debate, including taking us to task. However, BYTE moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. BYTE further reserves the right to disable the profile of any commenter participating in said activities.

Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.

Follow InformationWeek

By The Numbers

What Are Your Primary Concerns About Using Big Data Software?

Base: 417 respondents at organizations using or planning to deploy data analytics, BI or statistical analysis software
Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals, October 2012

What Do You Think?

What's your attitude about SQL analysis on top of Hadoop?
We want fast, standard SQL analysis capabilities on Hadoop ASAP
Hadoop is for unstructured data; SQL is for relational databases
We'll give SQL on Hadoop a try, but relational DBs will remain the mainstay
Given strong SQL support on Hadoop, we'd nix the data warehouse
We're not interested in Hadoop
No opinion



Related Content

From Our Sponsor

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Business leaders often need a visual snapshot of data to quickly grasp and use it. This paper identifies five challenges in presenting data and how visual analytics can resolve them. Solutions are suggested to overcome the challenges of: speed, data clarity, data quality, displaying meaningful results, and dealing with outliers.

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Today's competitive advantage requires a deeper understanding of your business, your market and your customers. As an IT executive, you can drive that knowledge transformation. In this white paper, learn how to make decisions as a strategic business leader and three steps to begin an analytics initiative within your enterprise.

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

High-performance data visualization turns sophisticated analyses into meaningful graphics, leading to faster and smarter decision making. In this white paper, learn how visual analytics can transform big data, with additional features such as real-time functionality, mobile compatibility, robust applications for technical groups and accessibility for nontechnical users.

Big Data: Lessons from the Leaders

Big Data: Lessons from the Leaders

Financial performance, competitive advantage, operational efficiency, strategic decision making - every business goal can extract value from big data, and the time for doubt or inaction has long passed. In this Economist Intelligence Unit report, in-depth interviews with data pioneers reveal the link between the effective use of big data and the bottom line among other results.

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Which came first, the data or the decision? This white paper makes the case for having a decision in mind, then tailoring big data's volume, variety and velocity to achieve business results such as overcoming customer dissatisfaction or creating well-informed strategies in real time.

Informationweek Reports

Research: The Big Data Management Challenge

Research: The Big Data Management Challenge

The challenge of big data is real, but most organizations don't differentiate 'big data' from traditional data, and nearly 90% of respondents to our survey use conventional databases as the primary means of handling data. We'll help you understand what constitutes big data (it's not just size) and the numerous management challenges it poses.