2 tech adoptions that pushed ArabiaWeather ahead of the competition

The story of ArabiaWeather is one referred to in the Middle East startup community again and again – so often that it’s almost become legend. Founder Mohammed al-Shaker started the weather prediction service when he was still in high school back in 2006, and since then the site has since grown into one of the largest content sites in the region. As of last month, the mobile app boasted 289,000 active users, 3.1 million unique users visited the website, and the site was ranked number one in the MENA in the travel category, and number five in the MENA in the portals category.

Part of its success, says Chief Product Officer Yousef Wadi on a call with Wamda, is that ArabiaWeather has leveraged existing, largely Europe- and US-specific weather algorithms to create an accurate and nuanced prediction mechanism, as we have covered previously. This has been a rapidly accelerating process over the course of the company’s nearly 10-year journey (ArabiaWeather has been operational for four years, having transitioned from a Jordan-specific site).

As the team gears up for its biggest year yet (which will include the launch of several new products and services), Wamda asked Wadi what other technologies the startup has adopted that have impacted its formula, as well as how the product is sold.

When the team made the shift from JordanWeather.jo to a region-oriented service, the way they collected and processed data had to change due to the sheer amount of information they were now dealing with. “The amount of data generated by these models [the weather algorithms as well as data from airport, public, and their own weather stations around the region] is dramatic, massive,” Wadi says, “around half a terabyte per hour.” With JordanWeather.jo, weather information was collected and analyzed using a more local, on-the-ground strategy.

The team began renting processing power from supercomputers in Helsinki, Slovenia, and more recently, Belarus. The weather stations send raw data for the computers to put into models (the WRF model among others), and receive back images, numbers, and weather pattern simulation forecasts that the meteorological and weather teams then translate into content.

Original article by Stephanie d’Arc Taylor

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2 tech adoptions that pushed ArabiaWeather ahead of the competition