Articles in “Data”

Ocean CO2 Data Since 1970

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Oceans absorb approximately half of man-made CO2 emissions. The warming of the ocean surface and changes in wind patterns can limit the transfer of CO2 to deeper levels and thus limit oceans ability to absorb greenhouse gases. Shifts in the biological pump and ecosystem functioning are also likely to appear.

This week we’ve added Surface Ocean CO2 Variability and Vulnerability (Socat V4) dataset to Planet OS Datahub.

With historical data since 1970, the Socat V4 dataset is the common format of all publicly available fugacity of CO2 data for the oceans surface. It has become the foundation for the marine carbon community upon which they build the future.

Curious to find out more? Explore all datasets on Planet OS Datahub.

35 Years of Sea Surface Temperature

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This week we’ve added several decades of historical NOAA Daily OISST data to our Datahub. The dataset is an analysis constructed by combining observations from different platforms (satellites, ships, buoys) on a regular global grid, with values from 1981 until now.

In addition to standard sea surface temperature, the Daily Optimum Interpolation Sea Surface Temperature dataset also includes SST anomalies, which means the daily OISST minus a 30-year climatological mean.

The Daily OISST data are great for investigating upwellings and downwellings, and have been used to track signs of El Niño and La Niña. In fact, forecasters now think there’s a 70% chance that La Niña conditions will develop this fall.

Looking for Earth data? Explore the Planet OS Datahub.

Baltic Sea Real Time Observations Now Available

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This week we’ve added Baltic Sea Real Time Observations to Planet OS Datahub.

The Baltic Sea recently experienced a significant sea level drop which can be easily viewed with this timeseries dataset. Due to three weeks long eastern winds and high air pressure, over 200 km3 of water was blown from the Baltic Sea into the ocean through the Scandinavian straits.

Baltic Sea observations data are collected from 150 stations and includes sea surface temperature, sea surface height and wave properties. The dataset provides observations within 24 to 48 hours of acquisition, with quality controlled according to standardized procedures that have been defined in collaboration with BOOS (Baltic Operational Oceanographic System).

Are you working with Earth science data? Planet OS Datahub streamlines the data integration process and reduces operational overhead. Stop wrestling with undocumented interfaces, outdated formats, and broken integration scripts. With Datahub you can focus on what truly matters – delivering valuable data-driven insights and applications. See what other high-quality data we have made available.

Planet OS Data Challenge at ExpeditionHack NYC

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We’re thrilled to be part of the Expedition Hackathon NYC happening on November 12-13! This is your chance to map the future of sustainability with NGA, Mapbox, IBM Bluemix, Planet OS and others. The hackathon’s focus areas are Oceans, Forests, Conservation and Indigenous People.

To add some motivation to the hours of intense coding and hustling, we decided to put out tons of high-quality environmental data, data integration and computational infrastructure, and reward the best teams with some cool prizes.

All hackathon participants will get free, unlimited access to:

The prizes:

  • All teams that use our data tools will secure an unlimited free access to Planet OS data tools
  • The team with the best solution will get special swag and surprises from Planet OS
  • The general Grand Prize of the hackathon is $3000 and a round trip to DC from NGA to meet NGA Executives.

We have already validated a few business ideas that the teams could work on. Stay tuned for updates! All the updates will be shared on this page so it would be wise to bookmark it. Contact us at aljash@planetos.com for further questions.

#PlanetOS  #DataChallenge

How Big Data Makes Renewable Energy More Competitive

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Two weeks ago we attended the Renewables UK Marketplace and I presented the Planet OS story by participating in a session about big data. I was joined by several other leaders from the renewable energy big data market. Paul Usher started off the session and made it clear that renewable energy companies could gain significant competitive advantage through advanced analytics and big data.

However, this business advantage doesn’t come easy because big data analytics are hard to implement, and the most time consuming part is simply preparing the data for analysis. Most big data teams spend 40-60% of their time preparing data or ‘data wrangling’ as it’s called by many. The problem is even worse with geospatial data which has many diverse formats including time series, raster, and vector data. Luckily, there are new emerging technologies like Kinetica and Datahub that help companies reduce the time they spend wrangling geospatial data. The time saved can be spent instead on developing advanced analytical models that deliver competitive advantage to the business.

I observed many parallel themes between this event and the Wind Europe Summit. The wind energy industry is clearly maturing, although in different ways between onshore and offshore. Innovation is a major goal across the entire supply chain as companies rush to increase revenues and drive costs down. New technologies like floating turbines and energy storage could have an enormous impact on the future of renewable energy. Meanwhile the industry is tirelessly optimizing the performance of both operations and assets to reduce costs and be more competitive with traditional energy sources like coal and gas. Advanced analytics is a critical tool for renewable energy companies to achieve this competitive advantage.

Join Planet OS Data Challenge At Garage48

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We’re thrilled to be part of the Garage48 Open & Big Data Hackathon happening this weekend in Tartu, Estonia! To add some motivation to the 48h of intense coding and hustling, we decided to put out tons of high-quality environmental data, data integration and computational infrastructure, and reward the best teams with some cool prizes.

All hackathon participants will get free, unlimited access to:

The prizes:
- All teams that use our data tools will secure an unlimited free access to Planet OS data tools
– Three teams with the best solutions will get special swag and surprises from Planet OS
– The team that comes out with the best prototype that runs on Planet OS Datahub, will be rewarded with €1,000!

We have already validated a few business ideas that the teams could work on. Stay tuned for updates! All the updates will be shared on this page so it would be wise to bookmark it. Note that you will definitely stand out if your solution uses some elements of Machine Learning.

#PlanetOS  #DataChallenge

Carbon Dioxide Data Now Available On Datahub

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The carbon dioxide data, measured at the premier atmospheric research facility, Mauna Loa Observatory in Hawaii, is now available in Datahub:

The data, measured as the mole fraction in dry air, constitute the longest record of direct measurements of CO2 in the atmosphere dating back to May of 1974. As this dataset has a long, properly measured time series, it’s been widely used to show the connection between the level of Carbon Dioxide and global climate warming. Also, the unique geographic location of Mauna Loa Observatory – far from big industries and air pollution – makes it a reliable source.

The data are reported as a dry mole fraction defined as the number of molecules of carbon dioxide divided by the number of molecules of dry air multiplied by one million (ppm).

In addition to the carbon dioxide data, there are thousands of variables of other high-quality Earth Science data available in Datahub. Now that different kind of weather, climate, and oceanic data is made easy to find and work with, it’s time to build your own application. Let me know at annika@planetos.com if you want to discuss your idea.

Thoughts From The Wind Europe Summit 2016

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As I reflect on what I observed at Wind Europe Summit and Wind Energy Expo in Hamburg last week, I am left with three lingering thoughts.

  • The wind energy industry is clearly maturing as the new projects get larger and less frequent and much more competitive for the operators and suppliers. Also, the market growth rate seems to have slowed some from recent years, and the number of operators and suppliers is beginning to consolidate. All of this adds up to fewer, larger industry players which is a clear signal of a maturing industry.
  • Another obvious trend is the continued innovation and drive to lower the cost of wind energy. There is a relentless theme of cost reduction across all the segments of the value chain, from equipment OEMs, to operations and maintenance, to energy trading and distribution. For example, Sentient Science introduced a new data science model that can predict gearbox component failure. New technology in floating turbines and energy storage has the potential to open up large new markets for wind energy which will increase the market size dramatically to further reduce costs.
  • Finally, many speakers and people I talked to suggested that the industry needs to move beyond component and segment level optimization and start to approach challenges with a full system level view. Hans Bunting, COO of Renewables at Innogy made this point several times during the Technologies for Transition session. To move the industry forward, we need to take a system wide, technology independent, full supply chain view of the problem from the equipment, to the operator, through distribution all the way to the end consumer. Breaking down the barriers across the supply chain and even between different renewable technologies (wind, solar, hydro) is the best way to move the industry forward.

New NAM Weather Forecast Data Now Available

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This week we’ve added an additional NAM weather forecast to the Planet OS Datahub, which now provides API access to seven North American Mesoscale Forecast System products:

NAM dataset provides a 60-hour forecast of pressure-level fields at 1-hourly and 3-hourly time steps and covers the contiguous United States at a 5 km resolution.

Researchers use NAM as a driver for complex air pollution simulations, but it is also used by public and private meteorologists to guide near-term weather forecasts within the United States. In a hierarchy of weather forecast models, it falls between the global medium range forecast GFS and the rapid refresh model HRRR.

Visit the Planet OS Datahub to access this dataset and over 2,100 variables from the world’s leading Earth Science data publishers. Interested in discussing different datasets and developer tools with researchers, developers and data scientists from around the world? Join our Slack community:

Planet OS Community on Slack

NASA Climate Data Webinar Resources

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On September 21, we hosted our very first Product Webinar with Dr. Sangram Ganguly (NASA Ames & BAERI) and myself, where we discussed How to Easily Analyze NASA Climate Models using the Planet OS OpenNEX Data Tool. I would now like to share some of the resources and materials we used during our session so that you could try them out yourself.

Please let me know at tom@planetos.com if you have questions or comments.

See you at the next #PlanetosWebinar!