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Category: data systems

Machine learning for probabilistic hydrological forecasting

Machine learning for probabilistic hydrological forecasting

Contributed by Georgia Papacharalampous and Hristos Tyralis We would firstly like to thank HEPEX for giving us the opportunity to set a background on how machine learning can be used in probabilistic hydrological forecasting.  In this blog post, we start by providing the schematic summary of the discussion given below. Hope you will enjoy the reading! Learning practical problems with data: A machine learning algorithm can be explicitly trained for probabilistic hydrological forecasting Let’s suppose one of our most familiar…

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Climate services help improve decision-making for weather-dependent industries

Climate services help improve decision-making for weather-dependent industries

Contributed by Ilias Pechlivanidis, SMHI, & HEPEX co-chair.  Many industries are in need of reliable and usable climate forecasts for the coming weeks and months. Such predictions can help energy companies and other weather-dependent sectors better manage climate-related risks.  Renewable energy – such as wind, solar and hydropower –  is the fastest growing source of electricity globally. Renewable energy comes from natural sources such as sunlight, wind, or rain, which are not continuously generated. The generation of renewable energy is…

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Data Drought and Data Flood

Data Drought and Data Flood

Contributed by Mark Trigg*.  It’s hot, and very, very dry. The rains have failed, and the animals are dying. Around the table people are concerned that it will be people dying next. The cycle seems to repeat every 10 years and the response is exactly the same, every time – we must do something and save lives. “Drill more boreholes and put in big pumps and generators”, someone cries, “no matter the cost!”. A timid voice rises above the ongoing…

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OpenIFS@home: Using citizen science to improve our understanding of weather and hydrological forecasts

OpenIFS@home: Using citizen science to improve our understanding of weather and hydrological forecasts

The history of HEPEX is deeply connected to ensemble forecasting and uncertainty analysis. Indeed, none of us could even imagine a forecast without uncertainties (apart from McFool). One direction of research is to investigate the value in improving forecasts using a coupled system and truly understand the interactions between the atmosphere and the land surface whilst analysing the associated uncertainties. However, any analysis is limited by the resources one has available. To run a large number of ensembles, one would…

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How writing an article can come out of the blue (KGE on log-transformed flows: a bad idea?)

How writing an article can come out of the blue (KGE on log-transformed flows: a bad idea?)

Contributed by Léonard Santos (Irstea, France). It is common to read articles in which the Kling and Gupta Efficiency (KGE, Gupta et al., 2009) or its modified version (KGE’, Kling et al., 2012) are used as a metric to evaluate the quality of streamflow simulations. They are often seen as a solution to substitute the Nash and Sutcliffe Efficiency (NSE, Nash and Sutcliffe, 1970). However, are these two criterion totally comparable? Can the KGE be used exactly in the same…

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