Representative Direct Ensemble Uncertainty Visualizations: Conveying Uncertainty Using A Small Portion of The Data

Representative Direct Ensemble Uncertainty Visualizations: Conveying Uncertainty Using A Small Portion of The Data

Contributed by Le Liu , PhD As we know, ensemble approaches are widely adopted to estimate forecasts uncertainty. In atmospheric sciences, these approaches are specifically categorized into two types: multi-model ensembles and perturbed parameter ensembles. The former runs multiple numerical prediction models with the same initial parameters to estimate the atmospheric evolution, while the latter runs a single model multiple times with slightly perturbed initial conditions. They are usually combined to form the final forecast. Visualizing forecast uncertainty is challenging: the…

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A HEPEX researcher in the times of a pandemic

A HEPEX researcher in the times of a pandemic

Contributed by Maria-Helena Ramos (INRAE) Many of us have probably never before received so many emails starting or ending with phrases such as “I hope you are doing fine”, “Stay healthy”, “Stay safe”, “Don’t get crazy” (okay, this one maybe does not appear that often). Probably this is what we are all trying our best to do: keep our bodies and minds in good shape, while managing research projects, operational activities, students and courses with as much attention as we…

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50 shades of green: lessons about business models for climate services

50 shades of green: lessons about business models for climate services

Contributed by Francesca Larosa (Euro-Mediterranean Center on Climate Change (CMCC), Ca’ Foscari University, and  UCL Energy Institute | UCL Institute for Sustainable Resources) Climate services are essential for adaptation to climate variability and change The transition towards a zero-carbon economy is profoundly reshaping the business-as-usual. If, on one hand, we must cut down our emissions and restructure the way our economies work, on the other hand we need to use the science we have and the tremendous technological progresses we…

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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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Celebrating the new hydrological year with a new HEPEX blog year: Let’s co-generate the HEPEX blog global pattern

Celebrating the new hydrological year with a new HEPEX blog year: Let’s co-generate the HEPEX blog global pattern

Happy New Hydrological Year!! According to USGS and based on meteorological and geographical factors, the hydrological year is defined as the period between October 1st of one year and September 30th of the next year. Driven by this, HEPEX will set for this year a new interactive approach for scheduling the blogs with and for the community. The blog has been our channel to communicate scientific achievements, insights and developments. As a blogger, you do not need to be outstanding…

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