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Recent Posts
- Climate Ambassadors and the future of Climate Education
- Why should we keep working on theory and fluid dynamics in climate sciences?
- Land Surface water controls on Atmospheric CO2 growth
- Exploring history and colonialism in my approach to climate science research
- Air Quality in a UK Town – A 10-year case study
Archives
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- Clouds
- Co-production
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- Data rescue
- Data Visualisation
- Diversity and Inclusion
- drought
- earth observation
- Eclipses
- Energy budget
- Energy meteorology
- ENSO
- Environmental hazards
- Environmental physics
- Equatorial waves
- Europe
- extratropical cyclones
- extremes
- Fieldwork
- Flooding
- Fluid-dynamics
- Forecasting Testbed
- Geoengineering
- Greenhouse gases
- Health
- High performance computing
- High-Resolution Climate modelling
- Historical climatology
- History of Science
- Hydrology
- IPCC
- land use
- Machine Learning
- Madden-Julian Oscillation (MJO)
- Maritime Continent
- Measurements and instrumentation
- Metadata
- Microphysics
- Monsoons
- net zero carbon
- North Atlantic
- Numerical modelling
- Oceanography
- Oceans
- Outreach
- Phenology
- Polar
- Predictability
- radar
- Radiation
- Rainfall
- Reanalyses
- Remote sensing
- Renewable energy
- Royal Meteorological Society
- Seasonal forecasting
- soil moisture
- Solar radiation
- Space
- space weather
- Statistics
- sting jet
- Stratosphere
- Students
- subseasonal forecasting
- Sunshine
- Teaching & Learning
- Teleconnections
- Temperature
- Thunder Storms
- Tropical convection
- Tropical cyclones
- Troposphere
- Turbulence
- Uncategorized
- University of Reading
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Tag Archives: Gaussian distribution
Characterising extreme event occurrence
By Reinhard Schiemann When presented with a new data sample, the first thing many of us scientists do is to characterise it in terms of two numbers: the average or mean value of the sample, and the spread or variance of … Continue reading