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 *  * ELEARNING - ONLINE RESOURCES
      
      Search ECMWF eLearning
      Search ECMWF eLearning
   
   
    * FORECASTING
      
      
      
       * THE EXTREME FORECAST INDEX (EFI) AND THE SHIFT OF TAIL (SOT) INDEX
         
         
         The EFI provides specialised forecast guidance for anomalous, extreme,
         or severe weather events. In this lesson you will learn how EFI, SOT
         and M-climate are built.
      
      
       * ENSEMBLE FORECASTING: SOURCES OF FORECAST UNCERTAINTY (INTRODUCTION)
         
         
         Ensembles are run to account for uncertainties in initial conditions.
         This lesson explores the sources of error in NWP, how they are
         quantified, and how ensembles are evaluated.
      
      
       * THE ECMWF EXTENDED RANGE FORECASTS: INTRODUCTION
         
         
         Extended range forecasts provide outlooks up to 46 days. This lesson
         examines sources of predictability, seasonal forecast skill and the
         ECMWF extended range forecasting system.
      
      
       * FORECAST JUMPINESS: AN INTRODUCTION
         
         
         There are times when consecutive forecasts can 'jump' significantly.
         This lesson will discuss the ways in which forecast jumpiness can
         appear and how it can be mitigated.
      
      
       * SEASONAL FORECASTING
         
         
         Seasonal forecasting is useful in planning for many sectors. This
         lesson will explore seasonal predictability, how numerical seasonal
         forecast models work and their outputs.
   
   
    * RESEARCH
      
      
      
       * LECTURE NOTES: AN INTRODUCTION TO DATA ASSIMILATION
         
         
      
      
       * CLOUD AND PRECIPITATION PARAMETRIZATION 2: ICE AND MIXED-PHASE
         MICROPHYSICS
         
         
         This lesson describes the key microphysical processes for ice and
         mixed-phase cloud and precipitation in the atmosphere, and the
         uncertainties in parametrization.
      
      
       * NUMERICAL WEATHER PREDICTION: PARAMETRIZATION OF DIABATIC PROCESSES -
         CONVECTION 1: CONVECTION IN THE CONTEXT OF LARGE-SCALE CIRCULATION
         
         
         This lesson will take you through what convection is and the phenomena
         it causes.
      
      
       * NUMERICAL WEATHER PREDICTION: PARAMETRIZATION OF DIABATIC PROCESSES -
         CONVECTION 2: THE MASS-FLUX APPROACH AND THE IFS SCHEME
         
         
         This lesson looks at the three classes of parametrization schemes and
         the main characteristics of the IFS scheme.
      
      
       * CLOUD AND PRECIPITATION PARAMETRIZATION 1: OVERVIEW AND WARM-PHASE
         MICROPHYSICS
         
         
         This lesson describes the key microphysical processes of cloud and
         precipitation parametrization with a focus on warm-phase processes and
         how these are used in NWP.
      
      
       * INTRODUCTION TO THE PARAMETRIZATION OF SUB-GRID PROCESSES
         
         Sub-grid-scale processes are not explicitly simulated in NWP so must be
         parameterized. This lesson describes how the parameterization is done
         at ECMWF and the challenges faced.
      
      
       * AN INTRODUCTION TO DATA ASSIMILATION
         
         
         Data assimilation is used in NWP to define ‘optimal' initial conditions
         for numerical forecasts. In this lesson you will define data
         assimilation and explore how it is used at ECMWF.
      
      
       * SOURCES OF UNCERTAINTY
         
         
         All numerical weather prediction models have sources of uncertainty. In
         this lesson you will learn about these uncertainties and chaotic
         behaviour, why ensemble prediction is needed, and about ECMWF model set
         up.
      
      
       * OVERVIEW OF SATELLITE OBSERVATIONS AND THEIR ROLE IN NUMERICAL WEATHER
         PREDICTION (NWP)
         
         
         This module will teach you about data sources, the role of satellite
         observations, satellite data measurements, assimilation, and monitoring
         of satellite observations.
      
      
       * USING STOCHASTIC PHYSICS TO REPRESENT MODEL UNCERTAINTY
         
         
         All numerical weather prediction models have uncertainty. This lesson
         will explore why there is uncertainty and sources of it and how model
         uncertainty is represented in the IFS using stochastic physics.
      
      
       * LAND SURFACE: INTRODUCTION TO COLD PROCESSES
         
         
         Snow’s specific properties impact forecast ranges from a few days to
         seasonal and climate. In this lesson, you will learn about the role of
         snow at different time scales.
      
      
       * NUMERICAL WEATHER PREDICTION: PARAMETRIZATION OF DIABATIC PROCESSES -
         CASE STUDIES (CONVECTION)
         
         
         This lesson contains four case studies which explore the conditions
         that cause and how to identify deep convection including predictability
         and forecast errors.
      
      
       * AN INTRODUCTION TO SINGLE-COLUMN MODELLING
         
         
         The SCM is a tool to investigate the physical processes of a global
         model in isolation. This lesson will introduce what a SCM is, its
         applications and limitations.
      
      
       * CLOUD AND PRECIPITATION PARAMETRIZATION 2: ICE AND MIXED-PHASE
         MICROPHYSICS
         
         
         This lesson will take you through the basic concepts for the design of
         a cloud and precipitation microphysics parametrization.
      
      
       * DATA ASSIMILATION NOTATION
         
         
   
   
    * SOFTWARE, DATA AND COMPUTING
      
      
      
       * USING ECMWF COMPUTING FACILITIES: THE BATCH SYSTEM
         
         
         This lesson will focus on ECGATE - ECMWF's server dedicated to the
         users' work. You will learn how to run tasks in batch, submit, query
         and cancel jobs, correct common errors and check the accounting
         database.
      
      
       * MARS ECMWF'S METEOROLOGICAL ARCHIVE
         
         
         The Meteorological Archival and Retrieval System (MARS) enables users
         to access ECMWF’s data. This lesson will teach you how to create a
         customised data retrieval.
      
      
       * METVIEW FOR THE SINGLE-COLUMN MODEL (SCM)
         
         
         This lesson will provide a quick overview of Metview's main features
         and enable you to use Metview to analyse and edit input data for the
         single-column model, run the model and visualise its output.
      
      
       * EXPLORING METEOROLOGICAL DATA THROUGH OGC WEB SERVICES
         
         
         Web services are used to visualise geographical data. This lesson
         describes web services, data standards and outlines what OGC and
         INSPIRE are.
      
      
       * MARS – ADVANCED RETRIEVALS, DATA MANIPULATION AND COMPUTATIONS
         
         
         The Meteorological Archival and Retrieval System (MARS) enables users
         to access ECMWF’s data. This lesson will look at MARS requests and
         explore its compute capability.
      
      
       * ECCODES: DECODING WITH GRIB TOOLS
         
         
         ecCodes is software developed by ECMWF to decode and encode in WMO GRIB
         and BUFR formats. This lesson will teach you how to inspect GRIB
         messages using GRIB tools.
      
      
       * INTRODUCTION TO BUFR DECODING WITH ECCODES
         
         
         ecCodes is software developed by ECMWF to decode and encode in WMO GRIB
         and BUFR formats. This lesson will introduce you to the BUFR format for
         decoding of BUFR data.
      
      
       * INTRODUCTION TO METVIEW
         
         
         Metview is a powerful meteorological workstation application that
         enables you to access, process and visualise meteorological data. In
         this lesson you will learn how to use MetView.
      
      
       * A STARTER GUIDE TO ECFLOW
         
         
         ecFlow is a workflow package that enables users to run programmes
         behind a firewall. During this lesson you will learn ecFlow vocabulary
         and how to run a simple suite.
      
      
       * ECCODES – MANIPULATING GRIB DATA WITH TOOLS AND API
         
         
         ecCodes is software developed by ECMWF to decode and encode in WMO GRIB
         and BUFR formats. This lesson focuses on handling GRIB data with
         ecCodes tools.
      
      
       * ECMWF COMPILING ENVIRONMENT - WORKING ON ECGATE AND HIGH PERFORMANCE
         COMPUTING FACILITY (HPCF)
         
         
         In this lesson you will learn compiling and linking on ECGATE and HPCF,
         the basics of make and makefiles and simple debugging and optimisation.
   
   
    * MOOCS
      
      
      
       * MOOC MLWC - 1. ML IN WEATHER & CLIMATE
         
         This course includes 6 modules and is as introduction to the main
         topics, from the processing of observations to data assimilation,
         forecasting and post-processing.
      
      
       * MOOC MLWC - 2. CONCEPTS OF MACHINE LEARNING
         
         This course includes 5 modules and focuses on the key concepts of
         Machine Learning.
      
      
       * MOOC MLWC - 3. PRACTICAL ML APPLICATIONS IN WEATHER & CLIMATE
         
         This course includes 6 hands-on modules on the main topics of numerical
         weather and climate prediction.
   
   
    * COPERNICUS CLIMATE CHANGE SERVICE (C3S)
      
      
      
       * CLIMATE DATA STORE AND TOOLBOX
         
         This lesson provides an introduction to the CDS and Toolbox. Users will
         learn how to search the CDS, how to download data and how the toolbox
         can be used.
      
      
       * HANDS ON CASE STUDY
         
         This lesson guides users through an adaptation case study of an
         (imaginary) olive farmer in Spain. The lesson contains clips that show
         how to use and adapt scripts in the toolbox.
      
      
       * INTRODUCTION TO THE CDS API
         
         This lesson provides a hands-on introduction to downloading data using
         the CDS API in Python. The lesson provides a number of short tutorial
         videos.
      
      
       * TOOLBOX ADVANCED APPLICATIONS
         
         In this lesson you will get to know more functionalities available in
         the toolbox, you will learn to work with climate projections, and you
         will build applications.
      
      
       * CLIMATE DATA DISCOVERY – INTRODUCTION
         
         This lesson provides an introduction to the different sources of
         climate data and guides you to find the data you need.
      
      
       * CLIMATE DATA DISCOVERY – ADVANCED
         
         This lesson provides details on the various data sources, and
         strategies to find the data needed: Processing steps, choosing
         projections, scenarios, ensembles, variables etc. The lesson is a
         follow-up of “Climate Data Discovery – Introduction”.
      
      
       * DATA RESOURCES - INTRODUCTION
         
         This lessen provides an overview of the various types of climate data
         resources, and teaches what Essential Climate Variables are. It will
         indicate the main advantages and disadvantages of the various data
         sources.
      
      
       * DATA RESOURCES - OBSERVATIONS
         
         This lesson provides training on observations data. The different types
         of measurements are explained, the types of observing systems and the
         measurement uncertainty are explained.
      
      
       * DATA RESOURCES - REANALYSES
         
         This lesson teaches users the basics of climate reanalysis. The lesson
         explains how reanalyses are made, an overview of global reanalyses
         datasets, and their strengths and limitations.
      
      
       * DATA RESOURCES - CLIMATE MODELS
         
         This lesson explains how climate models work and how the quality of
         climate models can be evaluated. Differences between climate
         projections, predictions and scenarios are explained.
      
      
       * BIAS CORRECTION AND DOWNSCALING
         
         This lesson teaches about downscaling and bias correction methods. An
         exercise for bias correction is included.
      
      
       * USING CLIMATE MODELS FOR CLIMATE SCENARIOS
         
         This lesson teaches how to use climate models in the development of
         national climate scenarios. Examples are provided for The Netherlands,
         Switzerland and the U.K.
      
      
       * UNCERTAINTY, ROBUSTNESS AND CONFIDENCE
         
         This lesson teaches about sources of uncertainty in climate
         projections, what robust signals are, and when we can be confident in a
         change.
      
      
       * CLIMATE PROJECTIONS
         
         This ‘hands-on’ lesson covers climate projections, differences between
         climate models, and how to choose from climate projection data.
      
      
       * DEVELOP YOUR OWN CLIMATE SERVICES CASE STUDY
         
         The aim of this lesson is to provide an introduction into Climate
         Services, and a seven step approach to develop a case study,
         illustrated with a practical example.
      
      
       * SECTORAL APPLICATION – AGRICULTURE
         
         This lesson covers how climate change impacts the agriculture sector.
         Responses of different crop types to climate change is explained.
         Adaptation measures are introduced and how CDS data can be used for
         this. Examples are given from the SIS Global Agriculture.
      
      
       * SECTORAL APPLICATION – ENERGY
         
         This lesson covers how climate change will affect the energy sector. An
         overview is provided of energy-related data and indicators available
         from the CDS, with an explanation of how these can be used in
         applications.
      
      
       * SECTORAL APPLICATION - HEAT HEALTH
         
         In this lesson you will learn how to use the Climate Data Store (CDS)
         for applications in the health sector. The lesson focuses on urban
         heat. The lessons shows data and indicators from SIS-European Health.
      
      
       * SECTORAL APPLICATION - HEAT HEALTH – ADVANCED
         
         This lesson provides further background information about urban heat
         and health, in the context of CDS applications in the health sector.
      
      
       * SECTORAL APPLICATION – TOURISM
         
         This lesson provides an introduction to C3S applications in the tourism
         sector. It identifies C3S data and tools useful for tourism
         stakeholders in supporting climate change adaptation.



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