Tag - observations | ECMWF eLearning

An introduction to Data Assimilation

Learn about data assimilation and how it is used to define ‘optimal' initial conditions for NWP at ECMWF.

Level: Fundamentals

Certification course: No

What you'll learn:

What is data assimilation and how it fits into numerical weather prediction
Which sources of observations are used by ECMWF in its data assimilation process
How data assimilation methods work, including 4D- VAR
Recent improvements in data assimilation, and future challenges

Estimated duration: 40 minutes

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Atmospheric composition: No

Climate: No

Computing services and tools: No

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

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Category: Numerical Weather Prediction

Satellite observations and their role in NWP

Learn about the role of satellite observations and measurements, and how these are assimilated and monitored for NWP.

Level: Fundamentals

Certification course: No

What you'll learn:

Different data sources and the role of satellite observations in NWP
Types of satellites, what they measure, and how measurements are taken
How satellite observations are monitored and assimilated

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Atmospheric composition: No

Climate: No

Computing services and tools: No

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

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Category: Numerical Weather Prediction

MLWC MOOC 3: Applications of Machine Learning in Weather and Climate

Six modules giving ML applications in observations, forecasting, data assimilation, post-processing, ocean and more.

Certification course: No

What you'll learn:

The application of ML methods to a range of problems in weather and climate
The details of practical code examples which can be used to apply to your own problems

Prerequisites:

Please complete MOOC MLWC - 2. Concepts of Machine Learning first, or ensure you are familiar with the topics covered there. Intermediate proficiency with Python, knowledge of statistics and experience in weather/climate is assumed.

Full description:

In this third tier of our MOOC on Machine Learning (ML) in Weather and Climate, we focus on the implementation of ML in weather and climate problems.

This six-module course gives code examples and explainers in topics including:

Satellite precipitation removal
Environmental modelling
Nowcasting
Data assimilation
Downscaling
Ocean modelling
Operational meteorology

This course is aimed at those with a technical/weather background and experience in Python. Ideally you will have already taken Tiers 1 and 2 of the MOOC on ML in Weather and Climate, or already be familiar with the concepts there.

Please note that this course ran live in early 2023 and reflected the state of the art at that point in time.

Estimated duration: 25 hours

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Hide number of enrollments: No

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Atmospheric composition: No

Climate: Yes

Computing services and tools: No

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

Click to enter this course

Category: Machine Learning

MLWC MOOC 1: Introduction to Machine Learning in Weather and Climate

Six modules introducing the main topics in machine learning in the context of weather and climate.

Level: Fundamentals

Certification course: No

What you'll learn:

An overview of machine learning in weather and climate
Unpack, at a conceptual level, key concepts and topics in ML
Applications and recent advances in the field

Prerequisites:

A basic knowledge of weather and climate, statistics and computing.

Full description:

In this first tier of our MOOC on Machine Learning (ML) in Weather and Climate, we give a broad overview of the key concepts ML and its applications in recent years to topics from forecasting and data assimilation to post-processing, observations and computing. This course is aimed as an introduction, which is accessible to those with an interest in ML, weather and climate, but without necessarily requiring a very technical background.

This course features a mixture of interactive modules, webinar recordings, quizzes, podcasts and code examples.

Please note that this course ran live in early 2023 and reflected the state of the art at that point in time.

Estimated duration: 14 hours

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Atmospheric composition: No

Climate: Yes

Computing services and tools: Yes

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

Click to enter this course

Category: Machine Learning

Introduction to the Scalable Acquisition and Pre-Processing (SAPP) system

The SAPP system is the ECMWF's operational acquisition and pre-processing system for observations and other input data.

Level: Fundamentals

Certification course: No

What you'll learn:

Raw skills to use the SAPP Jupyter notebook to get you started
How to modify the SAPP system for your requirements
Better understanding of the three-stage data workflow: Acquisition stage, Processing stage and Extraction stage

Full description:

The SAPP system is the ECMWF's operational acquisition and pre-processing system for observations and other input data. This lesson is aimed at users with little or no experience of SAPP and will provide the raw skills needed to use SAPP.

Estimated duration: 40 minutes

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Atmospheric composition: No

Climate: No

Computing services and tools: No

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

Click to enter this course

Category: Computing services and tools

Climate Data Discovery – Introduction

An introduction to the different sources of climate data and how to find them.

Level: Fundamentals

Certification course: No

What you'll learn:

The difference between weather and climate
Different sources of climate data, including observations, renalysis, model forecasts and sectoral products
How to find the data you need

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Atmospheric composition: No

Climate: No

Computing services and tools: No

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

Click to enter this course

Category: Climate

Data Resources - Introduction

Learn about Essential Climate Variables, the different types of climate data resources, and their respective pros and cons.

Level: Fundamentals

Certification course: No

What you'll learn:

What Essential Climate Variables (ECVs) are
Types of climate data resources, including observations and models
Pros and cons of different data sources

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Atmospheric composition: No

Climate: No

Computing services and tools: No

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

Click to enter this course

Category: Climate

Data Resources - Observations

Explore the different types of measurements, the types of observing systems and their measurement uncertainty.

Level: Fundamentals

Certification course: No

What you'll learn:

Which different types of measurements exist, including direct and indirect observations
The various meteorological observing systems (remote sensing, land, sea, air, etc) and their representativeness
How to account for uncertainties and propagate errors

Hide last updated info: No

Hide number of enrollments: No

Hide full description label: No

Atmospheric composition: No

Climate: No

Computing services and tools: No

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

Click to enter this course

Category: Climate

observations

Courses tagged with "observations"