WWRP Data Assimilation & Observing Systems
Mission
DAOS aims to provide guidance to the WWRP to optimize the use of the current WMO Global Observing System (GOS). DAOS will facilitate the development of data assimilation and observing system methodologies from the convective scale to planetary scales and for forecasts with time ranges of hours to weeks.
DAOS should promote research related to observing systems of the WWRP projects such as the development of observing systems (either to become more capable or at least less expensive), network design aspects (which variables, spatial and temporal sampling), and scientific methods to quantify the value of observations in numerical weather prediction and beyond.
DAOS should promote research related to data assimilation of the WWRP projects such as data assimilation techniques for earth system prediction systems including coupled data assimilation (including hydrological data assimilation), urban scale prediction (e.g., heat, air quality) and for severe weather events such as heavy precipitation.
DAOS should promote the research of cross-cutting applications of Artificial Intelligence and Machine Learning with data assimilation and observing systems.
Working Group Members
Co-chair: Sarah L. DANCE, University of Reading, United Kingdom
Co-chair: Lili LEI, University of Nanjing, China
Rossella ARCUCCI, Imperial College of London, United Kingdom
Sean HEALY, European Centre for Medium-Range Weather Forecasts (ECMWF), United Kingdom
Andrew M. MOORE, University of California, Santa Cruz, United States of America
Takemasa MIYOSHI, Riken Center for Computational Science (RIKEN), Japan
Phillip BROWNE, European Centre for Medium-Range Weather Forecasts (ECMWF), United Kingdom
Clara DRAPER, National Oceanic and Atmospheric Administration (NOAA), United States of America
Nahidul SAMRAT, Bureau of Meteorology, Australia
María Eugenia DILLON, National Meteorological Service, Argentina
Katia LAMER, BNL Center for Multiscale Applied Sensing, United States of America
Maria Eugenia DILLON, National Meteorological Service of Argentina, Argentina
Relevant information
- QJRMS special collection on Coupled Earth System data assimilation (open through the end of 2024)
- QJRMS special collection: Combined machine learning and data assimilation for the atmosphere and ocean sciences (open through July 2024)
- ECMWF training on DA and the use of satellite data (annual) (2024 – 2025)
- MOOC - Machine Learning in Weather and Climate (2023)
- General DA: https://events.ecmwf.int/event/272/timetable/
- Use of satellite data: https://events.ecmwf.int/event/273/timetable/