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WAMSI Node 2.1 - Dynamics and predictability of the Indo-Pacific Ocean as a global condition on marine climate impacts in WA - POAMA

Created 25/06/2017

Updated 09/08/2023

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The Predictive Ocean Atmosphere Model for Australia (POAMA) is a state-of-the-art seasonal to inter-annual seasonal forecast system based on a coupled ocean/ atmosphere model and ocean/atmosphere/land observation assimilation systems. This project will establish the limits of predictability of large-scale variations of the marine environment in Western Australia using POAMA.

POAMA-1.5 hindcast and realtime data consists of three types output.

  1. Hindcasts Comprehensive set of hind-casts with a 10 member ensemble

  2. Forecasts These are realtime forecasts, which are run one per day. Most products involve putting together forecasts for the last 30 days to form an ensemble.

  3. Model Climatologies Model climatologies in same format as the forecasts and hindcasts. These can be used to creat forecast anomalies and therefore removing model biases. Each set starts on the first of the month. Note, for realtime forecasts starting on different days of the month, one should interpolate the two surrounding climatologies starting on the first of the month.

Forecast model output includes monthly means of global fields of upper ocean temperatures, currents, and salinity. Atmospheric fields include global fields of all surface fluxes (momentum, radiation, sensible and latent heat), and winds, temperatures, humidity, clouds, etc.

A subset of daily data, suitable for driving offline mixed layer models and downscaled models, including surface fluxes, surface winds, surface currents and temperature, is also archived.

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Title WAMSI Node 2.1 - Dynamics and predictability of the Indo-Pacific Ocean as a global condition on marine climate impacts in WA - POAMA
Language English
Licence Not Specified
Landing Page https://data.gov.au/data/dataset/6079d41f-29e1-46ce-963f-4820877d06b9
Contact Point
Bureau of Meteorology (BOM)
h.hendon@bom.gov.au
Reference Period 25/06/2017
Geospatial Coverage
Map data © OpenStreetMap contributors
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Data Portal Data.gov.au