Datasets

Seasonal forecast using CY49R2b with stochastic sea ice scheme active. Used to support the analysis in the paper titled "The impact of stochastic sea ice perturbations on seasonal forecasts" submitted to Weather and Climate Dynamics by K. Strommen, M. Mayer, J. Spaeth and S. Tietsche. Further details in the paper. This forecast can be directly compared against the control counterpart "ikh7".

Examples

Seasonal forecast using CY49R2b without any stochastic sea ice scheme active. Used to support the analysis in the paper titled "The impact of stochastic sea ice perturbations on seasonal forecasts" submitted to Weather and Climate Dynamics by K. Strommen, M. Mayer, J. Spaeth and S. Tietsche. Further details in the paper. This control forecast can be directly compared to the forecast "imsu", which has stochastic sea ice schemes turned on.

Examples

The seasonal run (Nov 2018 - Feb 2019) performed on the Oak Ridge Summit supercomputer with a 1.4 km spatial resolution and a 3 hour temporal resolution. Currently only the initial 10 steps have been published as an example, but more data is available on request. Surface fields are available for the entire time range, model level and pressure level fields are available for only the first month. Model levels 1 to 137 are available. Pressure levels 1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000 are available.

This is an global forecast experiment for ALaDyn

Examples

Retrieving 2m-temperature at step 24

Retrieving accumulated total precipitation at steps 1 to 4

Standalone wave model CY47R1 forced by ERA5 hourly neutral 10m winds, air density, gustiness and sea ice fraction. Native grid is Tco639 (18km), 36 directions, 37 frequencies. No wave data assimilation. Hourly output, including 2d spectra.

Examples

At any location over the oceans, there is a spectrum of waves which describes how much wave energy is present for given wave frequencies and direction of propagation...

Significant wave height can be shown to correspond to the average wave height of the top one-third highest waves. The wave period of windsea is generally <10s,...

The AI Weather Quest (AI WQ), organised by ECMWF, is an ambitious international competition designed to harness artificial intelligence (AI) and machine learning (ML) in advancing weather forecasting.

At any location over the oceans, there is a spectrum of waves which describes how much wave energy is present for given wave frequencies and direction of propagation...

15-member coupled IFS (cycle 43R1) extended-range reforecast experiment covering the period 1989-2015. The atmosphere is configured with 91 vertical levels and uses the Tco399 cubic octahedral reduced Gaussian grid. The IFS is coupled hourly to the 75 level version of the NEMO v3.4 ocean model and the LIM2 sea-ice model, both of which use the ORCA025 tripolar grid. Coupling follows the implementation used in ECMWF operational forecasts.

15-member coupled IFS (cycle 43R1) extended-range reforecast experiment covering the period 1989-2015 with bias-corrected sea-surface temperatures (SSTs) in the North Atlantic region. This experiment can be compared with gkzp, which is the relevant control without bias-correction. The atmosphere is configured with 91 vertical levels and uses the Tco399 cubic octahedral reduced Gaussian grid.

The objective of UERRA is to produce ensembles of European regional meteorological reanalyses of Essential Climate Variables (ECVs) for several decades and to estimate the associated uncertainties in the data sets. It also includes recovery of historical (last century) data. UERRA datasets come from 5 Numerical Weather Predication models: COSMO, HARMONIE, MESAN, MESCAN-SURFEX and UM/4DVAR.

These diagrams show verification scores for significant wave height and 10 m wind for three ...