We present an ensemble prediction system using a Deep Learning Weather Prediction
(DLWP) model that recursively predicts six key atmospheric variables with six‐hour time …

Forecast Verification: A Practioner's Guide in Atmospheric Science, 2nd Edition provides an
indispensible guide to this area of active research by combining depth of information with a …

Sub‐seasonal forecasts have been routinely produced at ECMWF since 2002 with
reforecasts produced 'on the fly'to calibrate the real‐time sub‐seasonal forecasts. In this …

Multimodel combination is a pragmatic approach to estimating model uncertainties and to
making climate projections more reliable. The simplest way of constructing a multimodel is to …

A series of 46‐day ensemble integrations starting on the 15th of each month from 1989 to
2008 has been completed with the European Centre for Medium‐Range Weather Forecasts …

Numerical weather prediction has seen, in the past 25 years, a shift from a 'deterministic'
approach, based on single numerical integrations, to a probabilistic one, with ensembles of …

Advanced building controls and energy optimization for new constructions and retrofits rely
on accurate weather data. Traditionally, most studies utilize airport weather information as …

We present a parsimonious deep learning weather prediction model to forecast seven
atmospheric variables with 3‐hr time resolution for up to 1‐year lead times on a 110‐km …

Subseasonal predictions bridge the gap between medium‐range weather forecasts and
seasonal climate predictions. This time horizon is of crucial importance for many planning …

Anderson, J., H. van den Dool, AG Barnston, W. Chen, W. Stern, and J. Ploshay, 1999:
Present-day capabilities of numerical and statistical models for atmospheric extratropical …