A new radiation scheme for the IFS

Hogan, Robin J. ; Bozzo, Alessio

Année de publication
2018
Résumé
Radiation is a fundamental process that drives atmospheric flows at all scales. It is key to both improving short-range surface temperature forecasts and meeting ECMWF's strategic aim of pushing the boundaries of predictability at medium-range and longer timescales. In 2007, the 'McRad' radiation scheme became operational in ECMWF's Integrated Forecasting System (IFS). It incorporated two major advances: very accurate gas optical properties in both the shortwave and longwave from the Rapid Radiative Transfer Model for general circulation models (RRTM-G), and the Monte Carlo Independent Column Approximation (McICA) for efficient treatment of cloud sub-grid heterogeneity. Many weather and climate models have since incorporated one or both of these advances. Two shortcomings of McRad have motivated the recent development of a new ECMWF radiation scheme, 'ecRad'. Firstly, flexibility: to facilitate ongoing scientific development, we need the ability to swap individual components of the radiation scheme for faster and more accurate ones, but the non-modular design of McRad makes this very difficult. Secondly, efficiency: the large number of spectral intervals (252) required by RRTM-G made McRad 3.5 times slower than its predecessor. The result is that the radiation scheme has to be run on a much coarser grid than the rest of the model, and in all operational model configurations except high-resolution forecasts (HRES) we only call the radiation scheme every 3 hours. In HRES the scheme is called every hour. The new radiation scheme ecRad, which became operational in July 2017 (IFS Cycle 43r3), is faster and more flexible. It uses a new implementation of McICA that is less noisy in partially cloudy conditions. Improvements in longwave radiative transfer reduce biases in temperature profiles. As implemented in IFS Cycle 43r3, ecRad brings slight improvements in forecast skill. Its modular structure facilitates radiative transfer research and opens the door to more substantial improvements in forecast skill in the future.

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