Continued progress in numerical weather prediction requires sustained, collaborative research in weather observation processing, modelling and computing. Often research efforts in these different areas have to go hand in hand. For example, modelling the atmosphere and other Earth system components at higher resolution requires advances in numerical methods and computing.
ECMWF is well placed to achieve such coordination because its scientists carry out research and development across all areas of numerical weather prediction in close collaboration with partner organisations and fellow scientists across the world.
In 2018, several major research projects came to fruition or made decisive progress. Important advances were made in data assimilation and in our understanding of persistent biases in ECMWF’s medium-range forecasts of near-surface weather parameters. Progress was also made in ensemble size research, atmospheric composition priorities for numerical weather prediction and work on an alternative dynamical core using the Finite Volume Module.
Continuous data assimilation
Accurate weather predictions rely on an accurate specification of the initial state of the Earth system. Data assimilation combines tens of millions of Earth system observations with model information to arrive at a full set of initial conditions called the analysis. In ECMWF’s configuration operational in 2018, by the time the analysis was complete, the most recent observations that had gone into producing it were almost two hours old.
Building on previous work at the Centre and elsewhere, ECMWF scientists developed a revised configuration of the 4D-Var data assimilation system that enables the analysis to benefit from more recent observations. In the new, more continuous framework, observations taken around one and a half hours later can be assimilated. The change also makes it possible to add another iteration to the data assimilation calculations, thus improving their accuracy.
The new data assimilation configuration was found to improve forecast quality significantly. It was scheduled for inclusion in the 2019 upgrade of ECMWF’s Integrated Forecasting System to IFS Cycle 46r1.
50-member Ensemble of Data Assimilations
Since 2010, ECMWF has run an Ensemble of Data Assimilations (EDA) to help determine the initial conditions for its forecasts. The EDA is an ensemble of 4D-Var data assimilations that reflects uncertainties in observations, atmospheric boundary conditions and the model physics. It contributes to the high-resolution analysis by providing flow-dependent estimates of the errors in the short-range forecasts used in the data assimilation system. It is also used to determine the perturbed initial conditions for ensemble forecasts: EDA-based perturbations are combined with singular vectors to construct perturbations to the high-resolution analysis.
The year 2018 saw the successful conclusion of work on a new, optimised 50-member EDA configuration that has a comparable computational footprint to the previous 25-member configuration. The increase in ensemble size improves both the high-resolution analysis and the EDA-based perturbations to the initial conditions for the 50-member ensemble forecast. The change also means that ensemble forecast members are exchangeable. Among other things, this will increase the efficiency of research experimentation involving ensemble forecasts. The change was scheduled for inclusion in the 2019 upgrade of ECMWF’s Integrated Forecasting System to IFS Cycle 46r1.
Ensemble size in research
An ensemble forecast is a set of forecasts that represent the range of possible future weather evolutions. The larger the ensemble, the better a probability distribution can be estimated. But research experiments to test new ideas are computationally expensive if the number of ensemble members is high. This can slow down progress: the larger the ensemble, the longer scientists have to wait for the results of their tests.
Work carried out in 2018 showed that some of the most relevant forecast performance scores obtained with a small number of ensemble members can be valid for large ensembles too. This can be achieved by making suitable statistical corrections in the score computation.
An important condition is that ensemble members are exchangeable. In that case, meaningful results for the operational 50-member ensemble can be achieved in tests involving as few as eight ensemble members. These findings can help to speed up progress in numerical weather prediction under the constraint of limited computing resources.
Reducing forecast biases
Investigations carried out in 2018 showed that persistent biases in ECMWF’s medium-range forecasts of near-surface weather parameters are closely related to the coupling between the atmosphere and the land surface in the Integrated Forecasting System. They are also related to other processes, such as turbulent mixing, radiation and clouds.
The investigations were part of an ECMWF initiative entitled ‘Understanding uncertainties in surface–atmosphere exchange’ (USURF), which started in November 2017. Key to making progress was the availability of suitable in situ data from ECMWF’s Member and Co-operating States and the development of a conditional verification methodology, which helped to identify specific processes as likely causes of some of the biases. Work in 2018 focused on 2-metre temperature biases in Europe. However, because of the physical links between 2-metre temperature, 2-metre humidity and 10-metre wind speed, investigations have also included some aspects of humidity- and wind-related processes.
The results of the initiative will feed into the operational implementation of a multi-layer snow scheme and an improved land scheme as well as a revised framework for moist processes relying on consistent assumptions and improved coupling between the turbulent diffusion, convection and cloud schemes and the dynamics.
Atmospheric composition priorities
One of ECMWF’s strategic goals for 2025 is to develop an integrated global model of the Earth system to produce forecasts with increasing fidelity on time ranges up to a year ahead. This will be achieved through an increased level of complexity of physical and chemical processes as well as of Earth system interactions in the model. Atmospheric composition has the potential to be one of the sources of predictability at different timescales.
A cross-departmental working group on atmospheric composition was set up at ECMWF in spring 2018. It has worked to identify a set of priority developments that have the potential to improve forecasts at all scales, from a few days to seasons ahead.
The group made recommendations concerning modelling and data assimilation aspects for ozone, CO2 and aerosols; improving the atmospheric composition numerical infrastructure and code efficiency; and modifying the model performance evaluation process to account for atmospheric composition. In pursuing these objectives, the Centre will be able to leverage some of the capabilities of the Copernicus Atmosphere Monitoring Service (CAMS) implemented by ECMWF on behalf of the EU.
Finite Volume Module improvements
Over the next decade, many aspects of ECMWF’s Integrated Forecasting System (IFS) may need to change to enable the production of the computationally demanding higher-resolution global forecasts called for by ECMWF’s long-term Strategy. The dynamical core lies at the heart of the model infrastructure. It numerically solves the governing equations describing the resolved atmospheric dynamics.
The dynamical core is coupled to parametrizations of small-scale physical atmospheric processes and to models of other Earth system components. The current IFS dynamical core uses the spectral transform method to solve the governing equations. ECMWF is continuing to develop this dynamical core, which also includes a nonhydrostatic option, to make it as computationally efficient as possible.
For added flexibility, it is also developing a new, nonhydrostatic dynamical core which uses the finite-volume method. In 2018, this ‘Finite-Volume Module’ (FVM) was shown to perform well compared to the current dynamical core in benchmark tests, and its computational cost was reduced substantially. Tests have shown that it holds great promise in terms of computational efficiency for global nonhydrostatic forecasts at high resolution run on future exascale high-performance computing facilities.