The ECMWF Ensemble Prediction System: Looking Back (more than) 25 Years and Projecting Forward 25 Years
Reliable low precision simulations in land surface models
Improving weather forecast skill through reduced precision data assimilation
Abstract:
A new approach for improving the accuracy of data assimilation, by trading numerical precision for ensemble size, is introduced. Data assimilation is inherently uncertain due to the use of noisy observations and imperfect models. Thus, the larger rounding errors incurred from reducing precision may be within the tolerance of the system. Lower precision arithmetic is cheaper, and so by reducing precision in ensemble data assimilation, computational resources can be redistributed towards, for example, a larger ensemble size. Because larger ensembles provide a better estimate of the underlying distribution and are less reliant on covariance inflation and localization, lowering precision could actually permit an improvement in the accuracy of weather forecasts. Here, this idea is tested on an ensemble data assimilation system comprising the Lorenz ’96 toy atmospheric model and the ensemble square root filter. The system is run at double, single and half precision (the latter using an emulation tool), and the performance of each precision is measured through mean error statistics and rank histograms. The sensitivity of these results to the observation error and the length of the observation window are addressed. Then, by reinvesting the saved computational resources from reducing precision into the ensemble size, assimilation error can be reduced for (hypothetically) no extra cost. This results in increased forecasting skill, with respect to double precision assimilation.A simple pedagogical model linking initial-value reliability with trustworthiness in the forced climate response
Abstract:
Using a simple pedagogical model, it is shown how information about the statistical reliability of initial-value ensemble forecasts can be relevant in assessing the trustworthiness of the climate system’s response to forcing.
Although the development of seamless prediction systems is becoming increasingly common, there is still confusion regarding the relevance of information from initial-value forecasts for assessing the trustworthiness of the climate system’s response to forcing. A simple system which mimics the real climate system through its regime structure is used to illustrate this potential relevance. The more complex version of this model defines “REALITY” and a simplified version of the system represents the “MODEL”. The MODEL’s response to forcing is profoundly incorrect. However, the untrustworthiness of the MODEL’s response to forcing can be deduced from the MODEL’s initial-value unreliability. The nonlinearity of the system is crucial in accounting for this result.