Reduced numerical precision in data storage for weather and climate simulations Peter Düben, Martin Leutbecher, Linus Magnusson European Centre for Medium-Range Weather Forecasts; Contact: peter.dueben@ecmwf.int 1. Introduction: Data storage generates significant cost for weather and climate modelling. If precision in data storage can be reduced with no reduction in information content, we can reduce data volume and store more fields more frequently at the same cost. We investigate how a reduction in precision will influence data quality for weather and climate simulations. We focus on the reduction of data volume for ensemble forecasts. A single operational ensemble forecast at ECMWF generates 11 terabytes of data. 2. GRIB data format: GRIB is a standard data format for output of weather and climate models. Here, real numbers are stored as scaled integers: The maximal and minimal value of a field are identified and the space in between is divided into 2n equidistant numbers where n is the number of bits that is used to store field values (n=16 in standard model output). 3. Climate runs: We have reduced precision for model output of climate simulations and evaluated the impact on several diagnostics. If we half precision to n=8 bits we see only very small significant changes. The evaluation of blocking frequency at reduced precision for IFS model data (cycle 43R1) is presented in the figure below as an example. 5. Two new methods to achieve a stronger reduction in precision in ensemble simulations: Method 1: Reduced Mean. Global fields can vary significantly over the globe. → If we store perturbations from the ensemble mean instead of absolute fields, the max-min distance is greatly reduced. → Precision can be reduced stronger for data storage of ensemble members (50 at ECMWF). However, we need to store the ensemble mean as an additional field. See error plots below. Method 2: Reduced spread. Ensemble spread and predictability can vary significantly over the globe. → We should store data values as normalized perturbations against ensemble spread. → We calculate and store the minimal value (mi) and the ensemble spread (si) at each grid point (two fields) and store a field value vi as: vi‘=(vi−mi)/si. This method links precision of data to predictability. → Rounding errors will be large if ensemble spread is large and model values are uncertain. See errors plots above. 6. Conclusion: To reduce precision in order to reduce data volume for both weather and climate model output appears to be a promising approach. Different model fields should be stored at different precision levels and precision in storage may be reduced as model errors grow with forecast lead time. If the ensemble mean and ensemble standard deviation is used to normalise ensemble data, numerical precision can be reduced much stronger, keeping precision high at the beginning of the forecast where it is needed most. 4. Ensemble weather forecasts: We have reduced precision for model output of ensemble weather forecasts and investigated the influence on Continuous Ranked Probability Scores for different model quantities (Z, T, u, and v) and locations (southern and northern extra-tropics and tropics). Differences are hardly significant if we store data with n=12 bits. If precision is reduced stronger, the minimal precision level that should be used can be different for different locations and model quantities. The impact of a precision reduction is smaller towards the end of the forecast.