NCMRWF Ensemble Prediction System (NEPS)

• NCMRWF Ensemble Prediction System (NEPS) is a global medium range probabilistic forecasting system adapted from UK MET Office. The configuration consists of four cycles of assimilation corresponding to 00Z, 06Z, 12Z 18Z and 10-day forecasts are made using the 00Z initial condition. The N400L70 forecast model consists of 800x600 grid points on the horizontal surface and has 70 vertical levels. Horizontal resolution of the model is approximately 33 km in the midlatitudes. The 10 day control forecast run starts with N768L70 analysis of the deterministic assimilation forecast system and 44 ensemble members start from different perturbed initial conditions consistent with the uncertainty in initial conditions. The initial perturbations are generated using Ensemble Transform Kalman Filter (ETKF) method (Bishop et al., 2001). Uncertainty in forecasting model is taken into account by making small random variations to the model and using a stochastic kinetic energy backscatter scheme, (Tennant et al., 2010).

Ensemble forecast Products

The Ensemble Mean and Spread:

• The ensemble mean of a parameter is a simple mean of the parameter value predicted by all ensemble members. Normally, the ensemble mean verifies better than the control forecast as it smoothes out the unpredictable detail and shows the more predictable elements of the forecast.
• The ensemble spread is a measure of the difference between the members and is represented by the standard deviation with respect to the ensemble mean. On average, small (high) spread indicates a high (low) forecast accuracy.

Probabilistic forecasts of quantitative precipitation:

• In these charts, the probability that 24-hour accumulated precipitation over a 1deg x 1deg lat-lon grid box will exceed certain threshold values is given. The forecast probability is estimated directly from the 44-member global ensemble.
• At each grid point the number of ensemble members having a 24-hour precipitation amount greater than the threshold limit considered is counted (M) and the probability is expressed as 100*(M/44).

EPSGRAM:

• The model output variables at a particular location can be extracted from the grid point values. Ensemble meteogram or EPSGRAM is one of the most popular charts that uses a box and whisker plot to illustrate the percentile points of the forecast distribution for one or more variables at a particular location.

References:

• 1. Bishop CH, Etherton BJ, Majumdar SJ, 2001. Adaptive sampling with the ensemble transform Kalman filter, Part I: Theoretical aspects, Mon. Weather Rev., 129, 420-436
• 2. Tennant WJ, Shutts GJ, Arribas A and Thompson SA, 2010. Using a Stochastic Kinetic Energy Backscatter Scheme to Improve MOGREPS Probabilistic Forecast Skill. Mon. Weather Rev., 139, 1190-1206