Cloud predictions diagnosed from global weather model forecasts

The U.S. Air Force has a long history of investment in Cloud analysis and prediction operations. Their need for accurate cloud cover information has resulted in routine production of global cloud analyses (from their RTNEPH analysis model) and forecasts (using their ADVCLD cloud trajectory forecast model) over many years. With the advancement of global numerical weather prediction technology and resulting forecast accuracy of noncloud meteorological quantities, it is of interest to determine if such technology could be used to benefit cloud cover forecasting. In this paper, a model output statistics approach to diagnosing cloud cover from forecast fields generated by a global numerical weather prediction model is presented. Cloud characteristics information obtained from the RTNEPH cloud analysis supplied the cloud predictands, and forecast fields from the U.S. Navy Operational Global Atmospheric Prediction System global weather prediction model provided the weather variable predictors. RTNEPH layer cloud cover was assigned to three cloud decks (high, middle, and low) based on reported cloud-base altitude, and RTNEPH total cloud cover was used as a separate predictand. Multiple discriminant analysis (MDA) was used to develop the predictand-predictor relationships For each cloud deck and total cloud using 5 days of twice-daily cloud analyses and corresponding forecasts for 30 degrees latitude zones. The consequent relationships were applied to the forecasts fields from the forecast initialized on the day following each 5-day development period to diagnose cloud cover forecasts for the Northern or Southern Hemisphere. In this study, cloud cover forecasts were diagnosed from global NWP model forecasts on hemispheric polar stereographic map projections with a grid spacing of 96 km. The diagnosed cloud forecasts (DCFs) were verified against the RTNEPH analyses for forecast durations of 12-72 h at 12-h intervals. Also verified were 12-48-h cloud cover forecasts (deck and total) from the ADVCLD cloud trajectory model, and from persistence (RTNEPH at initial forecast rime). Biases from all three methods were generally small. The DCFs were significantly better than ADVCLD and persistence in all decks and total cloud, at almost all forecast durations in rmse and 20/20 score. ADVCLD scored better in these measures only at 12 h in total cloud, suggesting the possibility of a crossover in superior prediction skill from trajectory to diagnostic method somewhere between 12 and 24 h. DCF better preserved the cloud cover frequency distribution than did ADVCLD. ADVCLD displayed a greater degree of spatial variation inherent in RTNEPH cloud cover than did DCF Both ADVCLD and DCF visual depictions of hemispheric total cloud cover appeared to provide useful cloud cover forecast information when compared with RTNEPH depictions. The advantages of the diagnosed cloud forecast algorithm presented in this study make it an excellent candidate for operational cloud cover prediction. It is expected that as cloud cover analyses are improved, the trajectory and diagnostic methods will prove complementary with the former more skillful at short-term predictions, and the latter better at long-term forecasts.