Reliability Predictors for Solar Irradiance Satellite-Based Forecast

The worldwide growing development of PV capacity requires an accurate forecast for a safer and cheaper PV grid penetration. Solar energy variability mainly depends on cloud cover evolution. Thus, relationships between weather variables and forecast uncertainties may be quantified to optimize forecast use. An intraday solar energy forecast algorithm using satellite images is fully described and validated over three years in the Paris (France) area. For all tested horizons (up to 6 h), the method shows a positive forecast skill score compared to persistence (up to 15%) and numerical weather predictions (between 20% and 40%). Different variables, such as the clear-sky index (Kc), solar zenith angle (SZA), surrounding cloud pattern observed by satellites and northern Atlantic weather regimes have been tested as predictors for this forecast method. Results highlighted an increasing absolute error with a decreasing SZA and Kc. Root mean square error (RMSE) is significantly affected by the mean and the standard deviation of the observed Kc in a 10 km surrounding area. The highest (respectively, lowest) errors occur at the Atlantic Ridge (respectively, Scandinavian Blocking) regime. The differences of relative RMSE between these two regimes are from 8% to 10% in summer and from 18% to 30% depending on the time horizon. These results can help solar energy users to anticipate—at the forecast start time and up to several days in advance—the uncertainties of the intraday forecast. The results can be used as inputs for other solar energy forecast methods.

Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method

A relocatable ocean prediction system (ROPS) was employed to an observational data set which was collected in June 2014 in the waters to the west of Sardinia (western Mediterranean) in the framework of the REP14-MED experiment. The observational data, comprising more than 6000 temperature and salinity profiles from a fleet of underwater gliders and shipborne probes, were assimilated in the Regional Ocean Modeling System (ROMS), which is the heart of ROPS, and verified against independent observations from ScanFish tows by means of the forecast skill score as defined by Murphy(1993). A simplified objective analysis (OA) method was utilised for assimilation, taking account of only those profiles which were located within a predetermined time window W. As a result of a sensitivity study, the highest skill score was obtained for a correlation length scale C = 12.5 km, W = 24 h, and r = 1, where r is the ratio between the error of the observations and the background error, both for temperature and salinity. Additional ROPS runs showed that (i) the skill score of assimilation runs was mostly higher than the score of a control run without assimilation, (i) the skill score increased with increasing forecast range, and (iii) the skill score for temperature was higher than the score for salinity in the majority of cases. Further on, it is demonstrated that the vast number of observations can be managed by the applied OA method without data reduction, enabling timely operational forecasts even on a commercially available personal computer or a laptop.

Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method

A Relocatable Ocean Prediction System (ROPS) was employed to an observational data set which was collected in June 2014 in the waters to the west of Sardinia (Western Mediterranean) in the mainframe of the REP14-MED experiment. The observational data, comprising almost 5000 temperature and salinity profiles from a fleet of underwater gliders and shipborne probes, were assimilated in the Regional Ocean Modeling System (ROMS) which is the heart of ROPS, and verified against independent observations from ScanFish tows by means of the forecast skill score as defined by Murphy (1993). A simplified objective analysis (OA) method was utilised for assimilation, taking account of only those profiles which were located within a predetermined time window W. As a result of a sensitivity study, the highest skill score was obtained for a correlation length scale C = 12.5 km, W = 24 hours, and r = 1, where r is the ratio between the error of the observations and the background error, both for temperature and salinity. Additional ROPS runs showed that (i) the skill score of assimilation runs was mostly higher than the score of a control run without assimilation, (i) the skill score increased with increasing forecast range, and (iii) the skill score for temperature was higher than the score for salinity in the majority of cases. Further on, it is demonstrated that the vast number of observations can be managed by the applied OA method without data reduction, enabling timely operational forecasts even on a commercially available Personal Computer or a laptop.

Kullback–Leibler Divergence as a Forecast Skill Score with Classic Reliability–Resolution–Uncertainty Decomposition

This paper presents a score that can be used for evaluating probabilistic forecasts of multicategory events. The score is a reinterpretation of the logarithmic score or ignorance score, now formulated as the relative entropy or Kullback–Leibler divergence of the forecast distribution from the observation distribution. Using the information–theoretical concepts of entropy and relative entropy, a decomposition into three components is presented, analogous to the classic decomposition of the Brier score. The information–theoretical twins of the components uncertainty, resolution, and reliability provide diagnostic information about the quality of forecasts. The overall score measures the information conveyed by the forecast. As was shown recently, information theory provides a sound framework for forecast verification. The new decomposition, which has proven to be very useful for the Brier score and is widely used, can help acceptance of the logarithmic score in meteorology.

Verification of Precipitation Forecasts from Two Limited-Area Models over Italy and Comparison with ECMWF Forecasts Using a Resampling Technique

This paper presents the first systematic limited area model (LAM) precipitation verification work over Italy. A resampling technique was used to provide skill score results along with confidence intervals. Two years of data were used, starting in October 2000. Two operational LAMs have been considered, the Limited Area Model Bologna (LAMBO) operating at the Agenzia Regionale Prevenzione e Ambiente-Servizio Meteorologico Regionale (ARPA-SMR) of the Emilia–Romagna region, and the QUADRICS Bologna Limited Area Model (QBOLAM) running at the Agenzia per la Protezione dell’Ambiente e per i Servizi Tecnici (APAT). A 24-h forecast skill score comparison was first performed on the native 0.1° high-resolution grids, using a Barnes scheme to produce the observed 24-h accumulated rainfall analysis. Two nonparametric skill scores were used: the equitable threat score (ETS) and the Hanssen and Kuipers score (HK). Frequency biases (BIA) were also calculated. LAM forecasts were also remapped on a lower-resolution grid (0.5°), using a nearest-neighbor average method; this remapping allowed for comparison with ECMWF model forecasts, and for LAM intercomparisons at lower resolution, with the advantage of reducing the skill score sensitivity to small displacements errors. LAM skill scores depend on the resolution of the verification grid, with an increase when they are verified on a lower-resolution grid. The selected LAMs have a higher BIA compared to ECMWF, showing a tendency to overforecast precipitation, especially along mountain ranges, possibly due to undesired effects from the large-scale and/or convective precipitation parameterizations. Lower ECMWF BIA accounts for skill score differences. LAMBO precipitation forecasts during winter (adjusted for BIA differences) have less misses than ECMWF over the islands of Sardinia and Sicily. Higher-resolution orography definitely adds value to LAM forecasts.

Corrections and Additions to Verification Study of Performance of 1976 University of Wisconsin Model2

Revised and complete verification statistics for mainland United States long-range forecasts made for the period 1976–80 by the 1976 version of the University of Wisconsin model are presented. Corrections to earlier published values are given, as well as skill scores obtained using a much more complete set of stations for which forecasts were made. The overall skill score for the pentad temperature forecasts made for January, April, July, and October is negative (−0.14), while those for pentad precipitation and individual year July precipitation forecasts are positive (0.12 and 0.04, respectively). The individual year January temperature forecast skill score was unchanged at −0.08 overall.