Saverio Nilo - 24.06.2020 Fog forecast using WRF model output for applications in solar energy applications Critical issues Hourly CNR-IMAA WRF Ts, Td, T850, W850 forecast Hourly CNR-IMAA WRF RH forecast Hourly FSI Hourly CNR-IMAA WRF Ts, Td forecast Tdepr_thresh RH_thresh Test #3 - Tdepr_test FSI_thresh Hourly CNR-IMAA WRF WS forecast Hourly CNR-IMAA WRF RH forecast WSmin_thresh WSmax_thresh RHDiff_thresh Test #4 - WS_test Test #2 - RH_test Test #5 - RHDiff_test Test #1 - FSI_test & MBFog forecast flag Block diagram of MBFog method REVIEWER 1 1. Added value 2.1 Persistence 2. Quality comparison REVIEWER 3 6. Threshold calculation 7. Estimation of the impact of the study for solar energy applications Fog forecast using WRF model output for solar energy applications Critical issues 2.3 Operational (TAF messages) 2.4 Published methods (e.g. intern. Airports) 3.1 Type of fog 3. Result explanation 4. Specificity of fog forecast for solar energy applications 5. Dataset weaknesses Critical issues 2.2 WRF fog forecast (visibility or LWC) 3.2 Forecast score vs. Forecast time 4.1 Quality evaluation in terms of fog duration during daytime 5.1 Multi-modal behaviour of RH histograms 5.2 RH close or under 80% in presence of fog REVIEWER 1 Critical issues: 1. Added value of this study with respect to published work not established 2. Quality comparison of the forecast method with: • Persistence forecast • WRF fog forecast (visibility or LWC forecast) • Operational fog forecast (TAF messages issued from aeronautical authorities) • Published fog forecast methods (e.g. over international airport like Paris-CdG, New York or Casablanca) 3. Result explanation (i.e. POD (0.52-0.71) and FAR (0.18-0.46) variability for the different studied airports) with respect to: • Type of fog (e.g. with the climatological classification of Tardif et al.) • Relation between the forecast score and the forecast time (are the scores the same for the 6h-30h forecast time?) 4. Highlight the specificity of fog forecast for solar energy applications • Duration of fog events during day time is a crucial parameter to forecast for energy application • Evaluation of the quality of the proposed method in terms of fog duration during daytime 5. Dataset weaknesses • Multi-modal behaviour of the relative humidity measurements during fog events • Fog events observed with relative humidities close or under 80%
