Medium and long-term forecasts for assessing the danger of fires.

<p><strong>Medium and long-term forecasts for assessing the danger of fires.</strong></p><p><strong>V.Santarsiero<sup>1,2</sup>, A. Lanorte<sup>1</sup>, G. Nolè<sup>1</sup>, B. Murgante<sup>2</sup>, B. Tucci<sup>1</sup> e P. Baldantoni<sup>2</sup></strong></p><p><strong> </strong></p><p><strong> </strong></p><p>Seasonal fire forecasts are a challenge made possible in recent years thanks to availability of better time series of climatic data and wider statistical databases on fires. In addition, the long-term fire risk estimate is considered an element crucial for the preparation of prevention activities (Mavsar et al., 2013). Many of the studies related to seasonal fire forecasts follow an approach empirical based on statistical correlations between fires and climatic variables antecedents. All relevant changes in local and / or weather conditions changes in the local socio-environmental context can influence the regimes of the climate-related fires. Recent advances in seasonal climate forecasting systems based on the analysis of ocean-atmosphere-earth processes make it possible to use prediction models for fire hazard prediction. Such models based on physical processes use models global climate together with human factors to predict the fire hazard on a scale monthly or seasonal (Roads et al. 2005, 2010; Spessa et al. 2015; Field et al. 2015). Seasonal fire hazard predictions in the USA (Roads et al., 2010) are recorded on the NCEP-CFS (National Center for Environmental Prediction's Coupled Forecasting System) (Saha et al., 2006, 2014). The NCEP-CFS system generates forecasts for ensembles of global and regional spectral models over a period of 3 to 7 months. <br>The forecasts generated by the NCEP-CFS system were used to derive precipitation and temperature anomaly maps. Forecasts are made starting from the initial conditions of the last 30 days, with four runs per day. The Forecast ensembles are made up of 40 members from an initial period of 10 days. To provide high resolution seasonal forecasts has been developed and a generalized empirical statistical downscaling system is applied. On this basis precipitation and temperature anomaly maps were extrapolated. The values ​​of the precipitation and temperature anomalies in the various decades have been integrated in order to develop a meteorological index capable of highlighting the areas where these anomalies affect the increase or decrease in the fire hazard in relation to the average conditions for each specific decade. The index is built in a way such as to attribute a greater weight to the precipitation anomalies (70%) than the temperature anomalies (30%).</p><p> </p><p>Keywords: fire hazard prediction, long-term forecasting.</p><p> </p><p><sup>1</sup> IMAA-CNR C.da Santa Loja, zona Industriale, Tito Scalo, Potenza 85050, Italy;</p><p><sup>2</sup>School of Engineering, University of Basilicata, Viale dell'Ateneo Lucano 10, Potenza 85100, Italy.</p><p> </p>

Sub-seasonal prediction of Arctic sea ice concentration using time series forecasting technique

<div> <div> <div> <p>Recently, many studies have highlighted the importance of the ability to predict the Arctic sea ice concentration in the sub-seasonal time scales. Notably, the Arctic sea ice concentration has a potential for skillful predictions through their long-term trend memory. Based on the long-term memory of Arctic sea ice concentration, we evaluate the predictability of Arctic sea ice concentration (SIC) by applying a time-series analysis technique of the Prophet model on sub-seasonal time scales. A Prophet is a recently introduced method as a statistical approach inspired by the nature of time series forecasted at Facebook and has not been applied to the prediction of Arctic SIC before. Sub-seasonal prediction skills of Arctic SIC in the Prophet model were compared with the NCEP Climate Forecast System Reforecast (CFS-Reforecast) model as a dynamical approach and verified with the satellite observation during wintertime from 2000 to 2018 for 1 to 8 weeks lead times. The result shows that the Prophet model exhibits much better skill than the NCEP CFS-Reforecast model in the climatology prediction except for the 1 to 3 weeks lead times, as the Prophet model has mainly the ability to capture the long-term trend. In the anomaly prediction, however, the NCEP CFS-Reforecast model is superior to the Prophet model in the prediction of sub-seasonal time scales, as the NCEP CFS-Reforecast captures more effectively the sub-seasonal transition of the underlying dynamical system. Therefore, even if the Prophet model has shown a useful skill in predicting the climatological Arctic SIC, there is still a need to improve the accuracy and robustness of the predictions in an anomalous Arctic SIC. Further, we suggest that the bias correction method is needed to improve the forecast skill of Arctic SIC using the time-series analysis technique, and it will be critical to advance the field of the Arctic SIC forecasting on the sub-seasonal time scales.</p> </div> </div> </div>

Forecast Skill of Synoptic Conditions Associated with Santa Ana Winds in Southern California

Santa Ana winds (SAW) are synoptically driven mesoscale winds observed in Southern California usually during late fall and winter. Because of the complex topography of the region, SAW episodes can sometimes be extremely intense and pose significant environmental hazards, especially during wildfire incidents. A simple set of criteria was used to identify synoptic-scale conditions associated with SAW events in the NCEP–Department of Energy (DOE) reanalysis. SAW events start in late summer and early fall, peak in December–January, and decrease by early spring. The typical duration of SAW conditions is 1–3 days, although extreme cases can last more than 5 days. SAW events exhibit large interannual variations and possible mechanisms responsible for trends and low-frequency variations need further study. A climate run of the NCEP Climate Forecast System (CFS) model showed good agreement and generally small differences with the observed climatological characteristics of SAW conditions. Nonprobabilistic and probabilistic forecasts of synoptic-scale conditions associated with SAW were derived from NCEP CFS reforecasts. The CFS model exhibits small systematic biases in sea level pressure and surface winds in the range of a 1–4-week lead time. Several skill measures indicate that nonprobabilistic forecasts of SAW conditions are typically skillful to about a 6–7-day lead time and large interannual variations are observed. NCEP CFS reforecasts were also applied to derive probabilistic forecasts of synoptic conditions during SAW events and indicate skills to about a 6-day lead time.