راستي‌آزمايي پيش‌بيني بارش مدل منطقه‌اي MM5 روي ايران

Verification of MM5 forecast precipitation over Iran

During the last several years, application of numerical weather prediction models in the country have become common in both research and operations, even though systematic verification of the modelsʹ results using statistical methods has rarely been conducted (Sodoudi et al. 2009). This paper aims at comparing the MM5 24-hour precipitation forecasts with the corresponding observations using standard scores for categorical E-mail: hedayati@irimet.net -x\-tfkh-\\- ^vm cr ojU-i if? ojj^ tUoa j j^j forecasts associated with 2x2 contingency tables for different precipitation thresholds over different nine sub-regions of Iran. Comparison is conducted for +24h/+48h/+72h forecasts for a four winter month period from December 2004 to March 2005. Performance of the model results were assessed for all available synoptic and climatological stations scattered across the country at three different precipitation thresholds. The 0.1 mm/24h threshold was considered as the rain/no rain event. The other two intervals are: 0.1-10 and greater than 10 mm/24h for light and heavy precipitation respectively. Based on the long term means of precipitation of different parts of Iran, nine different sub-regions were defined and verification was conducted for the whole country and nine different sub-regions separately. In this study for verification scores the quantity of precipitation is considered as a dichotomous variable by considering different precipitation thresholds. The standard approach is to record the frequencies with which the precipitation was observed and forecasted in a two-by-two table, and then to quantify forecast quality with summary measures of the table. The structure of a typical contingency table is presented in table 1. Table 1. Rain contingency table is applied at each verification observation site over the period of verification. A threshold value (e.g., 0.1 mm day-1) is chosen to separate rain from no-rain events. Here, a is the number of correct rain forecasts or hits, b is the number of false alarms, c is the number of misses, and d is the number of correct predictions of rain amount below the specified threshold. From McBride and Ebert (2000). Observed Predicted Rain No rain Rain a b No rain c d The verification scores used in this study are as follows: Threat score (TS), or critical success index (CSI). In terms of table 1 the threat score is computed as a. TS = CSI = a + b + c The worst possible threat score is zero, and the best possible threat score is one. The bias a + b B = —— a + c Unbiased forecasts exhibit B = 1. Bias greater than one indicates that the event was forecasted more often than observed, which is called over forecasting. Conversely, bias less than one indicates that the event was forecasted less often than observed, or was under forecasted. Regarding only the occurrence of event as "the" event of interest, the hit rate is the ratio of correct forecasts to the number of times this event occurred. It is