The Spring Index (SI-x) forecasting

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The map above is the forecast of the Spring Index anomaly (ΔSI-x) in units of days. Their negative values mean early onset for a climatology defined for the period 1982-2010, and late onset for positive values. The black solid lines indicate region where the spring onset has already occurred. The actual forecasted day of the Spring Index (SI-x) is indicated in the Table below in calendar days or Day-Of-the-Year (DOY) units. The SI-x is computed as in Ault et al. (2015) with current climatic conditions. These data sets are multiple simulations of daily Tmax and Tmin of the CFSv2 model. The SI-x product was post-processing using an ensemble model statistic output (EMOS), Non-homogeneous Gaussian Regression (Gneiting et al., 2005). The different ensemble members are defined with four daily runs with different initializations: 00Z, 06Z, 12Z, and 18Z. Thus, a total of 120 model realizations are used in each issued SI-x forecast. The EMOS product is calibrated with a re-forecasting dataset from 1982-2010 and further details can be found in Carrillo et al. (2018). The products related to the SI-x forecast are shown in four field maps in the Table below for different initializations (Dec 15, Dec 31, Jan 15, etc): (1) the Spring Index anomaly; (2) the Spring Index; (3) a global skill that is defined as CDF of centered anomaly correlation for the entire domain (Wilks, 2011); two thresholds (0.5 and 0.7) identify skill for different initializations; (4) a local skill defined using Pearson correlation.

Toby R. Ault
Dept. of Earth & Atmos. Sciences, Cornell University

SI-x forecast : 2019
∆SI-x SI-x
May 15th 31th 15th 31th Global_Skill Local_Skill
Apr 15th 30th 15th 30th Global_Skill Local_Skill
Mar 15th 31th 15th 31th Global_Skill Local_Skill
Feb 15th 28th 15th 28th Global_Skill Local_Skill
Jan 15th 31th 15th 31th Global_Skill Local_Skill
Dec 15th 31th 15th 31th Global_Skill Local_Skill

Historical validation: 1982-2010
Dec time_series Global_Skill Local_Skill
Jan time_series Global_Skill Local_Skill
Feb time_series Global_Skill Local_Skill
Mar time_series Global_Skill Local_Skill