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Single case research is a viable way to obtain evidence for social and psychological interventions on an individual level. Across single case research studies various analysis strategies are employed, varying from visual analysis to the calculation of effect sizes. To calculate effect sizes in studies with few measurements per time period (<40 data points with a minimum of five data points in each phase), non-parametric indices such as Nonoverlap of All Pairs (NAP) and Tau-U are recommended. However, both indices have restrictions. This article discusses the restrictions of NAP and Tau-U and presents the description, calculation, and benefits of an additional effect size, called the Typicality of Level Change (TLC) index. In comparison to NAP and Tau-U, the TLC index is more aligned to visual analysis, not restricted by a ceiling effect, and does not overcompensate for problematic trends in data. The TLC index is also sensitive to the typicality of an effect. TLC is an important addition to ease the restrictions of current nonoverlap methods when comparing effect sizes between cases and studies. [ABSTRACT FROM AUTHOR]

Subseasonal‐to‐seasonal (S2S) prediction has gained momentum in the recent past as a need for predictions between the weather forecasting timescale and seasonal timescale exists. The availability of S2S databases makes prediction and predictability studies possible over all the regions of the globe. Most S2S studies are, however, relevant to the northern hemisphere. In this review, the S2S literature relevant to the southern hemisphere (SH) are presented. Predictive skill, sources of predictability, and the application of S2S predictions are discussed. Indications from the subseasonal predictability studies for the SH regions suggest that predictive skill is limited to 2 weeks in general, particularly for temperature and rainfall, which are the variables most frequently investigated. However, temperature has enhanced skill compared to rainfall. More S2S prediction studies that include the quantification of the sources of predictability and the identification of windows of opportunity need to be conducted for the SH, particularly for the southern African region. The African continent is vulnerable to weather‐ and climate‐related disasters, and S2S forecasts can assist in alleviating the risk of such disasters. [ABSTRACT FROM AUTHOR]

North American Multi‐Model Ensemble (NMME) precipitation forecast skill over Iran is evaluated using Taylor diagrams and ranked probability skill scores (RPSS) as determined over a 29‐year test period (1991–2019). The forecast skill for both monthly (October through June for lead‐times of 0.5–3.5 months) and seasonal (October–December [OND], January–March [JFM], and April–June [AMJ] for lead‐times of 1.5–3.5 months) timescales is evaluated using six NMME models as well as multi‐model ensemble means (MMM). The latest versions of these models for forecasting Iran's precipitation have not been evaluated thus far. The Global Precipitation Climatology Center (GPCC) version 2020 dataset is used to verify the models. Among individual NMME models, Geophysical Fluid Dynamics Laboratory‐Seamless System for Prediction and Earth System Research (GFDL‐SPEAR) has generally the highest forecast skill. Both Taylor diagrams and RPSS of most of the models have indicated that the highest forecast skill is found for the month of November such that the Pearson correlation for both SPEAR and MMM is statistically significant for all lead‐times. For both monthly and seasonal timescales, the temporal Pearson correlation (TPC) between the observed and forecasts of the MMM is higher than the TPC of the individual models. The spatial Pearson correlation (SPC) and normalized centred root mean square error (NCRMSE) of the SPEAR is close to MMM, but the normalized standard deviation (NSD) of the SPEAR is closer to one compared to the MMM for months from November to March and two seasons (OND and JFM seasons). The MMM precipitation forecasts are underestimated over the northern regions and Zagros mountains for JFM and OND seasons for both 1.5‐ and 2.5‐month lead‐times. The degree to which the forecast skill of MMM is dependent on the El Niño–Southern Oscillation (ENSO) connections with precipitation over Iran is examined. Significant Spearman correlations between simultaneous observed Niño3.4 index and Iran precipitation are found for OND, but not for JFM and AMJ. The MMM reproduces the observed ENSO teleconnections to the tropical Pacific in OND, consistent with forecast skill in that season. However, the MMM also produces forecast skill in JFM and AMJ when the ENSO influence is marginal, showing that ENSO is not the only source of skill in the models. [ABSTRACT FROM AUTHOR]