Comparison between the Operational and Statistical Daily Maximum and Minimum Temperature Forecasts on the Central Coast of Peru

Abstract

Abstract Multiple linear regression models were developed for 1–3-day lead forecasts of maximum and minimum temperature for two locations in the city of Lima, on the central coast of Peru (12°S), and contrasted with the operational forecasts issued by the National Meteorological and Hydrological Service—SENAMHI and the output of a regional numerical atmospheric model. We developed empirical models, fitted to data from the 2000–13 period, and verified their skill for the 2014–19 period. Since El Niño produces a strong low-frequency signal, the models focus on the high-frequency weather and subseasonal variability (60-day cutoff). The empirical models outperformed the operational forecasts and the numerical model. For instance, the high-frequency annual correlation coefficient and root-mean-square error (RMSE) for the 1-day lead forecasts were 0.37°–0.53° and 0.74°–1.76°C for the empirical model, respectively, but from around −0.05° to 0.24° and 0.88°–4.21°C in the operational case. Only three predictors were considered for the models, including persistence and large-scale atmospheric indices. Contrary to our belief, the model skill was lowest for the austral winter (June–August), when the extratropical influence is largest, suggesting an enhanced role of local effects. Including local specific humidity as a predictor for minimum temperature at the inland location substantially increased the skill and reduced its seasonality. There were cases in which both the operational and empirical forecast had similar strong errors and we suggest mesoscale circulations, such as the low-level cyclonic vortex over the ocean, as the culprit. Incorporating such information could be valuable for improving the forecasts. Significance Statement We wanted to compare the temperature of the operational forecast of the Meteorological and Hydrological Service, an atmospheric model, and persistence with the observed temperatures on the Peruvian central coast. In addition, we generated an empirical forecast model considering both atmospheric and local predictors. We got better results with this empirical model, considering the highest Pearson correlations and the lowest RMSE values. These results will allow us to use this empirical model as the main tool to automate the forecast on the central coast of Peru. Future work should be aimed at testing the skill of this model for forecasting in other cities of Peru.