Covariance-informed spatiotemporal clustering improves the detection of hazardous weather events

Abstract. Spatiotemporal clustering can be used to detect weather events in multi-dimensional datasets. This method requires that the resolution of a dataset equivalently resolves fluctuations across space and time, thereby normalizing the dataset for unbiased clustering across three dimensions. Yet, few studies test whether a dataset meets this requirement as there is no standard approach to do so. To address this methodological gap, we present a framework to quantify the relationship between space and time using space time separable covariance modelling. We demonstrate that, by defining a temporal resolution of interest (e.g. hours, days), the equivalent spatial resolution can be empirically derived using a space time metric. We present an application using the unsupervised machine learning method Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect heat waves and severe storms across the Southeastern US from 1940 to 2023 from ECMWF Reanalysis version 5 (ERA5) data. We analyse the seasonal behaviour of space time metrics for precipitation and heat index before selecting representative values. We find that both ERA5-derived daily heat index and hourly precipitation are insufficiently resolved for unbiased clustering at their native resolutions (i.e., 0.25 spatial degrees [degree] per day for heat index and 0.25 degree per hour for precipitation). We show that a resolution of 0.39 degree per day (0.05 degree per hour) prevents preferential clustering in either the spatial or temporal dimension for heat index (precipitation). We hypothesize that event identification will improve by resampling the data by the space time metric. Heat wave clusters that were produced using the unbiased resolution were compared against the NOAA Storm Events Database from 2019 to 2023. Recall of heat waves increased from 0.92 to 0.94 using the covariance-informed resolution, demonstrating the importance of normalization prior to weather event reconstruction. Ultimately, the inclusion of temporal geostatistics leads to improved reconstruction of historical weather events and enables evaluation of their scale and variability.