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1. Application of Clustering Algorithms to TRMM Precipitation over the Tropical and South Pacific Ocean

   https://doi.org/10.1175/JCLI-D-19-0537.1  

   Pike, Maxwell; Lintner, Benjamin R. (July 2020, Journal of Climate)

   Understanding multiscale rainfall variability in the South Pacific convergence zone (SPCZ), a southeastward-oriented band of precipitating deep convection in the South Pacific, is critical for both the human and natural systems dependent on its rainfall, and for interpreting similar off-equatorial diagonal convection zones around the globe. A k-means clustering method is applied to daily austral summer (December–February) Tropical Rainfall Measuring Mission (TRMM) satellite rainfall to extract representative spatial patterns of rainfall over the SPCZ region for the period 1998–2013. For a k = 4 clustering, pairs of clusters differ predominantly via spatial translation of the SPCZ diagonal, reflecting either warm or cool phases of El Niño–Southern Oscillation (ENSO). Within each of these ENSO phase pairs, one cluster exhibits intense precipitation along the SPCZ while the other features weakened rainfall. Cluster temporal behavior is analyzed to investigate higher-frequency forcings (e.g., the Madden–Julian oscillation and synoptic-scale disturbances) that trigger deep convection where SSTs are sufficiently warm. Pressure-level winds and specific humidity from the Climate Forecast System Reanalysis are composited with respect to daily cluster assignment to investigate differences between active and quiescent SPCZ conditions to reveal the conditions supporting enhanced or suppressed SPCZ precipitation, such as low-level poleward moisture transport from the equator. Empirical orthogonal functions (EOFs) of TRMM precipitation are computed to relate the “modal view” of SPCZ variability associated with the EOFs to the “state view” associated with the clusters. Finally, the cluster number is increased to illustrate the change in TRMM rainfall patterns as additional degrees of freedom are permitted. 

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2. Evaluating methods for addressing skewness in clustering: a focus on generalized hyperbolic mixture models

   https://doi.org/10.1080/00949655.2025.2502535  

   Tortora, Cristina (May 2025, Journal of Statistical Computation and Simulation)

   In model-based clustering, the population is assumed to be a combination of sub-populations. Typically, each sub-population is modeled by a mixture model component, distributed according to a known probability distribution. Each component is considered a cluster. Two primary approaches have been used in the literature when clusters are skewed: (1) transforming the data within each cluster and applying a mixture of symmetric distributions to the transformed data, and (2) directly modeling each cluster using a skewed distribution. Among skewed distributions, the generalized hyperbolic distribution is notably flexible and includes many other known distributions as special or limiting cases. This paper achieves two goals. First, it extends the flexibility of transformation-based methods as outlined in approach (1) by employing a flexible symmetric generalized hyperbolic distribution to model each transformed cluster. This innovation results in the introduction of two new models, each derived from distinct within-cluster data transformations. Second, the paper benchmarks the approaches listed in (1) and (2) for handling skewness using both simulated and real data. The findings highlight the necessity of both approaches in varying contexts. 

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3. Temporal variability and predictability predict alpine plant community composition and distribution patterns

   https://doi.org/10.1002/ecy.4450  

   Reed, William J; Westmoreland, Aaron J; Suding, Katharine N; Doak, Daniel F; Bowman, William D; Emery, Nancy C (October 2024, Ecology)

   Abstract One of the most reliable features of natural systems is that they change through time. Theory predicts that temporally fluctuating conditions shape community composition, species distribution patterns, and life history variation, yet features of temporal variability are rarely incorporated into studies of species–environment associations. In this study, we evaluated how two components of temporal environmental variation—variability and predictability—impact plant community composition and species distribution patterns in the alpine tundra of the Southern Rocky Mountains in Colorado (USA). Using the Sensor Network Array at the Niwot Ridge Long‐Term Ecological Research site, we used in situ, high‐resolution temporal measurements of soil moisture and temperature from 13 locations (“nodes”) distributed throughout an alpine catchment to characterize the annual mean, variability, and predictability in these variables in each of four consecutive years. We combined these data with annual vegetation surveys at each node to evaluate whether variability over short (within‐day) and seasonal (2‐ to 4‐month) timescales could predict patterns in plant community composition, species distributions, and species abundances better than models that considered average annual conditions alone. We found that metrics for variability and predictability in soil moisture and soil temperature, at both daily and seasonal timescales, improved our ability to explain spatial variation in alpine plant community composition. Daily variability in soil moisture and temperature, along with seasonal predictability in soil moisture, was particularly important in predicting community composition and species occurrences. These results indicate that the magnitude and patterns of fluctuations in soil moisture and temperature are important predictors of community composition and plant distribution patterns in alpine plant communities. More broadly, these results highlight that components of temporal change provide important niche axes that can partition species with different growth and life history strategies along environmental gradients in heterogeneous landscapes. 

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4. Variations in Tropical Cyclone Size and Rainfall Patterns Based on Synoptic‐Scale Moisture Environments in the North Atlantic

   https://doi.org/10.1029/2024JD043135  

   Addington, K_D; Zick, S_E; Wood, K_M; Matyas, C_J; Berislavich, K. (May 2025, Journal of Geophysical Research: Atmospheres)

   Abstract Prior research has shown that tropical cyclone (TC) size, which is integral in determining the spatial extent of TC impacts, is influenced by environmental wind shear and the overall moisture environment. This study considers North Atlantic TCs located within low to moderate wind shear and at least 100 km from major landmasses. An empirical orthogonal function (EOF) analysis is applied to distinguish moisture environments based on the spatial pattern of total column water vapor surrounding the TC. Using these EOF patterns, four separate categories (groups) are created. Principal component scores indicate the TC samples most contributing to each EOF pattern and ultimately determine the cases in each group. TC structural differences among the groups are compared using size metrics based on the wind and precipitation fields and shape metrics based on the precipitation field. These metrics are considered across a 48‐hr window centered on the sample times evaluated in the EOF analysis. There are no statistically significant differences in the TC wind field size, but TCs with abundant moisture to the southeast have larger rain areas with more outer rainbands. TCs in a dry environment or with dry air southeast of the TC center have generally smaller rain areas and less closed rainbands than TCs with moisture to the southeast. Future work will investigate the physical processes contributing to these spatial differences in precipitation. 

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5. Clustering and classification of vertical movement profiles for ecological inference of behavior

   https://doi.org/10.1002/ecs2.4384  

   Barbour, Nicole; Robillard, Alexander J.; Shillinger, George L.; Lyubchich, Vyacheslav; Secor, David H.; Fagan, William F.; Bailey, Helen (January 2023, Ecosphere)

   Abstract Vertical movements can expose individuals to rapid changes in physical and trophic environments—for aquatic fauna, dive profiles from biotelemetry data can be used to quantify and categorize vertical movements. Inferences on classes of vertical movement profiles typically rely on subjective summaries of parameters or statistical clustering techniques that utilize Euclidean matching of vertical movement profiles with vertical observation points. These approaches are prone to subjectivity, error, and bias. We used machine learning approaches on a large dataset of vertical time series (N = 28,217 dives) for 31 post‐nesting leatherback turtles (Dermochelys coriacea). We applied dynamic time warp (DTW) clustering to group vertical movement (dive) time series by their metrics (depth and duration) into an optimal number of clusters. We then identified environmental covariates associated with each cluster using a generalized additive mixed‐effects model (GAMM). A convolutional neural network (CNN) model, trained on standard dive shape types from the literature, was used to classify dives within each DTW cluster by their shape. Two clusters were identified with the DTW approach—these varied in their spatial and temporal distributions, with dependence on environmental covariates, sea surface temperature, bathymetry, sea surface height anomaly, and time‐lagged surface chlorophyllaconcentrations. CNN classification accuracy of the five standard dive profiles was 95%. Subsequent analyses revealed that the two clusters differed in their composition of standard dive shapes, with each cluster dominated by shapes indicative of distinct behaviors (pelagic foraging and exploration, respectively). The use of these two machine learning approaches allowed for discrete behaviors to be identified from vertical time series data, first by clustering vertical movements by their movement metrics (DTW) and second by classifying dive profiles within each cluster by their shapes (CNN). Statistical inference for the identified clusters found distinct relationships with environmental covariates, supporting hypotheses of vertical niche switching and vertically structured foraging behavior. This approach could be similarly applied to the time series of other animals utilizing the vertical dimension in their movements, including aerial, arboreal, and other aquatic species, to efficiently identify different movement behaviors and inform habitat models. 

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