An Empirical Study of the Relationship between Seasonal Precipitation and Thermodynamic Environment in Puerto Rico

Abstract

With limited groundwater reserves and few reservoirs, Caribbean islands such as Puerto Rico are largely dependent on regular rainfall to meet societal and ecological water needs. Thus, the ability to anticipate seasonal rainfall shortages, such as the 2015 drought, is particularly important, yet few reliable tools exist for this purpose. Consequently, interpolated surface precipitation observations from the Daymet archive are summarized on daily, annual, and seasonal time scales and compared to the host thermodynamic environment as characterized by the Gálvez–Davison index (GDI), a convective potential parameter designed specifically for the tropics. Complementing the Daymet precipitation totals, ≥1.1 million WSR-88D volume scans between 2002 and 2016 were analyzed for echo tops ≥ 10 000 ft (~3 km) to establish a radar-inferred precipitation activity database for Puerto Rico. The 15-yr record reveals that the GDI outperforms several midlatitude-centric thermodynamic indices, explaining roughly 25% of daily 3-km echo top (ET) activity during each of Puerto Rico’s primary seasons. In contrast, neither mean-layer CAPE, the K index, nor total totals explain more than 11% during any season. When aggregated to the seasonal level, the GDI strongly relates to 3-km ET (R2 = 0.65) and Daymet precipitation totals (R2 = 0.82) during the early rainfall season (ERS; April–July), with correlations weaker outside of this period. The 4-month ERS explains 51% (41%) of the variability to Puerto Rico’s annual rainfall during exceptionally wet (dry) years. These findings are valuable for climate downscaling studies predicting Puerto Rico’s hydroclimate in future atmospheric states, and they could potentially be adapted for operational seasonal precipitation forecasting.

Historical trends in the trade wind inversion in the tropical North Atlantic Ocean and Caribbean

“…A monotonic upward (downward) trend indicates that the variable consistently increases (decreases) over time, but it may not be a linear trend. Results are summarized seasonally via three 4‐month periods: December–March (DJFM), April–July (AMJJ), and August–November (ASON), which correspond closely to observed precipitation seasonality in the eastern Caribbean (Miller et al 2019; Miller and Ramseyer, 2020), a region often influenced by the TWI (Jury and Winter, 2010). Although different seasonal definitions may be suggested, the size of the analysis domain captures a large variability in hydroclimatic regimes.…”

Areal Probability of Precipitation in Moist Tropical Air Masses for the United States

“…Though the number of MT days for each SSC site/polygon vary, Stage IV precipitation is only analyzed across a polygon when its associated SSC site experienced a MT day. While not all areas within a polygon may have experienced the same air mass conditions as the central site, it is common practice to characterize convective environments over an adjacent region are using the nearest radiosonde launch site [26][27][28][29], a network even coarser than the SSC arrangement in Figure 1. Given the limited distribution of surface stations with SSC classifications, this study adopts the same wellestablished technique.…”

Changing our view of the Schistosoma granuloma to an ecological standpoint

“…The schistosomal granuloma thus satisfies the characteristics of a complex system, such as dynamism, reconstruction, homeostasis, and self‐organization. The same parameters are frequently applied to ecosystems in nature (Kay, 1991; Brulisauer, Bradfield & Maze, 1996; Levin, 1998; Wagendorp et al ., 2006; Xu et al ., 2012; Miller, Mote & Ranseyer, 2019) and, likewise, granulomas work as spatial units determined by a particular arrangement of their ECM products, showing well‐defined boundaries and providing a mechanical scaffold for adhesion, differentiation, and migration of distinct cell populations (el‐Meneza et al ., 1989; Lenzi et al ., 2006).…”