Event-based Precipitation Features from GPM IMERG Data Product

We extract statistical precipitation features from precipitation events tracked in both space (two-dimensional, 2D) and time (one-dimensional, 1D) using NASA Global Precipitation Mission’s IMERG data product. These features can be used to ensure IMERG product consistencies (since the combination of instruments and algorithms used to derive this product evolves with time) and study decadal precipitation variations. They may even reveal climate change signals.

We use connected component labeling (CCL) for tracking. Two-dimensional (2D) connected precipitating components (with precipitation rate ≥ 0.1 mm/hr and an area coverage ≥ 25 grid cells, i.e., ~2500 km²) are identified first in each half-hour (spatial) slice of IMERG data. We consider the components touching the space or time boundary incomplete and discard them from temporal tracking. Spatially overlapping 2D connected components in adjacent half-hour time slices are considered to be of the same precipitating events and are given unique labels. A precipitation event thus may start as disjoint 2D components, experience merging and splitting, and eventually disappear.

We extract event-based precipitation features based on tracked events instead of spatially connected 2D components. This is a significant departure from previous precipitation feature studies ( e.g., Liu et al., 2008; Hayden et al., 2021), in which precipitation features are based on 2D connected components in a half-hour IMERG slice, i.e., in space only. Hayden et al. (2021) perform tracking based on the overlap in adjacent IMERG time slices of equivalent-area circular discs derived from these 2D connected components, which may or may not have overlapping precipitating cells.

We report in this presentation statistical precipitation features extracted from 10 years of Northern Hemisphere IMERG data (2014-2023). Such features include distributions of event duration, maximum area coverage, maximum precipitation rate, event-integrated precipitation volume, etc. We also report these features filtered by season and geographical region for more detailed analysis.

References

Hayden, L., Liu, C., and Liu, N.: Properties of Mesoscale Convective Systems Throughout Their Lifetimes Using IMERG, GPM, WWLLN, and a Simplified Tracking Algorithm, Journal of Geophysical Research: Atmospheres, 126, e2021JD035264, https://doi.org/10.1029/2021JD035264, 2021.

Liu, C., Zipser, E. J., Cecil, D. J., Nesbitt, S. W., and Sherwood, S.: A Cloud and Precipitation Feature Database from Nine Years of TRMM Observations, Journal of Applied Meteorology and Climatology, 47, 2712–2728, https://doi.org/10.1175/2008JAMC1890.1, 2008.