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We evaluated the Integrated Multisatellite Retrievals for GPM (IMERG) V06B Early and Final Run products using data from a dense gauge network in Singapore as ground reference (GR). The evaluation is carried out at monthly, daily, and hourly scales, and conditioned on different seasons and rainfall intensities. Further, different spatial configurations and densities of the gauge networks (3–17 gauges per IMERG cell) used here allowed us to examine spatial sampling errors (SSE) in the GR. The results revealed a probability of detection of 0.95 (0.65), critical success index of 0.69 (0.35), and a correlation of 0.60 (0.41) for the daily (hourly) scale. Results also indicate an overestimation of rainy days (hours) by IMERG compared to GR, leading to a false alarm ratio of 0.29 (0.57) at daily (hourly) scales. Analysis of probability distributions and conditional error metrics showed overestimation of lighter (0.2–4 mm day −1 ) and moderate (4–8 mm day −1 ) rainfall by IMERG, but better performance for heavier rainfall (≥32 mm day −1 ). The seasonal analysis showed improved performance of IMERG during November–February compared to June–September months. The hourly analysis further revealed large discrepancies in diurnal cycles during June–September. The SSE are studied in a Monte Carlo framework consisting of several synthetic networks with varying spatial configurations and densities. The effect of SSE on IMERG evaluation results is characterized following the error variance separation approach. For the gauge networks studied here, the contribution of SSE variance to IMERG daily error variance ranges from 4% to 24% depending on gauge spatial configuration, and is as large as 36% during intermonsoon months when rainfall is highly convective in nature.

Evaluation of GPM IMERG Rainfall Estimates in Singapore and Assessing Spatial Sampling Errors in Ground Reference