Satellite-based precipitation observations provide near-global coverage with high spatiotemporal resolution in near-realtime. Their utility, however, is hindered by oftentimes large errors that vary substantially in space and time. Since precipitation uncertainty is, by definition, a random process, probabilistic expression of satellite-based precipitation product uncertainty is needed to advance their operational applications. Ensemble methods, in which uncertainty is depicted via multiple realizations of precipitation fields, have been widely used in other contexts such as numerical weather prediction, but rarely in satellite contexts. Creating such an ensemble dataset is challenging due to the complexity of errors and the scarcity of “ground truth” to characterize it. This challenge is particularly pronounced in ungauged regions, where the benefits of satellite-based precipitation data could otherwise provide substantial benefits. In this study, we propose the first quasi-global (covering all continental land masses within 50°N-50°S) satellite-only ensemble precipitation dataset, derived entirely from NASA’s Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (IMERG) and GPM’s radar-radiometer combined precipitation product (2B-CMB). No ground-based measurements are used in this generation and it is suitable for near-realtime use, limited only by the latency of IMERG. We compare the results against several precipitation datasets of distinct classes, including global satellite-based, rain gauge-based, atmospheric reanalysis, and merged products. While our proposed approach faces some limitations and is not universally superior to the datasets it is compared to in all respects, it does hold relative advantages due to its combination of accuracy, resolution, latency, and utility in hydrologic and hazard applications.

Development is well-advanced for the next version of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG), labeled Version 07. IMERG is a key output of the U.S. GPM Science Team, and V07 will be the second generation in which data from both the Tropical Rainfall Measuring Mission (TRMM) and GPM projects are combined into a single, uniformly processed record, currently starting in June 2000. This presentation will show several examples of successes and challenges in V06, and use these to illuminate the upgrades that have been pursued for V07. For example, the V06 IMERG near-real-time products (Early and Late Runs) show regional biases because they do not have climatological calibration (despite the documentation), and this will be done in V07. As well, the time series of precipitation-rate histograms shows a seam in the transition from TRMM calibration to GPM Core Observatory calibration at the start of June 2014. V07 will benefit from better continuity in the input calibration datasets across that boundary. A third issue is that the Kalman filter used in IMERG a) introduces a variable amount of smoothing, and b) depends on relatively simple measures of input data quality. Both of these are revisited in V07. We will report the status of IMERG Version 07 processing as of the conference time, and introduce some topics that are being considered for the future, including improved uncertainty estimates, addition of sub-monthly gauge information, and strategies for incorporating precipitation estimates from multiple, relatively short-lived small satellites.

Organization metrics were originally developed to measure how densely convective clouds are arranged at mesoscales. In this work, we apply organization metrics to describe tropical synoptic scale convective activity. Such activity is identified by cloud-precipitation (hybrid) regimes defined at 1-degree and 1-hourly resolution. Existing metrics were found to perform inadequately for such convective regime aggregates because the large domain size and co-existence of sparse aggregate occurrences with noisy isolated convection often violate assumptions inherent in these metrics. In order to capture these characteristics, the existing “convective organization potential” (COP) metric was modified so as to: (1) focus on local organization and (2) provide increased weight to aggregate size. The resulting “area-based COP” (ABCOP) is found to outperform existing metrics in tropical convective events at synoptic scales. Moreover, this new organization metric can match the performance of existing metrics, or arguably be better, over a wide range of domain sizes.