STREAM-Sat: a novel near-realtime quasi-global satellite-only ensemble precipitation

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 (cov..., This repository is to create near-realtime global precipitation ensembles that condition on satellite observations (e.g., IMERG: Integrated Multi-satellitE Retrievals for GPM; https://gpm.nasa.gov/data/imerg). We unified the methods proposed in Li et al. (2023) doi:10.1109/tgrs.2023.3235270 and Hartke et al. (2022) https://doi.org/10.1029/2021WR031650. We tried to solve the challenge of Near-Realtime (NRT) global precipitation generation due to the lack of ground-based gauge network and the complex error of satellite precipitation.
The highlights of this method are

no ground-based measurement is needed. The performance (e.g., ensemble spread and accuracy) is independent of gauge density;

It could be generated in Near-Realtime, which means its time latency is only affected by satellite products (e.g., IMERG Early has 4-hour latency);

It can be done globally, while the performance will be affected by satellite retrieval accuracy over different regions.

The inputs of STREAM-Sat are..., , # STREAM-Sat: A novel near-realtime quasi-global satellite-only ensemble precipitation

Kaidi Peng et al.

This repository is to create near-realtime global precipitation ensembles that condition on satellite observations (e.g., IMERG: Integrated Multi-satellitE Retrievals for GPM; ). We unified the methods proposed in Li et al. (2023) [doi:10.1109/tgrs.2023.3235270](https://ieeexplore.ieee.org/document/10011447) and Hartke et al. (2022) . We tried to solve the challenge of Near-Realtime (NRT) global precipitation generation due to the lack of ground-based gauge network and the complex error of satellite precipitation.

The highlights of this method are

1\.  no ground-based measurement is needed. The performance (e.g., ensemble spread and accuracy) is independent of gauge density;

2\.  It could be generated in Near-Realtime, which means its time latency is only affected by satellite products (e.g., IMERG Early has 4-hour latency);

3\.  It can be done globally, while the performance w...