DOI: 10.5061/dryad.c59zw3rfk
title: Data from: STREAM-Sat: a novel near-realtime quasi-global satellite-only ensemble precipitation

description: 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.
description: 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 a gridded input precipitation dataset, such as IMERG Early; an error model for that input dataset at individual grid cell scale (CSGD in this example); motion vector: MERRA2 U850/V850 or IMERG's motion vector; covariates (optional): It depends on the covariates you used in the error model (WAR in this example);<br>The output is a user-defined number of precipitation and noise ensemble (20 in this example). Details about CSGD are on https://github.com/KaidiWisc/CSGD_error_model.git
description: # 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 will be affected by satellite retrieval accuracy over different regions. The inputs of STREAM-Sat are 1\.  a gridded input precipitation dataset, such as IMERG Early; 2\.  an error model for that input dataset at individual grid cell scale (CSGD in this example); 3\.  motion vector: MERRA2 U850/V850 or IMERG's motion vector; 4\.  covariates (optional): It depends on the covariates you used in the error model (WAR in this example);     The output is a user-defined number of precipitation and noise ensemble (20 in this example). The performance of the ensemble can be evaluated by CR_calc.py and CRPS_calc.py. Details about CSGD are on #### Description of the data and file structure The period of the dataset is 2017 (more can be produced upon request). The coverage is land area within 50°N-50°S. The time resolution is half an hour. The spatial resolution is 0.1 deg. The name of each file is "STREAMSat_YYYYMMDD.nc". "YYYYMMDD" is the corresponding year/month/day of the data. The data format is Netcdf4 with dimensions time, latitude, and longitude. Each file has 48 indices over time dimension, the value of which is the start time of each half hour. Latitude and longitude are the center of each grid cell. For any questions, feel free to contact Kaidi Peng ().