.png){kind=icon}
.png){kind=icon}
Return
Deep Learning for Multisatellite Precipitation Retrievals: Impact of Tomorrow.io's Microwave Sounders
.png){kind=icon}
PRE
AI
F
P
B
S
R
E
J
C
C
S
A
DOI：10.1175/JHM-D-25-0007.1.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Understanding precipitation intensity and distribution is critical for applications such as real-time flood monitoring and water resource management. However, accurate monitoring often depends on costly, ground-based radar networks that are not globally available. This study introduces a satellite-based precipitation retrieval that combines publicly available geostationary imagers and polar-orbiting microwave sensors with two commercially launched Tomorrow.io microwave sounders. The retrieval produces near-surface precipitation rate estimates every 10 min at a spatial resolution of 0.04 degrees (;4 km), covering latitudes between 60 degrees S and 72 degrees N. Precipitation estimates are derived from a convolutional neural network that ingests sequences of multichannel geostationary imagery and polar-orbiting passive microwave observations within the preceding hour. The network is designed to accommodate sparse data from individual microwave swaths, delivering a spatially and temporally complete trajectory of precipitation rate across the hour-long input window. Results demonstrate improvements of 5%-10% in categorical and continuous precipitation metrics over previous algorithm versions, with 30%-50% skill gains relative to publicly available near-real-time products such as IMERG-Early. Verification was conducted against terrestrial radar networks (United States, Europe, Japan), U.S.-based gauge observations spanning all seasons, and several case studies outside the training domains. Data-denial experiments from impactful 2024 hurricanes further illustrate the benefit of incorporating microwave observations into precipitation retrievals using geostationary and polar-orbiting satellites.
Keywords:
Precipitation
Microwave observations
Satellite observations
Operational forecasting
Deep learning
Journal
IF:
2.9
Papers:
2.9K
Citations:
1.1W
Organization
Cited Papers