arXiv subjects

Jackson Tan

Publications and source records attributed to Jackson Tan.

4 recordsLinked to original sources

arXiv2026

GPROF-IR: An Improved Single-Channel Infrared Precipitation Retrieval for Merged Satellite Precipitation Products

Current merged precipitation products such as IMERG, GSMAP, and CMORPH combine satellite estimates from passive microwave (PMW) and infrared (IR) observations. However, the different information content of these sensors makes it challenging to produce consistent precipitation estimates, even for coincident observations. The resulting inconsistencies between PMW and IR retrievals can introduce artifacts in the temporal evolution of merged precipitation fields and lead to an overreliance on time-propagated PMW estimates. We introduce GPROF-IR, a novel IR precipitation retrieval that leverages a convolutional neural network to improve precipitation estimates from single-channel IR observations. We demonstrate that the proposed model is able to leverage the temporal information in half-hourly IR observations to improve precipitation estimates. GPROF-IR is designed for integration into the upcoming release of the Integrated Multi-Satellite Retrieval for GPM (IMERG V08) and produces estimates that are climatologically consistent with the GPROF-NN PMW retrieval. We evaluate GPROF-IR using independent, global reference measurements and demonstrate substantial improvements over conventional IR retrievals. GPROF-IR provides lower mean squared error and higher correlation coefficient than IMERG V07 PMW estimates over continental land masses but remains below the accuracy of PMW precipitation estimates over sea surfaces and climate regimes with a greater influence from shallow precipitation. By expoiting both spatial and temporal information content in geostationary IR observations, GPROF-IR establishes a new state of the art for single-channel IR precipitation retrievals. GPROF-IR can be used to quasi-global precipitation estimates at half-hourly resolution from 1998 onward, providing a consistent and accurate foundation for improving merged precipitation products.

physics.ao-ph↗

arXiv2025

A Benchmark Dataset for Satellite-Based Estimation and Detection of Rain

Accurately tracking the global distribution and evolution of precipitation is essential for both research and operational meteorology. Satellite observations remain the only means of achieving consistent, global-scale precipitation monitoring. While machine learning has long been applied to satellite-based precipitation retrieval, the absence of a standardized benchmark dataset has hindered fair comparisons between methods and limited progress in algorithm development. To address this gap, the International Precipitation Working Group has developed SatRain, the first AI-ready benchmark dataset for satellite-based detection and estimation of rain, snow, graupel, and hail. SatRain includes multi-sensor satellite observations representative of the major platforms currently used in precipitation remote sensing, paired with high-quality reference estimates from ground-based radars corrected using rain gauge measurements. It offers a standardized evaluation protocol to enable robust and reproducible comparisons across machine learning approaches. In addition to supporting algorithm evaluation, the diversity of sensors and inclusion of time-resolved geostationary observations make SatRain a valuable foundation for developing next-generation AI models to deliver more accurate, detailed, and globally consistent precipitation estimates.

physics.ao-ph↗

arXiv2023

Global Precipitation Nowcasting of Integrated Multi-satellitE Retrievals for GPM: A U-Net Convolutional LSTM Architecture

This paper presents a deep learning architecture for nowcasting of precipitation almost globally every 30 min with a 4-hour lead time. The architecture fuses a U-Net and a convolutional long short-term memory (LSTM) neural network and is trained using data from the Integrated MultisatellitE Retrievals for GPM (IMERG) and a few key precipitation drivers from the Global Forecast System (GFS). The impacts of different training loss functions, including the mean-squared error (regression) and the focal-loss (classification), on the quality of precipitation nowcasts are studied. The results indicate that the regression network performs well in capturing light precipitation (below 1.6 mm/hr), but the classification network can outperform the regression network for nowcasting of precipitation extremes (>8 mm/hr), in terms of the critical success index (CSI).. Using the Wasserstein distance, it is shown that the predicted precipitation by the classification network has a closer class probability distribution to the IMERG than the regression network. It is uncovered that the inclusion of the physical variables can improve precipitation nowcasting, especially at longer lead times in both networks. Taking IMERG as a relative reference, a multi-scale analysis in terms of fractions skill score (FSS), shows that the nowcasting machine remains skillful (FSS > 0.5) at the resolution of 10 km compared to 50 km for GFS. For precipitation rates greater than 4~mm/hr, only the classification network remains FSS-skillful on scales greater than 50 km within a 2-hour lead time.

cs.LG↗

arXiv2013

PriSM: A Private Social Mesh for Leveraging Social Networking at Workplace

In this work we describe the PriSM framework for decentralized deployment of a federation of autonomous social networks (ASN). The individual ASNs are centrally managed by organizations according to their institutional needs, while cross-ASN interactions are facilitated subject to security and confidentiality requirements specified by administrators and users of the ASNs. Such decentralized deployment, possibly either on private or public clouds, provides control and ownership of information/flow to individual organizations. Lack of such complete control (if third party online social networking services were to be used) has so far been a great barrier in taking full advantage of the novel communication mechanisms at workplace that have however become commonplace for personal usage with the advent of Web 2.0 platforms and online social networks. PriSM provides a practical solution for organizations to harness the advantages of online social networking both in intra/inter-organizational settings without sacrificing autonomy, security and confidentiality needs.

cs.DC↗