arXiv subjects

Christian D. Kummerow

Publications and source records attributed to Christian D. Kummerow.

5 recordsLinked to original sources

arXiv2026

A Sensor-Adaptive Incremental Learning Framework for Artifact Detection in Satellite Precipitation Data

Historically, retrieving rainfall data from satellite imagery has been the domain of space agencies. However, in recent years, the development of cheaper, more compact satellites (SmallSats) capable of detecting rainfall proxies has led to a significant increase in private-sector initiatives for satellite launch and surface precipitation products. This rapid growth has yet to be matched by data validation efforts. Consequently, the need for a robust tool to detect anomalies in near-real-time data before it is disseminated to the public has become critical. In this paper, we present the development of an anomaly-detection system to identify artifacts in global satellite-based rainfall products. The developed framework leverages pre-trained computer vision models and incorporates scarce human-labeled data to detect specific anomalies. Our proposed anomaly detection strategy is tested on data from the Special Sensor Microwave Imager (SSMI) and the Special Sensor Microwave Imager/Sounder (SSMIS). Results demonstrate the efficacy of our approach at separating regular orbits from artifact-containing orbits for each satellite, with performance comparable to state-of-the-art in-place methods. Additionally, the framework offers explainability and the capacity for iterative refinement following false-positive or false-negative classifications.

physics.ao-ph↗

arXiv2026

Prithvi-Precip: Integrating Satellite Observations into an Atmospheric AI Foundation Model for Precipitation Forecasting

Accurate precipitation forecasting remains one of the most challenging problems in weather prediction. While recent AI weather prediction (AIWP) systems have achieved substantial improvements in medium-range forecasting skill, precipitation often remains a secondary target and is commonly learned from reanalysis datasets that contain considerable uncertainty. In this work, we investigate two complementary strategies for improving AI-based precipitation forecasts. Building on the Prithvi-WxC foundation model, we develop Prithvi-Precip, a global precipitation forecasting system, and examine (1) the impact of training targets derived from satellite-based precipitation estimates rather than reanalysis fields and (2) the direct assimilation of satellite observations into the forecasting model. We systematically evaluate key design choices for finetuning the Prithvi-WxC AI foundation model for precipitation forecasting. We find that autoregressive rollout training produces substantially more accurate forecasts than direct conditioning on forecast lead time. Using independent radar-based precipitation estimates for evaluation, we show that training on satellite-derived precipitation targets yields improved forecast accuracy relative to training on MERRA-2 precipitation fields. Furthermore, direct ingestion of satellite observations provides additional improvements at short lead times, with the largest gains occurring in tropical and subtropical regions. Together, these advances enable Prithvi-Precip to substantially improve upon directly comparable precipitation forecasts from the Goddard Earth Observing System. Our results highlight the potential of improved precipitation targets and the direct integration of satellite observations as promising pathways for advancing medium-range AI precipitation forecasting.

physics.ao-ph↗

arXiv2026

Bridging the Sensitivity Gap in Precipitation Estimates from Spaceborne Radars using Passive Microwave Observations

Current global precipitation estimates from spaceborne precipitation radars are limited by their sensitivity to light and frozen precipitation, leading to systematic underestimation of precipitation at high latitudes. Because passive microwave retrievals (PMW) are commonly trained using these radar observations as reference data, this limitation is propagated into PMW This study introduces a novel PMW oceanic precipitation retrieval, GPROF-NN eXtended Precipitation Regime (XPR), that combines reference estimates from a cloud radar and a precipitation radar to overcome the sensitivity limitations of current spaceborne precipitation radars. The retrieval is trained to estimate light precipitation from CloudSat observations and moderate-to-heavy precipitation using observations from the GPM Dual-Frequency Precipitation Radar. The two estimates are combined using a fusion scheme to obtain a consistent precipitation estimate across precipitation regimes. Validation against in situ measurements from shipborne disdrometers shows a 26% improvement in the detection skill for high-latitude precipitation in terms of the critical success index and a reduction in the underestimation of high-latitude and frozen precipitation by more than 50% compared to retrievals constrained only by precipitation radar data. However, the fused retrieval does not improve the precision of instantaneous precipitation estimates, which is likely due to significant random errors in the CloudSat-based reference estimates of liquid precipitation. These results demonstrate that PMW retrievals can leverage the complementary sensitivities of cloud and precipitation radars to provide more consistent precipitation estimates across precipitation regimes than either reference instrument alone. The proposed retrieval provides a pathway to improve the representation of oceanic precipitation in future GPM precipitation products.

physics.ao-ph↗

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.

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