IPC 15 full program
Session 2 B
Session 2B: Bridging Models and Observations for Advancing Precipitation Science
IPC Session
June 16, 2026
Trustworthiness of Satellite and Reanalysis Precipitation Products in Extreme Rainfall: Insights from Historical Data and the 2024 Valencia Flood Event Authors: Eric Peino (NOAA/Atlantic Oceanographic and Meteorological Laboratory); Joan Bech (Universitat de Barcelona) Corresponding author email: eric.peino-calero@noaa.gov Abstract: Establishing the trustworthiness of gridded precipitation products requires rigorous validation during record-breaking extremes and an understanding of how these events fit within long-term climatic contexts. This study evaluates NASA’s IMERG V07B, JAXA’s GSMaP, and ECMWF’s ERA5-Land by focusing on the flood event of 29 October 2024 in Valencia, Eastern Spain, and situating it within the historical record of extreme rainfall across the Iberian Peninsula from 1998 to 2024. Ground validation of the extreme event against a dense network of over 1500 rain gauges reveals stark differences in product performance. GSMaP captured the extreme magnitude most accurately (peak 610.9 mm, R=0.72, slope 1.02), whereas IMERG V07B (197.6 mm) and ERA5-Land (91.7 mm) substantially underestimated the event. Placing this single event in a 26-year historical context, we analyze each product’s consistency in detecting historical maxima and regional precipitation trends, showing that there is no consensus among datasets regarding the ranking of the Valencia DANA by magnitude, although GSMaP identifies it as the second most intense event in its daily-resolution record. Despite its significance as a benchmark extreme, multi-product trend analysis reveals considerable spatial divergence, with datasets frequently disagreeing on the sign and magnitude of precipitation shifts along the Mediterranean coast. By using the 2024 Valencia flood event as a reference, this work highlights the limitations of current sub-daily precipitation retrieval algorithms and underscores the necessity of continuous ground validation to ensure that satellite-derived trends and extremes remain physically meaningful and reliable for operational decision-making.
A method to attribute Precipitation to Coherent Atmospheric Features for GPEX using Cloud Tracking applied to Geostationary Satellite Data Authors: Adrian McDonald (University of Canterbury) Corresponding author email: adrian.mcdonald@canterbury.ac.nz Abstract: The Global Precipitation EXperiment (GPEX) WCRP Lighthouse Activity aims to address the challenge of improving global precipitation prediction by enhancing understanding of the atmospheric features that drive precipitation. Within GPEX, attributing precipitation to dynamic systems such as atmospheric rivers, mesoscale convective systems, monsoons, and tropical cyclones has been identified as central to improving predictive skill, reducing product uncertainties, and advancing process understanding. This contribution demonstrates how feature tracking and high-resolution cloud tracking might this goal by linking measured precipitation and cloud evolution to identifiable atmospheric features. Using Advanced Himawari Imager data from the Himawari 9 geostationary satellite, coherent cloud objects are identified and tracked over the southwest Pacific, centred on New Zealand, during recent field campaigns. A watershedding segmentation scheme and the “tobac” tracking algorithm are applied to derive cloud object lifecycles from 10 minute, 4 km by 4km imagery. Tracked cloud features are then compared with ground-based and remotely sensed precipitation observations to assess precipitation properties and connected to specific atmospheric features, in particular atmospheric rivers in this context. Feature tracking approaches enable improved attribution of observed precipitation to coherent atmospheric features, providing a pathway toward connecting observed hydrometeor characteristics with global precipitation processes in the GPEX framework.
A Physically Consistent Model for Precipitation Particle Size Distributions: Evaluation with Disdrometer Observations from the KPOP-MS Campaign Authors: Livia J. Leganés Fernández (University of Castilla-La Mancha); Gabriela Juárez (University of Castilla-La Mancha); Sohaib Fakar (University of Castilla-La Mancha); Raúl Martín (University of Castilla-La Mancha); Francisco J. Tapiador (University of Castilla-La Mancha) Corresponding author email: livia.leganes@uclm.es Abstract: The probability distribution of small drops in the boundary between cloud droplets and raindrops are difficult to model specially in moist environments. Current approaches make assumptions that are often problematic, as they allow negative values for the mean of the distribution. While the statistical goodness of fit of those models might be reasonable for precipitation radar estimation, the situation is unsatisfactory if a fully consistent physical modeling of precipitation across scales is desired. This is the case of weather and climate models, where keeping all the variables within physical limits is a must. This work discusses a modeling that provides both a better fit for measured small, supercooled and medium size drops, plus a seamless integration in the parameterizations of the cloud microphysics. The model is tested on an extensive disdrometer dataset collected in the KPOP-MS campaign in Korea. Comparison with existing models shows that the new method has substantial practical and theoretical advantages for the modeling of the microphysical process in the cloud-precipitation boundary and for supercooled drops. The research has implications in elucidating the role of clouds in the climate sensitivity of climate models.
Development and Evaluation of Cloud Microphysics schemes Using Field Campaign Observations Authors: Kyo-Sun Lim (Seoul National University); Sun-Young Park (Seoul National University); Juhee Kown (Seoul National University); Gyuwon Lee (Kyungpook National University) Corresponding author email: kyosunlim02@gmail.com Abstract: In the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) cloud microphysics scheme, hydrometeors are represented using fixed densities, prescribed mass–diameter relationships, and predefined fall velocity–diameter relationships. This framework limits the representation of intermediate solid-phase hydrometeors, such as particles transitioning between snow and graupel or between graupel and hail. To improve ice-phase microphysics, this study introduces a double-moment treatment for solid hydrometeors together with a prognostic graupel density formulation. Graupel density is predicted from the graupel volume mixing ratio and allowed to vary between 100 and 900 kg m⁻³, enabling a more realistic representation of rimed particles. New fall velocity–diameter relationships are derived to account for density variability, allowing density-dependent terminal velocities. A generalized double-moment normalization (GDMN) method is also implemented to reduce variability in particle size distributions. The revised WDM6 scheme is evaluated using observations from the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field campaign and surface observations from the Boseong site. The results demonstrate the benefits of the proposed improvements and highlight the role of intensive field observations in advancing cloud microphysics parameterization. The revised WDM6 scheme has been adopted as the operational microphysics scheme in the weather forecast model of Korean Meteorological Administration (KMA) and has demonstrated improved precipitation forecast performance compared to the previous version.
NOAA’s Precipitation Prediction Grand Challenge (PPGC) Initiative in Supportive of GPEX: Progress, Contributions, and Future Opportunities Authors: Jin Huang (NOAA Climate Program Office); David Novak (NOAA/NCEP Weather Prediction Center); John Ten Hoeve (NOAA Weather Program Office) Corresponding author email: Jin.Huang@noaa.gov Abstract: Despite advances in numerical modeling and observations, precipitation forecast skill—particularly for high-impact events—has improved only modestly over recent decades, with persistent errors in the timing, location, and intensity of extremes. In response, NOAA established the Precipitation Prediction Grand Challenge (PPGC) Initiative to accelerate improvements in precipitation prediction across weather and subseasonal-to-seasonal (S2S) timescales. PPGC is a coordinated, end-to-end effort spanning NOAA’s research, operational, and satellite communities (OAR, NWS, and NESDIS), with six integrated objectives: enhancing user engagement; improving precipitation prediction products and applications; advancing prediction systems; sustaining and exploiting observations; improving process-level understanding and modeling; and advancing understanding of precipitation predictability. Many PPGC activities directly align with and contribute to the Global Precipitation EXperiment (GPEX) goals. Ongoing projects, supported by NOAA Climate Competitive Research Grants and the FY22 and FY25 Supplemental funds, span coordinated use of satellite and in situ observations including the planned field campaigns in Tropic Pacific, data and product development, machine-learning analysis of precipitation processes, and multi-scale modeling experiments aimed at reducing uncertainties in precipitation estimates and forecasts. This presentation will provide an overview of NOAA’s PPGC Initiative, highlight progress and emerging outcomes contributing to GPEX, and describe planned future activities. Opportunities for enhanced coordination between NOAA PPGC and International GPEX—particularly in field campaign planning, data standards, model evaluation, and capacity development—will also be discussed to maximize collective impact and mutual benefits
A Long-Term High-Resolution Precipitation Dataset over South Korea Based on Dynamically Updated Bias Correction and Adaptive Residual Radar–Gauge Merging Authors: Gyuwon LEE (Kyungpook National University); Soorok Ryu (Kyungpook National University); Kyo-Sun Lim (Seoul National University) Corresponding author email: gyuwon.lee@gmail.com Abstract: This study presents a long-term, high-resolution precipitation dataset over South Korea developed through an advanced radar–gauge merging framework that explicitly accounts for the spatiotemporal variability of radar bias. The dataset integrates composite radar reflectivity and dense rain gauge observations from the Korea Meteorological Administration (KMA), providing 0.5 km × 0.5 km spatial resolution (2305 × 2881 grids) and 10-minute temporal resolution over a nine-year period (2016–2024). The methodological novelty lies in a dynamically evolving bias correction scheme in which radar–gauge bias factors are recalculated and updated at 10-minute intervals during rainfall events. Unlike conventional static or event-mean adjustment approaches, this adaptive correction enforces temporal continuity while allowing rapid adjustment to evolving precipitation systems. This framework improves consistency across different precipitation, that is, microphysical regimes and reduces discontinuities in merged fields. In addition, spatial discrepancies between bias-corrected radar estimates and gauge observations are adaptively interpolated using a Residual Radial Basis Function (RRBF) approach. By modeling the residual structure rather than the raw precipitation field, the method preserves fine-scale radar-derived spatial features while incorporating gauge-based ground truth. This hybrid strategy mitigates smoothing artifacts commonly introduced by traditional kriging-based techniques. The merged precipitation product is rigorously evaluated against multiple geostatistical interpolation methods to quantify improvements in bias, variance structure, and spatial coherence. Analysis of the error characteristics demonstrates enhanced performance in both different precipitation regimes and regions. The resulting dataset provides a physically consistent, temporally continuous, and spatially detailed precipitation record suitable for ground validation, hydrological modeling, extreme rainfall analysis, urban flood forecasting, and high-resolution numerical weather prediction validation. This study details the methodological framework, validation results, and public data access. Reviewing past strategies and examining new approaches on the development of hybrid observational–modeling datasets Authors: Ana M. B. Nunes (Universidade Federal do Rio de Janeiro) Corresponding author email: ana.nunes@igeo.ufrj.br Abstract: More accurate initial conditions, together with better representation of physical processes, are still among the challenges that numerical weather prediction (NWP) faces. Developed for researchers at Florida State University, the assimilation of precipitation using a global spectral model emerged in the late 80s to provide more truthful NWP’s initial conditions in the tropics. Here, precipitation assimilation was applied in the Regional Spectral Model, which was originally developed at the National Centers for Environmental Prediction for improving hydroclimate simulations. This algorithm was developed at the Federal University of Rio de Janeiro in Brazil for the South American hydroclimate reconstruction. It uses a spectral nudging technique and assimilates precipitation estimates from the Climate Prediction Center (CPC) Morphing Technique (MORPH). Results using CMORPH show improvements in the soil moisture representation in tropical areas, which include the Amazon rainforest. A brief overview of the precipitation assimilation applications spanning from improving initial conditions for short-range precipitation forecasting to hydroclimate reconstructions will be given. The numerous applications of precipitation assimilation in atmospheric modeling will be discussed. Further developments will also be presented on the assimilation of higher-resolution, satellite-based precipitation estimates. Many of the results form part of a review paper in preparation.
Quantifying Thermodynamic and Dynamical Drivers of Tropical Cyclone Rainfall at Landfall Authors: Eric Peino (NOAA/Atlantic Oceanographic and Meteorological Laboratory); Gregory Foltz (NOAA/Atlantic Oceanographic and Meteorological Laboratory); Jun Zhang (NOAA/Atlantic Oceanographic and Meteorological Laboratory) Corresponding author email: eric.peino-calero@noaa.gov Abstract: Tropical cyclones (TCs) are among the most significant sources of extreme precipitation globally, yet the physical controls governing rainfall intensity and spatial asymmetry near landfall remain incompletely understood. In particular, the relative contributions of oceanic forcing, environmental conditions, and internal storm structure to rainfall variability are still debated. We present a global observational assessment of landfalling TCs during 1998–2021, integrating satellite-derived precipitation from the Global Precipitation Measurement IMERG product, sea surface temperature from NOAA OISST, environmental diagnostics from SHIPS, and track information from IBTrACS. Storms are required to remain over ocean for at least 48 hours prior to landfall. Rainfall is evaluated within storm-centered radii of 200–1000 km to distinguish inner-core and outer-rainband variability. Using stratified and partial correlation analyses, we isolate the independent effects of thermodynamic and dynamical variables while controlling for storm intensity, size, translation speed, and basin. Results indicate a clear hierarchy of drivers. Within the inner core (200 km), a 1°C increase in SST is associated with an increase of approximately 1.16 mm h⁻¹ in rainfall rate, corresponding to an estimated 6–10% enhancement per degree of ocean warming. This sensitivity is nearly four times larger than that identified in the outer rainbands (1000 km), highlighting a physically meaningful linkage between oceanic warming and pre-landfall TC rainfall. Storm intensity remains the dominant dynamical control (R ≈ 0.51 in the inner core), while mid-level humidity emerges as a consistent independent thermodynamic predictor across radii. By explicitly quantifying these sensitivities, this work advances process-level understanding of TC rainfall at landfall and supports efforts to reduce uncertainties in precipitation prediction within the World Climate Research Programme Global Precipitation Experiment (GPEX) framework.
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