Recently approved by the AMS Publications Commission, the Special Collection from the 10th and 11th International Precipitation Working Group (IPWG) Workshops and the 6th International Workshop on Space-based Snowfall Measurement (IWSSM) reviews the state of the knowledge in active areas of global precipitation research, explores common approaches that can lead to faster advances by the community, and recommends future directions on topics related to precipitation. Viviana Maggioni (pictured), one of the special collection organizers, offers a review of the special collection’s purpose, highlights, and outlook.
As noted in its name, the IPWG is an international working group. Similarly, the IWSSM studies measurements of snowfall from space on an international scale. What drew you to this field of study and how did you become involved in the IPWG and/or IWSSM?
Growing up in Milan, Italy, I was surrounded by an incredible diversity of climates and landscapes—from the towering Alps to the Mediterranean coastline—each producing its own complex precipitation patterns. I witnessed firsthand the impacts of floods, droughts, landslides, and earthquakes on local communities, and that experience sparked a deep fascination with understanding these natural processes. That curiosity, combined with a natural passion for mathematics and science, drew me toward hydrometeorology.
Precipitation and snowfall don’t respect borders, and neither should the science we use to study them. The IPWG and IWSSM represent exactly the kind of collaborative, global approach I believe is essential—pooling data, methods, and expertise across nations to tackle challenges no single country can solve alone.
I first encountered the IPWG when I was a postdoctoral researcher at the University of Maryland, and attended my first meeting in 2014 in Tsukuba, Japan. It was eye-opening. I discovered a genuinely welcoming community—one where important scientific questions were debated openly and thoughtfully—regardless of where you were in your career. I still remember sitting down at dinner with Gail Skofronick-Jackson and Chris Kummerow, true pioneers of satellite precipitation observations, and being engaged as a peer. That moment stayed with me, especially because we (unknowingly) ordered grilled chicken hearts! Contributing to this community ever since has been one of the most rewarding parts of my career.
Based on presentations and discussions from the recent IPWG and IWSSM workshops, how would you describe the current state of the science?
The Global Precipitation Measurement (GPM) mission continues to serve as the backbone of global precipitation measurement, and significant progress has been made on satellite precipitation product algorithms. At the same time, the community is actively wrestling with known persistent challenges: retrievals over complex terrain, cold surfaces, and high latitudes remain difficult.
Perhaps the most striking shift over the past workshops is how central machine learning (ML) has become. It is no longer a niche tool—it now has its own working group, with deep learning approaches being explored for everything from merging low Earth orbit (LEO) and geostationary Earth orbit (GEO) observations to hydrometeor classification and quantitative precipitation estimation. The conversation has matured from “Can ML help?” to “How do we validate, interpret, and operationalize ML-based products responsibly?”
Snowfall measurement remains a frontier. The workshop’s discussions highlighted how poorly constrained solid precipitation still is from space, particularly with respect to particle scattering properties. New platforms, including CubeSats and SmallSats, are generating excitement as potential means to increase constellation density and revisit time, though translating these observations into reliable retrievals is still a work in progress.
Data assimilation emerged as another area of growing momentum, with several groups presenting advances in all-sky microwave assimilation and cloud/precipitation-affected radiance use in numerical weather prediction (NWP) systems. And the land surface challenge—understanding and modeling surface emissivity to improve precipitation retrievals over complex backgrounds—continues to demand attention.
Taken together, the workshops reflect a community that is technically more capable than ever, but also increasingly honest about the remaining gaps. The focus on establishing baseline surface precipitation networks, benchmarking product uncertainty, and fostering reproducible validation frameworks signals a healthy push toward more rigorous, predictable science—which I find both encouraging and energizing.
Why is it important that there be international collaboration in the study of space-based precipitation measurements?
Satellite precipitation measurements are inherently global in nature—the sensors observe the entire Earth, and the constellations that make products like IMERG (Integrated Multi-satellite Retrievals for GPM) and GSMaP (Global Satellite Mapping of Precipitation) possible are themselves multinational efforts, with instruments contributed by NASA, JAXA (Japan Aerospace Exploration Agency), EUMETSAT, NOAA, and others. The science that underpins those products—retrieval algorithms, validation frameworks, merged datasets—draws on expertise that no single country possesses in full. Validation is perhaps the clearest example. Ground truth networks are distributed unevenly, with dense coverage in parts of North America, Europe, and Japan, and significant gaps across Africa, the tropics, and the polar regions. Filling those gaps requires coordinated, international efforts to share data and harmonize observing networks. And a retrieval algorithm developed and validated primarily over one region will inevitably underperform somewhere else, unless researchers working in those environments are part of the conversation.
What future directions have the workshops recommended?
The past two workshops have pointed toward several consistent future directions, all of which are reflected in the agenda for IPWG-12 in Kraków in July 2026.
Expanding CubeSat and SmallSat constellations to improve temporal sampling is a clear priority, alongside algorithm improvement. Machine learning will continue to advance, but the focus is shifting from demonstration toward operational robustness, interpretability, and uncertainty quantification.
Snowfall, orographic precipitation, and high-latitude retrievals remain persistently challenging and underdeveloped relative to rainfall, and improving performance in these regimes—including better surface emissivity modeling over complex terrain—is a recurring call to action.
IPWG-12 is framing its objectives around producing higher-resolution, more accessible global precipitation products; developing quality metrics in regions lacking traditional validation data; and making greater use of new satellite constellations, while also placing greater emphasis on applications in hydrology, climate, and weather forecasting. That linkage between retrieval science and the communities that depend on it is one of the most important directions the field is moving toward.
Are there articles in the collection that you would consider to be notable, and why?
Perhaps the one by Bytheway and Mahoney, which provides a critical reality check by revealing that even “gold-standard” global datasets like IMERG can struggle to accurately capture the intensity of extreme storms and sometimes report “spurious” or fake precipitation events. By comparing six major datasets over the United States, the study highlights a dangerous gap between satellite estimates and ground reality, serving as an essential guide for scientists and city planners who rely on this data for flood and disaster management.
Does the collection lack an area of study that you would like to see included?
I would say that one gap is the relatively limited treatment of uncertainty propagation from satellite retrievals through to hydrological and societal applications. The collection is strong at the retrieval and validation stage, but tends to stop there, before the data reaches the scientists, engineers, and decision-makers who ultimately depend on it. Understanding how uncertainty compounds as satellite precipitation estimates flow into hydrological models and impact assessments is essential for building user confidence and guiding product improvement in ways that are grounded in real-world consequences. Encouragingly, IPWG-12 has explicitly signaled a greater emphasis on applications in hydrology, climate, and weather forecasting, so there is reason to hope that future collections will more fully bridge that divide.
