Bulletin of the American Meteorological Society

Data-rich fields such as the Earth sciences benefit from transparent, structured documentation. Based on best practices in software engineering, we adapt for the Earth sciences a questionnaire format known as a datasheet which guides the author to document both biases and technical information in a dataset. Datasheets complement existing standards for documentation by eliciting information about technical aspects and biases together within the scope of a project. This combination of information is not easily obtained elsewhere and provides transparency, aids reproducibility, and informs subsequent uses of data. This information is broadly useful for all research applications and vital for data-driven methods such as machine learning which are strongly influenced by biases within training data. Datasheets synthesize information uniquely known to the creator of a dataset and provide easy and equitable access to information otherwise restricted to community networks. We adapted the datasheet format for the Earth sciences through our own knowledge and further tailored it through multiple years of community feedback. We address common concerns that arose through this feedback process, such as the time commitment needed for completion and distinctions between dataset creation and dataset usage. We also contrast our format with other well-known dataset documentation efforts.

Current global multisource merged precipitation datasets can facilitate better utilization of the complementary nature of gauge-, satellite-, and reanalysis-based precipitation estimates, particularly for capturing precipitation variability. However, merging these datasets at high resolutions of 1-hourly and 0.1° on a full global scale remains a substantial challenge for the scientific community owing to high spatiotemporal heterogeneities. This study proposes a merging-and-calibration framework to optimally integrate the advantages of gauge-, satellite-, and model-based precipitation estimates, focusing on precipitation occurrences and providing a new fully global multisource merging-and-calibration precipitation (GMCP: 1-hourly, 0.1°, global, 2000–the present) dataset. The main conclusions included 1) GMCP generally outperformed the input datasets, ERA5-Land, GSMaP–moving vector with Kalman filter (MVK), and IMERG-Late, across various spatiotemporal scales, both in regional statistics and extreme precipitation systems; 2) GMCP significantly outperformed IMERG-Final, calibrated by gauge analysis at the monthly scale, with the improvements in correlation coefficient (CC), root-mean-square error (RMSE), and Heidke skill score (HSS) by approximately 66.67%, 39.25%, and 26.83%, respectively, from 2016 to 2020 over the contiguous United States (CONUS); 3) compared to the state-of-the-art multisource merged product with a daily gauge correction scheme, Multisource Weighted-Ensemble Precipitation (MSWEP) V2 (3-hourly and 0.1°), GMCP demonstrated the notable improvements with an approximately 20% enhancement in accurately capturing the precipitation occurrences against approximately 67 000 rain gauges over mainland China in 2016; 4) in comparison to another well-known multisource merged quasi-global daily and 0.05° precipitation product, Climate Hazards Infrared Precipitation with Stations (CHIRPS) integrating the gauge-, satellite-, and reanalysis-based precipitation estimates, GMCP also demonstrated the notable improvements at the daily scale, achieving the increases in CC, RMSE, and HSS by around 57.45%, 38.18%, and 75.76%, respectively, against approximately 67 000 rain gauges over mainland China in 2016; and 5) this framework was suitable for generating the fully global precipitation datasets at 1-hourly and 0.1° scales, significantly mitigating the inherent shortcomings of each input dataset, with GMCP demonstrating the great potential as a valuable resource for worldwide scientific research and societal applications.

This paper introduces the Direct In Situ Study of Residual Layer Chemistry in Urban Beijing (DISRELCUB) experiment conducted on the roof of China International Trust and Investment Corporation (CITIC) tower, which stands 528 m above the ground in urban Beijing. The aim is to directly study in situ residual layer chemistry and its influence on the air quality on the ground surface. The experiments were performed during March–April 2022 and March–April 2024, respectively. For the first time, we can present a picture of the nanoparticle formation and growth owing to residual layer chemistry in a megacity. In the first part, we introduce the experimental design, the observation station, and the instruments; in the second part, we present boundary layer thermodynamics during observation periods as well as the characteristics of meteorological variables, aerosol, and trace gases measured on the 528-m platform, 143-m platform, and ground surface, respectively. In the third section, we show highlights of some key radical reservoir molecules and vapors associated with nanoparticle formation and growth on the 528-m platform; in the last part, we quantify the contribution of residual layer chemistry to the ground surface by turbulence with a large-eddy simulation model.

The 2022-2023 winter in the Western U.S., particularly in Southern California, experienced unusually wet and cold conditions, prompting vigilant water management. This study chronicles the water year, highlighting the challenges state water managers faced as California shifted from extreme drought to elevated flood risks due to an unprecedented “weather whiplash” and a subsequent record-setting snowpack. By analyzing precipitation and temperature data from 2002 to 2023, the research highlights the anomalous nature of these variables in California during this period. It focuses on the impacts of Atmospheric Rivers (AR) due to their proven influence on seasonal precipitation patterns and intensities, examining their hydrologic impacts—specifically, snow water equivalent (SWE) in Sierra Nevada and reservoir storage—compared to other high precipitation years in California to gauge the effects of this atypical weather on water resources. The study reveals that Southern California’s wintertime precipitation in 2022-2023 was the highest in over two decades. Precipitation was closely linked to the occurrence of 11 moderate to strong ARs, which alleviated the state’s drought conditions—94% of California was drought-free by the end of the water year. Additionally, the mean maximum temperature was below the long-term average during spring and summer, decelerating snowpack melt and mitigating flood potential. Notably, 2022-2023 saw the most significant increases in SWE and reservoir storage among the years analyzed. This research delves into the complex interplay between AR-driven precipitation, temperature, and snowpack, providing valuable insights into the precarious dynamics of CA’s regional hydrology with a real-world example.

Over the past 20 years, the Hydrological Ensemble Prediction Experiment (HEPEX) international community of practice has advanced the science and practice of hydrological ensemble prediction and its application in impact- and risk-based decision- making, fostering innovations through cutting-edge techniques and data that enhance water- related sectors. Here, we present insights from those 20 years on the key priorities for (co-)creating broadly applicable hydrological forecasting systems that add value across spatial scales and time horizons. We highlight the advancement of hydrological forecasting chains through rigorous data management that incorporates diverse, high-quality data sources, data assimilation techniques, and the application of AI to improve predictive accuracy. HEPEX has played a critical role in enhancing the reliability of water resources and water-related risk management globally by standardising ensemble forecasting. This effort complements HEPEX's broader initiative to strengthen research-to-operations, making innovative forecasting solutions both practical and accessible. Additionally, efforts have been made towards supporting the United Nations Early Warnings for All initiative through developing robust and reliable early warning systems by means of global training, education and capacity development, and the sharing of technology. Finally, we note that the integration of advanced science, user-centric methods and global collaboration can provide a solid framework for improving the prediction and management of hydrological extremes, aligning forecasting systems with the dynamic needs of water resource and risk management in a changing climate. To effectively meet future demands, it is crucial to accelerate the integration of innovative science within operational frameworks, fostering adaptable and resilient hydrological forecasting systems globally.

A wide range of important societal and economic applications on a national and international level strive for an integrated understanding and forecasting of weather and climate, at high spatial resolution ranging from days to decades. The global to regional model system ICON (Icosahedral Nonhydrostatic) has been applied to weather as well as to climate timescales with joint developments of the model infrastructure. However, ICON’s model configurations share the same dynamical core but differ substantially in their physical parameterization and the coupling of Earth System components, depending on whether they were designed for numerical weather prediction (NWP) or climate applications. Starting in 2020, a new modeling initiative has been launched as a joint project between climate modeling institutes and the Deutscher Wetterdienst. The initiative “vertically” integrates NWP, climate predictions, climate projections, and atmospheric composition modeling based on the ICON framework and targets a unified treatment of the respective subgrid-scale parameterizations. This initiative aims at the development of coupled model configurations of ICON to conduct operational weather and ocean forecasts for several days, climate predictions with timescales up to 10 years ahead as well as climate projections, and it provides a model baseline for joint research for NWP and climate. This paper illustrates the strategic direction of this modeling initiative, isolates key challenges, and reports on first results.

The Global Atmospheric Research Program (GARP) Atlantic Tropical Experiment (GATE) took place from June to September 1974. It remains the largest field campaign in atmospheric science history. Its 50th anniversary was celebrated at the 104th AMS Annual Meeting in Baltimore on 1 February 2024. The celebration featured a series of events including town halls, sessions, and a luncheon. These events provided a platform for reflection and knowledge sharing among surviving participants and others and highlighted GATE’s foundational role in advancing our understanding of tropical meteorology and oceanography. GATE was motivated by the need to address the challenge of global weather forecasting, and its science objectives remain relevant today. The campaign led to discoveries that continue to influence modern thinking about tropical meteorology and oceanography. It also impacted the design and goals of subsequent tropical field studies. This article briefly describes the 50th anniversary celebration, including some of the experiences of the participants, and summarizes seminal findings about tropical convection, the tropical atmospheric boundary layer over the ocean, easterly waves, oceanography, and air–sea interaction—fields where GATE’s insights have guided subsequent research.

How do the atmosphere and airborne insects respond to the abrupt cessation and restoration of sunlight during a total eclipse? The Flexible Array of Radars and Mesonets (FARM), including three mobile Doppler On Wheels (DOW) radars, Mobile Mesonets, Pod weather stations, and an upper-air sounding system, were deployed as an unprecedentedly dense observing network in the path of totality of the 21 August 2017 eclipse that spanned the United States from its Pacific to Atlantic coasts. This was the first targeted dual-polarization radar, multiple-Doppler, and micronet study of the impacts of totality on meteorology and insect behavior. The study area was chosen to be completely sunny, nearly devoid of trees, with homogeneous non-forested land-use, and very flat. This resulted in as near an ideal observational environment as realistically attainable to observe the effects of a total solar eclipse absent the confounding effects of variable cloud shading, terrain, and land use. Rapid and substantial changes in the boundary-layer and propagation of a prominent radar fine line associated with a post-totality wind shift mechanism different than previously hypothesized, were observed. Profound and rapid changes in airborne insect behavior were documented, including descent and then reascent during the minutes immediately surrounding totality, with implications related to solar-related insect navigational mechanisms and behavior.

Physics-based numerical models form the foundation of both scientific research and operational prediction of our environment, including the atmosphere, sea ice, oceans and more. With the increasing availability of compute resources for researchers and the general public, many such modeling efforts have been or are moving to an open-source and open-science development approach. Some examples of such models and modeling system in the US are the National Center for Atmospheric Research’s (NCAR) Weather Research and Forecasting (WRF) and Community Earth System Model (CESM). Similarly, The National Oceanic and Atmospheric Administration (NOAA) is moving to a community Unified Forecast System (UFS) approach for both research and operations. One of the early open-source and open-science environmental models is the WAVEWATCH III ® (WW3) wind-wave model as originally developed at NOAA. This essay provides a history of this model while extracting lessons learned for community modeling. It presents sixteen such lessons, ranging from coding principles to code management to community building and governance. It is expected that most lessons learned are generally applicable to community modeling, with the caveat that the WW3 community is relatively small, and that some lessons, as discussed, might not “scale up” to much larger modeling systems and communities.

The ‘warming stripes’ are an iconic climate data visualisation, adopted globally as a symbol of our warming world. We discuss their origin and uses for communication, including understanding long-term changes in the climate and consequences of future emission choices. We also extend the stripes concept to explore observed temperature variations throughout the climate system, revealing coherent warming for the troposphere and upper ocean, and cooling in the stratosphere, consistent with our understanding of human influences on our climate.

The World Weather Research Programme (WWRP)/World Climate Research Programme (WCRP) Subseasonal to Seasonal Prediction (S2S) project was launched in 2013 with the primary goals of improving forecast skill and understanding sources of predictability on the subseasonal timescale (from 2 weeks to a season) around the globe. Particular emphasis was placed on high-impact weather events, on developing coordination among operational centers, and on promoting the use of subseasonal forecasts by the applications communities. This 10-year project ended in December 2023. A key accomplishment was the establishment of a database of subseasonal forecasts, called the S2S database. This database enhanced collaboration between the research and operational communities, enabled studies on a wide range of topics and contributed to significant advances towards a better understanding of subseasonal predictability and windows of opportunity that contributed to improvements in forecast skill. It was used to train machine learning methods and test their performance in the S2S Artificial Intelligence/Machine Learning (AI/ML) Prize Challenge. The S2S project co-organized several coordinated research experiments to advance understanding of subseasonal predictability, and the Real-Time Pilot Initiative that provided real-time access to subseasonal data for 15 application projects. A sequence of training courses sustained over 10 years enhanced the capacity of national meteorological services in the Global South to make subseasonal forecasts. A major legacy of the S2S project was the establishment and designation of the World Meteorological organization (WMO) Global Producing Centres and Lead Centre for Sub-seasonal Predictions Multi-Model Ensemble, which will provide real-time subseasonal multi-model ensemble (MME) products to national and regional meteorological services.

Motivated by experiencing extreme turbulence during a mission into Hurricane Ian (2022), this research develops a novel “bumpiness index” to objectively quantify the three-dimensional turbulence felt by scientists, pilots, and crew members’ on NOAA’s WP-3D (Hereafter P-3) Orion Hurricane Hunter aircraft missions. The bumpiness is derived using physics first principles and accounts for translational and rotational accelerations about an aircraft’s three Cartesian axes. Since rotational motions are experienced differently depending on where someone is on a plane, the bumpiness index takes into account seat position. We then rank the bumpiest flights in recent history by gathering flight-level data from every tropical cyclone mission on the P-3 since 2004 when data needed from missions for this analysis became readily available, as well as data from the infamous flights into Hurricanes Allen (1980) and Hugo (1989). Based on the maximum bumpiness value, the objective algorithm shows that the flight through Hurricane Hugo was the most turbulent ever with the flight into Hurricane Ian ranked second. The flight into Hurricane Hugo was unique because of the large accelerations associated with back-front motions while the flight into Hurricane Ian was unique because of large accelerations associated with left-right motions. The next two bumpiest flights were in Hurricane Irma (2017) and Hurricane Sam (2021). Statistically, the bumpiest missions tend to be for stronger storms that will weaken in the subsequent 12 h. The largest values of bumpiness tend to be on the inner edge of the eyewall near a large gradient in radar reflectivity.

Clearly communicating heat warning information to the public is an important way to reduce heat mortality and morbidity. However, heat communication interventions from the National Weather Service commonly include technical and scientific terms, otherwise known as jargon. These terms, such as heat advisory or heat index, may not be understood by the public. Given the importance of message understanding in protective action decision making, the purpose of this study is to assess how the public understands National Weather Service heat information. Specifically, we asked 195 participants recruited via Amazon MTurk what the terms excessive heat watch, excessive heat warning, heat advisory, and heat index mean to them. This approach allows us to (a) evaluate how these terms are understood by examining how people give them meaning, and (b) determine if they are jargon by comparing the meanings between the National Weather Service and the public. Our results show that these terms mean something different to the public than the National Weather Service. Almost half of participants reported heat index was synonymous with air temperature, with less than 10% of participants indicating that heat index includes humidity. Furthermore, the timing of heat watches, warnings, and advisories was inconsistent with National Weather Service definitions. To address these differences in understanding, we suggest that researchers and practitioners explore plain language messaging alternatives to improve future heat communication from the National Weather Service and weather enterprise more broadly.

The sea surface and air-sea exchange processes have been identified as essential for both short- and long-term atmospheric and ocean forecasts. The two phases of the fluid layer covering our planet interact across a vast range of scales that we need to explore to achieve a better understanding of the exchange processes. While satellites provide a distributed large-scale view of the sea surface situation, highly detailed measurements, e.g., from oceanographic towers, are necessarily local. An intermediate solution can be provided by swarms of miniature surface buoys that measure waves and other key parameters. As size, weight, and cost are reduced, these can be deployed in large numbers to investigate specific processes that are at present only crudely parameterized in our models, also because of scarcity of good measurements. Perhaps the most crucial process is white-capping in stormy conditions, where air-sea exchanges are enhanced by one or two orders of magnitude. Other applications include wave-current interactions, wave-ice interactions, and plunging breakers in the coastal zone. Stimulated by a dedicated workshop, we summarize here the main findings and possibilities derived from the different approaches, and in particular the state of the art for a selection of miniature buoys. We list the presented solutions, as well as other, similar and larger, buoys, with their main characteristics and range of application. We describe the various possibilities of practical use and the scientific and engineering problems to be solved. Looking to the future, we also point out where the present technological improvements are leading to.

In northern China, the transition zone affected by summer monsoon and its neighboring areas frequently experience drought events. Especially since the 21st century, the number of major drought events in this region has significantly increased, making it a notable drought-prone area known as the northern drought-prone belt (NDPB). Till now, major drought events in this area have profoundly impacted China’s historical progress and socioeconomic development. In fact, similar drought-prone belts are common in other monsoon transition zones globally, making it highly relevant and scientifically valuable to study the major drought issues in this region. Therefore, we discuss the major drought issues in the NDPB for the first time, comprehensively analyze the basic characteristics of the NDPB, systematically review the main stages of drought research in northern China, and identify scientific challenges currently faced in major drought research in the NDPB. Based on this and in line with the current forefront of earth system science research, we outline five research directions and their basic approaches, including scientific experimental research focusing on major droughts in the NDPB, the impact of multifactor atmospheric circulation interactions on major drought formation, mechanisms promoting major drought development through the land–atmosphere coupling, the synergistic mechanisms of the atmospheric circulation and land–atmosphere coupling on major droughts, and fine monitoring technology and prediction methods for the full-chain progression of major droughts that have multifactor collaborative impacts. This research work holds significant scientific reference value for promoting innovative breakthroughs in the theory of major drought formation and monitoring and prediction techniques in northern China.

Radio occultation of Global Navigation Satellite System (GNSS) signals is a remote sensing technique that provides precise thermodynamic measurements of the Earth’s atmosphere, which are routinely assimilated into numerical weather prediction models. Polarimetric radio occultation (PRO) extends the traditional technique by using differences in the linear polarization components of the occulted GNSS signal to extract information about the hydrometeor content along the signal path. In early 2023, Spire launched the first three nanosatellites capable of collecting PRO measurements from low-earth orbit. This paper highlights the initial collection, processing, and calibration of PRO measurements from Spire satellites for the first time. Three Spire satellites equipped with a PRO payload are capable of producing a total of over 2000 PRO measurements per day, which is approximately 10 times the amount currently available to the community through the ROHP-PAZ instrument. PRO measurements are collected globally from all four of the major GNSS constellations. An antenna pattern analysis shows that the instrument provides stable measurements that require minimal calibration to demonstrate sensitivity to hydrometeors as determined by colocations with other precipitation products. Furthermore, Spire’s PRO data can be used to retrieve atmospheric bending angle profiles with the same statistical quality as Spire’s traditional RO profiles used in operational numerical weather prediction models.