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Abstract. Accurate precipitation predictions at the subseasonal timescale (beyond a week but within a season) could benefit a range of human activities, but are highly challenging to achieve. Research efforts have been made through multi-agency and international collaborations, resulting in numerous forecast products such as those included in the Subseasonal Consortium (SubC) and the Subseasonal-to-Seasonal (S2S) Prediction Project. However, a unified and comprehensive evaluation of the full suite of hindcast datasets from these efforts remains limited, partly due to inconsistencies in hindcast frequency and data periods across products. In this study, we employ the full suite of nineteen precipitation hindcast datasets from the SubC and S2S projects over the contiguous United States (CONUS). The hindcast datasets are temporally aggregated into weekly values and are assessed against a reference dataset derived from the Parameter-elevation Regressions on Independent Slopes Model (PRISM). Overall and seasonal evaluations are carried out using statistical metrics including percentage bias (PBIAS), anomaly correlation coefficient (ACC), and continuous ranked probability score (CRPS). Furthermore, we adopt a baseline-referenced skillfulness approach that accounts for differences in hindcast initialization, frequency, and data periods for a relatively fair comparison among the employed hindcast datasets. Our results indicate widespread overestimations in winter and spring across most hindcast datasets, while underestimations are more likely to be observed in summer and autumn. Predictive accuracy generally declines over forecast lead time and remains marginal beyond week three. Notable variations in predictive skill are observed across regions, seasons, and lead times, with no single hindcast dataset consistently outperforming others. In summary, this work provides valuable references for both forecast end-users and model developers, and highlights the need for context-specific selection of available subseasonal forecast products for downstream applications. 

Abstract The strongest geomagnetic storm in the preceding two decades occurred in May 2024. Over these years, ground‐based observational capabilities have been significantly enhanced to monitor the ionospheric weather. Notably, the newly established Sanya incoherent scatter radar (SYISR) (Yue, Wan, Ning, & Jin, 2022,https://doi.org/10.1038/s41550‐022‐01684‐1), one of the critical infrastructures of the Chinese “Meridian Project,” provides multiple parameter measurements in the upper atmosphere at low latitudes over Asian longitudies. Unique ionospheric changes on superstorm day 11 May were first recorded by the SYISR experiments and the geostationary satellite (GEO) total electron content (TEC) network over the Asian sector. The electron density or TEC displayed wavelike structures rather than a regular diurnal pattern. Surprisingly, two humps, a common feature in the daytime equatorial ionization anomaly structure, disappeared. The SYISR observations revealed that multiple wind surges accompanied the downward phase propagation caused by atmospheric gravity waves (AGWs) originating from auroral zones. Meanwhile, strong upward and large downward drifts were respectively observed in the daytime and around sunset. The Thermosphere‐Ionosphere Electrodynamics Global Circulation Model (TIEGCM) simulations demonstrated that abnormal ionospheric changes were attributed to meridional wind disturbances associated with AGWs and recurrent penetration electric fields corresponding to largerB_zsouthward excursions and disturbance dynamo. The complicated interplay between AGWs and disturbance electric fields contributed to this unique ionospheric variation. 

This design project arose with the purpose to intervene within the current landscape of content moderation. Our team’s primary focus is community moderators, specifically volunteer moderators for online community spaces. Community moderators play a key role in up-keeping the guidelines and culture of online community spaces, as well as managing and protecting community members against harmful content online. Yet, community moderators notably lack the official resources and training that their commercial moderator counterparts have. To address this, we present ModeratorHub, a knowledge sharing platform that focuses on community moderation. In our current design stage, we focused 2 features: (1) moderation case documentation and (2) moderation case sharing. These are our team’s initial building blocks of a larger intervention aimed to support moderators and promote social support and collaboration among end users of online community ecosystems. 

Abstract The classical way of studying the rainfall‐runoff processes in the water cycle relies on conceptual or physically‐based hydrologic models. Deep learning (DL) has recently emerged as an alternative and blossomed in the hydrology community for rainfall‐runoff simulations. However, the decades‐old Long Short‐Term Memory (LSTM) network remains the benchmark for this task, outperforming newer architectures like Transformers. In this work, we propose a State Space Model (SSM), specifically the Frequency Tuned Diagonal State Space Sequence (S4D‐FT) model, for rainfall‐runoff simulations. The proposed S4D‐FT is benchmarked against the established LSTM and a physically‐based Sacramento Soil Moisture Accounting model under in‐sample and out‐of‐sample simulation setups across 531 watersheds in the contiguous United States (CONUS). Results show that S4D‐FT is able to outperform the LSTM model across diverse regions under both simulation setups, especially for regions that feature snowmelt‐driven or intermittent flow regimes. In contrast, S4D‐FT tends to underperform in flashier, high‐magnitude flow regimes, likely due to its global state‐space convolution computation that emphasizes slow, storage‐driven dynamics, which makes it less effective at picking up short bursts and noisy spikes in the data. In summary, our pioneering introduction of the S4D‐FT for rainfall‐runoff simulations challenges the dominance of LSTM in the hydrology community and expands the arsenal of DL tools available for hydrological modeling.