What's Next for Snow: Insights From the NASA Terrestrial Hydrology Program Community Snow Meeting
Bibliographic record
Abstract
Abstract Earth's snow cover strongly influences the climate system and represents an important resource for agricultural, industrial, and domestic water use. The last decade of snow‐focused research has improved our understanding of snow across scales. These efforts have culminated in new snow measurement instruments and methods, operational models for tracking snowpack evolution and forecasting snowmelt, multi‐year and international snow and remote sensing field campaigns, and satellite mission proposals to measure snowpack water resources from space, with two submitted to NASA's Earth Explorer AO and the Environment and Climate Change Canada Terrestrial Snow Mass Mission moving closer to a launch opportunity. Yet, shortcomings in each snowpack observation system still exist, including uncertainty in product performance, mission proposal advancement, and synergies across methods. The snow community aims to navigate next actionable steps toward improved and global‐scale snow monitoring for climate and human purposes. Building from recent advances in snow research and operations and carrying momentum from the conclusion of the NASA SnowEx field campaigns, NASA's Terrestrial Hydrology Program (THP) sponsored a Community Snow Meeting in August 2024 in Boulder, Colorado, USA, with 200 total in‐person and virtual attendees. Meeting objectives were to outline existing and ongoing snowpack monitoring techniques and identify knowledge gaps and recommended next steps for the snow community. We broadly summarize the state of numerous snow science sub‐disciplines and share the insights and takeaways from the Community Snow Meeting, focused largely but not exclusively on NASA opportunities, and intended to support ongoing and future pathways toward the next decade of snow research and development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".