Chasing Snowstorms: The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Campaign
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) is a NASA-sponsored field campaign to study wintertime snowstorms focusing on East Coast cyclones. This large cooperative effort takes place during the winters of 2020–23 to study precipitation variability in winter cyclones to improve remote sensing and numerical forecasts of snowfall. Snowfall within these storms is frequently organized in banded structures on multiple scales. The causes for the occurrence and evolution of a wide spectrum of snowbands remain poorly understood. The goals of IMPACTS are to characterize the spatial and temporal scales and structures of snowbands, understand their dynamical, thermodynamical, and microphysical processes, and apply this understanding to improve remote sensing and modeling of snowfall. The first deployment took place in January–February 2020 with two aircraft that flew coordinated flight patterns and sampled a range of storms from the Midwest to the East Coast. The satellite-simulating ER-2 aircraft flew above the clouds and carried a suite of remote sensing instruments including cloud and precipitation radars, lidar, and passive microwave radiometers. The in situ P-3 aircraft flew within the clouds and sampled environmental and microphysical quantities. Ground-based radar measurements from the National Weather Service network and a suite of radars located on Long Island, New York, along with supplemental soundings and the New York State Mesonet ground network provided environmental context for the airborne observations. Future deployments will occur during the 2022 and 2023 winters. The coordination between remote sensing and in situ platforms makes this a unique publicly available dataset applicable to a wide variety of interests.
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.
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 | 0.001 | 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 | 0.001 |
| 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 it