Comparing Land Surface Model Performance between Fine-scale and Coarse-scale Assimilation: Leaf Area Index Retrievals versus GRACE / GRACE-FO Retrievals 
Bibliographic record
Abstract
Land surface models (LSMs) are useful for estimating land surface states and fluxes such as snow water equivalent, soil moisture content, vegetation, and river discharge. Although estimates are continuous in time and space, LSMs are flawed since they lack comprehensive representation of process physics and are dependent on uncertain boundary conditions. One way to improve LSM performance is by conditioning model states on space-borne retrievals using an ensemble Kalman filter framework. In this study, the Noah-MP version 4.0.1 LSM without conditioning (a.k.a., Open Loop; OL) is compared against the same model with conditioning (a.k.a., Data Assimilation; DA). Two different univariate DA experiments are conducted: 1) assimilation using terrestrial water storage (TWS) anomalies from GRACE / GRACE-FO, and 2) assimilation using leaf area index (LAI) retrievals from MODIS. Not only do the experiments assimilate different types of retrievals (i.e., TWS versus LAI), but also, they assimilate products of different spatial (~3° versus ~0.005°) and temporal (~monthly versus ~weekly) resolutions. Experiments are conducted across different watersheds in North America with a particular focus on basins with irrigated agriculture. Modeled states and fluxes from the OL and DA are then compared against independent, ground-based measurement networks including U.S. SNOTEL, Canadian CanSWE product, U.S. SCAN for soil moisture, and USGS measurement gauges for river discharge. Statistical analyses, including bias, RMSE, and normalized information content (NIC) are computed to quantify the marginal improvements via each assimilation experiment. Results provide a basis to better understand the coupling between different state variables (i.e., snow mass, soil moisture, and groundwater) as well as the utility of using coarse-scale and fine-scale retrievals in land data assimilation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".