NARR’s Atmospheric Water Cycle Components. Part II: Summertime Mean and Diurnal Interactions
Bibliographic record
Abstract
Abstract Summertime interactions in the North American Regional Reanalysis (NARR) atmospheric water cycle are examined from a user’s perspective over the 1980–99 period with a particular emphasis on the diurnal cycle, the nocturnal maximum of precipitation over the Midwest, and the impacts of precipitation assimilation. NARR’s full-year mean atmospheric water cycle and its annual variations are examined in Part I of this study. North American summertime (June–August) features substantial convective activity that is often organized on a diurnal scale, although diverse regional diurnal features are evident to various extents in high-resolution precipitation products. NARR’s hourly assimilation of precipitation observations over the continental United States allows it to resolve diurnal effects on the water cycle, but in other regions the diurnal cycle of precipitation is imposed from an external reanalysis model. The prominent nocturnal maximum in precipitation across the upper Midwest is captured in NARR, but different precipitation assimilation sources disrupt the propagation of convective systems across the Canadian border. Normalized covariances of NARR’s diurnal water cycle component interactions in the nocturnal maximum region reveal a strong relationship between moisture convergence and precipitation, and also measure the way in which the precipitable water column holds a lagged response between evaporation and precipitation. In many regions the diurnal cycle of rainfall is driven by interactions with water cycle components that differ from those driving the seasonal cycle. A comparison between NARR’s precipitation and an estimate of the model precipitation prior to precipitation assimilation distinguishes the portion of the water cycle captured in full by the model and that which is value added by the assimilation routine. The nocturnal rainfall maximum is not present in the model precipitation estimate, leading to diurnal-scale biases in the evaporation and moisture flux convergence fields that are not directly modified by precipitation assimilation.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".