Cloud, thermodynamic, and precipitation observations in West Africa during 2006
Bibliographic record
Abstract
In 2006, the ARM Mobile Facility (AMF) completed a 1‐year deployment at Niamey, Niger, Africa, in support of the Radiative Atmospheric Divergence using ARM Mobile Facility, GERB data and AMMA Stations (RADAGAST) field campaign, which is the subject of this special issue. Observations from the AMF instrumentation are used to analyze the relationship between clouds, precipitation, and the thermodynamic environment in this rarely observed region and to evaluate the cloud fields in the National Center for Environmental Prediction Global Forecast System (GFS) initialization product. The 1‐year deployment period enabled measurements in the dry and wet (monsoon) seasons and through the transitions in May and September, respectively. Cirrus clouds in the 10‐ to 15‐km layer with modest monthly cloud fraction and mean depth of ∼1 km are ubiquitous through the observing period as observed in other regions of the tropics. The monsoon season from May to September is characterized by convective clouds of varying depth that produce precipitation of varying intensity, as indicated by cloud radar. Peak surface rainfall is observed during August, and the largest daily rainfall rates are observed during the period from July to September. The lifting condensation level (LCL) is observed to decrease as the monsoon season progresses, and a strong correlation between the height of the LCL and precipitation is demonstrated. Cooling of the lower troposphere is implicated as the probable cause of the lowering of the LCL. Conversely, the amount of convective available potential energy is found to be poorly correlated with precipitation. As in other tropical regions, the physical height at which the zero‐degree isotherm is observed corresponds to gradients in the thermodynamic profiles and a gradient in the profile of cloud occurrence. Comparisons with the GFS initialization data, which are derived from a number of sources including satellites, show some systematic biases when compared to AMF measurements. There is general correspondence between the locations of clouds and the profile of vertical velocity diagnosed by the GFS initialization early in the monsoon season, but vague correspondence thereafter. The relative humidity in the GFS initialization is too large above 10 km and too small in the monsoon layer near the surface, and it seriously underestimates the amount of cloud below 10 km during August, which is the height of the West African monsoon in Niamey.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".