Spatiotemporal analysis of land surface temperature and wind dynamics during winter in Bangladesh
Bibliographic record
Abstract
Understanding regional climate variability is critical for addressing the escalating impacts of climate change on agriculture, public health, and environmental sustainability in South Asia. Bangladesh, with its diverse topography and dense population, is particularly vulnerable to winter climate anomalies, making it essential to investigate the underlying atmospheric and surface processes. The spatiotemporal patterns of land surface temperature (LST) and wind dynamics during winter (December–February) in Bangladesh from 2001 to 2023 were investigated using both district-level and pixel-wise analyses. Trends in LST, temperature gradients, and wind fields were examined to identify significant regional variations in thermal behavior. Elevated temperatures in the southeastern coastal districts were attributed to maritime influences, whereas daytime cooling trends in the northern regions and nighttime warming in coastal areas were observed. Sen's slope analysis was employed to quantify these trends, revealing notable cooling in northwestern districts and warming in coastal Chattogram. Temperature gradients were found to be most pronounced along the northern border near the Meghalaya Plateau and within the southeastern hill tracts, influenced by topographic features. A consistent westerly wind flow was identified, with higher wind speeds observed in northeastern areas. Inverse correlations between temperature and wind speed were detected in the northwestern and southwestern regions during December and January, transitioning to direct correlations in February across northwestern Bangladesh. These findings offer critical insights for guiding policy and supporting region-specific climate adaptation in Bangladesh. Potential applications include improved agroclimatic zoning, climate-resilient agriculture, and targeted public health interventions aligned with local thermal and wind dynamics. • Distinct seasonal LST patterns across Bangladesh during winter months (2001–2023). • Southeastern coastal districts show higher temperatures due to maritime influences. • Northwestern regions experience pronounced daytime cooling trends in February. • Strong temperature gradients observed near the Meghalaya Plateau and hill tracts. • Wind dynamics show consistent northwesterly flow, shaping local temperature regimes.
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 | 0.001 |
| 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".