mowumialabi/west-africa-climate-trap-2026: Production Final Code - Peer Review Ready.
Domaine:
climateagricultureType de record:
# Cloud-Scale GeoAI Processing Pipelines for Non-Linear West African Climate Trap Dynamics (January 2026 Phase)
* **Permanent Concept DOI Line**: https://doi.org/10.5281/zen…
* **Author Profile & Affiliations**: Dr. O. Alabi (NASRDA / ARCSSTEE PGD Program)
* **Production Build Release Code**: v1.0.5
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## 📌 Project Overview
This open-access repository archives the operational python computational environments, cloud-native ingestion scripts, and reconciled geostatistical datasets supporting a 4-manuscript publication pipeline investigating the January 2026 9°N synoptic atmospheric stalemate over West Africa.
By hardcoding real-time thermodynamic physical laws directly into distributed planetary data streams, this GeoAI architecture successfully separates localized atmospheric path noise (Mie scattering anomalies) from true biophysical canopy health parameters, eliminating radiometric vulnerability blocks inside automated multi-spectral crop monitoring networks.
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## 🛠️ Repository Architecture & File Directory
The project assets are structured to isolate your master computational code from journal-specific visual and statistical validation deliverables:
```text
📦 west-africa-climate-trap-2026 (Root Directory)
┣ 📂 scripts_and_notebooks
┃ ┗ 📜 era5_thermodynamic_pipeline.ipynb # Master Python code recipe book
┗ 📂 publication_figures
┣ 📂 ACCR_9N_Synoptic_Stall # Manuscript 1 Deliverables (ACCR)
┃ ┣ 📜 manuscript1_figure2_final_clean.png
┃ ┣ 📜 manuscript1_figure3_exposure.png
┃ ┣ 📜 manuscript1_section1_background_baselines.csv
┃ ┗ 📜 manuscript1_section4_climatological_anomalies.csv
┣ 📂 COMPAG_GeoAI_Smart_Physics # Manuscript 2 Deliverables (COMPAG)
┃ ┣ 📜 manuscript2_figure1_final.png
┃ ┣ 📜 manuscript2_figure2_ingestion.png
┃ ┣ 📜 manuscript2_figure3_synchronized.png
┃ ┣ 📜 manuscript2_section2_peak_diurnal.csv
┃ ┣ 📜 manuscript2_section5_geostatistical_profile.csv
┃ ┗ 📜 payload_reduction_report.txt
┣ 📂 AFM_Boiling_Cocoa_Leaf # Manuscript 3 Deliverables (AFM)
┃ ┣ 📜 manuscript3_figure1_flux_shift.png
┃ ┣ 📜 manuscript3_figure2_pipeline_perfect.png
┃ ┣ 📜 manuscript3_figure3_bottom_legend.png
┃ ┗ 📜 manuscript3_section6_exposure_matrix.csv
┗ 📂 ATENV_Particulate_Roadblock # Manuscript 4 Deliverables (ATENV)
┣ 📜 manuscript4_figure1_concept.png
┣ 📜 manuscript4_figure2_pipeline.png
┗ 📜 manuscript4_figure3_aod_peaks.png
```
---
## 📊 Publication & Visual Validation Guide
All code-generated visual assets and raw statistical outputs are mapped below by their corresponding journal article submissions. Reviewers can cross-verify the execution steps sequentially inside the master script pipeline: `scripts_and_notebooks/era5_thermodynamic_pipeline.ipynb`.
### 1. 📂 ACCR_9N_Synoptic_Stall
* **Target Venue**: *Advances in Climate Change Research* (Elsevier, Q1)
* **Pipeline Source Cells**: `SECTION 1` (24-Hour Continuous Baselines) & `SECTION 4` (30-Year Historical Baseline Anomaly Calculator)
* **Data Verification State**:
* *Relative Humidity Baselines*: Ile-Ife = 64.5% | Abuja = 38.7% | Kano = 15.4%
* *Standardized Departures (Z-Scores)*: Ile-Ife RH_Z = +0.945 | Abuja VPD_Z = +0.973 | Kano VPD_Z = +2.009 (Crosses +2\sigma Extreme Horizon)
* **Validation Assets (Stored in `publication_figures/ACCR_9N_Synoptic_Stall/`)**:
* 📜 `manuscript1_figure2_final_clean.png`: Clustered bar chart compiling 30-year historical departures (1996–2025) across the transect.
* 📜 `manuscript1_figure3_exposure.png`: Consolidated dual-bar plot isolating cumulative exposure duration spikes.
* 📜 `manuscript1_section1_background_baselines.csv`: Raw 24-hour diurnal background baseline matrix spreadsheet.
* 📜 `manuscript1_section4_climatological_anomalies.csv`: Output matrix tracking multi-decadal standard deviation calculations.
### 2. 📂 COMPAG_GeoAI_Smart_Physics
* **Target Venue**: *Computers and Electronics in Agriculture* (Elsevier, Q1)
* **Pipeline Source Cells**: `SECTION 2` (Diurnal Afternoon Heat-Stress Peaks) & `SECTION 5` (MODIS NDVI vs. ERA5 VPD Cross-Correlation Engine)
* **Data Verification State**:
* *Geostatistical Coupling Framework*: Pearson Product-Moment Coefficient = -0.7215 | Coefficient of Determination (R^2 = 0.5206
* *Payload Optimization Footprint*: Achieved a 99.9750% server-side memory compression efficiency, reducing raw network data matrices from 68.1152 MB down to a streamlined 17.4375 KB.
* *Physics-Guided Validation Rule*: y = -0.2013x + 1.1050 (Inverse slope coefficient mathematically falsifies true vegetative canopy failure)
* **Validation Assets (Stored in `publication_figures/COMPAG_GeoAI_Smart_Physics/`)**:
* 📜 `manuscript2_figure1_final.png`: Vector-drawn conceptual schematic modeling canopy-cloud near-infrared attenuation via atmospheric Mie scattering.
* 📜 `manuscript2_figure2_ingestion.png`: Flowchart mapping distributed server-side `ee.Reducer.mean()` payload reductions.
* 📜 `manuscript2_figure3_synchronized.png`: Ordinary Least Squares (OLS) linear trendline scatter plot tying satellite reflectance grids to reanalysis metrics.
* 📜 `manuscript2_section2_peak_diurnal.csv`: Spreadsheet mapping afternoon thermodynamic variables across all stations.
* 📜 `manuscript2_section5_geostatistical_profile.csv`: Raw cross-correlation matrix linking MODIS pixels to climate columns.
* 📜 `payload_reduction_report.txt`: Automated text summary tracking raw vs. compressed point stream data points.
### 3. 📂 AFM_Boiling_Cocoa_Leaf
* **Target Venue**: *Agricultural and Forest Meteorology* (Elsevier, Q1)
* **Pipeline Source Cells**: `SECTION 2` (Peak Diurnal Variables) & `SECTION 6` (Consolidated Cumulative Exposure Engine)
* **Data Verification State**:
* *Asynchronous Threshold Matrix*: Ingests 744 consecutive hourly atmospheric columns for January 2026
* *Cumulative Stress Profiles*: Abuja (Arid Frontline) = 250 Hours Absolute Heat vs. 3 Hours Humid-Heat | Ile-Ife (Moisture Trap) = 163 Hours Absolute Heat vs. 61 Hours Concurrent Transpirational Arrest
* **Validation Assets (Stored in `publication_figures/AFM_Boiling_Cocoa_Leaf/`)**:
* 📜 `manuscript3_figure1_flux_shift.png`: Biophysical energy balance block diagram showing net radiation partitioning shift from Latent Heat (LE) to Sensible Heat (H).
* 📜 `manuscript3_figure2_pipeline_perfect.png`: Software architecture model detailing the conditional filtering logic and parallel argument loop.
* 📜 `manuscript3_figure3_bottom_legend.png`: High-resolution dual-bar canopy exposure duration plot.
* 📜 `manuscript3_section6_exposure_matrix.csv`: Spreadsheet tracking continuous threshold hour accumulation values.
### 4. 📂 ATENV_Particulate_Roadblock
* **Target Venue**: *Atmospheric Environment* (Elsevier, Q1)
* **Pipeline Source Cells**: Extended Column Matrix Modules & Aerosol Grid Extractions
* **Data Verification State**:
* *Particulate Attenuation Matrix*: Multi-decadal Copernicus Atmosphere Monitoring Service (CAMS) Global Reanalysis (EAC4) vertical column integration
* *Frontline Roadblock Extremes*: Monthly mean Total Aerosol Optical Depth at 550 nm (AOD_550} over Abuja spikes from a historical background of 0.44 to an extreme peak of 1.48 (Z-score = +1.842)
* **Validation Assets (Stored in `publication_figures/ATENV_Particulate_Roadblock/`)**:
* 📜 `manuscript4_figure1_concept.png`: Schematic diagram contrasting standard dust advection against the synoptic atmospheric roadblock front.
* 📜 `manuscript4_figure2_pipeline.png`: Centralized vertical flowchart detailing the automated GeoAI data reduction architecture.
* 📜 `manuscript4_figure3_aod_peaks.png`: Spatial-temporal chart rendering the downwind aerosol transport block at the 9°N atmospheric moisture dam.
---
## 📜 Open-Access Terms of Use Licensing
The computational notebooks and custom Python/JavaScript architectures archived in this repository are officially licensed under the standard open-source **MIT License**. Downstream researchers are permitted to freely copy, modify, and build upon these models provided appropriate academic citation attribution is granted to the original author manuscripts.