Interannual Variability of Sea Level and Sea Surface Temperature in Djibouti and Yemen: Insights from the University of Hawaii Sea Level Center and NOAA OISST Data (1981–2022)

 

Figure 1.  Interannual sea level changes in Djibouti and Yemen over the period 2007 to 2018 from University of Hawaii, Sea Level Center

Figure 2.  Annual average Sea surface température (°C) from 1981 to 2022 , NOAA OISST. Abdi-Basid ADAN, 2022 The Abdi-Basid Courses Institute

 

Spatiotemporal Analysis of the Impacts of Early 2024 Wildfires in North America: A Study of Correlations Between Meteorological Conditions, Air Quality, and Wind Dynamics in Stinnett and Canadian, T

This study provides a detailed spatiotemporal analysis of the impacts of early wildfires occurring between February 26 and March 16, 2024, in North America, with a focus on the localities of Stinnett (35.82°N, -101.44°W) and Canadian, TX (35.91°N, -100.38°W). Utilizing data from NetCDF files (v10m, u10m, AOD500nm, t2m, coplev, comlev), the analysis employs the xarray library to process meteorological and atmospheric variables, including aerosol optical depth (AOD at 550 nm), 2-meter temperature (converted to °F), carbon monoxide (CO) at 1000 hPa and model level 1 (in ppb), and 10-meter wind components (u10, v10). Visualizations, created using matplotlib and cartopy, include spatial maps of daily averages of AOD, temperature, and CO, overlaid with wind vectors to illustrate their role in pollutant dispersion. Time series analyses reveal the daily evolution of these variables for both localities, highlighting significant AOD peaks in Stinnett, indicative of smoke plume passage. Hourly heatmaps confirm the immediate impact of wildfires on air quality, showing a marked increase in AOD within hours. Wind rose diagrams, generated for each locality, quantify the frequency and intensity of winds, emphasizing their influence on pollutant spread. Finally, a pixel-by-pixel Pearson correlation analysis between AOD and CO reveals a strong positive relationship (R close to +1) in affected areas, confirming the common origin of aerosols and CO from biomass burning. These findings, contextualized within an extreme meteorological event characterized by high temperatures and dry conditions, underscore the complex interactions between meteorological and atmospheric dynamics, with implications for air quality monitoring and environmental crisis management in North America.

🎯 The detailed methodology and results can be accessed through this link:

👉Click here now! :https://github.com/abdibasidadan

Abdi-Basid ADAN, 2025

The Abdi-Basid Courses Institute

Solar and Wind Potentials through Modeling and Comparative Analysis: Case Study

✳️ Evaluation of solar radiation (GHI and DNI) from Solargis data (1994–2018)

✳️Seasonal variability of clearness index and temperature (2005–2020)

✳️Spatial distribution of wind and wind power at 50 m (Global Wind Atlas)

✳️Model validation using Taylor diagram (reanalyses 2015–2018)

Abdi-Basid ADAN , 2022

The Abdi-Basid Courses Institute

Combined Statistical Approaches for the Analysis of EgaEnEstellaQts Streamflow: From ROC Curves to Goodness-of-Fit Measures

Description

This script illustrates a comprehensive approach to evaluating hydrological data (here, EgaEnEstellaQts, a series of streamflow measurements on the Ega River at the Estella station in Spain) through different methods. First, it applies a ROC (Receiver Operating Characteristic) analysis to test the discriminative capacity of the predictions. It then implements performance measures (goodness-of-fit) to compare observations and simulations, supported by graphical visualizations such as adjustment curves, scatter plots, and time series. Finally, the code provides descriptive statistics and an analysis of the data distribution. The purpose of this script is to combine statistical and graphical tools in order to assess the quality of the simulations, detect potential biases, and provide a comprehensive view of model performance when applied to environmental or hydrological datasets.

🎯 The detailed methodology and results can be accessed through this link:

👉click here NOW ! :  https://rpubs.com/abdibasidadan/

Abdi-Basid ADAN, 2025

Performance Evaluation of Climate and Precipitation Models Using Taylor Diagrams in R

Project Objective

This analysis evaluates the performance of various precipitation and climate models (CHIRPS, PERSIANN, TAMSATV3, ARCV2, ERA5) against ground-based observations using Taylor diagrams in R. Taylor diagrams provide a concise visualization of model performance by comparing correlation, standard deviation, and centered root-mean-square error (RMSE). The analysis uses synthetic data representing annual and seasonal (JF, MAM, JJAS, OND) precipitation measurements. By leveraging the plotrix and openair packages, we assess the models’ ability to replicate observed precipitation patterns, offering insights into their accuracy and reliability for climate studies.

🎯 The detailed methodology and results can be accessed through this link:

👉click here now! : https://rpubs.com/abdibasidadan

Abdi-Basid ADAN

Comprehensive Data Visualization: A Multidimensional Exploration

This R script report harnesses R’s advanced plotting libraries to explore built-in datasets (iris, mtcars, Titanic, volcano) through diverse visualization techniques, including scatter plots, boxplots, histograms, 3D scatter plots, and interactive visualizations. The code uncovers critical patterns, such as species clustering in iris or performance correlations in mtcars, making it a powerful tool for data analysis. By leveraging ggplot2, plotly, it produces publication-ready HTML output, ideal for data scientists, analysts, and educators aiming to communicate complex insights with clarity and interactivity.

🎯 The detailed methodology and results can be accessed through this link:

👉click here now! : https://rpubs.com/abdibasidadan/

Abdi-Basid ADAN
2025-08-21

The Abdi-Basid Courses Institute

Bayesian Analysis of Precipitation Fluxes in a Climatic Context Using MixSIAR

 Project Objective 

This module introduces the application of stable water isotopes (δ¹⁸O and δD) to quantify the origin of precipitation and hydrological inputs under varying climatic conditions. Using Bayesian mixing models implemented in MixSIAR (R package), participants will learn to:

Prepare and structure isotopic datasets from precipitation, potential water sources, and discrimination factors.

Apply a Bayesian mixing model to estimate the relative contributions of different hydrological sources (rainfall, snowmelt, groundwater).

Evaluate model outputs through statistical summaries and convergence diagnostics (e.g., MCMC trace plots, Gelman–Rubin statistics) to ensure robustness.

Visualize estimated source proportions and their posterior distributions, enabling clear interpretation in both climatic and hydrological studies.

By the end of the module, participants will be able to trace water fluxes within a watershed, assess the climatic influence on precipitation sources, and communicate their findings effectively using results derived from Bayesian analysis.

In this training context, simulated datasets are employed to provide order-of-magnitude examples and hands-on practice with MixSIAR.

δ¹⁸O (delta-O-18): The ratio of oxygen-18 (¹⁸O) to oxygen-16 (¹⁶O) in water. δD (δ²H, delta-Deuterium): The ratio of hydrogen-2 (²H, deuterium) to hydrogen-1 (¹H) in water.

🎯 The detailed methodology and results can be accessed through this link:

👉click here now! : https://rpubs.com/abdibasidadan

Abdi-Basid ADAN

2025-08-20

The Abdi-Basid Courses Institute