Statistical modeling of maximum temperature in Guinea.

Area of Science:

• Climatology
• Statistical Analysis
• Extreme Value Theory

Background:

• Understanding regional temperature variations is crucial for climate change impact assessments.
• Extreme value analysis provides a robust framework for studying climate extremes.

Purpose of the Study:

• To statistically analyze maximum temperature data from twelve weather stations in Guinea.
• To assess trends and estimate return levels of maximum temperatures.
• To apply the Generalized Extreme Value distribution to model temperature extremes.

Main Methods:

• Maximum likelihood estimation was employed to fit the Generalized Extreme Value distribution.
• The Generalized Extreme Value distribution was used to model maximum temperature data.
• Statistical significance testing was performed to identify trends.

Main Results:

• The Generalized Extreme Value distribution adequately fit the maximum temperature data from all twelve stations.
• Significant positive trends in maximum temperature were detected at four of the twelve stations.
• Return level estimates for maximum temperatures were calculated.

Conclusions:

• Maximum temperature data in parts of Guinea can be effectively modeled using the Generalized Extreme Value distribution.
• A subset of stations exhibits statistically significant increasing trends in maximum temperatures.
• These findings contribute to understanding regional climate variability and potential warming in Guinea.