Improved seasonal prediction of rainfall over East Africa for application in agriculture: Statistical downscaling of CFSv2 and GFDL-FLOR

Statistically downscaled forecasts of October–December (OND) rainfall are evaluated over East Africa from two general circulation model (GCM) seasonal prediction systems. The method uses canonical correlation analysis to relate variability in predicted large-scale rainfall (characterizing, e.g., predicted ENSO and Indian Ocean dipole variability) to observed local variability over Kenya and Tanzania. Evaluation is performed for the period 1982–2011 and for the real-time forecast for OND 2015, a season when a strong El Niño was active. The seasonal forecast systems used are the National Centers for Environmental Prediction Climate Forecast System, version 2 (CFSv2), and the Geophysical Fluid Dynamics Laboratory Forecast-Oriented Low Ocean Resolution (GFDL-FLOR) version of CM2.5. The Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) rainfall dataset—a blend of in situ station observations and satellite estimates—was used at 5 km × 5 km resolution over Kenya and Tanzania as benchmark data for the downscaling. Results for the case-study forecast for OND 2015 show that downscaled output from both models adds realistic spatial detail relative to the coarser raw model output—albeit with some overestimation of rainfall that may have been derived from the downscaling procedure introducing a wet response to El Niño more typical of historical cases. Assessment of the downscaled forecasts over the 1982–2011 period shows positive long-term skill better than that documented in previous studies of unprocessed GCM forecasts for the region. Climate forecast downscaling is thus a key undertaking worldwide in the generation of more reliable products for sector specific application including agricultural planning and decision-making

The Impact of Prior Exposure to Engineering Through the MUT Pre-College Course - A Case Study of Kangema Sub-County Secondary Schools

In Kenya, secondary schools have a great role in preparing learners for career progression. In order to realize industrial growth, it is important to prepare more students for careers in STEM. There is relatively little research that exists on the impact of prior exposure to Engineering through pre- college sessions to students' attitude in STEM subjects. In addition, Industry 4.0 requires that the 21st century student be exposed to current trends in the industry. The purpose of this research is to investigate the impact of the pre-college sessions as a mode of prior exposure to Engineering to secondary school students on learning STEM subjects. The pre-college exposure course entailed introducing the students to green energy through Solar photovoltaic systems, automation using Arduino, advanced manufacturing through 3D printing and robotics. The research was conducted in secondary school students from Kangema subcounty. The target population is Form 1 and Form 2. In this research, the first cohort entailed 30 students who were selected from 3 secondary Schools through stratified, systematic and purposive sampling. The students were taken through the pre-college sessions. The study explored the impact of the precollege sessions to the attitude learning of STEM subjects. The study established that the students exhibited an improved attitude in learning of the STEM subjects