The need to understand the role of statistical methods for the forecasting of climatological parameters cannot be trivialized. This study gives an in depth review on the different variations of the Mann-Kendall (M-K) trend test and how they can be applied, regression techniques (Simple and Multiple), the Angstrom-Prescott model for solar radiation, etc. The study then goes ahead to apply some of them with data obtained from the Nigerian Meteorological Agency (NiMet), and applying tools like the python programming language and Wolfram Mathematica. Results show that the maximum ambient temperature for Calabar is increasing (Z=2.52) significantly after the calculated p-value < 0.05 (significant level). The seasonal M-K test was also applied for the dry and wet seasons and both were found to be increasing (Z=3.23 and Z=4.04 respectively) after their calculated p-values < 0.05. The relationship between refractivity and other meteorological parameters relating to it was discerned using partial differential equations giving the gradient of each with refractivity; this was compared with results from the correlation matrix to show that the water vapour contents of the atmosphere contributes significantly to the variation of refractivity. Multiple linear regression has also been adopted to give an accurate model for the prediction of refractivity in the region after the residual error between the calculated refractivity and predicted refractivity was minimal.
Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations
Beijing University of Posts and Telecommunications（北京邮电大学）；National Meteorological Center, China Meteorological Administration（中国气象局国家气象中心）
A Branched Deep Convolutional Network for Forecasting the Occurrence of Hazes in Paris using Meteorological Maps with Different Characteristic Spatial Scales
FuXi-2.0: Advancing machine learning weather forecasting model for practical applications
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Shanghai Academy of Artificial Intelligence for Science（上海科学人工智能研究院）
W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting
University of Electronic Science and Technology of China（电子科技大学）；Institute of Plateau Meteorology, China Meteorological Administration（中国气象局高原气象研究所）；Institute of Urban Meteorology, China Meteorological Administration（中国气象局城市气象研究所）
Assessing influence of some meteorological parameters on airborne particulate matters in the Himalayan hill-station of Darjeeling - A preliminary assessment
Estimating Atmospheric Variables from Digital Typhoon Satellite Images via Conditional Denoising Diffusion Models
Imperial College London（帝国理工学院）；University of Cambridge（剑桥大学）；Grantham Institute for Climate Change and the Environment, Imperial College London（帝国理工学院格兰瑟姆气候变化与环境研究所）；National Center for Artificial Intelligence CENIA, Chile（智利国家人工智能中心CENIA）
Assessment of global warming in Al Buraimi, sultanate of Oman based on statistical analysis of NASA POWER data over 39 years, and testing the reliability of NASA POWER against meteorological measurements
