Advances and challenges in climate modeling
O Alizadeh - Climatic Change, 2022 - Springer
In spite of the chaotic nature of the atmosphere and involvement of complex nonlinear
dynamics, forecasting climate fluctuations over different timescales is feasible due to the …
dynamics, forecasting climate fluctuations over different timescales is feasible due to the …
Machine learning in weather prediction and climate analyses—applications and perspectives
B Bochenek, Z Ustrnul - Atmosphere, 2022 - mdpi.com
In this paper, we performed an analysis of the 500 most relevant scientific articles published
since 2018, concerning machine learning methods in the field of climate and numerical …
since 2018, concerning machine learning methods in the field of climate and numerical …
Enhancing short-term forecasting of daily precipitation using numerical weather prediction bias correcting with XGBoost in different regions of China
Accurate precipitation (P) short-term forecasts are important for engineering studies and
water allocation. This study evaluated a method for bias correction of the Numerical Weather …
water allocation. This study evaluated a method for bias correction of the Numerical Weather …
A review of the application of hybrid machine learning models to improve rainfall prediction
Rainfall is one of the most important meteorological phenomena that impacts many fields,
including agriculture, energy, water resources management, and mining, among others …
including agriculture, energy, water resources management, and mining, among others …
A new look into the South America precipitation regimes: observation and forecast
GWS Ferreira, MS Reboita - Atmosphere, 2022 - mdpi.com
South America is a vast continent characterized by diverse atmospheric phenomena and
climate regimes. In this context, seasonal climate predictions are helpful for decision-making …
climate regimes. In this context, seasonal climate predictions are helpful for decision-making …
Improving monthly rainfall forecast in a watershed by combining neural networks and autoregressive models
A Pérez-Alarcón, D Garcia-Cortes… - Environmental …, 2022 - Springer
The main aim of the rain forecast is to determine rain occurrence conditions in a specific
location. This is considered of vital importance to assess the availability of water resources …
location. This is considered of vital importance to assess the availability of water resources …
Data-driven models for atmospheric air temperature forecasting at a continental climate region
Atmospheric air temperature is the most crucial metrological parameter. Despite its influence
on multiple fields such as hydrology, the environment, irrigation, and agriculture, this …
on multiple fields such as hydrology, the environment, irrigation, and agriculture, this …
Trend analysis and forecasting of meteorological variables in the lower Thoubal river watershed, India using non-parametrical approach and machine learning models
Climate change, variability and their impact assessment are major concerns of the scientific
community across the world. Changes and variations in meteorological variables have …
community across the world. Changes and variations in meteorological variables have …
Analyzing trend and forecast of rainfall and temperature in Valmiki Tiger Reserve, India, using non-parametric test and random forest machine learning algorithm
Assessment of spatiotemporal dynamics of meteorological variables and their forecast is
essential in the context of climate change. Such analysis can help suggest possible …
essential in the context of climate change. Such analysis can help suggest possible …
Harnessing machine learning for sustainable futures: advancements in renewable energy and climate change mitigation
Background Renewable energy and climate change are vital aspects of humanity. Energy is
needed to sustain life on Earth. The exploration and utilisation of traditional fossil-based …
needed to sustain life on Earth. The exploration and utilisation of traditional fossil-based …