# Meteorological Phenomena and Simulations

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/meteorological-phenomena-and-simulations/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on advancements in numerical weather prediction models, including topics such as ensemble Kalman filtering, data assimilation, convective parameterization, mesoscale modeling, probabilistic forecasting, microphysics schemes, boundary layer processes, radiative transfer models, hydrological modeling, and atmospheric dynamics. |
| Domain | Physical Sciences |
| Field | Earth and Planetary Sciences |
| OpenAlex ID | t10466 |
| Works | 53 |

## Topic papers all

Showing 15 of 53.

- [The Community Earth System Model Version 2 (CESM2)](https://scholariq.org/papers/the-community-earth-system-model-version-2-cesm2/)
- [The Community Earth System Model: A Framework for Collaborative Research](https://scholariq.org/papers/the-community-earth-system-model-a-framework-for-collaborative-research/)
- [The Community Earth System Model (CESM) Large Ensemble Project: A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability](https://scholariq.org/papers/the-community-earth-system-model-cesm-large-ensemble-project-a-community/)
- [The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data](https://scholariq.org/papers/the-fluxnet2015-dataset-and-the-oneflux-processing-pipeline-for-eddy-covariance/)
- [Statistical Methods in the Atmospheric Sciences](https://scholariq.org/papers/statistical-methods-in-the-atmospheric-sciences/)
- [The first high-resolution meteorological forcing dataset for land process studies over China](https://scholariq.org/papers/the-first-high-resolution-meteorological-forcing-dataset-for-land-process/)
- [Fourier Neural Operator for Parametric Partial Differential Equations](https://scholariq.org/papers/fourier-neural-operator-for-parametric-partial-differential-equations/)
- [DEVELOPMENT OF A EUROPEAN MULTIMODEL ENSEMBLE SYSTEM FOR SEASONAL-TO-INTERANNUAL PREDICTION (DEMETER)](https://scholariq.org/papers/development-of-a-european-multimodel-ensemble-system-for-seasonal-to-interannual/)
- [Physics-informed machine learning: case studies for weather and climate modelling](https://scholariq.org/papers/physics-informed-machine-learning-case-studies-for-weather-and-climate-modelling/)
- [The Seasonal Cycle over the Tropical Pacific in Coupled Ocean–Atmosphere General Circulation Models](https://scholariq.org/papers/the-seasonal-cycle-over-the-tropical-pacific-in-coupled-ocean-atmosphere-general/)
- [Wavelet analysis of covariance with application to atmospheric time series](https://scholariq.org/papers/wavelet-analysis-of-covariance-with-application-to-atmospheric-time-series/)
- [Large‐eddy simulations over Germany using ICON: a comprehensive evaluation](https://scholariq.org/papers/large-eddy-simulations-over-germany-using-icon-a-comprehensive-evaluation/)
- [Western tropical Pacific multidecadal variability forced by the Atlantic multidecadal oscillation](https://scholariq.org/papers/western-tropical-pacific-multidecadal-variability-forced-by-the-atlantic/)
- [Air Temperature Forecasting Using Machine Learning Techniques: A Review](https://scholariq.org/papers/air-temperature-forecasting-using-machine-learning-techniques-a-review/)
- [An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation](https://scholariq.org/papers/an-interpretable-machine-learning-approach-based-on-dnn-svr-extra-tree-and/)

## Topic primary papers

- [Statistical Methods in the Atmospheric Sciences](https://scholariq.org/papers/statistical-methods-in-the-atmospheric-sciences/)
- [DEVELOPMENT OF A EUROPEAN MULTIMODEL ENSEMBLE SYSTEM FOR SEASONAL-TO-INTERANNUAL PREDICTION (DEMETER)](https://scholariq.org/papers/development-of-a-european-multimodel-ensemble-system-for-seasonal-to-interannual/)
- [Physics-informed machine learning: case studies for weather and climate modelling](https://scholariq.org/papers/physics-informed-machine-learning-case-studies-for-weather-and-climate-modelling/)
- [Large‐eddy simulations over Germany using ICON: a comprehensive evaluation](https://scholariq.org/papers/large-eddy-simulations-over-germany-using-icon-a-comprehensive-evaluation/)
- [Air Temperature Forecasting Using Machine Learning Techniques: A Review](https://scholariq.org/papers/air-temperature-forecasting-using-machine-learning-techniques-a-review/)
- [A Deep Learning Method for Bias Correction of ECMWF 24–240 h Forecasts](https://scholariq.org/papers/a-deep-learning-method-for-bias-correction-of-ecmwf-24-240-h-forecasts/)
- [Improving Nowcasting of Convective Development by Incorporating Polarimetric Radar Variables Into a Deep‐Learning Model](https://scholariq.org/papers/improving-nowcasting-of-convective-development-by-incorporating-polarimetric/)
- [Bridging Research to Operations Transitions: Status and Plans of Community GSI](https://scholariq.org/papers/bridging-research-to-operations-transitions-status-and-plans-of-community-gsi/)
- [Polarimetric Radar Signatures of Dendritic Growth Zones within Colorado Winter Storms](https://scholariq.org/papers/polarimetric-radar-signatures-of-dendritic-growth-zones-within-colorado-winter/)
- [Environmental Data Records From FengYun-3B Microwave Radiation Imager](https://scholariq.org/papers/environmental-data-records-from-fengyun-3b-microwave-radiation-imager/)
- [Advanced Quantitative Precipitation Information: Improving Monitoring and Forecasts of Precipitation, Streamflow, and Coastal Flooding in the San Francisco Bay Area](https://scholariq.org/papers/advanced-quantitative-precipitation-information-improving-monitoring-and/)
- [A Comprehensive Assessment of Machine Learning Techniques for Temperature and Rainfall Forecasting: SVR vs. Decision](https://scholariq.org/papers/a-comprehensive-assessment-of-machine-learning-techniques-for-temperature-and/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.