WRF Climate Simulations & Validation
Research Associate Project | Central Department of Hydrology and Meteorology, Tribhuvan University
July 2024 – Present | Ongoing Research
Project Overview
This research focuses on commissioning and improving the operational Weather Research and Forecasting (WRF) model at the Department of Hydrology and Meteorology (DHM), Nepal. The work involves comprehensive testing of diverse parameterization schemes, validation against observational data, and optimization of model performance for South Asian regional climate.
The project bridges operational weather prediction needs with research-grade simulations, enabling more accurate short-term climate forecasts and long-term climate simulations for Nepal and the broader South Asia region.
About WRF (Weather Research & Forecasting)
The WRF model is a state-of-the-art mesoscale numerical weather prediction system developed jointly by NCAR, NOAA, and other institutions. It is widely used for:
- Regional climate simulations and downscaling of global climate models
- Operational weather forecasting at resolutions from meters to kilometers
- Research on atmospheric dynamics, microphysics, and convective processes
- Sensitivity studies exploring impacts of parameterization choices
Simulation Design & Methodology
Multi-Year Simulations
Conducted extensive WRF simulations spanning multiple years to capture seasonal variability and different weather regimes. This approach ensures robust evaluation of model performance across diverse meteorological conditions.
Parameterization Scheme Testing
Tested diverse combinations of physical parameterization schemes including:
- Microphysics Schemes: WRF Single-Moment (WSM), Morrison, Thompson
- Cumulus Parameterization: Kain-Fritsch, Grell, Tiedtke
- Planetary Boundary Layer: YSU, MYJ, MYNN
- Land Surface Model: Noah-MP for improved surface physics
- Radiation: RRTM for accurate longwave and shortwave radiation
This comprehensive approach identifies optimal parameterization combinations for different seasons and weather phenomena over complex South Asian terrain.
Validation Against Observations
Model outputs validated against multiple independent observation networks:
- Precipitation: Ground-based rain gauge networks and satellite estimates
- Temperature: Surface station observations at multiple elevations
- Upper-Air Data: Radiosonde profiles from atmospheric soundings
- Spatial Coverage: Nepal and surrounding South Asian regions
Validation conducted at multiple temporal scales (daily, monthly) using standard meteorological verification metrics.
Performance Metrics & Assessment
Comprehensive evaluation using:
- Bias Assessment: Systematic over/under-prediction patterns
- Skill Metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE)
- Correlation Analysis: Pattern correlation with observations
- Categorical Metrics: Probability of Detection (POD), False Alarm Rate (FAR)
Key Findings & Recommendations
Seasonal Scheme Optimization
Identified that optimal parameterization schemes vary by season. Monsoon period requires stronger convective schemes, while pre-monsoon shows better performance with microphysics-dominant schemes.
Precipitation Performance
WRF demonstrates skill in capturing precipitation patterns, with particular strengths in monsoon month simulations and complex mountainous terrain representation.
Temperature Simulation
Temperature simulations show consistent behavior with minimal bias when appropriate land surface models are employed. Elevation-dependent variations are well-represented.
Operational Recommendations
Provided DHM with season-specific parameterization recommendations and bias-correction strategies for operational forecast implementation.
Model Configuration
Technologies & Tools
Modeling System
- WRF (v4.x)
- WRF Pre-processing System (WPS)
- Data Assimilation (WRF-DA)
Programming & Scripting
- Bash/Shell scripting for automation
- Python for data processing
- NCO (NetCDF operators)
Visualization
- Cartopy for map projections
- Matplotlib for publication-ready figures
- wrf-python for WRF-specific plotting
Data Management
- NetCDF4 data format
- High-performance computing clusters
- Large-scale data archiving
Related Publications
Research from this project contributes to the following peer-reviewed publications:
- Adhikari, S., Lamichhane, P., Karki, J., Paudel, H.K et al. (2025). "Analyzing Extreme Precipitation during the Prolonged Summer Monsoon of 2022 in Nepal: Insights from Hourly Observational Data."Journal of Institute of Science and Technology, 30(1), 179-188.
- Kuikel, S., Kuinkel, D., Paudel, H.K et al. (2026). "624 mm in 24 hours: Analysis and simulation of a new national rainfall record event in Nepal."Journal of Meteorological Research, Under Review.
Research Impact & Significance
- Operational Improvement: Directly enhances DHM's operational weather prediction capabilities for Nepal.
- Climate Research: Enables high-resolution regional climate simulations and downscaling studies.
- Extreme Weather: Improves understanding and forecasting of extreme precipitation and temperature events.
- Institutional Capacity: Builds research and operational capacity within DHM for advanced climate modeling.