Success Story: The multiphysics experiments of the Weather Research and Forecasting Model (WRF) on precipitation patterns of Turkey

NCC presenting the success story

TÜBİTAK-TRUBA, one of the two capacious HPC centers authoritatively in Turkey, coordinates NCC Turkey. The Middle East Technical University (METU), Sabanci University (SU), and Istanbul Technical University National Center for High-Performance Computing (UHeM) are third-party partners and cooperate with TÜBİTAK-TRUBA in the decision-making process. In a project, TÜBİTAK-TRUBA provides technical support to researchers and monitor running jobs. Furthermore, TÜBİTAK-TRUBA regularly organises online meetings with the researchers to discuss drawbacks and progressions. This project is conducted under the auspices of TÜBİTAK-TRUBA with the inspections mentioned above. On the researcher side, the METU researcher team performs the project and present the success story.    

Industrial Organisations Involved:

The collaboration between the METU research team and Eriktronik SME was based upon a European Union (EU) project proposal. After our EU project was rejected at the second stage for the second time due to limited funding availability, we were directed to the TÜBİTAK-TRUBA to maintain our works. The Eriktronik company and researchers at METU collaborated in this project, where an operational forecast of hydrometeorological variables are aimed to be acquired at high accuracy. The heavy lifting in terms of Numerical Weather Prediction (NWP) model setup and run has been undertaken by the METU team mainly. 

Technical/scientific Challenge:

The reliable weather research and forecast heavily rely on the choice of the model physics options. There are multiple physics options in the WRF model that should be tested in a combinational manner to improve the forecasts. The weather events are chaotic by nature, and any physic option may be time and domain-specific thus have some uncertainties. Therefore, the physics combinations doing a good job are hired as an ensemble to deal with these uncertainties. A sensitivity test can reveal the ensemble members of the multiphysics combinations. On the other hand, similar sensitivity tests are conducted for climate modelling but with relatively long runs (~year-long). To our knowledge, there are no studies comprehensively dealing with such sensitivity tests of the multiphysics options over the Turkey domain.

The WRF model is an NWP system designed to solve physics equations in the 3D and 4D architectures. It supports parallel computing in which end users need to utilize HPCs in weather forecasts and climate prediction. To a certain extent, high resolution is needed to achieve improved and creditable results in running WRF. The higher the resolution is tuned, the higher the computational costs are required.


The research team has the following benefits owing to this project:

  • The team has gained experience for the first time in the HPC domain.
  • The team has been encouraged to apply to the EuroHPC projects in the seasonal forecast and climate prediction areas through gained experience with this project.
  • The team improved their insight into driving mechanisms of precipitation over Turkey.


The project’s sensitivity tests were completed over the Turkey domain for 2020, with a 60-combination of model physics in 4-km resolution. The combination number, resolution, and simulation time are rather comprehensive for such sensitivity tests. We have valuable information now about which multiphysics ensemble responds favorably to the Turkey precipitation characteristics. Our findings pave the way for improving weather forecasts and climate predictions over Turkey. This study could not be performed without utilizing TÜBİTAK-TRUBA HPC facilities.

Business impact:

This project is to have several impacts on the private and public sectors. On the private sector side, high accuracy operational forecasts that are tailored to Turkey via specific parametrizations are obtained, where such a forecast was available before. Having such a high accuracy forecast is very valuable for many industries, in particular those are interested in prediction of renewable energy (wind, solar, and hydropower). The private sector companies dealing with renewable energy have an opportunity to access these data.

On the public sector side, institutions that are closely working on the prediction of hydrometeorological variables such as precipitation, temperature, and runoff can utilize the results of this study and improve their short term operational forecasts and climate predictions. In particular, they may improve early flood warning systems based on the new forecasts. The Ministry of Environment, Urbanization and Climate Change can act upon the climate change impacts on urban and rural areas of Turkey when climate predictions are completed based on the best configuration obtained with this project. When the predictions are completed, The General Directorate of Water Management can update their basin management plans. The Ministry of Agricultural and Forestry can monitor drought and flood events more precisely when the seasonal forecasts are performed, depending upon the multiphysics ensemble obtained here.


  • Keywords: Hydrometeorological forecast, precipitation, temperature, wind, renewable energy, numerical weather prediction
  • Industry sector: Earth science, Energy, Environment/climate/weather, Public services/Civil protection
  • Technology: HPC

Previously available model performances (solid gray box on the right with a median 0.58) are improved to the level of European state of the art model accuracy levels (left gray box with median 0.66). These are very encouraging results compared with existing state of the art models.


This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 951732. The JU receives support from the European Union’s Horizon 2020 research and innovation program and Germany, Bulgaria, Austria, Croatia, Cyprus, the Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia, Spain, Sweden, the United Kingdom, France, the Netherlands, Belgium, Luxembourg, Slovakia, Norway, Switzerland, Turkey, Republic of North Macedonia, Iceland, Montenegro