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Study Analyzes Performance of WRF and NICAM in Hyperarid Environment

Arid and semi-arid regions, known to be very sensitive to climate change, are expected to expand due to global warming and may experience more extreme weather conditions in the future. Therefore, it is necessary to correctly simulate specific extreme weather events and better understand the limitations of the numerical models used. 

One way to gain further insight into the deficiencies of a numerical model is to conduct an ensemble of simulations in which different sets of physics and dynamics options are chosen and the initial and boundary conditions are analyzed. This helps in determining the optimal model configuration for a given environment, with a further improvement of the model’s performance obtained by optimizing relevant tunable parameters defined in the parameterization schemes.

Another option is to compare the model’s performance to that of other numerical models that have different physics and dynamics but are forced by the same dataset.

In an attempt to understand the local limitations of numerical models in predicting the temperature profiles of arid regions like the UAE, Prof. Marouane Temimi, former Principal Investigator of the UAE Rain Enhancement Program (UAEREP) modeling integration project between Khalifa University (KU) and National Center of Meteorology (NCM), worked on a study analyzing Global Forecast System (GFS) data from the Weather Research and Forecasting (WRF) Model and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) that ran over the UAE for a 3-day period in the cold season from 16 to 18 December, 2017 and the warm season from 13 to 15 April, 2018.

The study aimed to better understand the limitations of the two models in a hyperarid environment, where numerical models are known to underperform and satellite-derived meteorological variables, such as surface temperature, are not very reliable.

The models’ performance was evaluated against four observational datasets: weather station observations, eddy-covariance flux measurements at Al Ain, microwave radiometer–derived temperature profile, and twice-daily radiosonde measurements at Abu Dhabi. An overestimation of the daily mean air temperature by 1°–3°C was noticed for both models and periods. This warm bias is attributed to the reduced cloud cover and resulting increased surface downward shortwave radiation flux.

The study also found that while the performance of both models for the near-surface fields is comparable, NICAM outperforms WRF in the simulation of vertical profiles of temperature, relative humidity, and wind speed, being able to partially correct some of the biases in the GFS data.

An extension of this work would be to consider additional cases and perturb the experimental set up by testing different physics options and changing the values of relevant surface parameters to improve the models’ performance. Once this is achieved, a long-term simulation can be conducted to learn about the local-scale meteorology.

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