On the analysis of the performance of WRF and nicam in a hyperarid environment

Ricardo Fonseca, Marouane Temimi, Mohan Satyanarayana Thota, Narendra Reddy Nelli, Michael John Weston, Kentaroh Suzuki, Junya Uchida, Kondapalli Niranjan Kumar, Oliver Branch, Youssef Wehbe, Taha Al Hosari, Noor Al Shamsi, Abdeltawab Shalaby

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

The Weather Research and Forecasting (WRF) Model and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) are forced with the Global Forecast System (GFS) data and run over the United Arab Emirates (UAE) for two 4-day periods: one in the cold season (16–18 December 2017) and another in the warm season (13–15 April 2018). The models’ performance is 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 is noticed for both models and periods. This warm bias is attributed to the reduced cloud cover and resulting increased surface downward shortwave radiation flux. A comparison with the eddy-covariance data suggested that both models also underestimate the observed albedo. However, when the models predict heavier amounts of precipitation, they tend to be colder than observations, typically by 2°–3°C. NICAM and WRF overpredict the strength of the near-surface wind speed at all weather stations by roughly 1–3 m s-1, which has been attributed to a poor representation of its subgrid-scale fluctuations and surface drag parameterization. WRF tends to be wetter and NICAM drier than the station observations, possibly because of differences in the cloud microphysics schemes. 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.

Original languageBritish English
Pages (from-to)891-919
Number of pages29
JournalWeather and Forecasting
Volume35
Issue number3
DOIs
StatePublished - Jun 2020

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