Seasonal and diurnal performance of daily forecasts with WRF V3.8.1 over the United Arab Emirates

[摘要] Effective numerical weather forecasting is vital in arid regions like the United Arab Emirates (UAE) where extreme events like heat waves, flash floods, and dust storms are severe. Hence, accurate forecasting of quantities like surface temperatures and humidity is very important. To date, there have been few seasonal-to-annual scale verification studies with WRF at high spatial and temporal resolution. This study employs a convection-permitting scale (2.7 km grid scale) simulation with WRF with Noah-MP, in daily forecast mode, from 1 January to 30 November 2015. WRF was verified using measurements of 2 m air temperature ( T 2 m ), 2 m dew point (TD 2 m ), and 10 m wind speed (UV 10 m ) from 48 UAE WMO-compliant surface weather stations. Analysis was made of seasonal and diurnal performance within the desert, marine, and mountain regions of the UAE. Results show that WRF represents temperature ( T 2 m ) quite adequately during the daytime with biases ≤ + 1   ∘ C. There is, however, a nocturnal cold bias ( − 1 to − 4  ∘ C), which increases during hotter months in the desert and mountain regions. The marine region has the smallest T 2 m biases ( ≤ - 0.75   ∘ C). WRF performs well regarding TD 2 m , with mean biases mostly ≤  1  ∘ C. TD 2 m over the marine region is overestimated, though (0.75–1  ∘ C), and nocturnal mountain TD 2 m is underestimated ( ∼ - 2   ∘ C). UV 10 m performance on land still needs improvement, and biases can occasionally be large (1–2 m s −1 ) . This performance tends to worsen during the hot months, particularly inland with peak biases reaching ∼  3 m s −1 . UV 10 m is better simulated in the marine region (bias ≤  1 m s −1 ) . There is an apparent relationship between T 2 m bias and UV 10 m bias, which may indicate issues in simulation of the daytime sea breeze. TD 2 m biases tend to be more independent. Studies such as these are vital for accurate assessment of WRF nowcasting performance and to identify model deficiencies. By combining sensitivity tests, process, and observational studies with seasonal verification, we can further improve forecasting systems for the UAE.

[学科分类] 天文学(综合)