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  • 1
    Publication Date: 2018-06-01
    Description: High-altitude balloons and rockets are regularly launched at the Esrange Space Center (ESC) in Kiruna, Sweden, with the aim of retrieving atmospheric data for meteorological and space studies in the Arctic region. Meteorological conditions, particularly wind direction and speed, play a critical role in the decision of whether to go ahead with or postpone a planned launch. Given the lack of high-resolution wind forecasts for this remote region, the Weather Research and Forecasting (WRF) Model is used to downscale short-term forecasts given by the Global Forecast System (GFS) for the ESC for six 5-day periods in the warm, cold, and transition seasons. Three planetary boundary layer (PBL) schemes are considered: the local Mellor–Yamada–Janjić (MYJ), the nonlocal Yonsei University (YSU), and the hybrid local–nonlocal Asymmetric Convective Model 2 (ACM2). The ACM2 scheme is found to provide the most skillful forecasts. An analysis of the WRF Model output against the launch criteria for two of the most commonly launched vehicles, the sounding rockets Veículo de Sondagem Booster-30 (VSB-30) and Improved Orion, reveals probability of detection (POD) values that always exceeds 60% with the false alarm rate (FAR) generally below 50%. It is concluded that the WRF Model, in its present configuration, can be used to generate useful 5-day wind forecasts for the launches of these two rockets. The conclusions reached here are applicable to similar sites in the Arctic and Antarctic regions.
    Print ISSN: 0882-8156
    Electronic ISSN: 1520-0434
    Topics: Geography , Physics
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  • 2
    Publication Date: 2020-07-10
    Description: The central aim of this work is to investigate the characteristics of fog events over the United Arab Emirates (UAE) and identify the underlying physical processes responsible for fog initiation and dissipation. To achieve this, hourly meteorological measurements at eight airport stations, along with ERA5 reanalysis data (1995–2018), are utilized. The analysis indicates the dominance of radiation fog (RAD) as, on average, 70% of the observed events fall under this category. Fog in the UAE typically forms between 2000 and 0200 local time (LT) and dissipates between 0600 and 0900 LT. During a typical dense fog event recorded during 22–23 December 2017, cooling and moistening tendencies of up to 1.2 K h−1 and 0.7 g kg−1 h−1 are observed ~5–6 h before fog onset. In the vertical, a dry and warm layer above 750 hPa gradually descends from above 500 hPa to promote the development of fog. Similar conclusions are reached when analyzing composites of fog events. Further, the variability of fog occurrence associated with El Niño–Southern Oscillation (ENSO) patterns is explored. It is concluded that the El Niño (warm) and La Niña (cold) phases exhibit very different spatial characteristics with respect to surface meteorological variables. In particular, during El Niño events, the near-surface atmosphere is cooler and moister compared to La Niña events, favoring RAD fog formation over the UAE. Besides, fog events during El Niño years tend to last longer compared to La Niña years due to an earlier onset.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
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  • 3
    Publication Date: 2020-09-03
    Description: A thorough evaluation of the Weather Research and Forecasting (WRF) model is conducted over the United Arab Emirates, for the period September 2017 - August 2018. Two simulations are performed: one with the default model settings (control run), and another one (experiment) with an improved representation of soil texture and land use land cover (LULC). The model predictions are evaluated against observations at 35 weather stations, radiosonde profiles at the coastal Abu Dhabi airport, and surface fluxes from eddy-covariance measurements at the inland city of Al Ain. It is found that WRF’s cold temperature bias, also present in the forcing data and seen almost exclusively at night, is reduced when the surface and soil properties are updated, by as much as 3.5 K. This arises from the expansion of the urban areas, and the replacement of loamy regions with sand, which has a higher thermal inertia. However, the model continues to overestimate the strength of the near-surface wind at all stations and seasons, typically by 0.5 to 1.5 m s-1. It is concluded that the albedo of barren/sparsely vegetated regions in WRF (0.380) is higher than that inferred from eddy-covariance observations (0.340), which can also explain the referred cold bias. At the Abu Dhabi site, even though soil texture and LULC are not changed, there is a small but positive effect on the predicted vertical profiles of temperature, humidity, and horizontal wind speed, mostly between 950 and 750 hPa, possibly because of differences in vertical mixing.
    Print ISSN: 1525-755X
    Electronic ISSN: 1525-7541
    Topics: Geography , Geosciences , Physics
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  • 4
    Publication Date: 2019-03-01
    Description: Estimates of the uncertainty of model output fields (e.g., 2-m temperature, surface radiation fluxes, or wind speed) are of great value to the weather and climate communities. The traditional approach for the uncertainty estimation is to conduct an ensemble of simulations where the model configuration is perturbed and/or different models are considered. This procedure is very computationally expensive and may not be feasible, in particular for higher-resolution experiments. In this paper, a new method based on Bayesian hierarchical models (BHMs) that requires just one model run is proposed. It is applied to the Weather Research and Forecasting (WRF) Model’s 2-m temperature in the Botnia–Atlantica region in Scandinavia for a 10-day period in the winter and summer seasons. For both seasons, the estimated uncertainty using the BHM is found to be comparable to that obtained from an ensemble of experiments in which different planetary boundary layer (PBL) schemes are employed. While WRF-BHM is not capable of generating the full set of products obtained from an ensemble of simulations, it can be used to extract commonly used diagnostics including the uncertainty estimation that is the focus of this work. The methodology proposed here is fully general and can easily be extended to any other output variable and numerical model.
    Print ISSN: 1558-8424
    Electronic ISSN: 1558-8432
    Topics: Geography , Physics
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  • 5
    Publication Date: 2020-04-17
    Description: 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.
    Print ISSN: 0882-8156
    Electronic ISSN: 1520-0434
    Topics: Geography , Physics
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