On the analysis of ground-based microwave radiometer data during fog conditions

Fonseca, Ricardo; Temimi, Marouane; Valappil, Vineeth Krishnan; Weston, Michael; Thota, Mohan; Wehbe, Youssef; Yousef, Latifa A.

doi:10.1016/j.atmosres.2019.104652

Cited by 37 publications

(65 citation statements)

References 70 publications

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“…9a are consistent with those with respect to the MWR measurements at Masdar Institute shown in Fig. 10 as expected since the MWR temperature profiles are found to be in close agreement with the radiosonde profiles (Temimi et al 2020). Here, the solid lines denote PBL height estimates, defined as the height in the lowest 5 km at which the vertical potential gradient is maximized.…”

Section: June 2020supporting

confidence: 88%

“…For example, it is concluded that having a correct estimate of the observed surface albedo is important, stressing the need to measure it in dedicated field campaigns, while the choice of the momentum roughness length seems to be less relevant. In addition, intensive in situ observations of soil moisture are needed in the region (Fares et al 2013;AlJassar et al 2019), where unlike midlatitude regions such measurements are scarce (Temimi et al 2014). Such effort could be useful to verify retrievals from missions like the NASA Soil Moisture Active Passive (SMAP) one, which could enhance its potential to be used in NWP models.…”

Section: Discussionmentioning

confidence: 99%

“…d Hourly automatic weather station (AWS) and airport station data at 35 sites spread out over the country, shown as circles in Fig. 1c Temimi et al 2020). The MWR also provides humidity measurements but only temperature profiles are used for evaluation.…”

Section: Observational Datasets and Verification Diagnosticsmentioning

confidence: 99%

“…This is justified because (i) the humidity biases have a rather large magnitude in the lower troposphere, exceeding 6 g kg 21 , and (ii) the elevation scanning mode gives high vertical resolution scans in the boundary layer for temperature only. Further details about the MWR performance can be found in Temimi et al (2020).…”

Section: Observational Datasets and Verification Diagnosticsmentioning

confidence: 99%

“…As for the other period, the scores are lower than for the air temperature, mostly because of a poor simulation of the observed water vapor mixing ratio variability (not shown). This may arise from errors in the PBLH is defined as the first level above the surface in the lowest 5 km at which the vertical potential temperature gradient du/dz is maximized, following Temimi et al (2020). The instrument is located at Masdar Institute in Abu Dhabi (24.43648N, 54.61198E).…”

Section: June 2020mentioning

confidence: 99%

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On the Analysis of the Performance of WRF and NICAM in a Hyperarid Environment

Fonseca

Thota

Temimi

et al. 2020

Weather and Forecasting

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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.

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Section: June 2020supporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Observational Datasets and Verification Diagnosticsmentioning

confidence: 99%

Section: Observational Datasets and Verification Diagnosticsmentioning

confidence: 99%

Section: June 2020mentioning

confidence: 99%

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On the Analysis of the Performance of WRF and NICAM in a Hyperarid Environment

Fonseca

Thota

Temimi

et al. 2020

Weather and Forecasting

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Characteristics of atmospheric aerosols over the UAE inferred from CALIPSO and Sun Photometer Aerosol Optical Depth

Nelli

Fissehaye

Francis

et al. 2020

Preprint

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Atmospheric aerosols have a wide range of impacts on the climate system. This includes interactions with the incoming solar radiation and clouds (Forkel et al., 2012), serving as a medium for transporting various organisms through vast distances (Azua-Bustos et al., 2019), and causing or enhancing respiratory, infectious, cardiovascular, and allergic diseases (Boersma et al., 2008). However, quantifying the effects of aerosols on the climate on both global and regional scales has never been a trivial problem, primarily due to their sporadic nature and large spatiotemporal variability (B. Liu et al., 2018). Hence, a considerable uncertainty of their net effect on the climate remains (Boucher et al., 2013).Atmospheric aerosols originate from different sources, namely biogenic (nature) and anthropogenic (human-related activities). Mineral dust aerosols are the dominant species in the atmosphere by virtue of their large contribution to the global aerosol loading (Tegen et al., 2002). These natural aerosols originate mostly from the Arabian, the Asian, and the Sahara deserts (Prospero, 2002). Dust mixed with biomass burning and urban pollution, frequently labeled as "polluted dust," is also an important contributor to atmospheric aerosols and is regularly observed in the Middle East, West Africa, and Central Asia (Kim et al., 2018).The cycle of atmospheric aerosols, from emission to transport and deposition, is strongly controlled by the atmospheric dynamics and thermodynamics (Kok et al., 2012). In the Arabian Gulf and surrounding region, the prevailing near-surface wind is from the northwest, locally known as Shamal winds (Bou Karam Francis et al., 2017;Yu et al., 2016). This large-scale wind interacts with local land-sea breeze circulations (Eager et al., 2008), which are stronger in the summer season when the land-sea temperature gradient is larger. As a result of the strong heating of the surface by the Sun, a thermal low develops in the region,

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