Sensitivity study of the planetary boundary layer and microphysical schemes to the initialization of convection over the Arabian Peninsula

Schwitalla, Thomas; Branch, Oliver; Wulfmeyer, Volker

doi:10.1002/qj.3711

Cited by 31 publications

(70 citation statements)

References 83 publications

(146 reference statements)

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“…As was the case for the winter period, and in line with other studies such as Gunwani and Mohan (2017), both WRF and NICAM generally underestimate the observed cloud cover, as concluded by visually comparing the two (not shown) and in line with other studies such as Schwitalla et al (2019) and Wehbe et al (2019), leading to enhanced downward shortwave radiation fluxes at the surface and warmer daytime air temperatures. The exception is when heavier than observed precipitation amounts are predicted, as seen by comparing Figs.…”

Section: June 2020supporting

confidence: 88%

“…The physics parameterization schemes are given in Table 1. A similar set up was used in previous studies over the UAE (e.g., Chaouch et al 2017;Weston et al 2018;Schwitalla et al 2019). Following Chaouch et al (2017), the QNSE PBL scheme is employed, while Weston et al (2018) highlighted the added value of the Noah-MP LSM, and Schwitalla et al (2019) of the Thompson cloud microphysics scheme.…”

Section: A Wrf Configurationmentioning

confidence: 99%

“…A similar set up was used in previous studies over the UAE (e.g., Chaouch et al 2017;Weston et al 2018;Schwitalla et al 2019). Following Chaouch et al (2017), the QNSE PBL scheme is employed, while Weston et al (2018) highlighted the added value of the Noah-MP LSM, and Schwitalla et al (2019) of the Thompson cloud microphysics scheme. The radiation and cumulus schemes considered here are those used in Chaouch et al (2017) and Weston et al (2018).…”

Section: A Wrf Configurationmentioning

confidence: 99%

“…As in Weston et al (2018) that considered eight cold season fog events, the authors reported a cold bias, which the latter managed to partially correct by switching from the Noah land surface model (Noah LSM; Chen and Dudhia 2001;Tewari et al 2004) to a modified version of the Noah LSM with multiparameterization options (Noah-MP; Niu et al 2011;Z.-L. Yang et al 2011). For a fivemember physics ensemble conducted for a typical warm season convective event, Schwitalla et al (2019) also reported a cold bias in the 2-m temperature during the morning and evening transition, in addition to an underestimation of the observed cloud cover, regardless of the cloud microphysics scheme employed. The model underestimates the observed precipitation, with the 10-m wind speed generally higher than that observed except in the evening hours.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: A Wrf Configurationmentioning

confidence: 99%

Section: A Wrf Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Fonseca

Thota

Temimi

et al. 2020

Weather and Forecasting

Self Cite

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“…The strong negative bias over the Sahara and the Arabian Peninsula is probably related to a toostrong cooling effect in the WRF model over sand surfaces at higher resolution. This effect was also observed in a study of Schwitalla et al (2019), who investigated the behaviour of a different WRF physics combination over the Arabian Peninsula. It is also interesting to note here that, although both simulations are forced by the same SST data set, a constant temperature bias over the Indian and topical Pacific Ocean is present.…”

Section: Spatial Distribution Of 2 M Temperatures and Precipitable Watersupporting

confidence: 61%

Near-global-scale high-resolution seasonal simulations with WRF-Noah-MP v.3.8.1

et al. 2020

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Abstract. The added value of global simulations on the convection-permitting (CP) scale is a subject of extensive research in the earth system science community. An increase in predictive skill can be expected due to advanced representations of feedbacks and teleconnections in the ocean–land–atmosphere system. However, the proof of this hypothesis by corresponding simulations is computationally and scientifically extremely demanding. We present a novel latitude-belt simulation from 57∘ S to 65∘ N using the Weather Research and Forecasting (WRF)-Noah-MP model system with a grid increment of 0.03∘ over a period of 5 months forced by sea surface temperature observations. In comparison to a latitude-belt simulation with 45 km resolution, at CP resolution the representation of the spatial-temporal scales and the organization of tropical convection are improved considerably. The teleconnection pattern is very close to that of the operational European Centre for Medium Range Weather Forecasting (ECMWF) analyses. The CP simulation is associated with an improvement of the precipitation forecast over South America, Africa, and the Indian Ocean and considerably improves the representation of cloud coverage along the tropics. Our results demonstrate a significant added value of future simulations on the CP scale up to the seasonal forecast range.

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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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Sensitivity study of the planetary boundary layer and microphysical schemes to the initialization of convection over the Arabian Peninsula

Cited by 31 publications

References 83 publications

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

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

Near-global-scale high-resolution seasonal simulations with WRF-Noah-MP v.3.8.1

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

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