Influence of cumulus convection and cloud microphysics parameterizations on the prediction of Western Disturbances

Sarkar, Abhijit; Dutta, Devajyoti; Chakraborty, Paromita; Dutta, Somenath; Majumdar, Susmita; Payra, Swagata; Bhatla, R.

doi:10.1007/s00703-019-00697-2

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…They found only a low sensitivity to the choice of microphysics scheme. Sarkar et al (2019) examined 30 WD case studies and also found that the choice of convection and microphysics scheme had little impact on precipitation biases, with a consistent wet bias over orography. In a parallel study, Sarkar et al…”

Section: Numerical Weather Prediction Modelsmentioning

confidence: 99%

“…The key difference with earlier studies of similar focus is increased resolution, which is sometimes sufficiently fine to permit explicit convection. (Sarkar et al, 2019) ran 30 case studies at 9 km with both explicit and parameterised convection, but found that the parameterised simulations produced more realistic rainfall and concluded that 9 km was too coarse to turn off convection schemes. Regardless, as we have already seen, studies have shown that WRF can produce fairly realistic heavy WD precipitation even at these grey-zone resolutions.…”

Section: Numerical Weather Prediction Modelsmentioning

confidence: 99%

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Western disturbances and climate variability: a review of recent developments

Hunt,

Baudouin,

Turner

et al. 2024

Preprint

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Abstract. Western disturbances (WDs) are synoptic-scale weather systems embedded within the subtropical westerly jet. Manifesting as upper-level troughs often associated with a lower-tropospheric low over Western India, they share some dynamical features with extratropical cyclones. WDs are most common during the boreal winter (December to March), during which they bring the majority of precipitation – both rain and snow – to the Western Himalaya, as well as to surrounding areas of north India, Pakistan and the Tibetan Plateau. WDs are also associated with weather hazards such as heavy snowfall, hailstorms, fog, cloudbursts, avalanches, frost, and coldwaves. In this paper, we review the recent understanding and development on WDs. Recent studies have collectively made use of novel data, novel analysis techniques, and the increasing availability of high-resolution weather and climate models. This review is separated into six main sections – structure and thermodynamics, precipitation and impacts, teleconnections, modelling experiments, forecasting at a range of scales, and paleoclimate and climate change – each motivated with a brief discussion of the accomplishments and limitations of previous research. A number of step changes in understanding are synthesised. Use of new modelling frameworks and tracking algorithms has significantly improved knowledge of WD structure and variability, and a more frequentist approach can now be taken. Improved observation systems have helped quantification of water security over the Western Himalaya. Convection-permitting models have improved our understanding of how WDs interact with the Himalayas to trigger natural hazards. Improvements in paleoclimate and future climate modelling experiments have helped to explain how WDs and their impacts over the Himalaya respond to large-scale natural and anthropogenic forcings. We end by summarising unresolved questions and outlining key future WD research topics.

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Section: Numerical Weather Prediction Modelsmentioning

confidence: 99%

Section: Numerical Weather Prediction Modelsmentioning

confidence: 99%

Western disturbances and climate variability: a review of recent developments

Hunt,

Baudouin,

Turner

et al. 2024

Preprint

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“…Hence, evaluating the performance of CPS in different regions with different geographical and climatic conditions, and the season is one of the fundamental steps to optimize WRF performance and further improve WRF precipitation products. In this context, Madala et al (2013) studied the performance of different CPSs in simulating pre‐monsoon thunderstorm events, Haghroosta et al (2014) in simulating rainfall associated with typhoons, Cruz and Narisma (2016) in predicting rainfall associated with tropical cyclones, Das et al (2019) in predicting rainfall associated with monsoon lows, Sarkar et al (2020) in predicting rainfall associated with westerly disturbances, Liu et al (2020) in simulating heavy rainfall, Castorina et al (2021) in forecasting weather for complex orographic areas. Recently, Khansalari et al (2021) showed that Grell‐Freitas CPS performs better than Kain‐Fritsch, Tiedtke, and Grell‐3 CPSs when convective uplift causes precipitation but fails in representing precipitation associated with frontal uplift in the northern part of Iran.…”

Section: Introductionmentioning

confidence: 99%

WRF prediction of an atmospheric river‐related precipitation event: Sensitivity to cumulus parameterization schemes

Maddah,

Mostamandi

2024

Meteorological Applications

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The present study aimed to evaluate performance sensitivity to the Cumulus Parameterization Scheme (CPS) used for the Weather Research and Forecasting (WRF) model to predict the atmospheric river‐related precipitation (ARP) event with 206 and 57 mm, highest and area‐averaged precipitation (AAP) per 24‐h, respectively, that occurred over the central mountainous basins of Iran on 31 March 2019. For this purpose, experiments were designed using the 12 (almost all) CPSs available in WRF v4. To verify the predicted precipitation (from the inner 4‐km domain), both point‐scale and grid‐scale comparisons were performed against gauge‐ and satellite‐based observational data at three accumulation time‐scales (12‐, 18‐, and 24‐h) and in three distinct sub‐regions. All scores obtained from the different statistical metrics used, are in complete agreement with a strongly dependent performance of WRF on the CPS used. In addition, the use of Kain‐Fritsch, KF‐CuP, and Grell‐3 CPSs could provide a realistic picture of impending heavy precipitation for WRF. Contrary, the New SAS, Tiedtke, and Zhang‐McFarlane CPSs did not perform satisfactorily in predicting the ARP event. As a result, CPSs with the “momentum transport” option in their modification mechanism are unlikely to adequately simulate the conversion of incoming low‐level moisture from atmospheric river to precipitation. However, precipitation predictions are more accurate at the 24‐h accumulation time‐scale than at the 12‐ and 18‐h. Also, a dry bias in the predictions is expected as the terrain elevation and accumulation time‐scale decrease and the distance from the core of the precipitation field increases.

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