Role of interactions between cloud microphysics, dynamics and aerosol in the heavy rainfall event of June 2013 over Uttarakhand, India

Hazra, Anupam; Chaudhari, Hemantkumar S.; Ranalkar, M. R.; Chen, Jen‐Ping

doi:10.1002/qj.2983

Cited by 38 publications

(12 citation statements)

References 57 publications

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“…Most of the studies conducted earlier over this region (Medina et al, 2010;Kumar et al, 2012;Thayyen et al, 2013;Kumar et al, 2014;Chevuturi et al, 2015;Shekhar et al, 2015;Chevuturi and Dimri, 2016;Rajesh et al, 2016;Hazra et al, 2017) are based on the general or default WRF configuration with WSM6 microphysics, a Kain-Fritsch cumulus parameterization scheme and planetary boundary layer of the Yonsei University scheme. In this paper, ensemble experiments are conducted using the WRF model with different grid spacing, four microphysics schemes, two cumulus parameterization schemes, two planetary boundary layer schemes and two land surface model conditions.…”

Section: Discussionmentioning

confidence: 99%

“…Medina et al (2010) used the WRF model to understand how topography and land surface conditions affect the extreme convection in western and eastern Himalayas. Particularly for the 2013 heavy rainfall episode in the Uttarakhand region, the WRF model is used in several studies, including those by Kotal et al (2014), Vellore et al (2016) and Hazra et al (2017), to understand the physical processes leading to the event. Shekhar et al (2015), Chevuturi and Dimri, (2016), and Dimri et al (2016) performed in-depth synoptic analysis of the June 2013 heavy rainfall event using the WRF model.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin

Chawla¹,

Osuri²,

Mujumdar³

et al. 2018

Hydrol. Earth Syst. Sci.

122

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Abstract. Reliable estimates of extreme rainfall events are necessary for an accurate prediction of floods. Most of the global rainfall products are available at a coarse resolution, rendering them less desirable for extreme rainfall analysis. Therefore, regional mesoscale models such as the advanced research version of the Weather Research and Forecasting (WRF) model are often used to provide rainfall estimates at fine grid spacing. Modelling heavy rainfall events is an enduring challenge, as such events depend on multi-scale interactions, and the model configurations such as grid spacing, physical parameterization and initialization. With this background, the WRF model is implemented in this study to investigate the impact of different processes on extreme rainfall simulation, by considering a representative event that occurred during 15-18 June 2013 over the Ganga Basin in India, which is located at the foothills of the Himalayas. This event is simulated with ensembles involving four different microphysics (MP), two cumulus (CU) parameterizations, two planetary boundary layers (PBLs) and two land surface physics options, as well as different resolutions (grid spacing) within the WRF model. The simulated rainfall is evaluated against the observations from 18 rain gauges and the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42RT version 7 data. From the analysis, it should be noted that the choice of MP scheme influences the spatial pattern of rainfall, while the choice of PBL and CU parameterizations influences the magnitude of rainfall in the model simulations. Further, the WRF run with Goddard MP, Mellor-Yamada-Janjic PBL and Betts-MillerJanjic CU scheme is found to perform "best" in simulating this heavy rain event. The selected configuration is evaluated for several heavy to extremely heavy rainfall events that occurred across different months of the monsoon season in the region. The model performance improved through incorporation of detailed land surface processes involving prognostic soil moisture evolution in Noah scheme compared to the simple Slab model. To analyse the effect of model grid spacing, two sets of downscaling ratios -(i) 1 : 3, global to regional (G2R) scale and (ii) 1 : 9, global to convection-permitting scale (G2C) -are employed. Results indicate that a higher downscaling ratio (G2C) causes higher variability and consequently large errors in the simulations. Therefore, G2R is adopted as a suitable choice for simulating heavy rainfall event in the present case study. Further, the WRF-simulated rainfall is found to exhibit less bias when compared with the NCEP FiNaL (FNL) reanalysis data.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin

Chawla¹,

Osuri²,

Mujumdar³

et al. 2018

Hydrol. Earth Syst. Sci.

122

View full text Add to dashboard Cite

show abstract

“…For example, more cloud water can be transported upward due to CCN‐induced initial suppression of warm rainfall and increase the freezing of cloud drops to ice phase. The increased releases of latent heat in a polluted environment invigorate the cloud systems to generate more ice hydrometeors and eventually more rain but at a later stage and probably at a downwind location compared to clean conditions (Hazra et al, , ; Prabha et al, ; Sarangi et al, , ). Recent years have witnessed devastating flash floods over various major cities of North India during the monsoon season, causing loss of properties and human life (Gupta & Nair, ; http://www.independent.co.uk/news/world/asia/india-floodsbangladesh-nepal-deaths-millions-homeless-latest-news-updates-a7919006.html).…”

Section: Introductionmentioning

confidence: 99%

Aerosol and Urban Land Use Effect on Rainfall Around Cities in Indo‐Gangetic Basin From Observations and Cloud Resolving Model Simulations

et al. 2018

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Coupling of urban land use land cover (LULC) and aerosol loading on rainfall around cities in the Gangetic Basin (GB) is examined here. Long‐term observations illustrate more rainfall at urban core and climatological downwind regions compared to the upwind regions of Kanpur, a metropolitan area located in central GB. In addition, analysis of a 15 day cloud resolving simulation using the Weather Research and Forecasting model also illustrated similar rainfall pattern around other major cities in the GB. Interestingly, the enhancement of downwind rainfall was greater than that over urban regions, and it was positively associated with both the urban area of the city and ambient aerosol loading during the propagating storm. Further, to gain a process‐level understanding, a typical storm that propagated northwestward across Kanpur was simulated using Weather Research and Forecasting under three different scenarios. Case 1 has realistic LULC representation of Kanpur, while the grids representing the Kanpur urban region were replaced by cropland LULC pattern in Case 2. Comparison illustrated that urban heat island effect caused convergence of winds and moisture in the lower troposphere, which enhances convection over urban region and induced more rainfall over the urban core compared to upwind regions. Case 3 is similar to Case 1 but lower aerosol concentration (by a factor of 100) over the storm region. Analysis shows that aerosol‐induced microphysical changes delay the initiation of warm rain (over the upwind region) but enhance ice phase particle formation in latter stages (over the urban and downwind regions) resulting in increase in downwind rainfall.

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“…and Kain-Fritsch (KF) for cumulus physics are adopted in this study (Srivastava et al, 2015;Hazra et al, 2017;Cai et al, 2018).…”

Section: Wrf Model and Configurationsmentioning

confidence: 99%

Typhoon rainstorm simulation with radar data assimilation in southeast coast of China

Tian

Liu

Ding

et al. 2020

Preprint

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Abstract. As an effective technique to improve the rainfall forecast, data assimilation plays an important role in meteorology and hydrology. The aim of this study is to explore the reasonable use of Doppler radar data assimilation to correct the initial and lateral boundary conditions of the Numerical Weather Prediction (NWP) systems. The Weather Research and Forecasting (WRF) model is applied to simulate three typhoon storm events in southeast coast of China. Radar data from Changle Doppler radar station are assimilated with three-dimensional variational data assimilation (3-DVar) model. Nine assimilation modes are designed by three kinds of radar data (radar reflectivity, radial velocity, radar reflectivity and radial velocity) and three assimilation time intervals (1 h, 3 h and 6 h). The rainfall simulations in a medium-scale catchment, Meixi, are evaluated by three indices including relative error (RE), critical success index (CSI) and root mean square error (RMSE). Assimilating radial velocity with time interval of 1 h can significantly improve the rainfall simulations and outperforms the other modes for all the three storm events. Shortening the assimilation time interval can improve the rainfall simulations in most cases, while assimilating radar reflectivity always leads to worse simulation as the time interval shortens. The rainfall simulation can be improved by data assimilation as a whole, especially for the heavy rainfall with strong convection. The findings provide references for improving the typhoon rainfall forecasts in catchment scale and have great significance on typhoon rainstorm warning.

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Role of interactions between cloud microphysics, dynamics and aerosol in the heavy rainfall event of June 2013 over Uttarakhand, India

Cited by 38 publications

References 57 publications

Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin

Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin

Aerosol and Urban Land Use Effect on Rainfall Around Cities in Indo‐Gangetic Basin From Observations and Cloud Resolving Model Simulations

Typhoon rainstorm simulation with radar data assimilation in southeast coast of China

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