Forecasting the heavy rainfall during Himalayan flooding—June 2013

Dube, Anumeha; Ashrit, Raghavendra; Ashish, Amit; Sharma, Kuldeep; Iyengar, G. R.; Rajagopal, E. N.; Basu, Swati

doi:10.1016/j.wace.2014.03.004

Cited by 77 publications

(30 citation statements)

References 11 publications

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“…The synthesis of the synoptic setting of the event has been carried out in a number of studies such as Dube et al (2014), Kotal et al (2014), Ray et al (2014), Shekhar et al (2015), Chevuturi and Dimri, (2016), and Rajesh et al (2016), but the mesoscale assessment pertaining to the simulation of this rainfall event is still lacking. Therefore, the present study emphasizes quantitatively evaluating and conducting the sensitivity analysis of the WRF model in predicting extreme rainfall.…”

Section: Description Of the Heavy Rainfall Eventmentioning

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

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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: Description Of the Heavy Rainfall Eventmentioning

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

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“…High intensity rainfall in 2013 that was 375 % above the daily average of monsoon season (Dube 2014 ) invited fl ash fl ood and landslide in various parts of the state. High intensity rainfall in 2013 that was 375 % above the daily average of monsoon season (Dube 2014 ) invited fl ash fl ood and landslide in various parts of the state.…”

Section: The Need For Hazard Map Of Sediment Disastersmentioning

confidence: 99%

Land Use Management in Disaster Risk Reduction

Banba¹,

Shaw²

2017

Disaster Risk Reduction

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Disaster risk reduction is a process, which leads to the safety of community and nations. After the 2005 World Conference on Disaster Reduction, held in Kobe, Japan, the Hyogo Framework for Action [HFA] was adopted as a framework of risk reduction. The academic research and higher education in disaster risk reduction has made/is making a gradual shift from pure basic research to applied, implementation-oriented research. More emphasis is given on the multi-stakeholder collaboration and multidisciplinary research. Emerging university networks in Asia, Europe, Africa and the Americas have urged for the process-oriented research in disaster risk reduction fi eld. Keeping this in mind, this new series will promote the outputs of action research on disaster risk reduction, which will be useful for a wider range of stakeholders including academicians, professionals, practitioners and students and researchers in the related fi eld. The series will focus on some of the emerging needs in the risk reduction fi eld, starting from climate change adaptation, urban ecosystem, coastal risk reduction, education for sustainable development, community-based practices, risk communication, human security, etc. Through academic review, this series will encourage young researchers and practitioners to analyse fi eld practices and link it to theory and policies with logic, data and evidences. Thus, the series emphasizes evidence-based risk reduction methods, approaches and practices.

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“…Dube et al . () used the NCMRWF NCUM forecasts to study the extreme rainfall event that occurred in June 2013 over Uttrakhand, India. They verified the model's skill and found that the NCUM could perform well up to 5 days ahead.…”

Section: Introductionmentioning

confidence: 99%

Medium‐range forecasts of extreme rainfall events during the Indian summer monsoon

Satyanarayana

Kar

2016

Meteorological Applications

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Extreme rainfall events occur rarely but are integral elements of climatic conditions in any region. The frequency of occurrence of extreme events has been studied using 57 years of observed rainfall data over India and its neighbouring countries. In this study, the rank method has been used to compute the threshold rainfall amount based on past observations. If the model forecasted rainfall is above the climatological threshold, then that event is identified as an extreme at that grid point and for that specific day. The results show an increasing trend in the number of extreme weather events during monsoons. The National Centre for Medium Range Weather Forecasting (NCMRWF) uses the Unified Model (NCUM) for operational weather forecasting on a medium-range timescale. The capability of the model in predicting extreme rainfall events over India and its surrounding regions has not been evaluated so far. Therefore, this study has been undertaken to identify extreme rainfall events from the medium-range rainfall forecasts and compare the same with observations so that forecasts for extreme events can be obtained. The rank method has been applied to the NCUM forecast of rainfall during the period June to September 2014. Several extreme rainfall cases are identified during this monsoon. The frequency of occurrence of extreme events is compared with observations, and it is found that the model has reasonable skill to predict up to day three forecasts. However, this skill reduces when the forecast period length increases.

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Forecasting the heavy rainfall during Himalayan flooding—June 2013

Cited by 77 publications

References 11 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

Land Use Management in Disaster Risk Reduction

Medium‐range forecasts of extreme rainfall events during the Indian summer monsoon

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