The role of land surface processes on the mesoscale simulation of the July 26, 2005 heavy rain event over Mumbai, India

Chang, Hsin I.; Kumar, Anil; Niyogi, Dev; Mohanty, U. C.; Chen, Fei; Dudhia, Jimy

doi:10.1016/j.gloplacha.2008.12.005

Cited by 100 publications

(53 citation statements)

References 15 publications

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“…However, the underestimation of rainfall is less in Domain 2b (G2C scale) compared to others, indicating the necessity of finer grid spacing as the first-order requirement for simulating the magnitudes of the extremely heavy rainfall events. The bias in the WRF simulations is typically due to a number of interactive factors: (i) scale feedback between mesoscale convection and large-scale processes within the model (Bohra et al, 2006), (ii) lack of local observations that can add mesoscale features Osuri et al, 2015), (iii) lack of proper land surface processes (Niyogi et al, 2006;Chang et al, 2009;Osuri et al, 2017a), and (iv) the inability of the model to fully resolve the complex topography (Argüeso et al, 2011;Cardoso et al, 2013;Chevuturi et al, 2015). To assess the performance of the WRF simulations, quantitative scores (MAE and RMSE) with respect to the observed data are computed for daily rainfall data, which is then averaged over the 4-day period.…”

Section: Model Configuration and Experimental Setupmentioning

confidence: 99%

“…The advanced research version of the Weather Research and Forecasting model (hereafter referred to as the WRF model) is a regional popular community model that is widely used for both studying as well as forecasting a variety of high-impact meteorological events, such as rainfall (Vaidya and Kulkarni, 2007;Deb et al, 2008;Kumar et al, 2008;Chang et al, 2009;Routray et al, 2010;Mohanty et al, 2012), tropical cyclones (Raju et al, 2011;Routray et al, 2016;Osuri et al, 2017b) and thunderstorms (Madala et al, 2014;Osuri et al, 2017a). Several works are reported in the literature which have considered the WRF model over the Himalayan region.…”

Section: Introductionmentioning

confidence: 99%

“…However, the influence of the interaction between model parameterization schemes on mesoscale rainfall simulations over India is still an understudied issue. In particular, heavy rain simulation studies have reviewed the impact of individual parameterization options such as the microphysics (MP) scheme (Rajeevan et al, 2010;Raju et al, 2011;, cumulus (CU) parameterization scheme (Deb et al, 2008;Mukhopadhyay et al, 2010;Srinivas et al, 2013;Madala et al, 2014), planetary boundary layer (PBL) scheme (Li and Pu, 2008;Hu et al, 2010;Hariprasad et al, 2014) and land surface model (LSM) options (Chang et al, 2009). However, the ensemble analysis that reviews the relative impact of different configurations and the associated variability (uncertainty) is lacking.…”

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.

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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: Model Configuration and Experimental Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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.

Self Cite

122

View full text Add to dashboard Cite

show abstract

“…However, the rainfall distribution also improved in the NUD experiment as compared to CNTL. Further improvement can be obtained by improving the model horizontal resolution, physical parameterizations such as land surface representation [21] and also inserting additional high-resolution Doppler Weather Radar (DWR) data and high quality dense network surface and upper-air observation data [11].…”

Section: Discussionmentioning

confidence: 99%

“…It would be of interest to analyze the spatial (latitude: [15][16][17][18][19][20][21][22][23][24][25] • N and longitude: 67-77 • E) correlation and RMSE of rainfall between TRMM and the model simulations from the three numerical experiments for day 1 and day 2 ( Table 7). Though CC are not very large in all experiments, it can be easily noted that the 3DV can estimate the rainfall pattern better than the other two experiments.…”

Section: Quantitative Verificationsmentioning

confidence: 99%

A Comparative Study on Performance of Analysis Nudging and 3DVAR in Simulation of a Heavy Rainfall Event Using WRF Modeling System

Routray

Osuri

Kulkarni³

2012

ISRN Meteorology

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The present study focuses on the performance-based comparison of simulations carried out using nudging (NUD) technique and three-dimensional variational (3DVAR) data assimilation system (3DV) of a heavy rainfall event occurred during 25-28 June 2005 along the west coast of India. The Indian conventional and nonconventional observations are used in the 3DV experiment. Three numerical experiments are conducted using WRF modeling system, the model is integrated upto 54 hours from the initial time 0000 UTC of 25 June 2005. It is noticed that the meteorological parameters are improved in the resulting high-resolution analyses prepared by NUD and 3DV compared to without data assimilation experiment (i.e., called CNTL experiment). However, after the successful inclusion of observations using the 3DVAR data assimilation technique, the model is able to simulate better structure of the convective organization as well as prominent synoptic features associated with the mid-tropospheric cyclones (MTC) than the NUD experiment and well correlated with the observations. The simulated location and intensity of rainfall is also improved in 3DV simulation as compared with other experiments. Similar results are noticed in the root mean squar errors, correlation coefficients, and Equitable Threat Scores between TRMM and model simulated rainfall for all the three experiments.

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Development and evaluation of a mosaic approach in the WRF‐Noah framework

et al. 2013

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[1] The current Weather Research and Forecasting (WRF)-Noah modeling framework considers only the dominant land cover type within each grid cell, which here is referred to as the "dominant" approach. In order to assess the impact of subgrid-scale variability in land cover composition, a mosaic/tiling approach (hereafter the "mosaic" approach) is implemented into the coupled WRF-Noah modeling system. In the mosaic approach, a certain number (N) of tiles, each representing a land cover category, is considered within each grid cell. WRF simulations of a clear sky day and a rainfall period over a heterogeneous urban/suburban setting show that the two approaches generate differences in the surface energy balance, land surface temperature, near-surface states, boundary layer growth, as well as rainfall distribution. Evaluation against a variety of observational data (including surface flux measurements, the MODIS land surface temperature product, and radar rainfall estimates) indicates that, compared to the dominant approach, the mosaic approach has a better performance. In addition, WRF-simulated results with the mosaic approach are less sensitive to the spatial resolution of the grid: Larger differences are observed in simulations of different resolutions with the dominant approach. The effect of increasing the number of tiles (N) on the WRF-simulated results is also examined. When N increases from 1 (i.e., the dominant approach) to 15, changes in the ground heat flux, sensible heat flux, surface temperature, and 2 m air temperature are more significant during nighttime. Changes in the 2 m specific humidity are more significant during daytime, and changes in the boundary layer height are most prominent during the morning and afternoon transitional periods.

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The role of land surface processes on the mesoscale simulation of the July 26, 2005 heavy rain event over Mumbai, India

Cited by 100 publications

References 15 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

A Comparative Study on Performance of Analysis Nudging and 3DVAR in Simulation of a Heavy Rainfall Event Using WRF Modeling System

Development and evaluation of a mosaic approach in the WRF‐Noah framework

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