Evaluation of Weather Research and Forecasting model downscaled rainfall and its variability over India

Kumar, P. Vinay; Viswanadhapalli, Yesubabu; Naidu, C. V.; Dasari, Hari Prasad

doi:10.1002/joc.7311

Cited by 15 publications

(8 citation statements)

References 70 publications

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“…The model evaluation statistics confirmed that this dynamically downscaled model WRF makes it suitable to simulate rainfall data. The overestimation of rainfall by WRF might be due to increased pressure gradients and enhances the convection in the upper streams and associated rainfall 25 , 68 , 69 . Some of the earlier studies by Hong et al .…”

Section: Discussionmentioning

confidence: 99%

Integrated use of regional weather forecasting and crop modeling for water stress assessment on rice yield

Rajasivaranjan

Anandhi

Patel

et al. 2022

Sci Rep

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This study evaluated the effects of water stress on rice yield over Punjab and Haryana across North India by integrating Weather Research Forecasting (WRF) and Decision Support System for Agrotechnology Transfer (DSSAT) models. Indian Remote Sensing Satellite datasets were used to define land use/land cover in WRF. The accuracy of simulated rainfall and temperature over Punjab and Haryana was evaluated against Tropical Rainfall Measuring Mission and automated weather station data of Indian Space Research Organization, respectively. Data from WRF was used as weather input to DSSAT to simulate rice yield in Punjab and Haryana for 2009 and 2014. After simulated yield has been evaluated against district-level observed yield, the water balance components within the DSSAT model were used to analyze the impact of water stress on rice yield. The correlation (R2) between the crop water stress factor and the rice yield anomaly at the vegetative and reproductive stage was 0.64 and 0.52 for Haryana and 0.73 and 0.68 for Punjab, respectively. Severe water stress during the flowering to maturity stage inflicted devastating effects on yield. The study concludes that the regional climate simulations can be potentially used for early water stress prediction and its impact on rice yield.

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Section: Discussionmentioning

confidence: 99%

Integrated use of regional weather forecasting and crop modeling for water stress assessment on rice yield

Rajasivaranjan

Anandhi

Patel

et al. 2022

Sci Rep

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“…The other model used for downscaling is the Weather Research and Forecasting model version 4, (WRF4) which is a mesoscale model designed to serve both operational and research purposes (Skamarock et al, 2008). The WRF model is one of the most widely acclaimed meso scale model for forecasting mesoscale weather events but recent studies have concluded that the model can be used for seasonal prediction and possess good predictability (Viswanadhapalli et al, 2017;Prathipati et al, 2021). Table 1 shows the brief description of the model domain and con guration of different parametrization schemes used in both the regional models.…”

Section: Methodsmentioning

confidence: 99%

“…The prediction of monsoon can be marginally improved by using the method of dynamical downscaling using regional models (Attada et al, 2014;Bhatla et al, 2016;Viswanadhapalli et al, 2017;Prathipati et al, 2021). In this study, two RCMs having a good predictive skill of the Indian summer monsoon rainfall have been used to downscale the high resolution CFSv2 output.…”

Section: Introductionmentioning

confidence: 99%

Inter-comparison of two regional climate models (RegCM and WRF) in downscaling CFSv2 for the seasonal prediction of Indian summer monsoon

Mohanty

Mohanty²

2022

Theor Appl Climatol

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The e cacy of two latest versions of regional climate models (RegCM and WRF) for simulating the Indian summer monsoon (JJAS) is tested in this study. The CFSv2 hindcast outputs are downscaled over the Indian monsoon domain for 11 contrasting monsoon seasons using the regional models. The April start ensembles of the CFSv2 are averaged to generate the initial and lateral boundary conditions for driving the WRF and RegCM. The regional models perform better in simulating the Indian summer monsoon features better than the parent CFSv2 model. The rainfall pattern as well as the intensities are improved with the dynamical downscaling and the errors in the rainfall are minimized over the GCM hindcast. On comparing the two regional models, the RegCM overestimates the rainfall during the excess and normal monsoon seasons. The RCMs improve the skill of rainfall prediction as compared to the GCM and WRF shows better skill in particular. One peculiar nding of this study is that the daily rainfall biases averaged over all the years of simulation shows that the two RCMs show similar biases with RegCM showing stronger biases occasionally. It may be implied that the errors from GCM in the form of the ICBC might be in uencing the simulation in the RCMs. The upper air and surface parameters analysis shows that the WRF performs better in representing the semi-permanent features of the Indian summer monsoon which may be helping in improving the rainfall over the RegCM. The wind pattern as well as the relative humidity along the vertical column of the atmosphere are captured better in the WRF model. Diagnostics of CAPE & vertically integrated moisture transport supports the nding of the rainfall being simulated better in the WRF model.

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“…RCMs and mesoscale models have been used in numerous past studies and some of the models captured to simulate the mean circulation whereas other failed to even represent the mean rainfall pattern (Bhaskaran et al 1996 Viswanadhapalli et al, 2019). For Indian summer monsoon, the RegCM and WRF models have been primarily and widely used over other RCMs (Maurya et al, 2018;Mohanty et al, 2019a;Viswanadhapalli et al, 2017Viswanadhapalli et al, , 2019Dasari et al, 2020;Prathipati et al, 2021). The RegCM is a climate model whereas the WRF is a mesoscale model but both the models have shown to possess good skill while simulating the summer monsoon.…”

Section: Introductionmentioning

confidence: 99%

Seasonal prediction of Indian summer monsoon using WRF: A dynamical downscaling perspective

Mohanty

Mohanty²

2022

Preprint

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Seasonal forecasting of the Indian summer monsoon by dynamically downscaling the CFSv2 output using a high resolution WRF model over the hindcast period of 1982–2008 has been performed in this study. The April start ensemble mean of the CFSv2 has been used to provide the initial and lateral boundary conditions for driving the WRF. The WRF model is integrated from 1st May through 1st October for each monsoon season. The analysis suggests that the WRF exhibits potential skill in improving the rainfall skill as well as the seasonal pattern and minimizes the meteorological errors as compared to the parent CFSv2 model. The rainfall pattern is simulated quite closer to the observation (IMD) in the WRF model over CFSv2 especially over the significant rainfall regions of India such as the Western Ghats and the central India. Probability distributions of the rainfall show that the rainfall is improved with the WRF. However, the WRF simulates copious amounts of rainfall over the eastern coast of India. Surface and upper air meteorological parameters show that the WRF model improves the simulation of the lower level and upper level winds, MSLP, CAPE and PBL height. The specific humidity profiles show substantial improvement along the vertical column of the atmosphere which can be directly related to the net precipitable water. The CFSv2 underestimates the specific humidity along the vertical which is corrected by the WRF model. Over the Bay of Bengal, the WRF model overestimates the CAPE and specific humidity which may be attributed to the copious amount of rainfall along the eastern coast of India. Residual heating profiles also show that the WRF improves the thermodynamics of the atmosphere over 700hPa and 400hPa levels which helps in improving the rainfall simulation. Improvement in the land surface fluxes are also witnessed in the WRF model.

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Evaluation of Weather Research and Forecasting model downscaled rainfall and its variability over India

Cited by 15 publications

References 70 publications

Integrated use of regional weather forecasting and crop modeling for water stress assessment on rice yield

Integrated use of regional weather forecasting and crop modeling for water stress assessment on rice yield

Inter-comparison of two regional climate models (RegCM and WRF) in downscaling CFSv2 for the seasonal prediction of Indian summer monsoon

Seasonal prediction of Indian summer monsoon using WRF: A dynamical downscaling perspective

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