Suitability of WRF model for simulating meteorological variables in rural, semi-urban and urban environments of Central India

Kadaverugu, Rakesh; Matli, Chandrasekhar; Biniwale, Rajesh B.

doi:10.1007/s00703-021-00816-y

Cited by 18 publications

(5 citation statements)

References 43 publications

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“…While a cold bias (∼-2 °C to -4 °C) is found during nighttime (18:30 to 4:30 LT), the daytime (5:30 to 17:30 LT) simulations exhibit a warm bias (∼1 °C to 2 °C) over Gadanki (See Fig. S1), similar to previous studies (Kadaverugu et al, 2021;Boadh et al, 2016;Hariprasad et al, 2014;Mohan and Bhati, 2011). Daytime (RMSE= 2.1 °C, MBE = -3.5 °C)…”

Section: Simulation Of Co 2 Mixing Ratiosupporting

confidence: 87%

“…All models to be greater when different LSMs are applied. A large inter-model spread is found in daytime, similar to previous studies (Hariprasad et al, 2014;Kadaverugu et al, 2021;Madala et al, 2013Madala et al, , 2015Sathyanadh et al, 2017). Figure 2f indicates a poor correlation between the simulated and observed wind directions, with a RMSE range of 30°to 65°.…”

Section: Simulation Of Co 2 Mixing Ratiosupporting

confidence: 85%

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Evaluating the meteorological transport model ensemble for accounting uncertainties in carbon flux estimation over India

Mathew,

Ravi,

Pillai

et al. 2024

Preprint

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Abstract. The existing climate change scenario calls for immediate intervention to curb rising greenhouse gas emissions. An improved understanding of the regional distributions of carbon sources and sinks under the perturbed climate system is vital for assisting the above mitigation efforts. The current uncertainties in estimation can potentially be reduced by employing a multi-data modelling system capable of representing atmospheric tracer transport adequately. This study focuses on the mesoscale transport patterns that can affect atmospheric tracer distribution and examines how well they are represented in the meteorological models employed. We investigate the capability of the Weather Research and Forecasting (WRF) model to predict meteorological fields such as temperature, humidity, wind, and planetary boundary layer height (PBLH) by comparing different model simulations with surface and vertical profile observations available at urban and rural stations, Cochin and Gadanki, and with global reanalysis data over India. Combining different model schemes and data products allows us to present a model ensemble of 11 members. Using these ensemble simulations, the impacts of changes in physics schemes, initial and boundary conditions, and spatial resolutions on meteorology and, consequently, on CO2 mixing ratio simulations are quantified. Most simulations capture variations in temperature and moisture very well (R2> 0.75). The wind (R2> 0.75 for height above 2 km) and PBLH simulations (R2> 0.75 for daytime) are also reasonably correlated with the observations. The sensitivity to changing planetary boundary layer (PBL) schemes and land surface model (LSM) schemes on meteorological and CO2 mixing ratio simulations is significant, thereby producing higher inter-model differences between experiments. Our analysis provides an assessment of expected CO2 transport errors when using WRF-like models in the inverse modelling framework. We emphasise the importance of treating these errors in the carbon data assimilation system to utilize the full potential of the measurements and conclude that WRF can be utilised as a potential transport model for the regional carbon flux estimations in India.

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Section: Simulation Of Co 2 Mixing Ratiosupporting

confidence: 87%

Section: Simulation Of Co 2 Mixing Ratiosupporting

confidence: 85%

Evaluating the meteorological transport model ensemble for accounting uncertainties in carbon flux estimation over India

Mathew,

Ravi,

Pillai

et al. 2024

Preprint

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“…The WRF/CALPUFF modeling system was employed to investigate the dispersion of pollutants from vehicles in GZP. The WRF model is the latest generation mesoscale numerical weather prediction model, which has a wide range of applications in meteorological forecasting, ranging from a few kilometers to thousands of kilometers [40][41][42]. This model has the advantages of high precision and high operation efficiency.…”

Section: Air Dispersion Modelmentioning

confidence: 99%

Spatial Distribution of Primary and Secondary PM2.5 Concentrations Emitted by Vehicles in the Guanzhong Plain, China

Pan

Deng

et al. 2022

Atmosphere

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With the rapid increase of the vehicle population in the Guanzhong Plain (GZP), the fine particulate matter (PM2.5) emitted by vehicles has an impact on regional air quality and public health. The spatial distribution of primary and secondary PM2.5 concentrations from vehicles in GZP in January and July 2017 was simulated in this study by using the Weather Research and Forecasting (WRF) model and the California Puff (CALPUFF) air quality model. The contributions of vehicle-related emission sources to total PM2.5 concentrations were also calculated. The results show that although the emissions of primary PM2.5, NOx, and SO2 in July were greater than those in January, the hourly average concentrations of primary and secondary PM2.5 in January were significantly higher than those in July. The highest concentrations of primary and total PM2.5 were mostly located in the urban areas of Xi’an and Xianyang in the central region of GZP. The contributions of exhaust emissions, secondary nitrates, brake wear, tire wear, and secondary sulfate to the total PM2.5 concentrations in GZP were 50.37%, 34.76%, 10.79%, 4.06%, and 0.04% in January and 71.91%, 11.14%, 11.89%, 5.03%, and 0.03% in July, respectively. These results will help us to further control PM2.5 pollution caused by vehicles.

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“…The seasonal variation in the surface wind forecasting performance of the WRF model's real-time four-dimensional data assimilation system has been validated against station observations over China [8]. In several studies, the WRF model was evaluated by comparing low-level winds with a network of wind profilers [9,10], the tropospheric wind speed and direction with GPS radiosonde measurements [11], and the vertical wind profile with radiosonde data [12,13]. In addition, HIRHAM [14] and NCEP RCM3 [15] were evaluated by quantifying their ability to simulate the tropospheric wind profiles via comparison with radiosonde observations.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Horizontal Winds Simulated by IAP-HAGCM through Comparison with Beijing MST Radar Observations

Tian

Chai

et al. 2023

Remote Sensing

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The performance of general circulation models (GCMs) in simulating horizontal winds is important because the distribution and variation in horizontal winds are central to investigating atmospheric dynamic characteristics and processes. Also, horizontal wind data can be used to extract some of the required information on gravity waves, tides, and planetary waves. In this context, the present paper evaluates the capability of the Institute of Atmospheric Physics atmospheric general circulation model high-top version (IAP-HAGCM) in simulating the horizontal winds and tides of the troposphere and lower stratosphere by presenting a climatological and statistical comparison against observations of the powerful Beijing mesosphere–stratosphere–troposphere (MST) radar (39.78°N, 116.95°E) during 2012–2014. The results illustrated that the IAP-HAGCM can successfully reproduce the time–altitude distribution of the monthly mean zonal wind and diurnal tide amplitude, albeit with some underestimation. The mean correlation coefficients and root-mean-square error for the zonal (meridional) winds were 0.94 (0.73) and 6.60 m s−1 (2.90 m s–1), respectively. Additionally, the IAP-HAGCM can capture the temporal variation in both the zonal and meridional winds. It is worth noting that, compared with the seven coupled model intercomparison project phase 6 (CMIP6) models, the IAP-HAGCM performs better in meridional wind simulations below 15 km. However, there are discrepancies in altitudinal ranges with large wind velocities, such as the westerly jet, in the transition region of the troposphere and stratosphere, and in February, April, July, and September. It is suggested that model users should take advantage of the model’s simulation ability by combining this information regarding when and where it is optimal with their own research purposes. Moreover, the evaluation results in this paper can also serve as a reference for guiding improvements of the IAP-HAGCM.

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Suitability of WRF model for simulating meteorological variables in rural, semi-urban and urban environments of Central India

Cited by 18 publications

References 43 publications

Evaluating the meteorological transport model ensemble for accounting uncertainties in carbon flux estimation over India

Evaluating the meteorological transport model ensemble for accounting uncertainties in carbon flux estimation over India

Spatial Distribution of Primary and Secondary PM2.5 Concentrations Emitted by Vehicles in the Guanzhong Plain, China

Evaluation of the Horizontal Winds Simulated by IAP-HAGCM through Comparison with Beijing MST Radar Observations

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