Conference proceeding
Sensitivity experiments of WRF-ARW PBL schemes over Singapore region: Impact of land use, land cover and model resolution
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Vol.2017-, pp.29-32
07/2017
DOI: 10.1109/IGARSS.2017.8126885
Abstract
In the present study, the surface meteorological parameters over Singapore are simulated using WRF-ARW mesoscale model by varying the planetary boundary layer (PBL) parameterization schemes, horizontal resolutions and two land cover data sets (USGS and MODIS). Simulations are conducted with four nested domains having horizontal resolution of 27, 9, 3 and 1 km; 51 vertical levels by using the 1° × 1° NCEP final analysis meteorological fields for initial and boundary conditions. Eight days (20-28 January 2015) are selected for simulating various surface meteorological parameters. The model-simulated parameters of surface temperature, relative humidity, wind speed and wind direction are validated with the available observations over Singapore. It has been found that, improvements in predicting surface meteorological parameters with the increase in model resolution up to 3 km. The experiment with the 3 km grid resolution showed better simulated surface meteorological variables than that of 1 km resolution grid. Further, MODIS land cover data considerably improved the prediction of surface meteorological variables compare to the USGS. The surface meteorological variables simulated using the ACM2 PBL scheme with MODIS data are in better agreement with the observations showing least error statistics than the other PBL schemes used in the study. The better performance by ACM2 could be due to the non-local turbulence closure during unstable conditions and local-closure during stable conditions formulated in this scheme.
Details
- Title: Subtitle
- Sensitivity experiments of WRF-ARW PBL schemes over Singapore region: Impact of land use, land cover and model resolution
- Creators
- Srikanth Madala - Centre for Remote Imaging Sensing & Process., Nat. Univ. of Singapore, Singapore, SingaporeSanto V Salinas - Centre for Remote Imaging Sensing & Process., Nat. Univ. of Singapore, Singapore, SingaporeJun Wang - Center for Global & Regional Environ. Res., Univ. of Iowa, Iowa City, IA, USASoo Chin Liew - Centre for Remote Imaging Sensing & Process., Nat. Univ. of Singapore, Singapore, Singapore
- Resource Type
- Conference proceeding
- Publication Details
- 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Vol.2017-, pp.29-32
- DOI
- 10.1109/IGARSS.2017.8126885
- eISSN
- 2153-7003
- Publisher
- IEEE
- Language
- English
- Date published
- 07/2017
- Academic Unit
- Electrical and Computer Engineering; Civil and Environmental Engineering; Physics and Astronomy; Chemical and Biochemical Engineering
- Record Identifier
- 9984106175602771
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