Weather Research and Forecasting (v3.6) model outputs from 2-km Kuala Lumpur urban climate experiments

Description

This dataset contains model outputs from the Weather Research and Forecasting model 3.6 over the Malay Peninsula at 2 km spatial resolution and covering the period 2008-2012.

It includes two different simulations: an experiment with urban areas and a second simulation where cities are replaced with the dominant surrounding vegetation type. Precipitation and 2m temperature were directly extracted from the raw model outputs. Near-surface water vapor mixing ratio divergence was calculated using 2-m mixing ratio and 10-m wind components from the raw outputs. 4D variables (i.e. temperature, water vapor mixing ratio, cloud water and ice mixing ratio and wind speed) were extracted from the raw outputs and interpolated to height above the ground levels from the model native levels.

The project that generated this data was aimed at understanding how cities modify precipitation extremes in a tropical region. We make use of a convection-permitting model coupled with an urban canopy model and in-situ observations to quantify the effect of cities on heavy rainfall, focusing on precipitation intensities at sub-daily scales.

This dataset was produced by Daniel Argueso for the ARC Centre of Excellence for Climate Extremes, as part of the Extreme Rainfall research program.

Access information

Preferred citation:

• Bureau of Meteorology, 2021. BARRA-R Product NCI Australia. (Dataset). https://dx.doi.org/10.25914/6198bcb35c1c9

NCI THREDDS Data Server: 

• https://thredds.nci.org.au/thredds/catalog/ks32/CLEX_Data/WRFv3-6_KL_Uclim/v1-0/catalog.html

If accessing from NCI thredds you can also acknowledge the service:
NCI Australia (2021): 

• NCI THREDDS Data Service. NCI Australia. (Service) https://dx.doi.org/10.25914/608bfc062f4c7

Local host

Direct access to the data is available on the NCI servers: 

Path:

• /g/data/ks32/CLEX_Data/WRFv3-6_KL_Uclim/v1-0

Register for local access:  

• https://my.nci.org.au/mancini/project/ks32/join

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