This post shows code to import OpenStreetMap and satellite images into Python’s Cartopy.
- style (map or satellite)
- radius (of circle)
- npoints (number of random points to plot within the circle)
This is just a code snippet to remind myself how to build an xarray dataset.
At the American Meteorological Society (AMS) 100th Annual Meeting we introduced Urban-PLUMBER: evaluation and benchmarking of land surface models in urban areas .
The project is a collaboration of modelling groups from around the world interested in improving the accuracy of weather and climate simulations in urban areas. Read how to get involved below, or download the Urban-PLUMBER poster for the 2020 AMS annual meeting here.
You can read more about UCLEM here, but in short it responds to local weather and calculates energy consumed inside buildings (from heating and cooling and other energy use) and then emits that energy as waste heat back into the environment. In dense urban areas that waste heat can raise air temperature and cause convection, changing local weather in a feedback loop. Apart from that, it’s useful to know how much energy is being used in different weather conditions.
This first animation shows the air temperature over Eastern Australia, along with the energy used within buildings. The second is nested within the first for a higher resolution simulation over Melbourne.
Next week I’ll be at the annual workshop for ARC Centre of Excellence for Climate Extremes (CLEX) group. The workshop is a chance to hear about the work researchers have been doing over the last 12 months or so, and discuss future research goals.
Below is the poster I’ll be presenting. The images in this PDF version are higher quality.
This post is a summary of our latest paper  on improving an urban climate model to better predict building energy consumption depending on local weather conditions, the structure of buildings and human behaviours.
It’s been a while since I posted as I’ve been focussed on finishing the PhD thesis, which is now submitted. In the coming months I’ll be trying to summarise the work here and keep current research more up to date. In the meantime I’ll document some of the posters I’ve presented at workshops over the last few years.
Writing an article in LaTeX produces crisp, quality documents and beautiful equations. However, it’s not very user friendly. Recently I’ve been using another language, Markdown, because I find it more readable and intuitive, but it still has LaTeX equation support. To me that’s the best of both worlds. So how about writing an entire PhD thesis in Markdown? This post shows you how.
I’ve recently finished developing a computational model which predicts the heating and cooling energy demands of a neighbourhood based on building characteristics, meteorological conditions and the behaviour of people. However, I don’t have a perfect and complete set of observations to describe the system, and I am finding it difficult to find appropriate values for some parameters. So I’ve used machine learning to help.
In a new paper1, we introduce a method to simulate how heat is conducted through roofs, walls and roads. We show it improves the simulation of heat storage and release, a very important process in urban climate.