This post takes a look at the default behavior of ICAR with regards to the calculation of the Brunt-Väisälä frequency $N$ and discusses an alternative method that is arguably better suited from a theoretical point of view. Note that the data and results here are not peer reviewed. However we have a manuscript in preparation that discusses aspects of this. I’ll add the corresponding links once they are available.
IMC has begun and the posters will be up shortly. Here you can view them online:
An evaluation of linear theory based downscaling with ICAR in complex topography
Extending limited in situ mountain weather observations to the baseline climate: A true verification case study
My poster is up and ready, so here’s the link to view it online. Just follow it to the dedicated ICAM 2019 Poster page.
In September I’ll be participating at poster sessions at the International Conference on Alpine Meteorology (ICAM 2019) and the International Mountain Conference (IMC 2019) respectively. The posters will focus on ICAR, it’s evaluation with a weather pattern based approach and potential issues due to the numerics at the top boundary of the model. I plan to focus on a real-life test case during ICAM while looking at idealized simulations at IMC. The IMC poster will also be preceeded by a flash talk.
Furthermore I’ll be a presenting author during the IMC workshop “Climate information for impact modeling” for a statistical downscaling approach used to extend limited in situ mountain weather observations to the baseline climate.
Read more for links, titles, locations and session details!
Just a quick note – we published a paper in HESS that evaluates ICAR thoroughly in the complex topography of the Southern Alps on the South Island of New Zealand. Specifically we’ve been looking at ICAR downscaled 4×4 km² precipitation fields and how they compare to weather station data and an operational gridded precipitation product supplied by NIWA. ICAR was forced with the ERA-Interim reanalysis.
Our results in two sentences: ICAR improves over the driving model but underestimates precipitation amounts and the performance strongly depends on your choice of the elevation of the top boundary (model top). Particular clear improvements are found for cross-alpine flow and flows of high linearity (as quantified with the inverse dimensionless mountain height).
Horak, J., Hofer, M., Maussion, F., Gutmann, E., Gohm, A., and Rotach, M. W.: Assessing the added value of the Intermediate Complexity Atmospheric Research (ICAR) model for precipitation in complex topography, Hydrol. Earth Syst. Sci., 23, 2715-2734, https://doi.org/10.5194/hess-23-2715-2019, 2019.
So far when ICAR used a forcing dataset where the z-levels did not vary with time it did extrapolate some quantities downward from the lowest forcing level by keeping them constant. This could potentially lead to unwanted behavior as I’m going to show with an example in the following. I’ll refer to the ICAR version before the code modification as “ICAR old” and the version after the modification as “ICAR new”.
For one of my applications it is necessary to know the geopotential at each model level of the ERA5 data. Since ERA5 and ERAI only store the geopotential at the surface, it may not be immediately obvious how to arrive at the remaining values. This post deals with how to achieve this. While shown for ERA5, the procedure is similar for ERA-Interim.
For a sensitivity study in a manuscript of ours that’s currently in the discussion phase at the HESS journal, we ran a couple of simulations with the Intermediate Complexity Atmospheric Research Model for the South Island of New Zealand. Horizontally the domain contained 205×225 grid points while the number of z-levels was varied between 7 and 25, corresponding to model top elevations above topography between 0.7 and 8.0 km. Here I talk a little bit about the computational performance of ICAR with regards to these simulations.