the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Hectometric-scale modelling of the urban mixed layer evaluated with a dense LiDAR-ceilometer network
Abstract. With development in recent years of hectometric (O(100 m); hm) scale numerical weather prediction (NWP) models, there is a need for their evaluation with high spatio-temporal scale observations. Here we assess UK Met Office Unified Model (UM) simulations with grid-spacing down to 100 m using a dense network of observations obtained during the urbisphere-Berlin campaign. A network of 25 automatic lidars-ceilometers (ALCs) provide aerosol attenuated backscatter observations from which mixed-layer height (MLH) is determined. UM simulated aerosol on two days (18 April and 4 August 2022) is used to determine model MLH with a novel algorithm (MMLH). MMLH is consistently able to reproduce the vertical extent of the mixed layer during late afternoon despite the two case-study days having different maxima. MMLH performance is better in the 100 m model domain compared to a 300 m configuration, which may be explained by the higher vertical resolution in the 100 m configuration. During the August case in which an extreme heat event occurred, a delayed MLH growth is seen in the morning and afternoon over the city compared to the rural surroundings in both the model and ALCs. Both days show a distinct influence of the city through the mixed layer, including a plume extending downwind of the city that is detectable in both the observations and model. The modelled urban plume has a deeper mixed layer compared to the rural surroundings (4 August: ~500 m; 18 April: ~200 m) for up to 15 km downwind of the city.
- Preprint
(2998 KB) - Metadata XML
-
Supplement
(887 KB) - BibTeX
- EndNote
Status: open (until 14 Jul 2025)
Viewed
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
182 | 27 | 5 | 214 | 12 | 5 | 8 |
- HTML: 182
- PDF: 27
- XML: 5
- Total: 214
- Supplement: 12
- BibTeX: 5
- EndNote: 8
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1