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Accurately predicting weather and climate in cities is critical for safeguarding human health and strengthening urban resilience. Multimodel evaluations can lead to model improvements; however, there have been no major intercomparisons of urban-focussed land surface models in over a decade. Here, in Phase 1 of the Urban-PLUMBER project, we evaluate the ability of 30 land surface models to simulate surface energy fluxes critical to atmospheric meteorological and air quality simulations. We establish minimum and upper performance expectations for participating models using simple information-limited models as benchmarks. Compared with the last major model intercomparison at the same site, we find broad improvement in the current cohort's predictions of short-wave radiation, sensible and latent heat fluxes, but little or no improvement in long-wave radiation and momentum fluxes. Models with a simple urban representation (e.g., ‘slab’ schemes) generally perform well, particularly when combined with sophisticated hydrological/vegetation models. Some mid-complexity models (e.g., ‘canyon’ schemes) also perform well, indicating efforts to integrate vegetation and hydrology processes have paid dividends. The most complex models that resolve three-dimensional interactions between buildings in general did not perform as well as other categories. However, these models also tended to have the simplest representations of hydrology and vegetation. Models without any urban representation (i.e., vegetation-only land surface models) performed poorly for latent heat fluxes, and reasonably for other energy fluxes at this suburban site. Our analysis identified widespread human errors in initial submissions that substantially affected model performances. Although significant efforts are applied to correct these errors, we conclude that human factors are likely to influence results in this (or any) model intercomparison, particularly where participating scientists have varying experience

This dataset contains observation (AWS and weather radar) and some WRF data (input, output, namelist) used in the article with the title "Understanding the role of sea surface temperature and urbanization on severe thunderstorms dynamics: a case study in Surabaya, Indonesia." This is also one of our commitments to support open data science.

Archive of: https://urban-plumber.github.io/AU-Preston/plots/ Files in this folder are associated with the manuscript: “Evaluation of 30 urban land surface models in the Urban-PLUMBER project: Phase 1 results” Files are an archive of the website https://urban-plumber.github.io/AU-Preston/plots/ as of 2nd December 2022. Use of any data must give credit through citation of the above manuscript, the data repository, and other site references as appropriate. Corresponding author: Mathew Lipson (mathew.lipson@bom.gov.au) Usage Load the "index.html" to navigate through plots and results subpages Description These files include results from Phase 1 of the Urban-PLUMBER model evaluation project for urban areas. Data includes: - individual model results (error metrics) and submission metadata - individual model plots (timeseries, subsets, energy closure, distributions) - collective timeseries for every submitted output in the baseline experiment - collective timeseries for every submitted output in the detailed experiment - supplementary material for the manuscript - variable definitions Authors Mathew Lipson, Sue Grimmond, Martin Best, Gab Abramowitz, Andrew Coutts, Nigel Tapper, Jong-Jin Baik, Meiring Beyers, Lewis Blunn, Souhail Boussetta, Elie Bou-Zeid, Martin G. De Kauwe, Cécile de Munck, Matthias Demuzere, Simone Fatichi, Krzysztof Fortuniak, Beom-Soon Han, Maggie Hendry, Yukihiro Kikegawa, Hiroaki Kondo, Doo-Il Lee, Sang-Hyun Lee, Aude Lemonsu, Tiago Machado, Gabriele Manoli, Alberto Martilli, Valéry Masson, Joe McNorton, Naika Meili, David Meyer, Kerry A. Nice, Keith W. Oleson, Seung-Bu Park32, Michael Roth33, Robert Schoetter34, Andres Simon35, Gert-Jan Steeneveld, Ting Sun, Yuya Takane, Marcus Thatcher, Aristofanis Tsiringakis, Mikhail Varentsov, Chenghao Wang, Zhi-Hua Wang

The surface energy balance is different over cities compared to the surrounding rural areas due to differences in heat storage, evapo-transpiration and anthropogenic activities. Consequently, the urban boundary layer varies substantially in its structure from its rural counterpart. This has implications for the interactions between urban areas and meso-scale atmospheric circulations. Understanding these interactions is essential to improve the representation of urban surface process in urban canopy models (UCMs) and their coupling with numerical weather prediction (NWP) models. This can lead to better weather forecasts for urban areas, with consequences for human health and economic activities in cities.The skill of a UCM in quantifying the surface energy balance strongly depends on the accurate prescription of many surface parameters, including some that are notoriously difficult to estimate (albedo, thermal conductivity, heat capacities etc.). Therefore, offline optimisation approaches for these parameters are often used, but these optimizations exclude the effects of the urban surface-atmosphere feedback mechanisms.In Chapter 1 we identify the feedback mechanisms and their importance on the surface energy balance using two modelling setup, the off-line single-layer urban canopy model (SLUCM) and the SLUCM coupled to the single-column  version of the Weather Research and Forecasting (1D-WRF) model. The response of the modelled energy balanced to changes in uncertain surface parameters is investigated, during 2 summer days over London. The model responds differently to changes in surface parameter when coupled to the atmosphere. The turbulent heat flux shows up to 22% lower variability in the on-line setup, because near surface potential temperature gradient and atmospheric stability are altered. Consequently, the energy is directed toward the urban fabric changing the variability of the storage heat flux up to 50% compared to the off-line model. Moreover, ent