Laboratory for
Climatology and Remote Sensing

Abstract:

Global evidence indicates that orography can exert a strong influence on rainfall, thereby giving rise to natural hazards such as flooding in mountain areas. Alongside orographic influences, urban areas can also act to intensify storms, but their impact on heavy rainfall in high elevations has not yet been investigated in detail. Here, we quantify the contribution of a high-altitude city to both rainfall intensification and its daytime and nighttime patterns, focusing on the city of Cuenca in the Ecuadorian Andes. Modeling several observed storms with and without the city suggests that the urban area can enhance downstream precipitation by over 20% over a pre-existing hotspot of orographically induced precipitation. This urban-induced rainfall intensification seems to exceed that observed for cities of comparable size outside mountainous regions. This may be explained by Cuenca’s valley setting and its high humidity compared to its surroundings, suggesting that high-altitude cities with similar morphologies could amplify combined orographic-dynamic and urban-thermodynamic effects.

Abstract:

The meteorological measurements in Brno, Czech Republic, is among the world's oldest measurements, operating since 1799. Like many others, station was initially installed in the city center, relocated several times, and currently operates at an airport outside the city. These geographical changes potentially bias the temperature record due to different station surroundings and varying degrees of urban heat island effects. Here, we assess the influence of land cover on spatial temperature variations in Brno, capitol of Moravia and the second largest city of the Czech Republic. We therefore use a unique dataset of half-hourly resolved measurements from 11 stations spanning a period of more than 3.5 years and apply this information to reduce relocation biases in the long-term temperature record from 1799 to the present. Regression analysis reveals a significant warming influence from nearby buildings and a cooling influence from vegetation, explaining up to 80{\%} of the spatial variability within our network. The influence is strongest during the warm season and for land cover changes between 300 and 500 m around stations. Relying on historical maps and recent satellite data, it was possible to capture the building densities surrounding the past locations of the meteorological station. Using the previously established land cover--temperature relation, the anthropogenic warming for each measurement site could be quantified and hence eliminated from the temperature record accordingly, thereby increasing the long-term warming trend.