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The correlation between the urban heat island and the different land use types
In the following study the local climate modifying impact of the man modified environment is to be discussed in detail. Such studies have come in demand nowadays, since the meteorological forecasts have just recently become detailed and accurate enough for this purpose. Furthermore the number and extension of huge sized artificial areas has also radically increased.
The appearance of these highly diversified artificial surfaces to such an extent can modulate the local climate so significantly that it can not be neglected. However, at the same time this extreme diversity of the surfaces and materials makes it very hard to analyse the impacts made on the urban climate, and it eventuates a very complex climate system. This makes forecasting very difficult. In this study the Urban Heat Island (UHI) will be discussed as a case study. The examined area of this study is an inner district of Budapest. The phenomena of the UHI has been well known for about 200 years, but nowadays its impact can be so great in huge cities that it is high time to discuss it as fully as possible.
There are many purposes of such studies, and there are basically two groups of these. In the first case the improved meteorological forecast is the purpose. This means that a so called ‘urban’ factor is also calculated on, which factor can be added to the value of the ordinary meteorological forecast. This can be very important especially when extreme weather occurs, for example when forecasting extreme heatwaves in cities. The other momentous purpose is providing feedback. This means that by understanding the artificial factors causing the UHI, a method can be provided for urban planning in order to decrease this phenomenon as much as possible
The VALUE perfect predictor experiment: Evaluation of temporal variability
Temporal variability is an important feature of climate, comprising systematic variations such as the annual cycle, as well as residual temporal variations such as short-term variations, spells and variability from interannual to long-term trends. The EU-COST Action VALUE developed a comprehensive framework to evaluate downscaling methods. Here we present the evaluation of the perfect predictor experiment for temporal variability. Overall, the behaviour of the different approaches turned out to be as expected from their structure and implementation. The chosen regional climate model adds value to reanalysis data for most considered aspects, for all seasons and for both temperature and precipitation. Bias correction methods do not directly modify temporal variability apart from the annual cycle. However, wet day corrections substantially improve transition probabilities and spell length distributions, whereas interannual variability is in some cases deteriorated by quantile mapping. The performance of perfect prognosis (PP) statistical downscaling methods varies strongly from aspect to aspect and method to method, and depends strongly on the predictor choice. Unconditional weather generators tend to perform well for the aspects they have been calibrated for, but underrepresent long spells and interannual variability. Long-term temperature trends of the driving model are essentially unchanged by bias correction methods. If precipitation trends are not well simulated by the driving model, bias correction further deteriorates these trends. The performance of PP methods to simulate trends depends strongly on the chosen predictors.EU COST Association, Grant/Award Number: ES1102; Ministry of Education, Youth, and Sports of the Czech Republic, Grant/AwardNumber:LD1202