The energetic basis of the urban heat island

City size and the urban heat island

A simple hardware scale model is used to simulate nocturnal cooling rates for rural and urban environments under calm and cloudless conditions. Comparison with field observations gathered under similar conditions shows the model capable of reproducing many of the features of the temporal development of urban heat islands and the long-wave radiative exchange in urban canyons. The model is used to investigate the roles played by rural/urban differences in geometry and thermal admittance. The results of the experiments show that canyon geometry in the central portion of a city (as measured by the sky view factor) is a relevant variable in producing nocturnal urban heat islands due to its role in regulating long-wave radiative heat loss. It is also demonstrated that this measure is central to the relationship between city size and heat island intensity. The importance of canyon geometry as a feature of urban design is discussed. Thermal admittance differences can also produce realistic heat island features but their magnitude requires quantification in the field.

Canyon geometry and the nocturnal urban heat island: Comparison of scale model and field observations

SUMMARY This paper discusses the fitting and use of models for daily rainfall observations. Nonstationary Markov chains are fitted to the occurrence of rain, and gamma distributions, with parameters which vary with-the time of year, are fitted to the rainfall amounts. Numerical methods are used to derive results from these models that are important in agricultural planning. Examples include the distributions of soil water content and lengths of dry spells. The process of fitting and using these models provides a straightforward and flexible analysis for rainfall records.

https://rss.onlinelibrary.wiley.com/doi/pdf/10.2307/2981736

A Model Fitting Analysis of Daily Rainfall Data

. The generation of rainfall and other climate data needs a range of models depending on the time and spatial scales involved. Most of the models used previously do not take into account year to year variations in the model parameters. Long periods of wet and dry years were observed in the past but were not taken into account. Recently, Thyer and Kuczera (1999) developed a hidden state Markov model to account for the wet and dry spells explicitly in annual rainfall. This review looks firstly at traditional time series models and then at the more complex models which take account of the pseudo-cycles in the data. Monthly rainfall data have been generated successfully by using the method of fragments. The main criticism of this approach is the repetitions of the same yearly pattern when only a limited number of years of historical data are available. This deficiency has been overcome by using synthetic fragments but this brings an additional problem of generating the right number of months with zero rainfall. Disaggregation schemes are effective in obtaining monthly data but the main problem is the large number of parameters to be estimated when dealing with many sites. Several simplifications have been proposed to overcome this problem. Models for generating daily rainfall are well developed. The transition probability matrix method preserves most of the characteristics of daily, monthly and annual characteristics and is shown to be the best performing model. The two-part model has been shown by many researchers to perform well across a range of climates at the daily level but has not been tested adequately at monthly or annual levels. A shortcoming of the existing models is the consistent underestimation of the variances of the simulated monthly and annual totals. As an alternative, conditioning model parameters on monthly amounts or perturbing the model parameters with the Southern Oscillation Index (SOI) result in better agreement between the variance of the simulated and observed annual rainfall and these approaches should be investigated further. As climate data are less variable than rainfall, but are correlated among themselves and with rainfall, multisite-type models have been used successfully to generate annual data. The monthly climate data can be obtained by disaggregating these annual data. On a daily time step at a site, climate data have been generated using a multisite type model conditional on the state of the present and previous days. The generation of daily climate data at a number of sites remains a challenging problem. If daily rainfall can be modelled successfully by a censored power of normal distribution then the model can be extended easily to generate daily climate data at several sites simultaneously. Most of the early work on the impacts of climate change used historical data adjusted for the climate change. In recent studies, stochastic daily weather generation models are used to compute climate data by adjusting the parameters appropriately for the future climates assumed.

/pdf/stochastic-generation-of-annual-monthly-and-daily-climate-3x33i8xuxx.pdf

Stochastic generation of annual, monthly and daily climate data: A review

This paper proposes means whereby a test for the goodness of fit and an estimate of the order of a Markov chain model may be obtained concurrently. Application of the method to daily precipitation data for two seasons of about 30 yr. in New Mexico, Colorado, and Oregon suggests two tentative conclusions. First, within a single climatic area, order estimates tend toward zero as precipitation events become rarer. This may occur at the drier stations or at higher thresholds defining a wet day. Second, between climatic areas, the station with the greater diversity of air mass types will tend to have order estimates greater than the station having the same seasonal mean precipitation but a less diverse climate.

Markov chains of order greater than one

observations of the urban heal island in a small city

This paper describes an error apparently unrecognized among atmospheric scientists engaged in infrared thermometry and radiometry. The error results from multiple reflections between the target environment viewed by a thermometer or radiometer and the background environment. The difference between the sum of this error and other known errors, on the one hand, and only the other known errors, on the other hand, is termed the “unrecognized error.” Its magnitude and sign are analyzed with respect to the ratio of the background and target temperatures and with respect to the emissivities of the target and the background. The error for thermometry increases as either the target or the background departs from the blackbody condition, and as the background becomes warmer relative to the target. Analogous errors for radiometry are examined with the same results.

William P. Lowry

Papers

Markov chains of order greater than one

observations of the urban heal island in a small city

Errors in Infrared Thermometry and Radiometry

Chapter 15 – THE CLIMATE OF THE CITY

Chapter 9 – INSTRUMENTATION AND DATA COLLECTION