Density estimate and its application to analysis of temperature series

Abstract

SUMMARYNonparametric density estimates attempt to reconstruct the probability density from which a random sample has come, using the sample values and as few assumptions as possible about the density. These methods are smoothing operations on the sample distribution. Methods of kernel estimates represent one of the most effective nonparametric methods. These methods are simple to understand, easy to implement and they have very good mathematical properties. We employed the automatic procedure for the selection of the bandwidth, the kernel and the order of the kernel. This procedure is used for analysis of air temperature fluctuations for series of Central England and Prague-Klementinum in the periods 1661-2000 and 1771-2000, respectively. Graphical representation of the family of estimated densities in three dimensional space provide a better explanation of the long-term trends in temperature distribution of both series.

Recent severe heat waves in central Europe: how to view them in a long‐term prospect?

“…in Hlaváč (1937) and Brázdil and Budíková (1999). The series has been frequently employed in studies of climatic variability (Horová et al, 2003;Kyselý, 2007) including the analysis of heat waves over 1901-1997in Kyselý (2002a. Herein, we examine heat waves over the whole period 1775-2006 although results in the late 18th and early 19th centuries must be interpreted with caution, due to a possible presence of inhomogeneities in the daily records before 1840.…”