A new instrumental precipitation dataset for the greater alpine region for the period 1800-2002

Abstract

The paper describes the development of a dataset of 192 monthly precipitation series covering the greater alpine region (GAR,(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18). A few of the time series extend back to 1800. A description is provided of the sometimes laborious processes that were involved in this work: from locating the original sources of the data to homogenizing the records and eliminating as many of the outliers as possible. Locating the records required exhaustive searches of archives currently held in yearbooks and other sources of the states, countries and smaller regional authorities that existed at various times during the last 200 years. Homogeneity of each record was assessed by comparison with neighbouring series, although this becomes difficult when the density of stations reduces in the earliest years. An additional 47 series were used, but the density of the sites in Austria and Switzerland was reduced to maintain an even coverage in space across the whole of the GAR. We are confident of the series back to 1840, but the quality of data before this date must be considered poorer. Of all of the issues involved in homogenizing these data, perhaps the most serious problem is associated with the differences in the height above ground of the precipitation gauges, in particular the general lowering of gauge heights in the late 19th century for all countries, with the exception of Italy. The standard gauge height in the early-to-mid 19th century was 15-30 m above the ground, with gauges being generally sited on rooftops. Adjustments to some series of the order of 30-50% are necessary for compatibility with the near-ground location of gauges during much of the 20th century. Adjustments are sometimes larger in the winter, when catching snowfall presents serious problems. Data from mountain-top observatories have not been included in this compilation (because of the problem of measuring snowfall), so the highest gauge sites are at elevations of 1600-1900 m in high alpine valley locations. Two subsequent papers will analyse the dataset. The first will compare the series with other large-scale precipitation datasets for this region, and the second will describe the major modes of temporal variability of precipitation totals in different seasons and determine coherent regions of spatial variability.

Interannual variability of winter precipitation in the European Alps: relations with the North Atlantic Oscillation.

“…To this end, a gridded dataset, constructed interpolating station records on a grid of given spatial resolution, is used. We have considered several databases (Global Air Temperature and Precipitation, NOAA-Center for Climatic Research Department of Geography University of Delaware, 2001; Analysis (Schmidli et al, 2001); HISTALP-ALP-IMP (Auer et al, 2005), and we have selected the monthly precipitation time series of the gridded database CRU TS 1.2 (Mitchell et al, 2003) (New et al, 1999(New et al, , 2000 interpolating station data with a procedure that considers latitude, longitude and elevation as parameters. However, the rain gauge records used for the interpolation in the CRU TS 1.2 data set are not corrected for rain gauge type, wind conditions or anthropogenic disturbances.…”

Estimativa da probabilidade do evento extremo de precipitação de janeiro de 2000 no Vale do Paraíba, baseada na distribuição generalizada de pareto

“…AC e AE indicam se o testeé para amostra completa (AC) ou amostra de valores extremos (AE). O segundo problema de homogeneidade consideradoé aquele queé mais comum nas séries de dados climáticos com poucas dezenas de anos em comprimento (Auer et al, 2005), com variação abrupta causada por fatores como troca de instrumento e mudança da estação para outra localidade. As séries escolhidas para pesquisar tal variação foram as medianas anuais de precipitação diária, q 0,50 .…”

Adapted Caussinus-Mestre Algorithm for Networks of Temperature series (ACMANT)

“…The main tools of IH detection are 1) examination of accumulated anomalies [1], 2) rank-order statistics [2,3], 3) multiple linear regression [4,5], 4) t-test based examinations [6,7], 5) multiple analysis with Fisher-test [8], 5) fitting step-function [9]. Looking through reviewing articles about homogenisation methods [10][11][12][13][14][15][16][17] it can be seen that we know many details about homogenisation methods, but uncertainties still exist about their efficiencies, or with other words, about the practical usefulness of individual methods.…”