the climate of daily precipitation in the alps: development … · · 2013-12-03the climate of...

INTERNATIONAL JOURNAL OF CLIMATOLOGYInt. J. Climatol. (2013)Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/joc.3794

The climate of daily precipitation in the Alps: developmentand analysis of a high-resolution grid dataset from

pan-Alpine rain-gauge data

Francesco A. Isotta,a* Christoph Frei,a Viktor Weilguni,b Melita Percec Tadic,c

Pierre Lassegues,d Bruno Rudolf,e Valentina Pavan,f Carlo Cacciamani,f Gabriele Antolini,f

Sara M. Ratto,g Michela Munari,h Stefano Micheletti,i Veronica Bonati,j

Cristian Lussana,k Christian Ronchi,l Elvio Panettieri,m Gianni Marigo,n and Gregor Vertacnik,oa Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland

b Bundesministerium fur Land- und Forstwirtschaft, Umwelt und Wasserwirtschaft, Abteilung VII/3—Wasserhaushalt, Vienna, Austriac Meteorological and Hydrological Service of Croatia, Zagreb, Croatia

d Direction de la Climatologie Meteo-France, Toulouse, Francee German Weather Service, Hydrometeorology, Offenbach am Main, Germany

f ARPA Emilia-Romagna, Bologna, Italyg Regione Autonoma Valle d’Aosta, Aosta, Italy

h Provincia Autonoma di Bolzano, Ufficio Idrografico, Bolzano, Italyi OSMER Regional Meteorological Observatory of ARPA Friuli Venezia Giulia, Visco, Italy

j ARPA Liguria, Genoa, Italyk ARPA Lombardia, Milan, Italy

l ARPA Piemonte, Turin, Italym Provincia Autonoma di Trento, Trento, Italy

n ARPA Veneto, Arabba, Italyo Slovenian Environment Agency, Meteorology Office, Ljubljana, Slovenia

ABSTRACT: In the region of the European Alps, national and regional meteorological services operate rain-gaugenetworks, which together, constitute one of the densest in situ observation systems in a large-scale high-mountain region.Data from these networks are consistently analyzed, in this study, to develop a pan-Alpine grid dataset and to describe theregion’s mesoscale precipitation climate, including the occurrence of heavy precipitation and long dry periods. The analysesare based on a collation of high-resolution rain-gauge data from seven Alpine countries, with 5500 measurements per dayon average, spanning the period 1971–2008. The dataset is an update of an earlier version with improved data density andmore thorough quality control. The grid dataset has a grid spacing of 5 km, daily time resolution, and was constructed with adistance-angular weighting scheme that integrates climatological precipitation–topography relationships. Scales effectivelyresolved in the dataset are coarser than the grid spacing and vary in time and space, depending on station density. Wequantify the uncertainty of the dataset by cross-validation and in relation to topographic complexity, data density and season.Results indicate that grid point estimates are systematically underestimated (overestimated) at large (small) precipitationintensities, when they are interpreted as point estimates. Our climatological analyses highlight interesting variations inindicators of daily precipitation that deviate from the pattern and course of mean precipitation and illustrate the complexrole of topography. The daily Alpine precipitation grid dataset was developed as part of the EU funded EURO4M projectand is freely available for scientific use.

KEY WORDS Alpine region; mountain climate; spatial analysis; extreme events; Alpine climatology

Received 4 April 2013; Revised 27 June 2013; Accepted 2 July 2013

1. Introduction

The Alps are one of the major mountain ranges inEurope. Their significance for the regional water cycleis commonly expressed in the notion ‘water tower ofEurope’, which hints to their climate with abundant

* Correspondence to: F. A. Isotta, Federal Office of Meteorology andClimatology, Krahbuhlstrasse 58, Postfach 514, Zurich, Switzerland.E-mail: [email protected]

precipitation and to the role of snow and ice in storingfreshwater and moderating variations in river runoff (e.g.EEA, 2009). Indeed, the Alps are the source region offour major European rivers (the Danube, Rhine, Po andRhone), to which the head-waters contribute a significantshare of the runoff. Near the mouth of river Rhine, forexample, almost 50% of the mean discharge originatesfrom the Alpine region, an area only about 20% of thetotal catchment (Viviroli et al., 2003). Precipitation inthe Alpine region contributes freshwater and hydropower,

2013 Royal Meteorological Society

F. A. ISOTTA et al.

within and beyond the region; it enables transportationon rivers and shapes the distribution and diversity ofecosystems. But the vigorous precipitation climate of theAlps can also be a threat to life and civil infrastructure.Water-related natural hazards (river floods, flash floods,landslides, debris flows and avalanches) cause loss of lifeand damages more frequently, and this requires regionalplanning and prevention (e.g. Greminger, 2003).

Accurate knowledge of the precipitation climate,including the occurrence of heavy precipitation and dryperiods, is fundamental to planning and managementtasks concerned with water resources, water use andthe protection against natural hazards. These tasks areincreasingly supported by quantitative models, such asriver runoff models in hydrology, crop models in agricul-ture and glacier mass balance models in glaciology (e.g.Viviroli et al., 2009; Holzkamper et al., 2012; Machguthet al., 2009). Concerns over impacts from future climatechange and the potential needs for adaptation have partic-ularly fostered such model applications over the past fewyears. An important pre-requisite, however, are reliablemeteorological input data, ideally provided on a regulargrid. In the Alpine region, spatial analyses of precipitationthat resolve the mesoscale distribution in complex terrainare particularly relevant to modelling the components ofthe hydrosphere. In its report on climate change adap-tation in the Alps, the European Environment Agencyhas identified a need for monitoring and data collectionto expand the knowledge base and widen the scope foranalysis of long data series (EEA, 2009).

There exist several sources of objective spatial precip-itation analyses for the Alps. Firstly, the Alpine regionis covered in gridded climate datasets that extend overglobal or continental domains (e.g. Adler et al., 2003;Hijmans et al., 2005; Haylock et al., 2008). While muchimprovement was made in these datasets with higherstation density, finer grid resolution and advanced inter-polation methods (e.g. Klok and Klein Tank, 2008; Hof-stra et al., 2008), their reliability in the Alpine region isstill handicapped by the heterogeneity of available mea-surements and the large spatial variation of precipitation(Hofstra et al., 2009, 2010). Secondly, gridded precip-itation datasets have been developed in many Alpinecountries but confined to the national territories or subre-gions (e.g. Ceschia et al., 1991; Gyalistras, 2003; PercecTadic, 2010; Brunetti et al., 2012; Rauthe et al., 2013).The focus of recent developments in this category wastowards high spatial and sub-daily temporal resolutions.This has fostered techniques that incorporate, in addi-tion to conventional rain-gauge measurements, also datafrom radar and from analyses with numerical weather pre-diction models (e.g. Paulat et al., 2008; Lussana et al.,2009; Vidal et al., 2010; Wuest et al., 2010; Haidenet al., 2011; Erdin et al., 2012; Mounier et al., 2012).The segmentation into national sub-domains and method-ological differences in these datasets still complicate aconsistent climatological overview for the region as awhole and hamper applications with a trans-national areaof interest.

A third category of precipitation analyses aims atfilling the gap between large-scale and national analysesby providing climate information consistently for theregion as a geographic entity. Building on ideas fromearlier trans-national climatologies (Fliri, 1974; Fliri andSchuepp, 1983), Frei and Schar (1998) have developed amesoscale Alpine-wide grid dataset, based on a collationof rain-gauge data from national climate networks. Thedense spatial coverage (more than 6000 stations), dailyresolution and multi-decadal extent (1971–1990) of thisdataset have contributed to a high-resolution climatology(Schwarb et al., 2001), analyses of daily statistics (Freiand Schmidli, 2006) and a description of long-termprecipitation changes (Schmidli et al., 2002). In themeantime, the daily trans-Alpine grid dataset was usedin numerous applications, including the validation ofregional climate models, the study of extreme events,river runoff modelling and the evaluation of climatedatasets (e.g. Suklitsch et al., 2008; Smiatek et al., 2009;Bougeault et al., 2001; Martius et al., 2006; Kleinn et al.,2005; Hofstra et al., 2009; Rubel and Rudolf, 2001). Aview on the greater Alpine region was later also pursuedby Efthymiadis et al. (2006), who developed a griddedprecipitation dataset, at monthly resolution and covering aperiod of more than 200 years (1800–2003). This datasetis based on 192 high-quality climate records (Auer et al.,2005).

This study introduces an update and enhancement ofthe trans-Alpine precipitation dataset of Frei and Schar(1998, hereafter referred to as FS98). Hence, we presenta new daily grid dataset and consistent analysis ofthe precipitation climate across seven Alpine countries(Austria, Croatia, France, Germany, Italy, Slovenia andSwitzerland). The enhancements of FS98 are threefold:firstly, the collated rain-gauge database is extended forthe recent years (after 1990) and newly expands over a38-year period (1971–2008). Our data collection effortshave also resulted in a significant improvement of datadensity in previously under-sampled regions and led to amore homogenous data coverage. This was achieved bythe inclusion of newly digitized data and by assemblingdatasets from additional institutions. In fact, the majorparts of the rain-gauge dataset of FS98 were newlyassembled. Secondly, substantial efforts have been madeto ensure the consistency and quality of the different datacontributions. Part of these efforts was the developmentand application of a data quality control proceduredesigned to detect typical problems of data coding inrain-gauge datasets. Thirdly, the spatial interpolationprocedure of FS98 was enhanced to include climato-logical precipitation–topography relationships in orderto improve the reliability of the resulting grid dataset.The daily spatial analyses are newly provided at a gridspacing of 5 km, as compared to 25 km in FS98. Apartfrom the technical information we also present and dis-cuss, in this publication, a selection of analyses utilizingthe new grid dataset. These highlight some of the mainfeatures of the Alpine precipitation climate, including theoccurrence of heavy precipitation and long dry periods.

2013 Royal Meteorological Society Int. J. Climatol. (2013)

CLIMATE OF DAILY PRECIPITATION IN THE ALPS

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Figure 1. Geographic and topographic (grey colours, altitude in metres) map of the domain. The cuboids are the regions used in Chapter 4.

This study is part of the EU project EURO4M (Euro-pean Reanalysis and Observations for Monitoring) thataims at preparing and analyzing datasets for monitor-ing European climate variations from in situ and satelliteobservations and from model-based regional reanalyses(International Innovation, 2011). The gridded Alpine pre-cipitation dataset is publicly available for scientific use.

This article is organized as follows: in Section 2 weintroduce the updated Alpine rain-gauge dataset and inSection 3 the procedures for data quality control. Themethod of spatial analysis is described in Section 4,together with an analysis of interpolation errors and aidsfor the professional interpretation of the gridded analyses.Section 5 illustrates example analyses of heavy precipi-tation events. Several statistics of the daily precipitationclimate, as derived from the new grid dataset, are dis-cussed in Section 6. The conclusions of this study areprovided in the final section.

2. Alpine precipitation dataset

2.1. Study domain

Our efforts for collating high-resolution rain-gaugedata have focused onto a rectangular longitude–latitudedomain from 2–17.5◦E to 43–49◦N (Figure 1). Thedomain extends over approximately 1200 km from Cen-tral France across Switzerland to eastern Austria and overabout 700 km from northern Italy to southern Germany.The territory of Slovenia and large parts of Croatia arealso included in the study area.

The main topographic feature of the domain is thearc-shaped mountain ridge of the European Alps, whichextends over about 1000 km from the French and ItalianMediterranean coasts across Switzerland, northern Italy

and Austria into the central parts of the continent. Thehighest Alpine mountain peaks reach elevations of morethan 4000 mMSL and major cross-ridge passes are at1500–2000 m above the adjacent flatland. The ridgeis intersected with deep valleys, some of which aremore than 100 km long and divide the ridge into majormountain massifs.

To the north and northwest, the Alpine mountain rangedescends into a wide area of flat and hilly orography witha base elevation at around 300 mMSL. There are severalsmall-scale hill ranges in this part of the Alpine foreland,including the Vosges and Jura mountains, the BlackForest and the Bohemian Forest, all with peak elevationsbelow 1500 mMSL. The Massif Central in France is awide region of plateaus and mountain ranges. With peakelevations up to 1900 mMSL it constitutes a prominentorographic feature upstream the south-western sector ofthe Alpine arc. To the south, the Alps run out into the PoValley of northern Italy, a vast basin below 200 mMSL.It is delimited also by the Ligurian Alps and the northernApennines. Finally, in the southeast of the domain, theDinaric Alps stretch southward from the Carnic andJulian Alps along the east coast of the Adriatic Sea.

The Alpine region is influenced by and delineatesbetween several large-scale European climate regimes(e.g. Schar et al., 1997; Auer et al., 2005). The widebelt of adjacent mountain foreland included in ourstudy domain allows to better distinguish betweentopographically influenced precipitation patterns andthose of the ambient climate regimes.

2.2. Dataset

The first version of the Alpine rain-gauge dataset of FS98was covering the period 1971–1990 and included records


F. A. ISOTTA et al.

Table 1. Providers (institutions by nation) that have contributed data to the renewed Alpine rain-gauge dataset. The right columngives the number of stations with valid data available for the national territory averaged over all days of the period 1971–2008.

Country Provider Mean number of stations

Austria Federal Ministry of Agriculture, Forestry, Environment and Water(BMLFUW), Division Water Balance, Vienna (data status of December2010)

950

Croatia Meteorological and Hydrological Service (DHMZ), Zagreb 140France MeteoFrance, Toulouse 2420Germany German Weather Service (DWD), Offenbach 800Italy Climatological Archive for Northern Italy (ARCIS, www.arcis.it) 760

Bolzano Alto Adige: Servizio Meteorologico and Ufficio IdrograficoValle d’Aosta: Centro Funzionale RegionaleEmilia-Romagna: Servizio Idro-Meteo-Clima ARPA Emilia-RomagnaFriuli Venezia-Giulia: Osservatorio Meteorologico Regionale, ARPALiguria: ARPA LiguriaLombardy: Servizio Meteorologico ARPA LombardiaPiedmont: ARPA PiemonteTrentino: Centro Funzionale di Protezione Civile, MeteotrentinoVeneto: ARPA Veneto and Unita Idrografica regionaleARPA is the Italian abbreviation for ‘Regional agency for the protection ofthe environment’

Slovenia National Meteorological Service of Slovenia, Ljubljana 200Switzerland Federal Office of Meteorology and Climatology MeteoSwiss, Zurich 480

of daily precipitation totals from about 6600 stations.Several updates of this dataset were made in the followingyears for some of the national territories. Since 2009 amajor renewal of the FS98 dataset was pursued as part ofthe EURO4M project. The renewal aimed at an extensionto more recent years (up to 2008) and an improvement ofspatial and temporal coverage where the earlier versionwas deficient. An extension of the dataset back intime (pre-1971) could not be accomplished because, forsome Alpine countries, earlier high-resolution data isnot available in digital form. With the present renewal,several national components were entirely recompiledfrom the original data provider. This ensures a more up-to-date status of the collated dataset, taking advantage ofthe data owners’ efforts into data quality and data rescuesince the first collection more than 15 years ago.

The new version of the dataset encompasses morethan 8500 time series in total over the 38-year period1971–2008. It integrates available measurements fromthe high-resolution rain-gauge networks operated in sevenAlpine countries. Table 1 lists the data providers that havecontributed to the renewed dataset. Except for Italy, theproviders are national meteorological and hydrologicalservices, each of which has contributed a large numberof time series for the respective national territories.

In Italy, the operation of high-resolution measurementnetworks is under the responsibility of regional meteoro-logical and environmental services. Therefore a centralpoint of contact was missing. A major contribution ofdata from Italy was obtained through project ARCIS(Archivio Climatologico per l’Italia Settentrionale,climatological archive for northern Italy). ARCIS is acoordination effort of the regional services in northernItaly promoting the exchange and common analysis oflong-term climate data (Pavan et al., 2013). Our contacts

with the ARCIS consortium were through ARPAEmilia-Romagna. ARCIS contributed 627 records, whichprovided a continuous coverage at climatological qualitystandards for all of northern Italy, yet with limitedspatial density. To further improve data density, we havecontacted the regional agencies individually (see Table 1)and could obtain many more time series. In generalthese are, however, more interrupted and extending overshorter periods only.

The renewed Alpine rain-gauge dataset comprises typ-ically 5500 observations on any day of the period1971–2008. Figures 2 and 3 show, respectively, the dis-tribution of stations in the domain and the evolutionof station density over time. For Austria, France, Ger-many, and Switzerland, the renewed dataset carries onthe high data density of the FS98 dataset, essentially byextending previously available station records in time. Inthese regions, data density varies between 7 and 14 sta-tions per 1000 km2 (one station per 80–150 km2) and alarge proportion of the stations dispose of long records(Figure 2). However, there is a tendency towards coarsercoverage in more recent years (Figure 3), which reflectsthe reduction of climatological networks implemented inthese countries. For Germany, the reduction is particu-larly pronounced after 2003, which is to be understoodas a compensation for improved coverage by radar.

In Italy, the ARCIS dataset and the contributions fromregional services, together, yield a fairly stable datadensity of six stations per 1000 km2 (one station per180 km2, Figure 3). However, this number is an averageover the entire Italian sector and the spatial coverage isinhomogeneous (Figure 2). Comparatively higher densitycould be achieved over the north-eastern part of thecountry, along the Ligurian coast and the Apennines(Emilia-Romagna), while the central and western parts



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Figure 2. Distribution of stations from which records of daily precipitation are integrated in the renewed Alpine rain-gauge dataset. Shadingrepresents the fraction of the full period (1971–2008) covered by the respective record.

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Figure 3. Evolution of station density (number of stations per 1000 km2) over time (1971–2008) in the renewed Alpine rain-gauge dataset.Density is defined as the number of available observations over the national territory divided by area. For comparison, the station density is also

shown for Italy and Slovenia as represented in the dataset before renewal (labelled ‘old’).

(Lombardy and Piedmont) have only coarse coverage.Note that the dense data for Lombardy is from a networkthat was established starting in year 2000 only, and hencethese records cover only a small portion of the full period(Figure 2). Clearly, over northern Italy the Alpine rain-gauge dataset has a more heterogeneous and a moreinterrupted data coverage compared to most other parts ofthe domain. But the renewal of the dataset in this regionwas also more fundamental and this brought a markedimprovement over previous versions of the FS98 dataset.Note, for example, that the latter had very limited datacoverage over Italy after 1986, which could be improvedsubstantially with this renewal (Figure 3).

For the south-eastern parts of the domain, new datasetsfor Slovenia and Croatia have been integrated in theAlpine rain-gauge dataset. In the case of Slovenia,advantage could be made of digitization efforts since thelast delivery, so that data density is increased by a factorof about four (Figure 3). With this contribution the region

of the Carnic and Julian Alps, a region of particularlyheavy precipitation in the Alps, is now covered fullyat high-resolution. As for Croatia, the station coverageis less dense than elsewhere (about one station per 300km2). Nevertheless, this contribution is valuable to followthe transition of the Alpine precipitation climate into thesouth-eastern flatlands.

The national and regional services of the Alpine regionoperate their rain-gauge networks with different instru-ments and observing practices. As for the instruments, thedifferences are not fundamental. The classical Hellmann-type collector and variants thereof are the most widelyused measurement devices. Since around 1980 (depend-ing on service), subsets of the networks are operated withautomatic devices, typically tipping bucket instruments.In most contributions, the proportion of automatic gaugesis small (less than 15%) and they are mostly at lower ele-vations. Although the systematic measurement error of arain-gauge varies between instrument type and height of


F. A. ISOTTA et al.

deployment, the exposition to wind and hence the envi-ronment of the rain-gauge is the most important factorfor measurement bias (e.g. Sevruk and Zahlavova, 1994;Sevruk, 2005). Therefore in our analysis we do not distin-guish between differences of instrument type. As for theobserving practice, the daily reading times vary slightlybetween the different services. For Switzerland they are06:30 UTC, for Italy 08:00 UTC and 06:00 UTC for allothers. These differences are ignored in our analysis.

During the collation of the 16 different data contribu-tions special attention was required to adequately accountfor differences in archiving conventions. Labelling ofmissing values, specification of station coordinates, theassignment of dates to the data, for example, were differ-ent and not always specified in sufficient detail. Hence,apart from the time needed to arrive at a formal agree-ment on the exchange of data, additional efforts wereneeded to resolve technical issues. Numerous compar-isons and checks have been conducted to test and ensurethe internal consistency of the resulting collated database.

3. Data quality control

There are numerous sources for gross errors in the col-lated Alpine rain-gauge dataset, ranging from occasionalinstrument failures, to transmission, digitization andstorage errors. Gross errors can significantly affect thereliability of statistical analyses. For example, erroneouscoding of data gaps by zero precipitation will result inunderestimates in the frequency of wet days. Indeed,deficiencies in data quality were spotted in several earlyanalyses, and were manifest particularly as isolated wetor dry reports.

All institutions contributing to the Alpine rain-gaugedataset have applied their native quality control proce-dures before providing the data. The testing employed ishowever very variable. In some cases it included labo-rious manual validation and versatile automatic spatialchecking. In others, just single-site tests for physicalplausibility.

To remedy frequent problems of data quality as evi-dent during the climatological analyses, we devised anelementary testing procedure that could be applied sys-tematically across the entire region. Its purpose was thedetection and flagging of gross errors. Other issues ofdata quality, such as the systematic measurement biasfrom wind-induced undercatch, wetting and evaporationlosses (e.g. Sevruk, 2005, Groisman and Legates, 1994)as well as temporal inhomogeneities due to station reloca-tions and instrument changes (e.g. Wijngaard et al., 2003,Begert et al., 2005) were not addressed with this proce-dure. Detectability of gross errors strongly depends onstation density. Hence, its large variation across the Alpsposes a particular challenge to the design of a suitableprocedure. The philosophy of our approach is conser-vative in the sense that flagging is only applied whenthere is strong evidence for implausibility. As a result, theAlpine rain-gauge dataset contains gross errors even after

checking and their rate is likely larger in areas with coarsestation coverage, where the testing is less powerful.

In the following we describe sequentially the threesteps of the applied quality checking procedure. Theseencompass (a) the scanning of time series for codingproblems, (b) a fully automatic spatial consistency check,and (c) the identification of over all suspicious timeseries.

3.1. Checks for coding problems

The initial testing step aims at identifying well-knownand recurring coding errors in the time series. Firstly,the internal consistency of the time coding (referencedate for the non-calendaric 24-h accumulation period)between the different data contributions was verified. Tothis end nearby time series from different providers werechecked for suspicious lags. Secondly, the time serieswere scanned for suspicious duplicate values, as well asfor negative and non-physical large values. In the lattercase values exceeding 450 mm per day were manuallychecked. Apart from an extreme flash flood event inFrance (more than 500 mm per day on 8 September 2002,see Anquetin et al., 2005), all exceedances turned out tobe implausible, likely the result of arithmetic shifts ormulti-day accumulations, and they were flagged. Finally,all instances were manually checked when a time seriesreported zero precipitation throughout an entire calendarmonth. Real dry periods over more than 30 days arerare in the Alps, except along the Mediterranean coast.Many of these occurrences in the dataset turned out tocome from erroneous coding of long data gaps. Theseepisodes were therefore flagged unless simultaneous longdry periods were found at nearby stations.

3.2. Spatial consistency check

A fully automatic spatial consistency test was appliedby comparing all individual daily reports to simultaneousreports at stations in the neighbourhood. The procedureis a modified version of that described in Scherrer et al.(2011) (see also Behrendt, 1992). It distinguishes betweenthree situations, namely, that of an isolated wet report,that of an isolated dry report and that of an out-of-rangereport.

The situation of an isolated wet report occurs if anon-zero test value is surrounded by dry conditions (allstations within a 50 km radius report less than 0.3 mmper day). The test value is considered implausible and isflagged if it exceeds a certain threshold. The thresholdis defined to vary with distance to the closest neighbourstation and ranges from 0.3 mm per day for a co-locatednearest neighbour to 3 mm per day for a nearest neigh-bour as far as 15 km. If the nearest neighbour is furtherthan 15 km no constraints are employed and the testvalue is retained anyway. To account for the short-scalevariability during the convective season, the tolerancewith far nearest neighbours is increased to 3.5 mm perday and the maximum distance is set to 20 km betweenMay and September. The definition of these thresholds isbased on supervised tests in different regions of the Alps.



The procedure for an isolated dry report is employed,if the test value is less than 0.3 mm per day but allstations in a 50 km radius are wet. In this case andif the closest neighbour station is at less than 15 kmdistance, an estimate of precipitation is calculated byspatial interpolation using the procedure described inSection 4 but without the test station itself. The dry testvalue is considered implausible, if this cross-validationestimate exceeds a threshold. The test threshold is definedsimilarly to the case of isolated wet reports, i.e. dependingon distance of closest neighbour and time of the year, butwith slightly larger values (larger by 0.5 mm per day).

The checking for out-of-range reports is adopted to allsituations not considered with the above cases. The pro-cedure compares the test value against a cross-validationestimate, again using the interpolation procedure ofSection 4. The tolerance (critical difference between testvalue and cross-validation estimate) is formulated forsquare-root transformed values and in dependence of thevalue range of reports in the surrounding. The latterensures that the procedure is more permissive in case oflarger spatial variations in the neighbourhood. To accountfor the limited detectability of gross errors with coarsenetwork density, the out-of-range test is only effective ifat least three stations are within a 20-km distance aroundthe test station and if at least one has an elevation differ-ence of less than 300 m. Like with the other automatictests the chosen threshold dependence is the result ofdetailed case studies and of experiments with differentsettings and in different regions of the Alps.

3.3. Identification of suspicious time series

The spatial consistency test as just described revealedseveral data records with a particularly frequent rejectionof reports. To check whether there were more funda-mental quality problems with these stations, the corre-sponding records were inspected manually and comparedto nearby stations. As a result, some of the time series(longer episodes of the whole record) had to be declaredas highly implausible and these were systematicallyflagged. In other cases, the inspection revealed thatrecords for isolated stations were lagged in time by 1 d,in which case these records were shifted. If the manualinspection could not clearly identify systematic data qual-ity problems, the non-rejected reports of these potentiallysuspicious time series were retained.

3.4. Rejection statistics

The rate with which original reports have been objectedby the adopted quality control procedure varies consid-erably between the different providers (countries). Thisreflects the variable level of native quality control, thecomplications for reliable measurement in complex ter-rain, and the reduced power for gross error detection incoarse networks. In total the rejection rates for the spa-tial consistency checks range from 0.2% to 0.8% betweenthe different providers. For the subset of stations above1500 mMSL, the rejection increases to 0.3–1.1%. Note

that the out-of-range test was not effective for manyof the high-elevation stations (lack of nearby stationsat comparable altitude) and the higher rate there likelyreflects technical difficulties (wind, snow and ice). Whenstratified between the various components of the spa-tial consistency test, the largest rejection rates are foundfor isolated dry reports (1.4–4.5‰) and for isolated wetreports (0.5–3.2‰). Objections of out-of-range reportswere less frequent (0.1–0.7‰). The number of deletedentries due to coding problems, especially when a wholemonth has no precipitation at a certain station, is moder-ate for most providers. Altogether, the employed controlprocedure has improved data quality and consistency inthe collated Alpine rain-gauge dataset. This is discernible,for example, in far less frequent occurrence of artefactsin the daily gridded analyses and in more coherent spatialpatterns of climatological summary statistics, such as thefrequency of wet/dry days and quantiles.

4. Spatial analysis

We have used the collated Alpine rain-gauge data toestablish a dataset of pan-Alpine daily precipitation fieldson a regular grid. This gridded dataset supersedes severalearlier versions that were based on the FS98 stationdatabase (e.g. Frei, 2006, Schmidli et al., 2007). Apartfrom the more extended temporal and spatial coverageof the underlying station data (see Section 2), the mostsignificant change in this new grid dataset is the adoptionof a finer grid spacing. The spatial analyses are nowprovided on a regular 5 × 5 km grid in the ETRS89-LAEA coordinate system (Lambert Azimuthal EqualArea Coordinate Reference System). Note that ETRScoordinates have become a mapping standard in Europe(Annoni et al., 2003). The finer resolution is motivatedby the improved station density and demands for newapplications, for which the previous datasets at 50 and20 km grid spacing were not satisfactory.

The spatial analysis (interpolation) employed for thenew grid dataset is not substantially altered from thatfor the earlier datasets. Essentially, the changes involvenew settings of the method’s parameters that allowbetter reproduction of fine-scale variations in regionswith dense station coverage. In the following sub-sectionswe recapitulate on the principles of the method, discussissues relevant for the application of the grid datasetand describe the structure of interpolation errors. Thegrid dataset provides the basis for our analysis ofdaily precipitation statistics in the Alps (Sections 5and 6).

4.1. Method

The adopted method of spatial analysis relies on thewidely used anomaly concept where separate analysesare calculated for some reference condition, typicallya long-term mean, and for the relative anomaly fromthat reference on the day under consideration (e.g.Widmann and Bretherton, 2000; Haylock et al., 2008).


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Multiplication of the anomaly and reference grids finallyyields the daily precipitation analysis.

In our case, the reference condition for a day is thelong-term mean precipitation (period 1971–1990) of thepertinent calendar month. The construction of the clima-tological reference fields is based on the PRISM methodof Daly et al. (1994, 2002). For the estimation at agrid point PRISM uses a linear precipitation–elevationrelationship that is estimated from stations in the neigh-bourhood of the target point by linear regression. A keyfeature of PRISM is to ensure that stations are repre-sentative for the physiographic conditions at the targetlocation (see also Daly et al., 2008). For this purpose,PRISM attributes larger weights in the regression to sta-tions with an exposition, slope orientation, etc. similar tothe target location. Schwarb (2000) has adapted PRISMfor the Alpine region and applied it with validated stationnormals from the Alpine rain-gauge dataset and a high-resolution digital elevation model to derive mean monthlyprecipitation fields at a 2-km resolution (see also Schwarbet al., 2001). The aggregation of these fields onto the 5-km ETRS grid serves as a climatological reference forthe daily interpolation employed in this study.

Note, that our climatological reference is valid for the20-year average from 1971 to 1990. This choice provedto yield averages for many more stations than if the full38-year period would have been chosen as reference (seealso Figure 3).

The interpolation of relative anomalies for a particularday is performed with a weighting scheme that empha-sizes measurements from stations closer to and/or withstronger directional isolation relative to the analysis gridpoint. To this end a modified version of the SYMAP algo-rithm by Shepard (1984) is employed. One of the modifi-cations is the adoption of a smoother radial dependence inthe weighting function (see Equation (1) in FS98), whichaims at estimating an area-mean value directly, ratherthan via preliminary analysis on a primary grid (see e.g.Legates and Willmott, 1990). Moreover, Shepard’s orig-inal ‘gradient correction’ is omitted in our applicationfor the same reason. In extension of the original proce-dure we apply a spatially and temporally variable searchneighbourhood, similarly to FS98. In the present applica-tion (5-km ETRS grid) the search radius is successivelyincreased from 15 to 60 km (in steps of 5 km) until atleast three valid station measurements are contained. Agrid point is left at ‘missing’ if less than three measure-ments are available within 60 km. The variation of thesearch radius allows to better account for the large spatialvariations in station density, so that fine-scale informationcan be exploited where the network is dense.

The main purpose of the anomaly concept in the dailyinterpolation is to reduce the risk of systematic errorsdue to the non-representative distribution of stations,especially the prevalence of valley and lowland stationsover high-elevation stations (see FS98, Konzelmannet al., 2007). Indeed, comparisons of daily interpolationswith and without the anomaly approach have showndifferences in long-term mean precipitation of several

10% (Widmann and Bretherton, 2000; Frei et al., 2003).Interpolation with a reference and anomalies typicallyyields larger mean values compared to interpolation ofdaily precipitation directly. The comparisons suggestthat interpolation without reference is more prone tosystematic precipitation underestimates in high-mountainregions due to the prevalence of stations in comparativelydry valley conditions. These biases would give rise tosubstantial inconsistencies in the long-term water balanceas is suggested by Schadler and Weingartner (2002)who compared the PRISM-derived long-term climatologyagainst water balance based precipitation estimates inSwitzerland (see also Weingartner et al., 2007).

4.2. Interpretation

It is important to point out some general issues of theconstruction of the grid dataset that may need attentionin its application and the interpretation of results.

Firstly, the variations in the daily precipitation analysesat scales near the grid spacing are mostly imprintsof the climatological reference fields. Because thesepatterns reflect long-term mean conditions, they maybe of limited representativeness for individual days.Care should therefore be exercised in interpreting thesesmall-scale variations. They represent the temporallystable component of small-scale variability and are morerealistic for precipitation sums over longer periods. Theirprimary function is to ensure consistency with the fine-scale pattern of the long-term climatology and, hence, toreduce biases from unrepresentative station distributions(see previous subsection).

Secondly, the procedure of spatial interpolation bydistance and angular weighting has a smoothing effect.The daily analyses do not replicate peak values atindividual stations even if these were located at gridpoints. It is therefore more appropriate to interpret gridpoint values as estimates of area-mean precipitation.However, there is ambiguity about the spatial scaleseffectively resolved by the analysis. At the daily time-scale the station spacing (10–15 km in high densityareas) can be considered as a lower bound for theeffective resolution. For longer-term averages, it may befiner, depending on the strength of systematic topographyimprints. In conclusion, the fine spacing of the underlyinggrid does not mean that daily precipitation patterns areeffectively resolved at these scales. The true resolutionvaries across the domain and with time, depending onthe density of stations.

Thirdly, the grid dataset is affected by biases in rain-gauge measurements (Neff, 1977, Yang et al., 1999).The ‘gauge undercatch’ is comparatively larger duringepisodes with strong wind and during weather with lowrainfall intensity or with snowfall. Hence the relatederrors in the grid dataset are quite variable. Sevruk (1985)and Richter (1995) have estimated the magnitude ofsystematic measurement errors on seasonal mean pre-cipitation in Switzerland and Germany. Their estimatesrange from 7% (5%) over the flatland regions in win-ter (summer) to 30% (10%) above 1500 mMSL in winter



(summer). Sevruk’s estimate of a season independent biasof 4% for southern Switzerland may be characteristic forthe Po valley.

Finally, our collection of data has not made prereq-uisites regarding the long-term consistency (temporalhomogeneity) of the time series. Artefacts from changingmeasurement conditions must therefore be expected in therain-gauge data series. These and the variations of net-work density over time are compromising the long-termconsistency of the grid dataset (see e.g. Hofstra et al.,2009). Therefore, the present dataset is not suitable forapplications where long-term consistency is crucial, suchas for trend analysis.

4.3. Interpolation errors

To allow for well-informed applications of the presentgrid dataset, it is desirable to quantify errors of thespatial interpolation. The primary source of reference datacurrently available for an evaluation of the daily gridpoint estimates is station data. This is far from ideal whengrid point estimates more likely represent area-meanconditions (see previous subsection). The scale mismatchcan inflate interpolation errors by sampling errors ofthe reference (see e.g. Villarini et al., 2008). Despitethis we proceed here with a comparison of grid pointvalues against point measurements, but will consider thestatistics as being representative for a user who interpretsgrid point values as point estimates. The statistics willprovide an upper bound of the errors for area means.

Our quantification of interpolation errors is based ona leave-one-out cross-validation (similar to jackknifing)where station observations are left out in turn andestimates are calculated by spatial interpolation from thesurrounding stations. The cross-validation runs over allwet observations (≥1 mm per day, period 1971–2008) ofall stations in three subregions of the Alps. The regionsrepresent variable conditions in station density and terraincomplexity (see domains in Figure 1): Region 1 is overCroatia, an area with coarse station coverage (one stationper 240 km2), Region 2 for high-Alpine topographywith a dense network (one station per 66 km2), andRegion 3 over a flatland area of France with a densenetwork (one station per 86 km2). At the daily time-scale,interpolation errors must be expected to vary considerablywith precipitation intensity. Absolute (relative) errors arelarger for large (small) daily precipitation. For this reason,the cross-validation errors are stratified with respect toprecipitation intensity.

Figure 4 depicts boxplots of the distribution of relativeinterpolation errors for each region, separately forwinter and summer (each of the subsamples comprisesseveral thousand values). The x -axis distinguishesbetween classes of precipitation intensity. These classesare defined in terms of quantiles of daily precipitation(station dependent, wet days only), so that each boxdescribes error statistics in a section of the distributionfunction. For all regions and seasons, there is a strongsensitivity of the error distribution on precipitation

intensity. Relative errors are largest at low-intensityand decrease continuously with higher intensity. Obvi-ously this is because even small absolute errors inthe interpolation can mean substantial relative errorswhen daily totals are small (the sensitivity for absoluteerrors is reverse). Interestingly, the error distributionsare slightly biased with a tendency for overestimates atlow intensities, particularly in summer, and a clear shifttowards underestimates at high intensities. This reflectsthe smoothing effect of the interpolation, which tendsto smear out localized rainfall peaks into low-intensityregions and to damp high-intensity peaks. The systematicerrors (intensity dependent biases) are more substantialin summer when rainfall patterns are more convective,with space scales closer to the station spacing and,hence, more detrimental smoothing.

Quantitatively, there are large differences in both,the systematic and random errors (offset and spread ofboxes) between the three regions. In Region 1 (lowstation density) relative errors are substantial throughoutthe intensity spectrum. At moderate intensity (near themedian of wet-day totals), in winter, the interpolationunder- or overestimates point observations by a factor of1.5 or more (relative error outside 0.6–1.5) in more thanhalf the cases. For Regions 2 (Alps) and 3 (Flatland),the same values of typical relative error are 1.25 and 1.2,respectively. In winter, high-intensity precipitation eventsare systematically underestimated by about 20, 12 and 8%in the three regions. All these variations are plausible inview of the different sampling conditions and climatecharacteristics in the three regions. Evidently, the qualityof the grid dataset is depending critically on the densityof the underlying rain-gauge network.

Between summer and winter, there is an interestingchange in the error ranking between Region 2 and Region3: despite its flat terrain, Region 3 exhibits larger errorsat high-intensity than Region 2 (Alps). This may reflectdifferences in the nature of heavy precipitation. Overthe northern flatlands, heavy precipitation in summeris almost exclusively from convection, whereas, in theAlps, heavy events can also occur in connection withstationary flow and more wide-spread precipitation (e.g.MeteoSwiss, 2006; Schmutz et al., 2008).

The measures of uncertainty in Figure 4 are represen-tative when grid point values are interpreted as pointestimates. Clearly, we do not promote such a practicefor applications of the grid dataset over the Alps. Thedilemma is posed by the lack of a suitable observationalreference, necessary for verifying the interpolation at thegrid-pixel scale or at some ‘effectively resolved’ scale,compliant with more suitable interpretations by users. Asalternative, statistical models of the spatial precipitationvariability could also serve to estimate uncertainties inarea means. For example the framework of geostatisticshas been used for quantifying interpolation uncertaintiesand their dependence on spatial scale (Ahrens and Jaun,2007; Frei et al., 2008; Vogel, 2013). Clearly, furtherdevelopments will be required for a systematic applica-tion of such methods over a large area such as the Alps.


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Figure 4. Boxplot of interpolation errors determined from a systematic leave-one-out cross-validation in three characteristic regions (see domainsin Figure 1). Region 1: area with a coarse station network, Region 2: mountainous area with a dense network, Region 3: flatland area with adense network. Errors are expressed as the ratio between the interpolation (at the location of the station) and the observation at the station. Theerrors are stratified into bins of precipitation intensity, which are defined in terms of quantiles (wet days only) with low (high) intensities onthe left (right). Bins are labelled by probabilities. For example, the second bin from the left, includes error measures from all days when theobserved daily precipitation fell between the 20% and 40% quantiles of all wet days at the same station. The upper plot is for winter (DJF), thelower for summer (JJA). The boxplots represent the median (bold line), the inter-quartile range (box) and the 10–90% quantile range (whisker)

of the error distribution.

5. Example cases

The high-resolution grid dataset is a comprehensiveresource of the day-by-day course of precipitation in theAlps. It lends itself to study episodes of specific interest,such as heavy precipitation events, in a wider spatialcontext than is usually available in case studies at thenational level. To illustrate this potential Figure 5 displaystwo well-known examples of the past decades. In bothcases heavy precipitation fell over large areas and severalnational territories.

The case in Figure 5(a) is for the 3 d from 23 to25 August 1987. The synoptic situation on these dayswas characterized by a strong cold front advancing fromthe northwest and a cyclone near the Gulf of Genoaadvecting maritime air masses towards the southern Alps(LHG, 1991). The combination of orographic, frontal andlarge-scale uplift as well as the continuous triggeringof convection in this dynamic environment resulted in

a richly structured rainfall pattern (Steinacker, 1988).Regions particularly affected were the coastal regions ofnorth-western Italy and the southern flanks of the Alps,where rainfall regionally exceeded 300 mm. But largeareal amounts were also recorded in inner-Alpine regionsand along the northern pre-Alps. The event caused seriousflooding and landslides in many valleys of the Italian,Swiss and Austrian Alps. In Switzerland, it was one of themost damaging flooding events of the past four decades(e.g. Hilker et al., 2009).

The event of 20–22 August 2005 (Figure 5(b)) wasassociated with a cyclone of type Vc in the cyclonetrack classification of van Bebber (1891) that developedover the Gulf of Genua and moved eastward over theAdriatic Sea and the Balkans. The sustained transport ofmoist air masses around the eastern Alps led to long-lasting and intense rainfall along the northern rim ofthe Alps. Rainfall totals reached well beyond 200 mm



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Figure 5. Precipitation sum in mm for the 3-d periods of 23–25 August 1987 (upper panel) and 20–22 August 2005 (lower panel). The thickline represents the 800 mMSL topographic contour.

over a large area from central Switzerland to westernAustria and southern Germany. Heavy rainfall was alsorecorded in the eastern Alps of Austria and Slovenia,likely due to the more south-easterly and hence upslopeflow direction there. At many stations in Switzerland,eastern Austria and Bavaria the rainfall reached thelocal 100-year return level (Frei, 2006). The floodscaused damages of nearly three billion Euros in thesethree countries (UVEK, 2008; Amt der VorarlbergerLandesregierung, 2005; Bayerisches Staatsministeriumfur Umwelt, Gesundheit und Verbraucherschutz, 2005).

6. The Alpine precipitation climate

In this section we analyse the Alpine dataset to workout and discuss some key characteristics of the precip-itation climate in the Alps. While previous pan-Alpineanalyses have focused mostly on long-term mean pre-cipitation (e.g. Fliri, 1974; Baumgartner et al., 1983,FS98), the present compilation emphasizes aspects ofthe daily statistics, including the occurrence of intenseprecipitation and long dry periods. For this purpose ouranalysis considers a subset of the descriptive indices that

have been proposed by the CCl/CLIVAR/JCOMM ExpertTeam on Climate Change Detection and Indices (ETC-CDI, Klein Tank et al., 2009). In the course of this studya comprehensive list of these indices was investigated.The subset presented aims at spanning a range of dailystatistics and portraying the most important regional vari-ations. The following sub-sections discuss the distributionof mean annual precipitation (6.1), key indices of dailyprecipitation statistics (6.2), and long dry and wet spells(6.3). An illustration of the annual cycle is given for theexample of heavy precipitation in subsection (6.4). All theresults were derived by calculating the indices directlyfrom the gridded daily precipitation analyses of the 38years from 1971 to 2008 (Section 4).

6.1. Mean annual precipitation

Figure 6 depicts the distribution of mean annual precipi-tation (1971–2008). There is a wide range of values fromapproximately 400 to more than 3000 mm per year, withoccasionally pronounced contrasts over short distances.Topographic effects manifest in patterns at variable spacescales. For example, wet conditions are found in an elon-gated zone along the northern rim of the Alps. Embedded


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in this zone are smaller scale variations that go alongwith prominent mountain massifs (e.g. the Glarneralpenin eastern Switzerland and the Allgauer Alps at the west-ern border between Austria and Germany). There is asimilar wet band extending along the southern rim of theAlps (towards the Po valley), but with a more pronouncedsectioning into two wet zones at major embayments ofthe main ridge. The first is centred over southern Switzer-land (Ticino) and the Italian regions of Lombardia andPiemonte. This anomaly is connected to the wet anomalyalong the northern rim across the section of St. Got-tard, a particularly narrow cross-section of the ridge. Thesecond wet region along the southern rim extends fromthe Dolomiti massif in north-eastern Italy eastward intothe Julian and Carnic Alps (boarder between Italy andSlovenia) where particularly large annual mean valuesare observed. Compared to the two ridge-scale anomalies,regions in the inner Alps are comparatively dryer withmean values comparable to those over the flatland regionsadjacent to the Alps. Particularly dry conditions are foundin inner-Alpine valleys that are shadowed against direc-tions with prevailing moisture bearing winds. Noteworthyexamples are the Valais, the Aosta valley (north-westernItaly), the upper Inn valley and the Vinschgau (northernItaly, near border to Austria).

In addition to the pattern of the main ridge, severalsmaller scale hill ranges in the region show wet anoma-lies too. Examples are the Jura mountains, the Vosgesmountains, Black Forest and Bohemian Forest. Meanvalues there are comparable to those along the northernAlpine rim, but the precipitation signal is mostly centredat the highest elevations. An exception to this is foundin the Massif Central, which exhibits two distinct wetanomalies, one at the north-western and one at thesouth-eastern flanks of the Plateau. The comparativelydryer conditions of the southern Alps (south-westernFrance), suggests that the Massif Central may have arain-shadow effect on the latter. The wet anomaly ofthe Apennine is linked to that of the southern Alpsby a narrow wet anomaly along the mountains of theLigurian coast. Particularly dry conditions in the Alpineregion are found at the mouth of river Rhone (FrenchMediterranean coast) and in eastern Austria where meanannual precipitation is less than 600 mm per year.

The general distribution of mean annual precipitationfound in the present analysis is very similar to thatderived from earlier versions of the Alpine rain-gaugedataset (FS98). The higher resolution of the presentanalysis (5 km vs 25 km grid spacing), however, revealspreviously unresolved and interesting patterns that canbe related to individual mountain massifs and provide abetter delineation of the topographic enhancement signalsfrom dry valley and flatland conditions. (Compare Figure6 with Figure 9 in FS98.)

6.2. Daily precipitation statistics

Figure 7 depicts four selected indices of daily pre-cipitation in the Alps representing basic characteristicsof the daily rainfall frequency distribution. The indices

considered are frequency of wet days, defined as occur-rences when the daily total is 1 mm or larger (Figure7(a)), the mean precipitation on wet days (Figure 7(b)),the mean of annual maximum daily totals (averaged overall years, Figure 7(c)) and the fraction of precipitationcoming from moderate to intense events, i.e. days whenthe precipitation is equal to or larger than the 75% quan-tile (wet-day quantile at the gridpoint, Figure 7(d)). Note,that our definition of the wet-day threshold refers tothe recommendation of the ETCCDI (Klein Tank et al.,2009), which is larger than the lowest measurable precipi-tation. We have investigated several other popular indicesbut found that these would not add substantially to theoverall picture. For example, the popular indices for the95% wet-day quantile and the frequency of days exceed-ing 20 mm show distributions that are qualitatively verysimilar to that of mean annual precipitation (Figure 6).

The four indices reveal a pronounced asymmetry of thedaily precipitation frequency distribution between regionsto the north and south of the Alpine main crest. Alongthe northern rim and in the northern and western flatlandregions, wet-day frequency is large (Figure 7(a)). In theseareas rain falls on every third to every second day inthe annual average. Over the southern rim and forelands,however, the wet-day frequency is lower with rainfall onone of three to five days. For wet-day mean precipitationthe asymmetry is reversed (Figure 7(b)): average rainfallintensities are typically much larger along the southerncompared to the northern Alpine rim with particularlylarge values observed at the two major embaymentswith large annual mean precipitation. These two regionsstand out even more clearly at the tail of the frequencydistribution (Figure 7(c)). Annual maxima are two tothree times larger there compared to the moist northernrim. The two regions Piedmont-Ticino-Lombardy andJulian/Carnic Alps seem to be the mesoscale hot spots ofheavy precipitation in the Alps. Note also that more thantwo thirds of the total precipitation comes from the 25%most intense wet days in these two regions (Figure 7(d)).Interestingly the anomaly along the northern rim is notmanifest in Figure 7(d). This indicates that the shape ofthe tail of the frequency distribution at the northern rim issimilar to that over the nearby flatlands and hence that thelarger mean precipitation there comes primarily from thehigher frequency and mean intensity of wet days. This isdifferent over the southern portion of the domain, wherethe fraction from moderate to high-intensity events is notonly larger but also shows spatial variation. In summary,these analyses imply that precipitation is more frequentto the north of the main ridge but more vigorous to thesouth, particularly so along the southern rim where muchof the total precipitation is from rare intense events.

Apart from the hot spots along the southern rim ofthe main ridge, areas of particularly heavy precipitationare also found along the Ligurian coast, along the south-eastern slopes of the Massif Central and in the DinaricAlps along the Adriatic coast (Figure 7(c) and (d)).The former two of these regions are characterized bylow wet-day frequency (Figure 7(a)). Note particularly



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Figure 7. Four key statistics of the daily precipitation climate (annual, reference period 1971–2008). (a) Frequency of wet days (≥1 mm, fraction),(b) mean precipitation on wet days (mm per day), (c) mean of annual maximum daily precipitation (mm per day), (d) fraction of precipitation

from days with moderate to high-intensity (≥75% percentile on wet days, fraction).

that the Massif Central shows a similar asymmetryin precipitation statistics like that for the Alps: thereis a marked north-west to south-east gradient in wet-day frequency and a much stronger prominence in theintensity indices (Figure 7(b)–(d)) of the south-easterncompared to the north-western wet anomaly. Along theFrench Mediterranean coast there are the smallest valuesof wet-day frequency across the whole domain.

The smaller scale hill ranges of the northern Alpineregion (Jura, Vosges and Black Forest) receive morefrequent and more intense precipitation compared to theadjacent flatlands (Figure 7(a) and (b)). But these hills arelesser distinguished from their surrounding in the othertwo indices (Figure 7(c) and (d)), suggesting that the

larger mean precipitation there is mostly from increasedfrequency of light to moderate events.

Noteworthy features of the indices over the flatlandregions are the very low wet-day frequency along theFrench Mediterranean coast and a marked decrease ofthe same statistic towards eastern Austria and Croa-tia (Figure 7(a)). The latter indicates the influence ofmore continental climate near the eastern border of thedomain. Finally, over the flatlands of central France(north-western corner of the domain) only a very smallproportion of total rainfall comes from moderate to high-intensity events (Figure 7(d)). Frequent light precipitationduring much of the winter half year may be responsiblefor this.


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Figure 8. Mean annual maximum consecutive wet-day (left) and dry-day (right) period (in days). Reference period 1971–2008.

6.3. Long wet and dry spells

The succession and duration of spells of wet and dry daysare an integral part of a region’s precipitation climate.Figure 8 depicts the length (in days) of the longestperiod of consecutive dry days and wet days (criticalthreshold 1 mm), averaged over all years of the studyperiod (1971–2008).

Extreme wet spells (Figure 8(a)) show a distribution,which is roughly mixture of those for mean precipitationand wet-day frequency (cf. Figures 6 and 7(a)). Partic-ularly long durations occur along the northern rim ofthe Alps and, to some extent, also along the southernrim. But the latter anomaly is far less pronounced inaccord with the understanding that large mean precipi-tation there is primarily related to higher intensity (cf.Figure 7). Note, that the anomalies of mean precipitationfound at the Julian and Carnic Alps as well as along thesouthern slopes of the Massif Central are barely distin-guished in the wet spell distribution. As for the flatlandlong wet spells show a north-south contrast with lengthsaround 10 days to the north and around 6 days to thesouth of the ridge. The longest wet spells in the regionoccur at the northern and western hill ranges (Vosges,Black Forest, Jura, western Massif Central), which mayreflect their exposition to moisture bearing flows duringpersistent weather situations.

Long dry spells (Figure 8(b)) exhibit a strong gradientbetween the Alps and the northern foreland on the onehand (18–26 days) and regions to the south on the other(28–44 days). Particularly long dry-day periods, withaverage extreme spells exceeding 35 days, occur alongthe French Mediterranean coast and in Liguria (westernPo valley). The two regions seem to be well shieldedfrom rain-bearing weather systems by the surroundingtopography. There, dry spells are met both in winter andin summer, with winter-time events being more frequentin Liguria and summer-time events in southern France.It is intuitive that long wet spells go mostly togetherwith short dry spells and vice versa (Figure 8). But thiscorrespondence is not universal. For example the con-tinental regions of the domain (eastern Austria, Croatiaand Slovenia) are characterized by comparatively shortdry and short wet spells, indicating some fundamen-tal differences in precipitation intermittency across thedomain.

6.4. Annual cycle of heavy precipitation

Spatial variations in the annual cycle of mean precipi-tation have been discussed in detail in FS98 (see theirFigure 12). The results of this earlier analysis have notaltered with the new dataset. Therefore, we rather exam-ine the annual cycle of heavy precipitation here, consid-ering that magnitude of intense events is of particularinterest.

Figure 9 depicts the evolution of the largest dailyprecipitation total in each month of the study period(1971–2008), averaged separately for each calendarmonth. The results for the sub-domains were obtainedby averaging over all grid points in the domain. Thereare remarkable differences in the annual cycle of heavyprecipitation between the domains: in the flatland regionsof France there are only small variations through the yearwith a slight maximum in summer. This turns into agentle pattern with two maxima at the Black Forest (rep-resentative also for the Jura and Vosges hills). The onein winter is associated with dynamically active weathersystems (fronts, low-pressure systems) in combinationwith orographic enhancement; the one in summer isrelated to convective activity. Further east (sub-domainsBavaria, northern Alps and eastern Austria), the annualcycle markedly increases in amplitude and turns into a 12-month cycle, with values in summer (convection) beingabout twice as large as in winter. Values along the north-ern Alps are generally larger than in the other two ofthese domains (see also Figure 7(c)).

In the southern sub-domains, monthly maximum dailyprecipitation shows larger values and larger variationsthrough the year, except over the Po valley (Figure 9).The maximum of the annual cycle is attained betweenSeptember and November in all these domains, but theevolution is more complex: in some of the regions thereis a secondary maximum in spring (south-eastern MassifCentral, Ticino, Julian and Carnic Alps) and the minimumoccurs either in winter (Ticino, Po valley) or in sum-mer (south-eastern Massif Central, Liguria). The complexand spatially variable pattern of the annual cycle in thesesouthern domains is related to several factors with distinctannual patterns, including the occurrence of southerlyflow conditions (most frequent in spring and sum-mer), the sea surface temperature of the Mediterranean(warm in late summer and autumn) and insolation-driven



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It is worth mentioning that the annual cycle of heavyprecipitation has interesting differences to that for meanprecipitation (cf. Figure 12 in FS98). For example inthe southern regions, the relative amplitude of springand autumn maxima is reversed, with mean (heavy)precipitation being relatively larger in spring (autumn).The larger frequency of wet days in spring (about 0.45 inMay in region Ticino) compared to autumn (about 0.29 inSeptember) compensates for the less frequent occurrenceof very high daily amounts. We have verified that thesevariations are not just an effect of the slight difference insub-domains between Figures 9 and 12 of FS98.

7. Conclusions

The grid dataset and climatological analyses, presentedin this study, offer a comprehensive trans-nationalinformation source on Alpine precipitation of the recent

past. The underlying rain-gauge dataset encompasses typ-ically 5500 measurements on any day of the study period(1971–2008), probably one of the densest in situ mon-itoring systems available from a high-mountain area ofthis size. The dataset builds on an earlier data compilationeffort (FS98) but major improvements in data coveragecould be achieved in areas and time periods with previ-ously poor coverage, notably the regions of northern Italyand Slovenia and the years after 1990. Also, the qualityof the dataset was fundamentally reviewed by upgrad-ing to the recent status of the national databases and byadopting a specifically designed quality control procedurethat addresses common data coding errors and involvedseveral semi-automatic checks.

Compared to its earlier version, the new grid datasetis assembled at a higher nominal resolution (5 km gridspacing as compared to 20 km in FS98) by employ-ing a new analysis method, which incorporates localprecipitation–topography relationships at the climatolog-ical time-scale. The procedure aims at reducing the risk ofsystematic underestimates at high elevations. The higher


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resolution of the grid dataset may not be expected toresolve precipitation at the scale of the grid spacing, butit facilitates the aggregation into and improves estimationof spatial averages over complex domain shapes. This isparticularly useful in hydrological applications requiringmean precipitation over catchments, or for the evaluationof regional climate models, when the observational griddataset needs to be assembled onto the model’s nativegrid structure.

It is important that users of the Alpine precipitationgrid dataset are aware of the limitations and uncertain-ties involved: while there are good prospects that the griddataset resolves scales near the 5-km grid spacing whenprecipitation totals over longer periods are considered, theeffective resolution for daily totals is clearly coarser thanthe grid spacing. At the daily scale the spatial resolutionis more likely in the order of 10–25 km (typical sta-tion spacing), eventually even coarser, notably in regionsand during time periods where the station coverage ismore limited. When grid point values are interpreted asestimates at finer scales (e.g. as point estimates) quitesubstantial random errors must be expected at the dailytime-scale: high precipitation intensities are systemati-cally underestimated and low intensities overestimated.The magnitude of these errors depends on season, sta-tion coverage and topographic complexity (measures ofthe error for the case of interpreting grid point valuesas point precipitation are given in Section 4.3). Mis-matches between the effective spatial resolution of thepresent dataset and the resolution of its application (e.g.that of a hydrological model to be driven by the datasetor that of a regional climate model to be verified againstit) can give rise to systematic representativeness errors(e.g. Tustison et al., 2001).

Further improvement of the effective resolution of thepresented grid dataset will be difficult from in situ dataalone, considering that most of the available data wasincorporated. Approaches using radar data in combina-tion with station data (e.g. Krajewski, 1987; DeGaetanoand Wilks, 2008; Erdin et al., 2012) or analyses withnumerical models (e.g. Haggmark et al., 2000; Quintana-Segui et al., 2008; Haiden et al., 2011) are promising, but,certainly in the case of radar, hardly applicable over cli-mate time scales. More immediately, some improvementscould be achieved with a more elaborate representationof small-scale precipitation–topography relationships, forexample by conditioning them on circulation types, ratherthan just on month of the year (e.g. Hewitson and Crane,2005). We are currently exploring the added value of suchan approach.

A further major limitation of the presented grid datasetis that we have to make reservations with regard to itslong-term climate consistency. Changes in station loca-tions and measurement devices have introduced inhomo-geneities to some of the records. Moreover, variations inthe station network over time can significantly compro-mise the consistency of the grid dataset. Such effectswere observed in other grid datasets relying on time-varying station networks (e.g. Hofstra et al., 2009, 2010).

We expect that inhomogeneities are relatively more evi-dent at small spatial scales (near the effective resolu-tion), but tend to average out when considering meanvalues over larger domains. Indicators of daily precip-itation extremes, such as the frequency of a thresholdexceedance, may be particularly sensitive to variationsin network density, due to the smoothing of the interpo-lation. We therefore call for caution when applying thepresent dataset for purposes with a strong requirementon long-term consistency. Remedy for this caveat maybe sought through statistical reconstruction of precipi-tation fields from a time-invariant homogeneous stationdataset, for example by using the technique proposed bySchmidli et al. (2001, 2002) and Schiemann et al. (2010).This would inevitably be accompanied by a reductionin effective resolution. Hence, such a climate consistentgrid dataset would not supersede the present grid datasetbut rather exist as a complement that satisfies otherrequirements.

Gridded datasets have become a popular and com-pact basis for describing the climate of a region. Thepresent grid dataset offers a valuable resource of infor-mation on the precipitation climate of the Alpine region.Indeed the example analyses of this study have providednew insights into the distribution of mesoscale daily pre-cipitation indicators, which would have been difficult toidentify without a trans-national consistent data source.The dataset also offers itself as a basis for numerousapplications, for example as input into quantitative mod-els of environmental sub-systems, for statistical down-scaling of climate change scenarios, the evaluation ofregional climate models and many more. An applicationof this dataset for the evaluation of global and continentalobservation datasets is currently underway. The griddedAlpine precipitation dataset is available for research fromhttp://www.meteoswiss.ch.

Acknowledgements

The research leading to these results has received fund-ing from the European Union, Seventh FrameworkProgramme (FP7/2007-2013) under grant agreement n◦

242093. We are grateful to the following people for assis-tance and valuable discussions: Christof Appenzeller,Marco Gaia, Mark Liniger and Christian Lukasczyk (allMeteoSwiss, Switzerland), Claudio Cassardo (Univer-ity of Turin, Italy), Jean-Pierre Ceron (Meteo-France,France), Albert Klein Tank (Royal Netherlands Mete-orological Institute, Netherlands) and Christoph Schar(ETH Zurich, Switzerland). We also thank two anony-mous reviewers for their valuable comments.

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