The climate of daily precipitation in the Alps

Transcription

INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. (2013)
Published online in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/joc.3794
The climate of daily precipitation in the Alps: development
and analysis of a high-resolution grid dataset from
pan-Alpine rain-gauge data
Francesco A. Isotta,a* Christoph Frei,a Viktor Weilguni,b Melita Perčec Tadić,c
Pierre Lassègues,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 Vertačnik,o
a
b
Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland
Bundesministerium für Land- und Forstwirtschaft, Umwelt und Wasserwirtschaft, Abteilung VII/3—Wasserhaushalt, Vienna, Austria
c
Meteorological and Hydrological Service of Croatia, Zagreb, Croatia
d Direction de la Climatologie Météo-France, Toulouse, France
e
German Weather Service, Hydrometeorology, Offenbach am Main, Germany
f ARPA Emilia-Romagna, Bologna, Italy
g Regione Autonoma Valle d’Aosta, Aosta, Italy
h
Provincia Autonoma di Bolzano, Ufficio Idrografico, Bolzano, Italy
i OSMER Regional Meteorological Observatory of ARPA Friuli Venezia Giulia, Visco, Italy
j
ARPA Liguria, Genoa, Italy
k
ARPA Lombardia, Milan, Italy
l ARPA Piemonte, Turin, Italy
m
Provincia Autonoma di Trento, Trento, Italy
n
ARPA Veneto, Arabba, Italy
o Slovenian Environment Agency, Meteorology Office, Ljubljana, Slovenia
ABSTRACT: In the region of the European Alps, national and regional meteorological services operate rain-gauge
networks, 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 the
region’s mesoscale precipitation climate, including the occurrence of heavy precipitation and long dry periods. The analyses
are based on a collation of high-resolution rain-gauge data from seven Alpine countries, with 5500 measurements per day
on average, spanning the period 1971–2008. The dataset is an update of an earlier version with improved data density and
more thorough quality control. The grid dataset has a grid spacing of 5 km, daily time resolution, and was constructed with a
distance-angular weighting scheme that integrates climatological precipitation–topography relationships. Scales effectively
resolved in the dataset are coarser than the grid spacing and vary in time and space, depending on station density. We
quantify 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) precipitation
intensities, when they are interpreted as point estimates. Our climatological analyses highlight interesting variations in
indicators of daily precipitation that deviate from the pattern and course of mean precipitation and illustrate the complex
role of topography. The daily Alpine precipitation grid dataset was developed as part of the EU funded EURO4M project
and 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 in
Europe. Their significance for the regional water cycle
is commonly expressed in the notion ‘water tower of
Europe’, which hints to their climate with abundant
* Correspondence to: F. A. Isotta, Federal Office of Meteorology and
Climatology, Krähbühlstrasse 58, Postfach 514, Zurich, Switzerland.
E-mail: [email protected]
 2013 Royal Meteorological Society
precipitation and to the role of snow and ice in storing
freshwater and moderating variations in river runoff (e.g.
EEA, 2009). Indeed, the Alps are the source region of
four major European rivers (the Danube, Rhine, Po and
Rhone), to which the head-waters contribute a significant
share of the runoff. Near the mouth of river Rhine, for
example, almost 50% of the mean discharge originates
from the Alpine region, an area only about 20% of the
total catchment (Viviroli et al., 2003). Precipitation in
the Alpine region contributes freshwater and hydropower,
F. A. ISOTTA et al.
within and beyond the region; it enables transportation
on rivers and shapes the distribution and diversity of
ecosystems. But the vigorous precipitation climate of the
Alps 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 life
and damages more frequently, and this requires regional
planning and prevention (e.g. Greminger, 2003).
Accurate knowledge of the precipitation climate,
including the occurrence of heavy precipitation and dry
periods, is fundamental to planning and management
tasks concerned with water resources, water use and
the protection against natural hazards. These tasks are
increasingly supported by quantitative models, such as
river runoff models in hydrology, crop models in agriculture and glacier mass balance models in glaciology (e.g.
Viviroli et al., 2009; Holzkämper et al., 2012; Machguth
et al., 2009). Concerns over impacts from future climate
change and the potential needs for adaptation have particularly fostered such model applications over the past few
years. An important pre-requisite, however, are reliable
meteorological input data, ideally provided on a regular
grid. In the Alpine region, spatial analyses of precipitation
that resolve the mesoscale distribution in complex terrain
are particularly relevant to modelling the components of
the hydrosphere. In its report on climate change adaptation in the Alps, the European Environment Agency
has identified a need for monitoring and data collection
to expand the knowledge base and widen the scope for
analysis of long data series (EEA, 2009).
There exist several sources of objective spatial precipitation analyses for the Alps. Firstly, the Alpine region
is covered in gridded climate datasets that extend over
global or continental domains (e.g. Adler et al., 2003;
Hijmans et al., 2005; Haylock et al., 2008). While much
improvement was made in these datasets with higher
station density, finer grid resolution and advanced interpolation methods (e.g. Klok and Klein Tank, 2008; Hofstra et al., 2008), their reliability in the Alpine region is
still handicapped by the heterogeneity of available measurements and the large spatial variation of precipitation
(Hofstra et al., 2009, 2010). Secondly, gridded precipitation datasets have been developed in many Alpine
countries but confined to the national territories or subregions (e.g. Ceschia et al., 1991; Gyalistras, 2003; Perčec
Tadić, 2010; Brunetti et al., 2012; Rauthe et al., 2013).
The focus of recent developments in this category was
towards high spatial and sub-daily temporal resolutions.
This has fostered techniques that incorporate, in addition to conventional rain-gauge measurements, also data
from radar and from analyses with numerical weather prediction models (e.g. Paulat et al., 2008; Lussana et al.,
2009; Vidal et al., 2010; Wüest et al., 2010; Haiden
et al., 2011; Erdin et al., 2012; Mounier et al., 2012).
The segmentation into national sub-domains and methodological differences in these datasets still complicate a
consistent climatological overview for the region as a
whole and hamper applications with a trans-national area
of interest.
A third category of precipitation analyses aims at
filling the gap between large-scale and national analyses
by providing climate information consistently for the
region as a geographic entity. Building on ideas from
earlier trans-national climatologies (Fliri, 1974; Fliri and
Schüepp, 1983), Frei and Schär (1998) have developed a
mesoscale Alpine-wide grid dataset, based on a collation
of rain-gauge data from national climate networks. The
dense spatial coverage (more than 6000 stations), daily
resolution and multi-decadal extent (1971–1990) of this
dataset have contributed to a high-resolution climatology
(Schwarb et al., 2001), analyses of daily statistics (Frei
and Schmidli, 2006) and a description of long-term
precipitation changes (Schmidli et al., 2002). In the
meantime, the daily trans-Alpine grid dataset was used
in numerous applications, including the validation of
regional climate models, the study of extreme events,
river runoff modelling and the evaluation of climate
datasets (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). A
view on the greater Alpine region was later also pursued
by Efthymiadis et al. (2006), who developed a gridded
precipitation dataset, at monthly resolution and covering a
period of more than 200 years (1800–2003). This dataset
is based on 192 high-quality climate records (Auer et al.,
2005).
This study introduces an update and enhancement of
the trans-Alpine precipitation dataset of Frei and Schär
(1998, hereafter referred to as FS98). Hence, we present
a new daily grid dataset and consistent analysis of
the precipitation climate across seven Alpine countries
(Austria, Croatia, France, Germany, Italy, Slovenia and
Switzerland). The enhancements of FS98 are threefold:
firstly, the collated rain-gauge database is extended for
the recent years (after 1990) and newly expands over a
38-year period (1971–2008). Our data collection efforts
have also resulted in a significant improvement of data
density in previously under-sampled regions and led to a
more homogenous data coverage. This was achieved by
the inclusion of newly digitized data and by assembling
datasets from additional institutions. In fact, the major
parts of the rain-gauge dataset of FS98 were newly
assembled. Secondly, substantial efforts have been made
to ensure the consistency and quality of the different data
contributions. Part of these efforts was the development
and application of a data quality control procedure
designed to detect typical problems of data coding in
rain-gauge datasets. Thirdly, the spatial interpolation
procedure of FS98 was enhanced to include climatological precipitation–topography relationships in order
to improve the reliability of the resulting grid dataset.
The daily spatial analyses are newly provided at a grid
spacing of 5 km, as compared to 25 km in FS98. Apart
from the technical information we also present and discuss, in this publication, a selection of analyses utilizing
the new grid dataset. These highlight some of the main
features of the Alpine precipitation climate, including the
occurrence of heavy precipitation and long dry periods.
CLIMATE OF DAILY PRECIPITATION IN THE ALPS
Paris
Stuttgart
Vosges
Region 3
GERMANY
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nA
Black
bia
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Bohemian
Forest
Wien
München
AUSTRIA
Morves
SWITZERLAND
Bern
J
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r
a
Zürich
FRANCE
Region 2
Valais
Lyon
Alto-Adige
Dolomiti
Ticino
Trentino
Ljubljana
Valle
d‘ Aosta
Lombardy
Milano
Piedmont
Po Valley
ITALY
Liguria
Marseille
Venezia
Ap
pe
Zagreb
CROATIA
ps
Al
Torino
Julian
Alps S L O V E N I A
ric
Massif
Central
Glarner
Alps
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Innsbruck
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Di
A
Allgäuer
Alps
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Gulf of
Genoa
in
o
Adriatic
Sea
Region 1
Firenze
Split
100 km
4200
3900
3600
3300
3000
2700
2400
2100
1800
1500
1200
900
600
300
0
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 (European Reanalysis and Observations for Monitoring) that
aims at preparing and analyzing datasets for monitoring European climate variations from in situ and satellite
observations and from model-based regional reanalyses
(International Innovation, 2011). The gridded Alpine precipitation dataset is publicly available for scientific use.
This article is organized as follows: in Section 2 we
introduce the updated Alpine rain-gauge dataset and in
Section 3 the procedures for data quality control. The
method of spatial analysis is described in Section 4,
together with an analysis of interpolation errors and aids
for the professional interpretation of the gridded analyses.
Section 5 illustrates example analyses of heavy precipitation events. Several statistics of the daily precipitation
climate, as derived from the new grid dataset, are discussed in Section 6. The conclusions of this study are
provided in the final section.
2. Alpine precipitation dataset
2.1. Study domain
Our efforts for collating high-resolution rain-gauge
data have focused onto a rectangular longitude–latitude
domain from 2–17.5◦ E to 43–49◦ N (Figure 1). The
domain extends over approximately 1200 km from Central France across Switzerland to eastern Austria and over
about 700 km from northern Italy to southern Germany.
The territory of Slovenia and large parts of Croatia are
also included in the study area.
The main topographic feature of the domain is the
arc-shaped mountain ridge of the European Alps, which
extends over about 1000 km from the French and Italian
Mediterranean coasts across Switzerland, northern Italy
and Austria into the central parts of the continent. The
highest Alpine mountain peaks reach elevations of more
than 4000 mMSL and major cross-ridge passes are at
1500–2000 m above the adjacent flatland. The ridge
is intersected with deep valleys, some of which are
more than 100 km long and divide the ridge into major
mountain massifs.
To the north and northwest, the Alpine mountain range
descends into a wide area of flat and hilly orography with
a base elevation at around 300 mMSL. There are several
small-scale hill ranges in this part of the Alpine foreland,
including the Vosges and Jura mountains, the Black
Forest and the Bohemian Forest, all with peak elevations
below 1500 mMSL. The Massif Central in France is a
wide region of plateaus and mountain ranges. With peak
elevations up to 1900 mMSL it constitutes a prominent
orographic feature upstream the south-western sector of
the Alpine arc. To the south, the Alps run out into the Po
Valley of northern Italy, a vast basin below 200 mMSL.
It is delimited also by the Ligurian Alps and the northern
Apennines. Finally, in the southeast of the domain, the
Dinaric Alps stretch southward from the Carnic and
Julian Alps along the east coast of the Adriatic Sea.
The Alpine region is influenced by and delineates
between several large-scale European climate regimes
(e.g. Schär et al., 1997; Auer et al., 2005). The wide
belt of adjacent mountain foreland included in our
study domain allows to better distinguish between
topographically influenced precipitation patterns and
those of the ambient climate regimes.
2.2.
Dataset
The first version of the Alpine rain-gauge dataset of FS98
was 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 column
gives the number of stations with valid data available for the national territory averaged over all days of the period 1971–2008.
Country
Provider
Austria
Federal Ministry of Agriculture, Forestry, Environment and Water
(BMLFUW), Division Water Balance, Vienna (data status of December
2010)
Meteorological and Hydrological Service (DHMZ), Zagreb
MétéoFrance, Toulouse
German Weather Service (DWD), Offenbach
Climatological Archive for Northern Italy (ARCIS, www.arcis.it)
Bolzano Alto Adige: Servizio Meteorologico and Ufficio Idrografico
Valle d’Aosta: Centro Funzionale Regionale
Emilia-Romagna: Servizio Idro-Meteo-Clima ARPA Emilia-Romagna
Friuli Venezia-Giulia: Osservatorio Meteorologico Regionale, ARPA
Liguria: ARPA Liguria
Lombardy: Servizio Meteorologico ARPA Lombardia
Piedmont: ARPA Piemonte
Trentino: Centro Funzionale di Protezione Civile, Meteotrentino
Veneto: ARPA Veneto and Unità Idrografica regionale
ARPA is the Italian abbreviation for ‘Regional agency for the protection of
the environment’
National Meteorological Service of Slovenia, Ljubljana
Federal Office of Meteorology and Climatology MeteoSwiss, Zurich
Croatia
France
Germany
Italy
Slovenia
Switzerland
of daily precipitation totals from about 6600 stations.
Several updates of this dataset were made in the following
years for some of the national territories. Since 2009 a
major renewal of the FS98 dataset was pursued as part of
the EURO4M project. The renewal aimed at an extension
to more recent years (up to 2008) and an improvement of
spatial and temporal coverage where the earlier version
was deficient. An extension of the dataset back in
time (pre-1971) could not be accomplished because, for
some Alpine countries, earlier high-resolution data is
not available in digital form. With the present renewal,
several national components were entirely recompiled
from the original data provider. This ensures a more upto-date status of the collated dataset, taking advantage of
the data owners’ efforts into data quality and data rescue
since the first collection more than 15 years ago.
The new version of the dataset encompasses more
than 8500 time series in total over the 38-year period
1971–2008. It integrates available measurements from
the high-resolution rain-gauge networks operated in seven
Alpine countries. Table 1 lists the data providers that have
contributed to the renewed dataset. Except for Italy, the
providers are national meteorological and hydrological
services, each of which has contributed a large number
of time series for the respective national territories.
In Italy, the operation of high-resolution measurement
networks is under the responsibility of regional meteorological and environmental services. Therefore a central
point of contact was missing. A major contribution of
data from Italy was obtained through project ARCIS
(Archivio Climatologico per l’Italia Settentrionale,
climatological archive for northern Italy). ARCIS is a
coordination effort of the regional services in northern
Italy promoting the exchange and common analysis of
long-term climate data (Pavan et al., 2013). Our contacts
Mean number of stations
950
140
2420
800
760
200
480
with the ARCIS consortium were through ARPA
Emilia-Romagna. ARCIS contributed 627 records, which
provided a continuous coverage at climatological quality
standards for all of northern Italy, yet with limited
spatial density. To further improve data density, we have
contacted the regional agencies individually (see Table 1)
and could obtain many more time series. In general
these are, however, more interrupted and extending over
shorter periods only.
The renewed Alpine rain-gauge dataset comprises typically 5500 observations on any day of the period
1971–2008. Figures 2 and 3 show, respectively, the distribution of stations in the domain and the evolution
of station density over time. For Austria, France, Germany, and Switzerland, the renewed dataset carries on
the high data density of the FS98 dataset, essentially by
extending previously available station records in time. In
these regions, data density varies between 7 and 14 stations per 1000 km2 (one station per 80–150 km2 ) and a
large proportion of the stations dispose of long records
(Figure 2). However, there is a tendency towards coarser
coverage in more recent years (Figure 3), which reflects
the reduction of climatological networks implemented in
these countries. For Germany, the reduction is particularly pronounced after 2003, which is to be understood
as a compensation for improved coverage by radar.
In Italy, the ARCIS dataset and the contributions from
regional services, together, yield a fairly stable data
density of six stations per 1000 km2 (one station per
180 km2 , Figure 3). However, this number is an average
over the entire Italian sector and the spatial coverage is
inhomogeneous (Figure 2). Comparatively higher density
could be achieved over the north-eastern part of the
country, along the Ligurian coast and the Apennines
(Emilia-Romagna), while the central and western parts
0.8
0.6
0.4
0.2
Figure 2. Distribution of stations from which records of daily precipitation are integrated in the renewed Alpine rain-gauge dataset. Shading
represents the fraction of the full period (1971–2008) covered by the respective record.
14
Germany
# stations/1000 km2
12
Switzerland
10
Slovenia
Austria
8
France
6
Italy
Italy old
4 Slovenia old
2
0
1970
Croatia
1975
1980
1985
1990
Year
1995
2000
2005
2010
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 network
that was established starting in year 2000 only, and hence
these records cover only a small portion of the full period
(Figure 2). Clearly, over northern Italy the Alpine raingauge dataset has a more heterogeneous and a more
interrupted data coverage compared to most other parts of
the domain. But the renewal of the dataset in this region
was also more fundamental and this brought a marked
improvement over previous versions of the FS98 dataset.
Note, for example, that the latter had very limited data
coverage over Italy after 1986, which could be improved
substantially with this renewal (Figure 3).
For the south-eastern parts of the domain, new datasets
for Slovenia and Croatia have been integrated in the
Alpine rain-gauge dataset. In the case of Slovenia,
advantage could be made of digitization efforts since the
last delivery, so that data density is increased by a factor
of about four (Figure 3). With this contribution the region
of the Carnic and Julian Alps, a region of particularly
heavy precipitation in the Alps, is now covered fully
at high-resolution. As for Croatia, the station coverage
is less dense than elsewhere (about one station per 300
km2 ). Nevertheless, this contribution is valuable to follow
the transition of the Alpine precipitation climate into the
south-eastern flatlands.
The national and regional services of the Alpine region
operate their rain-gauge networks with different instruments and observing practices. As for the instruments, the
differences are not fundamental. The classical Hellmanntype collector and variants thereof are the most widely
used measurement devices. Since around 1980 (depending on service), subsets of the networks are operated with
automatic devices, typically tipping bucket instruments.
In most contributions, the proportion of automatic gauges
is small (less than 15%) and they are mostly at lower elevations. Although the systematic measurement error of a
rain-gauge varies between instrument type and height of
F. A. ISOTTA et al.
deployment, the exposition to wind and hence the environment of the rain-gauge is the most important factor
for measurement bias (e.g. Sevruk and Zahlavova, 1994;
Sevruk, 2005). Therefore in our analysis we do not distinguish between differences of instrument type. As for the
observing practice, the daily reading times vary slightly
between the different services. For Switzerland they are
06:30 UTC, for Italy 08:00 UTC and 06:00 UTC for all
others. These differences are ignored in our analysis.
During the collation of the 16 different data contributions special attention was required to adequately account
for differences in archiving conventions. Labelling of
missing values, specification of station coordinates, the
assignment of dates to the data, for example, were different and not always specified in sufficient detail. Hence,
apart from the time needed to arrive at a formal agreement on the exchange of data, additional efforts were
needed to resolve technical issues. Numerous comparisons and checks have been conducted to test and ensure
the internal consistency of the resulting collated database.
3.
Data quality control
There are numerous sources for gross errors in the collated Alpine rain-gauge dataset, ranging from occasional
instrument failures, to transmission, digitization and
storage errors. Gross errors can significantly affect the
reliability of statistical analyses. For example, erroneous
coding of data gaps by zero precipitation will result in
underestimates in the frequency of wet days. Indeed,
deficiencies in data quality were spotted in several early
analyses, and were manifest particularly as isolated wet
or dry reports.
All institutions contributing to the Alpine rain-gauge
dataset have applied their native quality control procedures before providing the data. The testing employed is
however very variable. In some cases it included laborious manual validation and versatile automatic spatial
checking. In others, just single-site tests for physical
plausibility.
To remedy frequent problems of data quality as evident during the climatological analyses, we devised an
elementary testing procedure that could be applied systematically across the entire region. Its purpose was the
detection and flagging of gross errors. Other issues of
data quality, such as the systematic measurement bias
from wind-induced undercatch, wetting and evaporation
losses (e.g. Sevruk, 2005, Groisman and Legates, 1994)
as well as temporal inhomogeneities due to station relocations and instrument changes (e.g. Wijngaard et al., 2003,
Begert et al., 2005) were not addressed with this procedure. Detectability of gross errors strongly depends on
station density. Hence, its large variation across the Alps
poses a particular challenge to the design of a suitable
procedure. The philosophy of our approach is conservative in the sense that flagging is only applied when
there is strong evidence for implausibility. As a result, the
Alpine rain-gauge dataset contains gross errors even after
checking and their rate is likely larger in areas with coarse
station coverage, where the testing is less powerful.
In the following we describe sequentially the three
steps of the applied quality checking procedure. These
encompass (a) the scanning of time series for coding
problems, (b) a fully automatic spatial consistency check,
and (c) the identification of over all suspicious time
series.
3.1. Checks for coding problems
The initial testing step aims at identifying well-known
and recurring coding errors in the time series. Firstly,
the internal consistency of the time coding (reference
date for the non-calendaric 24-h accumulation period)
between the different data contributions was verified. To
this end nearby time series from different providers were
checked for suspicious lags. Secondly, the time series
were scanned for suspicious duplicate values, as well as
for negative and non-physical large values. In the latter
case values exceeding 450 mm per day were manually
checked. Apart from an extreme flash flood event in
France (more than 500 mm per day on 8 September 2002,
see Anquetin et al., 2005), all exceedances turned out to
be implausible, likely the result of arithmetic shifts or
multi-day accumulations, and they were flagged. Finally,
all instances were manually checked when a time series
reported zero precipitation throughout an entire calendar
month. Real dry periods over more than 30 days are
rare in the Alps, except along the Mediterranean coast.
Many of these occurrences in the dataset turned out to
come from erroneous coding of long data gaps. These
episodes were therefore flagged unless simultaneous long
dry periods were found at nearby stations.
3.2. Spatial consistency check
A fully automatic spatial consistency test was applied
by comparing all individual daily reports to simultaneous
reports at stations in the neighbourhood. The procedure
is a modified version of that described in Scherrer et al.
(2011) (see also Behrendt, 1992). It distinguishes between
three situations, namely, that of an isolated wet report,
that of an isolated dry report and that of an out-of-range
report.
The situation of an isolated wet report occurs if a
non-zero test value is surrounded by dry conditions (all
stations within a 50 km radius report less than 0.3 mm
per day). The test value is considered implausible and is
flagged if it exceeds a certain threshold. The threshold
is defined to vary with distance to the closest neighbour
station and ranges from 0.3 mm per day for a co-located
nearest neighbour to 3 mm per day for a nearest neighbour as far as 15 km. If the nearest neighbour is further
than 15 km no constraints are employed and the test
value is retained anyway. To account for the short-scale
variability during the convective season, the tolerance
with far nearest neighbours is increased to 3.5 mm per
day and the maximum distance is set to 20 km between
May and September. The definition of these thresholds is
based 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 all
stations in a 50 km radius are wet. In this case and
if the closest neighbour station is at less than 15 km
distance, an estimate of precipitation is calculated by
spatial interpolation using the procedure described in
Section 4 but without the test station itself. The dry test
value is considered implausible, if this cross-validation
estimate exceeds a threshold. The test threshold is defined
similarly to the case of isolated wet reports, i.e. depending
on distance of closest neighbour and time of the year, but
with slightly larger values (larger by 0.5 mm per day).
The checking for out-of-range reports is adopted to all
situations not considered with the above cases. The procedure compares the test value against a cross-validation
estimate, again using the interpolation procedure of
Section 4. The tolerance (critical difference between test
value and cross-validation estimate) is formulated for
square-root transformed values and in dependence of the
value range of reports in the surrounding. The latter
ensures that the procedure is more permissive in case of
larger spatial variations in the neighbourhood. To account
for the limited detectability of gross errors with coarse
network density, the out-of-range test is only effective if
at least three stations are within a 20-km distance around
the test station and if at least one has an elevation difference of less than 300 m. Like with the other automatic
tests the chosen threshold dependence is the result of
detailed case studies and of experiments with different
settings and in different regions of the Alps.
3.3. Identification of suspicious time series
The spatial consistency test as just described revealed
several data records with a particularly frequent rejection
of reports. To check whether there were more fundamental quality problems with these stations, the corresponding records were inspected manually and compared
to nearby stations. As a result, some of the time series
(longer episodes of the whole record) had to be declared
as highly implausible and these were systematically
flagged. In other cases, the inspection revealed that
records for isolated stations were lagged in time by 1 d,
in which case these records were shifted. If the manual
inspection could not clearly identify systematic data quality problems, the non-rejected reports of these potentially
suspicious time series were retained.
3.4. Rejection statistics
The rate with which original reports have been objected
by the adopted quality control procedure varies considerably between the different providers (countries). This
reflects the variable level of native quality control, the
complications for reliable measurement in complex terrain, and the reduced power for gross error detection in
coarse networks. In total the rejection rates for the spatial consistency checks range from 0.2% to 0.8% between
the different providers. For the subset of stations above
1500 mMSL, the rejection increases to 0.3–1.1%. Note
that the out-of-range test was not effective for many
of the high-elevation stations (lack of nearby stations
at comparable altitude) and the higher rate there likely
reflects technical difficulties (wind, snow and ice). When
stratified between the various components of the spatial consistency test, the largest rejection rates are found
for isolated dry reports (1.4–4.5‰) and for isolated wet
reports (0.5–3.2‰). Objections of out-of-range reports
were less frequent (0.1–0.7‰). The number of deleted
entries due to coding problems, especially when a whole
month has no precipitation at a certain station, is moderate for most providers. Altogether, the employed control
procedure has improved data quality and consistency in
the collated Alpine rain-gauge dataset. This is discernible,
for example, in far less frequent occurrence of artefacts
in the daily gridded analyses and in more coherent spatial
patterns of climatological summary statistics, such as the
frequency of wet/dry days and quantiles.
4.
Spatial analysis
We have used the collated Alpine rain-gauge data to
establish a dataset of pan-Alpine daily precipitation fields
on a regular grid. This gridded dataset supersedes several
earlier versions that were based on the FS98 station
database (e.g. Frei, 2006, Schmidli et al., 2007). Apart
from the more extended temporal and spatial coverage
of the underlying station data (see Section 2), the most
significant change in this new grid dataset is the adoption
of a finer grid spacing. The spatial analyses are now
provided on a regular 5 × 5 km grid in the ETRS89LAEA coordinate system (Lambert Azimuthal Equal
Area Coordinate Reference System). Note that ETRS
coordinates have become a mapping standard in Europe
(Annoni et al., 2003). The finer resolution is motivated
by the improved station density and demands for new
applications, for which the previous datasets at 50 and
20 km grid spacing were not satisfactory.
The spatial analysis (interpolation) employed for the
new grid dataset is not substantially altered from that
for the earlier datasets. Essentially, the changes involve
new settings of the method’s parameters that allow
better reproduction of fine-scale variations in regions
with dense station coverage. In the following sub-sections
we recapitulate on the principles of the method, discuss
issues relevant for the application of the grid dataset
and describe the structure of interpolation errors. The
grid dataset provides the basis for our analysis of
daily precipitation statistics in the Alps (Sections 5
and 6).
4.1.
Method
The adopted method of spatial analysis relies on the
widely used anomaly concept where separate analyses
are calculated for some reference condition, typically
a long-term mean, and for the relative anomaly from
that reference on the day under consideration (e.g.
Widmann and Bretherton, 2000; Haylock et al., 2008).
F. A. ISOTTA et al.
Multiplication of the anomaly and reference grids finally
yields the daily precipitation analysis.
In our case, the reference condition for a day is the
long-term mean precipitation (period 1971–1990) of the
pertinent calendar month. The construction of the climatological reference fields is based on the PRISM method
of Daly et al. (1994, 2002). For the estimation at a
grid point PRISM uses a linear precipitation–elevation
relationship that is estimated from stations in the neighbourhood of the target point by linear regression. A key
feature of PRISM is to ensure that stations are representative for the physiographic conditions at the target
location (see also Daly et al., 2008). For this purpose,
PRISM attributes larger weights in the regression to stations with an exposition, slope orientation, etc. similar to
the target location. Schwarb (2000) has adapted PRISM
for the Alpine region and applied it with validated station
normals from the Alpine rain-gauge dataset and a highresolution digital elevation model to derive mean monthly
precipitation fields at a 2-km resolution (see also Schwarb
et al., 2001). The aggregation of these fields onto the 5km ETRS grid serves as a climatological reference for
the daily interpolation employed in this study.
Note, that our climatological reference is valid for the
20-year average from 1971 to 1990. This choice proved
to yield averages for many more stations than if the full
38-year period would have been chosen as reference (see
also Figure 3).
The interpolation of relative anomalies for a particular
day is performed with a weighting scheme that emphasizes measurements from stations closer to and/or with
stronger directional isolation relative to the analysis grid
point. To this end a modified version of the SYMAP algorithm by Shepard (1984) is employed. One of the modifications is the adoption of a smoother radial dependence in
the weighting function (see Equation (1) in FS98), which
aims at estimating an area-mean value directly, rather
than via preliminary analysis on a primary grid (see e.g.
Legates and Willmott, 1990). Moreover, Shepard’s original ‘gradient correction’ is omitted in our application
for the same reason. In extension of the original procedure we apply a spatially and temporally variable search
neighbourhood, similarly to FS98. In the present application (5-km ETRS grid) the search radius is successively
increased from 15 to 60 km (in steps of 5 km) until at
least three valid station measurements are contained. A
grid point is left at ‘missing’ if less than three measurements are available within 60 km. The variation of the
search radius allows to better account for the large spatial
variations in station density, so that fine-scale information
can be exploited where the network is dense.
The main purpose of the anomaly concept in the daily
interpolation is to reduce the risk of systematic errors
due to the non-representative distribution of stations,
especially the prevalence of valley and lowland stations
over high-elevation stations (see FS98, Konzelmann
et al., 2007). Indeed, comparisons of daily interpolations
with and without the anomaly approach have shown
differences in long-term mean precipitation of several
10% (Widmann and Bretherton, 2000; Frei et al., 2003).
Interpolation with a reference and anomalies typically
yields larger mean values compared to interpolation of
daily precipitation directly. The comparisons suggest
that interpolation without reference is more prone to
systematic precipitation underestimates in high-mountain
regions due to the prevalence of stations in comparatively
dry valley conditions. These biases would give rise to
substantial inconsistencies in the long-term water balance
as is suggested by Schädler and Weingartner (2002)
who compared the PRISM-derived long-term climatology
against water balance based precipitation estimates in
Switzerland (see also Weingartner et al., 2007).
4.2. Interpretation
It is important to point out some general issues of the
construction of the grid dataset that may need attention
in its application and the interpretation of results.
Firstly, the variations in the daily precipitation analyses
at scales near the grid spacing are mostly imprints
of the climatological reference fields. Because these
patterns reflect long-term mean conditions, they may
be of limited representativeness for individual days.
Care should therefore be exercised in interpreting these
small-scale variations. They represent the temporally
stable component of small-scale variability and are more
realistic for precipitation sums over longer periods. Their
primary function is to ensure consistency with the finescale pattern of the long-term climatology and, hence, to
reduce biases from unrepresentative station distributions
(see previous subsection).
Secondly, the procedure of spatial interpolation by
distance and angular weighting has a smoothing effect.
The daily analyses do not replicate peak values at
individual stations even if these were located at grid
points. It is therefore more appropriate to interpret grid
point values as estimates of area-mean precipitation.
However, there is ambiguity about the spatial scales
effectively resolved by the analysis. At the daily timescale the station spacing (10–15 km in high density
areas) can be considered as a lower bound for the
effective resolution. For longer-term averages, it may be
finer, depending on the strength of systematic topography
imprints. In conclusion, the fine spacing of the underlying
grid does not mean that daily precipitation patterns are
effectively resolved at these scales. The true resolution
varies across the domain and with time, depending on
the density of stations.
Thirdly, the grid dataset is affected by biases in raingauge measurements (Neff, 1977, Yang et al., 1999).
The ‘gauge undercatch’ is comparatively larger during
episodes with strong wind and during weather with low
rainfall intensity or with snowfall. Hence the related
errors in the grid dataset are quite variable. Sevruk (1985)
and Richter (1995) have estimated the magnitude of
systematic measurement errors on seasonal mean precipitation in Switzerland and Germany. Their estimates
range from 7% (5%) over the flatland regions in winter (summer) to 30% (10%) above 1500 mMSL in winter
(summer). Sevruk’s estimate of a season independent bias
of 4% for southern Switzerland may be characteristic for
the Po valley.
Finally, our collection of data has not made prerequisites regarding the long-term consistency (temporal
homogeneity) of the time series. Artefacts from changing
measurement conditions must therefore be expected in the
rain-gauge data series. These and the variations of network density over time are compromising the long-term
consistency of the grid dataset (see e.g. Hofstra et al.,
2009). Therefore, the present dataset is not suitable for
applications where long-term consistency is crucial, such
as for trend analysis.
4.3. Interpolation errors
To allow for well-informed applications of the present
grid dataset, it is desirable to quantify errors of the
spatial interpolation. The primary source of reference data
currently available for an evaluation of the daily grid
point estimates is station data. This is far from ideal when
grid point estimates more likely represent area-mean
conditions (see previous subsection). The scale mismatch
can inflate interpolation errors by sampling errors of
the reference (see e.g. Villarini et al., 2008). Despite
this we proceed here with a comparison of grid point
values against point measurements, but will consider the
statistics as being representative for a user who interprets
grid point values as point estimates. The statistics will
provide an upper bound of the errors for area means.
Our quantification of interpolation errors is based on
a leave-one-out cross-validation (similar to jackknifing)
where station observations are left out in turn and
estimates are calculated by spatial interpolation from the
surrounding stations. The cross-validation runs over all
wet observations (≥1 mm per day, period 1971–2008) of
all stations in three subregions of the Alps. The regions
represent variable conditions in station density and terrain
complexity (see domains in Figure 1): Region 1 is over
Croatia, an area with coarse station coverage (one station
per 240 km2 ), Region 2 for high-Alpine topography
with a dense network (one station per 66 km2 ), and
Region 3 over a flatland area of France with a dense
network (one station per 86 km2 ). At the daily time-scale,
interpolation errors must be expected to vary considerably
with precipitation intensity. Absolute (relative) errors are
larger for large (small) daily precipitation. For this reason,
the cross-validation errors are stratified with respect to
precipitation intensity.
Figure 4 depicts boxplots of the distribution of relative
interpolation errors for each region, separately for
winter and summer (each of the subsamples comprises
several thousand values). The x -axis distinguishes
between classes of precipitation intensity. These classes
are defined in terms of quantiles of daily precipitation
(station dependent, wet days only), so that each box
describes error statistics in a section of the distribution
function. For all regions and seasons, there is a strong
sensitivity of the error distribution on precipitation
intensity. Relative errors are largest at low-intensity
and decrease continuously with higher intensity. Obviously this is because even small absolute errors in
the interpolation can mean substantial relative errors
when daily totals are small (the sensitivity for absolute
errors is reverse). Interestingly, the error distributions
are slightly biased with a tendency for overestimates at
low intensities, particularly in summer, and a clear shift
towards underestimates at high intensities. This reflects
the smoothing effect of the interpolation, which tends
to smear out localized rainfall peaks into low-intensity
regions and to damp high-intensity peaks. The systematic
errors (intensity dependent biases) are more substantial
in 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 of
boxes) between the three regions. In Region 1 (low
station density) relative errors are substantial throughout
the intensity spectrum. At moderate intensity (near the
median of wet-day totals), in winter, the interpolation
under- or overestimates point observations by a factor of
1.5 or more (relative error outside 0.6–1.5) in more than
half 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 events
are systematically underestimated by about 20, 12 and 8%
in the three regions. All these variations are plausible in
view of the different sampling conditions and climate
characteristics in the three regions. Evidently, the quality
of the grid dataset is depending critically on the density
of the underlying rain-gauge network.
Between summer and winter, there is an interesting
change in the error ranking between Region 2 and Region
3: despite its flat terrain, Region 3 exhibits larger errors
at high-intensity than Region 2 (Alps). This may reflect
differences in the nature of heavy precipitation. Over
the northern flatlands, heavy precipitation in summer
is almost exclusively from convection, whereas, in the
Alps, heavy events can also occur in connection with
stationary flow and more wide-spread precipitation (e.g.
MeteoSwiss, 2006; Schmutz et al., 2008).
The measures of uncertainty in Figure 4 are representative when grid point values are interpreted as point
estimates. Clearly, we do not promote such a practice
for applications of the grid dataset over the Alps. The
dilemma is posed by the lack of a suitable observational
reference, necessary for verifying the interpolation at the
grid-pixel scale or at some ‘effectively resolved’ scale,
compliant with more suitable interpretations by users. As
alternative, statistical models of the spatial precipitation
variability could also serve to estimate uncertainties in
area means. For example the framework of geostatistics
has been used for quantifying interpolation uncertainties
and their dependence on spatial scale (Ahrens and Jaun,
2007; Frei et al., 2008; Vogel, 2013). Clearly, further
developments will be required for a systematic application of such methods over a large area such as the Alps.
Q10–Q90
Q25–Q75
Q50
DJF
Region 1
Region 2
Region 3
Q10–Q90
Q25–Q75
Q50
JJA
0.80
1.25
Region 1
Region 2
Region 3
1.25
0.80
0.99–0.999
0.98–0.99
Category (wet-day quantile prob)
0.95–0.98
0.9–0.95
0.8–0.9
0.6–0.8
0.4–0.6
0.2–0.4
0.1–0.2
0.33
0.50
Error (factor)
2.00
3.00 0.33
0.50
Error (factor)
2.00
3.00
F. A. ISOTTA et al.
Figure 4. Boxplot of interpolation errors determined from a systematic leave-one-out cross-validation in three characteristic regions (see domains
in Figure 1). Region 1: area with a coarse station network, Region 2: mountainous area with a dense network, Region 3: flatland area with a
dense network. Errors are expressed as the ratio between the interpolation (at the location of the station) and the observation at the station. The
errors are stratified into bins of precipitation intensity, which are defined in terms of quantiles (wet days only) with low (high) intensities on
the left (right). Bins are labelled by probabilities. For example, the second bin from the left, includes error measures from all days when the
observed daily precipitation fell between the 20% and 40% quantiles of all wet days at the same station. The upper plot is for winter (DJF), the
lower 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 comprehensive
resource of the day-by-day course of precipitation in the
Alps. It lends itself to study episodes of specific interest,
such as heavy precipitation events, in a wider spatial
context than is usually available in case studies at the
national level. To illustrate this potential Figure 5 displays
two well-known examples of the past decades. In both
cases heavy precipitation fell over large areas and several
national territories.
The case in Figure 5(a) is for the 3 d from 23 to
25 August 1987. The synoptic situation on these days
was characterized by a strong cold front advancing from
the northwest and a cyclone near the Gulf of Genoa
advecting maritime air masses towards the southern Alps
(LHG, 1991). The combination of orographic, frontal and
large-scale uplift as well as the continuous triggering
of convection in this dynamic environment resulted in
a richly structured rainfall pattern (Steinacker, 1988).
Regions particularly affected were the coastal regions of
north-western Italy and the southern flanks of the Alps,
where rainfall regionally exceeded 300 mm. But large
areal amounts were also recorded in inner-Alpine regions
and along the northern pre-Alps. The event caused serious
flooding and landslides in many valleys of the Italian,
Swiss and Austrian Alps. In Switzerland, it was one of the
most damaging flooding events of the past four decades
(e.g. Hilker et al., 2009).
The event of 20–22 August 2005 (Figure 5(b)) was
associated with a cyclone of type Vc in the cyclone
track classification of van Bebber (1891) that developed
over the Gulf of Genua and moved eastward over the
Adriatic Sea and the Balkans. The sustained transport of
moist air masses around the eastern Alps led to longlasting and intense rainfall along the northern rim of
the Alps. Rainfall totals reached well beyond 200 mm
300
250
210
180
150
120
90
70
50
35
20
10
5
2
0.3
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 thick
line represents the 800 mMSL topographic contour.
over a large area from central Switzerland to western
Austria and southern Germany. Heavy rainfall was also
recorded in the eastern Alps of Austria and Slovenia,
likely due to the more south-easterly and hence upslope
flow direction there. At many stations in Switzerland,
eastern Austria and Bavaria the rainfall reached the
local 100-year return level (Frei, 2006). The floods
caused damages of nearly three billion Euros in these
three countries (UVEK, 2008; Amt der Vorarlberger
Landesregierung, 2005; Bayerisches Staatsministerium
für Umwelt, Gesundheit und Verbraucherschutz, 2005).
6. The Alpine precipitation climate
In this section we analyse the Alpine dataset to work
out and discuss some key characteristics of the precipitation climate in the Alps. While previous pan-Alpine
analyses have focused mostly on long-term mean precipitation (e.g. Fliri, 1974; Baumgartner et al., 1983,
FS98), the present compilation emphasizes aspects of
the daily statistics, including the occurrence of intense
precipitation and long dry periods. For this purpose our
analysis considers a subset of the descriptive indices that
have been proposed by the CCl/CLIVAR/JCOMM Expert
Team on Climate Change Detection and Indices (ETCCDI, Klein Tank et al., 2009). In the course of this study
a comprehensive list of these indices was investigated.
The subset presented aims at spanning a range of daily
statistics and portraying the most important regional variations. The following sub-sections discuss the distribution
of mean annual precipitation (6.1), key indices of daily
precipitation statistics (6.2), and long dry and wet spells
(6.3). An illustration of the annual cycle is given for the
example of heavy precipitation in subsection (6.4). All the
results were derived by calculating the indices directly
from the gridded daily precipitation analyses of the 38
years from 1971 to 2008 (Section 4).
6.1.
Mean annual precipitation
Figure 6 depicts the distribution of mean annual precipitation (1971–2008). There is a wide range of values from
approximately 400 to more than 3000 mm per year, with
occasionally pronounced contrasts over short distances.
Topographic effects manifest in patterns at variable space
scales. For example, wet conditions are found in an elongated zone along the northern rim of the Alps. Embedded
F. A. ISOTTA et al.
in this zone are smaller scale variations that go along
with prominent mountain massifs (e.g. the Glarneralpen
in eastern Switzerland and the Allgäuer Alps at the western border between Austria and Germany). There is a
similar wet band extending along the southern rim of the
Alps (towards the Po valley), but with a more pronounced
sectioning into two wet zones at major embayments of
the main ridge. The first is centred over southern Switzerland (Ticino) and the Italian regions of Lombardia and
Piemonte. This anomaly is connected to the wet anomaly
along the northern rim across the section of St. Gottard, a particularly narrow cross-section of the ridge. The
second wet region along the southern rim extends from
the Dolomiti massif in north-eastern Italy eastward into
the Julian and Carnic Alps (boarder between Italy and
Slovenia) where particularly large annual mean values
are observed. Compared to the two ridge-scale anomalies,
regions in the inner Alps are comparatively dryer with
mean values comparable to those over the flatland regions
adjacent to the Alps. Particularly dry conditions are found
in inner-Alpine valleys that are shadowed against directions with prevailing moisture bearing winds. Noteworthy
examples are the Valais, the Aosta valley (north-western
Italy), the upper Inn valley and the Vinschgau (northern
Italy, near border to Austria).
In addition to the pattern of the main ridge, several
smaller scale hill ranges in the region show wet anomalies too. Examples are the Jura mountains, the Vosges
mountains, Black Forest and Bohemian Forest. Mean
values there are comparable to those along the northern
Alpine rim, but the precipitation signal is mostly centred
at the highest elevations. An exception to this is found
in the Massif Central, which exhibits two distinct wet
anomalies, one at the north-western and one at the
south-eastern flanks of the Plateau. The comparatively
dryer conditions of the southern Alps (south-western
France), suggests that the Massif Central may have a
rain-shadow effect on the latter. The wet anomaly of
the Apennine is linked to that of the southern Alps
by a narrow wet anomaly along the mountains of the
Ligurian coast. Particularly dry conditions in the Alpine
region are found at the mouth of river Rhone (French
Mediterranean coast) and in eastern Austria where mean
annual precipitation is less than 600 mm per year.
The general distribution of mean annual precipitation
found in the present analysis is very similar to that
derived from earlier versions of the Alpine rain-gauge
dataset (FS98). The higher resolution of the present
analysis (5 km vs 25 km grid spacing), however, reveals
previously unresolved and interesting patterns that can
be related to individual mountain massifs and provide a
better delineation of the topographic enhancement signals
from dry valley and flatland conditions. (Compare Figure
6 with Figure 9 in FS98.)
6.2.
Daily precipitation statistics
Figure 7 depicts four selected indices of daily precipitation in the Alps representing basic characteristics
of the daily rainfall frequency distribution. The indices
considered are frequency of wet days, defined as occurrences when the daily total is 1 mm or larger (Figure
7(a)), the mean precipitation on wet days (Figure 7(b)),
the mean of annual maximum daily totals (averaged over
all years, Figure 7(c)) and the fraction of precipitation
coming from moderate to intense events, i.e. days when
the precipitation is equal to or larger than the 75% quantile (wet-day quantile at the gridpoint, Figure 7(d)). Note,
that our definition of the wet-day threshold refers to
the recommendation of the ETCCDI (Klein Tank et al.,
2009), which is larger than the lowest measurable precipitation. We have investigated several other popular indices
but found that these would not add substantially to the
overall picture. For example, the popular indices for the
95% wet-day quantile and the frequency of days exceeding 20 mm show distributions that are qualitatively very
similar to that of mean annual precipitation (Figure 6).
The four indices reveal a pronounced asymmetry of the
daily precipitation frequency distribution between regions
to the north and south of the Alpine main crest. Along
the northern rim and in the northern and western flatland
regions, wet-day frequency is large (Figure 7(a)). In these
areas rain falls on every third to every second day in
the annual average. Over the southern rim and forelands,
however, the wet-day frequency is lower with rainfall on
one of three to five days. For wet-day mean precipitation
the asymmetry is reversed (Figure 7(b)): average rainfall
intensities are typically much larger along the southern
compared to the northern Alpine rim with particularly
large values observed at the two major embayments
with large annual mean precipitation. These two regions
stand out even more clearly at the tail of the frequency
distribution (Figure 7(c)). Annual maxima are two to
three times larger there compared to the moist northern
rim. The two regions Piedmont-Ticino-Lombardy and
Julian/Carnic Alps seem to be the mesoscale hot spots of
heavy precipitation in the Alps. Note also that more than
two 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 not
manifest in Figure 7(d). This indicates that the shape of
the tail of the frequency distribution at the northern rim is
similar to that over the nearby flatlands and hence that the
larger mean precipitation there comes primarily from the
higher frequency and mean intensity of wet days. This is
different over the southern portion of the domain, where
the fraction from moderate to high-intensity events is not
only larger but also shows spatial variation. In summary,
these analyses imply that precipitation is more frequent
to the north of the main ridge but more vigorous to the
south, particularly so along the southern rim where much
of the total precipitation is from rare intense events.
Apart from the hot spots along the southern rim of
the main ridge, areas of particularly heavy precipitation
are also found along the Ligurian coast, along the southeastern slopes of the Massif Central and in the Dinaric
Alps along the Adriatic coast (Figure 7(c) and (d)).
The former two of these regions are characterized by
low wet-day frequency (Figure 7(a)). Note particularly
2600
2400
2200
2000
1800
1600
1400
1200
1000
800
600
Figure 6. Mean annual precipitation (mm per year) for the period 1971–2008.
(b)
(a)
20
18.5
17
15.5
14
12.5
11
9.5
8
6.5
5
0.48
0.45
0.42
0.39
0.36
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0.3
0.27
0.24
0.21
0.18
(c)
(d)
180
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150
135
120
105
90
75
60
45
30
0.75
0.73
0.71
0.69
0.67
0.65
0.63
0.61
0.59
0.57
0.55
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 asymmetry
in precipitation statistics like that for the Alps: there
is a marked north-west to south-east gradient in wetday frequency and a much stronger prominence in the
intensity indices (Figure 7(b)–(d)) of the south-eastern
compared to the north-western wet anomaly. Along the
French Mediterranean coast there are the smallest values
of wet-day frequency across the whole domain.
The smaller scale hill ranges of the northern Alpine
region (Jura, Vosges and Black Forest) receive more
frequent and more intense precipitation compared to the
adjacent flatlands (Figure 7(a) and (b)). But these hills are
lesser distinguished from their surrounding in the other
two indices (Figure 7(c) and (d)), suggesting that the
larger mean precipitation there is mostly from increased
frequency of light to moderate events.
Noteworthy features of the indices over the flatland
regions are the very low wet-day frequency along the
French Mediterranean coast and a marked decrease of
the same statistic towards eastern Austria and Croatia (Figure 7(a)). The latter indicates the influence of
more continental climate near the eastern border of the
domain. Finally, over the flatlands of central France
(north-western corner of the domain) only a very small
proportion of total rainfall comes from moderate to highintensity events (Figure 7(d)). Frequent light precipitation
during much of the winter half year may be responsible
for this.
F. A. ISOTTA et al.
14
13
12
11
10
9
8
7
6
5
4
38
36
34
32
30
28
26
24
22
20
18
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 days
are an integral part of a region’s precipitation climate.
Figure 8 depicts the length (in days) of the longest
period of consecutive dry days and wet days (critical
threshold 1 mm), averaged over all years of the study
period (1971–2008).
Extreme wet spells (Figure 8(a)) show a distribution,
which is roughly mixture of those for mean precipitation
and wet-day frequency (cf. Figures 6 and 7(a)). Particularly long durations occur along the northern rim of
the Alps and, to some extent, also along the southern
rim. But the latter anomaly is far less pronounced in
accord with the understanding that large mean precipitation there is primarily related to higher intensity (cf.
Figure 7). Note, that the anomalies of mean precipitation
found at the Julian and Carnic Alps as well as along the
southern slopes of the Massif Central are barely distinguished in the wet spell distribution. As for the flatland
long wet spells show a north-south contrast with lengths
around 10 days to the north and around 6 days to the
south of the ridge. The longest wet spells in the region
occur at the northern and western hill ranges (Vosges,
Black Forest, Jura, western Massif Central), which may
reflect their exposition to moisture bearing flows during
persistent weather situations.
Long dry spells (Figure 8(b)) exhibit a strong gradient
between the Alps and the northern foreland on the one
hand (18–26 days) and regions to the south on the other
(28–44 days). Particularly long dry-day periods, with
average extreme spells exceeding 35 days, occur along
the French Mediterranean coast and in Liguria (western
Po valley). The two regions seem to be well shielded
from rain-bearing weather systems by the surrounding
topography. There, dry spells are met both in winter and
in summer, with winter-time events being more frequent
in Liguria and summer-time events in southern France.
It is intuitive that long wet spells go mostly together
with short dry spells and vice versa (Figure 8). But this
correspondence is not universal. For example the continental regions of the domain (eastern Austria, Croatia
and Slovenia) are characterized by comparatively short
dry and short wet spells, indicating some fundamental differences in precipitation intermittency across the
domain.
6.4. Annual cycle of heavy precipitation
Spatial variations in the annual cycle of mean precipitation have been discussed in detail in FS98 (see their
Figure 12). The results of this earlier analysis have not
altered with the new dataset. Therefore, we rather examine the annual cycle of heavy precipitation here, considering that magnitude of intense events is of particular
interest.
Figure 9 depicts the evolution of the largest daily
precipitation total in each month of the study period
(1971–2008), averaged separately for each calendar
month. The results for the sub-domains were obtained
by averaging over all grid points in the domain. There
are remarkable differences in the annual cycle of heavy
precipitation between the domains: in the flatland regions
of France there are only small variations through the year
with a slight maximum in summer. This turns into a
gentle pattern with two maxima at the Black Forest (representative also for the Jura and Vosges hills). The one
in winter is associated with dynamically active weather
systems (fronts, low-pressure systems) in combination
with orographic enhancement; the one in summer is
related to convective activity. Further east (sub-domains
Bavaria, northern Alps and eastern Austria), the annual
cycle markedly increases in amplitude and turns into a 12month cycle, with values in summer (convection) being
about twice as large as in winter. Values along the northern Alps are generally larger than in the other two of
these domains (see also Figure 7(c)).
In the southern sub-domains, monthly maximum daily
precipitation shows larger values and larger variations
through the year, except over the Po valley (Figure 9).
The maximum of the annual cycle is attained between
September and November in all these domains, but the
evolution is more complex: in some of the regions there
is a secondary maximum in spring (south-eastern Massif
Central, Ticino, Julian and Carnic Alps) and the minimum
occurs either in winter (Ticino, Po valley) or in summer (south-eastern Massif Central, Liguria). The complex
and spatially variable pattern of the annual cycle in these
southern domains is related to several factors with distinct
annual patterns, including the occurrence of southerly
flow conditions (most frequent in spring and summer), the sea surface temperature of the Mediterranean
(warm in late summer and autumn) and insolation-driven
60
50
40
30
20
10
0
1
3
5
7
9 11
60
50
40
30
20
10
0
1
3
5
7
9 11
Northern Alps
Bavaria
Black Forest
Central France
60
50
40
30
20
10
0
1
3
5
7
9 11
60
50
40
30
20
10
0
1
3
5
7
9 11
Eastern Austria
60
50
40
30
20
10
0
60
50
40
30
20
10
0
S–E Massif Central
60
50
40
30
20
10
0
1
3
5
7
9 11
Liguria
60
50
40
30
20
10
0
1
3
5
7
1 3 5 7 9 11
Julian and Carnic Alps
1
3
Ticino
9 11
60
50
40
30
20
10
0
1
3
5
7
5
7
9
11
Po Valley
9
11
60
50
40
30
20
10
0
1
3
5
7
9
11
Figure 9. Annual cycle of heavy precipitation for sub-domains. Columns represent, for each calendar month, the mean (averaged over years) of
the monthly maximum daily precipitation (mm per day, reference period 1971–2008).
convection (more frequent in late spring and early
summer).
It is worth mentioning that the annual cycle of heavy
precipitation has interesting differences to that for mean
precipitation (cf. Figure 12 in FS98). For example in
the southern regions, the relative amplitude of spring
and 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 in
May in region Ticino) compared to autumn (about 0.29 in
September) compensates for the less frequent occurrence
of very high daily amounts. We have verified that these
variations are not just an effect of the slight difference in
sub-domains between Figures 9 and 12 of FS98.
7. Conclusions
The grid dataset and climatological analyses, presented
in this study, offer a comprehensive trans-national
information source on Alpine precipitation of the recent
past. The underlying rain-gauge dataset encompasses typically 5500 measurements on any day of the study period
(1971–2008), probably one of the densest in situ monitoring systems available from a high-mountain area of
this size. The dataset builds on an earlier data compilation
effort (FS98) but major improvements in data coverage
could be achieved in areas and time periods with previously poor coverage, notably the regions of northern Italy
and Slovenia and the years after 1990. Also, the quality
of the dataset was fundamentally reviewed by upgrading to the recent status of the national databases and by
adopting a specifically designed quality control procedure
that addresses common data coding errors and involved
several semi-automatic checks.
Compared to its earlier version, the new grid dataset
is assembled at a higher nominal resolution (5 km grid
spacing as compared to 20 km in FS98) by employing a new analysis method, which incorporates local
precipitation–topography relationships at the climatological time-scale. The procedure aims at reducing the risk of
systematic underestimates at high elevations. The higher
F. A. ISOTTA et al.
resolution of the grid dataset may not be expected to
resolve precipitation at the scale of the grid spacing, but
it facilitates the aggregation into and improves estimation
of spatial averages over complex domain shapes. This is
particularly useful in hydrological applications requiring
mean precipitation over catchments, or for the evaluation
of regional climate models, when the observational grid
dataset needs to be assembled onto the model’s native
grid structure.
It is important that users of the Alpine precipitation
grid dataset are aware of the limitations and uncertainties involved: while there are good prospects that the grid
dataset resolves scales near the 5-km grid spacing when
precipitation totals over longer periods are considered, the
effective resolution for daily totals is clearly coarser than
the grid spacing. At the daily scale the spatial resolution
is more likely in the order of 10–25 km (typical station spacing), eventually even coarser, notably in regions
and during time periods where the station coverage is
more limited. When grid point values are interpreted as
estimates at finer scales (e.g. as point estimates) quite
substantial random errors must be expected at the daily
time-scale: high precipitation intensities are systematically underestimated and low intensities overestimated.
The magnitude of these errors depends on season, station coverage and topographic complexity (measures of
the error for the case of interpreting grid point values
as point precipitation are given in Section 4.3). Mismatches between the effective spatial resolution of the
present dataset and the resolution of its application (e.g.
that of a hydrological model to be driven by the dataset
or that of a regional climate model to be verified against
it) can give rise to systematic representativeness errors
(e.g. Tustison et al., 2001).
Further improvement of the effective resolution of the
presented grid dataset will be difficult from in situ data
alone, considering that most of the available data was
incorporated. Approaches using radar data in combination with station data (e.g. Krajewski, 1987; DeGaetano
and Wilks, 2008; Erdin et al., 2012) or analyses with
numerical models (e.g. Häggmark et al., 2000; QuintanaSegui et al., 2008; Haiden et al., 2011) are promising, but,
certainly in the case of radar, hardly applicable over climate time scales. More immediately, some improvements
could be achieved with a more elaborate representation
of small-scale precipitation–topography relationships, for
example by conditioning them on circulation types, rather
than just on month of the year (e.g. Hewitson and Crane,
2005). We are currently exploring the added value of such
an approach.
A further major limitation of the presented grid dataset
is that we have to make reservations with regard to its
long-term climate consistency. Changes in station locations and measurement devices have introduced inhomogeneities to some of the records. Moreover, variations in
the station network over time can significantly compromise the consistency of the grid dataset. Such effects
were observed in other grid datasets relying on timevarying station networks (e.g. Hofstra et al., 2009, 2010).
We expect that inhomogeneities are relatively more evident at small spatial scales (near the effective resolution), but tend to average out when considering mean
values over larger domains. Indicators of daily precipitation extremes, such as the frequency of a threshold
exceedance, may be particularly sensitive to variations
in network density, due to the smoothing of the interpolation. We therefore call for caution when applying the
present dataset for purposes with a strong requirement
on long-term consistency. Remedy for this caveat may
be sought through statistical reconstruction of precipitation fields from a time-invariant homogeneous station
dataset, for example by using the technique proposed by
Schmidli et al. (2001, 2002) and Schiemann et al. (2010).
This would inevitably be accompanied by a reduction
in effective resolution. Hence, such a climate consistent
grid dataset would not supersede the present grid dataset
but rather exist as a complement that satisfies other
requirements.
Gridded datasets have become a popular and compact basis for describing the climate of a region. The
present grid dataset offers a valuable resource of information on the precipitation climate of the Alpine region.
Indeed the example analyses of this study have provided
new insights into the distribution of mesoscale daily precipitation indicators, which would have been difficult to
identify without a trans-national consistent data source.
The dataset also offers itself as a basis for numerous
applications, for example as input into quantitative models of environmental sub-systems, for statistical downscaling of climate change scenarios, the evaluation of
regional climate models and many more. An application
of this dataset for the evaluation of global and continental
observation datasets is currently underway. The gridded
Alpine precipitation dataset is available for research from
http://www.meteoswiss.ch.
Acknowledgements
The research leading to these results has received funding from the European Union, Seventh Framework
Programme (FP7/2007-2013) under grant agreement n◦
242093. We are grateful to the following people for assistance and valuable discussions: Christof Appenzeller,
Marco Gaia, Mark Liniger and Christian Lukasczyk (all
MeteoSwiss, Switzerland), Claudio Cassardo (Univerity of Turin, Italy), Jean-Pierre Céron (Météo-France,
France), Albert Klein Tank (Royal Netherlands Meteorological Institute, Netherlands) and Christoph Schär
(ETH Zürich, Switzerland). We also thank two anonymous reviewers for their valuable comments.
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The climate of daily precipitation in the Alps

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