Introduction
Advances in computer sciences and the growing power of computers enable highly resolved model outputs from regional climate models (RCMs)
with horizontal resolutions at a scale of 1 km. At this resolution RCMs
provide more realistic simulations, especially for regions with complex
terrain, and allow the investigation of weather and climate in small subregions
.
To evaluate RCMs and to improve the performance of such models,
meteorological observations and particularly gridded datasets in
correspondingly high spatial and temporal resolutions are needed. RCMs
generally represent area-averaged processes rather than on a point-scale
. Therefore, gridded fields of meteorological
data are the most appropriate evaluation datasets, with each grid value being
a best estimate average of the grid cell observations .
For investigating weather and climate on a local scale as well as evaluating
RCMs, the Wegener Center (WEGC) at the University of Graz acquires and
automatically processes data from two station networks: the WegenerNet
Feldbach Region (FBR) and the WegenerNet Johnsbachtal (JBT). The WegenerNet
FBR is located in southeastern Styria, Austria and covers a dense grid of
more than 150 meteorological stations within an area of about
22km×16km . The terrain of
the FBR is hilly and characterized by small differences in altitude, and the
region is quite sensitive to climate change . It exhibits rich weather variability, especially including
strong convective activity and severe weather in summer
. Recently,
also analyzed wind fields in this region.
(a) Location of the study area WegenerNet Johnsbachtal
(white rectangle, enlarged in b) in the north of Styria,
Austria. The WegenerNet Feldbach Region in the Alpine forelands of
southeastern Styria, Austria, is also indicated for reference in the
easternmost part of the European Alpine region (details in
; Fig. 1 therein). (b) Map of the WegenerNet
Johnsbachtal region (black rectangle) with its meteorological stations,
including the selected mountaintop pseudo-stations, with the legend
explaining map characteristics and station operators.
The focus of this study is on the WegenerNet JBT, a station network
consisting of 11 meteorological stations, located in a mountainous region
in upper Styria, which is characterized by a very complex terrain
(Fig. ). The WegenerNet JBT has been realized through an
interdisciplinary research cooperation and the stations are operated by the
WEGC and several different partner organizations (indicated in
Fig. ). Details related to the cooperation, partnerships and
first results can be found in .
All observations from the two WegenerNet regions are integrated into the
WegenerNet Processing System (WPS), a system to control and manage
meteorological station data . This WPS consists of
four subsystems: The Command Receive Archiving System transfers raw data via
General Packet Radio Service (GPRS) wireless transmission to the WegenerNet
database in Graz, the Quality Control System checks the data quality, the
Data Product Generator (DPG) produces regular station time series and gridded
fields of weather and climate products, and the Visualization and Information
System offers the data to users via the WegenerNet data portal
(http://www.wegenernet.org, last access: 3 October 2018).
The DPG-produced weather and climate products are stored to the WegenerNet
data archives and have included for many years the gridded fields of the
variables temperature, precipitation and relative humidity for the WegenerNet
FBR. These fields are generated based on a spatial interpolation of the
station observations and provided with a latency of about 1–2 h.
Temperature lapse rates estimated from the observational datasets at the many
different station altitudes are included in the generation of temperature
fields over the hilly terrain. Technically, for temperature and relative
humidity, the fields are constructed by an inverse-distance weighted
interpolation and for the precipitation the inverse-distance squared weighted
interpolation is used. Details related to the subsystems of the WPS can be
found in and .
Furthermore, since the recent work of , the DPG computes
spatially distributed wind fields for the WegenerNet FBR. Due to the
dependence of wind on many different conditions, including surface properties
such as topography and surface roughness, we use a newly developed
application (named Wind Product Generator or WPG, developed in Python) to
determine the gridded field of wind parameters .
The WPG uses the freely available empirical California Meteorological Model
(CALMET) as core tool and generates wind fields in near-real-time. The CALMET
model reconstructs 3D wind fields (we focus on the 10 and 50 m height
levels) based on meteorological observations, terrain elevations and
information about land usage. Before its routine use for the WegenerNet FBR,
the WPG has been evaluated for a month including mainly thermally induced
events and another month with frequently occurring strong wind events; the
statistics showed good results for these periods. A detailed description of
the WPG application, and the statistical results for the WegenerNet FBR, can
be found in .
The key goal of this study is the implementation, and evaluation of, the WPG to
automatically produce high-resolution wind fields in near-real-time also for
the second study area, the challenging WegenerNet JBT region with its terrain
from about 700 to 2300 m and less wind stations than for the WegenerNet FBR.
The requirement for our WPG application is to provide the JBT wind fields
also with a spatial resolution of 100m×100m and a
time resolution of 30 min to the WegenerNet data archives. An essential goal
is the evaluation of these wind fields for periods with representative
weather conditions and also the estimation of wind gust fields. Furthermore,
the WPG`s performance shall be estimated first-time also for seasonal
climate-averaged data for the WegenerNet JBT in comparison to the WegenerNet
FBR region.
The paper is structured as follows. Section provides a
description of the study area, the WegenerNet JBT region with its
meteorological stations. Section presents the methodology
for the empirical wind field modeling, where first the characteristics of the
CALMET model and the extensions integrated to the WPG
for the automated production of the wind fields are explained, in particular
the inclusion of a few auxiliary pseudo-stations (Fig. ).
Second, the estimation method for the gust fields and a description of
atmospheric weather conditions during the model evaluation periods and of the
evaluation methods is introduced here. Section describes the
results of the wind field modeling for the selected evaluation periods,
July 2014 and December 2013, for the WegenerNet JBT as well as the results of
the seasonal climate datasets from the WegenerNet JBT compared to those of
the WegenerNet FBR. Finally, Sect. provides the conclusions
as well as prospects for the next steps of follow-on work.
Study area and WegenerNet data
The study area WegenerNet JBT (Fig. ),
named after the Johnsbach river basin, is located in the Ennstaler
Alps, an eastern Alpine region in the north of Styria, Austria, and overlays
with the National Park Gesäuse. The area is surrounded by the
Gesäuse Mountains in the north, east and west and by the
Eisenerzer Alps in the south. The terrain is characterized by large
differences in elevation, ranging from below 700 m in the valleys to over
2300 m at the highest summits . The highest peak is the
Hochtor, with an elevation of 2369 m. The landscape is dominated by alpine
rock formations and sparsely vegetated areas (barren land), forests and
range land (Fig. a).
(a) Land cover and use of the WegenerNet Johnsbachtal
region (black rectangle) based on the CORINE Land Cover 2006 raster version.
(b) Example temperature field over the region during a summer
day in July (18 July 2014; 15:00 UTC).
The climate is Alpine with annual mean temperatures of around 8 ∘C
at lower elevations and below 0 ∘C at higher elevations and with an
annual precipitation of about 1500 to 1800 mm from the valley to the summit
regions . The summer-day temperature
field illustrated in Fig. b, produced by a modified version
of CALMET , shows the distinct decrease in temperatures
from lower to higher elevations. We implemented algorithms developed by
as part of this modified CALMET version to account for
topographic shading and height dependency in surface temperatures (more
details in Sect. ). The wind field in the study area is
characterized by thermally induced local flows and influenced from larger
scales mainly by westerly-flow synoptic weather conditions.
The WegenerNet JBT comprises 11 irregularly distributed meteorological
stations within its area of about 16km×17km. The
station with the highest altitude was installed in summer 2009 and is located
on the summit of the Zinoedl at a height of 2191 m. A second summit station
was installed in 2011 on top of the Blaseneck at a height of 1969 m
.
All stations are equipped with a diversity of meteorological sensors. The
observed variables wind speed (v), wind direction (ϕ), air
temperature (T), air pressure (p) and relative humidity (RH) are
continuously recorded at a 10 min sampling rate and used as input for the
WPG. Table summarizes the technical characteristics of
the WegenerNet JBT stations including the station operators, wind sensor
heights and observed variables for each station (including the ones used).
Due to a topography strongly influencing the local wind fields at the
Weidendom and the Tamischbachturm 1 stations, the observations of the wind
variables from these two stations are not used as input.
Characteristics of meteorological stations of the WegenerNet
Johnsbachtal (JBT).
Station name,
Station
Wind sensor
Recorded
ID (starta)
abbreviation
Operator
Lat (E)
Lon (N)
Alt [m]
height [m]
variablesb
Oberkainz,
501 (2010)
OBK
WEGC
47∘32′11.0"
14∘35′52.8"
920
14
v, ϕ, vg, ϕg, T, RH, P, Qg, Qr, sd, swe
Koelblwiese,
502 (2013)
KOE
WEGC
47∘31′54.0"
14∘36′37.0"
860
3
v, ϕ, vg, ϕg, T, RH, P, p, Qg, Qr, Qn
Schroeckalm,
503 (2010)
SCH
WEGC
47∘31′45.2"
14∘40′16.8"
1344
10
v, ϕ, vg, ϕg, T, RH, P, p, Qg, Qn, ρs
Blaseneck,
504 (2010)
BLA
WEGC
47∘29′57.7"
14∘37′07.9"
1969
6
v, ϕ, vg, ϕg, T, RH, Qg, Qn
Zinoedl,
505 (2009)
ZIN
WEGC
47∘33′55.4"
14∘39′57.8"
2191
6
v, ϕ, vg, ϕg, T, RH, Qg, Qn
Weidendom,
506 (2006)
WEI
NPG
47∘34′51.0"
14∘35′29.3"
590
2
v, T, h, P, Qg, wl
Gscheidegg,
507 (2008)
GSC
NPG
47∘30′52.0"
14∘40′28.2"
1690
6
v, ϕ, vg, ϕg, T, RH, p , Qg, sd, ρs
Tamischb. 1,
508 (2008)
TA1
ZAWS
47∘37′02.4"
14∘43′01.2"
1431
7
v, ϕ, vg, ϕg, T, RH, Qg, Ts, sd, Tsn
Tamischb. 2,
509 (2008)
TA2
ZAWS
47∘36′48.4"
14∘41′58.2"
1952
5
v, ϕ, vg, ϕg, T, RH
Gstatterboden,
510 (2007)
GST
AHYD
47∘35′29.0"
14∘37′44.0"
580
–
P
Gaishorn,
511 (2007)
GAI
AHYD
47∘35′29.0"
14∘37′44.0"
720
–
P
a start year of time series (earliest year in
WegenerNet archive is 2007) b v wind speed, ϕ wind direction,
vg peak gust, ϕg peak gust direction, T air
temperature, RH relative humidity, P precipitation, p air pressure,
sd
snow depth, swe snow water equivalent, ρs snow density,
Qg global radiation, Qr reflected radiation,
Qn net radiation, Ts surface temperature,
Tsn snow temperature and wl water level
The observations of the Wegener JBT stations are partly available since 2010,
and partly since 2007 (Table , first column). For this
study, wind fields have been calculated within the period 2012–2017, and
ongoing near-real-time data are to be provided to the users with a maximum
delay of 2 h.
Methods and evaluation periods
Advanced CALMET model
The core tool of the operational WPG is the CALMET model .
Based on the settings in the CALMET control file, a user has three different
options for the use of the meteorological information as input data: in the
no-observations approach, CALMET uses data from numerical prognostic models
as input data, the hybrid approach combines data from numerical models and
meteorological observations, and the observations-only approach solely uses
meteorological observations. We use the observations-only approach for the
WPG, to ensure genuinely empirical wind fields and to keep the key
operational input independent from data external to the WegenerNet
. We consider this also the best-possible
choice for later intercomparison to, and evaluation of, dynamical climate model
fields.
The CALMET model computes the wind fields in a two-step approach. The first
step (step 1) includes the adjustment of an initial-guess wind field for
kinematic effects of terrain, slope flows and terrain blocking effects. In
the observations-only approach the initial-guess wind field is produced by an
interpolation of observational data.
In a second step (step 2), the observational data are introduced again and
blended to the step 1 wind field by an inverse distance weighting
interpolation to produce the final step 2 wind field. Observations are
excluded from this interpolation method if the distance from a station
location to a particular grid point is greater than a user defined radius of
influence. Furthermore, relative weighting parameters are used to weight the
observed wind variables to the previously computed step 1 wind field
(Table ). The procedure ensures divergence-free
(mass-conserving) wind vector fields, i.e., provides physically consistent
fields under assumption of incompressible flow.
Settings of critical area-specific model parameters in CALMET, used in this study for the WegenerNet JBT.
Parameter
Value
Remarks
TERRAD [km]
5.0
Radius of influence of terrain features
RMAX1 [km]
5.0
Maximum radius of influence over land in the surface layer
RMAX2 [km]
5.0
Maximum radius of influence over land aloft
R1 [km]
1.1
Relative weighting of the first guess field and observations in the surface layer
R2 [km]
0.6
Relative weighting of the first guess field and observations in the layers aloft
IEXTRP (flag)
-4
Extrapolate surface wind observations to upper layers with similarity theory (layer 1 data at upper-air stations are ignored)
ZFACE [m]
0, 20, 80
Cell face heights in vertical grid (the vertical levels correspond to the mid-levels, 10 m and 50 m, of those layer boundaries)
BIAS (-1 ≤ BIAS ≤ 1)
0.0, 0.0, 0.0, 0.0
Layer-dependent biases modifying the weights of surface and upper-air stations (Negative BIAS reduces the weight of upper-air stations, positive BIAS reduces the weight of surface stations)
Based on extensive sensitivity tests, we determined the settings for the
WegenerNet JBT shown in Table . Comparing these to the
settings of , Table therein, for the
WegenerNet FBR, one can see that we in particular found it beneficial to
increase the influence of terrain features and the first-guess file in the
surface layer. A detailed description related to model parameters, settings
and options can be found in the CALMET Manual .
In the original CALMET model, the energy balance is calculated without
considering topographic shading through terrain. Furthermore, height
dependency of surface temperatures is not taken into account and the
temperature fields are produced by a simple interpolation of point-specific
observations. Especially in complex terrain like in the WegenerNet JBT, such
shading, vertical temperature gradients and the vegetation cover
significantly affect the energy balance and subsequently the wind field.
To improve the modeling of these physical effects in this challenging region,
we improved an advanced model by implementing algorithms developed by
. These algorithms empirically take into account the
topographic shading based on terrain heights, topography slope and aspect
and the position of the sun for the estimation of solar radiation. In
addition, temperature fields are modeled based on vertical temperature
gradients, estimated from the meteorological stations located at different
altitudes, and the influence of the vegetation cover is accounted for, based
on the leaf area index (LAI) obtained from a geophysical dataset
(Table ). Detailed information related to these algorithms
can be found in .
Geophysical parameters based on the CORINE Land Cover (CLC) 2006 dataset, used in this study for the WegenerNet JBT.
Surface
Bowen
Soil heat
Vegetative
Land use type
roughness length [m]
Albedo
ratio
flux constant
leaf area index
Discontinuous urban fabric
1.000
0.18
1.5
0.25
0.20
Agricultural land – unirrigated
0.250
0.15
1.0
0.15
3.00
Rangeland
0.050
0.25
1.0
0.15
0.50
Forest land
1.000
0.10
1.0
0.15
7.00
Small water body
0.001
0.10
0.0
1.00
0.00
Non-forest wetland
0.020
0.10
0.10
0.25
1.00
Barren land
0.050
0.30
1.0
0.15
0.05
The WPG runs this advanced CALMET model based on a surface meteorological
data file, upper-air data files and a geophysical data file. In a predecessor
step, the WPG automatically generates these meteorological data sets from the
station observations and auxiliary geophysical information stored in the
WegenerNet database. Detailed information related to the WPG, including all
processing steps, can be found in .
The geophysical dataset consists of terrain elevations and land use
categories and was created in a preparatory step. In this study we used a DEM
derived from airborne laser scanning point clouds (provided online by
http://gis.steiermark.at, last access: 3 October 2018), illustrated by the elevations scale in Fig. b.
The original spatial resolution of 10 m was resampled and averaged to 50
(DEM50), 100 (DEM100), and 200 m (DEM200). In order to check the influence
of the spatial resolution on the modeling performance, the model was tested
with the different spatial resolutions. These sensitivity tests showed very
small differences between wind field results modeled based on DEM50 and
DEM100, while somewhat higher differences (from smoothing effects) were found
when using DEM200. We hence selected the DEM100 as the most adequate and computationally
efficient resolution and the 100m×100m
gridding for this study, which also matches the resolution of the land cover
dataset discussed next.
Furthermore, the land use categories for the study were determined based on
the CORINE Land Cover 2006 dataset (CLC 2006) . The definition
and the maximum number of the land use categories of the CLC dataset differs
from the classification scheme of the CALMET model. The entire CLC dataset of
the third and most detailed level contains 44 different classes, while the
CALMET classification scheme only distinguishes up to 14 land use types
. Therefore, we reclassified the 17 CLC 2006 land use
categories found in the study area into seven compliant CALMET classes
(Fig. a); the corresponding parameters summarized in
Table were then used as the CALMET geophysical dataset.
The observations of the three highest stations, Zinoedl, Blaseneck and
Tamischbachturm 2 (Table ), are used to create vertical
profiles of wind speed, wind direction, temperature, pressure, and elevation,
stored in upper-air datasets. A detailed explanation of how the creation of
upper-air datasets works can be found in .
Auxiliary pseudo-stations for upper-air data
Based on finding a systematic underestimation of wind speed in summit regions
without any station, we extended the WPG with a user option that enables the
introduction of upper-air pseudo-stations in the modeling domain. These
user-defined pseudo-stations are included to raise wind speed at higher
altitudes. For the WegenerNet JBT we defined five pseudo-stations upon
extensive sensitivity studies testing various setups, located at the
unobserved summit regions (Table , Figs. and
). The magnitude of wind speed of a pseudo-station
(vp) is estimated for the highest defined vertical height level
(zmax), which corresponds to the highest ZFACE level
(Table ; 80 m). The estimation is based on a linear
interpolation between neighbor station altitudes, except for pseudo-station
5, which is located at somewhat lower altitude than its neighborhood
stations. The wind speed is hence calculated by a slight downward
extrapolation for this latter station. This magnitude is calculated by
vp(zmax)=vn1(zmax)+vn2(zmax)-vn1(zmax)zn2-zn1(zp-zn1),
where zp is the altitude of the pseudo-station and zn1 and
zn2 indicates the altitudes of the defined neighbor stations with real
wind observations (Table , rightmost column).
Characteristics of upper-air pseudo-stations defined in the WegenerNet JBT region.
Station
Neighbor stations
Station name
abbreviation
Latitude (E)
Longitude (N)
Altitude [m]
(Table )
Pseudo-station 1
PS1
47∘36′49.5"
14∘36′06.0"
2061
TA2; ZIN
Pseudo-station 2
PS2
47∘32′59.6"
14∘31′24.9"
2126
BLA; ZIN
Pseudo-station 3
PS3
47∘33′36.9"
14∘37′45.0"
2068
BLA; ZIN
Pseudo-station 4
PS4
47∘33′16.0"
14∘43′33.7"
2139
BLA; ZIN
Pseudo-station 5
PS5
47∘29′02.1"
14∘42′06.3"
1892
BLA; ZIN
The magnitude of the wind speeds vn1,2(zmax) at the highest
height level of the neighbor stations used in Eq. () are
calculated by a logarithmic wind profile given as
vn1,2(zmax)=vn1,2(zs1,2)ln(zmax/z0)ln(zs1,2/z0),
where vn1,2(zs1,2) are the wind speeds at the neighbor stations
observed at the sensor heights zs1,2 (typically 5–10 m above surface),
and z0 is the surface roughness length at the locations of the
corresponding neighbor stations (up to 1 m).
The wind direction at the pseudo-station ϕp(zmax) is
estimated through a vector-mean calculation by
ϕp(zmax)=arctan2(u,v)+180∘whenarctan2(u,v)<180∘arctan2(u,v)-180∘whenarctan2(u,v)>180∘,
where the mean values of the south component v and the west
component u are calculated from the wind directions and wind speeds
at the two neighbor stations by
v(zmax)=-12vn,1(zmax)cosϕn,1(zmax)+vn,2(zmax)cosϕn,2(zmax),
and
u(zmax)=-12vn,1(zmax)sinϕn,1(zmax)+vn,2(zmax)sinϕn,2(zmax).
For providing ϕn1,2 to these equations, the wind directions at the
neighbor stations are extrapolated to zmax based on the work of
by
ϕn1,2(zmax)=ϕn1,2(zs1,2)Θd11-exp-d2zmaxzs1,2,
where ϕn1,2(zs1,2) are the observed wind directions at the
neighbor stations at the sensor heights zs1,2. The empirical constants
d1 and d2 take the values 1.5 and 1.0, respectively. For this
extrapolation we assume neutral stability conditions, which means the turning
angle Θ is set to 12∘. Details can be found in
and the CALMET user guide .
Equations () and () are then used again, but in
this case to compute the wind speed and wind direction at the pseudo-stations
(Table ) for the defined height levels, based on the values
estimated at zmax from Eqs. () and
().
The temperatures at the pseudo-stations are estimated from the gridded
temperature field generated by an interpolation of the temperature
observations. To calculate the temperatures for the defined station altitudes
and height levels, temperature lapse rates are estimated from the temperature
observations of the meteorological stations; for the relevant details on the
generation of the upper-air datasets see .
An additional user option that we integrated into the WPG concerns the
replacement of missing observations from meteorological stations that are
used to create the upper-air datasets. If observations from such a station
show invalid values, indicated by quality flags, the WPG includes an
algorithm to replace these data with observations from the highest upper-air
station with valid wind data. To indicate the data quality to the users, we
additionally provide gridded quality flags, ranging from zero (good value) to
four (bad value).
Wind gust fields as add-on product
As an additional post-processed product, we let the WPG generate gridded
fields of peak gust speed and the corresponding gust direction for 10 m
height above ground, based on re-scaling the gridded mean wind fields with
the aid of complementary wind gust observations (vg, ϕg) of the
meteorological stations (Table ). While a detailed
evaluation of this add-on product is beyond the scope of this study it fits
to briefly introduce its generation and some example results here, because
these gust fields have also recently become routinely available via the
WegenerNet data portal (http://www.wegenernet.org, last access: 3 October 2018).
More specifically, the gridded gust speeds are generated by a spatial
interpolation of the ratio of the observed maximum 30 min gust speed to the
30 min average wind speed, where this speed ratio is determined at each
observing station location by
rgmv=vgvm,
where vg is the peak gust speed and vm the average wind speed. The
ratio field, generated by interpolating rgmv, is then multiplied to the
gridded mean speed field, yielding a gridded gust field. As interpolation
method for the wind speed ratio, a simple inverse distance algorithm is
employed in the WPG, which leads to a reasonably smooth gridded gust-to-mean
ratio field. To avoid the generation of unrealistic high gust speeds,
especially under calm weather conditions, rgmv values are excluded from
the interpolation algorithm if vm is lower than a user defined minimum
average wind speed (vmin). Based on sensitivity tests we defined a
vmin of 1.0 m s-1 for the WegenerNet JBT, and 0.2 m s-1 for
the WegenerNet FBR (an approach that may be further refined in future). This procedure is a rough but sound approximation of how
strong in any 30 min time slice the wind gustiness is pronounced, on top of
the prevailing mean wind speeds.
To generate the gridded wind gust directions, the approach is essentially the
same but with using direction differences instead of speed ratios. That is,
the WPG determines the difference between the gust direction of the peak gust
speed and the 30 min vector-mean wind direction. This wind difference is
computed by
ΔΦgm=Φg-Φm,
where Φg is the direction of the peak gust speed and Φm the
30 min vector-mean wind direction. The spatial interpolation of these
direction differences (ΔΦgm) to the grid is done in the same way as the
gridded speed ratios. As interpolation method again a simple inverse
distance algorithm is employed. Finally, these gridded direction difference
fields are added to the mean wind direction fields to obtain the wind gust
direction field.
Wind field evaluation periods
The modeling performance is first evaluated by periods with mainly two
representative types of wind events: thermally induced wind events and strong
wind events. We have chosen July 2014 and December 2013 as test months for
this purpose.
In July 2014 the study area was mainly controlled by autochthonous weather
conditions, characterized by small synoptic influences, cloudless sky, low
relative humidity and increased radiation fluxes between the Earth surface
and the atmosphere . These weather conditions mainly
led to thermally induced wind systems, meaning that the wind fields were
dominated by small-scale temperature and pressure gradients. In December 2013
several episodes of strong wind occurred, including wind storms with 30 min
wind speeds up to around 30 m s-1 and peak gusts up to 55 m s-1 .
Wind speeds <0.5ms-1 were classified “calm” and discarded as
to small for a reliable cross-validation.
For estimating the model performance we used a leave-one-out
cross-validation, as in our previous work. In this
methodology, wind observations at one wind station are removed from the
stations input to the WPG and generated wind fields are evaluated against the
wind data from this station. More specifically, we compared the output wind
field results at the station location with the observations of the respective
station for each 30 min sample. We then calculated the statistical
performance parameters summarized in Table from all data
over the full evaluation period, for all seven stations that contributed wind
sensors (all wind observing stations in Table except WEI
and TA1).
Statistical performance parameters used for the evaluation of the wind field modeling results.
Parameter
Equation
Remarks
Bias
B=1N∑i=1Nvm,i-vo,i
vm: modeled wind speed; vo: observed wind speed
Standard deviation of observed wind speed
SDo=1(N-1)∑i=1N(vo,i-vo‾)2
vo: observed wind speed; vo‾: mean observed wind speed
Root-mean-square-error
RMSE =1N∑i=1N(vm,i-vo,i)2
vm: modeled wind speed; vo: observed wind speed
Correlation coefficient
R=1(N-1)∑i=1Nvm,i-vm‾σmvo,i-vo‾σo
vm: modeled wind speed; vm‾: mean modeled wind speed; vo: observed wind speed; vo‾: mean observed wind speed; σm: standard deviation of modeled wind speed; σo: standard deviation of observed wind speed
Index of agreement
IOA =1.0-∑i=1Nvm,i-vo,ic∑i=1Nvo,i-vo‾,if∑i=1Nvm,i-vo,i≤cvo,i-vo‾c∑i=1Nvo,i-vo‾∑i=1Nvm,i-vo,i-1,if∑i=1Nvm,i-vo,i>cvo,i-vo‾
vm: modeled wind speed; vo: observed wind speed; vo‾: mean observed wind speed; c: factor set to 2
Mean absolute error of wind direction
MAEdir=1N∑i=1Narccoscosϕm,i-ϕo,i
ϕm: modeled wind direction; ϕo: observed wind direction
Regarding the index of agreement (IOA) parameter we note that in this study
we used a redefined IOA, which spans from -1 to +1 with values near
+1 indicating best model performance . An IOA of 0.5,
for example, implies that the sum of the difference magnitudes between
modeled and observed values is one-half of the sum of the observed deviation
magnitudes. An opposite value of -0.5 indicates that the sum of the
difference magnitudes is twice the sum of the observed deviation magnitudes.
In case of little observed variability or poorly estimated deviations about
vo‾, the IOA delivers a value near -1.
In addition, we calculated statistical performance parameters for 5-year
seasonal data of the WegenerNet JBT and compared the results to 9-year
seasonal data of the WegenerNet FBR. We used the WegenerNet independent wind
measurements from the ZAMG Feldbach and Bad Gleichenberg stations, located in
the FBR, and from the ZAMG Admont station, located near the JBT area (a few
kilometers west of it, see Fig. ) for this climatological
evaluation. For the WegenerNet JBT we used, in addition to the ZAMG Admont
station, the wind measurements from the representative “left-out” stations
KOE and BLA.
Results
Evaluation of representative summer and winter months
Figure illustrates typical examples of WPG-modeled wind
fields for morning (upper panels), afternoon (middle panels) and evening
(lower panels) winds at a height of 10 m. The left column
(Fig. a) shows thermally driven circulations in course of
18 July 2014 with varying wind speeds and directions caused by
temperature and pressure gradients on a local scale. The highest wind speeds
typically occurred in the summit regions, with maximum 30 min wind speeds of
around 7 m s-1 near sunrise at 04:00 UTC (05:00 LT).
Modeled wind fields typical for the study area:
(a) thermally induced wind fields (18 July 2014) and
(b) strong region-scale winds (7 December 2013), for near-sunrise
(top), afternoon (middle) and near-sunset (bottom) conditions. Time is shown
as UTC (corresponding to local time minus 1 h).
The right column (Fig. b) displays wind storm behavior
during 7 December 2013 caused by northwesterly weather conditions.
These synoptic-scale flow conditions led to strong wind speeds in the area
with prevailing northwesterly wind directions and maximum 30 min wind speeds
of around 30 m s-1 during the early morning at 04:00 UTC
(Fig. b, top). Later during the day slightly weaker wind
speeds occur and the air flow is more influenced by the terrain and partly
channeled through the valleys of the study area.
Modeled wind gust fields typical for the study area:
(a) thermally induced wind fields (18 July 2014) and
(b) strong region-scale winds (7 December 2013), for near-sunrise
(top), afternoon (middle) and near-sunset (bottom) conditions. Time is shown
as UTC (corresponding to local time minus 1 h).
The maps in Fig. , shown in the same layout as
Fig. , display the estimated distribution of the peak
gust speeds and the corresponding gust directions for the same days. Note
that these are neither instantaneous nor average gust fields but synthetic
field estimates of maximum wind peaks and associated directions that occurred
at the same time during the 30 min sample interval. The thermally driven
gust field on 18 July 2014 showed maximum gust speeds of around
18 m s-1 upstream to the Zinoedl summit and the ridge of TA1 at
14:30 UTC (15:30 LT) (Fig. a, middle). During the storm
event on 7 December 2013, the gusts reached a tremendous speed of near
55 m s-1 at 04:00 UTC (Fig. b, top) around the
Zinoedl summit and the summit pseudo-station PS2 (around 200 km h-1).
It is noticeable that the strongest gusts have a northerly direction whereas
the average wind comes from the northwest
(Figs. b and b).
Scatterplot of modeled vs. observed vector-mean wind speeds for
the WegnerNet Koelblwiese (KOE) station in the Johnsbach valley:
(a) July 2014 and
(b) December 2013.
Same as Fig. but for WegenerNet Blaseneck
(BLA) station at the Blaseneck summit.
Figures and illustrate the
modeling performance at the Koelblwiese (KOE) and the Blaseneck (BLA)
station, as typical examples for a valley and a summit station. The KOE
station is located in the Johnsbach valley at a height of 860 m to monitor
the climate at the valley floor. The environment of this station is often influenced by lakes of cold air, especially in fall and winter. The BLA
station is located at a height of 1969 m on the summit of the Blaseneck. The
environment of the latter station is characterized through an exposed high
Alpine location were strong wind speeds can occur in all seasons. In the
scatterplots we compared the observed 30 min vector-mean wind speeds to the
corresponding modeled values of the nearest neighbor gridpoints (located at
<50 m distance).
For the KOE station we estimated a reasonably good model performance with a
correlation coefficient R of 0.71 in July 2014 and 0.75 in December 2013.
In July 2014 the maximum observed and modeled wind speeds were around
5 m s-1 with a slightly positive bias B between observed and
modeled wind speeds (Fig. a). In December 2013 the
maximum observed wind speeds were around 13 m s-1 and the estimated
B is slightly negative (Fig. b).
The scatterplot for the BLA station indicates a wider spread of the observed
and modeled wind speeds compared to the Koelblwiese station
(Fig. ). Regarding the R value we estimated similar
good results with a value of 0.69 for July 2014 and 0.71 for December 2013.
The mean absolute error of wind direction MAEdir
exhibits similar results for both stations and periods, with values near
40∘ (except for 59∘ at KOE in December 2013).
Relative frequency of vector-mean wind directions for a range of
wind speed categories, for observed (upper row) and modeled (lower row) wind
directions for the WegenerNet Koelblwiese (KOE) station in the Johnsbach
valley: (a) July 2014 and
(b) December 2013.
Same as Fig. but for WegenerNet Blaseneck
(BLA) station at the Blaseneck summit.
Figures and show windroses
of the relative frequency of wind directions divided by wind speed categories
from the model compared to the observed wind directions for the KOE and BLA
station, respectively.
Regarding the KOE station in July 2014 (Fig. a), a
shift from the west-southwest to the west-northwest sectors can be seen in the modeled results. The
observations show about 18 % in the west-southwest sector, while the model estimates
just a few percent in this sector. Vice versa, the frequency of observed wind
directions is 7 % for the west-northwest sector, while the model shows 23 % in
this sector. This shift by about 40∘ in wind directions is explained
by the influence of the Oberkainz (OBK) station which is located in the
west-northwest
in a distance of only about 1 km from the KOE station. The magnitude of the
wind speed is calculated quite well by the model, with values below
5 m s-1 in accordance to the observations.
In December 2013 (Fig. b) the main observed wind
directions at the KOE station are from the north-northeast to the east sectors; however, wind
directions with high wind speeds can be observed in the westward sectors as
well. For this period, the model estimates a significantly narrower wind
directions corridor, with the highest proportion of wind directions in the
northwest
and the east-southeast sector (each about 22 %). Evidently, the upslope flow
conditions (northeast sector) cannot be captured well by the available observational
information.
Figure illustrates the BLA station results. In July
2014 (Fig. a), the observed prevailing wind
directions are from the north-northwest to the east-northeast sectors, while the model calculates
the highest proportion from the west-northwest to north sector. Regarding wind speed, the
model estimates values in good agreement with the observed wind speeds,
illustrated in Fig. a.
In December 2013 (Fig. b) a shift between observed
and modeled wind directions from the north-northwest to the west-northwest sector and from the SW
more to the west sector can be seen. These modeled westerly flows are caused by
the influence of the summit station Zinoedl (ZIN), which is mainly driven by
northwesterly flows in this period. As briefly explained in
Sect. above, the WPG implements a function to replace
missing upper-air data with valid observations from the highest upper-air
station, giving the reason for the influence of this station. In case of the
evaluation of the BLA station the missing upper-air data were replaced by
observations from the ZIN station. For this period, again the wind speeds
between the observations and the model results are in good overall agreement.
Statistical performance measures calculated for the WegenerNet JBT
meteorological stations with contributing wind sensors, for July 2014 and
December 2013 from the “leave-one-out” validation analysis; see
Table for more information on the calculation of the
performance parameters.
Station ID
July 2014
December 2013
and abbr.
B
SDo
RMSE
R
IOA
MAEdir
B
SDo
RMSE
R
IOA
MAEdir
[ms-1]
[ms-1]
[ms-1]
[1/1]
[1/1]
[∘]
[ms-1]
[ms-1]
[ms-1]
[1/1]
[1/1]
[∘]
501, OBK
-0.10
1.23
1.24
0.42
0.57
35
1.61
1.71
3.72
0.35
0.21
51
502, KOE
0.30
0.93
0.81
0.71
0.61
42
-0.15
1.71
1.35
0.75
0.56
59
503, SCH
0.67
0.89
1.82
0.39
0.25
54
1.45
1.59
3.32
0.61
0.22
40
504, BLA
0.15
2.52
2.41
0.69
0.55
37
1.01
4.54
4.41
0.71
0.55
40
505, ZIN
-0.67
3.44
2.56
0.70
0.66
36
-3.85
6.76
6.02
0.73
0.60
38
507, GSC
0.31
1.01
1.10
0.56
0.46
74
0.83
1.28
1.85
0.62
0.32
67
509, TA2
1.40
2.47
3.01
0.62
0.46
50
0.99
4.52
4.62
0.69
0.54
37
Mean Value
0.30
1.78
1.85
0.58
0.51
47
0.27
3.16
3.61
0.64
0.43
47
The statistical results from all meteorological stations are summarized in
Table . The absolute statistical parameters (bias B,
standard deviation SDo, root-mean-square-error RMSE, and mean
absolute error of wind direction MAEdir) applied to the
vector-mean of wind speed show considerably higher values in December 2013,
resulting from the overall higher wind speeds in this period. In general, the
B values are positive, except for the ZIN station and for the OBK station
in July 2014.
The mean R values show better results in December 2013 than in July 2014
and the estimated MAEdir is similar for both periods, and found
at near or below about 40∘. The RMSE values range from
0.8 to 3 m s-1 for July 2014, with the lowest value for the KOE
station and the highest value for the TA2 station. The data from December 2013
generally show higher RMSE values, with the lowest value (1.35 m s-1)
again for the KOE station and the highest value (6 m s-1) for the ZIN
station.
The SDo values are of similar size for both periods. The mean
R value is 0.58 for July 2014 and 0.69 for December 2013. For
December 2013, the R value is higher than 0.6 for all stations except for
OBK, compared to July 2014, where all stations show higher values than 0.5,
except for OBK and SCH. Regarding the mean IOA, we estimated a value of
0.51 for July 2014 and 0.43 for December 2013, with again remarkably low
values for the SCH station in July 2014 and for the OBK and SCH station in
December 2013.
These overall statistical results, but also the example results shown in
Figs. to , illustrate the
useful level of skill well but also the evident performance limits that the
developed WPG application can provide for empirical wind field modeling based
on a small set of seven stations in such complex terrain as the WegenerNet
JBT area.
Modeled 5-year or 4-year seasonal mean wind fields (maps, top)
and relative frequency of wind directions for the Koelblwiese (KOE) and
Blaseneck (BLA) station (windroses, bottom) for the WegenerNet JBT:
(a) summer month March 2012/(March 2013)–February 2017 and
(b) winter
month March 2012/(March 2013)–February 2017.
Evaluation based on multi-year climatological data
Modeled average wind fields for the WegenerNet JBT are presented in the
multi-year climatological data of Fig. (top panels),
showing 5-year climate data for the summer and winter season. In summer,
the seasonal average wind speed reaches maximum values of around
6 m s-1 at the highest summits and generally lower values in the
valley regions, with around 3 m s-1. The environment of the OBK, KOE
and SCH stations exhibits the lowest average wind speeds, directly linked to
the observations of these stations which are used as model input
(Fig. a, color shading). In comparison, the winter
months show generally higher average wind speeds, with a similar spatial
distribution but including in particular higher values at higher altitudes
and the summit regions. The maximum average wind speeds of around
8 m s-1 is observed at the highest summits
(Fig. b, color shading).
Same as Fig. but for 9-year seasonal means
in the WegenerNet FBR, and windrose results for the ZAMG Feldbach and Bad
Gleichenberg stations.
The vector-mean of wind directions for the summer season has directions
mainly from the south sectors with maximum vector-mean wind speeds of around
3 m s-1 (Fig. a, black arrows). In the winter
season, the prevailing wind directions are from the west sectors, with maximum
vector-mean wind speeds of around 5 m s-1
(Fig. b, black arrows).
The windroses of Fig. bottoms show the seasonal
relative frequency of wind directions for the summer and winter seasons
for the KOE and BLA stations, used as examples for a valley and a summit
station. The distribution of wind directions shows similar results as the
distribution for the individual months July 2014 and December 2013 (cf.
Figs. and ). This similar
pattern indicates a good representativeness of these months, including
evidently common weather conditions in the WegenerNet JBT.
Due to the valley location of the KOE station, the observations and
modeled values show narrow wind corridors with a flow mainly along the valley
axis during the summer. The largest part of the observed flow is from the
directions east to east-southeast and west-southwest to west, while the model estimates directions mainly
from the east-southeast to west-northwest sector (bottom-left panel of
Fig. a). In winter, most of the flow is from the northeast to
the east-southeast sector. The model again estimates wind directions mainly from the
east-southeast
and the west-northwest to the northwest sectors (bottom-left panel of
Fig. b). A shift between modeled and observed values
from west-southwest to west-northwest directions can be seen in both seasons; this shift is caused
by the observational influence of the nearby OBK station on the modeled wind
fields, which is located around 1 km northwest of the KOE station (cf. also
Fig. ).
The relative frequency of observed wind directions of the BLA station shows
prevailing directions from the northwest to the north in the summer and winter months,
while the model mainly estimates wind directions from the west to the
northwest
sectors. In both seasons, the largest fraction is estimated from the
west-northwest
sector, with around 12 % in the summer months and around 23 % in the
winter months (bottom-right panels of the second row in
Fig. a and b). The modeled more westerly flows are
caused by the influence of the ZIN station; as already indicated by the
individual month results of Fig. .
Statistical multi-year climatological performance measures
calculated for representative meteorological stations for the WegenerNet JBT
and the WegenerNet FBR (upper half 5-year or 4-year seasonal means for
three WegenerNet JBT stations; right half 9-year seasonal means for two
WegenerNet FBR stations); see Table for more information
on the calculations of the performance parameters.
B
SDo
RMSE
R
IOA
MAEdir
Season (per Station)
[ms-1]
[ms-1]
[ms-1]
[1/1]
[1/1]
[∘]
WegenerNet JBT
KOE: 03/2013–02/2017
spring (MAM)
0.18
1.50
1.06
0.75
0.68
39
summer (JJA)
0.25
1.16
0.89
0.75
0.67
38
fall (SON)
0.16
1.35
1.10
0.68
0.63
41
winter (DJF)
-0.17
1.57
1.35
0.67
0.58
47
all
0.13
1.41
1.09
0.71
0.65
41
BLA: 03/2012–02/2017
spring (MAM)
0.09
3.54
3.64
0.65
0.51
40
summer (JJA)
0.34
2.70
2.74
0.68
0.54
43
fall (SON)
0.74
3.50
3.67
0.67
0.52
39
winter (DJF)
0.04
4.91
5.09
0.64
0.54
41
all
0.73
3.63
3.65
0.67
0.54
41
ZAMG ADMa: 03/2012–02/2017
spring (MAM)
1.33
1.38
3.28
0.38
0.19
52
summer (JJA)
0.99
1.18
2.62
0.36
0.28
53
fall (SON)
1.17
1.15
2.89
0.47
0.07
40
winter (DJF)
1.38
1.09
3.59
0.43
-0.04
36
all
1.22
1.22
3.12
0.40
0.15
38
WegenerNet FBR
ZAMG FBb: 03/2008–02/2017
spring (MAM)
-0.28
1.36
0.75
0.86
0.78
22
summer (JJA)
-0.27
1.00
0.57
0.87
0.77
19
fall (SON)
-0.25
1.05
0.57
0.87
0.78
19
winter (DJF)
-0.21
1.07
0.54
0.89
0.80
16
all
-0.25
1.15
0.61
0.88
0.79
19
ZAMG BGc: 03/2008–02/2017
spring (MAM)
-0.17
1.22
0.83
0.76
0.71
31
summer (JJA)
-0.08
0.92
0.64
0.76
0.71
57
fall (SON)
-0.12
0.88
0.60
0.77
0.73
27
winter (DJF)
-0.11
0.87
0.57
0.79
0.73
26
all
-0.12
-1.00
0.67
0.78
0.72
28
a Admont station, b Feldbach station, c Bad Gleichenberg station
For the WegenerNet FBR we show 9-year average wind fields again for the
summer and winter season (Fig. ), in the same format as
Fig. shows for WegenerNet JBT. The maximum average
wind speeds occur around the highest WegenerNet FBR station 74, located at an
elevation of 394 m, with average wind speeds around 1.5 m s-1 in
summer (Fig. a, top) and near 2.0 m s-1 in
winter (Fig. b, top). The spatial distribution of the
wind speeds exhibits slightly lower values in summer than in winter. As
expected, overall both the modeled average-speed fields and the vector-mean
fields from the WegenerNet FBR (Fig. ) in the Alpine
forelands show much lower wind speeds than the WegenerNet JBT
(Fig. ) with its mountainous Alpine terrain.
The seasonal relative frequency of wind directions from 9-year climate
data for the ZAMG Feldbach station is similar among observations and modeled
values for both seasons (bottom-left panels of
Fig. a and b).
However, larger differences between modeled and observed values can be noticed for the
ZAMG Bad Gleichenberg station (bottom-right panels of
Fig. a and b). For this station, the model calculates
the largest fraction with about 10 % to 15 % from the northeast to the
east
sectors for both seasons while the observed wind directions show about
17 % percent from the north-northwest sector and around 10 % from the south sector.
These differences between modeled and observed values can be explained by the
environment of this station bringing in local influences that degrade the
representativeness of the wind observations for the 1 km scale
.
Table summarizes the statistical results of multi-year
seasonal mean data for selected stations including the ones illustrated in
the bottom row of Fig. and Fig.
and the ZAMG Admont station for JBT. The results of the statistical
parameters generally show better performance for the WegenerNet FBR stations
than for the WegenerNet JBT stations.
For the WegenerNet JBT stations the B is positive for all seasons, except
for the KOE station in winter. The resulting RMSE ranges from about
0.9 to 1.35 m s-1 for this station. Due to the more frequently
occurring episodes of strong wind in winter, the RMSE values are generally
higher for all stations in this season. Because of the higher wind speeds at
the summit regions, the RMSE shows higher values at a range from
2.7 to 5.1 m s-1 for the BLA station. The R value is for both the
KOE and BLA stations and all seasons clearly higher than 0.6. The
MAEdir shows for all seasons and both JBT stations similar
results of near 40∘.
For the ZAMG Admont station the statistical results are generally worse.
Despite lower observed wind speeds compared to the other stations, the B
and RMSE show high values. Additionally, the R and the IOA values
indicate poor performance, with a R value only around 0.4 and IOA
values in a range of just -0.04 to 0.28 for all seasons. These statistical
results for an independent location outside but nearby the JBT area in the
Enns valley indicate the value that an additional station with wind
observations also in the Enns valley could bring to the JBT network (see also
Sect. below). As noted in Sects. and
, the wind observations from the existing Weidendom station,
which is located in the Enns valley, are not suitable as model input due to a
non-representative location.
The WegenerNet FBR stations show a somewhat negative bias (B) and
generally low RMSE values for all seasons. The R values show good
results for all stations, with values higher than 0.75 throughout (ZAMG
Feldbach station even >0.85). This also applies to the IOA, with values
higher than 0.71. The higher values of the mean absolute error of wind
directions (MAEdir) for the ZAMG Bad Gleichenberg station,
compared to the ZAMG Feldbach station, indicate again the local influences
affecting the observations of this station .
Conclusions
In this work we further developed an operational weather
diagnostic application, the WegenerNet Wind Product Generator (WPG), recently
developed by , and applied it to the WegenerNet
Johnsbachtal (JBT), a dense meteorological station network located in a
mountainous Alpine region in the north of Styria, Austria. Based on an
advanced version of the CALMET model , the WPG automatically
generates gridded high-resolution wind fields in near-real-time with a
temporal resolution of 30 min and a spatial resolution of
100m×100m. In addition, the WPG produces gridded
wind gust fields with the same temporal and spatial resolution. As derived
products, half-hourly fields are averaged to hourly and daily weather data
products as well as monthly, seasonal and annual climate data products
.
The main purpose of the WPG products is the evaluation of wind fields from
convection-permitting regional climate models and the investigation of
weather and climate on a local scale, among other needs, such as monitoring
of wind storms.
We evaluated the new WegenerNet JBT wind fields by identifying representative
monthly periods with mainly thermally induced wind fields (July 2014) and
strong wind speeds including wind storm events (December 2013). Using a
“leave-one-station-out” validation approach, and then evaluating against
the observed wind data at the “left-out” station, we inspected the
reasonableness of individual wind fields and computed statistical performance
measures such as modeled vs. observed biases, root-mean-square-errors and
correlation coefficients. In case of wind speed, the statistics show
reasonably good results for both periods with somewhat better values for
December 2013. Compared to the wind speed, the analysis of wind direction
delivers somewhat higher errors, with directional deviations in the wind
sectors of typically around 40∘, depending on the station location
and period.
Overall the results discussed well illustrate the useful level of skill, but
also the evident performance limits, that the WPG application can provide for
empirical wind field modeling based on a small network of seven stations in
such a complex terrain as the WegenerNet JBT area.
We also evaluated seasonal statistical performance parameters for multi-year
data of both the WegenerNet JBT region and WegenerNet Feldbach region (FBR),
the latter initially analyzed by . For the WegenerNet
JBT, the statistical performance measures applied to wind speeds show
reasonably good overall statistical agreement as we showed for the
Koelblwiese and Blaseneck stations. The results related to wind direction
show a level of directional deviation around 40∘, similar to the
individual month results.
For the ZAMG Admont station, an independent station nearby the area in the
Enns valley, the statistics show generally poor values, reflecting the
missing meteorological wind information in the valley. The installation of an
additional wind-observing station in the Enns valley (no suitable JBT station
currently available there) could help to significantly improve the WPG
results in this subarea. Due to the denser distribution of stations in the
WegenerNet FBR, and the less challenging terrain in this Alpine foreland
region, the statistical evaluation shows clearly superior climatological wind
field performance for this network.
Ongoing next steps of work deal with the evaluation the dynamical wind fields
of non-hydrostatic weather analyses and climate model simulations for the two
WegenerNet regions FBR and JBT for selected challenging weather conditions.
For this purpose, we intercompare the empirical wind fields generated by the
WPG with wind field analysis data from the INCA model of the Austrian weather
service ZAMG as well as with climate model data
from the non-hydrostatic model COSMO-CLM . We expect
the WPG application to be a valuable tool for serving this and other
purposes.