Sodankylä, in the heart of Arctic Research Centre of the Finnish
Meteorological Institute (FMI ARC) in northern Finland, is an ideal site for
atmospheric and environmental research in the boreal and sub-Arctic zone.
With temperatures ranging from
Nocturnal and wintertime surface temperature inversions still pose a
difficult challenge to weather forecast models. Various atmosphere-to-surface
coupling issues are also problematic in climate models, especially at Arctic
latitudes. For the model development, versatile measurements are essential.
The Arctic Research Centre of the Finnish Meteorological Institute (FMI ARC,
FMI ARC dates back to the mid-nineteenth century when, in 1858, The Societas Scientiarum Fennica founded the first weather station in Sodankylä. Continuous meteorological measurements were started in 1908 and have been continued to this day (Savunen et al., 2014). Being accessible from all parts of the world, FMI ARC is also an excellent base for studying various themes of global change in a northern context.
Today, an extensive set of measurements, ranging from basic meteorological
data to heat and carbon fluxes as well as ozone and Arctic snow coverage
measurements, is being performed at FMI ARC. Sodankylä observatory also
provides facilities for receiving and processing polar satellite images,
and FMI has conducted systematic aurora observations in the Finnish Lapland
since late 1950s. The FMI ARC research sites belong to the Lapland
Biosphere–Atmosphere Facility (LAP-BIAT,
In the weather model verification, the traditional way is to perform detailed studies of model analyses and forecasts by comparing them with measurements afterwards. Another way to provide insight into model behaviour is to compare measurements with forecasts parallel with model runs in near-real time. Although based partly on less accurate (unchecked) measurements, this approach nevertheless provides valuable information about model behaviour and, when monitored frequently, can also act as a kind of alarm bell, alerting model developers when there are apparent problems with model forecasts. Data collected this way can also be used in model performance studies (Atlaskin and Kangas, 2006). As an added benefit, it provides means to monitor measurements.
Starting from 2000, the measurements at FMI ARC have been used to verify
weather model forecasts in near-real time. The verification was started with
the NWP model HIRLAM (Undén et al, 2002; Eerola, 2013) and Sodankylä
measurements, but has later been extended to cover several other NWP models
and mast measurement stations. Presently, a total of 12 models and seven
measurement masts are included. The models represent the activities of HIRLAM
(
The harmonized and quality-checked data sets collected in Sodankylä are also available for more detailed research and model development. From the point of view of research, the most valuable feature of the Sodankylä site is that it offers the possibility to combine various simultaneous measurements, including those from a micrometeorological mast and a radiation tower, as well as from dedicated snow and soil observations, AWS, and atmospheric soundings (see e.g. Coustau et al., 2014). In the present article, these data sets are utilized in a study of radiation from HARMONIE-AROME forecast system (Seity et al., 2011) versus measured radiation in Sodankylä.
The Sodankylä measurements are likewise important in the initialization of NWP models in operational forecasting. Of the measurements performed in Sodankylä, balloon soundings (temperature, humidity, wind components) and some SYNOP measurements (surface pressure, screen-level temperature, snow depth) are assimilated in the upper air and surface analysis of HIRLAM and HARMONIE-AROME models.
Section 2 contains description of Sodankylä site and Sect. 3 of the mast verification system. A comparative study on HARMONIE-AROME radiation schemes is presented in Sect. 4, and conclusions in Sect. 5.
The terrain around FMI ARC Sodankylä observatory (67.368
Due to the warming effect of the Gulf Stream, the area can be classified as continental sub-Arctic or boreal taiga, by Köppen classification climate region Dfc (continental sub-Arctic or boreal (taiga) climates). However, with regard to stratospheric meteorology, Sodankylä can be classified as an Arctic site, often lying beneath the middle or the edge of the stratospheric polar vortex and in a zone displaying intermittent polar stratospheric ozone depletion (Savunen et al., 2014).
Continuous meteorological measurements have been performed in Sodankylä since 1908. Ground-station observations every 3 h record information on weather conditions prevailing at ground level. In addition to standard weather observations, the basic observational duties at the observatory include regular recordings of solar radiation, sunshine and hydrological quantities. Radiosonde measurements are carried out twice a day. During the NOPEX/WINTEX measurement campaign, an aircraft campaign to measure boundary layer properties was performed (Kangas et al., 1998), the results of which were then used in studies on satellite-based reflectance measurements (Kangas et al., 2001) and on regional momentum and sensible heat fluxes (Batchvarova et al., 2001).
Data from most of the measurements are collected into a central database at
In 2000, a 48 m-high micrometeorological mast was erected in the
immediate vicinity of the Sodankylä observatory
(
Sodankylä micrometeorological mast measurements (see also Fig. 1).
Sodankylä micrometeorological mast (November 2015).
The mast is extensively instrumented with temperature, wind, humidity, and radiation measurements at various levels (Fig. 1, Table 1). The instruments used include HMP155 (Vaisala) for temperature and humidity as well as WAA25/WMT700 (Vaisala) and Thies 2-D (Thies Clima) anemometers for wind speed and direction. Downwelling and upwelling shortwave and longwave radiation components (CNR4, Kipp & Zonen), net radiation (Nr-Lite, Kipp & Zonen), and photosynthetically active radiation (PAR, LI190SZ, Licor) are measured near the top of the tower (45 m). Heat and momentum fluxes are measured at the 23 m level by the eddy covariance method (see more detailed description below).
Additional near-ground measurements including soil temperature and moisture
profiles, soil heat flux, snow depth, and below-canopy PAR are performed in
the vicinity of the mast
(
The in situ fluxes of sensible heat, latent heat, and momentum are measured
at the micrometeorological mast by the micrometeorological eddy covariance
(EC) method, which provides direct measurements of the fluxes averaged on an
ecosystem scale. In the EC method, the vertical flux is obtained as the
covariance of the high frequency (10 Hz) observations of vertical wind speed
and the variable in question (temperature, H
The eddy covariance measurement system at Sodankylä includes a USA-1
(METEK GmbH, Elmshorn, Germany) three-axis sonic anemometer/thermometer and
a closed-path LI-7000 (Li-Cor., Inc., Lincoln, NE, USA) CO
In addition to the basic synoptic measurements, a set of additional
measurements is performed on a 18 m-high solar radiation tower in the
observatory area. It contains measurements of main radiation components:
shortwave radiation (CM11, Kipp & Zonen), direct normal radiance (NIP,
Eppley), longwave radiation (CG4 Kipp & Zonen), and aerosol optical depth
(PFR-N32, PMOD/WRC) (
For consistency, all radiation data used in the mast verification are obtained from the radiation tower. The measurement instruments on the radiation tower are also easily reachable and allow more frequent maintenance than those on the micrometeorological mast. They are quality-controlled and e.g. snow on the instruments is removed if found to exist. All instruments except those used for the outgoing LW radiation are ventilated. No heating is applied as that would interfere with the measurements.
The automatic weather station (AWS) providing the official main weather
parameters from Sodankylä has been in use since February 2008. All the
instruments and sensors at the station are calibrated annually. The
parameters include screen-level temperature (PT100, Pentronic) and humidity
(HMP, Vaisala), air pressure (PTB201A, Vaisala), visibility (FD12P, Vaisala),
and cloudiness (CT25K, Vaisala). Wind speed and gust (WAA25, Vaisala) and
wind direction (WAV15, Vaisala) at the height of 22 m, as well as snow depth
(SR50, Campbell Scientific) are also provided
(
Example mast verification plot from 22 September 2015. Screen-level (2 m) temperature from HIRLAM forecasts compared to Sodankylä mast measurement (3 m height). Red continuous line (OBS) shows measurements, dotted coloured lines (FCST) show the first 24 h from a set of consecutive forecasts.
Since 2002, near-real-time comparisons of model forecasts and in situ measurements have been performed as a part of HIRLAM weather forecast model operational runs at FMI. Starting with the HIRLAM forecast and Sodankylä measurements, the comparison has expanded to comprise a total of 12 models and seven masts from around Europe. An eighth mast in Estonia is presently being introduced into the system (Table 2). In addition to the direct online comparison, long-term comparison statistics are provided. Table 3 lists the parameters included in the comparison.
Masts and weather forecast models included in the mast verification.
Mast verification comparison parameters and their measurement in Sodankylä. Parameters 1–5 and 12–15 are from the micrometeorological mast, 6–11 from the radiation tower. In Sodankylä, screen-level temperature and humidity measurements take place at the height of 3 m, wind speed at 18 m.
To enable rapid update of the comparison, the comparison plots are produced as a part of the operational HIRLAM forecast cycle (currently 4 times a day after synoptic hours 00:00, 06:00, 12:00, and 18:00 UTC) using the latest available data.
The HIRLAM program web site (
The parameters that are currently plotted include temperature, wind speed, and humidity at specified levels as well as various heat and radiation fluxes (Table 3). With the original aim in mind, the temperature difference between 2 m and a higher level (usually the first model level) is also included in the plots as a measure of the surface temperature inversion. A sample plot showing screen-level (2 m) temperature from the HIRLAM forecast as compared to Sodankylä mast measurement (at 3 m) is shown in Fig. 2.
An interactive web page for browsing the comparison results has been set up. The page enables side-by-side comparison of different mast–model combinations. Not all model–mast–parameter combinations are possible, however, because parameters measured at different masts vary and all mast locations are not covered by all model integration areas. In these cases, an appropriate subset of the plots is shown. Information about the parameters as well as brief descriptions of the masts and models is also included. The page is available to all HIRLAM and ALADIN consortia participants and to data suppliers as a part of the general HIRLAM forecast visualization pages.
Seasonal statistics compiled for individual observatories, or mast sites, containing the models available at each respective station are calculated in the mast comparison as well. Seasonal summaries of the daily comparisons, including a variety of descriptive and comparative statistics, are shown under a separate heading on the interactive web page.
Graphs include time series of observed and modelled variables and the departures of model output from the observations. They provide a qualitative view of how the models are doing, and how their performance has varied during the season, thus linking model performance to the prevailing conditions. These graphs are also useful for identifying gaps in the data.
Graphs of average model biases and rms errors (RMSEs) as a function of forecast lead time serve to quantify the errors, while scatterplots, histograms, and mean diurnal cycles help to interpret the errors physically by linking the average errors to specific conditions or hours of the day.
As an example, Fig. 3 shows as the plots of the RMSE and bias of screen-level
(3 m in the mast) temperature and upwelling longwave radiation (LWUP,
obtained from the 18 m radiation tower, see Table 3) for the spring
period (March–April–May) of 2014. The plots include data from four models,
HIRLAM (FMI), HARMONIE-AROME (FMI), IFS (ECMWF), and Arpege (Météo
France) and they show the first 24 h of the 00:00 UTC forecasts. One can
see that for the FMI operational HIRLAM there is a clear overestimation of
both LWUP and the screen-level temperature. Here, LWUP represents the surface
temperature over open land in the measurements and that of the whole
forest-covered 50 km
Statistical comparison of screen-level (3m in the mast) temperature and the upwelling LW radiation for the first 24 h of 00:00 UTC forecasts. Time period is March–April–May 2014, and the models HIRLAM (FMI), HARMONIE-AROME (FMI), IFS (ECMWF), and Arpege (Metéo France).
Variables as function of time (
Spectrally averaged shortwave and longwave radiation fluxes at the surface
are predicted output variables of the contemporary NWP models. They are
directly comparable to the observed radiation fluxes, which could thus be
used for the validation of the forecast along with the near-surface
temperature and humidity, anemometer-level wind, cloudiness, and other
variables diagnosed from the NWP model output in the standard station
verification. In particular, comparison of the simulated and observed
radiation fluxes can give useful insight for the development of the cloud and
radiation parameterization in the NWP models. Both in reality and in the
models, the short-term variability of the surface radiation fluxes is mostly
related to the variations of cloud and aerosol particles in air. In
Sodankylä, the influence of aerosol in the atmospheric radiation transfer
is minor. In this section, we will test different atmospheric radiation
parameterization in an experimental version of the HARMONIE-AROME forecast
system, based on the reference cycle 38h1.2,
For a model–observation comparison, six components of radiation fluxes
measured in the 18 m-high Sodankylä radiation tower are available
(Table 3): shortwave downwards (SWDN or global radiation) and upwards
(reflected); direct normal solar irradiance (DNI); diffuse shortwave solar
radiation; and longwave radiation downwards (LWDN) and upwards (LWUP). In this
study, we compared the observed SWDN and LWDN to their model counterparts
for the time period 15 January–15 May 2014. The available 1 min flux
measurements were averaged over 3 h periods and compared with the
3 h average fluxes derived from the accumulated radiation fluxes of
the
The default atmospheric radiation parameterization of AROME (Seity et al.,
2011) is based on the radiation transfer code in the Integrated Forecast
System (IFS cycle 25R1, European Centre for Medium-Range Weather Forecast
implementation in 2002), see ECMWF (2012) and Mascart and Bougeault (2011),
denoted here as ifsradia. An alternative radiation scheme originates in
ALADIN (Mašek et al., 2016), hereafter denoted as acraneb2. The radiation
scheme of HIRLAM (based on Savijärvi (1990), see also Nielsen et
al. (2014)), hereafter denoted as hlradia, was available for experimentation.
All three schemes were tested within the framework of AROME physical
parameterization by running three series of experiments using a dedicated
version (harmonie-38h1.radiation) of HARMONIE-AROME over a domain covering
Finland. A horizontal resolution of 2.5 km and 65 levels in vertical were
used. Lateral boundary conditions for the experiments were obtained from the
ECMWF analyses. For the initial state of each
Most of the winter days before mid-March 2014 were cloudy in Sodankylä. Most observed and predicted clouds were essentially nonprecipitating. The nonprecipitating clouds predicted by HARMONIE-AROME consisted mainly of (supercooled) liquid droplets while the ice crystal content was small. Some amount of the (precipitating) snow and graupel was practically always present in the simulated clouds and some liquid–ice condensate at the lowest model level was often predicted. This is due to a recent change in cloud microphysics treatment in the HARMONIE reference system (K.-I. Ivarsson, personal communication, 2015).
Every month, there were several days when more than 1 mm of precipitation, corresponding roughly to 1 cm of snowfall, was observed and predicted, while the first significant rainfall appeared in the end of April. These precipitation events were predicted well by the model. Falling precipitation was observed during the periods when HARMONIE also suggested significant snow and graupel content in the clouds. This indicates that in the model most particles classified as precipitating indeed reached the surface, in agreement with the observations. Typically, the simulated condensate content of the precipitating particles was 2–3 times the liquid droplet water content, which in turn was an order of magnitude larger than that of the ice water content. In our experiments, only the cloud liquid droplets and ice crystals, but not the precipitating particles, were allowed to influence the radiative transfer in the atmosphere. This deviated from the default HARMONIE (cycle 38h1.2) settings, according to which a fraction of the snow and graupel particles is accounted for when determining the cloud optical properties.
Figure 4 shows time series of the observed and forecasted (
In February, solar radiation flux (Fig. 4b) is small, Sodankylä being
located north from the polar circle. In February 2014, the maximum observed
SWDN value was ca. 160 Wm
Generally, the LWDN flux was predicted well (Fig. 4c and d). The largest differences between predicted and observed LWDN were found 1–2, 7–8, and 19–21 February. The results were best when using the ifsradia and acraneb2 schemes, while more deviations were found for hlradia.
Automatic weather station observations (not shown) indicated that during February 2014, only the afternoon and night after the 20th was cloudless in Sodankylä. In this truly clear sky case (both observed and simulated) all schemes correctly produced small LWDN fluxes and low screen-level temperatures. When observed clouds were not caught by the model, LWDN fluxes were underestimated by all schemes. This was the case e.g. on 21 February. Downwelling longwave radiation was overestimated by hlradia (Fig. 4c, d) when the simulated clouds were optically thick (due to the assumed large supercooled liquid water content, not shown), for example during 9–12 February. During some periods (7–8 and 17–19 February), the cold bias of the screen-level temperature was most evident for hlradia, which showed the most underestimated LWDN values these days. Also the integrated cloud liquid water content was then smaller in the experiment with hlradia than it was with other schemes. This might indicate secondary effects due to the cloud-radiation interactions in the model. However, more studies are needed to estimate the significance of this difference and to understand the mechanism behind it.
The simulated LWUP (Fig. 4e) followed observations generally much more closely than the screen-level temperature. This indicates that the surface (skin) temperature seen by the radiation parameterization was predicted well in most cases (with the exception of the first 2 days and 7–8 February). In the model, the properties of the snow cover on ground and, to some extent, the soil and vegetation properties under the snow, influence the surface temperature and the grid-average LWUP.
The different LWDN produced by the different radiation schemes does not, however, explain the systematic bias of the predicted screen-level temperature. LWDN is a part of the surface energy balance, which determines the (snow and soil) surface temperature that interacts with the atmosphere. In the model, the diagnostic screen-level temperature is obtained by interpolating between the predicted lowest model level (representing the layer up to ca. 28 m from the surface) and the surface temperatures. In the interpolation, the surface layer stability is taken into account. The diagnostic estimation of the screen-level temperature is likely to add uncertainty to the model–observation comparison. Thus, the simulated screen-level temperature was evidently strongly influenced by the lowest model level temperature, which in turn was dominated by the temperature advection in the low troposphere.
In a model–observation comparison at a single location, phase errors of the large-scale forecast in time and space show up if e.g. the arrival of an atmospheric frontal system has been forecasted incorrectly. However, a systematic bias is hardly explained by the phase errors. A comparison between the predicted lowest model level temperature with the corresponding measurements of the micrometeorological mast, as well as a comparison between the predicted surface temperature and the corresponding snow–soil surface temperatures, might shed light on the problem. Predicted solar radiation fluxes, although small in this period, deserve evaluation against the observations. This falls, however, outside the scope of the present study.
The near-real-time mast verification of NWP forecasts, starting in 2000, has proved to be very useful in NWP model verification and, after being started with only one model and one mast (HIRLAM and Sodankylä), has now expanded to include 12 forecasts and seven masts across Europe.
The mast verification system has been integrated with the operational runs of NWP model HIRLAM, with data for other models and masts obtained through a common data pool. The results are shown as a part of HIRLAM web-based visualization pages that are available to all data suppliers and members of HIRLAM and ALADIN NWP model consortia. The system is not dependent on HIRLAM runs, though, and could be also run separately.
Statistics of the comparisons with e.g. long-term bias are also included in the verification, although they are not updated daily but on seasonal basis. They provide seasonal summaries of the daily comparisons, including a variety of descriptive and comparative statistics.
A comparative study of different radiation schemes applicable within
HARMONIE-AROME NWP system was also presented for early spring 2014. Based on
this example, we conclude that the three different radiation schemes
produced generally good but somewhat different LWDN fluxes in cloudy days –
and in February 2014, there was only one afternoon and night free of clouds
in Sodankylä. The hlradia scheme behaved most differently from the other
two schemes – ifsradia and acraneb2. The hlradia scheme tended to overestimate LWDN in
case of optically thick clouds and possibly underestimate it in case of
optically thin clouds. However, when comparing the simulated screen-level
temperatures to those observed by AWS, the usage of any scheme seemed to
lead to a systematic cold bias of the order of 1–2
The cooperation of Riika Ylitalo and Ari Aaltonen from the Finnish Meteorological Institute is gratefully acknowledged. The cooperation and interest of the HIRLAM and ALADIN consortia as well as the efforts of the data providers have been essential in setting up the online verification system. Edited by: C. Ménard