We have used three standard meteorological observation types in this study. We include a description. We have also used non-standrad wind observations from two mountain tops, namely Kjølen west of Tromsø and Gjevingåsen southwest of Værnes. The data are of the same type as the SYNOP data. Since the data are a new source of information and has another location than the runway, we have chosen to call them mountain data in the present context.
The model system is designed for forecasting turbulent conditions. We have addressed the need for
turbulence measurement for verification/validation. So far the observational data for verification is limited to wind observations. The observations are rather sparse. In order to get supplementary information, a trial period started in May 2008. During this period Widerøe pilots delivered special turbulence reports on how they felt the quality of the forecasts were compared to the real experience of the conditions. After the trial period the feedback from the pilots was evaluated and the overall impression was that it was good
correlation between the forecast and the pilots experience of the actual wind conditions.
3.1 SYNOP
SYNOP is an acronym for surface based reports. A SYNOP report contains wind measurements at 10 meter using standard traditional surface measurements for the airport at standard height 10 meter above land surface.
3.2 TEMP
TEMP is an acronym for vertical soundings as measured from a balloon released and ascending due to lift of the balloon. A measurement device is attached to the balloon. Data are transmitted to the ground from this device in real time. The wind is calculated from the observed drift of the balloon relative to the ground. The observational error of wind from TEMP reports is of the order of a few m/s.
3.3 AMDAR
There are available data from higher levels in the surrounding areas of some of the Norwegian airports. Some aircrafts are equipped with installations necessary to transmit meteorological observations known as AMDAR (Aircraft Meteorological DAta Relay).
The following is referred from AMDAR REFERENCE MANUAL prepared by Derek Painting, World Meteorological Organization (WMO).
“Wind speed and direction are computed by resolution of the vectors: V = Vg – Va
Where V is the wind vector, Vg (ground velocity) is the velocity of the aircraft with respect to the earth and Va is the velocity of the air with respect to the aircraft. Va is calculated from true airspeed and heading. Heading and ground velocity are derived from the inertial reference unit (IRU).
True airspeed is a function of Mach number and static air temperature. Errors in Mach number are the most significant. For example with a Mach number error of 0.5% at cruise level, airspeed error is some1.2m/s. Thus with zero error from the navigation system, wind vector errors up to 1.2m/s are to be
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expected and are also dependent on the angle between the wind at flight level and the aircraft heading.
Errors in true airspeed combine with errors from the IRU. The basic calculations assume perfect alignment of the aircraft with the airstream and zero roll, pitch, yaw and perfect inertial platform alignment. At high pitch/roll angles wind vector errors, which are proportional to true airspeed can be significant. For
example at 150kt airspeed with 5 degrees pitch and 10 degrees roll a wind vector error of some 1m/s can be expected regardless of the true wind vector. At low wind speeds vector errors can lead to large errors in wind direction. Thus a more useful indication combining wind speed and direction error as vector error would suggest a typical uncertainty of 2-3m/s.”
In the AMDAR format there are possibilities for reporting turbulence measures. Turbulence is reported in one or more of three ways:
(i)As variation in vertical acceleration experienced by the aircraft. (ii)As 'derived equivalent vertical gust'.
(iii)As an index related to eddy dissipation rate (EDR). For more details see the reference manual referred to above.
In Europe quite many AMDAR reports includes the index listed EDR (see list above). We have investigated the AMDAR reports presently available from aircrafts operating in Norway for some arbitrary days in January 2008. We have for those aircrafts found only reports with this index given as missing. For the project there would be of great value if actions could be taken in order to include such an index in the AMDAR reports for Norwegian areas.
3.4 MOUNTAIN DATA
As already mentioned above we have for the first time also used wind obsersvations from two mountain tops, namely Kjølen west of Tromsø amd Gjevingåsen southwest of Værnes. The data are of the same type as the SYNOP data. Since they are a new data source and has another location than the runway, we have chosen to call them mountain data.
The data has been collected from via a separate system used for giving socalled TREND forecasts at met.no. The collection of data started in October 2010 and results were presented for the three last months October, November and December of 2010 in the previous report. We addressed then the possible data quality control problem connected to those data. For that reason we will come back to verification using those data in the next report.
4. Verification
Basis for the verification work at the Norwegian Meteorological Institute is operational storage of both observations and model output in databases with a high level stability and with secured look up systems. In this way all data for validation and verification is available for further investigation.
All model data used in the following validation is interpolated horizontally to the position of the observation with Bessel interpolation with good interpolation accuracy. In the vertical data are interpolated as linear in the logarithm of p. To compare with observations at times off the hourly available model data a linear interpolation in time is carried out.
In order to summarize the results for the wind in some way, we have computed the square root (s ) of the uv
mean of the squared standard deviations of the component errors of the two wind components u and v (su andsv ). The formula is:
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2
2
2
1
v
u
uv
s
s
s
5.1 Verification of horizontal wind forecasts against SYNOP reports
for the airports Honningsvåg, Hammerfest, Hasvik, Tromsø, Evenes,
Narvik, Mosjøen, Sandnessjøen, Brønnøysund, Værnes, Ørsta-Volda,
Sandane, Førde and Fagernes.
The present report is organized with several figures for each airport showing area, monthly means of verification scores and wind roses in a sequence organized by letters. Starting with Mehamn and figures 5a, 5b and 5c we end up with Fagernes, Leirin in figures 23a, 23b and 23c. Since there are two observing sites inside the Hasvik as well as the Tromsø model area, there are five figures for the two sites namely figures 8 a to e and 9 a to e respectively.
We have included (as the last figure for each site) a figure showing monthly means of s and Mean Error uv
based on SYNOP reports for each observation site. Those two presented parameters are used as a quality indicator for the wind forecasts.
Mehamn airport
We refer to figures 5a, 5b and 5c for this airport.
This airport is situated at the coast with open sea to the north. There is a valley south of the airport and a peninsula to the west. The distribution as given by the wind roses shows that for the first four months period UM 1-km gives the best wind distribution. There is an improvement from the 4 km models (UM 4-km and HIRLAM 4-km) by UM 1-km as a too high frequency of easterly winds in the 4-km models are reduced. For the last four months the influence of the land sea breeze is evident. Simra and UM 1-km has the most realistic wind distributions for the last four months, while the 4-km models fails to describe the high frequency of onshore northerly winds.
Looking at the wind errors, SIMRA has a slightly larger error than UM 1-km and UM 4-km. The mean errors are rather similar for those models. Overall Simra and UM 1.km gives the best wind forecast at the runway.
Honningsvåg airport, Valan
We refer to figures 6a, 6b and 6c for this airport.
This airport is situated on a peninsula at the coast with open sea to the north. For the first four month period the Um 1.km model gives the best wind distribution with rather similar results for all the other models. For the last four months for the effect of the land sea breeze is evident. The results when it comes to quality of the wind distribution is in fact more in favour of the 4-km models for the last four monyhs, although the difference is small.
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Simra has a marked under-estimation of the wind. The non-systematic are errors levels vary through the year and for the period as a whole is rather similar errors for all models.
Hammerfest airport
We refer to figures 7a, 7b and 7c for this airport.
The airport is situated in a valley with mountains of 300-400 meters height situated some 700 meters to the northwest and mountains at about 200 meters height to the south only a few hundred meters away and mountains further south reaching above 600 meters height.
The observation shows a clear channelling of the wind along the valley. For the first four months all models fails to describe the frequent southwesterly wind observed. The other aspects of the distribution are
forecasted a little better by the 1-km models than the 4-km ones. For the last period, however, the land sea breeze is again evident and for this period SIMRA has the far best distribution.
Looking at both systematic and non-systematic errors, the quality of both SIMRA and UM 1-km compared to the coarser mesh models UM 4-km and HIRLAm 4-km is evident trough smaller non-systematic errors although both Simra and UM 1-km underestimates wind in a way UM 4-km does not.
Hasvik airport
We refer to figures 8a-e for this airport since there at observations from the runway as well as from the mountain top Sluskfjellet (approximately 400 meters height) 15 km to the north east of the runway. The airport is situated on a peninsula with a mountain of 174 meters height to the south west.
At Hasvik airport the wind rose plot shows that SIMRA resolves better the details although is has too frequent SE wind compared to the observations. At Sluksfjellet all the models has weaknesses in the wind distribution. The distribution most similar to the observed one is for Um 1-km in the first period and SIMRA in the last period.
There is generally a smaller unsystematic error in SIMRA compared to UM 1km. Regarding the systematic error, SIMRA underestimates the wind while UM 1-km overestimates it for the airport while both models underestimates the wind at Sluskfjellet. This last finding is in agreement with the findings in previous reports for the mountain tops Kjølen and Gjevingåsen in the SIMRA-area for Tromsø and Værnes respectively.
Tromsø airport, Langnes
We refer to figures 9a-e for this airport.
Tromsø airport is situated on the western side of Tromsøya. The height of the island is around 100 m. On the island to the west, however, there is a complex mountain chain at approximately 5 km distance with nearby tops of several hundred meters. The highest top Kjølen is situated to northwest and reaches 790 meters. Within the SIMRA area there is an observation site on the top of Tromsøya (at Værvarslinga for Nord- Norge). We have included this site in the verification to shed more light on the model results.
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Bardufoss airport
We refer to figures 10a, 10b and 10c for this airport.
The runway is oriented across the valley with approach areas oriented along valleys ending in the main valley. Towards southwest there is mountains some 5 km away with distant tops above 1000 meters. There is, however, mountains in all directions. Modelling and forecasting wind at the site is probably difficult. The wind roses shows that the HIRLAM 4-km model has the most realistic wind distribution. The finer mesh models are dominated by winds form south and those models is obviously not representing the observation site.
Simra and UM 1-km has a rather large unsystematic error here as expected. Both models do also overestimate the wind here while the 4-km model does not.
Harstad/Narvik airport, Evenes
We refer to figures 11a, 11b and 11c for this airport.
The airport is situated in a valley between mountains of 300-400 meters height approximately 4 km away. The wind roses seems to be rather realistic for the two highest resolution models, SIMRA and Um 1-km. This is consistent with the result for this airport in the previous report.
Looking at the error levels, we see that SIMRA has a somewhat larger unsystematic error at the runway compared to the other models. The errors at this airport are, however, small compared to the same for most other airports.
Narvik airport, Framnes
We refer to figures 12a, 12b and 12c for this airport.
This airport is situated on a peninsula east of the fjord with mountains to the south and east. The minimum distance to the mountains is however more than 3 km.
When looking at the wind roses for Narvik airport, Framnes, we see as reported previously that the
agreement between the models and the observations are not so good. The best fit is found between the UM 4-km and the observations, in particular for the last four months.
Narvik airport, Framnes represent a location where the UM 4-km has the lowest error and as stated above also has the best wind distribution. This result is probably caused by very small-scaled features in terms of terrain and shoreline at this place. Because of these details, the models perform rather different as they resolvet those details in a different but not fully realistic way. The unsystematic errors are rather large as well, demonstrating the problems modelling the 10 m wind at the runway for this site.
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Svolvær airport, Helle
We refer to figures 13a and 13b for this airport.
The airport is situated on the eastern side of a peninsula. Locally there is flat terrain. 1-2 km to the west and northwest there are mountains reaching 600-800 meters.
During the first four months, all models give a reasonable wind distribution when compared to the observations while UM 1-km and Simra are best for the last four months.
SIMRA underestimates the wind here. The non-systematic errors are rather large for all models. SIMRA has the largest non-systematic error. Since we now have nearly one year of data we see that the models performs in a similar way as for the other airports.
Leknes airport
We refer to figures 14a-c for this airport.
The airport is situated on an island. Locally there is not flat, but the details in the terrain is rather smooth. 3- 4 km away there are mountains of 400-700 meters height.
The wind roses shows that SIMRA and UM 1-km gives the best wind distribution for the first four months. For the last four month it seen that the wind distribution from SIMRA is not so good whiel UM 1km gives the best distribution.
Neither SIMRA nor UM 1-km underestimates the wind here. Those two models has also a significantly smaller non-systematic error than UM 4-km. This airport is a site where 1km resolution improves the wind at the runway all year around.