iMETOS VWS – Virtual Weather Station
The indispensable partner for smart agriculture.
WHAT ARE THE ADVANTAGES OF THE VIRTUAL WEATHER STATION?
- Calculated sensor variables equal to iMETOS IMT300 sensor set: wind speed, solar radiation, soil temperature, air temperature, precipitation, relative humidity and leaf wetness, along with calculated values of ET0, vapor-pressure deficit (VPD) and DeltaT.
- All data and decision support services are accessible online through FieldClimate.
VIRTUAL STATION IS A FIT FOR YOU IF:
VIRTUAL STATION VS. iMETOS IoT STATION
|Virtual station||iMETOS IOT Stations|
|Variables||Same parameters as iMETOS IMT300 + soil temperature||Based on sensor set|
|Availability||Anywhere in the world||Only where the station is installed|
|Terrain||Not complex terrain||Any terrain|
|Maintenance||No maintenance||Regular hardware maintenance necessary|
|Suitability for high value decisions (frost, water management, disease modelling etc.)||Limited||High|
DATA QUALITY PROVIDED WITH ACTUAL CASE STUDIES
With actual case studies Virtual Weather Station is under continuous improvements.
Seven different environmental parameters have been validated at more than 50 different METOS® stations worldwide during the last 1 year period, by analysing the accuracy of virtual data coming from meteoblue with Pessl Instruments sensor readings.
In particular air temperature, relative humidity, solar radiation, wind speed, soil temperature are considered on an hourly basis and precipitation, leaf wetness on daily basis since the timing of an event can change quickly.
Results show that air temperature on an hourly basis has mean absolute error MAE <1.5K, relative humidity <10 %, wind speed <1.5 m/s and daily precipitation <2 mm.
The following measures have been defined to run statistical analysis of all the data.
- MAE (Mean absolute error) measures the average magnitude of the errors in a set of data, without considering their direction.
- MBE (Mean bias error) is an indicator of whether the model is over-predicting or under-predicting the measured values.
- RMSE (Root mean square error) is a quadratic scoring rule which measures the average magnitude of the error. This means the RMSE is most useful when large errors are particularly undesirable.
The 2 m air temperature is well modelled by the meteoblue Learning Multi-Model (mLM) with values of MAE < 1.2 K. Relative humidity shows a MAE<10% in most of the stations and the model tends to underestimate RH. The model uncertainty of the wind speed is 1.5 m s-1.
The model skill of daily precipitation events decreases with increasing precipitation intensity. In fact, MAE results to be less than 2 mm, but in some stations located in the tropics the variance becomes bigger. Here precipitation results unpredictable as it is only caused by thunderstorms. Simulations can thus predict well the trend like e.g. a drier or moister week but hard to tell the exact amount and when. Leaf wetness and soil temperature variables result to have discrepancy in most of the stations. As expected the accuracy of soil temperature is worse than the accuracy of the air temperature. MAE values are between 1.5-9K.
The Virtual Weather Station allows manual adjustments of local rain data if needed. The user can use a small rain gauge to measure actual rainfall and then correct data in FieldClimate. Manually adjusted rainfall data provides more accurate results on the water balance.
GOT A QUESTION? ASK THE EXPERT!*
Product manager, Weather forecast & Hardware