Agrometeo Data
In this section, a detailed and in-depth report is provided on the trend of micro-climatic data collected from the station.
Last updated
In this section, a detailed and in-depth report is provided on the trend of micro-climatic data collected from the station.
Last updated
The histograms show the instant sampling data of the current day (instantaneous value of the last measured data)
The typical time resolution (measurement frequency of the data visible on the software) is hourly. In reality, the actual measurement frequency in the field is usually every 15 minutes: the hourly data visible on the software is an average of the 4 hourly values measured every 15 minutes. Each measured data is validated by the software before being considered and graphed.
The number and type of values reported depends on the specific set of physical sensors (real sensors) installed on the measurement station and any enabled virtual sensors. Some values are in fact defined as "virtual sensors": estimated and/or calculated values based on other direct measurements.
This graph is only enabled for stations equipped with wind direction and intensity sensors. The wind rose is a percentage distribution graph of the winds measured in the last 24 hours (current day). In the circular diagram, the frequencies of the winds with a certain direction are represented, chromatically dividing them by intensity.
On the right-hand side, the interactive legend is reported: it is possible to select one or more ranges of wind intensity that you wish to display on the wind rose; by default, all intensities are selected and therefore all are graphed (if present). By selecting a specific range of wind intensity, if the wind rose chart remains empty, this means that the intensities measured in the last 24 hours have always been outside the selected range. The available intensity ranges represent a re-adapted Beaufort scale.
The gust data represents the instantaneous maximum wind speed data sampled in the last day.
Zoom Feature: It is possible to enlarge a portion of interest of the graph simply by holding down the left mouse button and dragging it inside the graph itself. It is then possible to return to the standard view by pressing the "Reset zoom" button in the upper right.
The preset time interval for displaying historical data corresponds to the last 7 days. It is possible to select the time period of interest by clicking on the "Select period" button (located in the upper right corner): all trends over time are recalculated and graphed again.
For each line chart, there are three commands that can be activated by clicking on one of the following three icons
The functions of the three icon commands are respectively:
Insert an alarm
Expand/reduce the chart
Save the chart as an image
The bell icon (Insert alarm) allows you to set an alarm (via email and/or SMS) for a specific parameter shown in the chart. Clicking on the bell icon brings up a form to fill out for setting the alarm:
Enter a name to identify the new alarm: we suggest setting a name that immediately reminds us of the alarm thresholds that we will set next (e.g. for a temperature, the name "Alarm 0-30" indicates an alarm that has minimum and maximum thresholds of 0°C and 30°C respectively); after entering a name, continue by checking the "active" option.
Select the desired alert method (SMS and/or email).
Enter the minimum threshold below which the alarm is triggered; enter the maximum threshold above which the alarm is triggered.
Select the option to graphically display the set alarm (it is also necessary to select a color).
Virtual sensors are values derived through formulas and algorithms that combine different parameters directly measured on the field by real sensors. Below is a description of some potentially strategic virtual sensors for agronomic management.
This parameter is used in predicting snowfall and represents the lowest temperature that can be obtained through evaporation/sublimation of water in an air mass, at constant pressure, until it becomes completely saturated. It is caused during precipitation processes in which raindrops falling from an external air source tend to evaporate/sublimate (until complete saturation) in the underlying air mass, extracting energy from the environment, resulting in a lowering of the temperature.
The wet-bulb temperature is particularly useful for forecasting frost (especially overhead). Compared to the dry bulb temperature (the traditional one), the wet bulb temperature forecast is in fact the lower limit of temperature attainable (and therefore the worst case when forecasting a frost).
The wet bulb temperature can also be used in predicting snowfall. Below are two alternative methods of prediction.
Precipitation will be snow or rain depending on the altitude at which the wet bulb temperature reaches the freezing point of 0°C:
≥ 915 m a.s.l.
Almost always rain, snow is rare
600 ÷ 915 m a.s.l.
Mostly rain, snow is unlikely
300 ÷ 600 m a.s.l.
Persistent rain can easily turn into snow
< 300 m a.s.l.
Almost always snow, only light or occasional water precipitation
The amount of water vapor that can be contained in a volume of air depends on its temperature and pressure: the higher the temperature, the more energy is available to keep water in the form of vapor. Therefore, higher temperatures correspond to higher water vapor contents.
The dew point temperature is the temperature at which a mass of air must be cooled, at constant pressure, in order to become saturated (i.e., when the percentage of water vapor reaches 100%) and can begin to condense if it loses further heat. This results in the formation of frost, dew, or fog due to the presence of tiny droplets of water in suspension.
The dew point is associated to relative humidity: a high relative humidity value indicates that the temperature is close to the dew point. If the relative humidity is 100%, the dew point coincides with the temperature.
If T.dew point < 0°C (or close to zero), and if T = T.dew point, frost formation will occur.
This parameter indicates the presence or absence of a water film on the leaf surface and is crucial from a plant protection standpoint since under wet conditions spore germination and cuticular penetration by certain fungal microorganisms are activated.
Leaf wetness is a binary value: 1 (high) when the leaf is wet, 0 (low) when the leaf is dry.
The actual leaf wetness parameter is measured in the field by an electronic wetness sensor (if present) and represents the real, measured, and therefore exact data.
The theoretical leaf wetness parameter instead reports the mathematically calculated wetness state based on other field-measured values: rain, humidity, and air temperature. It is a derived value (therefore a virtual sensor), however reliable but not always as accurate as the actual sensor
