Improving greenhouse operations with high precision weather data

Local weather conditions have a major influence on the climate conditions and the energy usage of greenhouse operations. This is the main reason that in all high-tech greenhouse operations, temperature, radiation, wind speed and -direction, directly affect the setpoints and anticipatory controls to achieve the desired greenhouse climate, necessary for the optimal production facilities for vegetables or flowers. However, the weather forecast itself is also an essential factor to take into consideration. By integrating high precision weather forecast data in your day to day operations, you can take full advantage of the given outside conditions.

The importance of a high precision weather forecast

Weather conditions can change rapidly. One of the effects of climate change is that some of these changes are more rapid and severe than in the past. Plant evaporation and plant growth are primarily driven by solar radiation. Exact radiation forecasts have a high impact on decisions about irrigation, screening and supplementary lighting.

Fig.1: The weather forecast can be seamlessly integrated into the controls of the iSii process computer used in greenhouses.

Temperature changes affect the energy use of the greenhouse. For example, heat has to be produced in advance to maintain a constant temperature in the greenhouse under colder outside conditions. Also, incoming heavy rain showers need to be noticed in advance to lower vents in time to prevent structural damage and rain coming inside and thus avoid fungal diseases.

5 steps to achieve a highly precise local weather forecast

1. Acquire the best forecast models

Access to excellent global and regional weather forecast models is a must for any meteorological company needing to produce precise weather forecasts. By applying a mix of world-leading weather models and using the strengths of each model, a superior quality forecast can be ensured.

2. Access to a dense network of professional weather stations

Information about hyperlocal weather conditions is required to detect the weak spots of atmospheric weather models. This local information is derived from observations of weather stations from several sources worldwide. All these incoming observations are quality controlled, and in this process, incorrect measurements are filtered out.

3. Machine learning connecting weather conditions with model strengths

Machine learning techniques are used to determine the strengths of each model and optimize its use. Time series of historic weather conditions are compared to model forecasts over recent months and years to learn from the consistencies between model forecasts and observations. Per location, the optimal mix of model forecast values is determined each day. This is done by continuously learning from the most recent weather patterns and taking weather model (and climate) changes into account.


4. Strong statistical processing

Additional calculations are added to always take the latest weather conditions into account. These include the observed weather station measurements but also latest satellite and radar measurements where relevant. This way developing weather events find their way into the forecast which is crucial in rapidly changing and highly uncertain weather conditions like developing thunder storms. A team of professional meteorologists adapts the forecasts manually where needed. They have the ability to make small but crucial changes, ensuring the highly accurate forecasts needed for decision-making processes allowing growers to be one step ahead of the weather.

5. Continuous verification

While the forecast system already makes adjustments based on the latest correlations between forecasts and observed conditions, separate and objective monitoring of forecast quality is also performed. Forecast verification is provided daily to researchers and forecasters ensuring a continuous improvement process.

Precipitation Radar data

Apart from temperature, wind, and radiation, precipitation also has a considerable influence on greenhouse operations. Most greenhouses already have a precipitation sensor to lower the vents when rain is detected. However, greenhouse ventilation systems can have a closing time from fully opened to entirely closed varying from 5 to 30 minutes. When the vents are fully open, while a rain shower is approaching, it can take a long time to close the vents towards the desired position. Thus, it is essential to react to (expected) precipitation if possible.

Expected precipitation is one of the most critical weather forecast parameters, especially for horticulture. Advanced short-term precipitation forecasts are provided to achieve the accuracy required for decision making. Detailed radar imagery is used to determine the detailed precipitation intensity forecasts for the next 3 hours with intervals up to 5 minutes.



Conclusion

Growing a crop in a protected environment delivers many advantages. By optimizing the greenhouse climate with weather data, plants are stimulated to achieve the highest production at a low cost and risk.

Partnership Hoogendoorn MeteoGroup

Over 25 years Hoogendoorn Growth Management and MeteoGroup have been closely involved in applying weather data in Horticulture. Hoogendoorn creates sustainable and user-friendly automation solutions for every kind of horticultural business worldwide. Growth, continuity, and innovation are the focus.
Hoogendoorn and MeteoGroup where the first companies to jointly introduce the integration of weather data in greenhouse climate controls by introducing two special software modules to optimize climate controls using weather data, they developed to products which enable growers to integrate this in their automatic controls:

  • Meteoscope includes forecasted weather data for over a week ahead that can be applied to strategically chosen setpoints.
  • Meteoradar includes forecasted precipitation data in order to anticipate in time by lowering the vents in time.

Additional research on radiation and cloudiness forecasting in the past years has resulted in an unrivaled precision of weather data, specialized on radiation, enabling greenhouse growers to adjust controls automatically in order to optimize plant growth and at the same increase the energy efficiency of their greenhouse operations.
Hoogendoorn is currently upgrading their systems worldwide with newly available weather data, forecasted by MeteoGroup, in order to provide new and improved functionality.

Click here for our Whitepaper 'Improving greenhouse operations with weather data'.

 

Local weather conditions have a major influence on the climate conditions and the energy usage of greenhouse operations. This is the main reason that in all high-tech greenhouse operations, temperature, radiation, wind speed and -direction, directly affect the setpoints and anticipatory controls to achieve the desired greenhouse climate.