Our start-up delivers a unique set of weather forecast insights.
We improve and extend existing weather prediction models using observations and a sophisticated software algorithm.
So what is an algorithm?
In simple terms, an algorithm is a set of rules or calculations designed to solve a problem. Our extended weather outlooks deliver risk advisories for growers, which can enable them to plan further ahead to mitigate the impacts of local variability in weather.
How are complex models composed?
When starting something big, we need to generalise. Complexity arises naturally from the combination of many small and logically connected steps through iterative improvements. We first created a simple framework to describe the general processes of the climate system – and then refined that system to best match past observations. Einstein reportedly said that: “Everything should be made as simple as possible, but not simpler.” Through stepwise testing and validation, we expanded our model into more detailed seasonal climate outlooks to capture some local weather extremes.
To build a housing complex, architects first create a schematic of how their construction fits into the landscape. The houses form the blocks, which are fitted together to make the complex. The fixtures and fittings are the last step.
Similarly, creating too much detail or complexity in a model before the framework is rigorously tested can later become problematic.
What is the process for model verification?
Useful models require detailed thought about which features are valuable to a customer.
Farmers for instance require field-scale weather information – so can we get access to direct observations in the field to compare our weather predictions against? Standardised weather stations are often essential, providing the most direct weather measurements.
Growers of carrots may need to know the timing of the first significant frost. To tackle this verification problem ground temperature data may be required, and our knowledge suggests that carrots grow well in sandy soil textures that expose them more to nighttime lows. Weather stations are housed in a Stevenson Screen with thermometers 1.25 metres above ground level. To optimise an air frost model, weather station data is not suitable for these growers. Soil sensors are a better option.
Some farmers begin drilling early, exploiting the milder coastlines for reliable crop emergence. So what happens if we examine local weather for farms near the coastlines? Does this tell how to modify our model for future modelling?
Verification is an ongoing process. If we conduct the right investigations, centred around critical customer requirements, this naturally steers product development to a usable solution more efficiently.
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