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How is the weather data interpolated in New Zealand?
How is the weather data interpolated in New Zealand?
Updated over a month ago

Why is Interpolation Required?

DAS receives monthly weather observations from New Zealand weather stations. However, due to factors beyond the vendor’s control, such as equipment malfunctions, network outages, or extreme weather events; approximately 5% to 15% of observations may be missing or contain incorrect values, also known as outliers.

To ensure a consistent and accurate supply of data to customers, these missing or incorrect values must be spatially interpolated using observed data and a range of historical precipitation datasets.


How Does Spatial Interpolation Work?

DAS uses an advanced algorithm called Kriging with External Drift (KED), which is widely used for spatial interpolation of precipitation at regional and national scales. This algorithm leverages historical precipitation data to detect rainfall distribution patterns across New Zealand and applies these insights to interpolate missing observations.

Step-by-Step Process:

1. Historical Climate Patterns

  • The Southern Oscillation Index (SOI) (NOAA) is used to analyze long-term rainfall distribution patterns.

2. Reference Data for Weather Patterns

  • Average annual rainfall datasets (1972-2013) from the NZ Ministry for the Environment (source) are classified into three categories:

    • El Niño

    • La Niña

    • Neutral

3. Selection of the Most Relevant Climate Pattern

  • The current SOI value determines which of these climate patterns was active during the previous month.

  • The corresponding rainfall raster dataset is used as an External Drift Surface in the KED model.

4. Running the KED Algorithm

  • The algorithm takes the available observed values and generates an interpolation surface to estimate missing data points.

5. Final Data Adjustment

  • The interpolated values replace only the missing or incorrect observations, ensuring data reliability while maintaining scientific accuracy.

📌 More scientific details can be found here: Regression-Kriging.


Limitations of KED Interpolation

While spatial interpolation is a robust solution for filling gaps in weather data, it does have limitations:


Reduced Accuracy Compared to Direct Observations

  • The Root Mean Square Error (RMSE) averages 53 over five years of test data.

Tendency Toward Long-Term Averages

  • When rapid precipitation changes occur, interpolated values may appear smoother and closer to historical monthly averages.

Accuracy Depends on the Amount of Missing Data

  • If a large percentage of observations is missing in a given month, the accuracy of the interpolation may decrease.


Key Takeaways

Interpolation ensures a consistent supply of weather data when raw observations are missing or incorrect.

✅ The process incorporates long-term climate patterns and the current SOI index for improved accuracy.

✅ While interpolated values may slightly differ from real-time data, they remain within scientifically acceptable accuracy ranges.

DAS ensures reliable weather data delivery through scientifically validated spatial interpolation methods.


By applying validated interpolation techniques, DAS ensures customers receive the most accurate and complete weather data possible, even when direct observations are unavailable.

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