DOI: 10.5281/zenodo.14613224
title: Station Adjusted Precipitation Records in Tairāwhiti

description: This dataset is an augmentation of WRF forecasts over the Tairāwhiti region using a range of station observations. 

Location and Time

The bounding latitudes and longitudes of the dataset are [177.90, 178.54, -39.03, -37.50], covering the Tairāwhiti region. 

The dataset is available between March 1st 2024 and June 30th 2024. 

Data sources

This dataset fuses data from multiple sources:



Weather Research and Forecasting (WRF) model forecasts were obtained from the New Zealand Meteorological Service, and are at a 4km resolution. WRF is a weather forecast, rather than a reanalysis. We take the WRF forecast initialised most recently to the hour of interest, excluding the first six hours of any forecast to allow for a spin up period.

National Climate Database (NCD) station observations were obtained through NIWA's Cliflo. There are 5 stations from the NCD producing hourly point-based precipitation observations. 

Station data from the Tethys database, including stations owned by Gisborne District Council and Fire and Emergency New Zealand. 60 sites are available in our region of interest at an hourly time period.

Precipitation satellite data from NASA. This data is half hourly, so we resample to hourly data. 

Station data from automatic weather stations placed at schools in Tairāwhiti as part of the Mātaki Marangai project. There are 8 of these stations producing data every few minutes, so this data is resampled to be hourly. The raw data is available here. 

Rain gauges monitored by students during the Mātaki Marangai project. These data are quality checked and used to compare daily accumulations of adjusted WRF fields - more will be explained in the methodology section. The raw rain gauge data is available here.


Methodology



All data listed above are processed to become hourly data if it is not already.

WRF data are interpolated onto station observation points (that is, the data from the NCD, Tethys, and automatic weather stations).

The difference between the station observation and the WRF interpolated point is calculated, and a 2d delta field can be made at these points. This delta field can then be used to perform kriging interpolation (using the OrdinaryKriging Python tool) and adjust the gridded WRF dataset towards observations. This version of the adjusted dataset is called V1.

Not all locations have station observations, and so we use the satellite data to further improve the adjusted dataset in areas without stations. If there is a 0.2 degree square without site data, we take the satellite precipitation value for that square. This precipitation satellite data are calculated from cloud top temperatures, and is therefore of inferior quality to observations on the ground, but is useful in large regions without any ground observational data. We combine the satellite precipitation values and the station observations into a dataset and recalculate the 2d delta field over the region, and reperform kriging on the original WRF data (not the V1 dataset). We weight the station observations more than the satellite observations in the kriging process. This kriged dataset is called V2.

Finally, we compare the V2 dataset daily accumulations of precipitation to rain gauge observations. A daily 2d delta field is created and used to perform kriging interpolation once more. This final version of the dataset is called V3, and this is what is uploaded here. 


Project Information

The Mātaki Marangai project was run by Bodeker Scientific, in collaboration with He Oranga Trust and the New Zealand Meteorological Service. The project was co-funded by New Zealand's Ministry of Business, Innovation and Employment (MBIE) Unlocking Curious Minds fund, and the MBIE Smart Ideas project DeepWeather. 

Links to read more about the project:



https://www.mataki-marangai.com/home 

https://www.bodekerscientific.com/projects/m%C4%81taki-marangai