Enhancing gridded climate products with third party weather data in a rainfall study from Western Australia.

Accurate estimation of weather variables is essential for climate science and real-world applications, yet sparse official weather station networks often limit data reliability in many regions. This study highlights the transformative potential of integrating third-party automatic weather station (TPAWS) data to improve gridded climate data products. Daily rainfall, one of the most important yet challenging weather variables to estimate, is used as a case study in southwestern Western Australia. By incorporating quality-controlled TPAWS observations, we reduce the root mean square error (RMSE) of rainfall estimates by over 15% and false no-rain rates by 30%, with notable improvements during extreme events. These results illustrate how TPAWS data can augment official networks, offering a scalable, cost-effective approach to improve the accuracy of diverse weather variables beyond rainfall alone. Our findings provide compelling evidence of the scientific and practical value of leveraging non-traditional datasets to address data sparsity, opening new avenues for research and development in climate data integration worldwide.