Exemplary Energy is set to undertake an important task that underscores our core mission: updating and enhancing weather and climate data to keep it relevant. We are doing this for 250 locations across Australia, covering the years 1990 to 2022. By comparison, the NatHERS climate data set (and the many-flawed CSIRO “replication” of them) covers 1990-2015 for only 69 locations. More importantly, we disseminate the full 33-year weather record in formats required by industry while NatHERS and CSIRO keep that underlying weather data record secret.
Along with the introduction of hourly precipitation data*, we are also enhancing the datasets with more accurate cloud cover information derived from the more recently deployed BOM ceilometer network. Together, these improvements provide a more complete and nuanced understanding of Australia’s weather patterns, making the data an invaluable tool for a wide range of applications, including 33-year simulations to establish the full range of variation in annual performance. This lies hidden when using just a distilled “climate year”, however well that data is derived.
The impact of these enhancements extends beyond academic research and leading-edge projects. They have significant practical implications, particularly for designers, developers and facility managers. With a more precise understanding of precipitation patterns and cloud cover, facility managers can better manage moisture-related issues (including rain washing of solar PV installations and farms). This leads to more accurate performance prediction, healthier indoor environments and lower maintenance and rectification costs.
In addition, these improvements in weather and climate data will contribute to the development of locally-specific construction standards and design guidelines. They ensure that our data meets the evolving requirements of AIRAH DA07 and NCC updates, supporting practitioners in their efforts to adapt to the changing climate.
This ongoing effort reflects our dedication to providing valuable resources for understanding Australia’s unique climate dynamics and promoting renewable energy systems and building sustainability in both design and operation.
* The cornerstone of this initiative is our use of advanced machine learning techniques. By employing a Markov Chain Monte Carlo (MCMC) based algorithm, we can transform daily precipitation totals into detailed hourly datasets. As described in our recent publication, “Precipitation Data for Enhanced Facility Management: A Novel Approach using Machine Learning“, this methodological advancement enables us to provide a more granular understanding of Australia’s climate. But precipitation is only part of the enhancement picture.
