Author Archives: climanrecon

Author: Dr. Michael Chase A recent post dealt with the flaw in ACORN-SAT that it makes the erroneous assumption that all step changes in temperature arise from persistent non-climatic influences. This post illustrates a potential “false alarm” problem with the … Continue reading →

Photo above: Kalgoorlie, Western Australia, circa 1930 Author: Dr. Michael Chase This post looks at monthly averages of daily maximum temperatures recorded at Kalgoorlie, and surrounding areas, in the inter-war years of 1920-40, during which ACORN-SAT makes two adjustments. The … Continue reading →

Author: Dr. Michael Chase “When breakpoints are removed, the entire record prior to the breakpoint is adjusted up or down depending on the size and direction of the breakpoint.” Extract above from: http://berkeleyearth.org/understanding-adjustments-temperature-data/ Temperature measurements have two classes of non-climatic … Continue reading →

Author: Dr. Michael Chase Source of the figure above: http://www.bom.gov.au/climate/change/#tabs=Tracker&tracker=timeseries Summary There is considerable climate distortion in the ACORN-SAT version of surface air temperatures of Australia from 1910 to present, with most of the distortion in the first half of … Continue reading →

The AEMO models the future adequacy of the NEM electricity system, one of the key outputs being an annual “Electricity Statement of Opportunities” (ESOO), whose stated purpose is to alert industry to potential opportunities for new generators. This post is … Continue reading →

The previous post looked at a demand-side issue with AEMO electricity forecasting, this post deals with an issue concerning the contribution of wind power to meeting peak demands. In summary, it appears that AEMO decouples historical wind power from historical … Continue reading →

The AEMO does a lot of modelling of the NEM, to assess such things as the adequacy of the future generating capacity to meet peak demands, and of the transmission system to cope with new and lost generators. A key … Continue reading →

Introduction This post repeats the analysis of the previous one, this time for the summer weather of 2014/15 in South Australia (SA), with 2016/17 wind farms. The maximum demand in SA in summer 2014/15 was quite low, an outcome that … Continue reading →

Summary The AEMO uses past wind power and demand data to model the future adequacy of the NEM, and they make the data used available to the public. This post presents some of the data available for the summer of … Continue reading →

This post proposes a method of characterising the “Capacity Credit” of renewable electricity generation methods, covering both the credit of the existing generators, and how the credit will increase with additional generators. Solar power is a good way to introduce … Continue reading →