Numerical Weather Modeling and Predictive Technology

My work on Pollen Counts, Low Pressure Systems, and Predictive Analysis was published last week in the Weather Modeling Journal of the National Association of Forecasters. The paper focuses on the high vector count of low pressure systems as they encounter areas with high pollen densities. It turns out that as low pressure systems converge into these areas the predictive capabilities of our current radar arrays and dopler systemics increase exponentially.

Take, for example, the unparalleled analysis that Channel 7 undertook of the weather last week. Their weatherman, me, was able to predict with 90% accuracy that each day last week would display some form of alternating weather systems. We predicted with 85% accuracy that it would rain on Monday and that this rain would subside on Tuesday.

The science behind this technology is based on pollen count memory fluctuations. The floating pollards essentially capture the weather from the previous day and recreate yesterday's weather today. A quick analysis of the pollen will reveal it's intrinsic properties and chemical compositions, allowing the meteorologist to ascertain the weather for upcoming days. Thus, if the pollen count is high enough, you can accurately predict the weather. Further research should be undertaken to ascertain how high the pollen needs to be before you can undertake this analysis. My best estimates suggest that the pollen count needs to be in the upper 60 echelons before accurate predictions exist.

I believe that this research will yield amazing outcomes if we continue to research the topic. I, for one, will not pursue this any further, as the couch beckons. I leave it to my colleagues to continue this work for me.