Assessment Of The Influence Of Astronomical Parameters On The Skill Of Rainfall Forecasting In East Africa.
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Date
2019Author
Ndichu, Roger Wambugu
Type
ThesisLanguage
enMetadata
Show full item recordAbstract
Seasonal forecasts generated in East Africa have mainly used the SST-based models in the last
two decades with challenges of poor forecasting skills. In particular, the method of forecasting
rainfall extremes has been too general and that these extremes occur much more frequently
than forecasted. And furthermore, no forecasting model in use in the region provides the
temporal variation or the intra-seasonal-to-interannual variability of rainfall. In addition to the
low skills, the traditional Indigenous Knowledge (IK) forecasting methods are falling out due
to climate change. One component of the IK, the astronomical observations, is viewed with a
lot of scepticism and is considered as a non-science, therefore, inhibiting its application in
science-based forecasting and research.
This study focuses on astronomical observations in its objective which is to determine the
influence of the orbital parameters of planets and the moon on the weather and climate patterns
in East Africa.
Results generated show that Saturn, Jupiter, Venus and Mars have a relationship with rainfall
but at different levels. Both MAM and OND in all zones seem to show a variation from year
to year that indicates strong astronomical influence in most cases. We also note that rainfall
characteristics during two similar celestial phases but which occur at different times of the year
are different, however, rainfall characteristics associated with the same observed phase and in
the same month or period were found to be nearly the same.
To get to the same phase in the same month of the year, would take Saturn 30 years, Jupiter 12
years, Mars 15 years and Venus 8 years giving rise to what is refered to here as Saturn Rainfall
Cycle, Jupiter Rainfall Cycle, Mars Rainfall Cycle and Venus Rainfall Cycle respectively. That
means that the East African rainfall varies in cycles of 8, 12, 15 and 30 years. The rainfall
cycles are easily determined by use of their key phases and can be predicted by use of
astronomical calculations with little error and with sufficient accuracy way ahead of time.
Further, by using historical information, it was found out that severe climate extremes occur
during the conjunctions of both Uranus and Neptune where Uranus takes ~83 years and
Neptune takes ~163 years to orbit to the next conjunction. These periods, now called Uranus
rainfall cycle and Neptune rainfall cycle respectively, coincide with the variation of severe
extreme events in the study area. We can attribute those variations to the two planets’ orbital
motions.
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The probabilistic models developed here use probability of occurrence or exceedance and have
five categories; “Extreme Low”, “Below Normal”, “Normal”, “Above Normal”, “Extreme
High” and “Phenomenal”. Using the probabilities of occurence on 2018 rainfall seasons, a
qualitative verification process indicated relatively high probability values of up to 67% under
“Above Normal” and “Extreme High” for MAM 2018 forecast in areas that mainly fall in the
highlands East of the Riftvalley, while the period OND indicated high probability values of
up to 83% under the category “Below Normal”. The season MAM 2018 was extremely wet
and OND 2018 was extremely dry which means the probabilities had captured the extremes as
projected.
Generally, from the results, it was found out that the planets have a relationship with the East
African rainfall. Each one of them showed a certain level of contribution to the variation of
monthly rainfall with the Planet Saturn indicating the biggest influence. The moon had
relatively little influence to the monthly rainfall variation compared to the planets.
The phases of the planet can be hindcasted back in time to allow a dependable determination
of rainfall variation of the past. In general, the use of these astronomical phases can be used to
generate past and future climate scenarios in the region that can add useful body of knowledge
to climate science that can be integrated in scientific reports like the IPCC Assessment Reports.
Publisher
University of Nairobi
Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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