Climate change for profit

Growing market volume of weather derivatives (million USD) in Japan. Source: Compiled by the author, based on Yamada, 2010, SEE article references.

Growing market volume of weather derivatives (million USD) in Japan. Source: Compiled by the author, based on Yamada, 2010, SEE article references.

Record of global average temperatures as compiled by the NASA's Goddard Institute for Space Studies. (2006) Figure originally prepared by Robert A. Rohde.

Record of global average temperatures as compiled by the NASA's Goddard Institute for Space Studies. (2006) Figure originally prepared by Robert A. Rohde.

Foreign investment in weather networks such as TAMHO & Smart Agric is proof of the value of meteorological data, not only in Africa, but all over the world. Weather related economic risks create financial opportunities on a scale of tens of billions of dollars globally. Weather insurance companies and weather risk management is thriving as weather derivatives have become a traded commodity in the field of weather risk management.

Managing weather risk

As natural vegetation yields to farm land expansion, reduction in rain water catchment makes not only our economies more susceptible to weather risk, but contributes to global warming, thus fueling the growth of weather derivatives which benefit from global warming and its related weather volatility. The good news is that both vegetation and water catchment can be managed and recaptured by supporting natural processes. Accurate monitoring of critical micro-climates on which natural processes rely to balance and slow global warming, first requires high-quality and high-density meteorological networks with superb long-term stability of measurement, whose data can be traded to create funding for climate stabilization projects.

Plan of action for climate change

Quality weather data complemented by detailed action plans will permit timely reaction to destabilizing effects of erosion of natural processes and to reverse micro-climate changes from human-machine interaction with the environment. Many countries, in an effort to manage water and mitigate future drinking water risk, have begun forming such action plans, many of which include two tiered networks of large and small meteorological and hydrological weather stations like the MeteoHelix.

Meteorological capacity building for profit

Africa, Central & South America, South Asia and Oceana countries require their own meteorological networks. Secondary/tertiary weather networks such as TAHMO and Smart Agric, and their benefits, remain mostly in the hands of foreigners. Such networks need not be an a red expenditure in their meteorological departments’ budgets but an opportunity for meteorological departments to become self-sustainable enterprises. Income from risk mitigation and weather insurance can also mean saving of lives, crops, livestock and local economies.

REFERENCES:

Prabhakar, S.V. R. K. & Srinivasa Rao, Gattineni & Fukuda, Koji & Hayashi, Shinano. (2013). Promoting Risk Insurance in the Asia-Pacific Region: Lessons from the Ground for the Future Climate Regime under UNFCCC. Climate Change Adaptation in Practice: From strategy development to implementation. 10.1002/9781118548165.ch22.

Available from: https://www.researchgate.net/figure/Growing-market-volume-of-weather-derivatives-million-USD-in-Japan-Source-Compiled-by_fig2_261296609 [accessed 24 Apr, 2019]

Big data vs. Accurate data

Shit in = Shit out,” the first law of numerical simulation

In meteorological weather prediction, every student of computational fluid dynamics (CFD) knows that one cannot obtain any relevant levels of accuracy without accurate boundary conditions and initial conditions (input data), which equates to accurate, surface, near surface and upper air meteorological data.

Above every square meter of ground there is over 7000 kg of air in the Troposphere alone. Troposphere is where weather happens. It also contains 99% of all atmospheric water vapor. It also contains most of the mass and energy of the whole atmosphere. [1]

The acceptable magnitude of error

Relevant weather forecasts depend on accurate input data.

Relevant weather forecasts depend on accurate input data.

The amount of energy trapped in the Troposphere is striking. On a 37°C summer day with 95% RH humidity just one kilogram of air contains about 137 kJ of energy. A +3°C error in air temperature reading due to inadequate solar shielding [2] will erroneously increase this figure a whopping 16% to about 159 kJ/kg. A large 5% RH humidity deviation will only cause one fourth the error. Applying even a fraction of this error to boundary conditions of a CFD weather model of the local troposphere will lead to uncorrectable errors in numerical simulation and erroneous results and forecasts.

Validation counts

Having worked on wing design for a company which arguably makes the most efficient airplanes in the world, I had a first-hand look at complexity of numerical fluid simulations and their susceptibility to error. Results from numerical weather prediction models require validation. Just like airplane companies and even Formula 1 use wind tunnels to validate their CFD simulations, meteorologists validate with surface, near surface and upper air meteorological data. Yet there is one big difference. The meteorological weather prediction models have much more complex inputs and boundary conditions, thus more chance for error. Unfortunately, false validation seems to be a trend as many meteorologists are love to claim high weather forecast accuracies. I think we all intuitively realize the reality in weather critical situations is quite different.

Want to know more?

Read more why "Garbage in = Garbage out" is the most important sentence a modern CEO needs to hear and about the cloud of difference data quality will make for your next "Big-Data" cloud platform in the 2019 Varysian Guide to be distributed at the Meteorological Technology World Expo 2019 in Geneva.

Rererences

[1] "Troposphere". Concise Encyclopedia of Science & Technology. McGraw-Hill. 1984.

[2] METEOMET: An experimental method for evaluation of the snow albedo effect on near surface air temperature measurements, by Chiara Musacchio  Graziano Coppa  Andrea Merlone 

About the author

Jan Barani is the inventor of the helical MeteoShield Professional and the CTO of BARANI DESIGN Technologies which brought to the professional meteorological world the 1st micro-weather station, called the MeteoHelix IoT Pro, to be able to meet WMO guidelines for precision meteorological measurement in all weather conditions and all climates.

MeteoShield Professional on the peaks of Africa

MeteoShield Professional, the patented helical design of a solar radiation shield for atmospheric air temperature sensors is making inroads on the African continent. It was installed at the GAW station on Mt. Kenya as part of the WMO Global Atmosphere Watch (GAW) project by the Kenya Meteorological Department. Mount Keya is the highest mountain in Kenya and the second-highest peak in all of Africa right after Mt. Kilimanjaro.

Helical MeteoShield Professional on Mt. Kenya GAW station.

Helical MeteoShield Professional on Mt. Kenya GAW station.