Short-term weather forecasting, i.e., nowcasting, is an important area of research, with application to logistics, insurance, and environmental and social governance. More accurate forecasting is required to achieve nowcasting, which implies the need for improvement in either the acquisition of weather data or the development of better forecasting algorithms. While much of the literature focuses on algorithmic improvements, we consider how one may incentivize better data acquisition. Our idea is to implement a market-based incentive scheme that rewards meteorological data acquisition in proportion to how useful that data is in improving weather forecasts.
Our scheme incentivizes optimal placement of meteorological sensors by issuing blockchain-backed digital currency in direct proportion to the sensitivity of the weather forecast to raw data inputs. The scheme consists of an algorithm whose outputs are weather forecasts and whose inputs are data from weather sensors. The algorithm issues digital tokens in direct proportion to the sensitivity of outputs, i.e., the errors in forecasting, to inputs, i.e., sensor data. The issuance has the effects of incentivizing better sensor placement and improved sensor quality. Placement is incentivized because poor placement results in a lower forecast sensitivity; for example, this could happen due to placement near a non-representative microclimate, causing an aberration, or near another, well-placed sensor, resulting in redundancy. Quality is incentivized because higher certainty results in higher forecast sensitivity. With improved sensor placement and quality, the scheme therefore incentivizes better weather forecasts.
The incentivization therefore encourages better data assimilation and data availability and also enables the development of solutions that encourage better forecasting with market mechanisms. For example, a digital token may be traded between market participants and tokens may be issued by some authority to incentivize improved data acquisition. This latter example can be extended to a situation where an authority seeks to improve data acquisition in an area with low monitoring frequency, such as the world's oceans.
We present the results of a demonstration performed using a network of three Intellisense weather stations with forecasts based on a modification of the NAM‑NMM, performing nowcasts for locations in Torrance, California. The model was implemented in an oracle that performs forecasts and uses the results of the forecasts to issue rewards on the Solana blockchain testnet. The three weather stations were place in two different locations, with the two sensors placed in the same location having different update frequencies, resulting in higher uncertainty in the data coming from the lower-fidelity sensor. The two sensors in the same location cannibalize each other's earnings, and the higher-fidelity sensor is rewarded with the larger share of earnings.
How to cite: Kalabic, U., Chiu, M., Nabi, S., Mitts, S., Norell, J., Telenta, B., and Radonjic, Z.: Distributed weather sensing incentivization, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-553, https://doi.org/10.5194/ems2022-553, 2022.