Instrumentation and Sampling Considerations for Geostationary Earth Radiation Budget Measurements

Abstract

Resolving uncertainties surrounding the nature of future climate change is currently one of the greatest challenges facing mankind. Validation of global climate model (GCM) predictions of the currently much miss-represented cloud radiative feedback requires measurements made from orbit of the Earth Radiation Budget (ERB), specifically targeted at clouds. The ERB parameters for measure are the scattered solar or short wave (SW, 0.3 5µm) and the emitted thermal or long wave radiance (LW, 5 100µm). Such measurements map out the heat source/sink locations that drive all weather and climate, which acts like a complex network of coupled heat engines. The Clouds and the Earth’s Radiant Energy System (CERES), as part of NASA’s Earth Observing System (EOS), uses thermistor bolometer detectors to provide global high spatial resolution ERB measurements from Low Earth Orbit (LEO) space platforms. However, comprehensive validation of GCM prediction of cloud processes requires ERB measurements on a sub-hourly timescale to sample throughout the cloud formation/dissipation process. This is not practically possible with the use of LEO platforms, so ERB measurements are needed from synchronous orbits such as geostationary in order to increase the frequency of measurements. The Geostationary Earth Radiation Budget (GERB) experiment is a European Space Agency (ESA) project on board the spin stabilized Meteosat second Generation (MSG) platform. Location in geostationary orbit and the use of an array of thermopile detectors enables sampling of ERB radiances from the entire Earth disc at an optimum 15 minute temporal resolution. This study describes the instrumentation and sampling capabilities of the current GERB mission. Given its success it is proposed that efforts be made to expand the project and place GERB-like instruments on multiple high orbit platforms. This will provide climatologists with much needed global ERB data at sub-hourly time resolution, making it more likely that model predictions of future climate change can be properly validated.