A constellation of small, low-cost satellites is able to make scientifically valuable measurements of the Earth which can be used for weather forecasting, disaster monitoring, and climate studies. Eight CYGNSS satellites were launched into low Earth orbit on December 15, 2016. Each satellite carries a science radar receiver which measures GPS signals reflected from the Earth surface. The signals contain information about the surface, including wind speed over ocean, and soil moisture and flooding over land. The satellites are distributed around their orbit plane so that measurements can be made more often to capture extreme weather events. Innovative engineering approaches are used to reduce per satellite cost, increase the number in the constellation, and improve temporal sampling. These include the use of differential drag rather than propulsion to adjust the spacing between satellites and the use of existing GPS signals as the science radars’ transmitter. Initial on-orbit results demonstrate the scientific utility of the CYGNSS observations, and suggest that a new paradigm in spaceborne Earth environmental monitoring is possible.

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The NASA Cyclone Global Navigation Satellite System (CYGNSS) constellation of eight satellites was successfully launched into low Earth orbit on 15 December 2016. Each satellite carries a radar receiver that measures GPS signals scattered from the surface. Wind speed over the ocean is determined from distortions in the signal caused by wind-driven surface roughness. GPS operates at a sufficiently low frequency to allow for propagation through all precipitation, including the extreme rain rates present in the eyewall of tropical cyclones. The spacing and orbit of the satellites were chosen to optimize frequent sampling of tropical cyclones. In this study, we characterize the CYGNSS ocean surface wind speed measurements by their uncertainty, dynamic range, sensitivity to precipitation, spatial resolution, spatial and temporal sampling, and data latency. The current status of each of these properties is examined and potential future improvements are discussed. In addition, examples are given of current science investigations that make use of the data.

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A study on the effect of weather parameters on the the population dynamics of Spodoptera litura (S.litura) in soybean and cotton during kharif season using six years pest data (pheromone trap catches) at Niphad and Rahuri in Maharashtra showed that rainfall two weeks prior, Tmax and Tmin during the week of incidence signifiantly contributed towards the occurrence of S.litura in soybean. Maximum temperature and morning humidity during the week and one week prior were found to be favourable for the incidence of S. litura in cotton. Temperature (maximum: 26-27°C & minimum: 21-22°C), morning relative humidity (above 90%) and rainfall during one week prior were found to be congenial weather parameters for the outbreak of the pest in soybean. Similarly, maximum temperature around 32-33°C, minimum temperature around 22-23°C, morning relative humidity around 90 per cent, sunshine hours about 4 hrs day-1 and rainfall during the previous 2 weeks favoured heavy incidence of S.litura in cotton crop during flowering to boll formation stages. It is also shown how the incidence of S.litura in soybean and cotton can be predicted well in advance using the observed relationship of the pest with weather parameters as well as weather forecast.