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Measurement of Antenna Patterns for Oceanographic Radars Using Aerial DronesAbstract
A new method is described employing small drone aircraft for antenna pattern measurements (APMs) of high-frequency (HF) oceanographic radars used for observing ocean surface currents. Previous studies have shown that accurate surface current measurements using HF radar require APMs. The APMs provide directional calibration of the receive antennas for direction-finding radars. In the absence of APMs, so-called ideal antenna patterns are assumed and these can differ substantially from measured patterns. Typically, APMs are obtained using small research vessels carrying radio signal sources or transponders in circular arcs around individual radar sites. This procedure is expensive because it requires seagoing technicians, a vessel, and other equipment necessary to support small-boat operations. Furthermore, adverse sea conditions and obstacles in the water can limit the ability of small vessels to conduct APMs. In contrast, it is shown that drone aircraft can successfully conduct APMs at much lower cost and in a broader range of sea states with comparable accuracy. Drone-based patterns can extend farther shoreward, since they are not affected by the surfzone, and thereby expand the range of bearings over which APMs are determined. This simplified process for obtaining APMs can lead to more frequent calibrations and improved surface current measurements.
Abstract
A new method is described employing small drone aircraft for antenna pattern measurements (APMs) of high-frequency (HF) oceanographic radars used for observing ocean surface currents. Previous studies have shown that accurate surface current measurements using HF radar require APMs. The APMs provide directional calibration of the receive antennas for direction-finding radars. In the absence of APMs, so-called ideal antenna patterns are assumed and these can differ substantially from measured patterns. Typically, APMs are obtained using small research vessels carrying radio signal sources or transponders in circular arcs around individual radar sites. This procedure is expensive because it requires seagoing technicians, a vessel, and other equipment necessary to support small-boat operations. Furthermore, adverse sea conditions and obstacles in the water can limit the ability of small vessels to conduct APMs. In contrast, it is shown that drone aircraft can successfully conduct APMs at much lower cost and in a broader range of sea states with comparable accuracy. Drone-based patterns can extend farther shoreward, since they are not affected by the surfzone, and thereby expand the range of bearings over which APMs are determined. This simplified process for obtaining APMs can lead to more frequent calibrations and improved surface current measurements.
Abstract
The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.
Abstract
The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.
