Heat pulse probes are widely used to monitor soil thermal and physical properties for agricultural and hydrological monitoring related to crop productivity, drought, snowmelt and evapotranspiration. Changes in the effective probe spacing distance can cause measurement inaccuracy. This paper uses a novel heat pulse probe and theory to compensate for changes in effective distance thereby enabling more accurate sensor outputs useful for forecasts and predictions of drought and flooding.

Current borehole receivers only measure a single parameter of the magnetic field component, which does not meet the special requirements of controlled source electromagnetic methods.This study proposes a borehole electromagnetic receiver which realizes multi-component synchronous acquisition of adding the vertical electric field component. Results of the experiments show that the design has the performance of low noise and wide band and that high quality CSEM signals were obtained.

Researchers are making increasing use of machine learning to improve accuracy, efficiency and consistency. During such a study of the aurora, it was noted that biases or distortions had crept into the data, because of the conditions (or ergonomics) of the human trainers. As using machine learning techniques to auroral research is relatively new, it is critical that such biases are brought to the attention of the academic and citizen science communities.

This paper describes GRUAN's strategy to a network-wide change in the operational radiosonde, from Vaisala RS92 to RS41. GRUAN's main goal is providing long-term data records that are free of inhomogeneities due to instrumental effects, which requires proper change management. The approach is to fully characterize the differences between both radiosonde types, using a.o. laboratory tests, twin soundings, ancillary data, and by drawing from the various fields of expertise available in GRUAN.