Engineering topics
Publications and source records attributed to Nithin Sivadas.
Freedom to explore the unknown is key to scientific discovery. Maximizing modern individualistic measures of scientific productivity like citations and number of publications may impede the progress of science as a whole.
Simultaneous solar wind measurements from the solar wind monitors, WIND and ACE, differ due to the spatial and temporal structure of the solar wind. Correlation studies that use these measurements as input may infer an incorrect correlation due to uncertainties arising from this spatial and temporal structure, especially at extreme and rare solar wind values. In particular, regression analysis will lead to a regression function whose slope is biased towards the mean value of the measurement parameter. This article demonstrates this regression bias by comparing simultaneous ACE and WIND solar wind measurements. A non-linear regression analysis between them leads to a perception of underestimation of extreme values of one measurement on average over the other. Using numerical experiments, we show that popular regression analysis techniques such as linear least-squares, orthogonal least-squares, and non-linear regression are not immune to this bias. Hence while using solar wind parameters as an independent variable in a correlation or regression analysis, random uncertainty in the independent variable can create unintended biases in the response of the dependent variable. More generally, the regression to the mean effect can impact both event-based, statistical studies of magnetospheric response to solar wind forcing.
Earth’s magnetosphere responds to solar wind input over time scales ranging from minutes to days. On minute times scales, researchers seek to identify discontinuities in the solar wind that may trigger transient magnetospheric phenomena such as flux transfer events or substorm onsets. On hour time scales, researchers seek to determine the solar wind phenomena that drive the growth phase of geomagnetic substorms and the storage of magnetic flux within the magnetotail. On longer time scales, researchers seek to determine the solar wind features than propel geomagnetic storms through their initial, main, and recovery phases. Researchers must, perforce, rely upon spacecraft located near the L1 libration point for observations of the solar wind input into the magnetosphere over periods ranging from hours to days. Some of the hazards of doing this are well known. Features seen far upstream at locations away from the Sun-Earth line may never arrive at Earth due to structure in the solar wind transverse to the Sun-Earth line. Alternatively, they may arrive at unexpected times due to errors in calculating their orientation and velocity. Other hazards when predicting the magnetospheric response to varying solar wind conditions are less well understood. When observations of large solar wind drivers at spacecraft far upstream and off the Sun-Earth line are used to select events for study, the ‘reversion to the mean’ effect associated with spatial structure and errors in arrival timing for solar wind features at Earth demands that the magnetosphere will actually be subjected to and respond to solar wind drivers with much lower strengths. Consequently, the magnetospheric response must saturate for the highest solar wind drivers.