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In blue-light photoreceptors using flavin (BLUF), the signaling state is formed already within several 100 ps after illumination, with only small changes of the absorption spectrum. The accompanying structural evolution can, in principle, be monitored by femtosecond stimulated Raman spectroscopy (FSRS). The method is used here to characterize the excited-state properties of riboflavin and flavin adenine dinucleotide in polar solvents. Raman modes are observed in the range 90-1800 cm(-1) for the electronic ground state S-0 and upon excitation to the S-1 state, and modes >1000 cm(-1) of both states are assigned with the help of quantum-chemical calculations. Line shapes are shown to depend sensitively on resonance conditions. They are affected by wavepacket motion in any of the participating electronic states, resulting in complex amplitude modulation of the stimulated Raman spectra. Wavepackets in S-1 can be marked, and thus isolated, by stimulated-emission pumping with the picosecond Raman pulses. Excited-state absorption spectra are obtained from a quantitative comparison of broadband transient fluorescence and absorption. In this way, the resonance conditions for FSRS are determined. Early differences of the emission spectrum depend on excess vibrational energy, and solvation is seen as dynamic Stokes shift of the emission band. The ne state is evidenced only through changes of emission oscillator strength during solvation. S-1 quenching by adenine is seen with all methods in terms of dynamics, not by spectral intermediates.
Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near-Earth space into a single parameter. Most of the best-known indices are calculated from ground-based magnetometer data sets, such as Dst, SYM-H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root-mean-square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices. Plain Language Summary One aspect of space weather is a magnetic signature across the surface of the Earth. The creation of this signal involves nonlinear interactions of electromagnetic forces on charged particles and can therefore be difficult to predict. The perturbations that space storms and other activity causes in some observation sets, however, are fairly regular in their pattern. Some of these measurements have been compiled together into a single value, a geomagnetic index. Several such indices exist, providing a global estimate of the activity in different parts of geospace. Models have been developed to predict the time series of these indices, and various statistical methods are used to assess their performance at reproducing the original index. Existing studies of geomagnetic indices, however, use different approaches to quantify the performance of the model. This document defines a standardized set of statistical analyses as a baseline set of comparison tools that are recommended to assess geomagnetic index prediction models. It also discusses best practices, limitations, uncertainties, and caveats to consider when conducting a model assessment.