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The modeling of weather and climate has been a success story. The skill of forecasts continues to improve and model biases continue to decrease. Combining the output of multiple models has further improved forecast skill and reduced biases. But are we exploiting the full capacity of state-of-the-art models in making forecasts and projections? Supermodeling is a recent step forward in the multimodel ensemble approach. Instead of combining model output after the simulations are completed, in a supermodel individual models exchange state information as they run, influencing each other’s behavior. By learning the optimal parameters that determine how models influence each other based on past observations, model errors are reduced at an early stage before they propagate into larger scales and affect other regions and variables. The models synchronize on a common solution that through learning remains closer to the observed evolution. Effectively a new dynamical system has been created, a supermodel, that optimally combines the strengths of the constituent models. The supermodel approach has the potential to rapidly improve current state-of-the-art weather forecasts and climate predictions. In this paper we introduce supermodeling, demonstrate its potential in examples of various complexity, and discuss learning strategies. We conclude with a discussion of remaining challenges for a successful application of supermodeling in the context of state-of-the-art models. The supermodeling approach is not limited to the modeling of weather and climate, but can be applied to improve the prediction capabilities of any complex system, for which a set of different models exists.

 

We present the results of our study of cross-correlations between long-term multiband observations of the radio variability of the blazar 3C 279. More than a decade (2008–2022) of radio data were collected at seven different frequencies ranging from 2 to 230 GHz. The multiband radio light curves show variations in flux, with the prominent flare features appearing first at higher-frequency and later in lower-frequency bands. This behaviour is quantified by cross-correlation analysis, which finds that the emission at lower-frequency bands lags that at higher-frequency bands. Lag versus frequency plots are well fit by straight lines with negative slope, typically ∼−30 day GHz−1. We discuss these flux variations in conjunction with the evolution of bright moving knots seen in multiepoch Very Long Baseline Array maps to suggest possible physical changes in the jet that can explain the observational results. Some of the variations are consistent with the predictions of shock models, while others are better explained by a changing Doppler beaming factor as the knot trajectory bends slightly, given a small viewing angle to the jet.

 

We report the flux and spectral variability of PG 1553 + 113 on intra-night (IDV) to short-term time-scales using BVRI data collected over 91 nights from 28 February to 8 November 2019 employing 10 optical telescopes: three in Bulgaria, two each in India and Serbia, and one each in Greece, Georgia, and Latvia. We monitored the blazar quasi-simultaneously for 16 nights in the V and R bands and 8 nights in the V, R, I bands and examined the light curves (LCs) for intra-day flux and colour variations using two powerful tests: the power-enhanced F-test and the nested ANOVA test. The source was found to be significantly (>99 per cent) variable in 4 nights out of 27 in R-band, 1 out of 16 in V-band, and 1 out of 6 nights in I-band. No temporal variations in the colours were observed on IDV time-scale. During the course of these observations the total variation in R-band was 0.89 mag observed. We also investigated the spectral energy distribution (SED) using B-, V-, R-, and I-band data. We found optical spectral indices in the range of 0.878 ± 0.029 to 1.106 ± 0.065 by fitting a power law (Fν∝ν−α) to these SEDs of PG 1553 + 113. We found that the source follows a bluer-when-brighter trend on IDV time-scales. We discuss possible physical causes of the observed spectral variability.

 

The Cosmic Ray Extremely Distributed Observatory (CREDO) pursues a global research strategy dedicated to the search for correlated cosmic rays, so-called Cosmic Ray Ensembles (CRE). Its general approach to CRE detection does not involve any a priori considerations, and its search strategy encompasses both spatial and temporal correlations, on different scales. Here we search for time clustering of the cosmic ray events collected with a small sea-level extensive air shower array at the University of Adelaide. The array consists of seven one-square-metre scintillators enclosing an area of 10 m × 19 m. It has a threshold energy ~0.1 PeV, and records cosmic ray showers at a rate of ~6 mHz. We have examined event arrival times over a period of over 2.5 years in two equipment configurations (without and with GPS timing), recording ~300 k events and ~100 k events. We determined the event time spacing distributions between individual events and the distributions of time periods which contained specific numbers of multiple events. We find that the overall time distributions are as expected for random events. The distribution which was chosen a priori for particular study was for time periods covering five events (four spacings). Overall, these distributions fit closely with expectation, but there are two outliers of short burst periods in data for each configuration. One of these outliers contains eight events within 48 s. The physical characteristics of the array will be discussed together with the analysis procedure, including a comparison between the observed time distributions and expectation based on randomly arriving events.