Solar irradiance from Nimbus-7 compared with ground-based photometry

Measurements of the solar Lyman alpha flux that were made over a seven‐and‐one‐half‐year period between October 11, 1981 and April 13, 1989 have been compared with ground‐based measurements of the solar 10.7 cm radio flux made over the same time period. There is a long‐term correlation between these two measures of solar flux during the declining part of the solar cycle. During the solar minimum period, there is only a poor correlation between the two solar fluxes because the 10.7 cm radio flux reaches a minimum of 65 × 10−22 W m−2 Hz−1 and does not vary below this value while the Lyman alpha flux continues to decline and show long‐term and short‐term variations. During the early ascending phase of the new solar cycle, there is again a correlation between the two fluxes, although the constant of proportionality between the two is different from the constant during the declining phase of the previous solar cycle. Somewhat later, during the period November 25, 1988 — April 13, 1989 (last period when observations of Lyman alpha were made) a medium‐term correlation exists and the proportionality of the two indices is once again similar to what it was during the declining phase of the previous solar cycle. A study of the correlation of the 10.7 cm flux with the Lyman alpha for a 999‐day period during the declining phase showed that for the short‐term (27‐day) variation there is a correlation between the two fluxes but the proportionality between them varies from one solar rotation to the next. The conclusion is that the solar 10.7 cm radio flux is not a useful index for the prediction of solar Lyman alpha flux for the short‐term, 27‐day variations.

During sunspot cycles 20 and 21, the maximum in smoothed 10.7-cm solar radio flux occurred about 1.5 yr after the maximum smoothed sunspot number, whereas during cycles 18 and 19 no lag was observed. Thus, although 10.7-cm radio flux and Zurich suspot number are highly correlated, they are not interchangeable, especially near solar maximum. The 10.7-cm flux more closely follows the number of sunspots visible on the solar disk, while the Zurich sunspot number more closely follows the number of sunspot groups. The number of sunspots in an active region is one measure of the complexity of the magnetic structure of the region, and the coincidence in the maxima of radio flux and number of sunspots apparently reflects higher radio emission from active regions of greater magnetic complexity. The presence of a lag between sunspot-number maximum and radio-flux maximum in some cycles but not in others argues that some aspect of the average magnetic complexity near solar maximum must vary from cycle to cycle. A speculative possibility is that the radio-flux lag discriminates between long-period and short-period cycles, being another indicator that the solar cycle switches between long-period and short-period modes.

We report a new solar extreme ultraviolet (EUV; 26–34 nm) proxy using solar radio fluxes at six different frequencies (410; 610; 1,415; 2,695; 4,995; and 8,800 MHz) from ground‐based observations. The radio fluxes (2002–2008) are used to estimate the solar EUV flux under nonsolar flare conditions through an artificial neural network trained with coincidently observed solar EUV (26–34 nm) fluxes from the SOlar and Heliospheric Observatory/Solar EUV Monitor instrument. The radio fluxes at 610; 1,415; and 8,800 MHz are the three top contributors in the estimation of the solar EUV flux. The 1,415 MHz flux has the highest correlation coefficient (0.97) with the observed EUV flux. The estimated EUV flux retained the highest correlation coefficient (0.97) and had the lowest error in comparison with the observed EUV flux. The popular F10.7cm (2,800 MHz) is between 2,695 and 4,995 MHz, which have a minor contribution to the estimated EUV flux. On the other hand, F30cm (1,000 MHz) is between 610 and 1,415 MHz, the two major contributors to the estimated EUV flux. These features are consistent with a significantly better neutral density modeling using F30cm versus F10.7cm (Bruinsman, 2015, https://doi.org/10.1051/swsc/2015001). Similar results are obtained by applying the coefficients trained using a subset of the radio data (2002, 2004, and 2007) to a different subset of the data (2003, 2005, 2006, and 2008). These indicate that solar radio fluxes at multiple frequencies can be used to reliably retrieve solar EUV flux, one of the key parameters for space weather studies.

The correlation coefficients of the linear regression of six solar indices versus 10.7 cm radio flux F10.7 were analysed in solar cycles 21, 22 and 23. We also analysed the interconnection between these indices and F10.7 with help of approximation by polynomials of second order. The indices we have studied in this paper are: the relative sunspot numbers – SSN, 530.3 nm coronal line flux – F530, the total solar irradiance – TSI, Mg II 280 nm core-to-wing ratio UV-index, the Flare Index – FI and the counts of flares. In most cases the regressions of these solar indices vs. F10.7 are close to the linear regression except the moments of time near the minimums and maximums of the 11-year activity. For the linear regressions, we found that correlation coefficients Kcorr(t) for the solar indices vs. F10.7 and SSN dropped to their minimum values twice during each 11-year cycle.

In this paper, we studied the relationship between sunspots numbers (SNs), solar 10.7 cm radio flux (SRF), solar irradiance (SI), solar proton events (SPEs) and local earthquakes.The location of the study is selected in Iran area and all earthquakes data chosen for 4 from 1970 to 2010.The study reveals the following conclusions: (i) The total number of local earthquakes in maximum years of solar activities is greater than the minimum years of solar activities from 1964 to 2010, (ii) The total local earthquakes frequency (EF) in the maximum period of solar activities is very close to the maximum annual means of sunspots numbers, (iii) The total local EF in the maximum period of solar activity is very close to the maximum annual means of SPE with negative correlation coefficient, (iv) The local earthquakes in the minimum period of solar activities is very close to the minimum annual means of sunspots numbers with negative correlation and (v) The local earthquake in the minimum period of solar activities is very near to SRF with negative correlation.

Models of the radiation belts that are currently used to estimate exposure for astronauts describe the environment at times of either solar minimum or solar maximum. Static models, constructed using data acquired prior to 1970 during a solar cycle with relatively low solar radio flux, have flux uncertainties of a factor of two to live and dose-rate uncertainties of a factor of about two. The inability of these static models to provide a dynamic description of the radiation belt environment limits our ability to predict radiation exposures for long-duration missions in low earth orbits. In an attempt to add some predictive capability of these models, we studied the measured daily absorbed dose rate on the Mir orbital station over roughly the complete 22nd solar cycle that saw some of the highest solar flux values in the last 40 y. We show that the daily trapped particle dose rate is an approximate power law function of daily atmospheric density. Atmospheric density values are in turn obtained from standard correlation with observed solar radio noise flux. This correlation improves, particularly during periods of high solar activity, if the density at roughly 400 days earlier time is used. This study suggests the possibility of a dose- and flux-predictive trapped-belt model based on atmospheric density.

Solar indices and solar u.v.-irradiances

Abstract. We use model simulations and observations to examine how well the F10.7 and F30 solar radio fluxes have represented solar forcing in the thermosphere during the last 60 years of weakening solar activity. We found that increased saturation of radio fluxes during the last two extended solar minima leads to an overestimation of solar energy deposition, which manifests as a change in the linear relation between thermospheric parameters and F10.7. On the other hand, the linear relation between thermospheric parameters and F30 remains nearly the same throughout the whole studied period because of a recently found relative increase of F30 with respect to F10.7. This explains the earlier finding that F30 correlates better with several ionospheric and thermospheric parameters than F10.7 during recent decades. We note that continued evaluation is needed to see how well F10.7 and F30 will serve as solar proxies in the future when solar activity may start increasing toward the next grand maximum.

Changed relation between sunspot numbers, solar UV/EUV radiation and TSI during the declining phase of solar cycle 23

A new method is presented here to forecast the solar 10.7 cm (2.8 GHz) radio flux, abbreviated F10.7, utilizing advanced predictions of the global solar magnetic field generated by a flux transport model. Using indices derived from the absolute value of the solar magnetic field, we find good correlation between the observed photospheric magnetic activity and the observed F10.7 values. Comparing magnetogram data observed within 6 hours of the F10.7 measurements during the years 1993 through 2010, the Spearman correlation coefficient, rs, for an empirical model of F10.7 is found to be 0.98. In addition, we find little change in the empirical model coefficients and correlations between the first and second 9 year intervals of the 18 year period investigated. By evolving solar magnetic synoptic maps forward 1–7 days, this new method provides a realistic estimation of the Earth‐side solar magnetic field distribution used to forecast F10.7. Spearman correlation values of approximately 0.97, 0.95, and 0.93 are found for 1 day, 3 day, and 7 day forecasts, respectively. The method presented here can be expanded to forecast other space weather parameters, e.g., total solar irradiance and extreme ultraviolet flux. In addition, near‐term improvements to the F10.7 forecasting method, e.g., including far‐side magnetic data with solar magnetic flux transport, are discussed.

The mean daily global solar radiation flux is influenced by astronomical, climatological, geographical, geometrical, meteorological, and physical parameters. This paper deals with the study of the effects of influencing parameters on the mean daily global solar radiation flux, and also with the computation of the solar radiation flux at the surface of the earth in locations without solar radiation measurements. The reference–real data were borrowed from the Iranian Meteorological Organization. The analysis of data showed that the mean daily solar radiation flux on a horizontal surface is related to parameters such as: mean daily extraterrestrial solar radiation, average daily ratio of sunshine duration, mean daily relative humidity, mean daily maximum air temperature, mean daily maximum dew point temperature, mean daily atmospheric pressure, and sine of the solar declination angle. Multiple regression and correlation analysis were applied to predict the mean daily global solar radiation flux on a horizontal surface. The models were validated when compared with the reference–measured data of global solar radiation flux. The results showed that the models estimate the global solar radiation flux within a narrow relative error band. The values of mean bias errors and root mean square errors were within acceptable margins. The predicted values of global solar radiation flux by this approach can be used for the design and performance estimation in solar applications. The model can be used in areas where meteorological stations do not exist and information on solar radiation flux cannot be obtained experimentally.

We present a new method which can be used to forecast the variations of solar indices on the time scale of a half solar rotation. This method uses the Lyα interplanetary glow data obtained by the SWAN instrument on SOHO. We show how the maps of solar Lyα flux distributions derived from the SWAN data can be linked to the variation of activity on the solar disk. Active regions which are known to be brighter in Lyα radiation than the quiet sun illuminate more interplanetary H atoms through resonance scattering. This excess of illumination related to active regions is clearly seen in full‐sky Lyα maps recorded by the SWAN instrument on SOHO. These maps include also those excesses resulting from active regions which are on the far side of the Sun, i.e. not visible to solar disk imagers near Earth. From these data, we can derive a farside to nearside flux ratio. This ratio is then used to predict the evolution of solar indices like the solar Lyα flux seen at Earth, the 10.7cm radio flux or the MgII solar index. This technique could be used in the future to improve the quality of space weather forecast, in particular to predict atmospheric heating and increased orbital drag of sensitive spacecraft.

There is a pressing need to model X-ray Ultra-Violet (XUV: 1–30 nm) stellar irradiances, given the scarcity of current measurements. One of the measurable effects of a stellar cycle is the significant (more than one order of magnitude) variation in XUV irradiance. As a first step in modelling stellar irradiances, we present Extreme Ultraviolet (EUV) irradiances in a sample of strong spectral lines formed in different layers and regions of the solar atmosphere, obtained from Solar Dynamics Observatory Extreme Ultraviolet Variability Experiment (SDO EVE). These irradiances span half a solar cycle. We present correlations with several proxies of solar activity, such as the Mg ii index, sunspot numbers, and cm radio fluxes. Among these, the sunspot number proves to be the poorest proxy, whereas the Mg ii index is a very good proxy for coronal lines (hotter temperature lines). We find a relatively strong linear relationship, which enables us to build a model essential for various applications. Additionally, we compare our results with the previous EUV standard solar irradiances reported by Del Zanna and Andretta 2014, derived from Solar and Heliospheric Observatory Coronal Diagnostic Spectrometer (SOHO CDS) data, as well as historical records from the literature. We have also run a DEM analysis on Quiet Sun (QS) and Active Regions (AR) and list the blends and formation temperatures for the strong lines. Finally, we provide a simple routine for deriving the irradiances of the strong lines based on proxy values.

Solar power-based photovoltaic energy conversion could be considered one of the best sustainable sources of electric power generation. Thus, the prediction of the output power of the photovoltaic panel becomes necessary for its efficient utilization. The main aim of this paper is to predict the output power of solar photovoltaic panels using different machine learning algorithms based on the various input parameters such as ambient temperature, solar radiation, panel surface temperature, relative humidity and time of the day. Three different machine learning algorithms namely, multiple regression, support vector machine regression and gaussian regression were considered, for the prediction of output power, and compared on the basis of results obtained by different machine learning algorithms. The outcomes of this study showed that the multiple linear regression algorithm provides better performance with the result of mean absolute error, mean squared error, coefficient of determination and accuracy of 0.04505, 0.00431, 0.9981 and 0.99997 respectively, whereas the support vector machine regression had the worst prediction performance. Moreover, the predicted responses are in great understanding with the actual values indicating that the purposed machine learning algorithms are quite appropriate for predicting the output power of solar photovoltaic panels under different environmental conditions.

The most crucial data requirement for all solar energy researches is solar radiation. As solar radiation is the quantity which is dependent on time, the desired power output of any solar power plant is also dependent on time. The objective of this paper is to utilise machine learning models to estimate the solar radiations on daily data of Indore region (22.7196°N, 75.8577°E). The speed of wind, temperature, pressure, humidity along with solar radiation are used and applied in the process of prediction. The evaluation of the model is done in terms of prediction efficiency. In the work, boosted decision tree algorithm is used for the solar radiation prediction, which gives an accuracy of 96.9%. Also, the multiple linear regression algorithm is utilised in the work for the real time estimation of hourly solar radiations. The method gives an accuracy of 91.8%.