Effect of lunar cycle on rainfall

ABSTRACT. Rainfall data for a period of 50 years from 1931 onwards have been analysed for three west, coast stations in Kerala for the southwest monsoon period, The period is divided into two halves, the first half, i.e.June-July, providing comparatively more rainfall and the second half, i.e. August-September, providing comparatively lesser rainfall. Rainy days, having rain amounts>6.25 cm/day, have only been utilized for this study. The lunar cycle, which is having 29.53 days, is divided into ten phases, each phase constituting of around three days. To consider the effect of solar activity, the period is divided into active and quiet sun by considering those years with sunspot number greater than the upper quartile and those with sunspot number less than the lower quartile respectively. The data were analysed using x2 test. It describes the magnitude of the discrepancy between theory and observation. Analysis has shown that there is some statistical significance between heavy rainfall and lunar cycle. The effect is more significant in active sun period which shows the effect of solar activity also.

Periodicities in the time-series of the annual mean temperatures of Central England

ABSTRACT .Maximum Entropy spectral Analysis (MESA) of the 8IUlua1 mean temperature series for Central England for 1659-1991 indicated significant periodicilies at T = 7.8, 11.1, 12.5, 15, 18, 23, 32, 37, 68, 81, l09 and 203 years. These compare well with T = 22, 30, 80, 200 years obtained for China. Also, a good comparison is obtained with some periodicities in the sunspot number series.

Spatiotemporal Variability of Drought and Its Multi-Scale Linkages with Climate Indices in the Huaihe River Basin, Central China and East China

With the aggravation of the ocean–atmosphere cycle anomaly, understanding the potential teleconnections between climate indices and drought/flood conditions can help us know natural hazards more comprehensively to better cope with them. This study aims at exploring the spatiotemporal patterns of drought and its multi-scale relations with typical climate indices in the Huaihe River Basin. First, the spatial patterns were identified based on the seasonal Standardized Precipitation Index (SPI)-3 during 1956–2020 by means of the Empirical Orthogonal Function (EOF). The two leading sub-regions of spring and winter droughts were determined. Then, we extracted the periodicity of spring and winter SPI-3 series and the corresponding seasonal climate indices (Arctic Oscillation (AO), Bivariate El Niño–Southern Oscillation (ENSO)Timeseries (BEST), North Atlantic Oscillation (NAO), Niño3, and Southern Oscillation Index (SOI)) and the sunspot number by using the Continuous Wavelet Transform (CWT). We further explored the teleconnections between spring drought, winter drought, and climate indices and the sunspot number by using Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC) analyses. The results show that there are in-phase multi-scale relations between spring/winter PC1 and AO, BEST, and Niño3, of which the climate indices lead spring PC1 by 1.5–2 years and the climate indices lag winter PC1 by 1.5–3 years. Anti-phase relations between spring PCs and SOI and the sunspot number were observed. NAO mainly affects the interdecadal variation in spring drought, while AO and Niño3 focus on the interannual variation. In addition, Niño3 and SOI are more related to the winter drought on interdecadal scales. Moreover, there is a positive correlation between the monthly average precipitation/temperature and Niño3 with a lag of 3 months. The results are beneficial for improving the accuracy of drought prediction, considering taking NAO, AO, and Niño3 as predictors for spring drought and Niño3 and SOI for winter drought. Hence, valuable information can be provided for the management of water resources as well as early drought warnings in the basin.

Number of Sunspot Groups and Individual Sunspots Recorded by Tevel for the Period 1816–1836 in the Dalton Minimum

Cornelis Tevel made sunspot observations during the period 1816–1836, including the Dalton Minimum. In this work, the first revision of these observations since Wolf incorporated them into his database is presented. On the one hand, the number of individual sunspots from Tevel’s drawings was counted. This is of special interest for the sunspot number reconstruction because this kind of information is not as common in historical sunspot records as the number of groups. Thus, Tevel could be considered for the future reconstruction of the sunspot number index. On the other hand, the number of groups counted according to modern sunspot group classifications finding significant misinterpretations with the number of groups assigned to Tevel in the existing databases. Tevel was a relevant sunspot observer in the Dalton Minimum. In fact, he was the observer with the highest number of groups observed in Solar Cycles 6 and 7 according to the existing sunspot group number databases. According to the raw group number recount in this work, the maximum amplitudes for Solar Cycles 6 and 7 are, respectively, 27% and 7% lower than those previously determined. Moreover, Solar Cycle 6 is the weakest solar cycle since the Maunder Minimum after applying these new counts. Group counts from Tevel’s observations were compared with those from relevant contemporary astronomers, demonstrating that Schwabe and Tevel systematically recorded a higher number of groups than Flaugergues and Derfflinger. In addition, sunspot areas and positions recorded by Tevel should be used with caution for scientific purposes.

Solar Activity Is Associated With Diastolic and Systolic Blood Pressure in Elderly Adults

Background Since solar activity and related geomagnetic disturbances modulate autonomic nervous system activity, we hypothesized that these events would be associated with blood pressure (BP). Methods and Results We studied 675 elderly men from the Normative Aging Study (Boston, MA) with 1949 BP measurements between 2000 and 2017. Mixed‐effects regression models were used to investigate the association of average 1‐day (ie, day of BP measurement) to 28‐day interplanetary magnetic field intensity, sunspot number, and a dichotomized measure of global geomagnetic activity (K p index) in 4‐day increments with diastolic and systolic BP. We adjusted for meteorological conditions and other covariates associated with BP, and in additional models adjusted for ambient air pollutants (particulate matter with an aerodynamic diameter ≤2.5 µm, black carbon, and particle number) and ambient particle radioactivity. There were positive associations between interplanetary magnetic field, sunspot number, and K p index and BP that were greatest with these exposures averaged over 16 through 28 days before BP measurement. An interquartile range increase of 16‐day interplanetary magnetic field and sunspot number and higher K p index were associated with a 2.5 (95% CI, 1.7‒3.2), 2.8 (95% CI, 2.1‒3.4), and 1.7 (95% CI, 0.8‒2.5) mm Hg increase, respectively, for diastolic BP as well as a 2.1 (95% CI, 0.7‒3.6), 2.7 (95% CI, 1.5‒4.0), and 0.4 (95% CI, −1.2 to 2.1) mm Hg increase, respectively, for systolic BP. Associations remained after adjustment for ambient air pollutants and ambient particle radioactivity. Conclusions Solar activity and solar‐driven geomagnetic disturbances were positively associated with BP, suggesting that these natural phenomena influence BP in elderly men.

Evaluation of F10.7, Sunspot Number and Photon Flux Data for Ionosphere TEC Modeling and Prediction Using Machine Learning Techniques

Considering the growing volumes and varieties of ionosphere data, it is expected that automation of analytical model building using modern technologies could lead to more accurate results. In this work, machine learning techniques are applied to ionospheric modeling and prediction using sun activity data. We propose Total Electron Content (TEC) spectral analysis, using discrete cosine transform (DCT) to evaluate the relation to the solar features F10.7, sunspot number and photon flux data. The ionosphere modeling procedure presented is based on the assessment of a six-year period (2014–2019) of data. Different multi-dimension regression models were considered in experiments, where each geographic location was independently evaluated using its DCT frequency components. The features correlation analysis has shown that 5-year data seem more adequate for training, while learning curves revealed overfitting for polynomial regression from the 4th to 7th degrees. A qualitative evaluation using reconstructed TEC maps indicated that the 3rd degree polynomial regression also seems inadequate. For the remaining models, it can be noted that there is seasonal variation in root-mean-square error (RMSE) clearly related to the equinox (lower error) and solstice (higher error) periods, which points to possible seasonal adjustment in modeling. Elastic Net regularization was also used to reduce global RMSE values down to 2.80 TECU for linear regression.

Chinese Sunspot Drawings and Their Digitizations-(VI) Extreme Value Theory Applied to the Sunspot Number Series from the Purple Mountain Observatory

Based on the daily sunspot number (SN) data (1954–2011) from the Purple Mountain Observatory, the extreme value theory (EVT) is employed for the research of the long-term solar activity. It is the first time that the EVT is applied on the Chinese SN. Two methods are used for the research of the extreme events with EVT. One method is the block maxima (BM) approach, which picks the maximum SN value of each block. Another one is the peaks-over-threshold (POT) approach. After a declustering process, a threshold value (here it is 300) is set to pick the extreme values. The negative shape parameters are obtained by the two methods, respectively, indicating that there is an upper bound for the extreme SN value. Only one value of the N-year return level (RL) is estimated: N = 19 years. For N = 19 years, the RL values of SN obtained by two methods are similar with each other. The RL values are found to be 420 for the POT method and the BM method. Here, the trend of 25th solar cycle is predicted to be stronger, indicating that the length of meridional forms of atmospheric circulation will be increased.

The Impact of Sunspots Number on Critical Frequencies foF2 for the IONOSPHERIC Layer-F2 Over Erbil Station During the Down Phase of Solar Cycle 24

The ionosphere layer (F2) is known as the most important layer for High frequency (Hf) radio communication because it is a permanent layer and excited during the day and night so it is able to reflect the frequencies at night and day due to its high critical frequency, and this layer is affected by daily and monthly solar activity. In this study the characteristics and behavior of F2 layer during Solar cycle 24 were studied, the effect of Sunspots number (Ri) on the critical frequency (foF2), were investigated for the years (2015, 2016, 2017, 2018, 2019, 2020) which represents the down phase of the solar cycle 24 over Erbil station (36° N, 44° E) by finding the critical frequency (foF2) values, the layer’ s impression times are determined for the days of solstice as well as equinox, where the solar activity was examined for the days of the winter and summer solstice and the days of the spring and autumn equinoxes for a period of 24 hours by applied the International Reference Ionosphere model IRI (2016). The output data for foF2 were verified by using the IRI-Ne- Quick option by specifying the time, date and Sunspot number parameters. Statistical analysis was caried out through the application of the Minitab (version 2018) in order to find the correlation between the critical frequency (foF2) of Ionospheric layer F2 and Sunspot number. It was concluded that the correlation is strong and positive, this indicate that critical frequency (foF2) increase with increasing Sunspots number (Ri) for solar cycle 24.

Reconstruction of the Sunspot Number Source Database and the 1947 Zurich Discontinuity

The recalibration of the sunspot number series, the primary long-term record of the solar cycle, requires the recovery of the entire collection of raw sunspot counts collected by the Zurich Observatory for the production of this index between 1849 and 1980.Here, we report about the major progresses accomplished recently in the construction of this global digital sunspot number database, and we derive global statistics of all the individual observers and professional observatories who provided sunspot data over more than 130 years.First, we can announce the full recovery of long-lost source-data tables covering the last 34 years between 1945 and 1979, and we describe the unique information available in those tables. We then also retrace the evolution of the core observing team in Zurich and of the auxiliary stations. In 1947, we find a major disruption in the composition of both the Zurich team and the international network of auxiliary stations.This sharp transition is unique in the history of the Zurich Observatory and coincides with the main scale-jump found in the original Zurich sunspot number series, the so-called “Waldmeier” jump. This adds key historical evidence explaining why methodological changes introduced progressively in the early 20th century could play a role precisely at that time. We conclude on the remaining steps needed to fully complete this new sunspot data resource.