Planet activeness: a new concept to enhance the accuracy of Astromet weather forecast.

Dheebakaran Ga; Kokilavani S; Santosh Ganapati Patil; Sankar T; Rathika K; Arul Prasath S; Balamurali B; Sathyamoorthy N.K.

doi:10.12688/f1000research.149941.1

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Research Article

Planet activeness: a new concept to enhance the accuracy of Astromet weather forecast.

[version 1; peer review: 1 approved with reservations]

Dheebakaran Ga ¹, Kokilavani S¹, Santosh Ganapati Patil², [...] Sankar T¹, Rathika K¹, Arul Prasath S¹, Balamurali B¹, Sathyamoorthy N.K.¹

Dheebakaran Ga ¹, Kokilavani S¹, [...] Santosh Ganapati Patil², Sankar T¹, Rathika K¹, Arul Prasath S¹, Balamurali B¹, Sathyamoorthy N.K.¹

PUBLISHED 05 Jul 2024

Author details Author details

¹ Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India
² Physical Science & Information Technology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India

Dheebakaran Ga
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Supervision, Validation, Writing – Original Draft Preparation

Kokilavani S
Roles: Data Curation, Validation, Writing – Review & Editing

Santosh Ganapati Patil
Roles: Data Curation, Methodology, Validation, Writing – Review & Editing

Sankar T
Roles: Investigation, Validation, Writing – Review & Editing

Rathika K
Roles: Formal Analysis, Investigation

Arul Prasath S
Roles: Formal Analysis, Investigation, Validation

Balamurali B
Roles: Formal Analysis, Investigation, Validation

Sathyamoorthy N.K.
Roles: Supervision, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Climate gateway.

Abstract

Background

Astrometeorology is an ancient science, that deals the relationship between planet position and weather events. Several Indian studies proved that Astrometeorology could be a complementary method to improve numerical weather forecast accuracy. Since 2011, Tamil Nadu Agricultural University is conducting astrometeorological research and devised a novel concept “Planet Activeness Chart”. The principle is that “planets’ influence on a location’s weather varies throughout the day and may be negative, inactive, active, highly active and rule depending on their angle to that location”. Most existing astromet studies used planetary position to predict the occurrence of weather events (yes/no) but failed to capture intensity of the event. The “Planet Activeness Concept” could address this limitation and enhance forecast usability.

Methods

A study was carried out from 2018 to 2021 with six years data (2011-16) to verify the “Planet activeness” on hourly rainfall and windspeed events in Tamil Nadu. The frequency of planet activeness for a weather event was calculated by dividing the number of times a planet was in the selected activeness during a specific event category by the total number of events.

Results

The results indicated that negative state of the Sun, active status of the Saturn, Uranus, Venus and Moon were positively associated with rainfall intensity. The windy planet Mercury and Neptune at active state, the Sun and Saturn at rule state, Venus and Uranus at negative state, Jupiter at highly active state had significant influence on the increased wind speed.

Conclusion

Applying the planet activeness concept with azimuth could enhance the accuracy and usability of Astrometeorological forecasts. This study establishes a mathematical relationship between planet activeness and weather as a first step to understand the science behind this relationship. It is suggested to study different combination of planet activeness during a weather event for more insights.

Keywords

Astrometeorology, Weather Forecast, Ephemeris, Planet Activeness, Rain, Wind.

Corresponding author: Dheebakaran Ga

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2024 Ga D et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Ga D, S K, Patil SG et al. Planet activeness: a new concept to enhance the accuracy of Astromet weather forecast. [version 1; peer review: 1 approved with reservations]. F1000Research 2024, 13:746 (https://doi.org/10.12688/f1000research.149941.1) First published: 05 Jul 2024, 13:746 (https://doi.org/10.12688/f1000research.149941.1) Latest published: 05 Jul 2024, 13:746 (https://doi.org/10.12688/f1000research.149941.1)

1. Introduction

Weather is the most important factor that determines the success of any life on earth, especially in agricultural sector. Rainfall and wind speed information are more important than other weather metrics, both low and high magnitudes cause severe destruction to life, infrastructures and economy of a region. The strongest proof of rainfall variability influence is good agriculture in Tamil Nadu, India, during 2011-12 due to good rainfall events, complete crop loss in 50% of cropped land during 2016-17 due to poor rainfall, and up-down agriculture activity throughout the between years. The North East Monsoon (NEM) rainfall, which is major source of water for southern peninsular India have significant contribution from cyclonic events from Bay of Bengal, which are highly unpredictable.

Nearly 40 per cent of the districts in India are facing the prospect of drought, while close to 25 per cent districts are having heavy rainfall of more than 100 mm in just a matter of hours.¹ Uncertainty is an integral part of weather, the occurrences of weather events are always been uncertain. The primary need of a farmer is location-specific and accurate weather forecast.² The Accurate weather information in advance helps farmers to reduce weather risk and to utilize the favourable weather for planning, protecting and making adjustments, particularly in agricultural production sector.

Globally, successful approaches to weather forecasting include climatological, statistical, numerical methods with different dynamical models, remote sensing, and hybrid approaches combining two or more fields. The rise of high-performance computing systems has led to increased popularity of numerical weather forecasting systems, causing traditional indigenous weather forecasting approaches, which rely on observing natural phenomena, animal behaviour, insect wandering, and astrometeorology (star and planet movement), to gradually diminish in significance. The IPCC AR IV acknowledged Indigenous Traditional Knowledge (ITK) as “an invaluable foundation for developing strategies for adaptation and natural resource management in response to environmental and other forms of change.”³ Traditional and indigenous knowledge was incorporated as a guiding principle for the Cancun Adaptation Framework (CAF), which was adopted by parties at the 2010 United Nations Framework Convention on Climate Change (UNFCCC) Conference in Cancun.⁴ This decision was made during the 32^nd Session of the IPCC and Chapter 12 of the IPCC V AR delineated the indigenous and customary wisdom pertaining to human security.⁵

India had glorious scientific and technological tradition in the past. Our ancient astronomers and astrologers made a systematic study on meteorology. Kautilya’s Artha shastra (300 BC), Brihat Samhita and Varahamihira (505-587 AD) had script of astromet rain forecast and measurement. A great deal of trustworthiness is still placed by some individuals on astrology even today. It was reported that the rain prediction made in Panchangs were better or at par with the predictions made through modern techniques and procedures.⁶^,⁷ Ramakrishna, the Director of Central Research Institute for Dry Land Agriculture, India opined that the astrometeorology had accuracy of 60-70 per cent and there is always relevance in ancient wisdom of weather forecasting.⁸ The mathematical science based astrological science could help to improve the weather prediction with more accuracy and this traditional wisdom may be explored with scientific knowledge to come out with an error-free system of forecasting, which is very crucial for agrarian India.⁹^,¹⁰ The astrometeorological calendars (Nakshatra Varsha Almanac) were already developed for many states of India and the astromet seasonal rainfall forecast accuracy in Gujarat, India was 74 – 88 per cent¹¹^,¹² Maharashtra was 73 – 86 per cent¹³ and Kerala was 79 per cent.¹⁴

An earlier study at Tamil Nadu Agricultural University determined that the hybrid forecast, which was generated through the integration of astrometeorology, numerical weather forecasting, and the probability method, yielded enhanced accuracy (0.75 to 0.88) and critical success index (0.56 to 0.76) for rainfall forecasts in Tamil Nadu.¹⁵ Further research in astrometeorology on wind speed in Tamil Nadu Agricultural University revealed that the efficacy of hourly astromet rules for wind speed forecast was 43–67 per cent.¹⁶ In addition, an astromet study on cyclone events found that Mercury, Venus, the Moon, Saturn, Uranus, and Neptune all have a considerable ability to modify cyclones to varied degrees (59 to 75 percent) between the azimuth ranges of 61 to 120 and 241 to 300 degrees.¹⁷ The indispensability of astrometeorology as a substitute for numerical methods and its potential to enhance the practicality of weather forecasts is indisputable from these studies.

Since 2011, the Agro Climate Research Centre at Tamil Nadu Agricultural University has been conducting astronomical investigations, focusing on the association between planet positions (ephemeris) and hourly weather events. Earlier in the investigation, hourly astromet rainfall forecast output had significantly lower accuracy (40%) than numerical approaches. A comprehensive review of the astrometeorological literature led to the conclusion that, while planets are not the root cause of weather events, they do have a highly variable influence on the weather events of a location. Many earlier literatures had noted the relationship of as Sun, Mars and Uranus with hotness, Venus, moon and Neptune with rain, Mercury with Wind, Saturn with cold. While reviewing the Indian Panchangam (the yearly Astrometeorological Journals for human astrology), it is observed that the degree of influence of planets is vary with their positions (degree of angle) to a particular location. These Panchangams have noted the influence of planet position on human being as friendly, enemy, hide, rule and obstruct.

Based on the earlier literatures, the first author of this article had developed a novel concept called “Planet Activeness Chart” for the weather of a particular location, similar to the planet position and human life relationship (Figure 1). This planet activeness concept had incorporated in generating astromet rainfall forecast as pilot study and the output had significant increase in the accuracy and critical success index.¹⁵ A detailed study on the influence of plant activeness in rainfall and wind speed events were studied during 2018 – 2021 and presented in this paper.

Figure 1. Planet activeness chart for weather developed from human astrology.

2. Methods

2.1 Study area and period

Astrometeorological forecast studies of rainfall and wind speed events were conducted during 2018 – 2021 in each one location of all the 31 districts of Tamil Nadu (Figure 2) and pooled to reduce the spatial variability of astrometeorological rules.

Figure 2. Astromet study location for rainfall and windspeed forecast.

Observed hourly rainfall data for the period of five years from 1.1.2011 to 31.3.2015 and observed daily hourly wind speed data for the period of six years from 1.1.2011 to 31.3.2016 were collected for all the study locations from the Automatic Weather Station (AWS) installed by Tamil Nadu Agricultural University under Tamil Nadu Agriculture Weather Network (TAWN). Collected data were checked for errors and the erroneous data were removed. Based on the intensity, the rainfall events were grouped in to six categories viz., above 0 mm, 0.5-2.5 mm, 3 to 10 mm, 10.5 to 25 mm, above 25 mm and all events (Table 1).

Table 1. Intensity wise hourly rainfall events used for the study.

Sl No	Category	Intensity mm/hour	Total Events (2011-2015)
1.	Light	0.0–2.5	18906
2.	Moderate	3.0–10.0	5416
3.	Heavy	10.5–25.0	1563
4.	Extreme	Above 25.0	431
5.	All	>0.0	26316

Similarly, wind speed also grouped in to seven categories viz., below 2, 2.1 to 6, 6.1 to 12, 12.1 to 19, 19.1 to 30, 30.1-45 and above 45 km/hour (Table 2).

Table 2. Category wise hourly wind speed events used for the study.

S.N	Category	Intensity km/hr	Total Events (2011-2016)
1.	Calm	<2.0	272437
2.	Light air	2.1 to 6.0	456988
3.	Light breeze	6.1 to 12.0	260355
4.	Gentle Breeze	12.1 to 19.0	55310
5.	Moderate	19.1 to 30.0	9918
6.	Strong	30.1 to 45.0	60
7.	Extreme	>45.0	6
8.	Total		1055074

2.2 Azimuth and ephemeris

Azimuth is the angle formed between a reference direction (north) and a line from the observer to a point of interest projected on the same plane as the reference direction orthogonal to the zenith (Figure 3).

Figure 3. Azimuth calculation form observer (Source: https://commons.wikimedia.org/w/index.php?curid=17727911).

The ephemeris is continuous time series values of planets’ azimuth and hourly scale was used in this study. The Azimuth and ephemeris of nine planets viz., Sun, Moon, Mercury, Venus, Mars, Jupiter, Saturn, Uranus and Neptune to each location during the event happening hours was calculated using Alcyone Ephemeris 4.3v calculator, which is simple and fast astronomical position calculation software. This is a proprietary software that can be purchased on line in the following link “https://www.alcyone.de/buy.html”. Alternately, a free open-source NASA product “General Mission Analysis Tool (GMAT)” can be used for this purpose from the following web link “https://software.nasa.gov/software/GSC-17177-1.

2.3 Planet activeness

Planet activeness is a new concept developed by the first Author of this article based on the information collected from multiple Tamil “Panjangams”, which have mentioning of ‘friendliness’ and ‘antipathy’ of each planet to a Zodiac sign at specific angle. This concept was conceived and altered as planet activeness to weather.

The principle is that “planets’ influence on a location’s weather is not uniform throughout the day and may be positive or negative or null based on their angle to that location”. Based on the Azimuth (angle from the north), the activeness of a planet on weather of particular location is classified as negative, inactive, active, highly active and rule. For example, Venus, the rain influencing planet has positive activeness in angle ranged between 1 and 90, 181 and 360, whereas negative activeness on rain in angle between 91and 180. During that active period it has more influence on rainfall process of particular location, accordingly to their strength. It can be easily observable that, if the sun is negative for a location and that period lesser brightness would be observed than normal. It is a new concept hence, the influence of planet activeness on rainfall and wind speed were test verified in this present investigation. Activeness of planets in respect to weather events happening hours were calculated using activeness chart. Based on the ephemeris, planet’s status were categorized as negative, inactive, active, highly active and rule and equalized to -1, 0, +1, +2 and +3, respectively for calculation purpose (Table 3).

Table 3. Planet activeness chart in respect to their angle from observer location.

Angle Planet	1-30	31-60	61-90	91-120	121-150	151-180	181-210	211 – 240	241-270	271-300	301-330	331-360
Sun	+2	-1	-1	-1	+3	-1	0	-1	+1	-1	+1	+1
Moon	-1	+2	+1	+3	-1	+1	-1	0	+1	-1	-1	+1
Mars	+3	+1	+1	0	-1	+1	+1	+3	-1	+2	-1	-1
Mercury	+1	+1	+3	+1	-1	+3	+1	+1	+1	+1	+1	0
Jupiter	-1	+1	+1	+2	+1	+1	+1	-1	+3	0	+1	+3
Venus	+1	+3	+1	-1	-1	0	+3	+1	+1	+1	+1	+2
Saturn	0	+1	+1	-1	-1	+1	+2	-1	+1	+3	+3	+1
Uranus	-1	0	+1	-1	-1	+1	+1	+2	+1	+1	-1	+1
Neptune	-1	0	+1	-1	-1	+1	+1	+2	+1	+1	-1	+1

During the event happening hours, the activeness status of each planet was established and counted separately for all the event category by overlaying the ephemeris data on activeness chart. The activeness frequency was calculated by employing following formula.

Activeness frequency (%) = \frac{No . of times the planet positioned in the selected activeness range during a particular event category}{Total number of events in the particular event category}

3. Results and discussion

3.1 Influence of individual planet activeness on hourly rainfall events

Individual planet activeness frequency distribution for all category of rainfall events during the period from 01.01.2011 to 31.12.2015 were expressed in percentage (Table 4 and Figure 4).

Table 4. Activeness frequency (%) of different planets during hourly rainfall events.

Activeness	>0 mm	0-2.5 mm	3-10 mm	10.5-25 mm	>25 mm	>0 mm	0-2.5 mm	3-10 mm	10.5-25 mm	>25 mm
	Sun					Mercury
Active	20.7	22.3	18.5	12.8	12.2	63.4	65.0	60.7	55.8	57.2
Hi. Active	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Rule	5.8	5.8	5.3	6.3	11.1	25.4	23.8	28.5	34.0	29.1
Inactive	2.4	2.6	1.8	2.4	1.4	3.2	3.4	3.1	2.4	0.9
Negative	71.2	69.4	74.3	78.5	75.3	7.9	7.9	7.7	7.8	12.8
	Venus					Moon
Active	58.3	60.8	55.0	49.1	45.5	42.5	42.2	42.2	42.3	49.5
Hi. Active	2.5	2.8	2.3	0.8	0.3	5.1	5.2	5.0	5.0	2.5
Rule	8.8	8.4	10.8	8.3	7.0	16.5	16.5	16.7	14.6	14.6
Inactive	3.3	2.9	3.7	4.9	4.5	4.8	4.5	5.0	6.0	7.8
Negative	27.0	25.1	28.2	36.9	42.7	31.1	31.5	31.0	32.1	25.6
	Mars					Jupiter
Active	28.7	28.4	30.4	32.8	27.9	26.3	25.8	28.1	27.2	27.4
Hi.Active	12.1	12.7	10.8	9.0	6.3	31.3	30.5	32.9	31.0	35.4
Rule	9.1	9.3	8.1	9.1	8.5	28.0	28.8	26.1	29.3	24.1
Inactive	18.1	16.8	19.6	20.9	17.7	3.3	3.5	2.9	2.4	1.5
Negative	32.2	32.7	31.0	28.1	39.5	11.2	11.5	10.1	10.1	11.6
	Saturn					Uranus
Active	54.6	54.0	55.9	58.7	60.3	64.7	64.4	65.9	67.9	69.2
Hi. Active	0.4	0.5	0.3	0.4	0.7	4.4	4.2	5.0	4.5	4.4
Rule	36.6	37.3	35.1	32.8	33.1	0.0	0.0	0.0	0.0	0.0
Inactive	4.9	4.8	4.7	5.0	3.1	2.4	2.5	2.2	2.6	2.3
Negative	3.5	3.4	3.9	3.1	2.8	28.5	28.9	26.9	25.0	24.1
	Neptune
Active	75.5	75.5	75.9	77.5	77.3
Hi. Active	0.5	0.5	0.5	0.3	0.1
Rule	0.0	0.0	0.0	0.0	0.0
Inactive	7.5	7.7	7.3	5.4	7.3
Negative	16.5	16.3	16.3	16.8	15.3

Figure 4. Influence of planets’ activeness on different category of rainfall events.

In general, most of the rainfall events (>0.0 mm) were occurred during ‘active’ status of Neptune (75.5%), Uranus (64.7%), Mercury (63.4%), Venus (58.3%), Saturn (54.6%) and moon (42.5%) and ‘negative’ status of the Sun (71.2%).

The results confirmed that the planets viz., Neptune, Uranus, Mercury, Venus and Moon are to be in ‘active’ status and the sun should be in ‘negative’ activeness for a rainfall event. This order is in line with the order of strength followed in general Astrology viz., Uranus, Neptune, Saturn, Mars, Mercury, Jupiter, Venus, Moon and the Sun.¹⁸^,¹⁹ The findings this study also corroborated with the findings of earlier research by Vandeep et al. that there will be little rainfall when Venus is ahead of all other planets.²⁰ It was also observed in this study that the frequency of ‘negative’ status of the Sun (69 to 79%), ‘active’ status of Saturn (54 to 60%) and Uranus (64 to 69%) were increased with the increase in rainfall intensity from 0 to 25 mm.

On other side, there was decrease in frequency of ‘active’ status of mercury (65 to 56%) and ‘active’ status of Venus (61 to 46%) with the increase in rainfall intensity. The other planets viz., Neptune (76 – 78%) and moon (42%) had maintained the frequency of ‘active’ status without much changes. Though there was no marked trend of changes in the activeness status of Mars during increase in intensity of rainfall, sudden raise in ‘inactive’ frequency (28 to 40%) was observed at very high intensity (>25 mm) rainfall.

The trend of activeness frequency ensured the influence of ‘active’ status of Neptune in all the rainfall events, whereas the nearer planets (Venus and Mercury) had more influence on low intensity rainfall and far away planets (Uranus and Saturn) had more influence of high intensity rainfall. It is also to be noted that the frequency in active status of Moon and Sun were increased for the heavy down pour (>25 mm).

Study clearly identified that under ‘inactive’ status, no other planets except Mars had marked influence on the rainfall events. Similarly, other planets had very low frequencies (0 – 9% only) under ‘highly active’ for any rainfall events, except Jupiter which had higher frequency (31-35%) of ‘highly active’ status during rainfall events. The Jupiter had more frequencies under ‘active’, ‘highly active’ and rule status during the rainfall events and very minimum under ‘inactive’ or ‘negative’ status. The influence of Jupiter on rainfall at 90 – 120^o azimuth (Highly active status) and 241–270^o azimuth (rule status) reported here are backed up the results of early studies that Jupiter at a multiple of 90^o aspect to the local meridian produces rain.²¹

The frequency of ‘rule’ status of Saturn was decreased from 37 to 33 per cent with increase in the intensity of rainfall. Another important observation that, except the Jupiter and Saturn, all other planets’ activeness frequencies were concentrated either in ‘active’ or ‘inactive’ status. The Saturn had high frequencies either in ‘active’ or ‘rule’ status and very low (1 – 3%) frequencies under other activeness status during any rainfall event. Unnikrishnan et al., (2011) reported that Saturn at 60^o azimuth delivers greater rainfall in Kerala, which supports the findings of this study that Saturn at 60^o azimuth is in an active state.²²

The findings of this study scientifically supported the conclusion of Varshneya, et al., (2009) that the Sun and Mars are hot planets, the Moon, Venus, and Neptune are moist/rainy planets, and Saturn is a cold planet.²³ Under ‘negative’ status, the Sun may not express its original character of hotness and may express just opposite character i.e coolness, which is much needed for rainfall events. Similarly, in the case of another hot planet Mars, the ‘negative’ and ‘inactive’ status had more rainfall events than other activeness category. The Neptune, which considered as moist planet had more rainfall events under its active status.

3.2 Influence of individual planet activeness on hourly wind speed events

Individual planet activeness frequency distribution for all wind speed events during the period from 01.01.2011 to 31.03.2016 were expressed in percentage (Table 5 and Figure 5).

Table 5. Activeness frequency (%) of different planets during hourly windspeed events.

Activeness	<2.0 km	2.1-6.0 km	6.1-12.0 km	12.1-19.0 km	19.1-30.0 km	30.1-45.0 km	<2.0 km	2.1-6.0 km	6.1-12.0 km	12.1-19.0 km	19.1-30.0 km	30.1-45.0 km
	Sun						Mercury
Active	28.4	28.7	24.6	17.8	13.7	19.8	75.1	67.6	64.7	69.7	72.1	78.5
Hi. Active	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Rule	3.6	5.2	7.4	10.7	13.1	15.2	17.9	22.2	22.9	15.4	9.1	4.6
Inactive	2.3	2.2	2.1	2.4	1.2	0.0	1.8	3.6	3.5	1.5	0.9	6.3
Negative	65.7	64.0	65.9	69.0	72.0	65.0	5.2	6.6	8.8	13.3	17.9	10.7
	Venus						Moon
Active	75.3	63.7	52.4	37.5	26.2	41.6	39.2	41.8	40.6	33.6	35.1	27.0
Hi. Active	1.8	3.2	3.2	1.6	1.5	1.3	4.3	4.7	4.7	3.5	2.9	1.7
Rule	6.9	9.1	9.5	7.3	7.2	10.3	19.4	17.7	18.2	21.7	21.5	27.2
Inactive	2.7	2.5	2.8	3.4	2.3	0.3	4.5	5.1	4.9	3.9	2.5	0.0
Negative	13.3	21.5	32.0	50.1	62.9	46.6	32.6	30.7	31.6	37.2	38.0	44.1
	Mars						Jupiter
Active	30.0	28.0	25.8	19.4	15.4	12.4	26.0	25.7	26.2	27.3	28.0	22.8
Hi. Active	19.0	17.2	16.0	20.6	25.6	16.7	31.1	29.2	30.2	41.5	45.2	57.9
Rule	7.5	7.8	7.7	6.7	5.1	1.7	28.0	30.5	29.2	19.3	15.3	15.7
Inactive	11.3	16.8	21.6	22.1	24.3	63.0	3.6	2.5	2.4	2.5	4.1	1.3
Negative	32.2	30.2	28.9	31.2	29.7	6.2	11.3	12.1	12.0	9.4	7.3	2.4
	Saturn						Uranus
Active	45.2	51.6	50.9	39.5	33.0	37.8	59.8	63.0	61.3	50.7	42.3	34.4
Hi. Active	0.1	0.2	0.2	0.2	0.1	0.0	4.8	4.4	4.4	5.5	7.3	12.8
Rule	46.8	39.8	40.9	53.4	62.0	62.2	0.0	0.0	0.0	0.0	0.0	0.0
Inactive	4.9	4.6	4.4	4.2	3.8	0.0	1.9	2.3	2.3	1.4	0.4	0.0
Negative	2.9	3.8	3.7	2.7	1.0	0.0	33.5	30.2	32.0	42.4	49.9	52.7
	Neptune
Active	81.4	77.7	77.9	83.2	81.8	88.3
Hi. Active	0.3	0.3	0.4	0.6	0.3	0.0
Rule	0.0	0.0	0.0	0.0	0.0	0.0
Inactive	6.0	7.2	6.6	3.4	2.3	0.0
Negative	12.3	14.8	15.2	12.8	15.6	11.7

Calm	Light air	Light breeze	Gentle Breeze	Moderate	Strong
< 2.0	2.1 to 6	6.1 to 12	12.1 to 19	19.1 to 30	30.1 to 45

Figure 5. Influence of planets’ activeness on different category of hourly wind speed events.

Unlike rainfall events, wind speed events are continuous, either one category of the event is always happening in the earth. The results of astromet activeness and windspeed analysis inferred the following findings. The active status of Sun was decreased (28.4 to 19.8%) and the rule status was increased (3.6 to 15.8%) with increasing wind speed from Calm air to strong wind. The negative status of the Sun was increased (64 to 72.0) from light air to moderate wind then again dropped.

The planet Mercury, considered as windy planet behaves differently that the active status had decreased, and the rule status was increased with increasing wind speed from calm to gentle breeze, and then react oppositely towards strong wind. The active state of Venus had decreased, and correspondingly negative state was increased with increase in wind speed. Similar to Venus, the active state of moon had decreased with increase in wind speed, but the rule and negative status of moon were increased with increase in wind speed.

In the case of Mars, both active and negative state were decreased with increase in wind speed whereas there was increase in inactive state. At Strong wind speed (>30 km), the Mars was mostly in inactive status (63%). The highly active state of the Jupiter had increased drastically with increase in wind speed, whereas the rule and inactive states were decreased remarkably. The ruling state of the Saturn had increased with increase in wind speed, costing the active status. In the case of Uranus, the active state had decreased against increase in negative state with increase in wind speed. But the Neptune had remarkable increase in active state and decrease in inactive state with increase in wind speed.

It was observed from the study that the Sun at rule, Mercury at active, Venus at negative, Moon at rule/negative, Mars at inactive, Jupiter at highly active, Saturn at rule, Uranus at negative, Neptune at active status would increase the wind speed than other status. Unlike rainfall where active status of most planets played major role in increasing rainfall intensity, the negative status of most planets increased the wind speed except Mercury and Neptune. Varshneya et al., (2009) reported that the Mercury had most wind influencing capability which supported the result of this study.²⁴

IT was observed that the Saturn and Neptune influenced low pressure and the Uranus influenced high pressure.²⁵ Atmospheric pressure gradient is important factor of wind speed and Riske’s observation were supporting the results of this study that negative state of Uranus (High Pressure), active state of Saturn and Neptune (Low pressure) increased the wind speed.

4. Conclusion

The precipitation and wind velocity of a specific geographical area are subject to the effect of various meteorological conditions, including but not limited to temperature, topography, and air pressure. Astrometeorology is an ancient wisdom of weather forecasting, which relates the meteorological phenomenon of a particular location in earth to the position of the planet. The rainfall quantity received in a single hour was found to be positively influenced by the negative status of the Sun, the active status of Saturn, Uranus, Venus, and the Moon, according to the findings of this astrometeorological study. The windy planets Mercury and Neptune in their active states, the Rule states of the Sun, Saturn, and Neptune, Venus and Uranus in their negative states, and Jupiter in its very active condition all had a major impact on the increased wind speed during wind events. In addition to azimuth and aspect, incorporation of planet activeness concept in astromet weather forecast may produce significant improvement in the accuracy of forecast. In contrast to numerical weather forecasts, astrometeorological forecasts can be prepared for a longer time period as the planet ephemeris are precisely defined and accessible for a longer time period. The generated Astromet forecast may be overlaid on numerical or other forecasting approaches, which would undoubtedly increase forecast accuracy. For easy implementation, a system for overlapping the astromet forecast on other forecasts must be created.

Data availability statement

Planet activeness: a new concept to enhance the accuracy of Astromet forecast is shared in Dryad Repository.

The DOI and link for the dataset in Dryad are https://doi.org/10.5061/dryad.gxd2547v5 and https://datadryad.org/stash/share/Mw7SHl2MVbC3xzVywUfxB9XQvvWYqjGk1bsjTOlnINg.²⁶

This project contains following dataset:

Ephemeris dataset for 2011-2016

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).

Acknowledgement

We the authors, sincerely thank our Tamil Nadu Agricultural University for providing opportunity to work on Astrometeorology under University Research Project and verification through student’s thesis research.

References

1. Chauhan C: Extreme weather in India taking a toll on people and economy. Hindustan times. 20 September 2017. [assessed 31.3.2020]. Extreme weather in India taking a toll on people and economy | Latest News India - Hindustan Times
2. Report on Northeast Monsoon – 2018. Chennai: Regional Meteorological Centre; 2018. Reference Source
3. IPCC AR IV: Intergovernmental panel on climate change fourth assessment report.2007; p. 104. Reference Source
4. UNFCCC: Report of the conference of the Parties on its 16th session. Germany: United Nations Framework Convention on Climate Change; 2010; 2010. .
5. IPCC AR V: Intergovernmental panel on climate change fifth assessment report summary for policy makers.2014. Reference Source
6. Mishra SK: Weather forecasting in Almanacs relating to farming Operations and Its relevance In Today’s Agriculture, ([PhD] BHU Varanasi.1998.
7. Mishra SK, Dubey VK, Pandey RC: Rain forecasting in Indian almanacs (Panchangs): A case for making Krishi-Panchang. Asian Agri-History. 2002; 6(1): 29–42.
8. Ramakrishna YS: Relevance of Ancient Wisdom on Weather forecasting for improving agro-advisories. Workshop on exploring the possibility for development a ‘common weather forecasting system by converging modern and ancient Indian sciences’. Hyderabad: CRIDA; 2006. June – 7, 2006.
9. Shankar KR, Maraty P, Murthy V, et al.: Indigenous rain forecasting in Andhra Pradesh. Central Research Institute for Dryland Agriculture; 2008; 2008.
10. Sivaprakasam S, Kanakasabai V: Traditional almanac predicted rainfall – A case study. Indian J Tradit Know. 2009; 8(4): 621–625.
11. Varshneya MC, Bhat VV, Joshi RM: Nakshatra-varsha almanac-prepared for Maharashtra. Berlin: Springer; 2002. Publisher Full Text
12. Varshneya MC, Vaidya VB, Pandey V, et al.: Forecasting of rainfall for 2011 of Gujarat based on astrometeorology. CASAM. 2009; 8(1): 2359–2370. Publisher Full Text
13. Vaidya VB: Long range forecast of monsoon with astronomy and local observations for Maharashtram. Pune: Indian Institute of Tropical Meteorology, MoES; 2004; 8(4): pp. 323–324.
14. Unnikrishnan T, Prasada Rao GSLHV: Climate Change Adaptation Strategies in Agriculture and Allied Sectors. Scientific Publishers; 2011.
15. Dheebakaran G, Arulprasad S, Kokilavani S, et al.: Astrometeorology: An option to improve the accuracy of numerical daily rainfall forecast of Tamil Nadu. J. Agrometeorol. 2017; 19(Special Issue - AGMET 2016): 205–211.
16. Rathika K, Dheebakaran G, Panneerselvam S, et al.: Studying the astrometeorological relationship between planet’s azimuth and wind speed events. Madras Agric. J. 2019; 106(4-6): 330–334. Publisher Full Text
17. Sankar T, Dheebakaran G, Panneerselvam S, et al.: Astrometeorological relationship between planet azimuth and cyclone events in bay of Bengal. Int. J. Agric. Sci. 2019; 11(11): 927–937. Publisher Full Text
18. Green HS: Weather Predicting by Astro-meteorology (Astrological Manuals). Kessinger Publishing, LLC; 2003; 18–21.
19. Seetharama Iyer KV: Serva Muhurtha Panjankam - Jaya Varusam. Lavanya Printers; 2015; 2015. .
20. Vanadeep K, Murty SS, Krishnaiah M: Meteorological Predictions Preserved in the Panchangam Versus Real-time Observations-a Case Study over Tirupati Region-a Semi-arid Tropical Site in India. Indian J. Sci. Technol. 2012; 5(4): 2491–2509.
21. Bradley DA: Your powwow corner American Astrology Appears under the pen-name of Garth Allen.1957; 42–69. Reference Source
22. Unnikrishnan T, Prasada Rao GSLHV: Climate Change Adaptation Strategies in Agriculture and Allied Sectors.2011; 2011. 9788172336790.
23. Varshneya MC, Vaidya VB, Pandey V, et al.: Forecasting of rainfall for Gujarat based on astrometeorology. Asian Agri-History. 2009; 13(1): 25–37.
24. Varshneya MC, Vaidya VB, Pandey V, et al.: Forecasting of rainfall for Gujarat based on astrometeorology. Asian Agri-History. 2009; 13(1): 25–37.
25. Riske KB: Predicting weather events with astrology. Llewellyn Publications; 2014; 2014. : p. 240. ISBN-10: 0738741582, ISBN-13: 978-0738741581.
26. Ganesan D: Ephemeris data: “Planet activeness: a new concept to enhance the accuracy of Astromet weather forecast”. [Dataset]. Dryad. 2024. Publisher Full Text

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Author details Author details

¹ Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India
² Physical Science & Information Technology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India

Dheebakaran Ga
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Supervision, Validation, Writing – Original Draft Preparation

Kokilavani S
Roles: Data Curation, Validation, Writing – Review & Editing

Santosh Ganapati Patil
Roles: Data Curation, Methodology, Validation, Writing – Review & Editing

Sankar T
Roles: Investigation, Validation, Writing – Review & Editing

Rathika K
Roles: Formal Analysis, Investigation

Arul Prasath S
Roles: Formal Analysis, Investigation, Validation

Balamurali B
Roles: Formal Analysis, Investigation, Validation

Sathyamoorthy N.K.
Roles: Supervision, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

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Ga D, S K, Patil SG et al. Planet activeness: a new concept to enhance the accuracy of Astromet weather forecast. [version 1; peer review: 1 approved with reservations]. F1000Research 2024, 13:746 (https://doi.org/10.12688/f1000research.149941.1)

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Reviewer Report 10 Sep 2025

Shankarappa Sridhara, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, India

Approved with Reservations

https://doi.org/10.5256/f1000research.164457.r408161

Planet activeness: a new concept to enhance the accuracy of Astromet weather forecast

This manuscript presents a novel and culturally rooted framework on Planet Activeness Concept to enhance the accuracy of astrometeorological forecasts, particularly for rainfall and ... Continue reading

Planet activeness: a new concept to enhance the accuracy of Astromet weather forecast

This manuscript presents a novel and culturally rooted framework on Planet Activeness Concept to enhance the accuracy of astrometeorological forecasts, particularly for rainfall and wind speed events. The integration of planetary azimuth with hourly weather data reflects a commendable attempt to bridge traditional wisdom with modern meteorological analytics. The authors have demonstrated methodological rigor by validating planetary influence across multiple districts in Tamil Nadu using a substantial volume of datasets.
The methodology is sound and well-articulated, with clear steps for categorizing rainfall and wind speed events and correlating them with planetary positions. The use of large volume of hourly weather data for the period from (2011–2016) and its linkage with planetary azimuths is innovatively grounded. The classification of rainfall into five distinct categories and wind speed event in to seven categories adds granularity and enhances the interpretability of the results.
The results and discussion are coherent and supported by appropriate citations, including references to historical astrometeorological practices and recent climatological studies. The inclusion of relevant literature strengthens the credibility of the findings. The authors’ effort to quantify planetary influence using frequency metrics and azimuthal mapping is a significant advancement in astrometeorological modelling.
This study is especially valuable for regions where conventional numerical models face limitations due to microclimatic variability and data sparsity. The proposed activeness chart offers a scalable tool for hybrid forecasting systems and could be instrumental in developing location-specific agro-advisories. The interdisciplinary approach—combining astronomy, climatology, and indigenous knowledge is both timely and innovative and aligns well with current efforts to localize climate services.
The manuscript is well-structured, and the figures and tables are informative. The discussion is grounded in both empirical data and historical references, which adds depth to the interpretation. Minor editorial corrections (as noted separately) will further improve clarity and presentation.

Minor Corrections & Suggestions
Page 1 – Methods

Clarify the rationale for using datasets from 2011–2016 while the study period is 2018–2021. A brief justification would enhance transparency.

Page 2

Line 1: Replace “status” with “states” for grammatical accuracy.
Keywords: Consider listing them in alphabetical order for consistency with journal formatting norms.

Page 3 – Introduction

Para 1, Lines 3–5: Rephrase “The strongest...between years” for clarity and smoother flow.
Para 3: Clarify the relevance of the IPCC review in the context of astrometeorology.
Para 4: Insert a comma before “Maharashtra” for correct punctuation.

Page 4

Figure 1: Correct spellings of “Sagittarius” and “Scorpio.”
Para 1: Add clarification—“studied during 2018–2021 with the hourly weather dataset for the period from 2011–2016 and presented in this paper.”
Para 3: Align rainfall categories with Table 1: “All events (>0 mm), 0.0 to 2.5 mm, 3.0 to 10 mm, 10.5 to 25 mm, and above 25 mm.”

Page 6

Figure Title: Replace “form” with “from.”
Para 1: Correct “on line” to “online.”

Page 7 – Results and Discussion

Para 2: Revise “>0.0 mm” to “All, >0.0 mm.”
Para 3: Add publication year for “Vandeep et al.” and italicize “et al.”
Para 4: Replace “raise” with “rise.”

Page 10

Para 6: Correct “Sun at rule” to “Sun at negative,” as per Table 3.
Para 7: Correct “It” instead of “IT.”

Overall, this manuscript makes a meaningful contribution to the evolving field of climate-resilient forecasting. With minor editorial revisions, it has strong potential for broader application and interdisciplinary impact.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Climate Change, Weather Forecasting

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Shankarappa Sridhara, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, India

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10 Sep 2025 | for Version 1

Shankarappa Sridhara, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, India

5 Views Cite this report Responses(0)

Approved With Reservations

Planet activeness: a new concept to enhance the accuracy of Astromet weather forecast

This manuscript presents a novel and culturally rooted framework on Planet Activeness Concept to enhance the accuracy of astrometeorological forecasts, particularly for rainfall and wind speed events. The integration of planetary azimuth with hourly weather data reflects a commendable attempt to bridge traditional wisdom with modern meteorological analytics. The authors have demonstrated methodological rigor by validating planetary influence across multiple districts in Tamil Nadu using a substantial volume of datasets.
The methodology is sound and well-articulated, with clear steps for categorizing rainfall and wind speed events and correlating them with planetary positions. The use of large volume of hourly weather data for the period from (2011–2016) and its linkage with planetary azimuths is innovatively grounded. The classification of rainfall into five distinct categories and wind speed event in to seven categories adds granularity and enhances the interpretability of the results.
The results and discussion are coherent and supported by appropriate citations, including references to historical astrometeorological practices and recent climatological studies. The inclusion of relevant literature strengthens the credibility of the findings. The authors’ effort to quantify planetary influence using frequency metrics and azimuthal mapping is a significant advancement in astrometeorological modelling.
This study is especially valuable for regions where conventional numerical models face limitations due to microclimatic variability and data sparsity. The proposed activeness chart offers a scalable tool for hybrid forecasting systems and could be instrumental in developing location-specific agro-advisories. The interdisciplinary approach—combining astronomy, climatology, and indigenous knowledge is both timely and innovative and aligns well with current efforts to localize climate services.
The manuscript is well-structured, and the figures and tables are informative. The discussion is grounded in both empirical data and historical references, which adds depth to the interpretation. Minor editorial corrections (as noted separately) will further improve clarity and presentation.

Minor Corrections & Suggestions
Page 1 – Methods

Clarify the rationale for using datasets from 2011–2016 while the study period is 2018–2021. A brief justification would enhance transparency.

Page 2

Line 1: Replace “status” with “states” for grammatical accuracy.
Keywords: Consider listing them in alphabetical order for consistency with journal formatting norms.

Page 3 – Introduction

Para 1, Lines 3–5: Rephrase “The strongest...between years” for clarity and smoother flow.
Para 3: Clarify the relevance of the IPCC review in the context of astrometeorology.
Para 4: Insert a comma before “Maharashtra” for correct punctuation.

Page 4

Figure 1: Correct spellings of “Sagittarius” and “Scorpio.”
Para 1: Add clarification—“studied during 2018–2021 with the hourly weather dataset for the period from 2011–2016 and presented in this paper.”
Para 3: Align rainfall categories with Table 1: “All events (>0 mm), 0.0 to 2.5 mm, 3.0 to 10 mm, 10.5 to 25 mm, and above 25 mm.”

Page 6

Figure Title: Replace “form” with “from.”
Para 1: Correct “on line” to “online.”

Page 7 – Results and Discussion

Para 2: Revise “>0.0 mm” to “All, >0.0 mm.”
Para 3: Add publication year for “Vandeep et al.” and italicize “et al.”
Para 4: Replace “raise” with “rise.”

Page 10

Para 6: Correct “Sun at rule” to “Sun at negative,” as per Table 3.
Para 7: Correct “It” instead of “IT.”

Overall, this manuscript makes a meaningful contribution to the evolving field of climate-resilient forecasting. With minor editorial revisions, it has strong potential for broader application and interdisciplinary impact.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Climate Change, Weather Forecasting

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Respond to this report

Responses (0)

[1] 1. Chauhan C: Extreme weather in India taking a toll on people and economy. Hindustan times. 20 September 2017. [assessed 31.3.2020]. Extreme weather in India taking a toll on people and economy | Latest News India - Hindustan Times

[2] 2. Report on Northeast Monsoon – 2018. Chennai: Regional Meteorological Centre; 2018. Reference Source

[3] 3. IPCC AR IV: Intergovernmental panel on climate change fourth assessment report.2007; p. 104. Reference Source

[4] 4. UNFCCC: Report of the conference of the Parties on its 16th session. Germany: United Nations Framework Convention on Climate Change; 2010; 2010. .

[5] 5. IPCC AR V: Intergovernmental panel on climate change fifth assessment report summary for policy makers.2014. Reference Source

[6] 6. Mishra SK: Weather forecasting in Almanacs relating to farming Operations and Its relevance In Today’s Agriculture, ([PhD] BHU Varanasi.1998.

[7] 7. Mishra SK, Dubey VK, Pandey RC: Rain forecasting in Indian almanacs (Panchangs): A case for making Krishi-Panchang. Asian Agri-History. 2002; 6(1): 29–42.

[8] 8. Ramakrishna YS: Relevance of Ancient Wisdom on Weather forecasting for improving agro-advisories. Workshop on exploring the possibility for development a ‘common weather forecasting system by converging modern and ancient Indian sciences’. Hyderabad: CRIDA; 2006. June – 7, 2006.

[9] 9. Shankar KR, Maraty P, Murthy V, et al.: Indigenous rain forecasting in Andhra Pradesh. Central Research Institute for Dryland Agriculture; 2008; 2008.

[10] 10. Sivaprakasam S, Kanakasabai V: Traditional almanac predicted rainfall – A case study. Indian J Tradit Know. 2009; 8(4): 621–625.

[11] 11. Varshneya MC, Bhat VV, Joshi RM: Nakshatra-varsha almanac-prepared for Maharashtra. Berlin: Springer; 2002. Publisher Full Text

[12] 12. Varshneya MC, Vaidya VB, Pandey V, et al.: Forecasting of rainfall for 2011 of Gujarat based on astrometeorology. CASAM. 2009; 8(1): 2359–2370. Publisher Full Text

[13] 13. Vaidya VB: Long range forecast of monsoon with astronomy and local observations for Maharashtram. Pune: Indian Institute of Tropical Meteorology, MoES; 2004; 8(4): pp. 323–324.

[14] 14. Unnikrishnan T, Prasada Rao GSLHV: Climate Change Adaptation Strategies in Agriculture and Allied Sectors. Scientific Publishers; 2011.

[15] 15. Dheebakaran G, Arulprasad S, Kokilavani S, et al.: Astrometeorology: An option to improve the accuracy of numerical daily rainfall forecast of Tamil Nadu. J. Agrometeorol. 2017; 19(Special Issue - AGMET 2016): 205–211.

[16] 16. Rathika K, Dheebakaran G, Panneerselvam S, et al.: Studying the astrometeorological relationship between planet’s azimuth and wind speed events. Madras Agric. J. 2019; 106(4-6): 330–334. Publisher Full Text

[17] 17. Sankar T, Dheebakaran G, Panneerselvam S, et al.: Astrometeorological relationship between planet azimuth and cyclone events in bay of Bengal. Int. J. Agric. Sci. 2019; 11(11): 927–937. Publisher Full Text

[18] 18. Green HS: Weather Predicting by Astro-meteorology (Astrological Manuals). Kessinger Publishing, LLC; 2003; 18–21.

[19] 19. Seetharama Iyer KV: Serva Muhurtha Panjankam - Jaya Varusam. Lavanya Printers; 2015; 2015. .

[20] 20. Vanadeep K, Murty SS, Krishnaiah M: Meteorological Predictions Preserved in the Panchangam Versus Real-time Observations-a Case Study over Tirupati Region-a Semi-arid Tropical Site in India. Indian J. Sci. Technol. 2012; 5(4): 2491–2509.

[21] 21. Bradley DA: Your powwow corner American Astrology Appears under the pen-name of Garth Allen.1957; 42–69. Reference Source

[22] 22. Unnikrishnan T, Prasada Rao GSLHV: Climate Change Adaptation Strategies in Agriculture and Allied Sectors.2011; 2011. 9788172336790.

[23] 23. Varshneya MC, Vaidya VB, Pandey V, et al.: Forecasting of rainfall for Gujarat based on astrometeorology. Asian Agri-History. 2009; 13(1): 25–37.

[24] 24. Varshneya MC, Vaidya VB, Pandey V, et al.: Forecasting of rainfall for Gujarat based on astrometeorology. Asian Agri-History. 2009; 13(1): 25–37.

[25] 25. Riske KB: Predicting weather events with astrology. Llewellyn Publications; 2014; 2014. : p. 240. ISBN-10: 0738741582, ISBN-13: 978-0738741581.

[26] 26. Ganesan D: Ephemeris data: “Planet activeness: a new concept to enhance the accuracy of Astromet weather forecast”. [Dataset]. Dryad. 2024. Publisher Full Text

Planet activeness: a new concept to enhance the accuracy of Astromet weather forecast.

Abstract

Background

Methods

Results

Conclusion

Keywords

1. Introduction

Figure 1. Planet activeness chart for weather developed from human astrology.

2. Methods

2.1 Study area and period

Figure 2. Astromet study location for rainfall and windspeed forecast.

Table 1. Intensity wise hourly rainfall events used for the study.

Table 2. Category wise hourly wind speed events used for the study.

2.2 Azimuth and ephemeris

Figure 3. Azimuth calculation form observer (Source: https://commons.wikimedia.org/w/index.php?curid=17727911).

2.3 Planet activeness

Table 3. Planet activeness chart in respect to their angle from observer location.

3. Results and discussion

3.1 Influence of individual planet activeness on hourly rainfall events

Table 4. Activeness frequency (%) of different planets during hourly rainfall events.

Figure 4. Influence of planets’ activeness on different category of rainfall events.

3.2 Influence of individual planet activeness on hourly wind speed events

Table 5. Activeness frequency (%) of different planets during hourly windspeed events.

Figure 5. Influence of planets’ activeness on different category of hourly wind speed events.

4. Conclusion

Data availability statement

Acknowledgement

References

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