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Obtaining deltaT for use in software

Obtaining deltaT for use in software


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I'm currently developing a javascript application in which I want to calculate the approximate position of the sun. This works quite fine but requires the value for deltaT (TT-UT) to be set depending on the year for which I want to calculate the solar position.

Currently, I'm using a default value of 67 for my calculation. However, since I want to calculate the solar position for several years I'm looking for a convenient way to obtain the deltaT value for each year.

To all of you, that have some experience with programming: Is there any interface (API) that provides me with the desired values? Of course, it would also be sufficient to get the universal and terrestrial time so that I can calculate deltaT on my own.


I'm not aware of any APIs that provide $Delta T$, but you may be able to parse https://datacenter.iers.org/eop/-/somos/5Rgv/latest/16 for the value $Delta T$.

Of course if you want to calculate the approximate position of the Sun, a few second more or less should not matter too much ;-)


See Where can I find/visualize planets/stars/moons/etc positions? for an extremely general answer on how to compute positions, but the files you're looking for specifically are the "leap second kernels" at http://naif.jpl.nasa.gov/pub/naif/generic_kernels/lsk/

There was a mild kerfuffle on spice-discussion lists when NASA failed to update the kernels after the end-of-2016 leap second was announced, but they have updated it now.


A historical table from 1961 to the present of TAI-UTC is maintained here:

ftp://hpiers.obspm.fr/iers/bul/bulc/UTC-TAI.history

Delta T can be calculated by adding 32.184s, the difference between TT and TAI, to the value (TAI-UTC) in the table.

So currently Delta T is about 68 and will likely be 69 in a few years. It's increasing by one every 3 years or so.

However, values of UTC are adjusted with leap seconds so we can use precision clocks that aren't continuously tweaked. UT1 is the precise measure of "Earth" time. You can modify this to reference UT1 using the DUT1 value which is distributed with NIST time signals, WWV etc. It's value is published here:

http://hpiers.obspm.fr/eoppc/bul/buld/bulletind.131


The actual equations used by NASA are located here:

https://eclipse.gsfc.nasa.gov/SEcat5/deltatpoly.html

I failed to find any pre-written code and consequently wrote my own in Swift. The equations are fairly straightforward and a list of the possible errors these equations may produce is linked to that page as well.

Here are the polynomials:

Using the ΔT values derived from the historical record and from direct observations (see: Table 1 and Table 2 ), a series of polynomial expressions have been created to simplify the evaluation of ΔT for any time during the interval -1999 to +3000.

We define the decimal year "y" as follows:

y = year + (month - 0.5)/12

This gives "y" for the middle of the month, which is accurate enough given the precision in the known values of ΔT. The following polynomial expressions can be used calculate the value of ΔT (in seconds) over the time period covered by of the Five Millennium Canon of Solar Eclipses: -1999 to +3000.

Before the year -500, calculate:

ΔT = -20 + 32 * u^2 where: u = (y-1820)/100

Between years -500 and +500, we use the data from Table 1, except that for the year -500 we changed the value 17190 to 17203.7 in order to avoid a discontinuity with the previous formula at that epoch. The value for ΔT is given by a polynomial of the 6th degree, which reproduces the values in Table 1 with an error not larger than 4 seconds:

ΔT = 10583.6 - 1014.41 * u + 33.78311 * u^2 - 5.952053 * u^3 - 0.1798452 * u^4 + 0.022174192 * u^5 + 0.0090316521 * u^6 where: u = y/100

Between years +500 and +1600, we again use the data from Table 1 to derive a polynomial of the 6th degree.

ΔT = 1574.2 - 556.01 * u + 71.23472 * u^2 + 0.319781 * u^3 - 0.8503463 * u^4 - 0.005050998 * u^5 + 0.0083572073 * u^6 where: u = (y-1000)/100

Between years +1600 and +1700, calculate:

ΔT = 120 - 0.9808 * t - 0.01532 * t^2 + t^3 / 7129 where: t = y - 1600

Between years +1700 and +1800, calculate:

ΔT = 8.83 + 0.1603 * t - 0.0059285 * t^2 + 0.00013336 * t^3 - t^4 / 1174000 where: t = y - 1700

Between years +1800 and +1860, calculate:

ΔT = 13.72 - 0.332447 * t + 0.0068612 * t^2 + 0.0041116 * t^3 - 0.00037436 * t^4 + 0.0000121272 * t^5 - 0.0000001699 * t^6 + 0.000000000875 * t^7 where: t = y - 1800

Between years 1860 and 1900, calculate:

ΔT = 7.62 + 0.5737 * t - 0.251754 * t^2 + 0.01680668 * t^3 -0.0004473624 * t^4 + t^5 / 233174 where: t = y - 1860

Between years 1900 and 1920, calculate:

ΔT = -2.79 + 1.494119 * t - 0.0598939 * t^2 + 0.0061966 * t^3 - 0.000197 * t^4 where: t = y - 1900

Between years 1920 and 1941, calculate:

ΔT = 21.20 + 0.84493*t - 0.076100 * t^2 + 0.0020936 * t^3 where: t = y - 1920

Between years 1941 and 1961, calculate:

ΔT = 29.07 + 0.407*t - t^2/233 + t^3 / 2547 where: t = y - 1950

Between years 1961 and 1986, calculate:

ΔT = 45.45 + 1.067*t - t^2/260 - t^3 / 718 where: t = y - 1975

Between years 1986 and 2005, calculate:

ΔT = 63.86 + 0.3345 * t - 0.060374 * t^2 + 0.0017275 * t^3 + 0.000651814 * t^4 + 0.00002373599 * t^5 where: t = y - 2000

Between years 2005 and 2050, calculate:

ΔT = 62.92 + 0.32217 * t + 0.005589 * t^2 where: t = y - 2000

This expression is derived from estimated values of ΔT in the years 2010 and 2050. The value for 2010 (66.9 seconds) is based on a linearly extrapolation from 2005 using 0.39 seconds/year (average from 1995 to 2005). The value for 2050 (93 seconds) is linearly extrapolated from 2010 using 0.66 seconds/year (average rate from 1901 to 2000).

Between years 2050 and 2150, calculate:

ΔT = -20 + 32 * ((y-1820)/100)^2 - 0.5628 * (2150 - y)

The last term is introduced to eliminate the discontinuity at 2050.

After 2150, calculate:

ΔT = -20 + 32 * u^2 where: u = (y-1820)/100

All values of ΔT based on Morrison and Stephenson [2004] assume a value for the Moon's secular acceleration of -26 arcsec/cy^2. However, the ELP-2000/82 lunar ephemeris employed in the Canon uses a slightly different value of -25.858 arcsec/cy^2. Thus, a small correction "c" must be added to the values derived from the polynomial expressions for ΔT before they can be used in the Canon

c = -0.000012932 * (y - 1955)^2

Since the values of ΔT for the interval 1955 to 2005 were derived independent of any lunar ephemeris, no correction is needed for this period.


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    Obtaining deltaT for use in software - Astronomy

    AA+ is a C++ implementation for the algorithms as presented in the book "Astronomical Algorithms" by Jean Meeus. Source code is provided with the book, but it includes (IMHO) a restrictive license, as well as not having been updated for the 2nd revision of the book which includes new and interesting chapters, on areas such as the Moons of Saturn and the Moslem and Jewish Calendars. To make the most of my code, you will really need a copy of the book. This can be purchased from Amazon or directly from the publishers Willman-Bell.

    Example areas covered include the positions of the planets, comets, minor planets and the Moon, calculation of times of Rising, Setting and Transit, calculation of times of Equinoxes and Solstices plus calculation of the positions of the moons of Jupiter and Saturn as well as many other algorithms presented in the book. This is one of the biggest frameworks I have ever developed and includes 415+ thousand lines of code!


    Obtaining deltaT for use in software - Astronomy

    Thank you for your interest to the article and library.

    I don't quite understand what you mean by "real world application for close loop control system": actually ANY real world functional control system is a close-loop one. Probably the first well-known such system is steam engine with fly-ball (centrifugal) governor invented by James Watt & Matthew Boulton back in 1788.

    Hi,
    I would like to know how to apply your solution into a simple PID control system.
    For example: How to regulate an output pressure in a plant using your solution? 1 input (set point) and 1 output (PV)

    Most of standard PID control loop is similar to the one shown below. Your solution is much more different from standard PID.
    Sorry, I am not really good in high level Math.

    Here is a simple software loop that implements a PID algorithm:
    previous_error := 0
    integral := 0

    loop:
    error := setpoint − measured_value
    integral := integral + error × dt
    derivative := (error − previous_error) / dt
    output := Kp × error + Ki × integral + Kd × derivative
    previous_error := error
    wait(dt)
    goto loop

    Thank you for your interest to the article. This approach is much more complex w.r.t. a simple PID. So, to use it you should have reason for this. If a simple PID satisfies your requirements then no need for such complications.
    The main idea that we control our system with a set of feedbacks by all coordinates (actual or simulated) and inputs. Actually a simple PID we can easily restructured to feedbacks - and feedbacks to PID (may be of higher order). In case of linear system PID with constant parameters can be generated from feedback coefficients - consider the first numeric example in the article. The system provides displacement of a mass to 10 m on 9 s without displacement overshoot. And regulator with a single input and two negative feedbacks on displacement and velocity may be converted to one with a single feedback of displacement and PID.
    IMHO an approach described in the article (variant of iLQR - iterative Linear-Quadratic Regulator) is especially useful for complex non-linear systems.

    Recursion is a powerful tool in terms of the ability to use it to write fairly concise code that is powerful in capability. The main problem with recursion is that it creates a great deal of extra overhead for the processor compared to a more linear solution. So, in the context of needing optimal code to keep a control system responsive in a dynamically changing environment, I would advise that recursive algorithms should be avoided. You could prove me wrong with simple recursive algorithms running on powerful processors, but getting into the habit of using such a paradigm in this type of situation is bound to lead to trouble down the road as you progress to using more and more complex recursive algorithms.

    What is the square optimal control problem? The term is not used anywhere else in the article.

    Thank you for reading.
    Article discusses just one possible way of control policy generation. PID is also just one possibility of the control - along with many others.
    Actually PID algorithms may be implemented with the set of feedbacks like shown in Close-Loop Control System picture in the article - it is intuitively understood and can be easily proofed for linear case.
    Best regards.
    Igor

    Suppose we have a tank which we fill with water through some valve. We have sensor of water volume (or level), say, some float connected to valve actuator. Valve can be in one of two positions: either open or closed. As soon as float indicates that level of water has reached 100 per cent the valve is closed. This is trivial control system with no real dynamic, and of course sophisticated control approach discussed in the article is no needed for such simple and evident case. According to the Russian proverb, to use such complications in this simple case is like shoot at sparrow with artillery gun.

    Now consider more complex problem. We need to move some object on 10 m in relatively short time, but during this movement its velocity should not exceed certain value (this is the first numeric example in the article).

    great article (you've got 5).
    Please can you update mathematical expressions in section numerical example?
    IE browser always shows only 'Math expression error'.

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    Tips and tricks for Solar imaging and processing with ASI290MM and ASI174 cameras

    Let say that we have Solar H-alpha telescope and we would like to buy an appropriate camera for Solar imaging in H-alpha light to make our first image of the Sun in H-alpha light. What should we know?

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    Sharpening the stacked image can be achieved by many different techniques and softwares. The simplest and very effective method for sharpening is using the free software called ImPPG. ImPPG uses Lucy Richard deconvolution technique. The Sigma slider can be finely tuned to estimate the point spread function of the particular image and the results can be seen quickly.

    After stacking process select the best image file for post-processing and add an artificial color of a final image. In this step, you can use Adobe Photoshop or any similar image processing tool.


    150-mm Solar telescope equipped with ASI290MM during the imaging near the sea. Your solar telescope should be mounted at the locations that minimizes the first meters of turbulence. Water surrounded locations show a minimal turbulence at low layers of atmosphere. The best seeing is usually in the morning from 10AM to 12AM.

    Our Sun is a fascinating target in H-alpha light even when the solar activity is low. If you will frequently observe the Sun, you will notice how active and dynamic it is from time to time. During imaging be creative and experiment with settings and processing techniques. There is no only one rule! We will be happy to hear your results and findings.


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    Comments

    "1. Short focal-length optics
    2. Fast focal-ratios
    3. Avoid tiny pixels
    4. Use a color camera instead of mono with filters"
    I definitely agree with 1 & 2.
    Tiny pixels is debatable depending on what one considers to be "tiny"!? And 1 & 2 can offset "tiny" to a degree.
    Color is nice but some of the best astro-images I've ever captured over 30+ years of shooting are monochrome. I think it depends on personal choice. I like mono. A lot of people really like color.
    bwa