Forecasts are arranged into the following categories:
Forecasts are arranged into the following categories:
For those who are interested in traditional or long range forecasts, I have repackaged the forecasts from weather.com into the following categories:
The data presented on these pages are derived from forecasts of vertical weather profiles supplied by the national weather services. Both 3.5 and 7.5 day forecasts are provided. The forecasts are based on computer models that use balloon soundings and GOES satellite soundings as input. The forecasts are for three towns (Elkins, WV, Lewisburg, WV, and Hot Springs, VA ) that are 45-60 miles from the Green Bank observatory. By 'averaging' the data from the three sites, it is hoped that the results represent the conditions over the observatory.
The 3.5 forecasts are based on the "North American Mesoscale" (NAM, forally known as ETA) model. ".... The model is run four times a day (00, 06, 12, 18 UTC) out to 84 hours. It is currently run with 12 km horizontal resolution and with 1 hour temporal resolution, providing finer detail than other operational forecast models." (http://en.wikipedia.org/wiki/North_American_Mesoscale_Model).
The 7.5-day forecasts are based on the the first half of the 16-day GFS (Global Forecast System, previously AVN) atmospheric models. Tthe half of the model I use has a 35 km horizontal resolution, a 3 hour temporal resolution, and is updated twice a day (http://en.wikipedia.org/wiki/Global_Forecast_System). The courser resolution of the GFS model suggests that one should only use the GFS results only beyond the 3.5 day cutoff of the NAM forecasts. Note that the 7.5-day forecasts do not include percentage cloud cover but clouds are still being used to estimate opacities, etc.
Each forecasts consists of a time series of ground weather conditions (pressure, temperature, wind speed and direction, dew point...), gross weather conditions (cloud cover, total precipitable water, ...) and, most importantly for the work presented here, pressure, humidity, dew point, cloud fraction, ... as a function of time and height above the above mentioned towns. The models divide the atmosphere into ~64 layers that extend to over 20,000 m. Since these forecasts and models are what the weather services use to predict weather for our area, it's one of the best data sets for local forecasting. The data are downloaded from ftp://ftp.meteo.psu.edu four times a day and archived for future use.
The calculations performed on these data, unlike most other attempts at calculating weather conditions for cm- and mm-wave observations, are not based on a model atmosphere with assumed pressure heights or temperature lapse rates. Rather, calculations are performed on each layer of the atmosphere, thereby eliminating any assumptions concerning the atmospheric profile above the observatory. Calculations are performed every two hours in TCL by a CLEO application (http://www.gb.nrao.edu/~rmaddale/GBT/CLEOManual/index.html) which also automatically generates these web pages.
The number of results that can be produced by the CLEO application is rather extensive and only a subset of what is possible is presented here. Nevertheless, there's usually about thirty graphs that are given here that I hope help our observers plan their observations and help with their data calibration. The contents of these pages will change as we learn more about what our observers and staff require.
In June 2005 I released a CLEO graphical interface that allows users the ability to generate graphs for just their desired weather parameters. Since the raw data are archived, one can use the archive to determine weather parameters for past observations or to use the archive to accumulate weather statistics.
The following sections give a general guide to what is usually available.
The displayed graphs usually consist of wind speeds and temperatures as a function of UT, which should help observers avoid times when the weather conditions either preclude observing (too cold or windy for the telescope to operate) or wind conditions that are unsuitable to high frequency observing. I also include here precipitable water vapor estimates which can be used to predict, roughly, whether the opacity will be good for a particular observing frequency. A better measure of opacity is provided off of the Opacity link above.
Opacities are derived via the MWP model of Liebe 1985), with some modifications by Danese and Partridge (1989). Opacities are calculated based on the contributions from 40 O2 resonance lines, three H2O resonance lines, H2O continuum, and the dry air. The model should be accurate for most purposes up to 120 GHz -- currently to save some computational time, the displayed graphs are limited to below 50 GHz.
As of Sept, 2005, I have added in the contributions to opacity from hydrosols (fog, cloud water droplets, etc.). I'm using the cloud model described by Schwab and Hogg (1989), combined with the Liebe hydrosol continuum model. It's a compromise technique and assumes a cloud is present in any layer of the atmosphere where the humidity is 95% or greater. The thickness of the cloud layer determines the density of water droplets -- 0.2 g/m^3 for clouds thinner than 120 m, 0.4 g/m^3 for clouds thicker than 500 m, with linearly-interpolated densities for clouds of intermediate thickness.
The opacity plots typically provided are:
Once the opacity is calculated for each layer in the atmosphere, one can then use radiative transfer to derive the contribution to the system temperature that is due to the atmosphere. This quantity is helpful since Tsys directly influences the noise in an observation. The derived graphs show only that part of Tsys that is contributed by the atmosphere. They do not include the contributions from the receiver, spillover, and the 3K microwave background.>
Plots consist of:
Neither system temperatures or opacities alone determine the affects of the atmosphere on observations. Firstly, the atmosphere attenuates the atmospheric signal and, secondly, the atmosphere emission can be a significant contributor to the system temperature. Both the atmospheric attenuation and emission are important factors in determining the amount of observing time needed to achieve a certain signal to noise.
I define the Effective System Temperature (EST) as Tsys*exp(Tau*AirMass). EST is proportional to the square root of the integration time needed to achieve a desired signal to noise. Tsys is the sum of the contributions from the atmosphere at the observing elevation (Tatm*(1-exp(tau*AirMass))), spillover (assumed to be 3 K for the GBT), the cosmic microwave background (3 K), and the receiver. Thus, EST is receiver, frequency, telescope, elevation, and weather dependent. (Note that I am not including in EST the contribution of any strong continuum source or background galactic emission.)
I next define the Relative Effective System Temperature (REST) as EST / EST0 where EST0 is the value of EST under the best possible weather conditions for Green Bank for the same elevation at which EST is determined. REST is exactly equal to sqrt(t/t0), where t is the integration time needed to perform an observation under the current weather conditions and t0 is the integration time needed under the best possible weather conditions. I used the weather conditions between 1 Oct 2004 and 1 May 2005 to determine values for EST0.
Plots on the forecast page consist of:
It might be useful to consider the following guiding principles in using REST plots.
Classically, observers use a measure of Tsys as a function of elevation (a 'tipping') to determine atmospheric opacity. The usual problem is that one has to assume a representative temperature (Tatm) for the atmosphere in this analysis. The degree to which the assumed Tatm is wrong is directly reflected in the inaccuracy of the derived opacity. These assumptions are no longer needed since one can determine Tatm from vertical weather data.
Plots consist of
Probably only staff will find the estimates of refraction interesting. Like the other calculations presented here, one can use vertical profiles and in-situ measures of the index of refraction to derive the amount by which the telescope's pointing needs to be adjusted for the difference between the refracted and true elevation of a source.
I provide a comparison between the refraction derived from vertical profiles to two other methods that are based on ground-level weather data. The first is that produced by the SlaLib refraction package which uses a standard lapse rate and pressure heights, with ground level weather parameters (temperature, pressure, and dew point). The other uses an empirical fit to the model presented in GBT Memo 112 (Maddalena 1994) and the same ground weather values. The latter model, except for the empirical fit, is that which is currently in use by the GBT.
Additionally, the GBT has an interesting optics problem due to refraction. Richard Simon (1994, private communication) first pointed out that, since the top and bottom edge of the GBT are at two very different elevation, the atmospheric paths for rays that hit the top of the dish will pass through a different atmosphere than the rays hitting the lower edge. This differential refraction will alter the shape of objects that are observed close to the horizon, essentially elongating sources in the elevation direction. The telescope will have a virtual astigmatism, then, that is due solely to the atmosphere. One of the plots I present illustrates the magnitude of this differential refraction on source size for each meter of aperture. The amount of virtual astigmatism is very weather dependent and poses a challenge to those wanting to observe close to the horizon. One way to correct for this astigmatism is to properly deform the telescope's shape out of a parabola in the elevation direction.
The refraction plots typically provided are:
K.D. Froome and L. Essen, "The Velocity of Light and Radio Waves", 1969, (New York: Academic Press).
W.S. Smart, "Textbook on Spherical Astronomy", 1977, (New York: Cambridge Univ. Press).
H.J. Liebe, "An Updated model for millimeter wave propagation in moist air", 1985, Radio Science, 20, 1069
H.J. Lehto, "High Sensitivity Searches for Short Time Scale Variability in Extragalactic Objects", 1989, Ph.D. Thesis, University of Virginia, Department of Astronomy, pp. 145-177.
L. Danese and R.B. Partridge, "Atmospheric Emission Models: Confrontation between Observational Data and Predictions in the 2.5-300 GHz Frequency Range", 1989, AP.J. 342, 604.
F.R. Schwab and D.E Hogg, "Analysis of Radiosonde Data for the MMA Site Survey and Comparison with Tipping Radiometer Data" (1989), from the IAU Symposium on "Radio Astronomical Seeing", pp 116-121.
J. Meeus, "Astronomical Algorithms", 1990 (Richmond: Willman-Bell).
J.M. Rueger, "Electronic Distance Measurements", 1990 (New York: Springer Verlag).
R.J. Maddalena "Refraction, Weather Station Components, and Other Details for Pointing the GBT", 1994, NRAO GBT Memo 112 (and references therein).
K. Rohlfs and T.L. Wilson, "Tools of Radio Astronomy, 2nd edition", 1996, pp. 165-168.
B. Butler, "Precipitable Water Vapor at the VLA -- 1990 - 1998", 1998, NRAO MMA Memo #237 (and references therein).
Bufkit (http://www.wbuf.noaa.gov/bufkit/bufkit.html) (and associated web pages.)
Slalib (http://star-www.rl.ac.uk/star/docs/sun67.htx/sun67.html/)
Weather Watcher (http://www.singerscreations.com)