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What can we expect from a HF propagation model ?

Electron concentration at 320 km of altitude (F2 layer) as predicted by the CTIP model at 16:00 UTC.

Performances and limitations of ionospheric models (II)

A propagation analysis and prediction program establishes its forecasts from statistical analysis using either empirical or real-time data from which are calculated critical frequencies, the ionosphere status at global scale or the reliability of the specified circuit among other parameters.

Empirical data are based on previous studies made in the field (using ballons or satellites) which results are stored in data sets (tables or database) or converted in functions (e.g. polynomial interpolations). Real-time data are downloaded online from websites receiving in near-real-time data from ionospheric sounding transmitting by observatories.

Depending on the accuracy and completness of stored data or functions, and the one of algorithms used to calculate effects of physico-chemical processes at various latitudes and heights, one easily understand that statistical analysis will be more or less complete and accurate.

If an application works with smoothed values, medians, estimated monthly or even at longer term (6 months to one year in the case of sunspot number) that also means that forecasts will be more optimistic than the ones provided by a more complete model taking into account the time and additional parameters. If you whish an optimistic forecast you must select a low reliability (30-50%), and conversely, select a high reliability (60-90%) to get a more pessimistic, conservative forecast.

In spite of these statistical laws, against which nobody can go, some publishers do not hesitate to pretend that their forecasts are very accurate using the nec plus ultra model, all solar and geomagnetic indices, etc. But looking closer their program, such arguments do not convince for long advanced amateurs. In fact, most of the time this is unfortunately nothing else that a marketing trick. 

Let's see the different reasons for which an ionospheric model shows, and will always show inaccuracies.

A. Utility of Ap and Kp indices

Let's take an example on the utility of some indices. Several editors state that their program uses Ap, Kp or Q index, or even the latest geomagnetic model (storm, etc) to improve the accuracy of their predictions. But in reality, those publishers do even don't know what they speak about. Those parameters and not suitable for automatic updates of the ionospheric model.

In fact you must know that there is no way of predicting HF propagation without taking into account the solar and geomagtnetic indices, and all VOACAP-based applications use both factors. But most of these programs use also median values because so does VOACAP. There is a good reason for that : the daily and hourly variations of the ionosphere are huge, and we simply do not have enough data to estimate these rapid and spatially diverse changes.

Effect of Kp index on the S/N ratio (SNR), MUF and TX takeoff angle estimation at high latitude where geomagnetic effects are the strongest. Propagation conditions calculated for the 20 meter band with Ham CAP for September 2004 using an emitting power of 100 W. At left forecast for Kp = 9, at right for Kp = 1. The cursor is pointed on Island (TF). A low K-index increases the MUF of 10% and the S/N ratio up to 20% at high latitudes. The best takeoff angle does not change more that 1 in average. So the impact of Kp is not important but it must be taken into account for all predictions implying a circuit via high latitudes, say surely above 50 N and S but its impact is already sensitive in tropical areas (where the S/N changes of a few dB only). In addition, without checking Kp you could not predict the auroral oval, excepting using satellites and ionosondes.

A good model, I mean accurate even at short term, can predict what happens when the Kp index increases. Thanks to Kp we know that a geomagnetic disturbance is developing aloft, but we cannot tell if it is accompanied by an ionospheric storm or not, and if it is, then which paths are enhanced and which ones are degraded and to what extent. It is thus very important to monitor the Kp index and auroral activity, but only a handful programs deliver accurate and real time information about the indices and the auroral oval. In fact these parameters are useless in the automatic propagation prediction because in no case the Kp input improves a prediction.

To produce an accurate global ionospheric map (at earth scale), there is a much better way : it is to use direct, real time ionosonde measurements of the F layer critical frequency. Instead of trying to guess how Kp influences foF2 (like NOAA' STORM model does), we can use the foF2 itself ! DXAtlas for example, to name a performing and recent application uses such a feature in tandem with a small application called IonoProbe to predict the auroral oval as good as SEC predictions based on NOAA satellites. Recall that Alex Shovkoplyas, VE3NEA, who developed DXATlas found and fixed an error in IRI-2001 model, his name being listed in the official IRI source code. DXAtlas is thus not a propagation model as ordinary as it looks at first sight...

At left, the statistical auroral oval extrapolated from NOAA-17 satellite data by SEC/NOAA for August 30, 2004 at 20:38 UTC. At right the real-time auroral oval as predicted by Alex Shovkoplyas, VE3NEA, author of DXAtlas using real-time ionosonde data and estimated for the same time. DXAtlas prediction is based on the same statistical patterns and real-time satellite data that the ones used by NOAA to generate their auroral map.

B. An inherent imprecision

Like weather forecasts based on numerics (models), ionospheric models will always show an inherent imprecision, IRI-2001 or its down-sized versions included. Up to date, no model go one step further and fits the IRI model to the actual foF2 values reported by the ionosonde network. Here also, IonoProbe from VE3NEA does this job and summarizes the real-time data in the effective SSN value and passes it to DX Atlas, along with other real-time parameters, allowing the latter to produce real-time maps.

But is it only possible to increase the accuracy of current models ? We can collect ionosonde data over long periods of time and perform statistical analysis on these data. But up to now the picture that has emerged is very disappointing. In generating a correlogram scientists discovered that the correlation distance of foF2 is about 2000 km, and the difference of the measured values from the modeled ones reaches an order of magnitude

That means that to build an accurate foF2 profile that could be used for ray tracing, one would need at least a 4000x4000 km grid of evenly distributed ionosondes; the existing ionosonde network does not even come close to that. Another approach is trying to use other available ionospheric parameters, such as Kp, Q index and auroral activity index, to model ionospheric disturbances, and to estimate the foF2 distribution more accurately.

At left the Signal Propagation model is a product dedicated to ray-bending. It takes advantage of VOACAP, Jones-Stephenson, RIBG and EICM ionospheric models. At right the electron concentration as a function of the height and latitude as predicted by the CTIP model.

But here also, both statistical analysis and a review of available publications have given very discouraging results. An ionospheric storm for example is a very complex process that evolves in space and time. To estimate its dynamics, one would need much more real-time measurements than we can hope to have in the foreseeable future.

To get an better view of difficulties that scientists meet with, see for example, the Effects of geomagnetic storms on the ionosphere and atmosphere paper, a very interesting document published in 2001 by AGU[2]. One approach that initially looked very promising was used in the STORM model[3] developed at NOAA and introduced earlier.

This model uses the 36-hour history of hourly Ap indices to model the effect of geomagnetic disturbances on the ionosphere. DXAtlas is the first amateur program having implemented the STORM algorithm. In addition, VE3NEA compared his results to AGU predictions, and discovered an error in AGU algorithm as well. But the striking thing is that the Kp/Ap based corrections that AGU has published since 2003 were absolutely wrong, and no one have ever noticed this !

Using Kp in some VOACAP-based application is a first attempt to catch the first order dependency between the geomagnetic activity and the depletion of the F2 layer. However, in DXAtlas documentation it is clearly stated that this option is experimental; it was added just to see if there is any improvement in the predictions due to an ad-hoc correction for geomagnetic activity. Today, we can conclude that it does not seem to improve the accuracy of predictions, a conclusion that some people expected for a while.

C. New improvements

However, there is a good news. When benchmarking VOACAP and Proppy against the D1 databank, Proppy is more accurate as it uses an entirely different method of prediction. The D1 dataset is an industry standard for evaluating propagation prediction applications based on ITU-R P.1148-1 describing how prediction tools may be compared in a systematic manner. Using this method, the standard deviation of error when predicting values with VOACAP is 19 dB, P.533 reports a 10 dB improvement in the error.

Comparisons between two point-to-point predictions calculated by Proppy (left part) and VOACAP Online (right part). The MUF (as the other data) can be different because the ITURHFProp model, thresholds, required reliability and SNR used by Proppy are not the same as in VOACAP. Poppy is more pessimistic (e.g. Proppy uses a SNR of 13 dB for a 3 kHz bandwidth where VOACAP uses 8 dB for a 3 kHz bandwidth), and data are derived from ITU-R F.240-7. In addition, circuits are designed for the military use assuming the full 3 kHz bandwidth. Future versions may extend this to 24 kHz bandwidth to support data.

Note that Proppy is able to calculate path lengths up to 7000 km, and beyond 9000 km using an empirical formulation based on the range defined by LUF and MUF. It is assumed to be along the great circle in E modes up to 4000 km and via F2 modes for all distances and specially the longest.

The engine takes into account all the usual parameters : MUF, time windows (currently limited to any month in the current and next year), location, power, SSN (Smoothed-Sunspot Number from SIDC), the field strength i.e. the transmitter frequency, power and antenna gain, required S/N, amplitude, etc. The model also calculates the equatorial scattering of HF signals.

D. The bottom line

What can we conclude from these observations ? Currently there is no way to adequately model global irregular variations in the ionosphere on a time scale smaller than a month. Unlike some shareware for which authors claim that their software produces more accurate predictions by using daily and hourly parameters, the authors of the engines used by VOACAP and Proppy have realized that random values cannot be predicted but can be described statistically. It is why their engine produces monthly medians, deciles, standard deviations, probabilities of service, etc. Such statistics are predictable and accurate (remember the accuracy of insurance statistics), predictions for a particular date and hour are just speculations.

Remember also that VOACAP was initially designed for experimenting with prediction algorithms, but the experimentation was never finished due to lack of funding. Just take a look at the list of available prediction methods available in VOACAP - there are 30 - and all output parameters to understand all the extent of the problem. For example, you can specify the month and day of prediction, but this will automatically force the program to use URSI coefficients - which should never be done because, according to the VOACAP authors, these coefficients produce incorrect predictions. (Now you know why Ham CAP does not use the day of the month either !). But as we will see in reviewing VOACAP, there are tens of ways to produce invalid VOACAP predictions by setting incorrect parameters. Many of them were explained in the VOACAP mailing list at QTH.net. If you are not subscribed yet, I highly recommend you this list.

Next chapter

Requisit and specification of amateur programs

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[2] Today, like most professional resources, the access to AGU documents requires to be identified on their website. The document can also be download, here also after subscription, from ZDNet UK White papers and several other scientific resources.

[3] Geomagnetic storms are probably the most important phenomenon among those related to solar wind and high-energy particles. They produce large and global disturbances in the ionosphere, but they affect also the neutral atmosphere, including the middle atmosphere and troposphere [e.g., Lastovicka, 1996]. The geomagnetic storm is a complex process of solar wind/magnetospheric origin. Various features of this complex process act at various altitudes in the ionosphere and neutral atmosphere. The STORM model is an experiment that has constructed a "vertical profile" and related scenarios of the geomagnetic storm effects on the Earth's atmosphere and ionosphere starting from the F2 -layer maximum down to the troposphere. However, being given that effects of geomagnetic storms at different altitudes and latitudes differ in development in time and in intensity, reflecting different features of geomagnetic storms, modeling of their mechanisms is a difficult task, to say the least.


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