# A High Latitude Model for the E Layer Dominated Ionosphere
## 4 Modeling Results
### NEEM-P: Model for the ELDI Occurrence Probability %ELDI  
As explained in Section 3.2.2, we divided the value ranges of the model drivers into intervals to compute the external %ELDI model coefficients. On the one hand, the number of intervals of a considered driver and, thus, of the resulting data points must be high enough to adequately represent the curve formed by the base functions included in the  
Figure 4: Observed (red) and predicted (black) wvE trends for the modeling dataset. Left column: Northern Hemisphere. Right column: Southern Hemisphere. Last row: RMS and NRMS deviation between observation and prediction.  
associated model term. On the other hand, this number is limited to ensure that each interval key is assigned a sufficiently large amount of profiles to obtain a good \(\%ELDI\) resolution. Our initial trend analysis showed that for describing the _MLAT_ dependency, a single Gaussian function is suitable. For describing the _LT_ and _DOY_ dependencies, the superposition of two cosine functions is appropriate in each case. All the listed function terms can be sufficiently approximated by six points each. In comparison, the basically linear \(F10.7\) and \(E_{Conv}\) dependencies can be described by three points each. This results in the following interval numbers for the model drivers: _MLAT_: 6, _LT_: 6, _DOY_: 6, _F10.7_: 3, _EConv_: 3. For the modeling dataset, we want to ensure a minimum \(\%ELDI\) resolution of 1% for fitting the model equation to the \(\%ELDI\)-model driver combinations of the keys. Hence, we set the threshold for the minimum number of assigned profiles required by a valid key to 100. Table 5 shows statistical information about the keys generated for both hemispheres. Thereafter, we give the \(\%ELDI\) model equation (Equation (6)) and its coefficients (Table 6).  
Considering the trends for both hemispheres, we see that the largest \(\%ELDI\) value is about 12%. Similar to \(NmE\), \(\%ELDI\) also shows a maximum between 65\({}^{\circ}\) and 70\({}^{\circ}\)_MLAT_, which fits well with the findings of [PERSON] et al. [8; 9]. In the case of a reduced solar zenith angle, as for example around local noon or in local summer, \(\%ELDI\) is decreased, which matches the observations of [PERSON] et al. [10] and [PERSON] et al. [8; 9]. A decrease in \(\%ELDI\) is also visible for increased solar activity, which was likewise found previously by [PERSON] et al. [8; 9]. These dependencies could be explained by the fact that both a small solar zenith angle and a high solar activity are associated with an increased amount of solar irradiance. This causes an enhanced ionization of the upper ionospheric layers, especially of the F2  
Figure 5: Observed (red) and predicted (black) \(\%ELDI\) trends for the modeling dataset. (**Left**) Northern Hemisphere. (**Right**) Southern Hemisphere. Last row: RMS and NRMS deviation between observation and prediction.  
layer, thereby hiding the ionization of the E layer produced by particle precipitation. In this way, the number of occurring ELDI events decreases. For an increasing convection electric field magnitude, we also see an increase in %\(ELDI\), which is in line with the findings for \(NmE\) and was also observed by [PERSON] et al. [9]. For both hemispheres, the range of the observed %\(ELDI\) is largest depending on \(LT\) (north: 9.1 %, south: 13.0 %).