## 4 Discussion
Furthermore, the loose definition of the time window to carry out the observations may lead to an incorrect evaluation of the current weather situation--as illustrated by the four cases with rapidly changing meteorological conditions, underpinned by the supplementary data from the visibility sensor. Using the proposed system, this type of inconsistency can be easily avoided by regular observations scheduled at a given time point within a period of 10 min before the METAR validity time. One of the further undisputable benefits of the proposed system is its functionality to hold a backup of all the observations from the past. It allows for verifying and eventually modifying the LO at any time, both for the visibility and the cloud coverage. The small size of the sample (mentioned above in the first bullet point) implies that the findings of the current study should not be overly generalized. They only indicate that the direction of our research is encouraging, and to derive generally valid conclusions, a much deeper and thorough analysis of the performance of the RO approach is required. A new, more elaborated analysis in the near future is expected to cover a period of length of about two years and will include a visual analysis of the camera records of several thousands of weather situations by remote observers. There is another specific feature of the current analysis, and it is related to the fact that the target destination is surrounded by mountains (the High Tatras in the north). Consequently, Steps #1-3 to determine the cloud base height (described in Section 2.2) can be performed with no limitations. In sites with no mountains or tall country features nearby, however, Step #2 has to be skipped. In such case, the LO is restricted to only follow Steps #1 and #3, and naturally, the same also holds true for the RO. This might lead to different results. Unfortunately, we have not received data from a different airport without mountains or tall features to verify the consequences of (the lack of) the particular settings. Nonetheless, we expect that both the local and the remote observers would be influenced in a similar way, as in the case of a non- mountainous airport, both LO and RO would suffer in the absence of Step #2. Note that both the LO and the RO should consider the fact that a cloud layer can have different height just above the airport and when it 'touches' the nearby mountains. However, the cloud observations in METAR should be representative of the area within a radius of approximately 16 km of the aerodrome reference point [15]; therefore, possible height differences over these 16 km are not taken into account. In principle, a very specific case would be an estimation of the cloud base height even without Step #3, i.e., with no ceilometer data. In fact, though, an airport without a ceilometer is a rarity. At less important, non-airport meteorological stations, it is more common to have a human observer without a support of a ceilometer. They even have different, far less precise reporting rules that can be better fulfilled by Step #1, i.e., by recognizing the cloud types and estimating their typical heights. This is, however, not our case. We target airports, and they tend to be the best-equipped meteorological stations. The near-future ambition of the team of authors is to upgrade all the subparts of the proposed system of remote observations to a fully automated one. Attempts for automated recognition of visibility were already begun in the 1980s [16; 17]. One of the key issues of the automated estimation of visibility is to recognize the contrast of a given object with respect to its background and evaluate it against pre-defined thresholds. To carry out these analyses, data-driven methods, such as deep learning, e.g., convolutional neural networks [4; 18], are generally used. It is expected that the artificial intelligence methods would also help in automated estimation of the cloud coverage.