Fig.16 Comparison Real (analysis, cyan) and forecasted (1h before) reflectivity

Weather analysis Vs. forecast: 20 dBZ threshold comparison

Fig.16 Comparison Real (analysis, cyan) and forecasted (1h before) reflectivity

A widely-used index for measuring accuracy in a region recognition in images is the JaccardTanimoto Index. Given the exact shape on grid of the region (in this case, the current weather analysis) and its approximation (in this case the forecast), the Tanimoto Index TI is defined as TI = TP/(FP+FN+TP) or, in other words, the number of “pixels” of intersection on the number of pixels of the union of the two images. A TI of 85-90% or above is usually considered, in image segmentation, a very accurate result. We compared each one of the weather analysis (current weather) in the 4 days considered (96 hours total) with the forecasts for that time from 1 to 6 hours before; then we computed Tanimoto index for each and the total clouds coverage. These calculations were made for the data with threshold at 20 dBZ (level 1 or more clouds) and for the data with threshold at 36 dBZ (level 2 or more clouds). In table below are reported the average results:

Jaccard-Tanimoto Index – 20 dBZ threshold

Clouds 1.Hour Forecast 2.Hour Forecast 3.Hour Forecast 4.Hour Forecast .5Hour Forecast

6.Hour Forecast 0,094122 0,910662 0,596208 0,469113 0,391596 0,33863 0,301704

Jaccard-Tanimoto Index – 36 dBZ threshold Clouds 1.Hour 2.Hour 3.Hour 4.Hour 5.Hour 6.Hour

Forecast Forecast Forecast Forecast Forecast Forecast 0,004701 0,812975 0,433031 0,260201 0,159075 0,099386 0,067976

Table 2 Clouds reflectivity prediction reliability

It can be seen that the weather forecast accuracy at 1 hour is quite good, but it becomes much worse very quickly. Only forecasts not older than 1 hour should be used in order to plan trajectories with a reliable knowledge about the “no flight zones” due to severe weather conditions.

6.1.1.2 Wind forecast accuracy

The same calculations were done also for wind direction and speed in the same days (applying a threshold to wind speed and direction to evaluate the changes). The data were selected from the same GRIB files used before. The results present a behavior, in data prediction accuracy over time, similar to the reflectivity.

6.2 Trajectory optimization Test cases

In this paragraph are considered 2 test cases (in climb and cruise phases) that will be considered also in Chapter 7 for the real-time graph generation proposed method. The 2 test cases consist in real flights of civil aircraft in real weather conditions. The emissions related to the optimized trajectories are compared with the emissions related to the real trajectories showing that there is a big margin of possible improvement.

6.2.1 Test Case 1

In this first test case is considered the real trajectory (downloaded from flightaware archive) of an A320 (DAL1888) in cruise phase in real weather conditions (downloaded from NOAA archive). In the following paragraphs, the emissions associated to the real aircraft trajectory and the trajectories optimized in accordance to different criteria are provided and compared. The considered trajectory is originated from the International Airport of Las Vegas (KLAS) (36.080°, -115.152°) on November 11th 2012 at about 4 p.m. (UTC): DAL1888. This flight is directed to the International Airport of Memphis. It is considered a part of the cruise phase of the trajectories. We suppose that the mass of the aircraft is 64000 kg.