## 4 Results
### Performance
In addition, here, accuracy is slightly higher in very cool conditions, MAD stays below 1 K for the linear, the [PERSON] et al., 2001 and the daytime Jimenez-Munoz and Sobrino 2003 methods and 0.4 K all the split-window methods, except the nighttime coefficients of Jimenez-Munoz and Sobrino 2008. At moderate temperatures, the accuracies get slightly lower again, while for higher temperature ranges, accuracies get better. These slight shifts are a result of available input data for the different ranges of columnar water vapour and initial guess LST. By narrowing the range of columnar water vapour to 0-25 kg\(\cdot\)m\({}^{-2}\), this effect would not show in the figure anymore. [PERSON] and [PERSON] 2008 might time performs least well at very low temperatures with columnar water vapour higher than 15 kg\(\cdot\)m\({}^{-2}\), therefore a peak value is shown in Figure 8. The Jimenez-Munoz and Sobrino 2003 algorithm is mostly out of axis range in Figure 8.  
In case of the split-window algorithms, the differences between the single methods are low; MAD and RMS differences stay below 0.2 K. Under normal atmospheric conditions the choice of algorithm does not seem to be crucial. The method of [PERSON] et al. 2008 incorporates the cosine of the view angle. However, obviously, this expression does not have an advantage over the other methods, when using the view angle optimized coefficients (Figures 7 and 8). In case of very high view angles (60\({}^{\circ}\)) the performance of this method is even worse (data not show). In its original version ([PERSON] et al.,  
Figure 7: Performance measures (**a**) \(r^{2}\); (**b**) MAD; (**c**) RMS; (**d**) STDEV of all revised algorithms, separated for different classes of CWV and an initial guess LST range from 270 to 310K. Results are retrieved from the case C coefficient set, a sensor view angle of 0\({}^{\circ}\), and daytime (given by symbol \(\Circle\)) and nighttime (given by symbol \(\times\)) conditions. 2008) the algorithm was used with coefficients regressed using all angles, the results are therefore not directly comparable. For both the mono-window and the split-window algorithms accuracy differences were found between the sensors due to their different spectral response. For most split-window equations the differences were found to be constant (difference between maximum and minimum MAD) mostly below 0.6 K), with decreasing differences in more humid conditions. The mono-window algorithms showed a higher spread with maximum differences between the sensors reaching 2 K for humid conditions. [PERSON] and [PERSON] (2003) has exceptional high deviations in humid conditions, as this algorithm is not suitable for such conditions. Table 5 shows the mean of all maximal deviations between sensors for given CWVs. Figure 8: Performance measures (**a**) \(r^{2}\); (**b**) MAD; (**c**) RMS; (**d**) STDEV of all revised algorithms, separated for different classes of initial guess LST and a columnar water vapour range of 0–35 kg\(\cdot\)m\({}^{-2}\). Results are retrieved from the case C coefficient set, a sensor view angle of 0\({}^{\circ}\), and daytime (\(\circ\)) and nighttime (\(\times\)) conditions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **CWV = 0** & **CWV = 10** & **CWV = 20** & **CWV = 30** & **CWV = 40** \\ \hline \multicolumn{6}{c}{**Mono-Window**} \\ \hline Price 1983 & 0.62 & 0.73 & 0.76 & 0.88 & 1.12 \\ [PERSON] et al., 2001 & 0.52 & 0.37 & 0.30 & 0.63 & 1.19 \\ [PERSON] and [PERSON] 2003 & 0.34 & 1.45 & 7.14 & 19.72 & 36.66 \\ Linear & 0.26 & 0.10 & 0.46 & 1.16 & 1.80 \\ \hline \multicolumn{6}{c}{**Split-Window**} \\ \hline Price 1984 & 0.70 & 0.66 & 0.52 & 0.31 & 0.21 \\ [PERSON] and [PERSON] 1990 & 0.71 & 0.67 & 0.52 & 0.32 & 0.20 \\ [PERSON] et al., 1994 & 0.61 & 0.66 & 0.52 & 0.32 & 0.19 \\ [PERSON] and [PERSON] 1996 & 0.72 & 0.66 & 0.53 & 0.32 & 0.19 \\ [PERSON] and [PERSON] 2008 & 0.64 & 0.64 & 0.46 & 0.28 & 0.29 \\ [PERSON] et al., 2008 & 0.60 & 0.64 & 0.50 & 0.32 & 0.23 \\ \hline \hline \end{tabular}
\end{table}