## 3 Results
### Temporal Sampling Scheme Induced Errors (Temporal Sampling Error)
#### 3.2.2 Errors Induced by Sampling Temporal Sequence  
As indicated in Figure 6, the shape of the estimated-MERO-measured flux fitting curve is strongly correlated with the distribution of the discretized temporal samples; if the temporal samples cover the vital control positions (the bottoms, the ascent and descent slopes and the peaks), the estimated-MERO-measured flux fitting curve would get close to the true value curve, thus resulting in less error. By contrast, fitting with the uncovering of these important positions would lead to a bigger measurement error. This indicates that a suitable sampling temporal sequence, which would alter the distribution of the discretized temporal samples under a fixed sampling interval, is crucial for a MERO mission. For example, under the fixed sampling interval of 2 h, a sampling temporal sequence with a start at local time of 16 min balanced covers the left and right slope bottoms, the ascend and descend slops and the top of the half-sine fit curve of the OSR measurement, which serves as a strong constraint on the opening extension and shape of the fitting curve  
Figure 7: Temporal sampling error induced by sampling intervals under a constant sampling temporal sequence (start time of 0 min); range of the sampling interval is 240 min–1 min; step is 10 min at the range of 240 min–20 min’ step is 1 min at the range of 20 min–1 min. (Figure 8a), whereas temporal sequence with a start at local time of 104 min (Figure 8b) tends to the left slope, making the opening extension and the shape of the fitting curve less constrained; therefore, sampling temporal sequence with a start time of 16 min would result in a smaller measurement error (3.9%) than the sampling temporal sequence with a start time of 104 min (6.9%). A detailed description of the effect of sampling temporal sequence on the measurement error is illustrated in Figure 9; such effects under different sampling intervals have different extent (maximum error minus minimum error). For example, the effect ranges of the OSR and OLR fluxes under 2 h sampling interval are 3.45% and 3.17%, respectively, which are higher than those under a 1.5 h sampling interval (3.14% for OSR and 2.35% for OLR). This is because smaller sampling intervals have more temporal samples, which cover larger parts of the half-sine fit curve; a change of the temporal sampling sequence would have less effect on the shape of the fitting curve, leading to less variation in the measurement error. To fully assess the sampling temporal sequence effect on the measurement error under different sampling intervals, we define the sampling temporal sequence influence at a certain sampling interval as the effect range mentioned above. For example, the effect ranges of sampling temporal sequence on measurement error under a sampling interval of 2 h for TOA OSR and OLR fluxes are 3.45% and 3.17%, respectively (Figure 9a). The full assessment of the effect of sampling temporal sequence on measurement error under different sampling intervals is illustrated in Figure 10. It is found that the TOA OSR flux  
Figure 8: Comparisons of the true and simulated MERO-measured TOA OSR fluxes under a fixed sampling interval of 2 h (**a**) with the sampling temporal sequence in the start time of 16 min and (**b**) with the sampling temporal sequence in the start time of 104 min. has a wide range of influence from the sampling temporal sequence (0.6-17.5%), while that of OLR is relatively smaller (0.2-4.9%). This is because the amplitude of the TOA OSR flux half-sine curve is much larger than the TOA OLR flux (OSR flux is larger than 300 W/m\({}^{2}\); OLR flux is less than 50 W/m\({}^{2}\)); the same change of sampling temporal sequence would make a larger offset to the OSR curve than the OLR curve, which would result in a more distinct OSR measurement error change than the OLR observations.