Correction: Kolbe, C., et al. Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit—Part 2: Precipitation Rates with Elektro-L2 and Insat-3D. Remote Sensing 2020, 12, 2114

The authors wish to make the following corrections to this paper [...]

. Daily precipitation sums for gauge calibrated MW precipitation rates with the predicted precipitation (a) and with IR only precipitation rates (b). (c,d) depict the gauge calibrated MW precipitation rates, the predicted precipitation rates the IR only precipitation rates on an averaged daily cycle as boxplots. The boxes display the percentiles (25th, 50th and 75th). The whiskers indicate extreme data up to 1.5 times of the interquartile range. Crosses mark outliers. The width of the boxes is relative to the number of validation scenes. Figure 9. Daily precipitation sums for gauge calibrated MW precipitation rates with the predicted precipitation (a) and with IR only precipitation rates (b). (c,d) depict the gauge calibrated MW precipitation rates, the predicted precipitation rates the IR only precipitation rates on an averaged daily cycle as boxplots. The boxes display the percentiles (25th, 50th and 75th). The whiskers indicate extreme data up to 1.5 times of the interquartile range. Crosses mark outliers. The width of the boxes is relative to the number of validation scenes. Figure 9. Daily precipitation sums for gauge calibrated MW precipitation rates with the predicted precipitation (a) and with IR only precipitation rates (b). (c,d) depict the gauge calibrated MW precipitation rates, the predicted precipitation rates the IR only precipitation rates on an averaged daily cycle as boxplots. The boxes display the percentiles (25th, 50th and 75th). The whiskers indicate extreme data up to 1.5 times of the interquartile range. Crosses mark outliers. The width of the boxes is relative to the number of validation scenes. In the section "4.3. Validation of PRETIP against 28 Chinese Rain Gauge Observations" we replaced Figures 11 and 12 with the correctly calculated precipitation sums and the updated validation measures.
We made following correction: The worst correlation is R = 0.21 and the best correlation is R = 0.71. We found an average MAE of 3.3 and an average RMSE of 5.9, which shows the high variability of the precipitation captured by the gauge observations. The lowest/highest MAE is 1.7/4.9 and the lowest/highest RMSE is 3.6/7.9 (see Figure 12 for details).
We originally wrote that the magnitude of PRETIP precipitation rates compared to the gauge observations strongly differs, however, there is just an slight overestimation of PRETIP.
In the section "5. Discussion" we wrote: "Further, we compared our product with Chinese gauge measurements and found a correlation coefficient of R = 0.49 while using the 4 km resolution of PRETIP. The MAE is 12.3 mm/day and the RMSE is 7.1 mm/day on average regarding the 4 km resolution." In the section "4.3. Validation of PRETIP against 28 Chinese Rain Gauge Observations" we replaced Figures 11 and 12 with the correctly calculated precipitation sums and the updated validation measures. We made following correction: The worst correlation is R = 0.21 and the best correlation is R = 0.71. We found an average MAE of 3.3 and an average RMSE of 5.9, which shows the high variability of the precipitation captured by the gauge observations. The lowest/highest MAE is 1.7/4.9 and the lowest/highest RMSE is 3.6/7.9 (see Figure 12 for details). We made following correction: The worst correlation is R = 0.21 and the best correlation is R = 0.71. We found an average MAE of 3.3 and an average RMSE of 5.9, which shows the high variability of the precipitation captured by the gauge observations. The lowest/highest MAE is 1.7/4.9 and the lowest/highest RMSE is 3.6/7.9 (see Figure 12 for details).
We originally wrote that the magnitude of PRETIP precipitation rates compared to the gauge observations strongly differs, however, there is just an slight overestimation of PRETIP. In the section "5. Discussion" we wrote: "Further, we compared our product with Chinese gauge measurements and found a correlation coefficient of R = 0.49 while using the 4 km resolution of PRETIP. The MAE is 12.3 mm/day and the RMSE is 7.1 mm/day on average regarding the 4 km resolution." We corrected the average MAE = 3.3 mm/day and RMSE = 5.3 mm/day. We corrected the average MAE = 3.3 mm/day and RMSE = 5.3 mm/day. The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected.

Conflicts of Interest:
The authors declare no conflict of interest.