Remote Sensing of Water-Related Hazards. Группа авторов

Snowfall Pattern, Evaluation, and Trend

The snowfall distributions of IMERG, ERA5, ERA‐Interim, MERRA2, and CGDPA are shown in Figure 2.7. CGDPA does not provide direct snowfall estimates. Therefore, we first calculate rainfall‐snowfall ratios from three reanalysis products and IMERG, and then use the ratios to estimate snowfall from CGDPA. According to Figure 2.7, snowfall is mainly distributed over mountainous regions in China, including the whole TP, the Tianshan Mountains in XJ, and the Altai Mountains, which cover a small area in the northern corner of XJ. For the TP, the Qiangtang Basin (30°–35°N and 80°–90°E) shows little snowfall due to its dry climate. The high latitude regions are widely covered by snowfall, whereas the total amount is not large. It is worth mentioning that the Brahmaputra Grand Canyon (about 30°N, 95°E) has a very large amount of snowfall benefiting from the high precipitation caused by the southwest monsoon.

      Meanwhile, these products also show substantial differences. The snowfall amounts produced by reanalysis products are much larger than those from CGDPA and IMERG. Among the three reanalysis products, ERA‐Interim has a coarse resolution and thus cannot represent the spatial details of snowfall. Moreover, ERA‐Interim fails to capture snowfall in southern regions. MERRA2 generates much stronger snowfall along the Himalayan Mountains than other products. CGDPA shows similar patterns with reanalysis products because it depends on reanalysis data to realize precipitation phase discrimination, whereas the snowfall intensity of CGDPA is smaller than that of reanalysis products, particularly over the northern and western TP. When station‐based snowfall measurement information is absent, the reanalysis‐based method (Figure 2.7a, b, c) and the temperature‐based method (using a temperature threshold to distinguish rain and snow, i.e., Figure 2.7d) are used to obtain snowfall estimates. As shown in Figure 2.7, both methods indicate that CGDPA shows lower snowfall amounts than reanalysis products. Although it is known that stations may have large errors in snowfall measurement, it is hard to conclude whether CGDPA underestimates or reanalysis products overestimate snowfall without further studies. IMERG generates the weakest snowfall across China.

Due to the lack of snowfall observations, we use MTC to evaluate the quality of CGDPA, ERA5, MERRA2, and IMERG snowfall estimates (Figure 2.8). ERA‐Interim is not included because it is similar to ERA5, as shown in Figure 2.7. Only samples meeting two criteria are selected: (1) all products detect the occurrence of a snowfall event (i.e., snowfall probability >50%), and (2) a grid must have at least 200 effective snowfall events. The criteria are designed to (1) reduce the effect of different rainfall‐snowfall classification schemes and (2) exclude zero snowfall samples, which are dominant in numbers and will greatly affect evaluation results. Two triplets are designed. The first includes CGDPA based on MERRA2 snowfall information, IMERG, and ERA5. The second includes CGDPA based on ERA5 snowfall information, IMERG, and MERRA2. Both triplets can satisfy the independence requirement of MTC. The two triplets produce similar CC and RMSE values for CGDPA and IMERG, which can partly verify the effectiveness of MTC. The metric maps for CGDPA and IMERG in Figure 2.8 are provided by the first triplet.

Figure 2.7 Spatial distributions of snowfall from 2000 to 2018 for (a–d) CGDPA, (e) ERA5, (f) ERA‐Interim, (g) MERRA2, and (h) IMERG. CGDPA1‐4 employs rainfall‐snowfall classification data provided by (e–h), respectively.

      Source: Based on Tang et al. (2020), Figure 12, p 13 / Elsevier.

In most regions of China, including XJ and NE, ERA5 performs better than other products with the highest CC, while MERRA2 shows the highest CC in the TP. It is noted that there is a hollow eastern to the TP, which is the relatively warm Sichuan Basin with limited snowfall. CGDPA exhibits relatively higher CC in east and south China than in north and west China. Overall, CGDPA is much worse than ERA5 and MERRA2 in snowfall estimation concerning CC, whereas it is hard to determine which one is the best based on RMSE. By comparing Figures 2.7 and 2.8, it is found that ERA5 and MERRA2 exhibit large RMSE in regions with limited snowfall, probably due to the overestimation of the frequency of light precipitation. In contrast, CGDPA exhibits large RMSE in regions with abundant snowfall caused by its underestimation of snowfall amount, indicating that the current design of rain gauges cannot satisfy snowfall measurements. Comparing with CGDPA and reanalysis products, IMERG shows smaller CC over China and much larger RMSE over TP, XJ, and NE due to its underestimation.

Figure 2.8 CC and RMSE of CGDPA, ERA5, MERRA2, and IMERG according to the triple collocation analysis using data from 2000 to 2018. Only grid cells with more than 200 effective snowfall samples are included. The first triplet includes CGDPA, IMERG, and ERA5, and the second triplet includes CGDPA, IMERG, and MERRA2.

      Source: Based on Tang et al. (2020), Figure 13, p 14 / Elsevier.

The trend of snowfall based on different products during the study period is shown in Figure 2.9. The trend according to reanalysis products can pass the significance test at the 95% significant level. According to ECMWF products (ERA5 and ERA‐Interim), the snowfall amount decreases at the rate of –2.90 and –2.29 mm/decade. This value is only –1.27 mm/decade for CGDPA. In contrast, MERRA2 and IMERG show that the snowfall amount increased from 2001 to 2018, with the increasing rate being 6.53 and 1.13 mm/decade, respectively. It is noted that the TP shows a very significant trend of snowfall according to all products except CGDPA, whereas MERRA2 and IMERG show an inverse trend compared to CGDPA, ERA5, and ERA‐Interim. It is amazing to find that the increasing rate of MERRA2 is as large as 55.63 mm/decade. Some studies attempted to explore the trend of snowfall in TP using various data sources. However, their conclusions are also inconsistent. In summary, IMERG needs to improve its snowfall estimation, and all products may need to increase the credibility of snowfall trends.

Figure 2.9 Snowfall trends of CGDPA, ERA‐Interim, MERRA2, and IMERG from 2001 to 2018 over China. Solid lines indicate that the trend is significant with a 95% level of confidence. The numbers on the top of the figure are the rate of trend (mm/decade).

      Source: Based on Tang et al. (2020).

      2.4.4. Applicability of IMERG in Flash Flood Warning

In this section, three types of rainfall products are used, including the IMERG Early run (IMERG‐E), IMERG Final run (IMERG‐F), and China Meteorological Administration hourly data (CMA). The three products are accumulated to 1 h, 3 h, 6 h, and 24 h, respectively, and then the RTI