**5. Discussion**

In this research, assessment and comparison of the aforementioned precipitation products have provided insights into how different errors vary with precipitation intensities, elevation, and climate zones. With respect to CC, MSWEP significantly yields better than other products in the whole domain in the range in most pixels. However, ERA5, followed by SM2RAIN, indicated low CC over the southern and western parts of the country and rather high CC in the area with low altitude (Figure 3). The similarities of both IMERG-V05B and -V06A products are very consistent across the scores in daily and monthly time-scales over the whole country. The southern part of the domain is characterized by high positive biases up to ±2 mm/day for IMERG-V05B, -V06A, and MSWEP, whereas bias of the northern and eastern parts are much lower. This can be due to the higher precipitation intensities in these regions, ranging from 1400 mm to 2600 mm during the time period of this study. Moreover, varied orography and complex precipitation processes might be the other reason for this high statistical errors. However, MSWEP significantly performs other products, followed by IMERG-V06 and -V05B products. Moreover, the low bias, RMSE, and MAE and high CC along the eastern part of the domain (Lower Austria) shows an interesting feature. A considerable underestimation of precipitation along the complex train at the southern and western parts of the domain that characterized the complex precipitation is common to IMERG-V05-RT and ERA5 products.

The average monthly data showed that SM2RAIN underestimated precipitation during the cold months over Austria (Figure 5). This underestimation in winter can be related to snowfall and/or frozen soil, which SM2RAIN is unable to estimate. This finding is consistent with those shown by Paredes-Trejo et al. [30], who evaluated the performance of SM2RAIN over Brazil. Moreover, the ERA5 and SM2RAIN products failed to capture the observed daily precipitation with CC < 0.5 in most stations over the high altitudes (elevation > 1000 m) and complex terrains. In general, one can say all products performed better in the low altitudes (elevation < 1000 m) compared to the high altitudes. As the ASCAT soil moisture product has severe limitations over frozen soil, snow-cover, rainforest, and complex topographical regions, SM2RAIN-ASCAT, which is derived based on the ASCAT soil moisture product, also has difficulties to estimate precipitation over these regions [21,31–33]. In general, MSWEP properly captured the precipitation gradients, most likely due to using daily in situ observation for bias correction in its algorithms while IMERG uses monthly in situ observation for its calibration (Figure 9). Another cause might be the higher native spatial resolution of MSWEP (0.1◦ × 0.1◦) than for example the ERA5 product (~0.28◦ × ~0.28◦), or higher temporal resolution of MSWEP (3-hourly) in compare to for example SM2RAIN (daily). The algorithm of MSWEP optimally merges the gauge, satellite, and reanalysis precipitation estimates combining the advantages of the different data sources. Moreover, even though at daily and monthly time scales, ERA5 only poorly agrees with the gauge-based data, patterns of accumulated precipitation agree well. The adequate representation of spatial accumulation patterns may be due to (i) the high number of observations assimilated in ERA5, and (ii) assimilate precipitation data from ground-based radar observations (2009 onwards), although ERA5 fails to place the precipitation in the correct areas, when compared with rain gauges.

With respect to detecting light-moderate precipitation events, ERA5, MSWEP, and SM2RAIN indicated higher average POD compared to all IMERG products. For the heavy precipitation threshold (P > 10 mm) MSWEP indicated the most robust and SM2RAIN were found as the less powerful product to detect precipitation with respect to POD and FAR values over the area (Figure 10). The results indicated IMERG-V05-RT (for the entire country, except eastern and southeastern regions), ERA5 the (entire country, except a narrow band in the upper middle of the domain which is extended from east to west), and SM2RAIN (the entire country) are unreliable at detecting precipitation at heavy precipitation category. Regarding the SM2RAIN precipitation products, this can be attributed to soil moisture retrieval errors, which highly affected the estimation of the precipitation quality derived from the SM2RAIN algorithm. SM2RAIN implemented a static correction procedure for climatological correction based on a cumulative density function (CDF) and the ERA5 reanalysis data [34]. Moreover, ERA5 showed some limitation, with emphasis on heavy precipitation, which can additionally affect the quality of SM2RAIN. In, overall, SM2RAIN-ASCAT, ERA5, and IMERG-V05-RT still face a significant challenge to estimate the amount of precipitation, while MSWEP-V2.2 and IMERG-V05B-FR, and V06A-FR revealed good performance to accurately estimate and detect precipitation over Austria. Thus, these products can offer a valuable alternative to in situ measurements for operational use in various applications.
