Evaluation of Integrated Nowcasting through Comprehensive Analysis (INCA) precipitation analysis using a dense rain-gauge network in southeastern Austria

Abstract

Abstract. An accurate estimate of precipitation is essential to improve the reliability of hydrological models and helps in decision making in agriculture and economy. Merged radar–rain-gauge products provide precipitation estimates at high spatial and temporal resolution. In this study, we assess the ability of the INCA (Integrated Nowcasting through Comprehensive Analysis) precipitation analysis product provided by ZAMG (the Austrian Central Institute for Meteorology and Geodynamics) in detecting and estimating precipitation for 12 years in southeastern Austria. The blended radar–rain-gauge INCA precipitation analyses are evaluated using WegenerNet – a very dense rain-gauge network with about one station per 2 km2 – as “true precipitation”. We analyze annual, seasonal, and extreme precipitation of the 1 km × 1 km INCA product and its development from 2007 to 2018. From 2007 to 2011, the annual area-mean precipitation in INCA was slightly higher than WegenerNet, except in 2009. However, INCA underestimates precipitation in grid cells farther away from the two ZAMG meteorological stations in the study area (which are used as input for INCA), especially from May to September (“wet season”). From 2012 to 2014, INCA's overestimation of the annual-mean precipitation amount is even higher, with an average of 25 %, but INCA performs better close to the two ZAMG stations. Since new radars were installed during this period, we conclude that this increase in the overestimation is due to new radars' systematic errors. From 2015 onwards, the overestimation is still dominant in most cells but less pronounced than during the second period, with an average of 12.5 %. Regarding precipitation detection, INCA performs better during the wet seasons. Generally, false events in INCA happen less frequently in the cells closer to the ZAMG stations than in other cells. The number of true events, however, is comparably low closer to the ZAMG stations. The difference between INCA and WegenerNet estimates is more noticeable for extremes. We separate individual events using a 1 h minimum inter-event time (MIT) and demonstrate that INCA underestimates the events' peak intensity until 2012 and overestimates this value after mid-2012 in most cases. In general, the precipitation rate and the number of grid cells with precipitation are higher in INCA. Considering four extreme convective short-duration events, there is a time shift in peak intensity detection. The relative differences in the peak intensity in these events can change from approximately −40 % to 40 %. The results show that the INCA analysis product has been improving; nevertheless, the errors and uncertainties of INCA to estimate short-duration convective rainfall events and the peak of extreme events should be considered for future studies. The results of this study can be used for further improvements of INCA products as well as for future hydrological studies in regions with moderately hilly topography and convective dominance in summer.

Analysing the Large-Scale Debris Flow Event in July 2022 in Horlachtal, Austria Using Remote Sensing and Measurement Data

“…Although this behaviour is also shown by the station measurements, the amount of precipitation differs between the two datasets. Although the number of meteorological stations in the study area is very limited for a linear regression and therefore prone to uncertainties, we detected a linear relationship between the INCA values and the rainfall measurements at the three stations, with the aforementioned and expected slight overestimation of the amount of rainfall by the INCA data [52]. Figure 7A…”

“…As INCA precipitation data are likely to overestimate peak rainfall values for extreme events [52], we used the recorded data from the meteorological stations in the study area to validate and correct the absolute values of the gridded INCA data. This was done by establishing a relationship through linear regression between the INCA cell values and the recorded precipitation values of the stations.…”

“…In comparison with WegenerNet reference data, they prove that the peak of INCA precipitation intensities as well as the rainfall durations can show differences for extreme convective short-duration events. In addition, the spatial pattern may also differ from the reference data [52]. Therefore, it is possible that the spatial pattern of the INCA rainfall dataset is slightly shifted in the case of the extreme event on 20 July 2022.…”

Analysing the impacts of extreme torrential events using multi‐temporal LiDAR datasets—The Schöttlbach catchment, Upper Styria, Austria

“…Both rain gauge measurements and radar observations are subject to considerable uncertainties (Ochoa‐Rodriguez et al, 2019). Additional errors and weaknesses of INCA data result from the low number of representative weather stations (Ghaemi et al, 2021; Haiden et al, 2011). The extreme values were reached very locally, in the source area of the Krumegger Bach (Figure 4).…”

Estimation of Real-Time Rainfall Fields Reflecting the Mountain Effect of Rainfall Explained by the WRF Rainfall Fields

“…The algorithm introduced in this study has been successfully applied to the Austrian INCA nowcasting system [27,28], and relatively recently applied to account for the mountain effect of rainfall near Beijing, China [29]. The rainfall field estimation algorithm considering the elevation effect on rainfall is designed depending on the magnitude of the rainfall intensity.…”