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To assess the potential of radar rainfall nowcasting for early warning, nowcasts for 659 events were used to construct discharge forecasts for 12 Dutch catchments. Four open‐source nowcasting algorithms were tested: Rainymotion Sparse (RM‐S), Rainymotion DenseRotation (RM‐DR), Pysteps deterministic (PS‐D), and probabilistic (PS‐P) with 20 ensemble members. As benchmark, Eulerian Persistence (EP) and zero precipitation input (ZP) were used. For every 5‐min step in the available nowcasts, a discharge forecast with a 12‐hr forecast horizon was constructed. Simulations using the observed radar rainfall were used as reference. Rainfall and discharge forecast errors were found to increase with both increasing rainfall intensity and spatial variability. For the discharge forecasts, this relationship depends on the initial conditions, as the forecast error increases more quickly with rainfall intensity when the groundwater table is shallow. Overall, discharge forecasts using RM‐DR, PS‐D, and PS‐P outperform the other methods. Threshold exceedance forecasts were assessed by using the maximum event discharge as threshold. Compared to benchmark ZP, an exceedance is, on average, forecast 223 (EP), 196 (RM‐S), 213 (RM‐DR), 119 (PS‐D), and 143 min (PS‐P) in advance. The EP results are counterbalanced by both a high false alarm ratio (FAR) and inconsistent forecasts. Contrarily, PS‐D and PS‐P produce lower FAR and inconsistency index values than all other methods. All methods advance short‐term discharge forecasting compared to no rainfall forecasts at all, though all have shortcomings. As forecast rainfall volumes are a crucial factor in discharge forecasts, a future focus on improving this aspect in nowcasting is recommended. Plain Language Summary: Flood warnings in quickly responding catchments are still challenging, as the timing and location of the rainfall forecast should be as accurate as possible. Radar rainfall nowcasting, a technique to statistically extrapolate the most recent rainfall observations, can potentially improve the rainfall forecasts up to several hours ahead. To evaluate the benefits and possible pitfalls of radar rainfall nowcasting for hydrological forecasting, we tested four different nowcasting algorithms for 659 rainfall events in the Netherlands. We used these nowcasts for the construction of discharge forecasts for 12 Dutch lowland catchments. Based on the evaluation of the large sample of discharge forecasts for these catchments, we conclude that all nowcasting methods advance short‐term discharge forecasting compared to no rainfall forecasts at all. Nevertheless, all techniques have shortcomings. Rainfall and discharge forecast errors increase with increasing rainfall intensity and spatial variability, though discharge forecast errors also strongly depend on the initial catchment wetness. Moreover, forecast rainfall volumes have shown to be a crucial factor in the quality of the discharge forecast. Hence, rainfall nowcasting can be a valuable addition to hydrological forecasting systems. Yet we recommend a future focus on improving area‐averaged rainfall volumes in nowcasting algorithms to further advance hydrological forecasting. Key Points: The potential of nowcasting for discharge forecasting was evaluated with 659 rain events spread over 12 lowland catchmentsDischarge forecast errors depend on initial catchment wetness and increase with both rainfall intensity and spatial variabilityDischarge exceedance thresholds can be on average forecast between 119 and 223 min earlier than with no rainfall forecast at all [ABSTRACT FROM AUTHOR]

Accurate and timely precipitation forecasts are crucial for early warning. Rainfall nowcasting, the process of statistically extrapolating recent rainfall observations, is increasingly used for short‐term forecasting. Nowcasts are generally constructed with high‐resolution radar observations. As a proof of concept, we construct nowcasts with country‐wide rainfall maps estimated from signal level data of commercial microwave links (CMLs) for 12 summer days in the Netherlands. CML nowcasts compare well to radar rainfall nowcasts. Provided well‐calibrated CML rainfall estimates are employed, CML nowcasts can outperform unadjusted real‐time radar nowcasts for high rainfall rates, which are underestimated as compared to a reference. Care should be taken with the sensitivity of the advection field derivation to areas with low CML coverage and the inherent measurement scale of CML data, which can be larger than the application scale. We see potential for rainfall nowcasting with CML data, for example, in regions where weather radars are absent. Plain Language Summary: Capturing the amount, timing and location of rainfall are of key importance for water management and the agricultural sector. Nowcasting of rainfall, a computational approach where the most recent rainfall observations are spatially extrapolated, is increasingly used for short‐term rainfall forecasts. The required rainfall observations normally originate from weather radars, but it has previously been demonstrated that rainfall can also be estimated from rain‐induced signal attenuation from the commercial microwave links (CMLs) in cellular telecommunication networks, which are used worldwide. In this study, we use such estimates to construct rainfall nowcasts for 12 summer events in the Netherlands, which we compare to nowcasts using operational radars. The results demonstrate that there is potential for using rainfall estimates from CML data to nowcast rainfall up to a few hours ahead, for example, in (low‐ and middle‐income) regions where high‐resolution rainfall observations or weather radars are absent. Key Points: Probabilistic rainfall nowcasts are constructed with rainfall estimates from commercial microwave linksThe nowcasts are compared to radar rainfall nowcasts for 12 summer daysNowcast skill depends on link network density and is generally comparable to that of radar rainfall nowcasts [ABSTRACT FROM AUTHOR]

Radar rainfall nowcasting, the process of statistically extrapolating the most recent rainfall observation, is increasingly used for very short range rainfall forecasting (less than 6 hr ahead). We performed a large‐sample analysis of 1,533 events, systematically selected for 4 event durations and 12 lowland catchments (6.5–957 km2), to determine the predictive skill of nowcasting. Four algorithms are tested and compared with Eulerian Persistence: Rainymotion Sparse, Rainymotion DenseRotation, Pysteps deterministic, and Pysteps probabilistic with 20 ensemble members. We focus on the dependency of nowcast skill on event duration, season, catchment size, and location. Maximum skillful lead times increase for longer event durations, due to the more persistent character of these events. For all four event durations, Pysteps deterministic attains the longest average decorrelation times, with 25 min for 1‐hr durations, 40 min for 3 hr, 56 min for 6 hr, and 116 min for 24 hr. During winter, with more persistent stratiform precipitation, we find three times lower mean absolute errors than for convective summer precipitation. Higher skill is also found after spatially upscaling the forecast. Catchment location matters too: Given the prevailing storm movement, two times higher skillful lead times are found downwind than upwind toward the edge of the domain. In most cases, Pysteps algorithms outperform the Rainymotion benchmark algorithms. We speculate that most errors originate from growth and dissipation processes which are not or only partially (stochastically) accounted for. Plain Language Summary: Early warning systems are a key instrument for timely responses to flood risk. These warnings depend on accurate weather forecasts. Most numerical weather prediction models have trouble to accurately forecast the timing and especially the location of rainfall on time scales of less than 6 hr. This time frame is important for decisions in small, mountainous, polder, and urban basins, where river discharge responds quickly to rainfall. Radar rainfall nowcasting, the process of statistically extrapolating the most recent rainfall observations, has the potential to provide forecasts up to 6 hr. We considered 1,533 rainfall events to evaluate the quality of five nowcasting methods. Nowcasts are better for longer‐lasting rain storms with relatively low intensity, which typically occur in winter, than for short, intensive storms, which typically occur in summer. Forecasts are useful up to 2 hr ahead for events of 1 day, while this is only 25 min for 1‐hr events. Moreover, nowcasts are better for larger basins and in the downwind direction with respect to the prevailing storm movement. Hence, nowcasts are useful, but improvements are needed to be able to forecast longer ahead. Key Points: A large‐sample analysis of 1,533 events is used to determine the skill of radar rainfall nowcastsEvaluation takes place with four nowcasting algorithms from the Pysteps and Rainymotion librariesNowcast skill is found to depend on event duration, season, catchment size, and location [ABSTRACT FROM AUTHOR]