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Despite efforts to detect and mitigate wind farm clutter in weather radar observations of rainfall, these signatures propagate to quantitative precipitation estimates. In this study, the authors investigate the hydrologic impact of wind farm clutter in the Multi‐Radar Multi‐Sensor rainfall products. The study uses the probability of detection method to identify wind farm clutter in data from Iowa for the years 2016 and 2017. Using the physically based distributed hydrologic model called the Hillslope‐Link Model, the authors show that streamflow (flood) prediction errors are generally significant at smaller basin communities where wind farms occupy a large portion of the upstream basins. These errors due to wind farm clutter show systematic decrease with the basin scales. The results from this study have implications for real‐time streamflow forecasts provided automatically by the National Water Model at the National Oceanic and Atmospheric Administration, particularly at small riverine communities. Plain Language Summary: Wind farms corrupt radar‐rainfall estimates used for flood forecasting. Erroneous streamflow forecasts affect small communities where wind farms occupy a large portion of the upstream basins. The effect is difficult to detect at larger scales typically monitored by the USGS streamflow gauging network as the wind farms occupy a small fraction of the basins at those scales. Key Points: Impact of wind farm clutter on streamflow predictions is higher at communities where wind farms cover a large portion of the upstream basinsDetection of wind farm effect is difficult at the USGS‐monitored basin scales as they occupy a small fraction of the drainage areaWind farm effect on streamflow predictions shows systematic decrease with increasing basin scales [ABSTRACT FROM AUTHOR]

This study proposes a new estimation method for vertically integrated liquid water content (VIL) using radar reflectivity volume data and temperature sounding retrieved from the numerical weather model analysis. This method addresses uncertainty factors in conventional VIL estimation associated with the effects from the bright band (BB) and radar beam geometry near the radar site. The new VIL is then used for precipitation classification (convective/stratiform) and wind turbine clutter detection in the hope that the estimated VIL indicating vertical activities or development of precipitation systems will account for the two independent subjects together, in opposite ways. The non-precipitation radar echoes returned from wind turbines do not likely generate significant degree of VIL, compared to the one estimated from actual convective cells, which contain comparable reflectivity strength. We tested the proposed VIL estimation, precipitation classification, and wind turbine clutter detection methods using various Iowa cases and illustrated their successful application. We also performed a quantitative evaluation of precipitation classification using ground reference data from a dense rain gauge network over the Turkey River basin in Iowa. The evaluation results show improved performance for most non-convective event cases estimated by the stratiform estimator (Z = 200 R 1.6) because we applied the convective estimator (Z = 300 R 1.4) to all event cases without classification. In addition, we demonstrated the potential of the new classification to mitigate significant BB effects in quantitative precipitation estimation using a correction method based on the vertical profile of reflectivity. • The study proposes a new estimation method for vertically integrated liquid water content (VIL) that can be used for precipitation classification and wind turbine clutter detection. • The new method mitigates VIL estimation uncertainties affected by the effects from the melting layer and radar beam geometry near the radar site. • The performance evaluation demonstrated the utility of the VIL application to the classification of precipitation types and the detection of wind turbine effects present in radar observations. [ABSTRACT FROM AUTHOR]