Radar Nowcasting Research Articles

Radar Nowcasting of Cloud-to-Ground Lightning over Houston, Texas

Abstract Ten years (1997–2006) of summer (June–August) daytime (1400–0000 UTC) Weather Surveillance Radar-1988 Doppler data for Houston, Texas, were examined to determine the best radar-derived predictors of the first cloud-to-ground lightning flash from a convective cell. Convective cells were tracked using a modified version of the Storm Cell Identification and Tracking (SCIT) algorithm and then correlated to cloud-to-ground lightning data from the National Lightning Detection Network (NLDN). Combinations of three radar reflectivity values (30, 35, and 40 dBZ) at four isothermal levels (−10°, −15°, −20°, and updraft −10°C) and a new radar-derived product, vertically integrated ice (VII), were used to optimize a radar-based lightning forecast algorithm. Forecasts were also delineated by range and the number of times a cell was identified and tracked by the modified SCIT algorithm. This study objectively analyzed 67 384 unique cells and 1 028 510 lightning flashes to find the best lightning forecast criteria. Results show that using 30 dBZ at the −15° or −20°C isotherm on cells within 75 km of the radar that have been tracked for at least two consecutive scans produces the best lightning forecasts with a critical success index (CSI) of 0.68. The best VII predictor values were 0.42 or 0.58 kg m−2 on cells within 75 km of the radar that have been tracked for at least two consecutive scans, producing a CSI of 0.67. Lead times for these predictors were 10.0 and 13.4 min, respectively. Lead times greater than 10 min occurred with less stringent predictors (e.g., 30 dBZ at −10°C or VII greater than 0.25 kg m−2 on cells within 125 km with a minimum track count of 2), but lower CSI values result. In general, cells tracked for multiple scans provide higher CSIs and lead times than decreasing the range from the radar or changing the reflectivity threshold and height.

Flash flood detection through a multi-stage probabilistic warning system for heavy precipitation events

Abstract. The deadly combination of short to no warning lead times and the vulnerability of urbanized areas makes flash flood events extremely dangerous for the modern society. This paper contributes to flash flood early warning by proposing a multi-stage warning system for heavy precipitation events based on threshold exceedances within a probabilistic framework. It makes use of meteorological products at different resolutions, namely, numerical weather predictions (NWP), radar-NWP blending, and radar nowcasting. The system is composed by two main modules. First, a European Precipitation Index based on a simulated Climatology (EPIC) and probabilistic weather forecasts is calculated to pinpoint catchments at risk of upcoming heavy precipitation. Then, a Probabilistic Flash Flood Guidance System (PFFGS) is activated at the regional scale and uses more accurate input data to reduce the estimation uncertainty. The system is tested for a high flow event occurred in Catalonia (Spain) in November 2008 and results from the different meteorological input data are compared and discussed. The strength of coupling the two systems is shown in its ability to detect areas potentially at risk of severe meteorological conditions and then monitoring the evolution by providing more accurate information with higher spatial-temporal resolution as the event approaches.

Application of the HBV hydrological model in a flash flood case in Slovenia

Abstract. An atmospheric depression passed over northwest Slovenia on 18 September 2007 producing precipitation that exceeded 300 mm/d and a 100-year return period runoff in Zelezniki tributary. The resultant flash flood in the study area, which consisted of five basins, was simulated with the conceptual distributed hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning). The model was calibrated and validated with past rainfall – runoff events with satisfactory results producing values of Nash – Sutcliffe coefficient between 0.82 and 0.96. The validated model was applied to the flash flood case with stream gauge failure, driven by spatiotemporal precipitation produced by a set of rain gauges and radar data. The model delivered satisfactory results on three out of five basin outlets while the other two had stream gauge failure during the event. The internal basin dynamics of the most affected area in Zelezniki, was successfully tested in eight of its sub-basins by comparing the peak discharges with the ones evaluated by the slope-conveyance method during a detailed intensive post event campaign. The added value of this method is in the reduced uncertainty in peak discharge estimation and event interpretation and in an effective flash flood warning system for the study area when it is combined with radar nowcasts and operational high resolution short range weather forecast models.

Downburst-producing thunderstorms in southern Germany: Radar analysis and predictability

Three days with downburst-producing thunderstorms during the VERTIKATOR intensive observation period in June and July 2002 are studied by means of the C-band polarisation diversity radar POLDIRAD and its network of three bistatic receivers. We present the first wind vector fields from a downburst measured by such a bistatic network. The polarimetric radar data allowed testing the recent hypothesis that a dominant trigger mechanism for wet downbursts might be the cooling due to melting of small hail or graupel in the storm, and we found some evidence for this process in the VERTIKATOR storms. This could be exploited by polarimetric radar nowcasting algorithms for downburst detection. The predictability of the downburst potential was further investigated from proximity soundings and their derived indices WINDEX as well as different formulations of GUSTEX. In particular, a new formulation of GUSTEX is proposed here which shows promising predictive skill for the VERTIKATOR cases and a number of other severe (and non-severe) situations from the same region in southern Germany.

Consideration of online rainfall measurement and nowcasting for RTC of the combined sewage system

In Berlin, Germany, the demand for enhanced protection of the environment and the growing economic pressure have led to an increased application of control concepts within the sewage system. A global control strategy to regulate the pumpage of the combined sewage system to the treatment plant was developed and evaluated in a theoretical study. The objective was to reduce CSO. In this paper an extension of the existing control algorithm by information from online rainfall measurement and radar nowcasting is described. The rainfall information is taken into account by two additive terms describing the predicted volume from rainfall runoff. On the basis of numerical simulation the potential of these two complementary forecast terms in the global control algorithm to further reduce CSO is evaluated. The investigations are based on long-time simulations that are conducted with the dynamic flow routing model InfoWorks for three subcatchments of the Berlin drainage system. The results show that at the current Berlin system a CSO reduction of only 0.8% is possible. The effect of the forecast terms is limited by operational constraints. Limits are set to both, the delivery from each individual pump station and the total pumpage to the treatment plant.

Error Propagation of Radar Rainfall Nowcasting Fields through a Fully Distributed Flood Forecasting Model

Abstract This study presents a first attempt to address the propagation of radar rainfall nowcasting errors to flood forecasts in the context of distributed hydrological simulations over a range of catchment sizes or scales. Rainfall forecasts with high spatiotemporal resolution generated from observed radar fields are used as forcing to a fully distributed hydrologic model to issue flood forecasts in a set of nested subbasins. Radar nowcasting introduces errors into the rainfall field evolution that result from spatial and temporal changes of storm features that are not captured in the forecast algorithm. The accuracy of radar rainfall and flood forecasts relative to observed radar precipitation fields and calibrated flood simulations is assessed. The study quantifies how increases in nowcasting errors with lead time result in higher flood forecast errors at the basin outlet. For small, interior basins, rainfall forecast errors can be simultaneously amplified or dampened in different flood forecast locations depending on the forecast lead time and storm characteristics. Interior differences in error propagation are shown to be effectively averaged out for larger catchment scales.

Evaluating the effects of image filtering in short‐term radar rainfall forecasting for hydrological applications

AbstractRadar rainfall nowcasting at short lead times has important hydrometeorological applications in the fields of weather prediction and flood forecasting. The predictability of rainfall events can vary significantly with scale as smaller storm features are less predictable than the storm envelope motion. As a result, various techniques have been developed for filtering a radar image and deriving short‐term forecasts from the more predictable, larger storm scales. In this study, the effects of image filtering on radar nowcasting performance using the Storm Tracker Nowcasting Model (STNM) are evaluated. Radar rainfall nowcasts are evaluated for three storms exhibiting varying degrees of organisation over the Arkansas‐Red River basin. In each case, it is found that the nowcast skill decreases with the forecast lead time, increases with the verification area used around a forecast location, and decreases with higher rainfall thresholds. Furthermore, it is demonstrated that a set of properly tuned filtering nowcasts are superior to simple ‘persistence’ and slightly better than ‘uniform advection’. At the scale of a large hydrologic basin (∼6000 km2), filter‐based nowcasting is shown to capture the temporal variation in rainfall amount and its spatial distribution based on a set of catchment‐based metrics. Finally, a method for relating changes in nowcasting skill to errors associated with storm dynamics not captured by image filtering techniques is evaluated. Copyright © 2006 John Wiley & Sons, Ltd.

Extending the Predictability of Hydrometeorological Flood Events Using Radar Rainfall Nowcasting

Abstract The predictability of hydrometeorological flood events is investigated through the combined use of radar nowcasting and distributed hydrologic modeling. Nowcasting of radar-derived rainfall fields can extend the lead time for issuing flood and flash flood forecasts based on a physically based hydrologic model that explicitly accounts for spatial variations in topography, surface characteristics, and meteorological forcing. Through comparisons to discharge observations at multiple gauges (at the basin outlet and interior points), flood predictability is assessed as a function of forecast lead time, catchment scale, and rainfall spatial variability in a simulated real-time operation. The forecast experiments are carried out at temporal and spatial scales relevant for operational hydrologic forecasting. Two modes for temporal coupling of the radar nowcasting and distributed hydrologic models (interpolation and extended-lead forecasting) are proposed and evaluated for flood events within a set of nested basins in Oklahoma. Comparisons of the radar-based forecasts to persistence show the advantages of utilizing radar nowcasting for predicting near-future rainfall during flood event evolution.

Atmospheric-hydrological modeling of severe precipitation and floods in the Huaihe River Basin, China

Our study focuses on the simulation of heavy precipitation and floods over the Huaihe River Basin (270,000 km2), one of the seven major river basins in China. The simulation covers two periods in 1998 (June 28–July 3, July 28–August 17) and a third period in 2003 (June 26–July 22). The former two periods, with eight meteorological cases each of duration 72-h, correspond to the Intensive Observation Period of HUBEX/MAGE (Huaihe River Basin Experiment/Monsoon Asian GEWEX Experiment). The period in 2003 with 10 cases is the second most severe flooding event on record. The Canadian atmospheric Mesoscale Compressible Community Model (MC2) is used for precipitation simulation in the hindcast mode for all cases. The Chinese Xinanjiang hydrological model driven by either rain gauge or MC2 precipitation is used to simulate hydrographs at the outlet of the Shiguanhe sub-basin (5930 km2), part of the Huaihe River Basin. The MC2 precipitation is also evaluated using observations from rain gauges. Over the Huaihe River Basin, MC2 generally overestimates the basin-averaged precipitation. Three of the eight 1998 cases have a percentage error less than 50% with the fourth having an error of 54%, while six of the ten 2003 cases have errors less than 50%. The precipitation over five different sub-regions and the Shiguanhe sub-basin of the Huaihe River Basin from MC2 are also compared with values from the Chinese operational weather prediction model; the latter data are only available for the ten 2003 cases. An excellent result is obtained in the hydrological simulation using rain gauge precipitation as revealed by the Nash–Sutcliffe coefficients of 0.91 for both summers of 1998 and 2003. The simulation using MC2 precipitation shows a reasonable agreement of flood timing and peak discharges with Nash–Sutcliffe coefficients of 0.63 and 0.87 for the two 1998 periods, and 0.60 for 2003. The encouraging results demonstrate the potential of using mesoscale model precipitation for flood forecast, which provides a longer lead time compared to traditional methods such as those based on rain gauges, statistical forecast or radar nowcasts.

Precipitation forecast skill of numerical weather prediction models and radar nowcasts

Short term precipitation forecasts based on Lagrangian advection of radar echoes are robust and have more skill than numerical weather prediction models over time scales of several hours. This is because the models do not generally capture well the initial precipitation distribution. We will refer to the advection‐based methods as radar nowcasts. Over longer time scales, we expect the models to perform better than nowcast methods as they resolve dynamically the large scale flow. We verify this conceptual picture of the relative accuracy of radar nowcasts and model forecasts using conventional skill scores. We identify the cross‐over point in time where model forecasts start to have more skill than nowcast methods. This occurs at about 6 hours after the forecast is initiated.