Radar-based Precipitation Data Research Articles

Application of Radar-Based Precipitation Data Improves the Effectiveness of Urban Inundation Forecasting

Using radar to estimate and forecast precipitation as input for hydrological models has become increasingly popular in recent years because of its superior spatial and temporal simulation compared with using rain gauge data. This study used radar-based quantitative precipitation estimation (QPE) to select the optimal parameter set for the MIKE URBAN hydrological model and radar-based quantitative precipitation forecasting (QPF) to simulate inundation in Nam Dinh city, Vietnam. The results show the following: (1) radar has the potential to improve the modeling and provide the data needed for real-time smart control if proper bias adjustment is obtained and the risk of underestimated flows after heavy rain is minimized, and (2) the MIKE URBAN model used to calculate two simulation scenarios with rain gauge data and QPE data showed effectiveness in combining the application of radar-based precipitation for the forecasting and warning of urban floods in Nam Dinh city. The results in Scenario 2 with rainfall forecast data from radar provide better simulation results. The average relative error in Scenario 2 is 9%, while the average relative error in Scenario 1 is 15%. Using the grid radar-based precipitation forecasting as input data for the MIKE URBAN model significantly reduces the error between the observed water depth and the simulated results compared with the case using an input rain gauge measured at Nam Dinh station (the difference in inundation level of Scenario 2 using radar-based precipitation is 0.005 m, and it is 0.03 m in Scenario 1). The results obtained using the QPE and QPF radar as input for the MIKE URBAN model will be the basis for establishing an operational forecasting system for the Northern Delta and Midland Regional Hydro-Meteorological Center, Viet Nam Meteorological and Hydrological Administration.

Investigating the feasibility of using precipitation measurements from weather RaDAR to estimate potential recharge in regional aquifers: the Majella massif case study in Central Italy

Rain gauge spatial sparsity and temporal discontinuity of data represent one of the major issues for reliable recharge estimations. In the past decades, the use of ground-based microwave weather RaDAR has dramatically improved quantitative rainfall estimation by providing spatially continuous estimates of rainfall over an area of more than 400 km2 every 10 minutes. Furthermore, weather RaDAR data have also proved relatively reliable in mountainous areas. These paramount features of RaDAR-derived precipitation data could improve the estimation of potential recharge of aquifers, which rely on geospatializations (e.g., Thiessen polygons) of rainfall data collected by a sparse rain gauge network which often shows lacking at high altitude (i.e., recharge areas), introducing additional uncertainty in the inflow volumes. Weather RaDAR rainfall estimation is also affected by various sources of error, which can be reduced by proper post-processing; however, uncertainties remain, especially for surface rain rate estimations. Despite the currently necessary complex numerical processing, the purpose of the study is to evaluate the use of the weather RaDAR data as an alternative or in addition to meteorological data. Based on the above considerations, the feasibility of using RaDAR-based precipitation data to estimate aquifer potential recharge and calculate a detailed water budget in the areas characterized by high elevations, such as the Majella massif in the central Apennines, has been evaluated. To address this objective, the water budget has been calculated in the 2017-2018 period using both RaDAR-based precipitation data and rain gauge data, as well as adopting different methods (i.e., Turc and Thornthwaite). Although intrinsically uncertain, the RaDAR-based precipitation data provided solid results, pointed out by comparing it with water budget obtained by rain gauge data, and especially with experimental literature data. This interdisciplinary work may pave the way for continuous monitoring of aquifer potential recharge at extremely high temporal and spatial resolution.

A simple model for predicting the statistics of spatiotemporal extremes of sub-daily precipitation

For a single site (of a rain gauge, say), the statistics of extreme precipitation are conveniently summarized by depth-duration-frequency (DDF) curves. Considering a spatial domain of some extent, one may ask: how often does it happen that the precipitation depth accumulated over 10 min exceeds 60 mm somewhere on this domain? Naturally, this frequency is higher than the frequency of exceedance of the same depth at a site; it depends on the size and shape of the domain and on the spatial dependence of extreme precipitation. In the present study, statistics of this spatial dependence are estimated from 11 years of gauge-adjusted radar-based precipitation data collected over the Netherlands, assuming spatial homogeneity. These statistics are the values of the extremal coefficient function (ECF) for selected spatial domains. From these values, a simple model is derived for predicting return periods or return values of the highest precipitation depth within an arbitrary spatial domain, based on a given DDF relation. In the model, the footprint of an individual downpour over its lifetime is simplified to a rectangular strip of fixed size ranging from 1.7 km × 12 km for a duration of 10 min to 5.1 km × 21 km for a duration of 12 h. Confidence intervals of the predictions are estimated by bootstrapping. The model is checked for fitness for its application to the design and maintenance of the drainage of highways, and the scope for further improvement is discussed.

Precipitation interpolation using digital terrain model and multivariate regression in hilly and low mountainous areas of Hungary

The relationship between precipitation and elevation is a well-known topic in the field of geography and meteorology. Radar-based precipitation data are often used in hydrologic models, however, they have several inaccuracies, and elevation can be one of the additional parameters that may help to improve them. Thus, our aim in this article is to find a quantitative relationship between precipitation and elevation in order to correct precipitation data input into hydrologic models. It is generally accepted that precipitation increases with elevation, however, the real situation is much more complicated, and besides elevation, the precipitation is dependent on several other topographic factors (e.g., slope, aspect) and many other climatic parameters, and it is not easy to establish statistically reliable correlations between precipitation and elevation. In this paper, we examine precipitation-elevation correlations by using multiple regression analysis based on monthly climatic data. Further on, we present a method, in which these regression equations are combined with kriging or inverse distance weighting (IDW) interpolation to calculate precipitation fields, which take into account topographic elevations based on digital terrain models. Thereafter, the results of the different interpolation methods are statistically compared. Our study areas are in the hilly or low mountainous regions of Hungary (Bakony, Mecsek, Börzsöny, Cserhát, Mátra and Bükk montains) with a total of 52 meteorological stations. Our analysis proved that there is a linear relationship between the monthly sum of precipitation and elevation. For the North Hungarian Mountains, the correlation coefficients were statistically significant for the whole study period with values between 0.3 and 0.5. Multivariate regression analysis pointed out that there are remarkable differences among seasons and even months. The best correlation coefficients are typical of late spring-early summer and October, while the weakest linear relationships are valid for the winter period and August. The vertical gradient of precipitation is between one and four millimetres per 100 metres for each month. The statistical comparison of the precipitation interpolation had the following results: for most months, co-kriging was the best method, and the combined method using topography-derived regression parameters lead to only slightly better results than the standard kriging or IDW.

Using radar-based quantitative precipitation data with coupled soil- and groundwater balance models for stream flow simulation in a karst area

A distributed soil water balance model, combined with a lumped karst groundwater model was developed, aiming to simulate stream discharge in karst catchments. Both modelling frameworks, which can be used separately, are presented. They were tested for the Lauchert catchment in the south-west of Germany, using radar based precipitation data, with the focus on simulating flood events. A soil water balance model with 100m grid resolution was combined with groundwater model compartments on a subcatchment base for the catchment covering 450km2. Results show good fits to observed runoff data and could be verified with soil-moisture and groundwater level observations. Specifically, spatially distributed precipitation data has proven to be very helpful for modelling local storm events, which are often not captured by station based weather data. The presented approach is a promising step towards an early warning system for floods in karst catchments.

Mud Flow Reconstruction by Means of Physical Erosion Modeling, High-Resolution Radar-Based Precipitation Data, and UAV Monitoring

Storm events and accompanying heavy rain endanger the silty soils of the fertile and intensively-used agricultural landscape of the Saxon loess province in the European loess belt. In late spring 2016, persistent weather conditions with repeated and numerous storm events triggered flash floods, landslides, and mud flows, and caused severe devastation to infrastructure and settlements throughout Germany. In Saxony, the rail service between Germany and the Czech Republic was disrupted twice because of two mud flows within eight days. This interdisciplinary study aims to reconstruct the two mud flows by means of high-resolution physical erosion modeling, high-resolution, radar-based precipitation data, and Unmanned Aerial Vehicle monitoring. Therefore, high-resolution, radar-based precipitation data products are used to assess the two storm events which triggered the mud flows in this unmonitored area. Subsequently, these data are used as meteorological input for the soil erosion model EROSION 3D to reconstruct and predict mud flows in the form of erosion risk maps. Finally, the model results are qualitatively validated by orthophotos generated from images from Unmanned Aerial Vehicle monitoring and Structure from Motion Photogrammetry. High-resolution, radar-based precipitation data reveal heavy to extreme storm events for both days. Erosion risk maps show erosion und deposition patterns and source areas as in reality, depending on the radar-based precipitation product. Consequently, reconstruction of the mud flows by these interdisciplinary methods is possible. Therefore, the development of an early warning system for soil erosion in agricultural landscapes by means of E 3D and high-resolution, radar-based precipitation forecasting data is certainly conceivable.

Aerosol connections between three distant continental stations

The present study is based on hourly aerosol particle and ancillary data taken at three stations in Northeastern Germany 150–200 km apart in the years 2009 through 2015 in order to investigate systematic process related differences that might show up when connecting their data with forward and backward air mass trajectories. The analysis of changes in the atmospheric aerosol during air mass transport between the stations focused on two low-pollution-to-high-pollution pathways of similar distance around 200 km. Despite rather different initial size distributions the increases in total concentration of particle number, volume, and black carbon agreed within 15%. Systematic variations were found in the different concentration increases as a function of time of day at which an interstation air mass transport took place that could be explained with related source and transport processes.With Radar-based precipitation data at the stations and along connecting trajectories sub-cloud particle scavenging was investigated. At each station sub-cloud particle scavenging coefficients were determined as a function of particle size between 10 and 700 nm diameter, and as a function of precipitation sums. Median particle scavenging coefficients taken over the central part of the size range of the study strongly increased between 0.2 and 1 mm of precipitation and then planed out towards higher precipitation values. With the particle scavenging coefficients determined at the individual stations and precipitation data along connecting trajectories the sub-cloud particle scavenging analysis was extended to wet scavenging along the transport paths yielding median sub-cloud scavenging ratios over the range 0.2–25 mm of precipitation sums with a non-linear shape similar to that of the median particle scavenging coefficients at the individual stations. Whereas in light precipitation more than 80% of the initial particles in the considered range survived 150–200 km of transport, more than 50% was scavenged by more than 15 mm of precipitation during aerosol transport.

Catchment tomography - An approach for spatial parameter estimation

The use of distributed-physically based hydrological models is often hampered by the lack of information on key parameters and their spatial distribution and temporal dynamics. Typically, the estimation of parameter values is impeded by the lack of sufficient observations leading to mathematically underdetermined estimation problems and thus non-uniqueness. Catchment tomography (CT) presents a method to estimate spatially distributed model parameters by resolving the integrated signal of stream runoff in response to precipitation. Basically CT exploits the information content generated by a distributed precipitation signal both in time and space. In a moving transmitter-receiver concept, high resolution, radar based precipitation data are applied with a distributed surface runoff model. Synthetic stream water level observations, serving as receivers, are assimilated with an Ensemble Kalman Filter. With a joint state-parameter update the spatially distributed Manning's roughness coefficient, n, is estimated using the coupled Terrestrial Systems Modelling Platform and the Parallel Data Assimilation Framework (TerrSysMP-PDAF). The sequential data assimilation in combination with the distributed precipitation continuously integrates new information into the model, thus, increasingly constraining the parameter space. With this large amount of data included for the parameter estimation, CT reduces the problem of underdetermined model parameters. The initially biased Manning's coefficients spatially distributed in two and four fixed parameter zones are estimated with errors of less than 3% and 17%, respectively, with only 64 model realizations. It is shown that the distributed precipitation is of major importance for this approach.

Comprehensive Framework for Assessment of Radar-Based Precipitation Data Estimates

AbstractAssessment of radar-based precipitation estimates using rain gauge observations is a critical exercise in evaluating pre-and postcorrected (gauge-adjusted) radar-based precipitation data. A comprehensive assessment framework combining several visual, quantitative, and statistical measures, indexes, and skill scores is proposed and developed for evaluation of radar-based precipitation estimates in space and time. Contingency measures, skill scores, and a few new metrics are proposed and are evaluated along with several indexes. Visual measures provide a quick check of agreement between radar and rain gauge data sets. Quantitative measures provide information about errors, and skill scores assess the quality of radar data for dichotomous (rain and no-rain) events. Summary statistics and hypothesis tests in statistical categories provide insights into distributional aspects of the rain gauge and radar data sets. The framework is used for evaluation of 15-min radar-based precipitation data obtained fr...

A climate change projection for summer hydrologic conditions in a semiarid watershed of central Arizona

Potential climate change impacts on summer precipitation and subsequent hydrologic responses in the southwestern U.S. are poorly constrained at present due to a lack of studies accounting for high resolution processes. In this investigation, we apply a distributed hydrologic model to the Beaver Creek watershed of central Arizona to explore its utility for climate change assessments. Manual model calibration and model validation were performed using radar-based precipitation data during three summers and compared to two alternative meteorological products to illustrate the sensitivity of the streamflow response. Using the calibrated and validated model, we investigated the watershed response during historical (1990–2000) and future (2031–2040) summer projections derived from a single realization of a mesoscale model forced with boundary conditions from a general circulation model under a high emissions scenario. Results indicate spatially-averaged changes across the two projections: an increase in air temperature of 1.2 °C, a 2.4-fold increase in precipitation amount and a 3-fold increase in variability, and a 3.1-fold increase in streamflow amount and a 5.1-fold increase in variability. Nevertheless, relatively minor changes were obtained in spatially-averaged evapotranspiration. To explain this, we used the simulated hydroclimatological mechanisms to identify that higher precipitation limits radiation through cloud cover leading to lower evapotranspiration in regions with orographic effects. This challenges conventional wisdom on evapotranspiration trends and suggest that a more nuanced approach is needed to communicate hydrologic vulnerability to stakeholders and decision-makers in this semiarid region.

Assimilation of hydrologic and hydrometeorological data into distributed hydrologic model: Effect of adjusting mean field bias in radar-based precipitation estimates

This paper investigates the effect of adjusting the mean field bias (MFB) in radar-based precipitation data on analysis and prediction of streamflow and soil moisture in assimilating streamflow or streamflow and in situ soil moisture data into distributed hydrologic models. To evaluate the effect of adjusting the MFB under realistic as well as idealized conditions, both real-world and synthetic experiments are carried out for the Eldon Catchment on the border of Oklahoma and Arkansas in the US. In the synthetic experiment, the MFB is modeled as a stationary Markov chain process. The synthetic experiment showed that adjusting the MFB in the assimilation process significantly improves streamflow analysis when the initial conditions are known with reasonable certainty, and that assimilating soil moisture in addition to streamflow improves analysis of streamflow as well as soil moisture if the initial conditions are largely uncertain. Adjusting the MFB during the assimilation process noticeably improved streamflow analysis over ranges of the MFB and random noise in the precipitation data. On the other hand, increasing the MFB and random noise in the precipitation data tended to degrade soil moisture analysis due possibly to over-adjusting soil moisture to mitigate the precipitation error. The real-world experiment with one-year dataset showed that adjusting the MFB during the assimilation process helped capture the peak as well as volume of outlet flow analysis as well as prediction, and that additionally assimilating interior flow observations was necessary to improve analysis and prediction of peak flows at interior locations.

Comparative statistical study of hourly precipitation determined by radar-based Stage IV and ground-based methods in the North Central United States

Detailed hourly precipitation data are required for long-range modeling of dispersion and wet deposition of particulate matter and water-soluble pollutants using the CALPUFF model. In sparsely populated areas such as the north central United States, ground-based precipitation measurement stations may be too widely spaced to offer a complete and accurate spatial representation of hourly precipitation within a modeling domain. The availability of remotely sensed precipitation data by satellite and the National Weather Service array of next-generation radars (NEXRAD) deployed nationally provide an opportunity to improve on the paucity of data for these areas. Before adopting a new method of precipitation estimation in a modeling protocol, it should be compared with the ground-based precipitation measurements, which are currently relied upon for modeling purposes. This paper presents a statistical comparison between hourly precipitation measurements for the years 2006 through 2008 at 25 ground-based stations in the north central United States and radar-based precipitation measurements available from the National Center for Environmental Predictions (NCEP) as Stage IV data at the nearest grid cell to each selected precipitation station. It was found that the statistical agreement between the two methods depends strongly on whether the ground-based hourly precipitation is measured to within 0.1 in/hr or to within 0.01 in/hr. The results of the statistical comparison indicate that it would be more accurate to use gridded Stage IV precipitation data in a gridded dispersion model for a long-range simulation, than to rely on precipitation data interpolated between widely scattered rain gauges. Implications: The current reliance on ground-based rain gauges for precipitation events and hourly data for modeling of dispersion and wet deposition of particulate matter and water-soluble pollutants results in potentially large discontinuity in data coverage and the need to extrapolate data between monitoring stations. The use of radar-based precipitation data, which is available for the entire continental United States and nearby areas, would resolve these data gaps and provide a complete and accurate spatial representation of hourly precipitation within a large modeling domain.

Adjustment of Radar-Based Precipitation Estimates for Great Lakes Hydrologic Modeling

For the Great Lakes region, radar-based precipitation estimates provide valuable data for over-lake areas and may help to improve mean areal precipitation estimates over land areas, particularly where precipitation gauges are sparse. To this end, potential biases in daily radar-based precipitation data are evaluated for several Great Lakes watersheds. Statistical analysis of gauge data (MAP) and radar-based data (MAPX) for the period 1996–2004 shows that, except for some summer months, MAPX values were consistently lower than MAP values. Possible reasons for this underestimation include truncation error, beam blockage and overshoot, and variability in radar reflectivity-surface precipitation relationships. Application of the precipitation data to continuous simulation and forecasting models of the Great Lakes shows that the observed biases are generally magnified by the hydrology models, i.e., even larger discrepancies are observed between simulated runoff values generated with MAP and MAPX inputs. Discre...

A distributed hydrologic model and threshold frequency-based method for flash flood forecasting at ungauged locations

A method to use a distributed hydrologic model in conjunction with threshold frequencies (DHM-TF) is proposed to improve the accuracy of flash flood forecasts at ungauged locations. The model produces high-resolution grids of peak flow forecast frequencies during rainfall events. Forecasters can compare these grids to locally derived threshold frequency (TF) grids to aid in warning decisions. TF grids may be derived from several sources of information such as known flood frequencies at selected river locations and local hydraulic engineering design standards. The proposed model characterizes flood severity at ungauged locations, and provides an inherent bias correction to reduce systematic errors in model predicted peaks. The distributed model basis for the approach offers advantages over current lumped model-based national weather service (NWS) flash flood guidance (FFG) procedures because it makes hydrologic calculations at spatial and temporal scales that are more commensurate with flash flooding. In this study, simulation results for 10 basins (eight nested) in Oklahoma and Arkansas, USA, were analyzed to (1) improve our understanding of distributed hydrologic model accuracy in small, interior basins, when forced by operational quality radar-based precipitation data, (2) validate that a distributed model can improve upon the lumped model-based NWS FFG, and (3) validate that the DHM-TF method can provide an inherent bias correction using available operational data archives to develop statistical parameters. We analyzed eight years of continuous hourly stream flow simulations and peak flows from 247 individual events. Both uncalibrated and calibrated distributed model results improved upon lumped model-based FFG at interior points. The DHM-TF method provided an average inherent bias correction for both uncalibrated and calibrated models in most basins examined. These positive results suggest that further development and testing of this technique should proceed.

Hydrology laboratory research modeling system (HL-RMS) of the US national weather service

This study investigates an approach that combines physically-based and conceptual model features in two stages of distributed modeling: model structure development and estimation of spatially variable parameters. The approach adds more practicality to the process of model parameterization, and facilitates an easier transition from current lumped model-based operational systems to more powerful distributed systems. This combination of physically-based and conceptual model features is implemented within the Hydrology Laboratory Research Modeling System (HL-RMS). HL-RMS consists of a well-tested conceptual water balance model applied on a regular spatial grid linked to physically-based kinematic hillslope and channel routing models. Parameter estimation procedures that combine spatially distributed and ‘integrated’ basin-outlet properties have been developed for the water balance and routing components. High-resolution radar-based precipitation data over a large region are used in testing HL-RMS. Initial tests show that HL-RMS yields results comparable to well-calibrated lumped model simulations in several headwater basins, and it outperforms a lumped model in basins where spatial rainfall variability effects are significant. It is important to note that simulations for two nested basins (not calibrated directly, but parameters from the calibration of the parent basin were applied instead) outperformed lumped simulations even more consistently, which means that HL-RMS has the potential to improve the accuracy and resolution of river runoff forecasts.

Simulation of precipitation fields from probabilistic quantitative precipitation forecast

To assimilate Probabilistic Quantitative Precipitation Forecast (PQPF) into ensemble hydrologic forecasting, a procedure is needed that generates ensemble traces of future precipitation fields from PQPF. This paper describes one such procedure used in the National Weather Service's (NWS) Ensemble Precipitation Processor (EPP). Given PQPF for the first 24 h valid at the forecast scale (approximately 64×64 km 2), the procedure generates ensemble traces of 24- or 6-h precipitation at the simulation scale (approximately 4×4 km 2) in space or in space and time, respectively. The steps involved are quasi-analytical downscaling of PQPF and conditional simulation of precipitation fields based on optimal linear estimation techniques that explicitly account for both intermittency and inner variability. To evaluate the procedure, radar-based precipitation data from the Ohio River Forecast Center (OHRFC) are used to develop a climatological PQPF, and to compare ensemble statistics between simulated and observed precipitation.