Radar Rainfall Measurements Research Articles

Reanalysis of multi-year high-resolution X-band weather radar observations in Hamburg

Abstract. This paper presents an open-access data set of reanalysed radar reflectivities and rainfall rates at sub-kilometre spatial and minute temporal scales. Variability at these scales is a blind spot for both operational rain gauge networks and operational radar networks. In the urban area of Hamburg, precipitation measurements of a single-polarized X-band weather radar operating at high temporal (30 s), range (60 m), and azimuthal sampling (1°) resolutions are made available for a period of more than 8 years. We describe in detail the reanalysis of the raw radar data, outline the radar performance for the years 2013 to 2021, and discuss open issues and limitations of the data set. Several sources of radar-based errors were adjusted gradually, affecting the radar reflectivity and rainfall measurements, e.g. noise, alignment, non-meteorological echoes, radar calibration, and attenuation. The deployment of additional vertically pointing micro rain radars yields drop size distributions at the radar beam height, which effectively reduces errors concerning the radar calibration and attenuation correction and monitors the radar data quality. A statistical evaluation revealed that X-band radar reflectivities and rainfall rates are in very good agreement with the micro rain radar measurements. Moreover, the analyses of rainfall patterns shown for an event and accumulated rainfall of several months prove the quality of the data set. The provided radar reflectivities facilitate studies on attenuation correction and the derivation of further weather radar products, like an improved rainfall rate. The rainfall rates themselves can be used for studies on the spatial and temporal scales of precipitation and hydrological research, e.g. input data for high-resolution modelling, in an urban area. The radar reflectivities and rainfall rates are available at https://doi.org/10.26050/WDCC/LAWR_UHH_HHG_v2 (Burgemeister et al., 2024).

Can stormwater runoff measurements be used for weather radar rainfall adjustment?

ABSTRACT Predicting the response to rainfall in urban hydrological applications requires accurate precipitation estimates with a high spatiotemporal resolution to reflect the natural variability of rainfall. However, installing rain gauges under nearly ideal measurement conditions is often difficult in urban areas, if not impossible. This paper demonstrates the potential of deriving rainfall measurements in urban areas and bias-adjusting weather radar rainfall measurements using stormwater runoff measurements. As a supplement to point rainfall measurements from rain gauges, the developed bias adjustment approach uses catchment runoff-rainfall estimates derived from water level measurements of a stormwater detention pond. The study shows that the bias-adjusted radar product correlates highly with rain gauge measurements in the catchment. Moreover, the presented approach enables rainfall measurements within a catchment independent of rain gauges located in the catchment, making the technique highly applicable for increasing the density of ground observations and thus improving weather radar precipitation estimates over urban areas. The method also derives the catchment-specific runoff coefficient independently of expensive flow measurements in the catchment, making the method very scalable. This paper highlights the potential of using easily achievable catchment runoff-rainfall measurements to increase the density of available ground observations and thereby improve weather radar precipitation estimates.

Program to Create Precipitation Data Using Radar Rainfall Measurements

Program to Create Precipitation Data Using Radar Rainfall Measurements

Uncertainty analysis of radar rainfall estimates induced by atmospheric conditions using long short-term memory networks

The unstable performance of radar rainfall measurement hinders its application in hydrology. Radar rainfall uncertainties induced by atmospheric conditions as raindrops fall from the radar sampling altitude to the ground are critical to radar-based quantitative precipitation estimation. However, these have rarely been considered in previous radar rainfall correction procedures. This study first demonstrates that the correlations between the radar–gauge rainfall discrepancy (RGD) and atmospheric fields are strong. A systematic radar-rainfall adjustment method is then proposed to decrease the discrepancies originating from changes in atmospheric conditions using a long short-term memory (LSTM) network. Three RGD adjustment models were established using a mass-variation scheme, a wind-drift scheme, and a combined mass-variation and wind-drift scheme, based on long-term (2013–2017) data covering most of the United Kingdom. The evaluation results demonstrate that all of the designed models performed well from overall, single-site, and event perspectives. Overall, the combined model, which exhibited the best performance, decreased the root-mean-square error between the rainfall levels measured by radar and gauges by 23.84%, increased Pearson’s correlation coefficient from 0.23 to 0.53, and improved the critical success index of the radar rainfall estimation from 0.56 to 0.92. The results indicate that the performances of the proposed models improved with an increase in average relative humidity or wind speed, which demonstrates that they can correct rainfall under high levels of relative humidity and wind speed. This study establishes a highly capable and comprehensive adjustment framework for radar rainfall estimation uncertainty induced by atmospheric conditions, which is essential for achieving high-quality radar products.

Ocean Vector Wind Retrievals From TRMM Using a Novel Combined Active/Passive Algorithm

This article presents a novel ocean vector wind (OVW) retrieval algorithm for remote sensing of ocean winds, using the precipitation radar (PR) and tropical rainfall measurement mission (TRMM) microwave imager (TMI) that operated for >17 years on the TRMM observatory. In this study, the wind speed and anisotropic wind direction (WD) characteristics of the ocean backscatter and brightness temperature (Tb) were empirically characterized to produce geophysical model functions for 49 PR beam positions and three TMI frequency channels (10, 19, and 37 GHz). For passive TMI measurements, the modified second stokes parameter (weighted difference of V and H-pol Tb's) was used to cancel the atmosphere contribution, which enhances the WD signal. Thus, combining linearly polarized Tb measurements with the anisotropic backscatter signature of the Ku-band PR, the feasibility of retrieving ocean surface wind vector is demonstrated. The OVW retrieval algorithm is validated by comparing retrievals with collocated numerical weather models wind vectors, and the statistics of OVW retrieval accuracy is presented.

Adjustment of Radar‐Gauge Rainfall Discrepancy Due to Raindrop Drift and Evaporation Using the Weather Research and Forecasting Model and Dual‐Polarization Radar

AbstractRadar‐gauge rainfall discrepancies are considered to originate from radar rainfall measurements while ignoring the fact that radar observes rain aloft while a rain gauge measures rainfall on the ground. Observations of raindrops observed aloft by weather radars consider that raindrops fall vertically to the ground without changing in size. This premise obviously does not stand because raindrop location changes due to wind drift and raindrop size changes due to evaporation. However, both effects are usually ignored. This study proposes a fully formulated scheme to numerically simulate both raindrop drift and evaporation in the air and reduces the uncertainties of radar rainfall estimation. The Weather Research and Forecasting model is used to simulate high‐resolution three‐dimensional atmospheric fields. A dual‐polarization radar retrieves the raindrop size distribution for each radar pixel. Three schemes are designed and implemented using the Hameldon Hill radar in Lancashire, England. The first considers only raindrop drift, the second considers only evaporation, and the last considers both aspects. Results show that wind advection can cause a large drift for small raindrops. Considerable loss of rainfall is observed due to raindrop evaporation. Overall, the three schemes improve the radar‐gauge correlation by 3.2%, 2.9%, and 3.8% and reduce their discrepancy by 17.9%, 8.6%, and 21.7%, respectively, over eight selected events. This study contributes to the improvement of quantitative precipitation estimation from radar polarimetry and allows a better understanding of precipitation processes.

Semiparametric estimation for isotropic max-stable space-time processes

Regularly varying space-time processes have proved useful to study extremal dependence in space-time data. We propose a semiparametric estimation procedure based on a closed form expression of the extremogram to estimate parametric models of extremal dependence functions. We establish the asymptotic properties of the resulting parameter estimates and propose subsampling procedures to obtain asymptotically correct confidence intervals. A simulation study shows that the proposed procedure works well for moderate sample sizes and is robust to small departures from the underlying model. Finally, we apply this estimation procedure to fitting a max-stable process to radar rainfall measurements in a region in Florida. Complementary results and some proofs of key results are presented together with the simulation study in the supplement [Buhl et al. (2018)].

Offshore wind turbine clutter characteristics and identification in operational C‐band weather radar measurements

The aim of this work is to update ground‐clutter classification methods in weather radar rainfall measurements to more accurately identify clutter pixels from wind farms. Measurements from two dual‐polarised weather radars, based in the United Kingdom, will be used to determine the characteristics of multiple wind farms in the North Sea and the Irish Sea. Currently 21 of the top 25 largest offshore wind farms are located in these regions. The extensive area occupied by the wind farms creates problems for weather radars located in the neighbouring European countries. Datasets of wind‐farm, precipitation and ground‐clutter pixels were aggregated from Thurnham Radar measurements to form novel membership functions that can be used in a fuzzy logic classification system to identify wind‐farm clutter. When only ground‐clutter datasets were used for classification, areas of the radar scans taken up by wind‐farm clutter were misclassified as rainfall. The inclusion of wind‐farm measurements led to an increase in the ability of the algorithm to detect these pixels as clutter, as the Heidke Skill Score increased from 67.4 to 97.8%. However there was a slight increase in the number of precipitation pixels incorrectly classified as clutter, with the false alarm rate increasing from 0.05 to 1.24% when all variables are used. The algorithm performed slightly better when applied to another radar on Hameldon Hill, showing promise for application to the UK network without recalibration of membership functions.

Estimation of Light Rainfall Using Ku-Band Dual-Polarization Radar

The light rain (less than or equal to a few mm hr-1) is critical to the Earth's ecosystem due to the high occurrence rate, particularly in middle and high latitude (over 80%). However, it is challenging to use rainfall gauge to measure light rain due to the sampling time and bucket volume resolution. Dual-polarization radar has become an important tool for quantitative precipitation estimation because of its relatively large covering area and ability to fill the sampling void. This paper presents the application of Ku-band dual-polarization radar for light rainfall estimation. The Ku-band radar rainfall algorithms and their error structure are described. The Ku-band observations from the National Aeronautics and Space Administration (NASA) Dual-frequency Dual-polarization Doppler Radar (D3R) during the NASA Iowa Flood Studies (IFoodS) field campaign are used to derive the rainfall products. The comparisons are performed between radar rainfall estimates and ground rainfall measurements from rain gauge and Autonomous Parsivel Unit (APU). It is shown here that the radar rainfall measurements at different timescales (i.e., 5, 10, and 15 min) agree with the APU observations very well. The normalized difference error is about 26.1%, 24.8%, and 23.7%, for 5-min, 10-min, and 15-min rainfall accumulations, respectively. The excellent performance of Ku-band rainfall algorithm for light rain estimation indicates the great potential of using D3R as a ground validation tool for the Global Precipitation Measurement (GPM) satellite precipitation product evaluations.

Experimentally Based Estimates of Relations between X-Band Radar Signal Attenuation Characteristics and Differential Phase in Rain

AbstractCorrecting observed polarimetric radar variables for attenuation and differential attenuation effects in rain is important for meteorological applications involving measurements at attenuating frequencies such as those at X band. The results of estimating the coefficients in the correction-scheme relations from dual-wavelength polarimetric radar measurements of rainfall involving attenuating and nonattenuating frequencies are described. Such coefficients found directly from measurements are essentially free from different assumptions about drop shapes, drop size distributions, and/or relations between different radar variables that are typically used in many attenuation and differential attenuation correction schemes. Experimentally based estimates derived using dual-wavelength radar measurements conducted during a project in northern Colorado indicate values of the coefficients in the attenuation–differential phase quasi-linear relations at X band in the approximate range of 0.20–0.31 dB deg−1. The corresponding coefficients in the differential attenuation–differential phase relations are in the range of 0.052–0.065 dB deg−1.

Probabilistic online runoff forecasting for urban catchments using inputs from rain gauges as well as statically and dynamically adjusted weather radar

• Rainfall nowcasts from rain gauges and 2 types of adjusted radar data are compared. • Probabilistic runoff forecasts are generated in 2 urban catchments in an on-line mode. • Time-statically adjusted radar data as model input yield best runoff forecasts. • Radar adjustment and online runoff forecast should be considered as a whole. • Improved spatial resolution in on-line rainfall runoff models improves forecasts. We investigate the application of rainfall observations and forecasts from rain gauges and weather radar as input to operational urban runoff forecasting models. We apply lumped rainfall runoff models implemented in a stochastic grey-box modelling framework. Different model structures are considered that account for the spatial distribution of rainfall in different degrees of detail. Considering two urban example catchments, we show that statically adjusted radar rainfall input improves the quality of probabilistic runoff forecasts as compared to input based on rain gauge observations, although the characteristics of these radar measurements are rather different from those on the ground. Data driven runoff forecasting models can to some extent adapt to bias of the rainfall input by model parameter calibration and state-updating. More detailed structures in these models provide improved runoff forecasts compared to the structures considering mean areal rainfall only. A time-dynamic adjustment of the radar data to rain gauge data provides improved rainfall forecasts when compared with rainfall observations on the ground. However, dynamic adjustment reduces the potential for creating runoff forecasts and in fact also leads to reduced cross correlation between radar rainfall and runoff measurements. We conclude that evaluating the performance of radar rainfall adjustment against rain gauges may not always be adequate and that adjustment procedure and online runoff forecasting should ideally be considered as one unit.

Probabilistic radar rainfall nowcasts using empirical and theoretical uncertainty models

Weather radar has a potential to provide accurate short-term (0–3 h) forecasts of rainfall (i.e. radar nowcasts), which are of great importance in warnings and risk management for hydro-meteorological events. However, radar nowcasts are affected by large uncertainties, which are not only linked to limitations in the forecast method but also because of errors in the radar rainfall measurement. The probabilistic quantitative precipitation nowcasting approach attempts to quantify these uncertainties by delivering the forecasts in a probabilistic form. This study implements two forms of probabilistic quantitative precipitation nowcasting for a hilly area in the south of Manchester, namely, the theoretically based scheme [ensemble rainfall forecasts (ERF)-TN] and the empirically based scheme (ERF-EM), and explores which one exhibits higher predictive skill. The ERF-TN scheme generates ensemble forecasts of rainfall in which each ensemble member is determined by the stochastic realisation of a theoretical noise component. The so-called ERF-EM scheme proposed and applied for the first time in this study, aims to use an empirically based error model to measure and quantify the combined effect of all the error sources in the radar rainfall forecasts. The essence of the error model is formulated into an empirical relation between the radar rainfall forecasts and the corresponding ‘ground truth’ represented by the rainfall field from rain gauges measurements. The ensemble members generated by the two schemes have been compared with the rain gauge rainfall. The hit rate and the false alarm rate statistics have been computed and combined into relative operating characteristic curves. The comparison of the performance scores for the two schemes shows that the ERF-EM achieves better performance than the ERF-TN at 1-h lead time. The predictive skills of both schemes are almost identical when the lead time increases to 2 h. In addition, the relation between uncertainty in the radar rainfall forecasts and lead time is also investigated by computing the dispersion of the generated ensemble members. Copyright © 2013 John Wiley & Sons, Ltd.

Rainfall-runoff modeling using Doppler weather radar data for Adyar watershed, India

Precipitation is a significant input for hydrologic models; so, it needs to be quantified precisely. The measurement with rain gauges gives the rainfall at a particular location, whereas the radar obtains instantaneous snapshots of electromagnetic backscatter from rain volumes that are then converted into rainfall via algorithms. It has been proved that the radar measurement of areal rainfall can outperform rain gauge network measurements, especially in remote areas where rain gauges are sparse, and remotely sensed satellite rainfall data are too inaccurate. The research focuses on a technique to improve rainfall-runoff modeling based on radar derived rainfall data for Adyar watershed, Chennai, India. A hydrologic model called ‘Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS)’ is used for simulating rainfall-runoff processes. CARTOSAT 30 m DEM is used for watershed delineation using HEC-GeoHMS. The Adyar watershed is within 100 km radius circle from the Doppler Weather Radar station, hence it has been chosen as the study area. The cyclonic storm Jal event from 4-8 November, 2010 period is selected for the study. The data for this period are collected from the Statistical Department, and the Cyclone Detection Radar Centre, Chennai, India. The results show that the runoff is over predicted using calibrated Doppler radar data in comparison with the point rainfall from rain gauge stations.

Research on attaining methods of rainfall "truth" by assessment of radar measurement of rainfall

Using the precipitation data given by high-density regional automated weather stations during main flood season in Hunan Province, in 2013, Cressman algorithm is introduced to calculate the regional precipitation value in 0.01°×0.01° resolution and is compared with the methods of a reciprocal of distance square, distance exponent, and distance square exponent. It is shown that the precipitation construction of Cressman algorithm is finer and closer to the features of gage data than other methods. Precipitation accumulation amount given by Cressman algorithm is in agreement with those obtained using methods of reciprocal of distance square and range exponent as well. Cross-validation tests show that the number with the absolute error less than 0.5 mm accounts for 98.71% of total gage number. The standard deviation of error is 0.24 mm, which is the least of all methods. As to 0.01°×0.01° resolution, the regional precipitation calculated by Cressman algorithm is adaptable to validate the radar estimation rainfall.

Gravity wave generation by convection and momentum deposition in the mesosphere‐lower thermosphere

The Tropical Warm Pool International Cloud Experiment campaign centered on Darwin (12°S, 131°E) in northern Australia in January–February 2006 provided an opportunity to study gravity wave generation by convection and the associated wave propagation and momentum transport. In this study, we discuss wave generation by a single mesoscale convective system (MCS) that occurred on 23 January. The project used a variety of radars to study the spatial and temporal variability of rainfall and the associated latent heat release during the storm. A high‐resolution numerical model utilized the latent heat release derived from radar rainfall measurements to compute the spatial and geographic variation of gravity wave generation and propagation into the lower stratosphere. Gravity wave ray‐tracing techniques were then used to estimate the wave energy flux penetrating to heights near 90 km, where the results were compared with direct measurements made with a meteor wind radar. This comparison is used to calibrate the momentum fluxes derived from the model and the ray‐tracing results using an iterative technique. The momentum was deposited in a relatively compact region. Body forces computed from the flux divergences had their maximum values at heights near 98 km with a peak values of about 400 m s−1h−1. The effects of secondary gravity wave generation are discussed, as is the overall contribution of gravity waves generated by MCSs to the momentum budget of the tropical middle atmosphere.

Accuracy Assessment of Unit Hydrographs: Point versus Spatially Integrated Rainfall Input

A unit hydrograph (UH) reflects the hydrologic response of watershed processes to a rainfall event. Most UH analyses use measured rainfall from a single rain gauge even though rainfall from a single gauge may not adequately reflect either the spatial or temporal variation during a storm event. The intent of this study was to better understand factors that influence UH accuracy, especially the spatial characteristics of rainfall. Analyses were made by using runoff and rainfall data, including radar rainfall measurements, from the densely monitored Walnut Gulch watershed, Arizona. Transmission loss (TL), as a physical process, is shown to be an important factor that influences UH shape. Multiple-storm, multiple rain gauge analyses show that rainfall from a single gauge is unlikely to yield a UH that reflects the watershed response. A comparison of UHs spatially averaged by using the temporal Thiessen method agree with UHs developed by using radar rainfall measurements. Intra-watershed analyses indicate that...

Rain Spatial Nonuniformity and Spatial Smoothing Error of Radar Rainfall Measurement

Rain Spatial Nonuniformity and Spatial Smoothing Error of Radar Rainfall Measurement

A virtual sensor system for user-generated, real-time environmental data products

With the advent of new instrumentation and sensors, more diverse types and increasing amounts of data are becoming available to environmental researchers and practitioners. However, accessing and integrating these data into forms usable for environmental analysis and modeling can be highly time-consuming and challenging, particularly in real time. For example, radar-rainfall data are a valuable resource for hydrologic modeling because of their high resolution and pervasive coverage. However, radar-rainfall data from the Next Generation Radar (NEXRAD) system continue to be underutilized outside of the operational environment because of limitations in access and availability of research-quality data products, especially in real time. This paper addresses these issues through the development of a prototype Web-based virtual sensor system at NCSA that creates real-time customized data streams from raw sensor data. These data streams are supported by metadata, including provenance information. The system uses workflow composition and publishing tools to facilitate creation and publication (as Web services) of user-created virtual sensors. To demonstrate the system, two case studies are presented. In the first case study, a network of point-based virtual precipitation sensors is deployed to analyze the relationship between radar-rainfall measurements, and in the second case study, a network of polygon-based virtual precipitation sensors is deployed to be used as input to urban flooding models. These case studies illustrate how, with the addition of some application-specific information, this general-purpose system can be utilized to provide customized real-time access to significant data resources such as the NEXRAD system. Additionally, the creation of new types of virtual sensors is discussed, using the example of virtual temperature sensors.

Radar Hydrology: New Z/R Relationships for Quantitative Precipitation Estimation in Klang River Basin, Malaysia

The use of Quantitative Precipitation Estimation (QPE) in radar-rainfall measurement for hydrological purposes is significantly important. For more than 50 years, radars have been deployed to monitor and estimate the precipitation routine in several countries. However in Malaysia, radar application in QPE is still new and needs to be explored. This paper focuses on the Z/R derivation work of radar- rainfall estimation. The work developed new Z/R relationships for the Klang River in the Selangor area for three different classes of rain events, namely low ( 10mm/hr, 30mm/hr). Looking at the high potential of Doppler radar in QPE, the newly formulated Z/R equations will be useful in improving the measurement of rainfall for any hydrological application, especially for flood forecasting.

LMODEL: A Satellite Precipitation Methodology Using Cloud Development Modeling. Part II: Validation

Abstract A new satellite-based rainfall monitoring algorithm that integrates the strengths of both low Earth-orbiting (LEO) and geostationary Earth-orbiting (GEO) satellite information has been developed. The Lagrangian Model (LMODEL) algorithm combines a 2D cloud-advection tracking system and a GEO data–driven cloud development and rainfall generation model with procedures to update model parameters and state variables in near–real time. The details of the LMODEL algorithm were presented in Part I. This paper describes a comparative validation against ground radar rainfall measurements of 1- and 3-h LMODEL accumulated rainfall outputs. LMODEL rainfall estimates consistently outperform accumulated 3-h microwave (MW)-only rainfall estimates, even before the more restricted spatial coverage provided by the latter is taken into account. In addition, the performance of LMODEL products remains effective and consistent between MW overpasses. Case studies demonstrate that the LMODEL provides the potential to synergize available satellite data to generate useful precipitation measurements at an hourly scale.