Vertical Profile Of Reflectivity Correction Research Articles

Improving Operational Radar Rainfall Estimates Using Profiler Observations Over Complex Terrain in Northern California

Quantitative precipitation estimation (QPE) using operational weather radars in the western United States is still a challenging issue due to the beam blockage in the mountainous areas and complex rainfall microphysics induced by the orographic enhancement. This article aims to improve operational radar rainfall estimates in complex terrain by incorporating auxiliary remote sensing observations. An innovative vertical profile of reflectivity (VPR) correction scheme is developed for operational radar using observations from multiple vertically pointing profilers to represent the vertical structure of precipitation at various locations. A demonstration study in the Russian River basin in Northern California is detailed. Results show that the QPE performance is significantly improved after VPR correction, and this new VPR correction approach is superior to the conventional approach currently applied in the operational radar rainfall system. The normalized standard error of hourly rainfall estimates for the two precipitation events presented in this article is improved by ~20% after applying the proposed VPR correction scheme.

Generation and Verification of Rainfall Estimates from 10-Yr Volumetric Weather Radar Measurements

AbstractVolumetric measurements from a C-band weather radar in Belgium are reprocessed over the years 2005–14 to improve the quantitative precipitation estimation (QPE). The data quality is controlled using static clutter and beam blockage maps and clutter identification based on vertical gradients, horizontal texture, and satellite observations. A new QPE is obtained using stratiform–convective classification, a 40-min averaged vertical profile of reflectivity (VPR), a brightband identification, and a specific transformation to rain rates for each precipitation regime. The rain rates are interpolated on a 500-m Cartesian grid, linearly accumulated, and combined with hourly rain gauge measurements using mean field bias or kriging with external drift (KED). The algorithms have been fine-tuned on 13 cases with various meteorological situations. A detailed validation against independent daily rain gauge measurements reveals the importance of VPR correction. A 10-yr verification shows a significant improvement of the new QPE, especially at short and long range, with roughly 50% increase in coverage. Adding the KED allows average improvements of 38%, 35%, and 80% for the mean absolute difference, the multiplicative error spread, and the fraction of good estimates, respectively. The benefit is higher in widespread situations and increases when considering higher rainfall amounts. The mitigation of radar artifacts is clearly visible on 10-yr statistics, including mean annual totals, probabilities to exceed 10 mm, and maxima for hourly and daily accumulation. The correlation of mean totals with rain gauges increases from 0.54 to 0.66 with the new QPE and to 0.8 adding KED.

Radar Vertical Profile of Reflectivity Correction with TRMM Observations Using a Neural Network Approach

Abstract Complex terrain poses challenges to the ground-based radar quantitative precipitation estimation (QPE) because of partial or total blockages of radar beams in the lower tilts. Reflectivities from higher tilts are often used in the QPE under these circumstances and biases are then introduced due to vertical variations of reflectivity. The spaceborne Precipitation Radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite can provide good measurements of the vertical structure of reflectivity even in complex terrain, but the poor temporal resolution of TRMM PR data limits their usefulness in real-time QPE. This study proposes a novel vertical profile of reflectivity (VPR) correction approach to enhance ground radar–based QPEs in complex terrain by integrating the spaceborne radar observations. In the current study, climatological relationships between VPRs from an S-band Doppler weather radar located on the east coast of Taiwan and the TRMM PR are developed using an artificial neural network (ANN). When a lower tilt of the ground radar is blocked, higher-tilt reflectivity data are corrected with the trained ANN and then applied in the rainfall estimation. The proposed algorithm was evaluated with three typhoon precipitation events, and its preliminary performance was evaluated and analyzed.

Estimation of Ground-Level Reflectivity Factor in Operational Weather Radar Networks Using VPR-Based Correction Ensembles

AbstractAn operational method is presented that corrects the bias of radar-based quantitative precipitation estimations (QPE) in radar networks that is due to the vertical profile of reflectivity (VPR) factor. It is used in both rain and snowfall. Measured average VPRs are obtained from the volume scans of each radar at ranges of 2–40 km. At each radar, two time ensembles of the bias estimates are made use of: the first ensemble contains 0–24 members at each range gate, calculated by beam convolution from the measured VPRs at 15-min intervals during the most recent 6 h. The second ensemble similarly contains 24 members calculated from parameterized climatological VPRs. In each scan the precipitation type classification and the climatological VPR are matched with the freezing level obtained from a numerical weather prediction model. The members of the two ensembles are weighted for both time lapse and quality and are then combined. At each composite grid point, the value of the networked VPR correction is then determined as a distance-weighted mean of the time ensembles of biases from all radars located closer than 300 km. In the absence of calibration errors, the resulting estimate of the reflectivity factor at ground level Ze is a seamless continuous field. As verified by radar–radar and radar–gauge comparisons in the Finnish network of eight C-band Doppler radars, the method efficiently reduces the range-dependent bias in QPE. For example, at radar ranges of 141–219 km, the average bias in the ground level Ze was −8.7 and 1.2 dB before and after the VPR correction, respectively.

The variability of vertical structure of precipitation in Huaihe River Basin of China: Implications from long-term spaceborne observations with TRMM precipitation radar

The current study investigates the variability of vertical structure of precipitation in the Huaihe River Basin (HRB) of China. The precipitation characteristics have been revealed by the long-term observations of vertical profile of reflectivity (VPR) from the first spaceborne precipitation radar (PR) onboard the National Aeronautics and Space Administration (NASA)'s Tropical Rainfall Measuring Mission (TRMM) satellite. This study has statistically analyzed the latest TRMM 2A-23 and 2A-25 products (version 7, released in 2012) with ∼15 years time span (from 11 December 1997 to 19 August 2012). First, the spatial and seasonal variations of storm height and freezing level have been investigated. The results show a climatological relation connecting the storm height with the rainfall rate in HRB. Second, mean VPRs have been studied for the stratiform and convective precipitation. The VPR variability has been analyzed for different seasons and rain intensities. Third, the characteristics of rain intensification and weakening in the vertical direction have been examined by the statistical analysis of VPR slope below the melting layer. The results show that the rainfall tends to be reduced (or intensified) with the height changing downward in the light (or moderate and heavy) precipitating clouds, no matter stratiform or convection. Finally, the S-band climatological VPRs have been characterized by converting the VPR from Ku-band to S-band. Considering the wide application of national radar network for weather surveillance in China, the developed S-band climatological VPRs can be potentially applied in a VPR correction scheme to improve the ground radar-based quantitative precipitation estimation (QPE) in this river basin.

Quantitative Assessment of Operational Weather Radar Rainfall Estimates over California’s Northern Sonoma County Using HMT-West Data

Abstract An evaluation of Weather Surveillance Radar-1988 Doppler (WSR-88D) KMUX and KDAX radar quantitative precipitation estimation (QPE) over a site in California’s northern Sonoma County is performed and rain type climatology is presented. This site is next to the flood-prone Russian River basin and, because of the mountainous terrain and remoteness from operational radars, is generally believed to lack adequate coverage. QPE comparisons were conducted for multiyear observations with concurrent classification of rainfall structure using measurements from a gauge and an S-band profiler deployed at the location of interest. The radars were able to detect most of the brightband (BB) rain, which contributed over half of the total precipitation. For this rain type hourly radar-based QPE obtained with a default vertical profile of reflectivity correction provided results with errors of about 50%–60%. The operational radars did not detect precipitation during about 30% of the total rainy hours with mostly shallow nonbrightband (NBB) rain, which, depending on the radar, provided ~(12%–15%) of the total precipitation. The accuracy of radar-based QPE for the detected fraction of NBB rain was rather poor with large negative biases and characteristic errors of around 80%. On some occasions, radars falsely detected precipitation when observing high clouds, which did not precipitate or coexisted with shallow rain (less than 10% of total accumulation). For heavier rain with a significant fraction of BB hourly periods, radar QPE for event totals showed relatively good agreement with gauge data. Cancelation of errors of opposite signs contributed, in part, to such agreement. On average, KDAX-based QPE was biased low compared to KMUX.

Correction of Radar QPE Errors Associated with Low and Partially Observed Brightband Layers

AbstractThe melting of aggregated snow/crystals often results in an enhancement of the reflectivity observed by weather radars, and this is commonly referenced as the bright band (BB). The locally high reflectivity often causes overestimation in radar quantitative precipitation estimates (QPE) if no appropriate correction is applied. When the melting layer is high, a complete BB layer profile (including top, peak, and bottom) can be observed by the ground radar, and a vertical profile of reflectivity (VPR) correction can be made to reduce the BB impact. When a melting layer is near the ground and the bottom part of the bright band cannot be observed by the ground radar, a VPR correction cannot be made directly from the Weather Surveillance Radar-1988 Doppler (WSR-88D) radar observations. This paper presents a new VPR correction method under this situation. From high-resolution precipitation profiler data, an empirical relationship between BB peak and BB bottom is developed. The empirical relationship is combined with the apparent BB peak observed by volume scan radars and the BB bottom is found. Radar QPEs are then corrected based on the estimated BB bottom. The new method was tested on 13 radars during seven low brightband events over different areas in the United States. It is shown to be effective in reducing the radar QPE overestimation under low brightband situations.

Correction of Radar QPE Errors for Nonuniform VPRs in Mesoscale Convective Systems Using TRMM Observations

Abstract Mesoscale convective systems (MCSs) contain both regions of convective and stratiform precipitation, and a bright band (BB) is often found in the stratiform region. Inflated reflectivity intensities in the BB often cause positive biases in radar quantitative precipitation estimation (QPE). A vertical profile of reflectivity (VPR) correction is necessary to reduce such biases. However, existing VPR correction methods for ground-based radars often perform poorly for MCSs owing to their coarse resolution and poor coverage in the vertical direction, especially at far ranges. Spaceborne radars such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), on the other hand, can provide high resolution VPRs. The current study explores a new approach of incorporating the TRMM VPRs into the VPR correction for the Weather Surveillance Radar-1988 Doppler (WSR-88D) radar QPE. High-resolution VPRs derived from the Ku-band TRMM PR data are converted into equivalent S-band VPRs using an empirical technique. The equivalent S-band TRMM VPRs are resampled according to the WSR-88D beam resolution, and the resampled (apparent) VPRs are then used to correct for BB effects in the WSR-88D QPE when the ground radar VPR cannot accurately capture the BB bottom. The new scheme was tested on six MCSs from different regions in the United States and it was shown to provide effective mitigation of the radar QPE errors due to BB contamination.

Identification and uncertainty estimation of vertical reflectivity profiles using a Lagrangian approach to support quantitative precipitation measurements by weather radar

This paper presents a novel approach to estimate the vertical profile of reflectivity (VPR) from volumetric weather radar data using both a traditional Eulerian as well as a newly proposed Lagrangian implementation. For this latter implementation, the recently developed Rotational Carpenter Square Cluster Algorithm (RoCaSCA) is used to delineate precipitation regions at different reflectivity levels. A piecewise linear VPR is estimated for either stratiform or neither stratiform/convective precipitation. As a second aspect of this paper, a novel approach is presented which is able to account for the impact of VPR uncertainty on the estimated radar rainfall variability. Results show that implementation of the VPR identification and correction procedure has a positive impact on quantitative precipitation estimates from radar. Unfortunately, visibility problems severely limit the impact of the Lagrangian implementation beyond distances of 100 km. However, by combining this procedure with the global Eulerian VPR estimation procedure for a given rainfall type (stratiform and neither stratiform/convective), the quality of the quantitative precipitation estimates increases up to a distance of 150 km. Analyses of the impact of VPR uncertainty shows that this aspect accounts for a large fraction of the differences between weather radar rainfall estimates and rain gauge measurements.

Correction of Radar QPE Errors for Non-Uniform VPRs Using TRMM Observations

Mesoscale Convective Systems (MCSs) contain both regions of convective and stratiform precipitation, and a bright band (BB) or equivalent high-reflectivity region is often found in the stratiform precipitation. Inflated reflectivity intensities in the BB often cause positive biases in radar quantitative precipitation estimation (QPE), and a vertical profile of reflectivity (VPR) correction is necessary to reduce the error. VPR corrections of the radar QPE is more difficult for MCSs than for a widespread cool season stratiform precipitation because of the spatial non-homogeneity of MCSs. Further, microphysical processes in the MCS stratiform region are more complicated than in the large-scale cool season stratiform precipitation. A clearly defined BB bottom, which is critical for accurate VPR corrections, is often not found in ground radar VPRs from MCSs. This is a big challenge when the stratiform region of MCSs is far away from the radar where the radar beam is too high or too wide to resolve the BB bottom. Further, variations of reflectivity below the freezing level are much more significant in MCSs than in a large-scale cool season precipitation, requiring high-resolution radar observations near the ground for an effective VPR correction. The current study seeks to use the vertical precipitation structure observed from Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) to aid VPR corrections of the ground radar QPE in MCSs. High-resolution VPRs are derived from TRMM data for MCSs and then applied for the correction of ground radar QPEs.

VPR correction of bright band effects in radar QPEs using polarimetric radar observations

Vertical profile of reflectivity (VPR) correction of bright band (BB) effects has been a challenge for single‐polarization radar quantitative precipitation estimations (QPEs) for mesoscale convective systems and for cool season stratiform precipitation when the freezing level is low. BB is often found in the radar observations of stratiform precipitation, and the inflated reflectivity intensities in the BB often cause positive biases in radar QPEs. A VPR correction is desirable to mitigate the BB contamination and reduce the bias. However, a well‐defined BB bottom, while critical for an effective correction of the bias, is often not found in the VPRs. Fortunately, polarimetric radar variables, especially the copolar correlation coefficient (ρHV), can provide a much better depiction of vertical BB structure than does reflectivity. In the current study, an apparent vertical profile of ρHV (AVPρHV) correction scheme is developed. For each tilt of the radar volume scan data, the precipitation echoes are segregated into convective and stratiform regions. An apparent VPR (AVPR) and AVPρHV are computed for the stratiform region in the given tilt. Then the bright band top, peak, and bottom are identified from the AVPR and AVPρHV, and a linear VPR correction model is fit to the AVPR in the BB layer. VPR corrections are applied to the reflectivity field in the given tilt based on the linear correction model, and radar QPEs are derived from the corrected reflectivity field. The new AVPR and AVPρHV combined scheme was tested on two mesoscale convective system events and one cold season event in the United States and was shown to be more effective in reducing the radar QPE bias associated with the BB than did the AVPR correction alone.

Statistical and Physical Analysis of the Vertical Structure of Precipitation in the Mountainous West Region of the United States Using 11+ Years of Spaceborne Observations from TRMM Precipitation Radar

AbstractThis study presents a statistical analysis of the vertical structure of precipitation measured by NASA–Japan Aerospace Exploration Agency’s (JAXA) Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) in the region of southern California, Arizona, and western New Mexico, where the ground-based Next-Generation Radar (NEXRAD) network finds difficulties in accurately measuring surface precipitation because of beam blockages by complex terrain. This study has applied TRMM PR version-7 products 2A23 and 2A25 from 1 January 2000 to 26 October 2011. The seasonal, spatial, intensity-related, and type-related variabilities are characterized for the PR vertical profile of reflectivity (VPR) as well as the heights of storm, freezing level, and bright band. The intensification and weakening of reflectivity at low levels in the VPR are studied through fitting physically based VPR slopes. Major findings include the following: precipitation type is the most significant factor determining the characteristics of VPRs, the shape of VPRs also influences the intensity of surface rainfall rates, the characteristics of VPRs have a seasonal dependence with strong similarities between the spring and autumn months, and the spatial variation of VPR characteristics suggests that the underlying terrain has an impact on the vertical structure. The comprehensive statistical and physical analysis strengthens the understanding of the vertical structure of precipitation and advocates for the approach of VPR correction to improve surface precipitation estimation in complex terrain.

A real‐time automated convective and stratiform precipitation segregation algorithm in native radar coordinates

AbstractA new convective/stratiform (C/S) precipitation segregation algorithm was developed for applications with single radar volume scan data and in its native (spherical) coordinates. The new algorithm consists of two parts: the first is to find convective rainfall cores based on physical characteristics of different rainfall types, and the second is to delineate the full convective area through seeded region growing. The new scheme takes into account radar sampling characteristics and a variety of precipitation scenarios where the C/S delineation was relatively challenging. The new C/S delineation scheme has two impacts on radar quantitative precipitation estimation (QPE): (i) correctly separate convective and stratiform regions such that appropriate Ze–R relationships can be applied; (ii) correctly define the stratiform area such that a vertical profile of reflectivity (VPR) correction can be applied. The VPR correction is very important to reduce overestimation errors in radar QPEs associated with bright band. The new algorithm was tested on many events and showed improved performance over previous schemes, especially when handling strong bright band and melting graupels in stratiform precipitation. The new scheme was also tested in radar quantitative precipitation estimation (QPE) and it consistently reduced the root mean square error and mean absolute bias in the radar QPE when compared with gauges.

Radar rainfall estimation of stratiform winter precipitation in the Belgian Ardennes

Radars are known for their ability to obtain a wealth of information about spatial storm field characteristics. Unfortunately, rainfall estimates obtained by this instrument are known to be affected by multiple sources of error. Especially for stratiform precipitation systems, the quality of radar rainfall estimates starts to decrease at relatively close ranges. In the current study, the hydrological potential of weather radar is analyzed during a winter half‐year for the hilly region of the Belgian Ardennes. A correction algorithm is proposed which corrects the radar data for errors related to attenuation, ground clutter, anomalous propagation, the vertical profile of reflectivity (VPR), and advection. No final bias correction with respect to rain gauge data was implemented because such an adjustment would not add to a better understanding of the quality of the radar data. The impact of the different corrections is assessed using rainfall information sampled by 42 hourly rain gauges. The largest improvement in the quality of the radar data is obtained by correcting for ground clutter. The impact of VPR correction and advection depends on the spatial variability and velocity of the precipitation system. Overall during the winter period, the radar underestimates the amount of precipitation as compared to the rain gauges. Remaining differences between both instruments can be attributed to spatial and temporal variability in the type of precipitation, which has not been taken into account.

A Real-Time Algorithm for the Correction of Brightband Effects in Radar-Derived QPE

Abstract The bright band (BB) is a layer of enhanced reflectivity due to melting of aggregated snow and ice crystals. The locally high reflectivity causes significant overestimation in radar precipitation estimates if an appropriate correction is not applied. The main objective of the current study is to develop a method that automatically corrects for large errors due to BB effects in a real-time national radar quantitative precipitation estimation (QPE) product. An approach that combines the mean apparent vertical profile of reflectivity (VPR) computed from a volume scan of radar reflectivity observations and an idealized linear VPR model was used for computational efficiency. The methodology was tested for eight events from different regions and seasons in the United States. The VPR correction was found to be effective and robust in reducing overestimation errors in radar-derived QPE, and the corrected radar precipitation fields showed physically continuous distributions. The correction worked consistently well for radars in flat land regions because of the relatively uniform spatial distributions of the BB in those areas. For radars in mountainous regions, the performance of the correction is mixed because of limited radar visibility in addition to large spatial variations of the vertical precipitation structure due to underlying topography.

Real-Time Comparisons of VPR-Corrected Daily Rainfall Estimates with a Gauge Mesonet

Abstract The relative skill of two vertical-profile-of-reflectivity (VPR) correction techniques for daily accumulations on a selected dataset and a real-time dataset has been verified. The first technique (C1) adjusts the 1-h rainfall amounts already derived on a Cartesian CAPPI map at an altitude of 1.5 km in a “one step” procedure using the range-dependent space–time-averaged VPR over the 1-h interval. The C2 technique corrects the nonconvective polar reflectivity measurements of each 5-min radar cycle that are also centered at a height of 1.5 km according to a VPR that is similarly derived but over a shorter time interval. The results emphasize the importance of applying a VPR correction scheme—in particular, in a climatic regime in which most of the liquid precipitation falls from stratiform echoes. The crucial importance of the choice of datasets is also underlined, causing differences in the final assessment that may be greater than those between the various algorithms. Both techniques perform well with selected events of low bright band and thus with the greatest potential for improvement—in particular, when the bias is removed in a post facto analysis. However, when the VPR algorithm is tested in a real-time environment consisting of less strong or higher brightband situations and faces a variety of day-to-day precipitation, the improvement is substantially lower. RMS errors are reduced only from 61% to 48% in contrast with the reduction from 117% to 43% seen with the smaller sample of selected events. This is because other sources of error—in particular, the variability in the radar reflectivity–rainfall rate (Z–R) relationship—are often of the same magnitude as the VPR errors. An example is provided that shows how the bias from an improper Z–R relationship can reduce the true skill of a real-time VPR correction scheme.

Influence of the Vertical Profile of Reflectivity on Radar-Estimated Rain Rates at Short Time Steps

Abstract The present study aims to demonstrate the major influence of the vertical heterogeneity of rainfall on radar–rain gauge assessment. For this purpose, an experimental setup was deployed during the HYDROMET Integrated Radar Experiment (HIRE-98) based on a conventional S-band weather radar operating at long range (90 km), an X-band vertically pointing radar, and a network of 25 tipping-bucket rain gauges. After calibration and attenuation corrections, the X-band radar data enables the estimation of the vertical profile of reflectivity (VPR) time series. Screening and VPR correction factors are derived for the distant S-band radar measurements. The raw and corrected S-band radar estimates are compared to rain gauge measurements for various integration time steps (6–30 min). Considering about 12 h of intense Mediterranean precipitation, the VPR influence at the X-band radar site is clear for all the time steps considered. For instance, a continuous increase in the Nash efficiency for the corrected rad...