Showing papers on "Weather radar published in 2009"

Evaluation of radar-gauge merging methods for quantitative precipitation estimates

Abstract: . Accurate quantitative precipitation estimates are of crucial importance for hydrological studies and applications. When spatial precipitation fields are required, rain gauge measurements are often combined with weather radar observations. In this paper, we evaluate several radar-gauge merging methods with various degrees of complexity: from mean field bias correction to geostatistical merging techniques. The study area is the Walloon region of Belgium, which is mostly located in the Meuse catchment. Observations from a C-band Doppler radar and a dense rain gauge network are used to estimate daily rainfall accumulations over this area. The relative performance of the different merging methods are assessed through a comparison against daily measurements from an independent gauge network. A 4-year verification is performed using several statistical quality parameters. It appears that the geostatistical merging methods perform best with the mean absolute error decreasing by 40% with respect to the original data. A mean field bias correction still achieves a reduction of 25%. A seasonal analysis shows that the benefit of using radar observations is particularly significant during summer. The effect of the network density on the performance of the methods is also investigated. For this purpose, a simple approach to remove gauges from a network is proposed. The analysis reveals that the sensitivity is relatively high for the geostatistical methods but rather small for the simple methods. The geostatistical merging methods give the best results for all tested network densities and their relative benefit increases with the network density.

Weather Radar Ground Clutter. Part II: Real-Time Identification and Filtering

Abstract: The identification and mitigation of anomalous propagation (AP) and normal propagation (NP) ground clutter is an ongoing problem in radar meteorology Scatter from ground-clutter targets routinely contaminates radar data and masks weather returns causing poor data quality The problem is typically mitigated by applying a clutter filter to all radar data, but this also biases weather data at near-zero velocity Modern radar processors make possible the real-time identification and filtering of AP clutter A fuzzy logic algorithm is used to distinguish between clutter echoes and precipitation echoes and, subsequently, a clutter filter is applied to those radar resolution volumes where clutter is present In this way, zero-velocity weather echoes are preserved while clutter echoes are mitigated Since the radar moments are recalculated from clutter-filtered echoes, the underlying weather echo signatures are revealed, thereby significantly increasing the visibility of weather echo This paper describ

Algorithm for Estimation of the Specific Differential Phase

Abstract: The specific differential phase Kdp is one of the important parameters measured by dual-polarization radar that is being considered for the upgrade of the current Next Generation Weather Radar (NEXRAD) system. Estimation of the specific differential phase requires computing the derivative of range profiles of the differential propagation phase. The existence of possible phase wrapping, noise, and associated fluctuation in the differential propagation phase makes the evaluation of derivatives an unstable numerical process. In this paper, a robust algorithm is presented to estimate the specific differential phase, which is able to work on wrapped phases and keep up with the spatial gradients of rainfall, to provide a high-resolution specific differential phase.

Extreme rainfall analysis and estimation of depth-duration-frequency curves using weather radar

Abstract: [1] Rain gauge data are often employed to estimate the rainfall depth for a given return period. However, the number of rain gauge records of short-duration rainfall, such as 15 min, is sparse. The obvious advantage of radar data over most rain gauge networks is their higher temporal and spatial resolution. Furthermore, the current quality of quantitative precipitation estimation with radar and the length of the available time series make it feasible to calculate radar-based extreme rainfall statistics. In this paper an 11-year radar data set of precipitation depths for durations of 15 min to 24 h is derived for the Netherlands (3.55 × 104 km2). The radar data are adjusted using rain gauges by combining an hourly mean-field bias adjustment with a daily spatial adjustment. Assuming a generalized extreme value (GEV) distribution, the index flood method is used to describe the distribution of the annual radar rainfall maxima. Regional variability in the GEV location parameter is studied. GEV parameters based on radar and rain gauge data are compared and turn out to be in reasonable agreement. Furthermore, radar rainfall depth-duration-frequency (DDF) curves and their uncertainties are derived and compared with those based on rain gauge data. Although uncertainties become large for long durations, it is shown that radar data are suitable to construct DDF curves.

3D Convective Storm Identification, Tracking, and Forecasting—An Enhanced TITAN Algorithm

Abstract: Storm identification, tracking, and forecasting make up an essential part of weather radar and severe weather surveillance operations. Existing nowcasting algorithms using radar data can be generally classified into two categories: centroid and cross-correlation tracking. Thunderstorm Identification, Tracking, and Nowcasting (TITAN) is a widely used centroid-type nowcasting algorithm based on this paradigm. The TITAN algorithm can effectively identify, track, and forecast individual convective storm cells, but TITAN tends to provide incorrect identification, tracking, and forecasting in cases where there are dense cells whose shape changes rapidly or where clusters of storm cells occur frequently. Aiming to improve the performance of TITAN in such scenarios, an enhanced TITAN (ETITAN) algorithm is presented. The ETITAN algorithm provides enhancements to the original TITAN algorithm in three aspects. First, in order to handle the false merger problem when two storm cells are adjacent, and to isola...

Rain Rate Estimation Using Measurements From Commercial Telecommunications Links

Abstract: In this paper, we propose a novel method for estimating the rain rate at any given point within a two-dimensional plain using measurements of the received signal level extracted from power control records of an existing deployed fixed wireless communication network. The path-average rainfall intensity along each microwave radio link is estimated from the rainfall-induced attenuation using an empirical relationship. The proposed algorithm consists of appropriate preprocessing of the links data, followed by a modified weighted least squares algorithm to infer on the rain level at any given point in space. The algorithm can be used to interpolate measurements onto a regular grid to construct a two-dimensional rainfall intensity field. The novelty of the proposed estimation method comes from its ability to be applied on an arbitrary geometry network comprising different microwave links lengths and frequencies and allowing easy integration of rain gauge observations into the model to improve estimation accuracy. The technique has been applied to an existing fixed wireless communication network comprising 22 microwave links covering an area of about 15times15 km2 and operating at carrier frequencies of about 20 GHz. The resulting rainfall field estimates have been compared to rain gauge stations in the vicinity and to weather radar data, showing good agreement.

Derivation of a 10-Year Radar-Based Climatology of Rainfall

Abstract: Weather radars give quantitative precipitation estimates over large areas with high spatial and temporal resolutions not achieved by conventional rain gauge networks. Therefore, the derivation and analysis of a radar-based precipitation “climatology” are highly relevant. For that purpose, radar reflectivity data were obtained from two C-band Doppler weather radars covering the land surface of the Netherlands (≈3.55 × 104 km2). From these reflectivities, 10 yr of radar rainfall depths were constructed for durations D of 1, 2, 4, 8, 12, and 24 h with a spatial resolution of 2.4 km and a data availability of approximately 80%. Different methods are compared for adjusting the bias in the radar precipitation depths. Using a dense manual gauge network, a vertical profile of reflectivity (VPR) and a spatial adjustment are applied separately to 24-h (0800–0800 UTC) unadjusted radar-based precipitation depths. Further, an automatic rain gauge network is employed to perform a mean-field bias adjustment to ...

Weather radar ground clutter. Part I: Identification, modeling, and simulation

Abstract: Real-time ground-clutter identification and subsequent filtering of clutter-contaminated data is addressed in this two-part paper. Part I focuses on the identification, modeling, and simulation of S-band ground-clutter echo. A new clutter identification parameter, clutter phase alignment (CPA), is presented. CPA is a measure primarily of the phase variability of the in-phase and quadrature-phase time series samples for a given radar resolution volume. CPA is also a function of amplitude variability of the time series. It is shown that CPA is an excellent discriminator of ground clutter versus precipitation echoes. A typically used weather model, time series simulatoris shown to inadequately describe experimentally observed CPA. Thus, a new technique for the simulation of ground-clutter echo is developed that better predicts the experimentally observed CPA. Experimental data from the Denver Next Generation Weather Radar (NEXRAD) at the Denver, Colorado, Front Range Airport (KFTG), and NCAR’s S-band dual-polarization Doppler radar (S-Pol) are used to illustrate CPA. In Part II, CPA is used in a fuzzy logic algorithm for improved clutter identification.

High-resolution precipitation estimates for hydrological uses

Abstract: The use of precipitation estimates from weather radar for hydrological applications has been limited by the quantitative accuracy, reliability and resolution. The adoption of a more centralised approach to radar data processing, upgrades to telecommunications links and the installation of additional radars in the UK weather radar network have enabled some of these limitations to be addressed. The development of more flexible product generation software, which more fully exploits the resolution of the radar measured reflectivity, now provides for the mapping of precipitation on scales of 1 km and even below, thus approaching the resolution requirements for applications in urban hydrology. This paper describes the methods by which these high-resolution precipitation products are now generated. Illustrations of the products are given and their use in predicting flow using an urban drainage model is demonstrated. Issues affecting data quality, and the advantages and disadvantages of using radar products at hi...

Quantifying Bird Density During Migratory Stopover Using Weather Surveillance Radar

Abstract: Increasingly, data from weather surveillance radars are being used by biologists investigating the ecology and behavior of birds, insects, and bats in the aerosphere. Unfortunately, these radars quantify echoes caused by layered biological targets such as migrating birds in a manner that introduces bias in radar measures. We investigated the performance of a bias-adjustment algorithm that adjusts radar measures for vertical variation of reflectivity, nonstandard beam refraction, and spatial displacement of radar targets. We evaluated the efficacies of four variations of this algorithm by their ability to increase correspondence between radar reflectivity measured at two weather radar sites and the ground density of migrating birds measured during two autumn seasons and two spring seasons among 24 hardwood forest sites along the northern coast of the Gulf of Mexico. The algorithm integrated close-range reflectivity data from the five lowest elevation angle sweeps to derive high-resolution vertical profiles of reflectivity (VPRs) that closely corresponded to the observed vertical target density profiles based on a vertically oriented portable radar. The radar reflectivity of birds aloft near the onset of migratory flight was positively correlated with the bird density on the ground. All four radar data adjustment schemes that we tested produced significant improvement in the accuracy of bird density estimates relative to unadjusted radar data. In general, adjusting reflectivity based solely on the VPRs derived using observed refractive conditions yielded the most accurate radar-based estimates of bird density.

Using SWAT to Model Streamflow in Two River Basins With Ground and Satellite Precipitation Data

Abstract: Both ground rain gauge and remotely sensed precipitation (Next Generation Weather Radar – NEXRAD Stage III) data have been used to support spatially distributed hydrological modeling. This study is unique in that it utilizes and compares the performance of National Weather Service (NWS) rain gauge, NEXRAD Stage III, and Tropical Rainfall Measurement Mission (TRMM) 3B42 (Version 6) data for the hydrological modeling of the Middle Nueces River Watershed in South Texas and Middle Rio Grande Watershed in South Texas and northern Mexico. The hydrologic model chosen for this study is the Soil and Water Assessment Tool (SWAT), which is a comprehensive, physical-based tool that models watershed hydrology and water quality within stream reaches. Minor adjustments to selected model parameters were applied to make parameter values more realistic based on results from previous studies. In both watersheds, NEXRAD Stage III data yields results with low mass balance error between simulated and actual streamflow (±13%) and high monthly Nash-Sutcliffe efficiency coefficients (NS > 0.60) for both calibration (July 1, 2003 to December 31, 2006) and validation (2007) periods. In the Middle Rio Grande Watershed NEXRAD Stage III data also yield robust daily results (time averaged over a three-day period) with NS values of (0.60-0.88). TRMM 3B42 data generate simulations for the Middle Rio Grande Watershed of variable qualtiy (MBE = +13 to −16%; NS = 0.38-0.94; RMSE = 0.07-0.65), but greatly overestimates streamflow during the calibration period in the Middle Nueces Watershed. During the calibration period use of NWS rain gauge data does not generate acceptable simulations in both watersheds. Significantly, our study is the first to successfully demonstrate the utility of satellite-estimated precipitation (TRMM 3B42) in supporting hydrologic modeling with SWAT; thereby, potentially extending the realm (between 50°N and 50°S) where remotely sensed precipitation data can support hydrologic modeling outside of regions that have modern, ground-based radar networks (i.e., much of the third world).

SEVIRI rainfall retrieval and validation using weather radar observations

Abstract: [1] This paper presents and validates a new algorithm to detect precipitating clouds and estimate rain rates from cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) The precipitation properties (PP) algorithm uses information on cloud condensed water path (CWP), particle effective radius, and cloud thermodynamic phase to detect precipitating clouds, while information on CWP and cloud top height is used to estimate rain rates An independent data set of weather radar data is used to determine the optimum settings of the PP algorithm and calibrated it For a 2-month period, the ability of SEVIRI to discriminate precipitating from nonprecipitating clouds is evaluated using weather radar over the Netherlands In addition, weather radar and rain gauge observations are used to validate the SEVIRI retrievals of rain rate and accumulated rainfall across the entire study area and period During the observation period, the spatial extents of precipitation over the study area from SEVIRI and weather radar are highly correlated (correlation ≈ 090), while weaker correlations (correlation ≈ 063) are found between the spatially mean rain rate retrievals from these instruments The combined use of information on CWP, cloud thermodynamic phase, and particle size for the detection of precipitation results in an increase in explained variance (∼10%) and decrease in false alarms (∼15%), as compared to detection methods that are solely based on a threshold CWP At a pixel level, the SEVIRI retrievals have an acceptable accuracy (bias) of about 01 mm h−1 and a precision (standard error) of about 08 mm h−1 It is argued that parts of the differences are caused by collocation errors and parallax shifts in the SEVIRI data and by irregularities in the weather radar data In future studies we intend to exploit the observations of the European weather radar network Operational Programme for the Exchange of Weather Radar Information (OPERA) and extend this study to the entirety of Europe

Detailed Observations of Wind Turbine Clutter with Scanning Weather Radars

Abstract: The wind power industry has seen tremendous growth over the past decade and with it has come the need for clutter mitigation techniques for nearby radar systems. Wind turbines can impart upon these radars a unique type of interference that is not removed with conventional clutter-filtering methods. Time series data from Weather Surveillance Radar-1988 Doppler (WSR-88D) stations near wind farms were collected and spectral analysis was used to investigate the detailed characteristics of wind turbine clutter. Techniques to mask wind turbine clutter were developed that utilize multiquadric interpolation in two and three dimensions and can be applied to both the spectral moments and spectral components. In an effort to improve performance, a nowcasting algorithm was incorporated into the interpolation scheme via a least mean squares criterion. The masking techniques described in this paper will be shown to reduce the impact of wind turbine clutter on weather radar systems at the expense of spatial res...

Bollène-2002 Experiment: Radar Quantitative Precipitation Estimation in the Cévennes–Vivarais Region, France.

Abstract: The Bollene-2002 Experiment was aimed at developing the use of a radar volume-scanning strategy for conducting radar rainfall estimations in the mountainous regions of France. A developmental radar processing system, called Traitements Regionalises et Adaptatifs de Donnees Radar pour l’Hydrologie (Regionalized and Adaptive Radar Data Processing for Hydrological Applications), has been built and several algorithms were specifically produced as part of this project. These algorithms include 1) a clutter identification technique based on the pulse-to-pulse variability of reflectivity Z for noncoherent radar, 2) a coupled procedure for determining a rain partition between convective and widespread rainfall R and the associated normalized vertical profiles of reflectivity, and 3) a method for calculating reflectivity at ground level from reflectivities measured aloft. Several radar processing strategies, including nonadaptive, time-adaptive, and space–time-adaptive variants, have been implemented to a...

Weather radar system and method using data from a lightning sensor

Abstract: A weather radar system or method can be utilized to determine a location of a weather hazard for an aircraft. The weather radar system can utilize processing electronics coupled to an antenna. The processing electronics can determine presence of the hazard in response to data related to returns received by the weather radar antenna and data from a lightning sensor. The system can include a display for showing the hazard and its location.

Towards flash flood prediction in the dry Dead Sea region utilizing radar rainfall information

Abstract: Flash-flood warning models can save lives and protect various kinds of infrastructure. In dry climate regions, rainfall is highly variable and can be of high-intensity. Since rain gauge networks in such areas are sparse, rainfall information derived from weather radar systems can provide useful input for flash-flood models. This paper presents a flash-flood warning model which utilizes radar rainfall data and applies it to two catchments that drain into the dry Dead Sea region. Radar-based quantitative precipitation estimates (QPEs) were derived using a rain gauge adjustment approach, either on a daily basis (allowing the adjustment factor to change over time, assuming available real-time gauge data) or using a constant factor value (derived from rain gauge data) over the entire period of the analysis. The QPEs served as input for a continuous hydrological model that represents the main hydrological processes in the region, namely infiltration, flow routing and transmission losses. The infiltration function is applied in a distributed mode while the routing and transmission loss functions are applied in a lumped mode. Model parameters were found by calibration based on the 5 years of data for one of the catchments. Validation was performed for a subsequent 5-year period for the same catchment and then for an entire 10-year record for the second catchment. The probability of detection and false alarm rates for the validation cases were reasonable. Probabilistic flash-flood prediction is presented applying Monte Carlo simulations with an uncertainty range for the QPEs and model parameters. With low probability thresholds, one can maintain more than 70% detection with no more than 30% false alarms. The study demonstrates that a flash-flood warning model is feasible for catchments in the area studied.

Evaluation of CMORPH Precipitation Products at Fine Space–Time Scales

Abstract: In this study, a comparison of the spatial patterns of high-resolution precipitation products obtained from the Climate Prediction Center’s morphing technique (CMORPH), which is a satellite-only product, and gauge-adjusted Next Generation Weather Radar (NEXRAD) rainfall observations is performed using a variety of statistical techniques for the Little Washita watershed region in Oklahoma for a 3-yr period. Results show that 1) the performance statistics of CMORPH show tremendous variability from one hour to the next, suggesting that the performance statistics are dynamic in time, and therefore each satellite rainfall product should be accompanied by an error product to make it more meaningful; 2) CMORPH is positively biased in summer and negatively biased in winter, consistent with the findings of previous studies; 3) CMORPH spatial fields tend to be smoother than NEXRAD output; 4) the errors are temporally correlated, in particular within the range from 1 to 6 accumulation hours, implying that a...

Classification of Convective Areas Using Decision Trees

Abstract: This paper presents an automated approach for classifying storm type from weather radar reflectivity using decision trees. Recent research indicates a strong relationship between storm type (morphology) and severe weather, and such information can aid in the warning process. Furthermore, new adaptive sensing tools, such as the Center for Collaborative Adaptive Sensing of the Atmosphere’s (CASA’s) weather radar, can make use of storm-type information in real time. Given the volume of weather radar data from those tools, manual classification of storms is not possible when dealing with real-time data streams. An automated system can more quickly and efficiently sort through real-time data streams and return value-added output in a form that can be more easily manipulated and understood. The method of storm classification in this paper combines two machine learning techniques: K-means clustering and decision trees. K-means segments the reflectivity data into clusters, and decision trees classify eac...

On the propagation of uncertainty in weather radar estimates of rainfall through hydrological models

Abstract: The generation of flow forecasts using rainfall inputs to hydrological models has been developed over many years. Unfortunately, errors in input data to models may vary considerably depending upon the different sources of data such as raingauges, radar and high resolution Numerical Weather Prediction (NWP) models. This has hampered the operational use of radar for quantitative flow forecasting. The manner with which radar rainfall input and model parametric uncertainty influence the character of the flow simulation uncertainty in hydrological models has been investigated by several authors. In this paper an approach to this problem based upon a stochastic hydrological model is considered. The errors in the input data, although they may be constrained, do propagate through the model to the flow predictions. Previous work on this error propagation through a fully distributed model is described, and a similar analysis for a stochastic hydrological model implemented in a mixed rural and urban area in north-west England carried out. Results are compared with those previously published for an American catchment. A possible approach to selecting flow forecast ensemble members is proposed. Copyright  2009 Royal Meteorological Society

Theory and Characterization of Weather Radar Networks

Abstract: A dense weather radar network is an emerging concept advanced by the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). In a weather radar environment, the specific radar units employed and the network topology will influence the characteristics of the data obtained. To define this, a general framework is developed to describe the radar network space, and formulations are obtained that can be used for weather radar network characterization. The models developed are useful for quantifying and comparing the performance of different weather radar networks. Starting with system characteristics that are used to specify individual radars, a theoretical basis is developed to extend the concept to network configurations of interest. A general network elemental cell is defined and employed as the parameterized domain over which different coverage aspects (such as detection sensitivity, beam size, and minimum beam height) are studied using analytical tools developed in...

Methods and systems for detection of hazard to aviation due to convective weather

Abstract: Systems and methods for improving output of weather information. A weather radar system receives weather reflectivity values. A processing device stores the received weather reflectivity values into a three-dimensional buffer, calculates a sum of the reflectivity value stored in a column of cells within the three-dimensional buffer, and assigns a first hazard indication to the cells of the column when the result of the calculation is above a first threshold. A display device generates a weather display based on data stored in the three-dimensional buffer. The weather display includes a display icon associated with the hazard indication when a cell from the three-dimensional buffer has been selected for the weather display.

The interaction of large scale and mesoscale environment leading to formation of intense thunderstorms over Kolkata Part I: Doppler radar and satellite observations

Abstract: The weather systems that predominantly affect the eastern and northeastern parts of India during the pre-monsoon summer months (March, April and May) are severe thunderstorms, known as Nor’westers. The storms derive their names from the fact that they frequently strike cities and towns in the southern part of West Bengal in the afternoon from the north-west direction while traveling far from its place of genesis over the Bihar plateau. The storms are devastating in nature particularly due to strong (gusty) winds, heavy rains and hails associated with it. Although these storms are well known for its power of causing damages, studies on them are relatively few due to their small size and sparse network of observations. To address this important issue, the evolution of two Nor’westers of 12 March and 22 May 2003 over Kolkata is studied in detail in this paper using hourly Doppler weather radar (DWR) observations and high resolution Meteosat-5 imageries. In addition, supporting meteorological reports are used to find the large scale conditions that influence the moisture convergence and vertical wind shear. The genesis of both the storms is found to be over Bihar-Jharkhand region and beyond the range of the DWR. The satellite observations are found to be useful in identifying the location and initiation of the storms. The movements of the storms are captured by the DWR estimated vertical cross-section of reflectivities. The Doppler estimate shows that the 12 March storm had a vertical extent of about 10–12 km at the time of maturity and that of 22 May reaching up to 18 km signifying deep convection associated with these events. The genesis, maturity and dissipation are well brought out by the hourly DWR and satellite imageries. The DWR observations suggest that the systems move at a speed of 20–25 m/s. The DWR estimated precipitation shows a detailed spatial distribution around Kolkata with several localized zones of heavy rain and this is found to be well supported by the nearby station observations. This study establishes that DWR observations along with hourly satellite imageries are able to capture the evolution of Nor’westers. The study also shows that the composite DWR-satellite information is a reliable tool for nowcasting the location, time and path of movement of Nor’westers. Based on these observations, a conceptual model of the Nor’wester is proposed.

Systems and methods for preparing ground-based weather radar information for use in an installation vehicle

Abstract: Systems and methods prepare ground-based supplemental weather radar information for integration with onboard weather radar information. An exemplary embodiment receives ground-based weather radar information from a ground-based weather radar station, the ground-based weather radar information referenced in a first coordinate system; generates supplemental weather radar information from the received ground-based weather radar information, wherein the supplemental weather radar information is referenced to a second coordinate system based upon at least latitude and longitude; and communicates the supplemental weather radar information, wherein the communicated supplemental weather radar information is integrated with weather radar information of an onboard weather radar system of an installation vehicle.