Search Results

You are looking at 1 - 10 of 69 items for

  • Author or Editor: Witold F. Krajewski x
  • Refine by Access: All Content x
Clear All Modify Search
Emmanouil N. Anagnostou
and
Witold F. Krajewski

Abstract

The WSR-88D Precipitation Processing Subsystem (PPS) is a multicomponent rainfall-estimation algorithm with a large number of parameters controlling its performance. Currently, the parameter values of the PPS are set based on limited experimental studies and do not account for rainfall-regime differences. This translates into potential increase of uncertainty in the system-estimated precipitation products.

The authors propose to formulate the PPS calibration as a global optimization problem. The parameter values are determined by optimizing a selected criterion at the level of gridded hourly rainfall-accumulation products. The criterion is the root-mean-square difference between the hourly radar rainfall products and rainfall accumulations from rain gauges under the radar umbrella. The main advantages of this approach are 1) it simultaneously estimates the optimal parameters providing an integral assessment of the algorithm’s performance, and 2) it allows for an assessment of the relative importance of the PPS parameters in the full context of rainfall estimation.

The optimization approach is illustrated using two months of Melbourne, Florida, WSR-88D radar-reflectivity data and the corresponding rain gauge measurements. Global optimization of the PPS parameters yields a reduction of 10% on average and up to 22% on individual days with respect to the default system. The illustration is completed by a sensitivity analysis of the PPS to identify the most significant parameters.

Full access
Gabriele Villarini
and
Witold F. Krajewski

Abstract

It is well acknowledged that there are large uncertainties associated with the operational quantitative precipitation estimates produced by the U.S. national network of the Weather Surveillance Radar-1988 Doppler (WSR-88D). These errors result from the measurement principles, parameter estimation, and the not fully understood physical processes. Even though comprehensive quantitative evaluation of the total radar-rainfall uncertainties has been the object of earlier studies, an open question remains concerning how the error model results are affected by parameter values and correction setups in the radar-rainfall algorithms. This study focuses on the effects of different exponents in the reflectivity–rainfall (ZR) relation [Marshall–Palmer, default Next Generation Weather Radar (NEXRAD), and tropical] and the impact of an anomalous propagation removal algorithm. To address this issue, the authors apply an empirically based model in which the relation between true rainfall and radar rainfall could be described as the product of a systematic distortion function and a random component. Additionally, they extend the error model to describe the radar-rainfall uncertainties in an additive form. This approach is fully empirically based, and rain gauge measurements are considered as an approximation of the true rainfall. The proposed results are based on a large sample (6 yr) of data from the Oklahoma City radar (KTLX) and processed through the Hydro-NEXRAD software system. The radar data are complemented with the corresponding rain gauge observations from the Oklahoma Mesonet and the Agricultural Research Service Micronet.

Full access
James A. Smith
and
Witold F. Krajewski

Abstract

In this paper procedures are developed for estimating the mean field bias of radar rainfall estimates. Mean field bias is modeled as a random process that varies not only from storm to storm but also over the course of a storm. State estimates of mean field bias are based on hourly raingage data and hourly accumulations of radar rainfall estimates. The procedures are developed for the precipitation processing systems used with products of the Next Generation Weather Radar (NEXRAD) system. To implement the state estimation procedures, parameters of the bias model must be specified. Likelihood-based procedures are developed for estimating these parameters. A simulation experiment is carried out to assess performance of the parameter estimation procedure. Convergence of parameter estimators is rapid for the cases studied, with data from approximately 25 storms providing parameter estimates of acceptable accuracy. The state estimation procedures are applied to radar and raingage data from the 27 May 1987 storm, which was centered near the NSSL radar in Norman, Oklahoma. The results highlight dependence of the state estimation problem on the parameter estimation problem.

Full access
Emmanouil N. Anagnostou
and
Witold F. Krajewski

Abstract

A multicomponent radar-based algorithm for real-time precipitation estimation is developed. The algorithm emphasizes the combined use of weather radar observations and in situ rain gauge rainfall measurements. The temporal and spatial scales of interest are hourly to storm-total accumulations for areas of 4 km2 to approximately 16 km2. The processing steps include beam–height-effect correction, vertical integration, convective–stratiform classification, conversion from radar observables to rainfall rate, range-effect correction, and transformation of the estimated rainfall rates from polar coordinates to a Cartesian grid. Additionally, the algorithm applies advection correction to the gridded rainfall rates to minimize the temporal sampling effect and, subsequently, aggregates the corrected rainfall rates to 1-hourly, 3-hourly, and storm-total accumulations. The system applies different parameter values for convective and stratiform regimes. The calibration of the system is formulated as a global optimization problem, which is solved using the Gauss–Newton adaptive stochastic method. The algorithm is cast in a recursive formulation with parameters adjusted in real time. Evaluation of the system is based on an extensive dataset from the Melbourne, Florida, WSR-88D radar site.

Full access
Emmanouil N. Anagnostou
and
Witold F. Krajewski

Abstract

The performance of a real-time radar rainfall estimation algorithm is examined based on an extensive dataset of volume scan reflectivity and rain gauge rainfall measurements from the WSR-88D site in Melbourne, Florida. Radar rainfall estimates are evaluated based on the following radar–rain gauge statistics: mean difference (bias), normalized root-mean-square difference, and correlation coefficient. The spatiotemporal scales of interest are hourly accumulations over 4 km × 4 km grids. First, the authors demonstrate the convergence properties of the algorithm’s adaptive parameter estimation procedure and conduct sensitivity tests of the system with respect to changes in the parameter values. Second, the major components of the algorithm are compared with the operational WSR-88D Precipitation Processing Subsystem. The authors show reduction in the radar–rain gauge root-mean-square difference up to 40%, resulting from the new parameterization schemes and the real-time calibration procedure. When rainfall classification is included, the reduction is higher (up to 50%). The authors show that correction for rain field advection moderately improves estimation accuracy (up to 20%). Finally, the authors show that the algorithm can effectively remove range-dependent systematic errors in radar observations.

Full access
Jeffrey R. McCollum
and
Witold F. Krajewski

Abstract

The relationship between monthly mean area-averaged rainfall and monthly mean fractional rainfall occurrence is used to develop a new method of open ocean rainfall estimation. This method uses acoustic sensors attached to drifting buoys to sample rainfall occurrence in space and time. The fractional rainfall occurrences measured by the sensors are used in a linear relationship to estimate monthly rainfall averaged over large (i.e., 2.5° × 2.5°) areas. This estimation method is tested for different scenarios using a stochastic model. Results support the feasibility of this new rainfall estimation scheme. Simulations show that the existing density of drifting buoys is inadequate, but densities around 10 times the existing density will give correlation coefficients between estimated and true rainfall around 0.55. Estimates obtained with this method may be used to calibrate and/or validate the satellite-based methods of open ocean rainfall.

Full access
Mircea Grecu
and
Witold F. Krajewski

Abstract

To detect anomalous propagation echoes in radar data, an automated procedure based on a neural network classification scheme has been developed. Earlier results had indicated that algorithms used to detect anomalous propagation must be calibrated before they can be applied to new sites. Developing a calibration dataset is typically laborious as it involves a human expert. To eliminate this problem, an efficient methodology of calibrating and validating neural network–based detection is proposed. Using volume scan radar reflectivity data from two WSR-88D locations, the authors demonstrate that the procedure can be calibrated easily and applied successfully to different sites.

Full access
Witold F. Krajewski
and
Bertrand Vignal

Abstract

A method of detecting anomalous propagation echo in volume scan radar reflectivity data is evaluated. The method is based on a neural network approach and is suitable for operational implementation. It performs a classification of the base scan data on a pixel-by-pixel basis into two classes: rain and no rain. The results of applying the method to a large sample of Weather Surveillance Radar-1988 Doppler (WSR-88D) level II archive data are described. The data consist of over 10 000 volume scans collected in 1994 and 1995 by the Tulsa, Oklahoma, WSR-88D. The evaluation includes analyses based on radar data only and on various comparisons of radar and rain gauge data. The rain gauge data are from the Oklahoma Mesonet. The results clearly show the effectiveness of the procedure as indicated by reduced bias in rainfall accumulation and improved behavior in other statistics.

Full access
Anton Kruger
and
Witold F. Krajewski

Abstract

This paper describes the design and operation of a two-dimensional video disdrometer (2DVD) for in situ measurements of precipitation drop size distribution. The instrument records orthogonal image projections of raindrops as they cross its sensing area, and can provide a wealth of information, including velocity and shape, of individual raindrops. The 2DVD is a sensitive optical instrument that is exposed to rain, high humidity, and possibly high temperatures. These and other issues such as calibration procedures impact its performance. Under low-wind conditions, the instrument can provide accurate and detailed information on drop size, terminal velocity, and drop shape in a field setting, and the instrument's advantages far outweigh its disadvantages.

Full access
Mekonnen Gebremichael
and
Witold F. Krajewski

Abstract

The main objective of this study is to assess the ability of radar-derived rainfall products to characterize the small-scale spatial variability of rainfall. The authors use independent datasets from high-quality dense rain gauge networks employed during the Texas and Florida Underflights (TEFLUN-B) and Tropical Rainfall Measuring Mission component of the Large-Scale Biosphere–Atmosphere (TRMM-LBA) field experiments conducted by NASA in 1998 and 1999. A detailed comparison between gauge- and radar-derived spatial variability estimates is carried out by means of a correlation function, covariance, variogram, scaling characteristics, and variance reduction due to spatial averaging. Emphasis is given to the correlation function because it is involved in most of these statistics. The approach followed in the analysis addresses the problems associated with the traditional estimation methods and the recognized differences in the scales of observation. The performance of the radar-derived correlation function is evaluated in two ways: by direct comparison with gauge-derived correlation function and by quantifying its effect on one of the applications, that is, gauge sampling uncertainty estimation. Results show that, at separation distances shorter than about 5 km, radar-derived correlations are lower than those obtained from gauges. Three sources of uncertainty that may have caused the discrepancy between gauge- and radar-derived correlations are identified, and their effects are quantified to the extent possible. The error introduced in gauge sampling uncertainty estimates due to the use of radar-derived correlation function is within 30%. Discrepancies between gauge- and radar-rainfall fields are also observed in terms of the other spatial statistics.

Full access