Performance Evaluation of a New Dual-Polarization Microphysical Algorithm Based on Long-Term X-Band Radar and Disdrometer Observations

Abstract Accurate estimation of precipitation at high spatial and temporal resolution of weather radars is an open problem in hydrometeorological applications. The use of dual polarization gives the advantage of multiparameter measurements using orthogonal polarization states. These measurements carry significant information, useful for estimating rain-path signal attenuation, drop size distribution (DSD), and rainfall rate. This study evaluates a new self-consistent with optimal parameterization attenuation correction and rain microphysics estimation algorithm (named SCOP-ME). Long-term X-band dual-polarization measurements and disdrometer DSD parameter data, acquired in Athens, Greece, have been used to quantitatively and qualitatively compare SCOP-ME retrievals of median volume diameter D0 and intercept parameter NW with two existing rain microphysical estimation algorithms and the SCOP-ME retrievals of rain rate with three available radar rainfall estimation algorithms. Error statistics for rain rate estimation, in terms of relative mean and root-mean-square error and efficiency, show that the SCOP-ME has low relative error if compared to the other three methods, which systematically underestimate rainfall. The SCOP-ME rain microphysics algorithm also shows a lower relative error statistic when compared to the other two microphysical algorithms. However, measurement noise or other signal degradation effects can significantly affect the estimation of the DSD intercept parameter from the three different algorithms used in this study. Rainfall rate estimates with SCOP-ME mostly depend on the median volume diameter, which is estimated much more efficiently than the intercept parameter. Comparisons based on the long-term dataset are relatively insensitive to path-integrated attenuation variability and rainfall rates, providing relatively accurate retrievals of the DSD parameters when compared to the other two algorithms

Correction of Polarimetric Radar Reflectivity Measurements and Rainfall Estimates for Apparent Vertical Profile in Stratiform Rain

AbstractA method for correcting the vertical profile of reflectivity measurements and rainfall estimates (VPR) in plan position indicator (PPI) scans of polarimetric weather radars in the melting layer and the snow layer during stratiform rain is presented. The method for the detection of the boundaries of the melting layer is based on the well-established characteristic of local minimum of copolar correlation coefficient in the melting layer. This method is applied to PPI scans instead of a beam-by-beam basis with the addition of new acceptance criteria adapted to the radar used in this study. An apparent vertical profile of reflectivity measurements, or rainfall estimate, is calculated by averaging the range profiles from all of the available azimuth directions in each PPI scan. The height of each profile is properly scaled with melting-layer boundaries, and the reflectivity, or rainfall estimate, is normalized with respect to its value at the lower boundary of the melting layer. This approach allows variations of the melting-layer boundaries in space and time and variations of the shape of the apparent VPR in time. The application of the VPR correction to reflectivity and rainfall estimates from a reflectivity–rainfall algorithm and a polarimetric algorithm showed that this VPR correction method effectively removes the bias that is due to the brightband effect in PPI scans. It performs also satisfactorily in the snow region, removing the decrease of the observed VPR with range but with an overestimation by 2 dB or more. This method does not require a tuning using climatological data, and it can be applied on any algorithm for rainfall estimation

Mobile high-resolution X-band polarimetric Doppler weather radar measurements (XPOL): Evaluation and application

This Ph.D dissertation focuses on applications of a mobile high resolution X-band polarimetric Doppler weather radar observations in quantitative rainfall and microphysical estimation. X-band tends to be an attractive radar frequency for hydrologists and hydrometeorologists who are more interesting in high-resolution measurements over small watersheds. However, the drawback with X-band radar is severe attenuation of the electromagnetic signal in significant rainfall, which affects the radar observations and introduces errors in rainfall estimation. ^ The major advantage of the polarimetric weather radar is that it has the ability to transmit and receive both horizontal and vertical polarization. This capability introduces two radar measurements apart from the horizontal reflectivity (ZH). These are the differential reflectivity (ZDR), which is the ratio of horizontal (H) to vertical (V) polarization and the differential phase shift (ΦDP), which is the difference in phase between the H and V polarization signals. ^ This additional information helps to increase the correlation (r 2 \u3e 0.95) between attenuation-corrected (National Observatory of Athens X-band polarimetric) XPOL versus the non-attenuated ZH and ZDR X-band parameters derived from (NCAR S-band polarimetric radar) S-Pol. Error statistics show that the selected algorithm with the least systematic error than the other methods and axial ratio models, converge to below 10% (50%) at path integrated attenuation (differential PIA) values greater than 10 dB (2.5 dB). Overall, the combined uncertainty in the estimation of specific and differential attenuation parameters represent about 28% (in ZH) and 38% (in ZDR). ^ The first part of this thesis focuses on the development of an algorithm that corrects for rain-path attenuation. The second part of this thesis describes a methodology that estimates drop size distribution (DSD) from the attenuation-corrected radar measurements. Two algorithms that estimate the three-parameter \u27normalized\u27 Gamma DSD model are developed for X-band radar polarimetric observations and compared against S-Pol radar and disdrometer spectra observations. The constrained-gamma method is so named because of the constrained μ-Λ relation and the β beta is so named because of the estimation of the mean axis ratio of drops. ^ From the statistical analysis and comparisons of disdrometer spectra observations and S-Pol DSD retrievals, it is found that the β-method introduces errors from the use of KDP, while the constrained-method works reasonably well at low and high rain rates and provides relatively accurate retrieval of the DSD parameters. Error statistics show that the β-method introduces an additional 20% and 30% error in NW and μ while for the estimation of D 0 both algorithms have similar performance.

Statistical characterization and modeling of raindrop spectra time series for different climatological regions

A large data set of raindrop size distribution (RSD) measurements collected with the Joss–Waldvogel disdrometer (JWD) and the 2-D video disdrometer (2DVD) in the U.K., Greece, Japan, and the U.S. are analyzed and modeled. This work extends a previous effort devoted to the exploitation of U.K. data and the design of a stochastic procedure to randomly generate synthetic RSD intermittent time series. This study seeks to: 1) explore the differences of RSD-derived moments for distinct hydroclimate regions, ranging from tropics to subtropics and mid and northern latitudes; 2) compare the governing parameters of the normalized gamma RSD for both stratiform and convective events and perform a sensitivity analysis by using different best fitting techniques; 3) exploit the time-correlation structure of the estimated RSD parameters as the input of a vector autoregressive stationary model used to simulate time series (or horizontal profiles) of RSDs and, consequently, its moments as the rain rate and concentration; and 4) characterize the distribution of the inter-rain duration and rain duration to design a semi-Markov chain to represent the intermittency feature of the rainfall process in a climatological framework. This climatological analysis and the related stochastic RSD generation model may find useful applications within both hydrometeorology and radio propagation

Performance evaluation of a new dual-polarization microphysical algorithm based on long-term X-band radar and disdrometer observations

Accurate estimation of precipitation at high spatial and temporal resolution of weather radars is an open problem in hydrometeorological applications. The use of dual polarization gives the advantage of multiparameter measurements using orthogonal polarization states. These measurements carry significant information, useful for estimating rain-path signal attenuation, drop size distribution (DSD), and rainfall rate. This study evaluates a new self-consistent with optimal parameterization attenuation correction and rain microphysics estimation algorithm (named SCOP-ME). Long-term X-band dual-polarization measurements and disdrometer DSD parameter data, acquired in Athens, Greece, have been used to quantitatively and qualitatively compare SCOP-ME retrievals of median volume diameter D0 and intercept parameter NW with two existing rain microphysical estimation algorithms and the SCOP-ME retrievals of rain rate with three available radar rainfall estimation algorithms. Error statistics for rain rate estimation, in terms of relative mean and root-mean-square error and efficiency, show that the SCOP-ME has low relative error if compared to the other three methods, which systematically underestimate rainfall. The SCOP-ME rain microphysics algorithm also shows a lower relative error statistic when compared to the other two microphysical algorithms. However, measurement noise or other signal degradation effects can significantly affect the estimation of the DSD intercept parameter from the three different algorithms used in this study. Rainfall rate estimates with SCOP-ME mostly depend on the median volume diameter, which is estimated much more efficiently than the intercept parameter. Comparisons based on the long-term dataset are relatively insensitive to pathintegrated attenuation variability and rainfall rates, providing relatively accurate retrievals of the DSD parameters when compared to the other two algorithms. © 2013 American Meteorological Society.The potential of satellite passive microwave sensors to provide quantitative information about near-source volcanic ash cloud parameters is assessed. To this aim, ground-based microwave weather radar and spaceborne microwave radiometer observations are used together with forward-model simulations. The latter are based on 2-D simulations with the numerical plume model Active Tracer High-Resolution Atmospheric Model (ATHAM), in conjunction with the radiative transfer model Satellite Data Simulator Unit (SDSU) that is based on the delta- Eddington approximation and includes Mie scattering. The study area is the Icelandic subglacial volcanic region. The analyzed case study is that of the Grímsvötn eruption in May 2011. ATHAM input parameters are adjusted using available ground data, and sensitivity tests are conducted to investigate the observed brightness temperatures and their variance. The tests are based on the variation of environmental conditions like the terrain emissivity, water vapor, and ice in the volcanic plume. Quantitative correlation analysis between ATHAM/SDSU forward-model columnar content simulations and available microwave radiometric brightness temperature measurements, derived from the Special Sensor Microwave Imager/Sounder (SSMIS), are encouraging in terms of both dynamic range and correlation coefficient. The correlation coefficients are found to vary from −0.37 to −0.63 for SSMIS channels from 91 to 183±1 GHz, respectively. The larger sensitivity of the brightness temperature at 183 ± 1 GHz to the columnar content, with respect to other channels, allowed us to consider this channel as the basis for a model-based polynomial relationship of volcanic plume height as a function of the measured SSMIS brightness temperature

Optimum estimation of rain microphysical parameters from X-band dual-polarization radar observables

Modern polarimetric weather radars typically provide reflectivity, differential reflectivity, and specific differential phase shift, which are used in algorithms to estimate the parameters of the rain drop size distribution (DSD), the mean drop shape, and rainfall rate. A new method is presented to minimize the parameterization error using the Rayleigh scattering limit relations multiplied with a rational polynomial function of reflectivity-weighted raindrop diameter to approximate the Mie character of scattering. A statistical relation between the shape parameter of the DSD with the median volume diameter of raindrops is derived by exploiting long-term disdrometer observations. On the basis of this relation, new optimal estimators of rain microphysical parameters and rainfall rate are developed for a wide range of rain DSDs and air temperatures using X-band scattering simulations of polarimetric radar observables. Parameterizations of radar specific path attenuation and backscattering phase shift are also developed, which do not depend on this relation. The methodology can, in principle, be applied to other weather radar frequencies. A numerical sensitivity analysis shows that calibration bias and measurement noise in radar measurements are critical factors for the total error in parameters estimation, despite the low parameterization error (less than 5%). However, for the usual errors of radar calibration and measurement noise (of the order of 1 dB, 0.2 dB, and 0.3 deg km(-1) for reflectivity, differential reflectivity, and specific differential propagation phase shift, respectively), the new parameterizations provide a reliable estimation of rain parameters (typically less than 20% error).Modern polarimetric weather radars typically provide reflectivity, differential reflectivity, and specific differential phase shift, which are used in algorithms to estimate the parameters of the rain drop size distribution (DSD), the mean drop shape, and rainfall rate. A new method is presented to minimize the parameterization error using the Rayleigh scattering limit relations multiplied with a rational polynomial function of reflectivityweighted raindrop diameter to approximate the Mie character of scattering. A statistical relation between the shape parameter of the DSD with the median volume diameter of raindrops is derived by exploiting long-term disdrometer observations. On the basis of this relation, new optimal estimators of rain microphysical parameters and rainfall rate are developed for a wide range of rain DSDs and air temperatures using X-band scattering simulations of polarimetric radar observables. Parameterizations of radar specific path attenuation and backscattering phase shift are also developed, which do not depend on this relation. The methodology can, in principle, be applied to other weather radar frequencies. A numerical sensitivity analysis shows that calibration bias and measurement noise in radar measurements are critical factors for the total error in parameters estimation, despite the low parameterization error (less than 5%). However, for the usual errors of radar calibration and measurement noise (of the order of 1 dB, 0.2 dB, and 0.3 deg km−1 for reflectivity, differential reflectivity, and specific differential propagation phase shift, respectively), the new parameterizations provide a reliable estimation of rain parameters (typically less than 20% error)

Correction of polarimetric radar reflectivity measurements and rainfall estimates for apparent vertical profile in stratiform rain

A method for correcting the vertical profile of reflectivity measurements and rainfall estimates (VPR) in plan position indicator (PPI) scans of polarimetric weather radars in the melting layer and the snow layer during stratiform rain is presented. The method for the detection of the boundaries of the melting layer is based on the well-established characteristic of local minimum of copolar correlation coefficient in the melting layer. This method is applied to PPI scans instead of a beam-by-beam basis with the addition of new acceptance criteria adapted to the radar used in this study. An apparent vertical profile of reflectivity measurements, or rainfall estimate, is calculated by averaging the range profiles from all of the available azimuth directions in each PPI scan. The height of each profile is properly scaled with melting-layer boundaries, and the reflectivity, or rainfall estimate, is normalized with respect to its value at the lower boundary of the melting layer. This approach allows variations of the melting-layer boundaries in space and time and variations of the shape of the apparentVPRin time. The application of theVPRcorrection to reflectivity and rainfall estimates from a reflectivity-rainfall algorithm and a polarimetric algorithm showed that this VPR correction method effectively removes the bias that is due to the brightband effect in PPI scans. It performs also satisfactorily in the snow region, removing the decrease of the observed VPR with range but with an overestimation by 2 dB or more. This method does not require a tuning using climatological data, and it can be applied on any algorithm for rainfall estimation. © 2013 American Meteorological Society.A method for correcting the vertical profile of reflectivity measurements and rainfall estimates (VPR) in plan position indicator (PPI) scans of polarimetric weather radars in the melting layer and the snow layer during stratiform rain is presented. The method for the detection of the boundaries of the melting layer is based on the well-established characteristic of local minimum of copolar correlation coefficient in the melting layer. This method is applied to PPI scans instead of a beam-by-beam basis with the addition of new acceptance criteria adapted to the radar used in this study. An apparent vertical profile of reflectivity measurements, or rainfall estimate, is calculated by averaging the range profiles from all of the available azimuth directions in each PPI scan. The height of each profile is properly scaled with melting-layer boundaries, and the reflectivity, or rainfall estimate, is normalized with respect to its value at the lower boundary of the melting layer. This approach allows variations of the melting-layer boundaries in space and time and variations of the shape of the apparent VPR in time. The application of the VPR correction to reflectivity and rainfall estimates from a reflectivity–rainfall algorithm and a polarimetric algorithm showed that this VPR correction method effectively removes the bias that is due to the brightband effect in PPI scans. It performs also satisfactorily in the snow region, removing the decrease of the observed VPR with range but with an overestimation by 2 dB or more. This method does not require a tuning using climatological data, and it can be applied on any algorithm for rainfall estimation

Underwater Acoustic Measurements to Estimate Wind and Rainfall in the Mediterranean Sea

Oceanic ambient noise measurements can be analyzed to obtain qualitative and quantitative information about wind and rainfall phenomena over the ocean filling the existing gap of reliable meteorological observations at sea. The Ligurian Sea Acoustic Experiment was designed to collect long-term synergistic observations from a passive acoustic recorder and surface sensors (i.e., buoy mounted rain gauge and anemometer and weather radar) to support error analysis of rainfall rate and wind speed quantification techniques developed in past studies. The study period included combination of high and low wind and rainfall episodes and two storm events that caused two floods in the vicinity of La Spezia and in the city of Genoa in 2011. The availability of high resolution in situ meteorological data allows improving data processing technique to detect and especially to provide effective estimates of wind and rainfall at sea. Results show a very good correspondence between estimates provided by passive acoustic recorder algorithm and in situ observations for both rainfall and wind phenomena and demonstrate the potential of using measurements provided by passive acoustic instruments in open sea for early warning of approaching coastal storms, which for the Mediterranean coastal areas constitutes one of the main causes of recurrent floods

Estimating Reservoir Storage Variations by Combining Sentinel-2 and 3 Measurements in the Yliki Reservoir, Greece

Inland water resources are facing increasing quantitative and qualitative pressures, deriving from anthropogenic causes and the ongoing climate change. The monitoring of reservoirs is essential for sustainable management and preparation against water scarcity and extreme events, such as droughts. This research, relying on the Sentinel-2 and 3 missions, attempts to demonstrate the efficiency of combining remotely sensed water level and water area estimations, in order to estimate the water storage variation of Yliki reservoir. The case study is conducted in one of the few sufficiently monitored reservoirs in Greece, enabling a direct comparison of the proposed methodology results with in situ observations. Moreover, this research work proposes a weekly time interval for pairing level and area estimations, instead of shorter time intervals. The results strongly demonstrate the efficiency of remote sensing in the production of empirical level–area–storage (L–A–S) curves. Correlation to in situ monitored storage- and satellite-derived water level, area stand for 98.81% and 99.27% respectively. Water storage variation is estimated and compared to the observed time series, resulting in an RMSE of 1.28% of the reservoir capacity and a correlation of 96.14%. The empirical L–S relationship underestimates storage, while the A–S relationship overestimates storage when compared to the existing L–A–S curve