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Chandrasekar Radhakrishnan and V. Chandrasekar

Abstract

This study targeted improving Collaborative Adaptive Sensing of the Atmosphere’s (CASA) 6-h lead time predictive ability by blending the radar-based nowcast with the NWP model over the Dallas–Fort Worth (DFW) urban radar network. This study also depicts the recent updates in CASA’s real-time reflectivity nowcast system by assessing nine precipitation cases over the DFW urban region. CASA’s nowcast framework displayed better primer outcomes than the WRF Model forecast for the lead time of 1 h and 30 min. After that time, the predictive ability of the nowcast framework began decreasing compared to the WRF Model. To broaden CASA’s predictive system lead time to 6 h, the WRF Model forecasts were blended with Dynamic and Adaptive Radar Tracking of Storms (DARTS) nowcast. The HRRR model analysis was used as initial and boundary conditions in the WRF Model. The high-resolution dual-pol radar observations were assimilated into the WRF Model through the 3DVAR data assimilation technique. Three kinds of blending strategies were used and the results were compared: 1) hyperbolic tangent curve (HTW), 2) critical success index (CSIW), and 3) salient cross dissolve (Sal CD). The sensitivity studies were conducted to decide desirable parameters in the blending techniques. The outcomes proved that blending enhanced the prediction skills. Also, the overall performance of blending relies on the accuracy of the WRF forecast. Even though blending results are mixed, the HTW-based technique performed better than the other two techniques.

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V. Chandrasekar and V. N. Bringi

Abstract

In Part II of this study, simulations of multiparameter radar observables to include X-band specific attenuation (A) are performed in order to study the relationship between A, Z, and Z DR. We also compute the triplet (A, Z, Z DR) from simulations of disdrometer raindrop spectra. As in Part I, our simulations include the fluctuations due to both measurement errors and physical variations of the gamma raindrop spectra parameters (N 0, D 0, m). We examine the correlation between (A/Z) and Z DR derived from both disdrometer and radar simulations, and show that the disdrometer-based data yields a negative collation (∼ −0.9) between (A/Z) and Z DR, whereas for radar data the correlation ≈ 0. We emphasize that these correlations are due only to measurement fluctuations, and not to physical variations. The large magnitude for the negative correlation compresses the scatter in plots of (A/Z) versus Z DR based on disdrometer RSD samples whereas the same scatter plots using multiparameter radar data show very large scatter. We also simulate A, Z and Z DR from three separate disdrometers (all sampling the same gamma RSD) and show that the scatter is more realistic and much larger than when using a single disdrometer.

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V. Chandrasekar and V. N. Bringi

Abstract

Fluctuations in the radar measurements of Z DR are due to both signal power fluctuations and the cross-correlation between the horizontal and vertical polarized signals. In Part I of this study, these signals are simulated for an S-band radar for backscatter from rain media, which is characterized by a gamma model of the raindrop size distribution (RSD). The parameters N 0, D 0, m of the gamma RSD are then varied over the entire range found in natural rainfall. Thus, the radar simulations contain the effects of both statistical fluctuations and physical variations. We also simulate sampling of raindrops by disdrometer. The sampling errors are related to the Poisson statistics of the total number of drops in the fixed sample volume and to the statistics that govern the gamma distribution of drops as a function of size. We simulate disdrometer RSD samples over the entire range of N 0, D 0, m values found in rainfall, so that the effects of statistical fluctuations and physical variations are introduced.

It is shown that Z DR, computed from disdrometer RSD samples, is correlated with Z and with other moments of the RSD when the same disdrometer data is used. This correlation is purely statistical and is independent of the physical correlation. We use the radar and disdrometer simulations to intercompare the rain rate as derived by the radar Z DR-method with the rain rate estimated by the disdrometer. Our simulation results are used to explain the correlation and error structure of radar/disdrometer-derived rain rate intercomparison data reported in the literature.

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V. Chandrasekar and V. N. Bringi

Abstract

Raindrop size distributions (RSDs) are often estimated using surface raindrop sampling devices (e.g., disdrometers) or optical array (2D-PMS) probes. A number of authors have used these measured distributions to compute certain higher-order RSD moments that correspond to radar reflectivity, attenuation, optical extinction, etc. Scatter plots of these RSD moments versus disdrometer-measured rainrates are then used to deduce physical relationships between radar reflectivity, attenuation, etc., which are measured by independent instruments (e.g., radar), and rainrate. In this paper we simulate RSDs of the gamma form as well as radar reflectivity (via time series simulation) to study the correlation structure of radar estimates versus rainrate as opposed to RSD moment estimates versus rainrate. Simulations offer a powerful method of studying the statistics of radar and surface RSD measurements since the “natural” RSD fluctuations can be introduced separately. In our simulations we vary the parameter N o, D o and m of a gamma distribution over the range normally found in rainfall, as well as varying the device sampling volume. We apply our simulations to explain some possible features related to discrepancies which can arise when radar rainfall measurements are compared with surface or aircraft-based sampling devices.

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Eugenio Gorgucci and V. Chandrasekar

Abstract

Monitoring of precipitation using high-frequency radar systems, such as the X band, is becoming increasingly popular because of their lower cost compared to their S-band counterpart. However, at higher frequencies, such as the X band, the precipitation-induced attenuation is significant, and introduces ambiguities in the interpretation of the radar observations. Differential phase measurements have been shown to be very useful for correcting the measured reflectivity for precipitation-induced attenuation. This paper presents a quantitative evaluation of two attenuation correction methodologies with specific emphasis on the X band. A simple differential phase–based algorithm as well as the range-profiling algorithm are studied. The impact of backscatter differential phase on the performance of attenuation correction is evaluated. It is shown that both of the algorithms for attenuation correction work fairly well, yielding attenuation-accurate corrected reflectivities with a negligible bias.

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Hongping Liu and V. Chandrasekar

Abstract

A fuzzy logic and neuro-fuzzy system for classification of hydrometeor type based on polarimetric radar measurements is described in this paper. The hydrometeor classification system is implemented by using fuzzy logic and a neural network, where the fuzzy logic is used to infer hydrometeor type, and the neural network learning algorithm is used for automatic adjustment of the parameters of the fuzzy sets in the fuzzy logic system according to prior knowledge. Five radar measurements, namely, horizontal reflectivity (Z H), differential reflectivity (Z DR), differential propagation phase shift (K DP), correlation coefficient [ρ HV(0)], and linear depolarization ratio (L DR), and corresponding altitude, have been used as input variables to the neuro-fuzzy network. The output of the neuro-fuzzy system is one of the many possible hydrometeor types: 1) drizzle, 2) rain, 3) dry and low density snow, 4) dry and high-density crystals, 5) wet and melting snow, 6) dry graupel, 7) wet graupel, 8) small hail, 9) large hail, and 10) a mixture of rain and hail. The neuro-fuzzy classifier is more advantageous than a simple neural network or a fuzzy logic classifier because it is more transparent (instead of a “black box”) and can learn the parameter of the system from the past data (unlike a fuzzy logic system). The hydrometeor classifier has been applied to several case studies and the results are compared against in situ observations.

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Jason Fritz and V. Chandrasekar

Abstract

The surface-layer moisture field can be obtained by estimating the refractive index of air, measured in parts per million, and is referred to as refractivity. A technique to estimate the refractivity by using radar has been demonstrated experimentally using the measured change in phase from stationary ground targets. Recently, a new network-based algorithm was proposed within the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) as an alternative approach, especially when dealing with multiple radars. That work presented the algorithm and applied it to purely simulated data. The research presented here provides more detail and takes the new networked radar approach to the next level by independently validating and demonstrating the output with data collected during a refractivity field experiment in Colorado during the summer of 2006. The practical aspects of implementing the network-based algorithm are presented along with a more complete mathematical representation. The results are then compared with the previously fielded technique starting from the same filtered phase data. From this comparison, the authors conclude that the networked algorithm has potential for providing a good refractivity estimate from a radar network once some of its own shortcomings are addressed.

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V. Chandrasekar and S. Lim

Abstract

A system for reflectivity and attenuation retrieval for rain medium in a networked radar environment is described. Electromagnetic waves backscattered from a common volume in networked radar systems are attenuated differently along the different paths. A solution for the specific attenuation distribution is proposed by solving the integral equation for reflectivity and attenuation. The set of governing integral equations describing the backscatter and propagation of common resolution volume are solved simultaneously with constraints on total path attenuation. The proposed algorithm is evaluated based on simulated X-band radar observations synthesized from S-band measurements collected by the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar.

Retrieved reflectivity and specific attenuation using the proposed method show good agreement with simulated reflectivity and specific attenuation. Preliminary demonstration of the network-based retrieval using data from the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) IP-1 radar network are also presented.

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N. Bharadwaj and V. Chandrasekar

Abstract

This paper evaluates the retrieval of polarimetric variables when phase-coded waveforms are employed to suppress range overlaid echoes. A phase-coded waveform tags transmitted pulses with a phase code and then decodes the received signal to separate the overlaid echoes. Two methods suggested for separating overlaid echoes use random and systematic phase-coding techniques. In this paper, random phase and systematic phase-coded waveforms are evaluated for dual-polarized operation. The random phased-coded and systematic phase-coded waveforms are known to provide fairly good estimates of the Doppler spectral moments. This paper presents results at S band to quantify the performance of phase-coded waveform in retrieving polarimetric variables. It is shown that the polarimetric variables for both strong and weak trip echoes are estimated with acceptable accuracy.

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Evan Ruzanski and V. Chandrasekar

Abstract

The short-term predictability of precipitation patterns observed by meteorological radar is an important concept as it establishes a means to characterize precipitation and provides an upper limit on the extent of useful nowcasting. Predictability also varies on the basis of spatial and temporal scales of the observed meteorological phenomena. This paper describes an investigation of the short-term predictability of precipitation patterns containing microalpha (0.2–2 km) to mesobeta (20–200 km) scales using high-resolution (0.5 km–1 min–1 dBZ) composite radar reflectivity data, extending the analysis presented in previous work to smaller space and time scales. An experimental approach is used in which continuous and categorical lifetimes of radar reflectivity fields in Eulerian and Lagrangian space are used to quantify short-term predictability. The space–time scale dependency of short-term predictability is analyzed, and a practical upper limit on the extent of Lagrangian persistence-based nowcasting is estimated. Connections to the predictability of larger scales are made within the context of previous work. The results show that short-term predictability estimates in terms of lifetime are approximately 14–15 and 20–21 min in Eulerian and Lagrangian space, respectively, and suggest that a linear relationship exists between predictability and space–time structure from microalpha to macrobeta (2000–10 000 km) scales.

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