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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

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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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

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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Francesc Junyent and V. Chandrasekar

Abstract

The CSU–CHILL radar is a dual-wavelength, dual-polarization weather radar system operating at S and X bands with coaxial beams. One of the capabilities of this radar system is the possibility of developing and/or validating algorithms across dual wavelengths and dual polarizations. This paper presents one such instance, showing how the rainfall field can be estimated either from the S- and X-band reflectivities or from the differential propagation phase at X band. To do so, the paper first presents a dual-wavelength attenuation correction method that uses the reflectivity measured at S band, as the constraint for the correction of the reflectivity measured at X band, and it describes how Mie scattering regions at X band may be detected from the retrieved path-integrated attenuation field. Then, the paper describes how the resulting specific attenuation field relates to rainfall and specific phase at X band, which can be obtained from dual-polarization data at a single wavelength as well, and shows examples. Finally, the paper looks at the relation between attenuation and the differential phase as a function of elevation angle for a few cases, which may be related to the drop size distribution and mean diameter, as well as temperature.

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Minda Le and V. Chandrasekar

Abstract

Extensive evaluations have been performed on the dual-frequency classification module in the Global Precipitation Mission (GPM) Dual-Frequency Precipitation Radar (DPR) level-2 algorithm. Both rain type classification and melting-layer detection continue to show promising results in the validations. Surface snowfall identification is a feature newly added in the classification module to the recently released version to provide a surface snowfall flag for each qualified vertical profile. This algorithm is developed upon vertical features of Ku- and Ka-band reflectivity and dual-frequency ratio from DPR. In this paper, we validate this surface snowfall identification algorithm with ground radars including NEXRAD, NASA Polarimetric Radar (NPOL), and CSU–CHILL radar during concurrent precipitation events and GPM validation campaign Olympic Mountain Experiment (OLYMPEX). Other ground truth such as Precipitation Imaging Package (PIP) and ground report is also included in the validation. Based on 16 validation cases in the years 2014–18, the average match ratio between surface snowfall flag from space radar and ground radar is around 87.8%. Promising agreements are achieved with different validation sources. Algorithm limitation and potential improvement are discussed.

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Evan Ruzanski and V. Chandrasekar

Abstract

Short-term automated forecasts (nowcasts) of liquid water equivalent (LWE) values can be used to assist aviation deicing decision-making activities. Such decisions can mitigate hazards that cause losses of life and property and increase costs because of travel delays. The Weather Support to Deicing Decision Making (WSDDM) system provides LWE nowcasts and is currently deployed at several major airports in the United States. WSDDM produces these nowcasts in two steps. First, an equation relating radar reflectivity to LWE rate is calibrated by correlating radar and surface observations. Then, nowcasts of reflectivity are converted to nowcasts of LWE using this calibrated equation. This paper shows that the incorporation of the Dynamic and Adaptive Radar Tracking of Storms (DARTS) radar–based nowcasting method into WSDDM can provide more accurate and efficient nowcasts of LWE relative to the correlation-based nowcasting method currently used. Results of an evaluation considering approximately 92 h of data collected during four winter weather events show the incorporation of DARTS into WSDDM provides an approximate 14% average improvement in the accuracy of 60-min LWE nowcasts and reduces runtime by two orders of magnitude.

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V. Chandrasekar and Nitin Bharadwaj

Abstract

Dual-polarization weather radars typically measure the radar reflectivity at more than one polarization state for transmission and reception. Historically, dual-polarization radars have been operated at copolar and cross-polar states defined with respect to the transmit polarization states. Recently, based on the improved understanding of the propagation properties of electromagnetic waves in precipitation media, the simultaneous transmit and receive (STAR) mode has become common to simplify the hardware. In the STAR mode of operation, horizontal and vertical polarization states are transmitted simultaneously and samples of both horizontal and vertical copolar returns are obtained. A drawback of the current implementation of STAR mode is its inability to measure parameters obtained from cross-polar signals such as linear depolarization ratio (LDR). In this paper, a technique to obtain cross-polar signals with STAR mode waveform is presented. In this technique, the horizontally and vertically polarized transmit waveforms are coded with orthogonal phase sequences. The performance of the phase-coded waveform is determined by the properties of the phase codes. This orthogonal phase coding technique is implemented in the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar. This paper outlines the methodology and presents the performance of the cross-polar and copolar parameter estimation based on the simulation as well as data collected from the CSU–CHILL radar.

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Evan Ruzanski and V. Chandrasekar

Abstract

Short-term automated forecasting (nowcasting) of precipitation has traditionally been done using radar reflectivity data; recent research, however, indicates that using specific differential phase K dp has several advantages over using reflectivity for estimating rainfall. This paper presents an evaluation of the characteristics of nowcasting K dp-based rainfall fields using the Collaborative Adaptive Sensing of the Atmosphere K dp estimation and nowcasting methods applied to approximately 42 h of X-band radar network data. The results show that K dp-based rainfall fields exhibit lifetimes of ~17 min as compared with ~15 min for rainfall fields derived from reflectivity Zh in a continuous (cross correlation based) sense. Categorical (skill score based) lifetimes of ~26 min were observed for K dp-based rainfall fields as compared with ~30 min for Zh-based rainfall fields. Radar–rain gauge verification showed that K dp-based rainfall estimates consistently outperformed Zh-based estimates out to a lead time of 30 min, but the difference between the two estimators decreased in terms of normalized standard error with increasing lead time.

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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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