Search Results

You are looking at 1 - 10 of 142 items for

  • Author or Editor: V. Chandrasekar x
  • Refine by Access: All Content x
Clear All Modify Search
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.

Free access
R. Raghavan
and
V. Chandrasekar

Abstract

Multiparameter radar measure one or more additional parameters in addition to the conventional reflectivity factor. The combination of radar observations from a multiparameter radar is used to study the time evolution of rainstorms. A technique is presented to self-consistently compare the area–time integral (ATI) and rainfall volume estimates from convective storms, using two different measurements from a multiparameter radar. Rainfall volumes for the lifetime of individual storms are computed using the reflectivity at S hand (10-cm wavelength) as well as one-way specific attenuation at X band (3-cm wavelength). Area-time integrals are computed by summing all areas in each radar snapshot having reflectivities (S band) in excess of a preselected threshold. The multiparameter radar data used in this study were acquired by the NCAR CP-2 radra during the Cooperative Huntsville Meteorological Experiment (COHMEX) and the Convection and Precipitation/Electrification Experiment(CaPE),respectively. ATI studies were accomplished in this work using multiparameter radar data acquired during the lifetime of six convective events that occurred in the COHMEX radar coverage area. A case study from the COHMEX field campaign (20 July 1986) was selected to depict the various stages in the evolution of a storm over which the ATI and rainfall volume computations were performed using multiparameter radar data. Another case study from the CaPE field campaign (12 August 1991) was used to demonstrate the evolution of a convective cell based on differential reflectivity observations.

Full access
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.

Full access
Joaquin Cuomo
and
V. Chandrasekar

Abstract

Nowcasting based on weather radar uses the current and past observations to make estimations of future radar echoes. There are many types of operationally deployed nowcasting systems, but none of them are currently based on deep learning, despite it being an active area of research in the last few years. This paper explores deep learning models as alternatives to current methods by proposing different architectures and comparing them against some operational nowcasting systems. The methods proposed here, harnessing residual convolutional encoder–decoder architectures, reach a level of performance expected of current systems and in certain scenarios can even outperform them. Finally, some of the potential drawbacks of using deep learning are analyzed. No decay in the performance on a different geographical area from where the models were trained was found. No edge or checkerboard artifact, common in convolutional operations, was found that affects the nowcasting metrics.

Full access
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.

Full access
Renzo Bechini
and
V. Chandrasekar

Abstract

The atmospheric state evolution is an inherently highly complex three-dimensional problem that numerical weather prediction (NWP) models attempt to solve. Although NWP models are being successfully employed for medium- and long-range forecast, their short-duration forecast (or nowcast) capabilities are still limited because of model initialization challenges. On the lower end of the complexity scale, nowcasting by extrapolation of two-dimensional weather radar images has long been the most effective tool for nowcasting precipitation. Attempts are being made to take advantage of both approaches by blending extrapolation and numerical model forecasts. In this work a different approach is presented, relying on the additional Doppler radar wind information and a simplified modeling of basic physical processes. Instead of mixing the outputs of different forecasts as in blended approaches, the idea behind this study is to combine extrapolation and precipitation modeling in a new technique with a higher level of complexity with respect to conventional nowcasting methods, although still much simpler than NWP models. As a preliminary step, the Variational Doppler Radar Analysis System (VDRAS) is used to provide an initial analysis exploiting all the available dual-polarization and Doppler radar observations. The rainwater and wind fields are then advected using an optical flow technique that is subject to simplified physical interactions. As a result precipitation and wind nowcasting are obtained and are successively validated up to a 1-h lead time, showing potential improvement upon standard extrapolation.

Full access
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.

Full access
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.

Full access
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.

Full access
Reino Keränen
and
V. Chandrasekar

Abstract

In operational weather radar, precipitation echoes are often weak when compared to the underlying noise. Coherence properties of dual polarization can be used for enhancing the detection and for the improved estimation of weak echoes of precipitation. The enhanced detectability results from utilizing coherent averages of precipitation signals, while the uncorrelated noise vanishes asymptotically, explicit in the off-diagonal element R hv of the echo covariance matrix. In finite sums, the noise terms as well as the uncertainties associated with them are suppressed. A signal can be detected in weaker echo by an analytically derived censoring policy. The coherent sums are readily available as the cross-correlation function of the antenna voltages H and V, which estimates R hv in the mode of simultaneous transmission and reception. The magnitude of R hv is a consistent estimate of the copolar echo power, leading to the copolar radar reflectivity of precipitation, which refers to the geometric mean of the reflectivities in H and V polarizations. Because of the intrinsic noise suppression, estimates of the copolar reflectivity are, in relative terms, more precise and more accurate than the corresponding estimates of reflectivity in specific channels, for weak signals of precipitation. These aspects are discussed quantitatively with validation of the key features in real conditions. The advances suggest for dedicated dual-polarization surveillance scans of weak echo of precipitation.

Full access