Atmospheric Instrumentation

National atmospheric research laboratory NARL has taken up a major augmentation project the coherent radar imaging CRI which will enhance the capabilities of the present MST radar by many fold The existing MST radar with a 32x32 size phased ...

... AK Patra. National MST Radar Facility, Tirupati, India. TV Chandrasekhar Sarma. National MST Radar Facility, Tirupati, India. BV Krishna Murthy. Space Physics Laboratory, Vikram Sarabhai Space Centre, Trivandrum, India. KSV Subba Rao. ...

Wind Profilers (WP) are coherent pulsed Doppler radars in UHF and VHF bands. They are used for vertical profiling of wind velocity and direction. This information is very useful for weather modeling, study of climatic patterns and weather prediction. Observations at different height and different wind velocities are possible by changing the operating parameters of WP. A set of Doppler power spectra is the standard form of WP data. Wind velocity, direction and wind velocity turbulence at different heights can be derived from it. Modern wind profilers operate for long duration and generate approximately 4 megabytes of data per hour. The radar data stream contains Doppler power spectra from different radar configurations with echoes from different atmospheric targets. In order to facilitate systematic study, this data needs to be segregated according the type of target. A reliable automated target classification technique is required to do this job. Classical techniques of radar target identification use pattern matching and minimization of mean squared error, Euclidean distance etc. These techniques are not effective for the classification of WP echoes, as these targets do not have well-defined signature in Doppler power spectra. This paper presents an effective target classification technique based on range-Doppler features.

Wind Profiler (WP) radars are used for 3-D vertical profiling of atmospheric winds. They transmit VHF/UHF band radio pulses towards vertical as well as off-vertical directions and receive the echoes from atmospheric targets. WP signal processor performs Doppler analysis on the received signal and derives wind velocities at different heights. This processing faces difficulty as the atmospheric echoes are very weak and the received signal is often contaminated by noise, clutter and Radio Frequency Interference (RFI). Wind profile estimation is greatly improved if all non-atmospheric echoes are removed at preprocessing stage. WP radar sessions generate large data volumes. The pre-processing of such a large data need to be handled by an automated program capable of removing the unwanted echoes. The present techniques for the removal of RFI and clutter are computationally complex and they require parameter changes for different radars and environmental conditions. These methods have limited utility when used for automated preprocessing. This paper presents an objective algorithm of RFI and clutter removal that is computationally simple and can be used on all radars and weather conditions without changing any parameters. The test results of this algorithm on the mesosphere-stratosphere-troposphere (MST) radar and Lower Atmospheric Wind Profiler (LAWP) radar, at Gadanki, are presented.

In atmospheric radars for wind measurements, adequate time domain data is first collected, and then subjected to Fourier domain analysis. This does not explicitly utilize the sparsity of the underlying signal, which is sparse in Doppler domain as well as across altitudes. We therefore propose the acquisition and recovery of atmospheric wind profiles using sparse signal processing techniques. In particular, Compressed Sensing (CS) suggests that a compressible signal can be recovered from a small number of measurements. Incorporating this sparseness, we apply CS recovery techniques such as Orthogonal Matching Pursuit (OMP) and Compressive Sampling Matching Pursuit (CoSaMP) to recover the Doppler speeds from a few noisy radar observations. Furthermore, a dynamic program based on Viterbi Data Association (VDA) is employed to reconstruct the wind profile, further exploiting the sparsity across altitudes. We employ a suitable cost metric factoring the kinematics of wind velocities to optimally track the profile. The proposed algorithm is applied on a few offline Indian MST radar datasets, and the performance is compared with that of existing algorithms like Adaptive Moment Estimation (AME) and Multiparameter Cost Function (MPCF). Our experiments prove that sparse signal recovery techniques can identify the true Doppler velocities with improved tracking accuracy.

The mesosphere–stratosphere–troposphere (MST) radar is a very high frequency (VHF) pulsed coherent Doppler radar used for ground-based remote sensing. In an MST radar, the backscattered pulses due to fluctuations in atmospheric refractive index are observed at the receiver. Echoes from higher altitudes are typically weaker, resulting in a low signal-to-noise ratio (SNR). They could also be contaminated with clutter and interference, making conventional spectral estimation techniques in the Fourier domain unreliable. In this article, we propose an algorithm to estimate the atmospheric wind profile using a dynamic programming approach employing the Viterbi data association (VDA) algorithm. Progressively for each range bin, the proposed algorithm chooses a set of probable wind speeds as nodes in a trellis. A branch metric incorporating the kinematic parameters inclusive of the differential wind shear is then constructed for state transitions within the trellis, from nodes in one range bin to the subsequent ones. A dynamic programming approach then chooses the optimal wind profile across altitudes. The proposed algorithm is experimented on simulated as well as real MST radar data sets to verify its robustness. Our experiments show that the suggested method has improved performance when compared to previously established techniques.