Wind Profiler Signal & Data Processing -Anil Anant Kulkarni SAMEER, IIT Campus,Powai Mumbai

Wind Profiler Signal & Data Processing Background Signal Processing Steps Data Analysis Step Data QA/QC

Wind Profiler : Basics….. Clear Air Doppler Radar Detects Reflection from Turbulence and eddies Typical frequencies used in wind profiling –45-65 MHz – MHz – MHz – MHz

Wind Profiler Basics …. Electromagnetic pulse is sent into the Atmosphere Detection of the signal backscattered from refractive index in-homogeneities in the atmosphere In clear air the scattering targets are the temperature and humidity fluctuations produced by turbulent eddies Scale is about half of the wavelength for the transmitted radiation (the Bragg Condition)

Wind Profiler : Back Scatter Signals

Wind Profiler : Scattering Mechanism Scattering from atmospheric targets: –irregularities in the refractive index of the air –hydrometeors, particularly wet ones (rain, melting snow, water coated ice) Scattering from Non-atmospheric targets: –birds and insects (frequency dependant) – smoke plumes Interfering signals: – Ground and sea clutter – Aircraft and migrating birds – RFI (depends on frequency band)

Wind Profiler : Scattering Mechanism When a pulse encounters a target... It is scattered in all directions. Of interest is the signal component received back at the radar. This signal is typically much weaker than the original sent from the transmitter and is called the "return signal". The larger the target, the stronger the scattered signal.

Wind Profiler : Scattering Mechanism Refractive index fluctuations are carried out by the wind; are used as tracers Irregularities exist in a size range of a few centimeters to many meters Different methods of wind measurement used with numerous variations: – SA (Spaced Antenna) – DBS (Doppler Beam Swinging) Doppler shift in the backscattered signal is used to derive the wind speed and direction as function of height

Doppler Beam Swinging (DBS) DBS method for wind vector calculations (u,v,w) Radial velocities measured with one vertical and 2 off-zenith beams Beam-pointing sequence is repeated every 1-5 minutes Electronic beam pointing with phase shifters using one antenna Local horizontal uniformity of the wind field is assumed

Doppler Shift Doppler Formula: f d = - 2 *V r / Doppler Measurement of wind speed based on the Doppler shift in the received signal: – where V r is the radial velocity of the scatterers Examples of Wind Profiler Doppler shift (radial velocity 10m/s) –50MHz, wavelength 6m, Doppler shift 3.34Hz –449MHz, wavelength m, Doppler shift 29.9Hz –1290MHz, wavelength 0.23m, Doppler shift 86Hz

Time Domain Processing (1.0) Spectral Domain Processing (2.0) Doppler Profile Analysis (3.0) Wind Profiles Rx I/Ps WP Signal Processing Steps

DSP System : Data Flow Diagram Power Spectra + Moments Power Spectra Radar Control PC Post Processor PC Front End PCI DSP Card(1) PCI DSP Card(2) I & Q I/P

Time Domain Signal Processing……. ADC Sampling Coherent Integration –Affects data rate, Nyquist frequency, SNR 8 bit Decoding –Improving the Range Resolution Fourier Transform –Broadens spectral features Power Spectral Computation.

Moments of the Average Doppler

Spectral Averaging –Reduces data rate,improves detectability Estimation of Noise Level Identification of Doppler Signals –Maximum Peak Construction of Doppler Profile Computation of Moments and SNR Spectral Domain Processing……

Basic Signal Processing Steps

Doppler Profile Analysis: The Doppler profiles from three beam directions from lower heights and higher heights are available as inputs To analyse input data to generate the 6 minute and hourly wind profiles. In this process the input Doppler profiles are subjected extensive quality assurance checks before generating the 6 minute and hourly wind profiles.  Separation of Precipitation echoes  Mode Merging  Calculation of Radial velocity and height (6 min)  Computation of Absolute Wind Velocity Vectors (UVW)  Quality Assurance of sub-hourly velocity profiles  Computation of Horizontal Wind Speed & direction (6 min)  Computation of Hourly Averages

Basic Issues in Signal Processing…. Signal Detection –Discrimination between signal and noise. (Hildebrand/Sekhon) –Are one or more non-noise signals present in spectrum? Signal Identification –If more than one signal is detected, which one is due to the (clear (clear-air) atmospheric return? air) atmospheric return? –What kind of What kind of a-priori information priori information can be used to select it? –Can unwanted contamination be effectively filtered out without affecting (biasing) the desired

Identification of Doppler Peaks… Basic Assumptions…. –There exist temporal and spatial continuities in a time series of spectral profiles which can which can be be employed. –Echoes back-scattered from the atmosphere exhibit continuity in time and height that can restrict the search of restrict the search of signal peaks to a certain part of the spectrum.

Identification of Doppler Peaks… Multiple Peak Identifications…. –Identify maximum 5 Spectral Peaks in each range bin –Mark spectral peaks which are below the noise level threshold –Compute three Moments for remaining spectral peaks. –Build the spectral chain across different range bins using wind shear criteria

Doppler Peak Identification continued.. Challenges … –Identification of Atmospheric Targets but not the Clear Air echoes –Precipitation echoes –Identification Interference Signal –Identification of Clutter –Identification of Non-Atmospheric Targets –Birds, Planes, non-stationary objects from near by buildings, roads (from Radar Side lobes)

Interferences…. Interference from migrating birds: –Birds act as large radar targets so that signals from birds overwhelm the weaker atmospheric signals This can produce biases in the wind speed and direction Precipitation interference: –During precipitation, the profiler measures the fall speed of rain drops Ground clutter: –Ground clutter occurs when a transmitted signal is reflected off of objects such as trees, power lines, or buildings instead of the atmosphere. Data contaminated by ground clutter can be detected as a wind shift or a decrease in wind speed at affected altitudes. RF Interference: –The RF Interference signals looks similar to the CAT echoes and some times are inseparable

Power Spectra : Vertical Beam with Precipitation echoes

Power Spectra : North Beam with Precipitation echoes During precipitation, the profiler measures the fall speed of rain drops

Power Spectra : East Beam with Precipitation echoes

Power Spectra Higher Heights

Power Spectra: Lower Heights

QA/ QC of Data Definition: –The process of identifying and if possible eliminating inconsistent observations (outliers) Outliers: –Data that are spatially, temporally, or physically inconsistent.

Recent development in QA/QC Coherent Integration –Wavelet pre-processing / No coherent integration / Low-pass filter Windowed FFT : –No windowing for long time series. Spectral Averaging –Statistical Averaging Method (SAM-ICRA) Signal Identification –Multi-Peak Picking (MPP) / ETL Signal Processing System (SPS) /NCAR Improved Moments Algorithm (NIMA) Wind finding –NCAR Winds and Confidence Algorithm (NWCA) –ETL Signal Processing System (SPS) –Weber/Wuertz (QC)