SCATTEROMETER.

SCATTEROMETER

What is a Scatterometer?
A scatterometer is a microwave radar sensor used to measure the reflection or scattering effect produced while scanning the surface of the earth from an aircraft or a satellite.

Background Two kinds of oceanic backscatter
Reflection and scattering of a radiance that is normally and obliquely incident on specular and wave-coverd ocean surface

Bragg scatter Strong oceanic backscatter for incident angles θ as up to 70º λw = λ/2sinθ where, λw = surface wavelength λ = surface projection of the radar wavelength For near nadir incidence angles, σ0 ↓ as U ↑ For oblique angles, σ0 ↑ as U ↑ Bragg scatter generated by the interaction between and incident radiance and a specific water wavelength

Backscatter modulation by surface roughness
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Backscatter as a Function of Wind Speed and Incidence Angle
Most sensitivity to wind at moderate incidence angles 30°-60° ® Z.Jelenak

Most sensitivity to wind at moderate incidence angles 30°-60° ® Z.Jelenak 10

Backscatter Sensitivity to Wind Direction
5m/s ® Z.Jelenak

20m/s 15m/s 10m/s 5m/s ® Z.Jelenak 14

30m/s 25m/s 20m/s 15m/s 10m/s 5m/s ® Z.Jelenak 15

Back Scattering Theory
Bragg scattering Incoming microwave radiation in resonance with short waves (dominant for 30°< q < 70 °) lB = l/(2sin(q) Specular reflection Ocean facets normal to incident radiation (non-negligible for q < 30°) Accuracy of theoretical models ~1 dB and not adequate Caps of waves tend to align perpendicular to local wind direction Sharp shape of leeward side of the capillary wave results more ocean radar return upwind than in the downwind direction l ~ 2cm (Ku-band) ; l ~ 5cm (C-band)

History of Scatterometry
Observations by radar mounted on aircrafts indicated relationship between surface wind and radar return Scatterometer flown onboard aircraft and Skylab ( S-193) showed good results and hence was planned for Seasat (Seasat-A Satellite Scatterometer :SASS) The first scatterometer flew as part of the Skylab missions in 1973 and 1974 The Seasat-A Satellite Scatterometer (SASS) operated from June to October 1978

History of Scatterometry
A single-swath scatterometer flew on the European Space Agency's Remote Sensing Satellite-1 (ERS-1) mission. The NASA Scatterometer (NSCAT) which launched aboard Japan's ADEOS-Midori Satellite in August, 1996, was the first dual-swath, scatterometer to fly since Seasat. From September 1996 when the instrument was first turned on, until premature termination of the mission due to satellite power loss in June 1997, The NSCAT mission proved so successful, that plans for a follow-on mission were accelerated to minimize the gap in the scatterometer wind database. The QuikSCAT mission launched SeaWinds in June 1999

Table of Satellite Scatterometers
Short Background Period in Service Spatial Resolution Scan Characteristics Operational Frequency SeaSat-A Scatterometer 1978/7/ /10/10 50 km with 100 km spacing two sided, double swath Ku band (14.6 GHz) ERS-1 Scatterometer 1991/ /5/21 50 km one sided, single swath C band (5.3 GHz) ERS-2 Scatterometer 1997/5/21 - current NSCAT 1996/9/ /6/30 25 km, and 50 km Ku band ( GHz) SeaWinds on QuikSCAT 1999/7/19 - current 25 km conical scan, one wide swath Ku band (13.4 GHz) SeaWinds on ADEOS II Launch Date Dec. 2001 25 x 6 km conical scan one wide swath

EUMETSAT The European Space Agency has its own scatterometers in orbit, such as Envisat. It is now running on a backup transmitter and have other problems, this satellite could fail at any moment. Now ASCAT on board METOP provides sea surface winds

ESCAT refers to the Active Microwave Instrument (AMI) scatterometer aboard the European Space Agency's (ESA's) Earth Remote Sensing (ERS)-1 and -2 satellites. The advanced scatterometer (ASCAT) on the European Space Agency's Met Operational (MetOp) platforms are the follow-on to European wind scatterometers. Metop -1 was launched on 19 Oct 2006 The NASA Scatterometer (NSCAT) flew on the Japanese Aerospace Exploration (JAXA, formerly known as NASDA) Advanced Earth Observing Satellite (ADEOS-I) The SeaWinds scatterometer flies on NASA's Quick Scatterometer (QuikSCAT) satellite. A similar scatterometer, SeaWinds-II, flew on JAXA's ADEOS-II satellite before a power failure terminated the mission.

QuikSCAT The SeaWinds on QuikSCAT mission is a "quick recovery" mission to fill the gap created by the loss of data from the NASA Scatterometer (NSCAT), when the satellite it was flying on lost power in June The SeaWinds instrument on the QuikSCAT satellite is a specialized microwave radar that measures near-surface wind speed and direction under all weather and cloud conditions over Earth's oceans In light of the 2003 failure of the ADEOS II satellite that was meant to succeed the NSCAT, QuickSCAT is currently the only US-owned instrument in orbit that measures surface winds over the oceans

Principle Of Scatteromery
The estimation of wind velocity from scatterometers require multiple co-located measurement of backscatter from different azimuth angles The multiple azimuth viewing is met using either fan beam antennas or scanning spot beams Earlier instruments relied on fan beam antennas The fan beam antenna configuration is essentially a side looking radar However, since the measurements have to be made at different zenith angles, multiple antennas oriented at different angles is used A given surface is first viewed by the forward antenna and later by the aft antenna Thus o measurement of the same place is provided by two azimuth angles separated by 90

Sea Surface Roughness - Oblique-viewing
microwave radiometers Microwave scatterometer is based on the principle of the resonant Bragg scattering. For a smooth surface, oblique viewing of the surface with active radar yields virtually no return. If the surface is rough, significant backscatter occurs.

Bragg’s Resonance

Sea Surface Roughness - Oblique-viewing microwave radiometers
Tropical cyclone observed by QuikSCAT in June 2007.

SEASAT-A SCATTEROMETER ANTENNA PATTERN
SASS uses four fan beam antennas — two on either sides of the sub-satellite track The two antennas on each side are aligned so that they are pointed 45° and 135° relative to the spacecraft flight direction

Waves induces waves of all wavelengths on the sea surface
Waves that are near the wavelength of 1.75cm backscatter radiation through Bragg Scattering As SASS wavelength is 2cm, capillary waves are responsible for backscatter Many parameters influence  o (wind speed, polarisation, viewing angle, relative azimuth between the radar beam and wind direction etc.) Max backscatter occurs in the upwind and downwind directions and minimum near the cross wind direction To account for the wind shear in the boundary layer, wind speed U taken for calculations is the 19.5m neutral stability wind U is calculated from boundary layer model and one of the input for the model is the height of the anemometer

MODEL FUNCTIONS Empirical relationship between  o and wind vector
The Model functions available for Ku and C bands are known as SASS-1 and CMOD4. SASS-1 Model functions  o = 10G(θ,,p) + 10H(θ,,p) .log(W) Where θ – incident angle ,- aspect angle G and H are model functions. The tabulated values of G and H were determined empirically using pre launch aircraft and post launch SASS observations of  o

MODEL FUNCTIONS 1 o = 10G(θ1,1,p1) + 10H(θ1,1,p1) .log(W)
Suppose SASS has only two observations, then we have two equations 1 o = 10G(θ1,1,p1) + 10H(θ1,1,p1) .log(W) 2 o = 10G(θ2,1+90,p2)+10H(θ2,1+90,p2).log(W) There are two non linear Eqns in two unknowns W and 1. They can be solved by plotting W versus 1. The two eqns give two curves Since  o is max. at up and downwind and minimum at cross winds, the curve has two maxima and two minima and they are separated by 90 The points where the curves intersect are the solutions (aliases) One of them represent the true wind To reduce the no. of aliases the next generation scatterometers had two additional antennas to reduce the no. of aliases

Justifications for making measurements at various azimuth angles

NSCAT ANTENNA PATTERN Two sets of antennae covering both sides of the track Each set has three antennae covering a swath of 800Km Polarisation of MW radiation emitted and received by the mid beam is both vertical (VV) and horizontal(HH) For others only VV polarisation is used

PENCIL BEAM ANTENNA PATTERN
Consists of two off nadir beams inner and outer Each point in the inner swath is scanned twice by the inner and twice by outer beam These four enable retrieval with better accuracy

ADVANTAGES OF PENCIL BEAM SCATTEROMETER
High o measurement accuracy due concentrated pencil beam High directional accuracy as each point is viewed four times No nadir gaps Simplified Model Functions: Only two incident angle Easier signal processing Smaller in size

RETRIEVAL ALGORITHMS Radar backscatter has a harmonic dependence on wind direction which results in multiple solutions Two retrieval algorithms are used Prioritisation Wind ambiguity removal The retrieval algorithm give multiple solutions along with prioritisation For a noise free signal, highest priority solution gives the true wind vector The first two prioritised winds invariably give the correct wind vector

Retrieval Algorithm for Prioritisation
The average and SD of wind speed is calculated from the entire range of aspect angles Then, the minima of the SD of the wind speeds are searched along the aspect angle The solution having the minimum SD is selected as the first priority solution and the next lowest as the second solution and so on. These are now need to be cleared of directional ambiguity

EXAMBLE OF PRIORITISED WIND VECTOR SOLUTION FOR NOISE FREE o
CMOD4 Model Function for ERS! Aft Mid Forward Mean SD

MEDIAN FILTER How It Works
Like the mean filter, the median filter considers each pixel in the image in turn and looks at its nearby neighbors to decide whether or not it is representative of its surroundings. Instead of simply replacing the pixel value with the mean of neighboring pixel values, it replaces it with the median of those values. The median is calculated by first sorting all the pixel values from the surrounding neighborhood into numerical order and then replacing the pixel being considered with the middle pixel value. (If the neighborhood under consideration contains an even number of pixels, the average of the two middle pixel values is used.) Figure 1 illustrates an example calculation.

By calculating the median value of a neighborhood rather than the mean filter, the median filter has two main advantages over the mean filter: The median is a more robust average than the mean and so a single very unrepresentative pixel in a neighborhood will not affect the median value significantly. Since the median value must actually be the value of one of the pixels in the neighborhood, the median filter does not create new unrealistic pixel values when the filter straddles an edge. For this reason the median filter is much better at preserving sharp edges than the mean filter.

Figure 1 Calculating the median value of a pixel neighborhood
Figure 1 Calculating the median value of a pixel neighborhood. As can be seen, the central pixel value of 150 is rather unrepresentative of the surrounding pixels and is replaced with the median value: 124. A 3×3 square neighborhood is used here --- larger neighborhoods will produce more severe smoothing.

This figure shows a typical region with each of the wind ambiguities displayed at each cell. Blue is the first (most likely) ambiguity, red is the second, green the third and aqua the fourth. In order to determine an estimate of the wind across the entire swath, an ambiguity selection algorithm is needed.

The Point-wise Median Filter
In order to select the proper ambiguity, we assume that the estimates are correlated from one cell to the next. In other words, we assume that the wind is unlikely to shift radically from one cell to the next. Using this assumption, we may use a variety of techniques to select a single ambiguity at each wvc. A common technique used to correlate the estimates is based on the point-wise median filter. This method does not alter any of the wind estimates generated; rather is selects between the ambiguities at each cell. Like median filters used in image processing, the point-wise median filter attempts to preserve edges and avoid blurring. The entire swath is initialized to the first ambiguity. For each cell in the swath, a 7-cell window is generated around the cell in question, and an average of all of the wind vectors is taken. The ambiguity closest to this windowed average is selected. This process is repeated until there are no more changes (or until a maximum number of iterations is achieved).

DIRECTIONAL AMBIGUITY REMOVAL
Uses two highest priority wind vectors In ERS-1 scatterometer antenna configuration more than half of first priority winds represent the true winds and the remaining solutions are opposite to the true wind Median Filtering approach is used to remove the ambiguity- Trend or consensus of highest priority directions is derived over median window For this, the highest priority vectors are resolved in to zonal and meridional components. With a moving window of optimal size, medians of both scalars at each data location are determined

DIRECTIONAL AMBIGUITY REMOVAL
The median scalar yield median wind direction at each data location which is used to select the direction out of the two highest prioritised winds The procedure is followed for entire satellite pass to select the solution nearer to the median direction At this stage the winds mostly resemble the true wind field. The direction closer to the median direction is retained and not replaced by the median direction(Gohil1992) If the data is noisy then some ambiguities may still prevail and may be removed by using a supplementary method.

RESULTS OF ERS-1 Ocean surface winds for the period Aug 1992- Jul 1993
ERS-1 data used to prepare monthly and fortnightly averaged windfields Strong SWly winds noticed off Somali coast Branching of winds over Arabian sea noticed Intensity is weak in May and strong in Jun Data compared with near co located data taken from ships( within 6hrs and 0.5deg) at 47 locations Showed god agreement with SAT instrument specifications of 2m/sec or 10% (whichever is more) Underestimated when spped was less than 5m/sec and over estimation when spped was more than 5m/ses The RMS deviation of 20deg was observed as against the desired 10deg specification

ADEOS: Advanced Earth Observing Satellite
AMI: Active Microwave Instrument DAAC: Distributed Active Archive Center ERS-1/2: European Remote Sensing Satellite ESCAT: AMI scatterometer on ERS-1/2 satellites NASA: National Aeronautics and Space Administration NSCAT: NASA Scatterometer QuikSCAT: Quick Scatterometer SASS: Seasat-A Scatterometer System

Where to Get Scatterometer Data
NRL Monterey NOAA/NESDIS QuikSCAT Storms page – includes ambiguities: Alternative NOAA site, with SSMI wind speeds: FNMOC Remote Sensing Systems Scatterometer winds from NRL page and QuikSCAT are the same though display is different. They are overlayed on sat pic on the NRL page which is useful. Note that RSS is not operational – delay in processing means that it is really only useful as a post analysis tool. APSATS 2002, Melbourne Australia

APSATS 2002, Melbourne Australia
Differences Wind retrieval RSS uses KU-2000 wind retrieval method Others use QuikSCAT1 wind retrieval method Rain Flags Generally Multidimensional Histogram (MUDH) procedure – a statistical method based on “noisiness” of data RSS has similar approach though it is less conservative and hence rain affected areas are often smaller Rain flags – NRL – circles at end of wind barb QuikSCAT – black wind barbs FNMOC – Green circles at ends of barbs – the brown dots indicate “edge vectors” RSS – Brown dots at end of barbs APSATS 2002, Melbourne Australia

Ambiguity Selection NOAA/NESDIS use rain flagged data in ambiguity selection process FNMOC does not – rain flagged data is put in “as is” after ambiguity removal. Therefore generally bigger discontinuity in data for FNMOC around rain flagged areas. APSATS 2002, Melbourne Australia

What is it GOOD for? Detection of circulations and determination of windspeeds. X FNMOC NRT plot. 09/04/ Z TC Bonnie in its early stages. Note gales on the southern side. APSATS 2002, Melbourne Australia

Small system (X) could be followed for 3 days --no help from NWP model 20S 170W 170W O O X X X O 25 Jan 1800Z Jan 0517Z Jan 1800Z 170W Slide from Roger Edson. 160W 170W 160W O X O APSATS 2002, Melbourne Australia 27 Jan 1712Z Jan 0427Z

What is it GOOD for? Location of fronts/troughs. Wind speeds in data sparse areas. Useful for high seas forecasts over the Southern and Indian Oceans where observations are few and far between. Can be useful in determining the surface location of fronts when IR imagery disguises it. APSATS 2002, Melbourne Australia

When is it BAD? Edge of swath (~ 7 wind vector cells) Rain effects Sensitivity to errors in NWP Practical wind regime 5-45 m/s (problems with both very light and very strong winds) Resolution (25km) – impact in tight gradients Ambiguity Removal Process and rain flag process can affect final solution Although there are many problem areas, there is useful information to be gained from most passes. On any individual pass, the problem areas must be identified and worked around. APSATS 2002, Melbourne Australia

Edge Problems Along the whole edge… or small portion… In the FNMOC display, the edge wind vectors are identified with a brown dot at the stem of each wind vector plot. In the examples, above, the left case shows a large area almost along the entire swath where the winds seem to be incongruous with the rest of the swath (both in excess speed and for winds pointing parallel to the swath direction). In the blow up section on the right, the edge data is indicated by the brown dots (except where a rain flag is indicated—where both effects are occurring). In this example only a small portion appears to not fit the pattern. (Note, even in the edge region, both light winds and rain-affected winds can be well represented). Remember, the data are not always bad. If it looks good, use it!. (from Edson) FNMOC DISPLAY APSATS 2002, Melbourne Australia

Rain Flags RSS TC Chris 05/02/ Z All the operational scat pages have the same rain flag determination. RSS is the only one that differs. Which one is correct? Answer probably somewhere in between. Rain flagged areas APSATS 2002, Melbourne Australia NRL

Typical Rain Patterns Rain effects: Cross swath vectors Higher wind speeds Some intense rain not flagged APSATS 2002, Melbourne Australia RSS slide

Rain Effects One or two bad wind solutions may affect neighboring wind vector cells through buddy-checking system causing a ‘rain contagion’ effect. TC Chris 05/02/ Z Direction of swath Uni-directional winds across obvious eye on sat pic. Rain block – direction perpendicular to swath Do not use direction – speed may be OK APSATS 2002, Melbourne Australia

Streamlines TC Chris 03/02/ Z Beware of winds perpendicular to the swath, even when they are not flagged X Use the good winds outside the rain blocks. Look for non-rain flagged winds APSATS 2002, Melbourne Australia

Model initialisation errors In this case, poor model initialization combined with a lower skill nadir position, picks proper wind speed, but NO circulation center 20/2356Z AVN 19/12Z tau 24 (Light winds?) -----low skill c 10S c 10S ? Max Wind 55 KTS ? 20S TC Paul 20S APSATS 2002, Melbourne Australia

ASCAT WINDS

ASCAT WINDS OVER INDIA :13 AUG 12

OCEANSAT 2 WINDS:13 AUG 12

WINDSAT is a joint NOAA Integrated Program Office
/Department of Defense/NASA demonstration project, intended to measure ocean surface wind speed and wind direction from space using a polarimetric radiometer. It was launched aboard the Coriolis satellite by a Titan II rocket on 6 January 2003 into a 830-km 98.7-degree orbit, and is designed for a three-year lifetime.

WINDSAT WindSat is a satellite-based polarimetric microwave radiometer developed by the Naval Research Laboratory Remote Sensing Division, the Naval Center for Space Technology, and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO). It was launched in January 2003 aboard the joint DoD/Navy platform Coriolis, with a planned 3-year life. Despite its extended lifespan, it continues to function quite well. WindSat measures the ocean surface wind vector, as well as cloud liquid water, sea surface temperature, total precipitable water, and rain rate (over water only). Derived products include soil moisture and sea ice.

The Navy, NOAA, and UK Met Office frequently use WindSat data in several operational forecast models. Coriolis is sun-synchronous (1800 UTC equator crossing time) and WindSat has a swath width of ~1000 km, with a resolution is ~50 km. WindSat data had always been a secondary wind observation with QuikSCAT (launched 1999) as the primary source, because of its better resolution and accuracy. However, when QuikSCAT failed in 2009, NRL researchers were encouraged to push their improved WindSat algorithm into operations.

Conclusions Provides coverage over data sparse areas Wind speeds generally good – useful for areas of gales etc Use the data if it makes sense Be aware of low skill areas and different ambiguity removal processes (compare!) Do not use in isolation APSATS 2002, Melbourne Australia

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