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Search in: Search Results second seconds Settings Shift Back to top Slide Text Space SUBMIT ALL submit all SUBMIT submit Submit Unmute volume principles of doppler meteorological radar clutter filtering instructor: andy wood review lesson take your time and review the lesson content provided in this presentation. review lesson technical problems? if you encounter any technical problems with this lesson, please contact the rac team directly by e-mail at: nws.wdtd.rachelp@noaa.gov technical problems? complete the quiz at the end of this lesson, there is an embedded quiz. complete this quiz by selecting the best answer for each question. you need to correctly answer 70% of the quiz questions to receive completion credit in the lms. complete the quiz course completion review lesson review lesson complete the quiz technical problems? technical problems? introduction in order for nws forecasters to receive credit for this course in the nws learning center, you will need to take the following steps roadmap learning objectives identify the purpose, strengths and limitations of the following clutter suppression algorithms: clutter mitigation decision (cmd) gaussian model adaptive processing (gmap) identify examples of moving ground-based targets that cannot be identified by cmd ground clutter contamination returns from non-moving ground targets affects all radar products two types of clutter contamination normal ground clutter anomalous propagation clutter if it’s moving, it’s not clutter the following returns not identifiable or removable: windfarms traffic on roads traffic on i-10 clutter filtering algorithms clutter mitigation decision (cmd) clutter identification gaussian model adaptive processing (gmap) clutter supression rda signal processor cmd in a nutshell cmd identifies normal & ap clutter every scan builds “dynamic bypass map” every rotation cmd maps used for bin by bin signal removal reflectivity based inputs to cmd fuzzy logic with many inputs z texture smooth (low) = weather rough (high) = clutter z spin z gradient sign changes cpa (phase consistency) high cpa (i.e. low variation) = clutter low cpa (i.e. highly variable) = weather standard deviation of zdr & cmd standard deviation of zdr: zdr high stdzdr = clutter low stdzdr = weather standard deviation of φdp & cmd standard deviation of φdp: high stdphi = clutter low stdphi = weather cmd implementation cmd builds a map for every rotation & every elevation split cuts: one for cs, another for cd batch and higher: one for each elevation bypass map vs. all bins keep bypass map in control all the time default clutter regions file all bins suppression rarely needed only impacts location, not what gmap removes the clutter dual-pol “footprint” cc (unfiltered) cc (filtered) zdr (unfiltered) zdr (filtered) clutter filtering algorithms gmap filter noise level power +vmax -vmax 0 (pre) noise weather clutter cmd has identified clutter for the bins identified by cmd, filtering performed by the gmap algorithm gmap filter noise level restored weather signal power +vmax -vmax 0 (post) noise weather clutter key concepts behind gmap weather and clutter signals look different gmap filter noise level power +vmax -vmax 0 noise weather clutter can be approximated by gaussian curve remove power from narrow zero velocity spike once power removed, gmap attempts to rebuild lost weather signal how gmap works remove power near zero velocity gmap filter noise level power +vmax -vmax 0 noise weather clutter restored weather signal rebuild lost weather across the gap (need sufficient data) gmap example: no weather z without clutter filtering, v with clutter filtering note nd gates associated in v with terrain clutter z v gmap example: squall line passes z & v as squall line passes through: clutter has been filled in with data z v gmap example: failure due to ducting large area of clutter due to ducting non-zero velocities prevent gmap from removing power removed control (prc) product power removed control 0.5° reflectivity helps show where filtering applied & what was remove available from rpg (shown) and awips power removed control product summary cmd identifies clutter! gmap filters clutter! press “next” to advance to the lesson quiz when ready cmd identifies normal & ap clutter gmap only filters clutter identified bins vcp 215 better than vcps 12, 212, & 112 for general clutterremoval incorrect you did not select the correct response. continue continue continue continue continue continue correct that's right! you selected the correct response. continue continue continue continue continue continue select all of the following statements that are a benefit of the clutter mitigation decision (cmd) algorithm? cmd uses both legacy and dual-polarization radar data to detect ground clutter cmd uses both legacy and dual-polarization radar data to detect ground clutter cmd uses both legacy and dual-polarization radar data to detect ground clutter cmd uses both legacy and dual-polarization radar data to detect ground clutter cmd uses both legacy and dual-polarization radar data to detect ground clutter cmd uses both legacy and dual-polarization radar data to detect ground clutter cmd builds a dynamic bypass map for every elevation cmd builds a dynamic bypass map for every elevation cmd builds a dynamic bypass map for every elevation cmd builds a dynamic bypass map for every elevation cmd builds a dynamic bypass map for every elevation cmd builds a dynamic bypass map for every elevation cmd identifies both ground clutter and anomalous propagation clutter cmd identifies both ground clutter and anomalous propagation clutter cmd identifies both ground clutter and anomalous propagation clutter cmd identifies both ground clutter and anomalous propagation clutter cmd identifies both ground clutter and anomalous propagation clutter cmd identifies both ground clutter and anomalous propagation clutter cmd removes all the returned signal from a clutter target cmd removes all the returned signal from a clutter target cmd removes all the returned signal from a clutter target cmd removes all the returned signal from a clutter target cmd removes all the returned signal from a clutter target cmd removes all the returned signal from a clutter target question #1 of 7 incorrect you did not select the correct response. continue continue continue continue continue continue correct that's right! you selected the correct response. continue continue continue continue continue continue select all of the following statements that are a benefit of the gaussian model adaptive processing (gmap) algorithm? gmap applies clutter filtering only to the bins identified by the clutter mitigation detection algorithm. gmap applies clutter filtering only to the bins identified by the clutter mitigation detection algorithm. gmap applies clutter filtering only to the bins identified by the clutter mitigation detection algorithm. gmap applies clutter filtering only to the bins identified by the clutter mitigation detection algorithm. gmap applies clutter filtering only to the bins identified by the clutter mitigation detection algorithm. gmap applies clutter filtering only to the bins identified by the clutter mitigation detection algorithm. if a sufficient number of pulses remain after clutter filtering, gmap can rebuild the weather signal. if a sufficient number of pulses remain after clutter filtering, gmap can rebuild the weather signal. if a sufficient number of pulses remain after clutter filtering, gmap can rebuild the weather signal. if a sufficient number of pulses remain after clutter filtering, gmap can rebuild the weather signal. if a sufficient number of pulses remain after clutter filtering, gmap can rebuild the weather signal. if a sufficient number of pulses remain after clutter filtering, gmap can rebuild the weather signal. gmap is run at the radar product generator with easy access to adaptable parameters. gmap is run at the radar product generator with easy access to adaptable parameters. gmap is run at the radar product generator with easy access to adaptable parameters. gmap is run at the radar product generator with easy access to adaptable parameters. gmap is run at the radar product generator with easy access to adaptable parameters. gmap is run at the radar product generator with easy access to adaptable parameters. gmap isolates the clutter signal before applying the signal removal. gmap isolates the clutter signal before applying the signal removal. gmap isolates the clutter signal before applying the signal removal. gmap isolates the clutter signal before applying the signal removal. gmap isolates the clutter signal before applying the signal removal. gmap isolates the clutter signal before applying the signal removal. question #2 of 7 incorrect incorrect: cmd does not perform range unfolding continue continue continue continue continue continue incorrect incorrect: gmap does not amplify the clutter signal continue continue continue continue continue continue incorrect incorrect: cmd does not remove power continue continue continue continue continue continue incorrect correct. continue continue continue continue continue continue the cmd algorithm _____ both normal and ap clutter targets, while the gmap algorithm _____ the clutter signal. identifies, removes identifies, removes identifies, removes identifies, removes identifies, removes identifies, removes removes, identifies removes, identifies removes, identifies removes, identifies removes, identifies removes, identifies identifies, amplifies identifies, amplifies identifies, amplifies identifies, amplifies identifies, amplifies identifies, amplifies range unfolds, removes range unfolds, removes range unfolds, removes range unfolds, removes range unfolds, removes range unfolds, removes question #3 of 7 incorrect incorrect: cmd is designed to identify targets with near zero velocity continue continue continue continue continue continue correct that's right! you selected the correct response. continue continue continue continue continue continue true true true true true true false false false false false false true or false: the cmd algorithm can identify ground targets with non-zero velocity. question #4 of 7 incorrect incorrect: cmd will not identify targets with non-zero velocity as clutter continue continue continue continue continue continue incorrect incorrect: cmd will not identify targets with non-zero velocity as clutter continue continue continue continue continue continue incorrect incorrect: cmd will not identify targets with non-zero velocity as clutter continue continue continue continue continue continue incorrect correct. continue continue continue continue continue continue which one of the following is a type of ground based target that the cmd algorithm was designed to identify as clutter? mountains mountains mountains mountains mountains mountains trucks on highways trucks on highways trucks on highways trucks on highways trucks on highways trucks on highways rotating wind turbines rotating wind turbines rotating wind turbines rotating wind turbines rotating wind turbines rotating wind turbines a large flock of geese taking off a large flock of geese taking off a large flock of geese taking off a large flock of geese taking off a large flock of geese taking off a large flock of geese taking off question #5 of 7 incorrect you did not select the correct response. continue continue continue continue continue continue correct that's right! you selected the correct response. continue continue continue continue continue continue true true true true true true false false false false false false true or false: after filtering, gmap can recover the weather portion of the returned signal if there is sufficient weather signal outside the gap. question #6 of 7 incorrect incorrect: both of these algorithms perform better with an increasing number of pulses continue continue continue continue continue continue correct that's right! you selected the correct response. continue continue continue continue continue continue true true true true true true false false false false false false true or false: the number of pulses per radial has no impact on cmd or gmap performance. question #7 of 7 roadmap <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style LeadingMargin="0" FirstLineMargin="0" Justification="Left" ListLevel="1" LineSpacingRule="Multiple" LineSpacing="20" SpacingBefore="4.32" SpacingAfter="0"> <ListStyle ListType="None" ListTypeFormat="Plain" Start="0" Color="#000000,00" BulletChar="•" BulletFont="Arial"> <BulletPicture Size="0x0" /> </ListStyle> </Style> <Span Text=" Welcome to this lesson on Clutter Filtering. This lesson is part of the Radar & Applications Course Principles of Meteorological Doppler Radar topic. Let’s get started!"> <Style FontFamily="+minor" FontSize="11" FontIsBold="False" FontIsItalic="False" FontIsUnderline="False" FontIsStrikeout="False" ForegroundColor="dk1,00" Elevation="Normal" DisplayCase="AsIs" LinkColor="dk1,00" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style LeadingMargin="0" FirstLineMargin="0" Justification="Left" ListLevel="1" LineSpacingRule="Multiple" LineSpacing="20" SpacingBefore="4.31999969" SpacingAfter="0"> <ListStyle ListType="None" ListTypeFormat="Plain" Start="0" Color="#000000,00" BulletChar="•" BulletFont="Arial"> <BulletPicture Size="0x0" /> </ListStyle> </Style> <Span Text=" Here is the complete roadmap for the entire “Principles of Doppler Weather Radar” topic of RAC. You are currently in the Base Data Generation section of this topic."> <Style FontFamily="Open Sans" FontSize="11" FontIsBold="False" FontIsItalic="False" FontIsUnderline="False" FontIsStrikeout="False" ForegroundColor="#464646" Elevation="Normal" DisplayCase="AsIs" LinkColor="#464646" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" This lesson has one learning objective. Please take a moment to read it over and advance to the next slide when you are ready."> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" In general, ground clutter occurs on WSR-88D radar products because returns from stationary or nearly stationary ground targets weren't filtered out. Clutter suppression gets applied at the signal processor just before the base data are built. So, unfiltered clutter negatively impacts all radar products. There are two types of clutter contamination. The first type, which you can call normal ground clutter, exists all the time because it results from elevated terrain, buildings, etc. The second type, which is more transient, depends on atmospheric conditions and is known as anomalous propagation clutter.
"> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> <Block> <Style /> <Span Text=" For the reflectivity (left) and velocity (right) images, clutter filtering was only applied very close to the radar. Extensive Anomalous Propagation (AP) clutter contamination exists to the east through the south of the radar. Note the near zero velocities throughout the AP clutter areas. "> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" It’s important to remember that moving objects are not considered to be ground clutter. The WSR-88D clutter algorithms require clutter echoes to have near zero velocity and spectrum width to be detected. So, anticipate contamination from moving ground targets such as wind farms and traffic on highways when the proper conditions exist to detect them. "> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" The clutter filtering process relies on two different algorithms. First, the Clutter Mitigation Decision (CMD) algorithm identifies where clutter exists on a bin by bin basis. Then, for each bin identified by CMD, the Gaussian Model Adaptive Processing (GMAP) algorithm applies a signal reduction, or a suppression, to the clutter signal. Both CMD and GMAP are run at the RDA signal processor which is a literal black box!"> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" Clutter Mitigation Decision (CMD) offers an automated approach to the management of clutter filtering. CMD can identify both normal and AP clutter every volume scan, which eliminates the need for manually defining and downloading regions files to address AP clutter. The alternative would be to filter for clutter in every bin, which introduces other issues that we will discuss later. CMD builds what’s known as a dynamic Bypass Map that shows each bins that the algorithm identifies as containing clutter. GMAP then suppresses clutter only in the those bins identified in the Bypass Map.
"> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> <Block> <Style /> <Span Text=" The image on the right shows a visualization of areas that have been identified as needing clutter filtering by CMD. The locations labeled in green, yellow, and red are bins where CMD detected clutter and varying degrees of power were ultimately removed from the signal. The purple regions indicate areas where either point clutter was identified or only the dual-pol data were filtered for clutter. "> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" The CMD algorithm uses fuzzy logic with multiple inputs, some of which are based on the Reflectivity base data:
"> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> <Block> <Style /> <Span Text="
"> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> <Block> <Style /> <Span Text="1. The first input is Reflectivity (or Z) texture. Weather signals tend to have a smoother Reflectivity texture than clutter.
"> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> <Block> <Style /> <Span Text="2. Another input is called Reflectivity spin. This input provides information on how the reflectivity gradient changes sign as you move along the radial.
"> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> <Block> <Style /> <Span Text="3. A third input is called the Clutter Phase Alignment (CPA). This variable captures the variance of pulse to pulse phase changes. When CPA is high, the phasors have good alignment which means the returns have a higher likelihood of containing clutter. "> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" " /> <Span Text="Two inputs to CMD come from the dual-pol base data. The first of these inputs is the standard deviation of Differential Reflectivity (or ZDR). The higher standard deviation of ZDR in a particular range bin, the greater the likelihood that it contains clutter. In this example, you can likely tell the difference in the ZDR appearance in the two areas pointed out by the arrows. The weather signal dominates the smoother area visible toward the middle part of the image. The upper right-hand corner, where the ZDR returns are more variable, likely contains significantly more clutter signal."> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" The second CMD input based on dual-pol base data is the standard deviation of ΦDP. The higher the standard deviation of ΦDP, the greater the likelihood that the range bin contains clutter. In this example, compare the noisiness of the ΦDP data surrounding the radar to the south and southeast with the areas of to the west that contain weather signal (you’ll just have to trust me that precipitation was occurring there). The standard deviation data captures that variation (or lack of it), which can be very useful for the CMD algorithm. "> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" CMD builds a bypass map for every elevation and rotation. For the Split Cuts, that means separate Bypass maps get built for both the Contiguous Surveillance (CS) and Contiguous Doppler (CD) scans. For the remaining elevations in the volume scan, CMD builds a new map for each tilt. Segments don’t get built for each elevation, thus you cannot see every Bypass Map CMD builds. This process makes CMD troubleshooting efforts challenging. "> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" CMD offers “hands off” clutter suppression, though some data quality monitoring may be needed from time to time. In order for CMD to build maps for every elevation, the Bypass Map must be in control for all elevations. The “Default” clutter regions file is designed to do just that. With CMD active, All Bins suppressions is rarely needed. However, some offices may choose to implement All Bins suppression in certain areas to ensure clutter filtering occurs in area where it is needed. Just remember that All Bins only defines WHERE suppression occurs. GMAP determines how much power gets removed when the suppression is performed. If you routinely see returns that you believe to be clutter, remember to check the Velocity and Spectrum Width data to see if the values are near zero!"> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" Differential Phase, Correlation Coefficient, and Differential Reflectivity are all more susceptible to clutter contamination than Velocity and, especially, Reflectivity. As a result, it’s possible to notice a clutter “footprint” in the dual-pol products that they wouldn’t see in the legacy Doppler products. You can see from the example on the slide, less clutter should be noticeable near the RDA in dual-pol base products on the left. By filtering just the dual-pol data in these areas, we notice a significant improvement in these products. This capability means sites shouldn’t turn CMD off to remove the dual-pol clutter footprint near the radar. CMD handles this issue well."> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" Now that CMD has identified where clutter exists, the filtering (or removal) of the clutter from the returned signal can occur. The GMAP algorithm applies filtering only to those bins identified on the Clutter Filter Control product. Let’s see how that works. "> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" Weather and clutter signals have different characteristics. A clutter signal (like the blue green spike in the graphic) contains high power centered at zero Velocity with a narrow Spectrum Width. A weather signal (the broader pink curve) will have varying power returns, Velocity. and Spectrum Width. This difference between clutter signals and weather signals can be used to remove the clutter signal with minimal damage to the weather signal. A key principle in GMAP’s design suggests that both clutter and weather signals can often be well represented by Gaussian curves. We’ve done just that in this simplified example. Another important concept to remember is that weather signals are not usually centered at zero velocity. 
"> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> <Block> <Style /> <Span Text=" So, keeping these points in mind, GMAP first removes power from the returned signal near zero velocity. Hopefully, as much of the “spike” as possible gets suppressed. If enough of the weather signal remains, GMAP rebuilds the weather signal that was lost. "> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" Once the width for signal removal is determined, GMAP applies filtering to all of the signal within that width. In this case, both the clutter and weather signal within this interval will be removed. However, GMAP can rebuild the weather signal that was lost, depending on the availability of data points outside of the gap. When sufficient weather signal data outside the gap exists, GMAP rebuilds the weather signal across the gap using the Gaussian estimate. 
"> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> <Block> <Style /> <Span Text=" The available number of pulses per radial impacts GMAP’s performance, especially with respect to the rebuilding of the signal. GMAP performs better rebuilding the signal with data collected in VCP 215 versus VCPs, 12, 212, and 112. So, you definitely want to use the general surveillance VCP when stratiform, or other slow-moving, non-severe echoes are expected."> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" As an example of GMAP performance, we start with a scan where no discernible weather is present. Clutter filtering has been turned off in order to identify the local terrain in the Reflectivity image on the left. Note the ridge line to the southwest. The Velocity image on the right has had clutter filtering applied. A second step, known as clutter censoring, has occurred to remove additional signal for bins with only clutter in them. Clutter filtering and censoring have produced the bins with no data on the velocity product in the area associated with the ridge line to the southwest. "> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" At a later time, a squall line passes through the area with clutter filtering turned on for both products. The squall line appears in both the Reflectivity image on the left and the Velocity product on the right, but the Velocity product has been zoomed in compared to the Reflectivity. Note that, in both products, the ridge and other terrain clutter areas are no longer apparent. The weather signal was strong enough (and enough pulses were available), for GMAP to rebuild the weather for the bins that contained clutter. "> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" Here’s an example where the CMD and GMAP combo didn’t do so well. A huge area of reflectivity exists with values exceeding 30 dBZ where we know there’s no precipitation. These echoes exist in the outflow area behind a line of storms that has pass through the region in the late evening, early overnight hours. CMD correctly identified these returns (likely due to ducting in that shallow, stable air mass) as clutter. However, look at the velocity data in this region. Too much data occurs at non-zero velocities for GMAP to remove all of the clutter returns. As a result, the clutter remains in the final product."> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" The RPG now generates the Power Removed Control (PRC) product to help radar operators. The PRC shows where clutter filtering was applied and how much suppression occurred in dB. The product can be found in two locations. On the RPG Human Control Interface, it can be found by clicking on the “Clutter Regions” button in the upper right-hand side of the display. The product can also be accessed from the Radar menu in CAVE under the Data Quality sub-section as PRM."> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" This image shows what the Power Removed Control product looks like on the RPG. Here are the important features of this display. We’ll start on the right side where we have the clutter map. This map indicates whether the Bypass Map (orange), All Bins (sage green), or No Filter (black) is in control for a particular area. In this example, the Bypass Map is in control everywhere. You can change the clutter map at the bottom of the window.
"> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> <Block> <Style /> <Span Text=" On the left side, you see what interventions were taken. Since the Bypass Map is in control, you use the legend info towards the top of the page. The colors indicate how much power was removed. Additionally, the Point Clutter and DP Filtered legend items apply to range bins that fall into these categories. "> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style /> <Span Text=" In summary, Clutter Mitigation Decision (CMD) and Gaussian Model Adaptive Processing (GMAP) work together to identify and suppress clutter, respectively. CMD identifies both normal and AP clutter on a bin by bin basis for every rotation and every elevation tilt in a VCP. These Bypass Maps are then handed off to GMAP, which performs the suppression by applying filtering only to the bins identified by CMD (or for regions defined as All Bins) and removing power from the signal near zero velocity. GMAP can also rebuild the weather signal in the filtered area if sufficient data points remain outside the gap. 
"> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> <Block> <Style /> <Span Text=" The performance of both CMD and GMAP are impacted by the number of pulses per radial. Both algorithms work better using VCP 215 than on VCPs 12, 212, and 112 because more pulses are available per radial for the general surveillance VCP. This fact is one of several reasons to use VCP 215 for general surveillance situations, like stratiform rain."> <Style FontFamily="+minor" FontSize="11" /> </Span> </Block> </Content> </Document> <Document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <Content> <Block> <Style LeadingMargin="0" FirstLineMargin="0" Justification="Left" ListLevel="1" LineSpacingRule="Multiple" LineSpacing="20" SpacingBefore="4.31999969" SpacingAfter="0"> <ListStyle ListType="None" ListTypeFormat="Plain" Start="0" Color="#000000,00" BulletChar="•" BulletFont="Arial"> <BulletPicture Size="0x0" /> </ListStyle> </Style> <Span Text=" Here is the complete roadmap for the entire “Principles of Doppler Weather Radar” topic of RAC. You are currently in the Base Data Generation section of this topic."> <Style FontFamily="Open Sans" FontSize="11" FontIsBold="False" FontIsItalic="False" FontIsUnderline="False" FontIsStrikeout="False" ForegroundColor="#464646" Elevation="Normal" DisplayCase="AsIs" LinkColor="#464646" /> </Span> </Block> </Content> </Document>