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**CLUTTER MITIGATION DECISION (CMD) THEORY AND PROBLEM DIAGNOSIS**

RADAR MONITORING WORKSHOP ERAD 2010 SIBIU ROMANIA Mike Dixon National Center for Atmospheric Research Boulder, Colorado

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**Why do we need to know where clutter is in order to filter it effectively?**

We need to filter normal-propagation (NP) clutter and anomalous-propagation (AP) clutter The filters used remove weather power in some circumstances: Velocity close to 0 Spectrum width close to 0 These conditions occur both in: Clutter Stratiform precipitation We therefore need a technique which identifies likely locations of clutter The filter is applied only at those gates with a high probability of clutter.

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**Problem - weather and clutter combined Reflectivity plot**

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**Problem - weather and clutter combined Velocity plot**

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**What happens if we filter everywhere?**

Weather power removed Filtered reflectivity – applying clutter filter everywhere

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And if we use CMD? Filtered reflectivity using CMD

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**Combined clutter and weather. Weather and clutter are distinct.**

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**Combined clutter and weather spectrum**

Combined clutter and weather spectrum. Weather and clutter overlap somewhat.

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**Combined clutter/weather spectrum**

Combined clutter/weather spectrum. Weather and clutter overlap completely.

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**Notch filter – removes weather information**

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**Adaptive filter – does not remove weather**

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**Adaptive filter has a difficult time when clutter and weather overlap**

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Doppler Radar CMD

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Motivation Adaptive spectral clutter filters show great promise for intelligently filtering clutter power while leaving weather power largely unaffected. However, these filters still remove weather power under the following circumstances: the weather return has a velocity close to zero; the weather return has a narrow spectrum width. This tends to occur with stratiform weather in the region of the zero isodop. In order to mitigate the problem, information other than that used by the filters must be used to determine whether clutter exists at a gate.

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**Principal feature fields for CMD**

In order to identify gates with clutter, we use a number of so-called feature fields. These contain information which is independent of that used by the clutter filter. The feature fields used in the latest CMD version are: The TEXTURE of the reflectivity field – TDBZ. The SPIN of the reflectivity field. This is a measure of how often the reflectivity gradient changes sign. The Clutter Phase Alignment or CPA, which is a measure of the pulse-to-pulse stability of the returned signal.

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**TEXTURE of reflectivity - TDBZ**

TDBZ is computed as the mean of the squared reflectivity difference between adjacent gates. TDBZ is computed at each gate along the radial, with the computation centered on the gate of interest. TDBZ at a gate is computed using the dBZ values for the 4 gates on either side of the gate of interest. Computed for this gate Using data from these gates

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**TDBZ feature field Example of TDBZ – Denver Front Range NEXRAD - KFTG**

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Reflectivity SPIN x y For a point at which a gradient sign change occurs, let x be the reflectivity change from the previous gate and y be the reflectivity change to the next gate. Then SPIN CHANGE = (|x| + |y|) / 2

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SPIN feature field Example of SPIN – Denver Front Range NEXRAD – KFTG (SPIN is noisy in low SNR regions) SPIN DBZ

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**Clutter Phase Alignment - CPA**

In clutter, the phase of each pulse in the time series for a particular gate is almost constant since the clutter does not move much and is at a constant distance from the radar. In noise, the phase from pulse to pulse is random. In weather, the phase from pulse to pulse will vary depending on the velocity of the targets within the illumination volume.

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**I,Q data is a complex number**

power phase I

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**CPA – theoretical phasor diagrams**

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CPA feature field CPA is computed as the length of the cumulative phasor vector, divided by the sum of the power for each pulse. CPA is computed at a single gate. It is a normalized value, ranging from 0 to 1. In clutter, CPA is typically above 0.9. In weather, CPA is often close to 0, but increases in weather with a velocity close to 0 and a narrow spectrum width. In noise, CPA is typically less than 0.05. CPA was originally developed as a quality control field for clutter targets used for refractivity measurements.

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**Example of CPA – Denver Front Range NEXRAD - KFTG**

CPA feature field Example of CPA – Denver Front Range NEXRAD - KFTG CPA DBZ

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**Combining TDBZ, SPIN and CPA**

The individual feature fields, TDBZ, SPIN and CPA, are combined into a single interest field using fuzzy logic. First, each feature field is converted into an interest field, using a membership transfer function. Interest fields have a range from 0.0 to 1.0. The interest fields are assigned a weight. The combined interest field is computed as a weighted mean of the individual interest fields.

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**Steps in computing the single-pol CMD**

Compute TDBZ and TDBZ-interest Compute SPIN and SPIN-interest Compute CPA and CPA-interest Step 2: Compute Texture-interest = maximum of (TDBZ-interest, SPIN-interest) Step 3: Compute CMD value = fuzzy combination of CPA-interest and Texture-interest Compute CMD flag: true if CMD >= 0.5, false if CMD < 0.5

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**Membership functions for single-pol CMD**

-> (30,1) -> (15,0)

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**Membership function combination as used in single-pol CMD**

Weight=1.0 -> (30,1) -> (15,0) Weight=1.01

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**Creating combined interest field - CMD**

TDBZ SPIN CMD CPA

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**Logic for setting the clutter flag**

3. CMD > 0.5? 1. SNR > 3dB? 3. Set Flag.

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**EXAMPLE OF APPLYING CMD**

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KFTG 2006/10/26, 1200 UTC Reflectivity

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VELOCITY, WIDTH Radial velocity Spectrum width

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TDBZ, SPIN TDBZ SPIN

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CPA, CMD CPA CMD

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Clutter flag CMD flag

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**Unfiltered reflectivity**

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**Filtered reflectivity**

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DIAGNOSING ERRORS

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**THERE ARE 3 MAIN ERROR TYPES**

FALSE DETECTIONS: the algorithm detects clutter incorrectly, so that the filter is applied excessively. This is particularly problematic when it occurs in the region of 0 velocity, since the filter cannot distinguish between clutter and weather. MISSED DETECTIONS: the algorithm fails to identify clutter, and the filter is therefore not applied where it should be. FILTER FAILURE: the CMD algorithm works correctly, but the filter fails to work effectively.

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**This is the most common error type.**

FALSE DETECTIONS This is the most common error type.

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**Example 1 - Filtered DBZ Filtered reflectivity using CMD**

Filtered everywhere

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**Example 1 – VELOCITY and WIDTH**

Radial velocity Spectrum width

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**Example 2 : Filtered DBZ Filtered everywhere Filtered using CMD**

Weather power removed Filtered using CMD Filtered everywhere

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**Example 2 : VEL, filtered VEL**

Velocity Filtered velocity

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**Example 3 : KFTG 2006/10/10, 1000 UTC Filtered everywhere**

Weather power removed Filtered everywhere Filtered using CMD

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**Example 3 : VEL, filtered VEL**

Velocity Filtered velocity

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**Example 4 : KFTG 2006/10/09, 2200 UTC Filtered everywhere**

Weather power removed Filtered using CMD Filtered everywhere

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**Example 4 : VEL, filtered VEL**

Velocity Filtered velocity

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**Example 4 : Operational NEXRAD**

Filtered reflectivity Filtered velocity

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**ERROR TYPE 2: MISSED DETECTIONS**

It was found that in some clutter regions, CPA values can be lower than expected.

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**KEMX reflectivity 2009/04/22 21:22 UTC Also showing counties, interstates and US highways**

We will investigate the region indicated by the ellipse

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**Unfiltered reflectivity**

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**Filtered reflectivity**

Ellipses highlight missed detections

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**Max interest for TDBZ/SPIN**

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Region 1 – CPA This region has CPA values with considerable variability, with some values in the clutter region being as low as 0.2.

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**Characteristics of the CPA field**

It was noted that in some of the clutter regions, there are a considerable number of gates with low CPA values. The CPA values can vary from high to low in adjacent gates. Examining the phase time series for these gates shows that the phase can change significantly over a small part of the time series. It is probable that 2 separate targets are illuminated during the dwell. This phase change reduces CPA. However, for the remainder of the time series the phase is stable.

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**Example - spectrum of clutter point with low CPA CPA = 0.19**

A-scope X-axis: range (km) Magenta line shows range Red – unfiltered spectrum X-axis: samples Pink – filtered spectrum Power time series X-axis: time Phase time series X-axis: time Pulse-to-pulse phase Difference time series X-axis: time Change in phase for part of the time series leads to low CPA value

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**ERROR TYPE 3: FILTER DOES NOT WORK EFFECTIVELY**

The clutter filter can have problems dealing with multi-mode spectra.

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**Traffic clutter spectra**

It appears that some of the missed detections and poor clutter filter performance are caused by traffic echoes. The following slides show spectra from normal clutter echoes and suspected traffic echoes. The traffic echoes exhibit multi-modal spectra. This makes them both difficult to detect as clutter, and difficult to filter with the current adaptive filters.

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**Unfiltered reflectivity**

Note interstate 10 – shown in bold – traversing this area, and smaller roads

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**Filtered reflectivity showing gates at which CMD and the clutter filter failed**

Ellipse high-lights gates for which CMD and/or the clutter filter failed

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**Normal-propagation clutter signature CPA = 0.88**

A-scope X-axis: range (km) Magenta line shows range Red – unfiltered spectrum X-axis: samples Pink – filtered spectrum Power time series X-axis: time Phase time series X-axis: time Pulse-to-pulse phase difference time series X-axis: time

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**Spectrum of suspected traffic targets CPA = 0.17**

A-scope X-axis: range (km) Magenta line shows range Red – unfiltered spectrum X-axis: samples Pink – filtered spectrum Power time series X-axis: time Multi-modal spectrum Phase time series X-axis: time Pulse-to-pulse phase difference time series X-axis: time

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**Spectrum of suspected traffic targets CPA = 0.30**

A-scope X-axis: range (km) Magenta line shows range Red – unfiltered spectrum X-axis: samples Pink – filtered spectrum Power time series X-axis: time Multi-modal spectrum Phase time series X-axis: time Pulse-to-pulse phase difference time series X-axis: time

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THANK YOU

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