Applications of a Velocity Dealiasing Scheme to Data from the China New Generation Weather Radar System (CINRAD)

Transcription

1 218 W E A T H E R A N D F O R E C A S T I N G VOLUME 27 Applications of a Velocity Dealiasing Scheme to Data from the China New Generation Weather Radar System (CINRAD) GUANGXIN HE AND GANG LI Center of Data Assimilation for Research and Application, Nanjing University of Information Science and Technology, Nanjing, China XIAOLEI ZOU Center of Data Assimilation for Research and Application, Nanjing University of Information Science and Technology, Nanjing, China, and Department of Earth, Ocean and Atmospheric Science, The Florida State University, Tallahassee, Florida PETER SAWIN RAY Department of Earth, Ocean and Atmospheric Science, The Florida State University, Tallahassee, Florida (Manuscript received 6 May 2011, in final form 30 August 2011) ABSTRACT An improved velocity dealiasing algorithm is developed as an extension of the Next Generation Weather Radar (NEXRAD) dealiasing algorithm. The algorithm described in this paper is evaluated on selected China Next Generation Weather Radar (CINRAD) S-band radar radial velocity observations. This algorithm includes four modules for removing weak signals and determining the starting radial as a prelude to identifying and correcting aliased velocities. The proposed dealiasing algorithm was tested on 14 different weather systems, composed of typhoons, squall lines, and heavy rains. The results show that the algorithm is robust and stable for dealiasing S-band CINRAD radial velocity measurements. The performance levels for the typhoon and heavy rain cases are slightly better than for squall-line cases. 1. Introduction Radar observations play an increasingly important role in numerical weather prediction (NWP) where real-time forecasts of actual storms, initialized by current data, are within reach. A radar network of 158 Doppler radars in China, called the China Next Generation Weather Radar (CINRAD) network, is one source that could provide the needed observations of precipitation, wind, and hail in near real time. These data will soon be assimilated into the Global and Regional Assimilation and Prediction System (GRAPES), which is a 3D variational data assimilation system developed in China. It is anticipated that radar data assimilation at the convective scale has the potential to improve the prediction of hazardous weather in China and elsewhere. Corresponding author address: Dr. X. Zou, Dept. of Earth, Ocean and Atmospheric Science, The Florida State University, Tallahassee, FL xzou@fsu.edu The integration of radar data into real-time NWP products requires substantial automation, adequate data accuracy, and robust quality control (QC) procedures. One challenge with radar data is correcting velocity aliasing. The unambiguous velocity interval derived from the complex time sample for a given pulse repetition frequency (PRF) and transmitted radar wavelength is given by [2V max, V max ] (Ray and Ziegler 1977), where V max 5 (PRF)l/4 (1) is the maximum observed velocity, called the Nyquist velocity. Any frequency shift exceeding PRF/2 will be aliased. The true velocity (V T ) is related to the observed velocity (V O ) as follows: V T 5 V O 6 2n 3 V max, n 5 0, 1, 2,, (2) where 2V max is the Nyquist cointerval. DOI: /WAF-D Ó 2012 American Meteorological Society

2 FEBRUARY 2012 H E E T A L. 219 Velocity aliasing in the Doppler radar radial velocity measurements is a challenge for radar data assimilation. An effective velocity dealiasing scheme must be applied to recover the true signals (V T ) from the raw measurements (V O ) before the data are assimilated into GRAPES or any other system incorporating radar data into a forecast model. Software or hardware approaches can be used to solve the velocity aliasing problem. Based on the expected structure of the velocity data, such as spatial continuity, dealiasing is accomplished here using a software algorithm. The hardware approach adopts the staggered-prt (Doviak and Zrnić 1993) method, and can appreciably enlarge the maximum unambiguous interval. Pulsed radars send out a succession of pulses at a scheduled pulse repetition time (PRT). The reciprocal of that is the PRF or the number of pulses per second. Currently, the staggered-prt technique is most often used with shortwavelength weather radars (because of their much smaller Nyquist cointerval) to reduced the dealiasing burden. However, the staggered-prt method has the disadvantage that measurements acquired at slightly different times or locations are combined, which can lead to representativeness errors (Haase and Landelius 2004) and that spectral estimation is made difficult because of the nonuniform sampling. Even with the staggered- PRT method, velocity aliasing can still occur when the wind velocity is very large. Therefore, it is necessary to develop a robust and computationally efficient dealiasing algorithm. a. Literature review Since the 1970s, there have been many dealiasing algorithms proposed. Ray and Ziegler (1977) put forward the first dealiasing algorithm: a one-dimensional scheme using only single radial velocity information to remove aliased velocities in that radial. They proposed that all the velocity gates along the same radial should be normally distributed, thus identifying the outlying gates that need to be dealiased. This technique is sensitive to noise and multiple folds [when n is greater than 1 in Eq. (2)] and is effective only when minor aliasing occurs. Bargen and Brown (1980) developed another one-dimensional dealiasing scheme. They assumed that the first gate in each radial was correct. Using the averages of previously dealiased gates in the radial as a reference, they dealiased the gates through minimizing the difference between the previous gates in the radial and the next velocity. For aliased gates that cannot be correctly dealiased, this scheme permits user intervention to manually intervene. Thus, their scheme is not feasible for very large datasets or real-time applications. Merritt (1984) introduced a twodimensional dealiasing algorithm that allows successive testing for both the radial and azimuthal directions. First, all the data in each elevation angle were separated into different regions. The data in each region have the same Nyquist interval. Then, the shear along the boundary of adjacent regions was minimized by determining the proper aliasing interval for each region. Finally, a wind model was used to determine the proper Nyquist interval for areas that are spatially isolated with respect to the major echo regions. Bergen and Albers (1988) enhanced Merritt s algorithm by adding a noise filter and using a sounding instead of the wind model. This improved the dealiasing of isolated echoes. Eilts and Smith (1990) developed a new two-dimensional algorithm. They used a vertical wind profile from a sounding taken at nearly the same time that the radar data were collected. Previously dealiased gates in the same and previous radials (or velocity azimuth display, VAD) wind profile information were used as reference data to dealias each elevation angle, radial by radial. This algorithm is effective when the Nyquist velocity is between 20 and 35 m s 21. Each gate was processed with a radial continuity check by comparing it with a reference velocity, which is (the closest of five preceding gates) in the same radial. If the difference between the dealiased velocity and the reference is less than a threshold (e.g., 15 m s 21 ), then the dealiased velocity was retained and the algorithm proceeds with the next gate in the same radial. If the initial radial continuity check failed or there is no point within five gates, then an average of nine neighboring gates (four in the same radial and five in the adjacent preceding radial) was used as the new reference for comparison. If the dealiased velocity fell within the threshold of the average, then it is dealiased, and the algorithm proceeded to the next set of gates. If the dealiased result is not within this domain, then it is deleted. Or if there are no valid points to compute the average, the algorithm looks back within 5 km toward the radar in the same radial and forward 2.5 km in the previous radial to find the first valid gate for reference. The sounding or VAD wind information will place the velocity in question into the proper aliasing interval when no points are found for comparison. Two types of error checks were incorporated into the algorithm during dealiasing. If five consecutive points are removed by the algorithm, these points are replaced by initially comparing the first point removed with the preceding velocity when dealiased by a larger threshold (1.5 times the previous threshold). If the difference between two azimuthally adjacent velocities was larger than 1.2 times the Nyquist velocity and all the azimuth pairs within 2.5 km met this criterion, an azimuthal shear check was used. These data were reexamined by a least squares minimizing technique until no errors exist. Finally, a gate-to-gate check was made if there were any unrealistic discontinuities (e.g., large than 1.7 times the Nyquist interval). If an

3 220 W E A T H E R A N D F O R E C A S T I N G VOLUME 27 unrealistic jump exists, the previous radial was used instead of the current radial of data for dealiasing the velocities in subsequent radials. The error checks can stop the propagation of errors caused by the improper dealiasing. Jing and Wiener (1993) assumed that the average radial velocity of the local wind observed by radar is less than the Nyquist velocity. They dealiased a twodimensionally connected region based on the assumption of a smooth velocity field, using the principle of minimizing shear with respect to the environmental wind field or VAD wind profile. Based on the twodimensional dealiasing algorithm, James and Houze (2001) added information from different elevation angles and volume scans. This technique first eliminates the low signal-to-noise ratio (SNR) data that may be caused by ground clutter or second trip echo; a Bergen and Albers (1988) filter is used to remove isolated data. Because high-elevation angles suffer less from ground clutter, dealiasing can first be performed at high-elevation angles and then extended to lower-elevation angles. Highelevation velocities are considered to be a reference when dealiasing the lower-elevation data. This new algorithm was then applied to an extensive C-band radar velocity dataset that incorporated 4300 different elevation angles. Ninety-three percent of the data were correctly dealiased. Gong et al. (2003) produced a three-step dealiasing algorithm to conduct quality control on the radial velocity data. The VAD is the data collected on the surface of a cone that is at a constant elevation angle. All the data are at the same elevation but vary with azimuth and range. Since the elevation is not necessarily zero, the height of the data also increases with range from the radar. The wind profile from a VAD algorithm is not always correct when aliasing exists. Therefore, they used the improved VAD algorithm (Tabary et al. 2001) to select and precondition possibly ambiguous velocity gates. Then, using the more accurate reference wind field computed from the traditional VAD algorithm, all the gates of the preconditioned velocity field were dealiased as needed. Zhang and Wang (2006) proposed a two-dimensional multipass dealiasing algorithm (2DMPDA), which is independent of external data sources. They minimized the velocity gradients between unfolded velocities and adjacent folded velocities in each scan. After multipass searching, the search area is successively relaxed to incorporate more reliable velocities for the minimization of the gradient. This algorithm was then assessed by dealiasing over 1000 volume scans from Taiwan and from Weather Service Radar-1988 Doppler (WSR-88D) data. More than 99% of the velocity data were correctly dealiased. Witt (2007) compared the dealiasing results between the current Supplemental Product Generator (SPG) algorithm and 2DMPDA (Zhang and Wang 2006) on data from the Terminal Doppler Weather Radar (TDWR) base data for eight severe weather events including squall lines, hailstorms, and supercells. Based on the scoring methodology procedure (Brown and Wood 2005), the 2DMPDA performed slightly better than current SPG algorithm. The 2DMPDA offers the potential for improved TDWR velocity data especially at the smaller Nyquist intervals. However, despite the overall improvement of the 2DMPA, the SPG can show better performance in severe weather signatures, such as those from mesocyclones. Witt (2007) also suggested that the velocity values with reflectivity less than 25 dbz should be treated as noise, or errors may propagate into higher-reflectivity regions. Removing noise before dealiasing will help improve the velocity dealiasing process. Witt et al. (2009) proposed a new, improved algorithm named the two-dimensional velocity dealiasing algorithm (2DVDA) based on the Jing and Wiener (1993) algorithm. They found that the current Next Generation Weather Radar (NEXRAD) algorithm can easily cause velocity dealiasing errors when NEXRAD radars operate in volume coverage pattern 31 (VCP 31) and wind speeds or vertical wind shear are high. The new algorithm consistently outperformed the current NEXRAD algorithm for convective storm and hurricane events. The performance improvement of the 2D-VDA was substantially greater for the VCP-31 cases versus the VCP-12/212 cases. Neither algorithm performs very well at the lowest elevation angle, which potentially has more aliased velocities and stronger horizontal wind shear regions. The new algorithm is planned to replace the current NEXRAD algorithm after more extensive testing is done in the future, particularly for the VCP-31 data. Lim and Sun (2010) introduced a new dealiasing scheme that uses the forecast field from a radar assimilation model as the reference field. The radar velocity data were first interpolated from the original two-dimensional polar grid onto a Cartesian coordinate system with a 1-km resolution. Then, the forecast field (4-km resolution), derived from a four-dimensional storm-scale radar assimilation system (the Variational Doppler Radar Analysis System, VDRAS; Sun and Crook 1997), was employed to dealias the interpolated radar data. They compared the differences in the dealiasing results when using a blend of data from the Weather Research and Forecasting (WRF) surface network and the VAD profiles mesoscale background and VDRAS analysis as references. They reported that the algorithm can reduce dealiasing errors when VDRAS analysis is used as the reference wind. WSR-88D is also sometimes referred to as the NEXRAD radar. The NEXRAD velocity dealiasing algorithm (VDA; Eilts and Smith 1990) is fundamentally based on minimizing velocity gradients along a radial, and has proven to

4 FEBRUARY 2012 H E E T A L. 221 FIG. 1. The average of the absolute values of the measured velocities at all of the valid gates along each radial (solid curve) and the total number of valid gates along each radial (dashed dotted curve). The dashed horizontal line is the average number of all the valid gates and the solid horizontal line indicates the average number of all the valid gates multiplied by 2 /3. be a robust and reliable algorithm, having been used for a number of years for dealiasing radial velocity measurements and producing operationally useful products in the United States. In this study, we apply the NEXRAD algorithm to CINRAD data for different weather regimes in China. Modifications to the NEXRAD algorithm are proposed, and the results are compared with those from the original scheme. b. Paper organization We include the following sections: section 2 describes the four steps in the modified NEXRAD algorithm in detail; section 3 introduces a dealiasing software from SOLO II (Oye et al. 1995), and uses the dealiasing result from it as the true value needed for our assessment; section 4 provides the dealiasing result of CINRAD velocity data utilizing the CINRAD improved dealiasing algorithm (CIDA), and compares it with the dealiasing result from the previous NEXRAD algorithm; section 5 provides a statistical analysis of dealiasing results of velocity data from CINRAD by the improved NEXRAD algorithm under different weather conditions; and, finally, section 6 concludes with a brief summary. FIG. 2. The dealiasing processes are carried out clockwise and counterclockwise for 1808 in both directions of the initial reference radial. Variables r and u indicate the radial direction and azimuth direction, respectively. reflectivity is less than 20 dbz. Different radar systems may require different thresholds depending on transmitted power, antenna gain, etc. Step 2: Noisy velocity data near aliased areas that are not completely removed in step 1 are further removed in this step. First, the sum of the absolute velocities of all the valid gates in each radial, V, iscalculated. Velocities smaller than a threshold value, a, are removed, where a is larger if V is larger. For examples, 2. Algorithm description The following new modifications and additions to the NEXRAD algorithm were considered and proposed: 1) noise removal, 2) selection of the first radial, 3) dealiasing, and 4) an error check. Each step is outlined below. a. Module 1: Noise removal (preconditioning of the data) This module consists of the following two steps: Step 1: Velocity data are removed if the corresponding spectrum width is higher than 8 m s 21 and the FIG. 3. The r and u indicate the radial direction and azimuth direction, respectively. (a) All the radials are marked with a flag of 0 before dealiasing. Black dots indicate the valid gates in each radial. (b) When dealiasing in the clockwise direction in step 1 of module 3, the gates that have preceding neighbors as a reference velocity are marked with black dots after dealiasing. The radial is marked with a flag of 1 if all the valid gates in the current radial are marked with black dots after dealiasing. Those gates without valid preceding neighbors (missing gates) both in the current and previous radials will be segregated and marked with black triangles (in radials l 3, l 4, and l 5 ) in step 1. Then, the gates with black triangles will be dealiased by the reference radial l 6 in the counterclockwise direction in step 2 of module 3.

5 222 W E A T H E R A N D F O R E C A S T I N G VOLUME 27 FIG. 4. Track of Typhoon Fungwong from 0000 UTC 24 Jul to 1800 UTC 31 Jul The large black open circle indicates the radius of 34-kt winds and the smaller shaded circle indicates the maximum observation domain of the Wenzhou radar station. if V 5 20 m s 21, a 5 2:5ms 21 ; and if V 5 15 m s 21, a 5 1:5 ms 21. It is acknowledged that noise removal will remove some good data. But retaining bad data will cause errors to propagate into regions of otherwise good data. It is a better strategy to lose some (as little as possible) good data to avoid the consequences of retaining noisy data. b. Module 2: Selection of the first radial Velocities are dealiased along a radial based on velocities of the previous radial (see module 3). If the velocities in the initial radial are not in the proper Nyquist interval, then the dealiasing results in the following radials will also be wrong; if the initial gate in the initial radial is ambiguous, then the discrepancy between the dealiased results and the true velocity value of all data in the sweep might be off by 62n 3 V max. It is therefore important to choose an initial radial along which there is no aliasing present. In the original NEXRAD dealiasing algorithm, the sounding data from the neighboring height level at nearly the same time was used as the reference value to dealiase the initial gate in the initial radial. However, the time resolution (12 h) and space resolution (over 300 km) of sounding data are far more sparse than the time (around 6 min) and space (0.25 km) resolutions of radar data, which can lead to a false dealiasing result for the initial gate (Zhang and Wang 2006). In addition, this algorithm neglects the specific definition of the initial radial. An initial radial must be selected to start the process of determining which velocities need to be unfolded or dealiased. A starting angle (e.g., the azimuth angle of 08) must be selected as the initial radial in each sweep. This does not ensure that that there are no aliased velocities in the initial radial. We propose a methodology that nearly ensures that the initial radial does not contain velocities that are aliased. Figure 1 illustrates how the first radial is chosen. First, the average of the absolute values of the measured velocities at all the valid gates along each radial is calculated (solid curve in Fig. 1). The total number of the valid gates along each radial (dashed dotted curve) and the average number n y of the valid gates of all the radials (dashed horizontal line) are recorded meanwhile. Then, the initial radial is chosen among radials whose total number of the valid gates is larger than (2/3) 3 n y (solid horizontal line). There are two bottoms of the symmetric V shaped curve that are separated by nearly 1808 in the red curve in Fig. 1. A radial with the minimum average velocity and more valid gates was chosen to represent the initial radial (in the left-hand bottom of the solid curve). Since NEXRAD and many other algorithms depend on previous unfolded radials in addition to radial shear, it is important that the initial radial is devoid of aliases velocities. If there is only one azimuth for which this is satisfied based upon this model, then implementation of module 3 is mandated. c. Module 3: Dealiasing Starting from the radial next to the first radial chosen in module 2, the NEXRAD algorithm by Eilts and Smith (1990) is implemented. The original NEXRAD algorithm performs contiguous radial-by-radial dealiasing in a clockwise direction. Each pass goes through In the new algorithm, dealiasing starts from the radials on both sides of the initial radial in two passes: one in the clockwise direction and the other in the counterclockwise direction.

6 FEBRUARY 2012 H E E T A L. 223 FIG. 5. Radar radial velocity at a 1.58-elevation angle from Wenzhou station at 1045 UTC 29 Jul 2008: (a) raw velocity, (b) reference velocity, (c) dealiased by the NEXRAD algorithm, and (d) dealiased by the improved NEXRAD algorithm. The yellow circles in (a) indicate areas of aliased velocity. Each pass goes through 1808 (Fig. 2). This strategy restricts any potential error propagation from extending beyond 1808 from the initial radial (Zhang and Wang 2006). The core concept of the original NEXRAD algorithm is the comparison between the current gate and radial and the proximal gate(s) in the preceding correctly dealiased radial. Dealiasing was performed through minimizing the difference between the current gate and the proximal gate(s). When there are no valid reference gates within 5 km in the current radial and 2.5 km away from the radar in the previous radial, sounding data or VAD wind information are used to dealiase the current gate. But, the resolution of the sounding data cannot represent small-scale wind shear information well. While VAD winds have similar spatial and time resolutions as the radial velocity field, the wind profile from the VAD algorithm is not always correct when aliasing exits (Gong et al. 2003). To reduce the dependence on sounding or VAD wind fields, the new algorithm will dealiase the gates that have preceding neighbors as the reference. Those gates without valid preceding neighbors in the current and previous radials will be dealiased in two steps. The following describes these two steps of the clockwise dealiasing shown in Fig. 2; the counterclockwise part can be dealiased in the same manner. First, all of the radials are marked with a flag of 0 and all of the valid gates in each radial are marked with a black dot (Fig. 3a). Then, the process continues as follows. 1) Starting from the radial next to the reference radial, the dealiased gates with valid preceding neighbors will be still marked with a black dot after dealiasing (Fig. 3b). Those gates without valid preceding neighbors (missing gates) both in the current and previous radials will be segregated and marked with black triangles. When dealiasing in the current radial is completed, if all gates are properly dealiased, then the whole radial is labeled with flag of l. Otherwise, the flag 0 for the current radial is kept if there are still some velocities with black triangles. 2) If the current radial is marked with flag of 1 or 0, the process of unfolding will proceed to the next radial.

7 224 W E A T H E R A N D F O R E C A S T I N G VOLUME 27 FIG. 6. Radar radial velocity at a 3.38 elevation angle from Xiamen station at 1023 UTC 8 Aug 2008: (a) raw velocity, (b) reference velocity, (c) dealiased by the NEXRAD algorithm, and (d) dealiased by the improved NEXRAD algorithm. The yellow circle in (d) indicates a problem area after dealiasing. However if the current radial is labeled with a flag of 1 (good), and the previous radial is marked with a flag of 0 (unfolding needed), the current radial labeled with a flag of 1 will be a new reference radial and the radial(s) labeled with a flag of 0 are reexamined for dealiasing with the new reference radial (e.g., radial L6 in Fig. 3b). The algorithm will go back (counterclockwise) to dealias the isolated gates indicated by the black triangles in the radials with the flag of 0, until it reaches a radial with the flag of 1 (e.g., radial L2 in Fig. 3b). Then modules 2 and 3 are repeated, continuing dealiasing the radial next to radial L6 in a clockwise direction until all the radials are finished dealiasing in the 1808 semicircle. d. Module 4: Error check Some residual errors are likely after implementing module 1, because of electronic stability, signal processing accuracy, and other sources that create errors in the data field (Rossa et al. 2005). All of these errors can lead to the failure of the algorithm. When dealiasing errors are made, they tend to propagate in the azimuthal or radial directions. Thus, an error check is incorporated into the new algorithm. Before replacing the observed value with the dealiased velocity, the dealiased result is compared with the average velocity of all the valid gates in the previous four radials and seven gates nearest to the radar. If the difference between the dealiased result and the average value is less than Nyquist velocity, then the dealiased result is correct. Otherwise, module 3 is repeated or the observed value is just kept. 3. Algorithm applications CIDA, as outlined above, is applied to 35 different weather systems, including 5 typhoon cases, 3 squall line cases, and 27 heavy rain cases as observed by the CINRAD S-band radar system. The CINRAD S-band data have a range gate spacing of 250 m and an azimuth spacing of

8 FEBRUARY 2012 H E E T A L. 225 FIG. 7. Radar radial velocity at a 3.38 elevation angle from Wenzhou station at 10:45 UTC 29 Jul 2008: (a) raw velocity, (b) reference velocity, (c) dealiased by the NEXRAD algorithm, and (d) dealiased by the improved NEXRAD algorithm. nearly 18. Several preset scanning modes of operations are referred to as VCPs. In VCP-21 mode, each radar volume contains nine elevation scans: 0.58, 1.58, 2.48, 3.48, 4.38, 6.08, 9.98, 14.68, and The Nyquist velocity is about 26 m s 21 at low-elevation angles (,9.98)andabout 32 m s 21 for high-elevation angles ($9.98). Several case examples of the dealiasing results using CIDA are shown in following sections. The NEXRAD algorithm is illustrated for comparison. a. The true dealiasing result serving as reference The National Center for Atmospheric Research (NCAR) SOLO II package is a widget-driven, interactive program that displays sweeps of radar data and enables the user to manually edit (i.e., unfold or dealias) the velocity data. The SOLO II assisted, manually edited, and subjectively generated velocity fields will be used as the true velocity field for developing and testing an automatic dealiasing algorithm for CINRAD data. b. Dealiasing points near range folding and missing gates The dealiasing results from both the existing NEXRAD and CIDA algorithms work very well when the radial velocity field is continuous. But when aliased gates are in the midst of range folding, missing gates, and noise, the dealiasing algorithms often fail. 1) SYNOPTIC OVERVIEW Tropical Cyclone Fungwong formed in the northwest Pacific Ocean at 0600 UTC 25 July 2008 and moved in the northwest direction at km h 21. It intensified slowly and was upgraded to severe typhoon status at 1800 UTC 27 July. Fungwong made its first landfall at around 0000 UTC 28 July near the border of Hualien County in Taiwan with a maximum wind speed of 45 ms 21. The tropical storm made its second landfall over mainland China at 1400 UTC, 60 km south of Fuzhou. Fujian Province was hard hit, receiving wind gusts of up to 155 km h 21. Figure 4 shows the path of Typhoon Fungwong from 0000 UTC 24 July to 1800 UTC 31 July

9 226 W E A T H E R A N D F O R E C A S T I N G VOLUME 27 FIG. 8. The vector wind fields for the (a) NCEP FNL results at 900 hpa at 1200 UTC 3 Jun (b) Wind fields observed by all the automatic weather stations near Shangqiu radar station at 1340 UTC 3 Jun The surface station data have been interpolated onto a Cartesian grid. The Shangqiu radar station is located at the center of the domains The small circle with a radius of 230 km indicates the maximum range of the radial velocity sampling at the Wenzhou, China, radar station. The large circle is the area of near-gale-force winds at 1100 UTC 29 July ) DEALIASING RESULTS Figure 5a shows the raw radial velocity at the elevation angle of 1.58 at 1045 UTC 29 July 2008 for Typhoon Fungwong, as observed by the Wenzhou radar station. Two areas of velocity aliasing are indicated by yellow circles. A significant area of range folding and missing data is evident in the radial velocity fields. The truth (i.e., dealiased by SOLO II and then manually edited) is presented in Fig. 5b. Dealiased results using the NEXRAD algorithm without the proposed modifications are shown in Fig. 5c. Some aliased gates near the area of the range folding and missing gates caused an area of incorrect dealiasing to the south of the radar. In contrast, the desired result is obtained if all the modules outlined in section 3 are implemented (Fig. 5d). Module 3 in section 3 improved the dealiasing results for this case. c. Dealiasing near noise gates Radar data include noise, which can arise from clutter, second-trip echoes, birds, weak signals, and sidelobe echoes. If these errors are not removed from the data, they will cause incorrect dealiasing results. 1) SYNOPTIC OVERVIEW Typhoon Morakot (international designation: 0908) was the deadliest typhoon to impact Taiwan in recorded history. It formed early on 2 August 2009 as a tropical depression and developed into a typhoon at 1230 UTC 5 August. It made its first landfall near the border of Hualien County at 1545 UTC 7 August and its second landfall in Fujian Province on mainland China at 1030 UTC 9 August. The pressure was 970 hpa and the maximum wind was 33 m s 21 when it made landfall in mainland China. 2) DEALIASING RESULTS Figure 6a shows the raw radial velocity at the elevation angle of 3.38 at 1023 UTC 8 August 2008 for Typhoon Morakot as observed from the Xiamen, China, station. There are many noisy gates near the radar. The aliased velocities adjacent to the noise caused significant errors in the NEXRAD algorithm, as shown to the northeast of the radar in Fig. 6c. Figure 6d is the result of dealiasing by the CIDA algorithm. The noise to the northeast of the radar center was almost removed after module 1 as outlined in section 3. The remaining noise northwest of the radar station leads to some dealiasing errors as shown in the yellow circle in Fig. 6d. But the dealiasing results were much better than those from the old NEXRAD algorithm when comparing to the reference velocity field in Fig. 6b. Figure 7 shows the raw radial velocity with an elevation angle of 3.38 at 1045 UTC 29 July 2008 as observed by the Wenzhou radar station. As a result of noise located southeast and southwest of the radar, the old NEXRAD scheme incorrectly modified two areas in Fig. 7c. In constant, the CIDA algorithm dealiased the velocity field correctly.

10 FEBRUARY 2012 H E E T A L. 227 FIG. 9. Radar radial velocity at a 4.38 elevation angle from Shangqiu station at 1339 UTC 3 Jun 2009 (a) raw velocity, (b) reference velocity, (c) dealiased by the NEXRAD algorithm, and (d) dealiased by the improved NEXRAD algorithm. d. Dealiasing wind shear velocity field When strong wind shear exists in the velocity field, such as is often found with squall lines, tornadoes, and other severe convective weather, the NEXRAD algorithm can easily fail. It is a challenge for any algorithm. 1) SYNOPTIC OVERVIEW A squall line containing a line of severe thunderstorms developed in Henan Province of China between 0746 and 1600 UTC 3 June The 140-km-long squall line moved toward the southeast with winds of km h 21. The squall line developed its maximum strength when it passed through Shangqiu, around 1300 UTC. A maximum wind speed of 29 m s 21 was observed in Yongcheng. Twenty-seven people died and the economic losses were estimated to be $2.212 billion in U.S. dollars. Figure 8a shows the vector wind field at the 900-hPa level from the NECP Final Analyses (FNL) data at 1200 UTC 3 June There was a wind confluence in Fig. 8a extending to the west of the radar station. Figure 8b is the surface wind field extrapolated onto a Cartesian grid at 1340 UTC 3 June The radar station is located at the center of Figs. 8a,b. 2) DEALIASING RESULTS The dealiasing results for the squall-line case are shown in Fig. 9. The missing velocities to the north in Fig. 9b occur because the spectrum widths for these velocities TABLE 1. The total tilts and total valid gate numbers of each type of weather condition radar data in the 14 test cases. All test cases are classified into 3 different types of weather conditions: typhoon, squall line, and heavy rain. Weather condition No. of tilts No. of valid gates Typhoon Squall line Heavy rain All

11 228 W E A T H E R A N D F O R E C A S T I N G VOLUME 27 TABLE 2. Dealiasing results from all 14 of the test cases, which are classified into three different types of weather conditions by different algorithms. Acronyms used here are NEXRAD1, the current NEXRAD algorithm; NEXRAD2, the NEXRAD algorithm; CIDA, the CINRAD improved dealiasing algorithm; A, the total number of aliased gates in each case; B, the total number of correctly dealiased gates in each case; C, the total number of incorrectly dealiased gates in each case; and D, the total number of missed dealiased gates in each case. Typhoon Squall line A B C D A B C D NEXRAD NEXRAD CIDA Heavy rain All A B C D A B C D NEXRAD NEXRAD CIDA exceeded the noise threshold of 8 m s 21. The original NEXRAD scheme works well except in a small region highlighted by the yellow circle in Fig. 9c. Aliased velocities are not completely corrected near the outer edge of the scan (i.e., larger radius). The best results, again, are obtained after implementing all the modifications proposed in section Statistical results The CIDA for velocity dealiasing has been applied to CINRAD S-band VCP-21 data for 35 different weather systems, including 5 typhoon cases, 3 squall-line cases and 27 heavy rain cases. There were some very difficult dealiasing problems because of noise (e.g., ground clutter), range folding, missing gates, and strong azimuthal and radial shears. Out of the total 35 cases, there are 14 cases (5 typhoon, 3 squall line, and 6 heavy rain) for which aliased radial velocities are found to be present. One volume with aliased velocities was selected for dealiasing from each case separately. Table 1 shows the total tilts and total valid gates number of each type of weather condition radar data. Table 2 presents the details of different dealiasing results among the current NEXRAD, new NEXRAD (Witt et al. 2009), and CIDA algorithms the for each type of weather condition cases. The statistical results in Table 3 and Fig. 10 are calculated by the following formulas: probability of detection (POD) 5 B/A, false alarm rate (FAR) 5 C/A, and (3) critical success index (CSI) 5 B/(B 1 C 1 D), where A is the total number of aliased gates in each case, B is the total number of correctly dealiased gates in each case, C is the total number of incorrectly dealiased gates in each case, and D is the total number of missed dealiased gates in each case. It is seen that the dealiasing results of both the CIDA and new NEXRAD algorithms are better than the current NEXRAD algorithm, which has the lowest POD and CSI, as well as the highest FAR, for all 14 cases, when CINRAD radar operates in VCP-21. The POD of the new NEXRAD algorithm (99.41%) is higher than those of the CIDA algorithm (99.02%) and the current NEXRAD algorithm (96.15%), which means that the new NEXRAD algorithm can identify and correctly dealias more aliased data. But the FAR of the new NEXRAD algorithm (2.07%) is also higher than the improved algorithm (1.19%). And the CSI of the new NEXRAD algorithm (97.39%) is slightly lower than that for CIDA (97.85%), but still higher than for the current NEXRAD algorithm. Also, both the CIDA and new NEXRAD algorithms improve the dealiasing results when dealing with different weather conditions. The dealiased results for the typhoon and heavy rain cases TABLE 3. Statistical dealiasing results (%) from all 14 of the test cases classified into three different types of weather conditions by different algorithms. Typhoon Squall line Heavy rain All POD FAR CSI POD FAR CSI POD FAR CSI POD FAR CSI NEXRAD NEXRAD CIDA

12 FEBRUARY 2012 H E E T A L Summary and conclusions FIG. 10. Different statistical results for (a) POD, (b) FAR, and (c) CSI among the current NEXRAD (light gray bars), new NEXRAD (dark gray bars), and CINRAD improved dealiasing (hatched bars) algorithms for different weather condition cases with aliased data. Acronyms used are T, typhoon; SL, squall line; and HR 5 heavy rain. are better than for the squall-line cases, partly because there are quite large, meteorological shears present that the algorithms still struggles to handle. The CIDA performs very well, with 99.02% of the aliased velocity observations being successfully compared with the SOLO II assisted and manually edited results (section 3). Only 2.17% of the dealiased velocities [(C 1 D)/A] were either improperly dealiased (1.19%) or missed being dealiased (0.98%) due to range folding or noises. A quality flag is then added to these data for their future applications in data assimilation. A CINRAD improved dealiasing algorithm (CIDA) is developed based on the NEXRAD (and prior) schemes. The performance of the proposed algorithm is examined for CINRAD S-band observations. This algorithm includes four modules: 1) removing weak signals, 2) determining the starting radial, 3) identifying aliased velocities, and 4) correcting aliasing in the observed velocities. Radar measurements include noisy data and can be contaminated by clutter, second-trip echoes and sidelobe echoes. When gates are near noise, the noise can cause incorrect dealiasing results. Therefore, it is necessary to remove all noise before beginning dealiasing. This algorithm has a method that utilizes the range, echo strength, and variance of surrounding data to identify noise. It also incorporates an important improvement, choosing an initial radial along which there is no or minimal aliasing present. The velocities near the zero line are smaller and less likely to be aliased. So choosing a reference radial near the zero line can reduce the error rate of dealiasing. The new algorithm dealiases the gates such that the data will be in the same Nyquist interval as the preceding gates, which act as reference velocities. The chosen gates are from a different location than the NEXRAD algorithm. Gates without correctly dealiased neighbors are marked with a flag. An attempt to dealias these gates with flags is made by approaching them from the other side. This scheme does not require an additional sounding or a VAD wind field as is used in other dealiasing schemes. It can also improve the dealiasing result when nearby gates have been range folded or are missing gates. Before replacing the observed value with the dealiased velocity, the dealiased result is compared with the average velocity of all the valid gates in the previous four radials and the seven gates nearest to the radar. This error check can prevent the propagation of error caused by any remaining noise and observation errors. The proposed dealiasing algorithm was tested for 14 different weather scenarios consisting of typhoons, squall lines, and heavy rains. The results show that the algorithm is robust and stable for dealiasing S-band CINRAD radial VCP-21 velocity data. The proposed algorithm can improve the dealiasing results when dealiasing near noisy gates, range folding, missing gates, and velocity fields with strong wind shear. The performance for typhoon and heavy rain cases was slightly better than the results for squall lines. The algorithm correctly dealiased more than 99% of the aliased radial velocity data when compared to the manually edited results. Future investigations are being conducted to improve the remaining 2.17% improperly dealiased velocities and velocities that missed

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