3D Multi-Radar Reflectivity Mosaic

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Presentation transcript:

3D Multi-Radar Reflectivity Mosaic Jian Zhang CIMMS/NSSL I’m very glad to have this opportunity to present our 3D multi-radar mosaic work. More importantly, this is a good opportunity for us to get inputs from users and potential users of the 3D high-resolution radar mosaic data. Please feel free to ask questions at anytime. And I would really appreciate your comments and inputs. April 21, 2003 FAA Project Review

OUTLINE Motivation Challenges WSR-88D data characteristics Demo of various gridding schemes Demo of various mosaicking schemes Summary This is a list of the topics that I’ll be talking about today. 11/30/2018

Why Mosaic Single radar has limited coverage Weather systems span over multiple radar umbrellas FAA ARTCCs span multiple radar umbrellas (multiple states) Multi-sensor storm algorithms require radar data on a common grid as other data (e.g., satellite, lightning, etc) The reasons for gridding and mosaicking multi-radar data are rather simple. Single radar has limited coverage and weather often span over multiple radar umbrellas. In addition, the regions for which warning decision makers are responsible often require multiple radar coverage. 11/30/2018

Why 3D Mosaic 2D radar mosaic products have been very useful and efficient in many operational weather decision making processes Adding high resolution vertical structure data can: Provide more info about aviation hazardous weather Help develop more and better aviation weather products More efficient air traffic routing and improved aviation safety Other: Help improve storm growth and decay analysis More accurate QPE Help improve convective scale numerical weather prediction There are many existing 2D radar mosaic products and they have been very useful and very efficient in operational weather applications. But to further improve the weather forecast and warning decisions, vertical structure is very important. The CRAFT project has demonstrated feasibility of disseminating high-resolution base radar data in real-time. And the rapid advance in computer power makes it possible to do high-resolution multi-radar mosaic in real-time. By adding the high resolution vertical structure, we can provide more info about storms and can thus improve air traffic safety. 11/30/2018

Challenges for 3D Multi-radar Mosaic Extremely non-uniform data resolution Different weather types/regimes Non-weather echo contamination/quality control Calibration differences Synchronization among radars Computational efficiency in real-time application We’ve run into many challenges with gridding and mosaicking radar data. For single radar data gridding, we need to deal with: extremely non-uniform radar data resolution, different weather regimes/scales. For mosaicking multiple radar data, we encountered synchronization and calibration problems among radars. And even with the ever-increasing computer power, the computational efficiency remains a challenge due to the mass volume of radar data, especially when we deal with large domains and fine grids such as the FAA northeast corridor. 11/30/2018

Choose a Filter for Gridding: Uniform or Adaptive? Uniform: (Trapp and Doswell, 2000, JTECH, 17) Scale uniformity Adaptive: (Askelson et al. 2000, MWR, 128) Minimize RMS error Retain high-resolution info in radar data Depends on radar data characteristics and applications There have been two point of views when choosing a gridding schem for radar data. One prefers a uniform scaled filter, and the other adaptive filter. Here adaptive means the filter’s space scale is adaptive to the radar data spatial resolution. The reason for a uniform filter is mainly the scale uniformity which would simplify model simulations. And the argument for an adaptive filter is the large non-uniformity of the radar data resolution. But the filter is really dependent on the data resolution. So next we’ll look at the spatial resolution of radar data. 11/30/2018

WSR-88D Radar Beam Propagation Effective earth’s radius model (Doviak and Zrnic,1984): All our beam propagation calculations are based on the four/third effective earth’s radius model shown in Doviak and Zrnics book: <<Doppler radar and weather observations>>. Here h is the height of the beam above the earth surface, s is the distance from radar to a gate along the earth surface. R is the slant range, ae is the effective earth radius, and theta-e is the elevation angle. h: height above the earth surface r: slant range s: distance along the earth surface 11/30/2018

Radar Beam Propagation Assumptions: Spherically stratified atmosphere Refractive index of air, n(h), linearly changes with height, h; |n-1|<< 1 Predicts beam height with sufficient accuracy for exponential reference atmosphere Severe departures at lower atmosphere (“AP”): Temperature inversions Large moisture gradients The assumptions behind the model are: … Doviak and Zrnic have compared the four-third earth radius model with an exponential reference atmosphere and found that the effective earth radius model predicts beam height with sufficient accuracy. The exceptions exist at the very lower part of the atmosphere when there are T-inversions and large moisture gradients. 11/30/2018

WSR-88D Beam Geometry (VCP 11) This is a plot of the radar beam geometry for the NEXRAD VCP 11. Note that the size of radar beams expand with range rapidly. At a range of 200km, the beam is more than 3km wide. 11/30/2018

Power Density Distribution (VCP 11) This plot shows the WSR-88D radar power density distribution. Basically the power density function shows that targets at the center of radar beams contribute to the return power more than targets that are at the edge of the beams. The edge is the region shown in green and cyan. 11/30/2018

WSR-88D Beam Geometry (VCP 21) And this is a same plot for VCP21. In addition to the expand of the beam size, we also notice the large gaps between high tilts. 11/30/2018

Power Density Distribution (VCP 21) This is a plot showing power density function for VCP 21. 11/30/2018

WSR-88D Data Resolution Spatial resolution of radar data: In spherical coordinates: ~1x 1km x 1 at lower tilts (azimuth:range:elevation) higher tilts are as far as 5 apart Power density distribution  not point data “Effective” spatial resolution of radar data: Physical distance between centers of adjacent radar bins in space As you see, radar data are not point data, rather they are a weighted mean of all targets in a resolution volume. The weight is dependent on radar power density function. The center of the resolution volumes are approximately 1km by 1deg apart on a cone plane. In the vertical direction, the tilts can be as far as 5 degrees apart. For easy describe the data distribution, we define “effective” spatial resolution by using the physical distance between centers of the radar resolution volume scans (or radar bins). 11/30/2018

WSR-88D Azimuthal Resolution This is a plot showing the distance between two adjacent radials on the same tilt, we call it “azimuthal resolution”. The red arrow indicates the range where the resolution become worse than 1km.  11/30/2018

WSR-88D Azimuthal Resolution (VCP11 and VCP21) Range (km) 50 100 150 200 300 460 Distance (km) 0.8 1.7 2.5 3.3 4.9 8 This table just lists a few values read from the previous plot. Azimuthal resolution is worse than 1km beyond 60 km range 11/30/2018

WSR-88D Elevational Resolution (VCP11) This plot shows the distance between two adjacent tilts at the same range and same azimuth. Majority of the centers of data bins are more than 1km apart, many are more than 5km apart. To achieve an uniform filtered field, we must use the worst resolution (longest wavelength) in the data. That means at least 5km in horizontal (azimuthal) and 10km in vertical. This would not be acceptable storm-scale applications.  11/30/2018

WSR-88D Elevational Resolution (VCP21) Same as previous plot but for VCP 21. The resolution is even worse. Therefore, the WSR-88D data is not really ideal for studying stratiform weathers.  11/30/2018

Choose a Filter: Uniform or Adaptive? Our choice: adaptive Largely different spatial resolution An adaptive filter is needed for gridding radar data in order to retain high resolution info in radar data Data sampling resolution are worse than 1km in most places: Grid resolution of 1km is sufficient From previous discussion, we found that an adaptive filter is needed for gridding radar data, especially for high-resolution grid such as of 1km. 11/30/2018

Demo of Gridding Schemes “Resolution volume mapping” Nearest neighbor Vertical interpolation (adaptive) Vertical interpolation plus gap-filling Cases: convective, 6/25/02, 2036Z, KIWX stratiform, 2/15/98, 0859Z, KIWA From previous discussion, we found that an adaptive filter is needed for gridding radar data, especially for high-resolution grid such as of 1km. 11/30/2018

Convective Case 1: KIWX, 6/25/02, 2036Z Now we begin to demonstrate our 3D mosaic project using real cases. The first case is a convective storm case in the CIWS domain and this image shows the composite reflectivity from North Webster or Fort Wayne radar on June 25, 2002. The white circle lines are range rings 50km apart and straight lines are azimuth lines 45 degrees apart. The x- and y- axis show distance in kilometers from the radar. There are strong convective cells at both near radar ranges and far ranges. The color table is shown at the upper right and it will be used for all our images unless mentioned otherwise. 11/30/2018

Stratiform Case 2: KIWA, 2/15/98, 0859Z The second case is a stratiform winter storm case in the state of Arizona. And the image shows the composite reflectivity from KIWA (Phoenix) radar at about 9Z on Feb. 15, 1998. 11/30/2018

Gridding Scheme I: Resolution Volume Mapping Assuming each radar bin (resolution volume) size =1x 1km x 1 Resample on Cartesian grid Usually 1km x 1km x 500m For any given grid cell, if the center of the grid cell is inside a radar bin volume, then the grid cell will take the value of the radar bin. Like nearest neighbor, but within the beam width. Gaps may exists. The first gridding scheme we developed is to simply map the radar resolution volumes assuming the size of the volume is 1x 1km x 1. We’ll show some results together with the second scheme, nearest neighbor remapping. 11/30/2018

Gridding Scheme II: Nearest Neighbor For each grid cell: If the center is above (below) the highest (lowest) elevation angle, but within half of a beam width, then it will take the value of the nearest bin value on the highest (lowest) tilt. If the center is in between the lowest and highest elevation angles, then it will take the value of the nearest radar bin. No gaps between the lowest and highest tilts 11/30/2018

Convective Case1: RHI, 263° Resolution Volume Mapping Nearest Neighbor RHI images of the case 1 from bin volume mapping and the nearest neighbor remapping. The systems are characterized by large vertical scales and small horizontal scale. Note the beam spreading and the discontinuities in the vertical due to the power density function. Resolution Volume Mapping Nearest Neighbor 11/30/2018

Convective Case1: RHI, 173° Resolution Volume Mapping Nearest Neighbor RHI images from a different azimuth angle, with a longer range. Again it shows the small horizontal scale and large vertical scale. Resolution Volume Mapping Nearest Neighbor 11/30/2018

Convective Case1: CAPPI, 5km These are CAPPI images from the convective case. It is basically a horizontal cross section cut at a constant height with the same assumptions taken for the previous RHI images. The height here is defined with respect to the mean sea level. The image look quite consistent and smooth, even with the gaps between the tilts. It appears that a simple horizontal interpolation would fix the gaps. 11/30/2018 Resolution Volume Mapping Nearest Neighbor

Convective Case1: CAPPI, 12km Another CAPPI taken at 12km. This near the top of the system, therefore some of the arc shaped discontinuities are due to lack of data for interpolation. 11/30/2018 Resolution Volume Mapping Nearest Neighbor

Stratiform Case 2: RHI, 0° Resolution Volume Mapping Nearest Neighbor Now let’s look at the RHI images for the case 2. The discontinuity between tilts are much more pronounce, indicating that there are strong vertical gradients. When there are strong vertical gradients, the difference between centers of adjacent tilts are larger. Thus when fill in the whole radar bin volumes with the gate values, the discontinuity at the boundary is more noticeable. 11/30/2018 Resolution Volume Mapping Nearest Neighbor

Stratiform Case2: CAPPI, 2.3km CAPPI image for the winter case. The gaps are filled by the vertical expand of the radar beams. However, the ring-shaped discontinuities still exist. 11/30/2018 Resolution Volume Mapping Nearest Neighbor

Stratiform Case2: CAPPI, 5km Another CAPPI taken at 5km. 11/30/2018 Resolution Volume Mapping Nearest Neighbor

Gridding Scheme I Summary: Resolution Volume Mapping Shows raw data distribution Gaps between high tilts Ring-shaped discontinuities, especially for stratiform echoes. Caused by the coarse vertical resolution. 11/30/2018

Gridding Scheme II Summary: Nearest Neighbor No smoothing Gaps are filled Ring-shaped discontinuities 11/30/2018

Gridding Scheme III: Vertical Adaptive Barnes For each grid cell: If the center is above (below) the highest (lowest) elevation angle, same as the nearest neighbor. If the center is in between the lowest and highest elevation angles, then it will take the weighted mean of the two nearest radar bin values, one at the tilt above and one at below. 11/30/2018

Convective Case1: RHI, 263° Nearest Neighbor Vertical Adaptive Barnes The vertical interpolation provide a continuous and more consistent reflectivity field. Nearest Neighbor Vertical Adaptive Barnes 11/30/2018

Convective Case1: RHI, 173° Nearest Neighbor Vertical Adaptive Barnes 11/30/2018 Nearest Neighbor Vertical Adaptive Barnes

Convective Case1: CAPPI, 5km 11/30/2018 Nearest Neighbor Vertical Adaptive Barnes

Convective Case1: CAPPI, 12km 11/30/2018 Nearest Neighbor Vertical Adaptive Barnes

Stratiform Case 2: RHI, 0° Nearest Neighbor Vertical Adaptive Barnes 11/30/2018

Stratiform Case2: CAPPI, 2.3km Another CAPPI taken at 10km. Note the gaps between different tilts. 11/30/2018 Nearest Neighbor Vertical Adaptive Barnes

Stratiform Case2: CAPPI, 5km Vertical interpolation smoothed this field. 11/30/2018 Nearest Neighbor Vertical Adaptive Barnes

Gridding Scheme III Summary: Vertical Adaptive Barnes Filled gaps Alleviated discontinuities Bright-band rings still exist 11/30/2018

Gridding Scheme IV: Adaptive Barnes + Gap Filling For each grid cell: If the center is above (below) the highest (lowest) elevation angle, same as the nearest neighbor. If the center is in between the lowest and highest elevation angles, then it will take the weighted mean of four radar bin values on the tilt above and below, all at the same azimuth. Two of the radar bins are at the same range and two others are at the same height as the grid cell (See Figure next) 11/30/2018

Gridding Scheme IV: Adaptive Barnes + Gap Filling o o + o o 11/30/2018

Stratiform Case 2: RHI, 0° Vertical Adaptive Barnes Now a RHI image for the case 2. The discontinuity between tilts are much more pronounce, indicating that there are strong vertical gradients. When there are strong vertical gradients, the difference between centers of adjacent tilts are larger. Thus when fill in the whole radar bin volumes with the gate values, the discontinuity at the boundary is more noticeable. Vertical Adaptive Barnes Adaptive Barnes + Gap Filling 11/30/2018

Stratiform Case2: CAPPI, 2.3km Another CAPPI taken at 10km. Note the gaps between different tilts. 11/30/2018 Vertical Adaptive Barnes Adaptive Barnes + Gap Filling

Convective Case1: RHI, 263° Vertical Adaptive Barnes Artifacts show horizontal interpolation is not suited for convective storms. Vertical Adaptive Barnes Adaptive Barnes + Gap Filling 11/30/2018

Gridding Scheme IV Summary: Adaptive Barnes + Gap Filling Filled gaps Eliminated discontinuities Alleviated bright-band rings Not suited for upright convective systems, so…. Solution: Apply gap filling component only when bright-band is identified (Gourley and Calvert 2003) Constraints: h/s > scale_ratio; range < range_limit; 11/30/2018

Mosaic Data from individual radars have now been gridded (resampled on Cartesian grid) Now we need to combine or mosaic data from multiple radars 11/30/2018

Mosaic Scheme I: Nearest Neighbor At each grid cell: If there is only one radar’s grid value, then the value is used If there are multiple radar values, then the value from the nearest radar is used. 11/30/2018

Mosaic Scheme II: Maximum At each grid cell: If there is only one radar’s grid value, then the value is used If there are multiple radar values, the the maximum value among the multiple radars is used. 11/30/2018

Mosaic Scheme III: Distance Weighted Mean At each grid cell: If there is only one radar’s grid value, then the value is used If there are multiple radar values, then a weighted mean of the multiple values is used. The weighting function is dependent on the distance from the grid cell to the radars. R is the radius of influence; d is the distance between radar and the given grid cell. 11/30/2018

Convective Case 1: 6/25/02, 2036Z KLOT and KIWX Is KLOT radar too cold or KIWX radar too hot? 11/30/2018 CREF_KLOT CREF_KIWX

Composite Reflectivity Difference KIWX-KLOT Statistics along the equidistance line (white): Mean: 7.7dBZ Min: -9.5dBZ Max: 26dBZ Blue line is for a vertical cross section shown next 11/30/2018

RHI for Reflectivity Difference KIWX-KLOT Blue line shows the equidistance. 11/30/2018

Convective Case 1: Nearest Neighbor 11/30/2018

Convective Case 1: Maximum 11/30/2018

Convective Case 1: Distance Weighted Mean An exponential function with 50km scale radius provide “better” mosaicking. 11/30/2018

Convective Case 1: Distance Weighted Mean Cressman weighting function with 300km radius of influence damped intensity for this case. 11/30/2018

Mosaic Scheme Summary Nearest Neighbor: Taking maximum value: Produces discontinuities when there are calibration differences Taking maximum value: Retain highest intensity everywhere Giving 100% weight to the “hot” radars May expand bright-band features at far ranges Weighted mean: Retain highest intensity at near ranges Weighting function based on distance -- giving high weight to closer range observations Smoothing Smoothing (“cold” radar can damp echo intensity at mid-ranges) 11/30/2018

Summary An adaptive filter is required to retain highest possible resolution in the data. Interpolation in vertical direction produces smooth and consistent reflectivity analysis. A gap-filling is needed for stratiform precip echoes with strong vertical gradient, especially when there is bright-band. Weighted mean mosaicking helps alleviate calibration discontinuities and provide continuity in the mosaic field. 11/30/2018

Future Work Further improve data QC Apply advection when mosaicking Alleviate synchronization problems among radars Incorporate TDWR, ASR-9, and other radar data Develop toward a 4D dynamic grid Demo of preliminary results 11/30/2018

Current 3D mosaic comp. refl. (every 5 min) 01:20Z - 01:30Z, 2002-08-14 KTLX KFDR 11/30/2018

Rapid update mosaic comp. refl. (every 10s) 01:20Z - 01:30Z, 2002-08-14 KTLX KFDR 11/30/2018

Lines for vertical cross sections 2 1 01:20Z - 01:30Z, 2002-08-14 KTLX 2 KFDR 1 11/30/2018

Cross section-1 loop (~ every 10 s) 01:17:00Z - 01:30:01Z, 2002-08-14 KTLX KFDR 11/30/2018

Cross section 2 loop (~every 10s) 01:17:00Z - 01:30:01Z, 2002-08-14 11/30/2018

3D Multi-Radar Mosaic Thank You! Questions? Email: Jian.zhang@noaa.gov This ppt file is available at: ftp://ftpnssl.nssl.noaa.gov/pub/jzhang/FAA_PR/ 11/30/2018