0   INITIAL OBSERVATION RESULTS         FOR PRECIPITATIONON THE KU-BAND BROADBAND RADAR             NETWORKEi-ichi Yoshika...
1Outline 1. Introduction 2. The Ku-band Broadband Radar (Ku-BBR)   – Design concept   – Observation accuracy   – Observati...
IntroductionResolution of Conventional Weather Radar                                    ○Fronts, Hurricanes               ...
IntroductionResolution of the Ku-BBR Network                                   ○Fronts, Hurricanes              ○Cumulus, ...
4IntroductionThe Ku-BBR Network                       • High resolution                         ⇒ Range resolution : sever...
5Design Concept of the Ku-BBRHigh resolution                             Wide Band Widthsolution Pulse compression        ...
6ConfigurationD/A Converter generates IQ signals                               Bistatic Lens Antennaarbitrarily. (170 MHz ...
7     Signal Processing Unit           Pulse compression Signal Processing Unit                       Doppler estimation  ...
8Fast Scanning System Luneburg Lens   (φ450 mm)                                      Azimuth rotation                     ...
Specifications     Name                                                  Specifications         Remarks     System        ...
10Observation AccuracyCross-validation with Joss-Waldvogel Disdrometer (JWD)•Impact type disdrometer•Rain drop diameter: 0...
11        Observation Accuracy                                 Scatter plot                  40                           ...
12Examples of Ku-BBR ObservationVertical Pointing Mode                                                  Time-Height Cross ...
13Examples of Ku-BBR ObservationVolume Scanning Mode 3D construction of    structure of strong vortex (Resolution of sever...
142(3)-BBR Network in JapanToyonaka radar(Dual-pol) 135.455748E 34.804939N                             Nagisa radar       ...
15Initial Observation ResultsIntegration clearlyshows precipitation           Integration by usingpatterns because of     ...
16Initial Observation Results                              13:55:09 09/14/10
17Initial Observation Results                              13:56:13 09/14/10
18Initial Observation Results                              13:57:17 09/14/10
19Initial Observation Results                              13:58:21 09/14/10
20Initial Observation ResultsClear shapesof precipitationare retrieved                 Thin-plate shaped                  ...
21Conclusion• We developed the Ku-BBR Network, a short-range and  high-resolution weather radar network.   – Range resolut...
22Thank you for listening! End
23Initial Observation ResultsIntegration clearlyshows precipitation           Integration by usingpatterns because of     ...
24Initial Observation Results                              13:55:09 09/14/10
25Initial Observation Results                              13:56:13 09/14/10
26Initial Observation Results                              13:57:17 09/14/10
27Initial Observation Results                              13:58:21 09/14/10
Introduction Resolution of the BBR                                             ○Fronts, Hurricanes                       ○...
29 Introduction The BBR Network                        ○ Unobservable area in low altitudes                          ⇒ in ...
30 Outline     1. The Ku-band BroadBand Radar (The Ku-BBR)          – Theory          – Observation accuracy          – In...
31 Ch.1: The Ku-BBR    1. The Ku-band BroadBand Radar (The Ku-BBR)         – Theory         – Configuration         – Init...
32 Concept High resolution                                 Wide Band Width  solution Pulse compression                    ...
Pulse Compression for Distributed Particles    •     Pulse Compression         – gives us sufficient energy on a target fo...
Pulse Compression for Distributed Particles    •     Radar Equation for Precipitation Particles with          Pulse Compre...
35 Configuration      D/A Converter generates IQ signals                           Bistatic Lens Antenna      arbitrarily....
36 Bistatic Lens Antenna System                   About 1450 mm                                                           ...
37 Bistatic Lens Antenna System                                Azimuth rotation                                 40 RPM (ma...
38    Signal Processing Unit              Pulse compression             Signal Processing Unit         Doppler estimation ...
Specifications               Name                                                   Specifications            Remarks     ...
40 Observation Accuracy  Cross-validation with Joss-  Waldvogel Disdrometer (JWD)  •Impact type disdrometer  •Rain drop di...
41       Observation Accuracy                                 Scatter plot                  40                            ...
42 Comparison to C-band Radar                           Highly attenuated                                                 ...
43 Observation Result (Time series example)                            Toyonaka,                       Osaka universityCha...
44 Example –tornado? 2nd elevation (in altitudes of 0 m – 270 m, roughly)Chapter 1 The Ku-BBR
45 Example –tornado? 3rd elevation (in altitudes of 0 m – 450 m, roughly)Chapter 1 The Ku-BBR
46 Example –tornado? 4th elevation (in altitudes of 0 m – 640 m, roughly)Chapter 1 The Ku-BBR
47 Example –tornado? 5th elevation (in altitudes of 0 m – 820 m, roughly)Chapter 1 The Ku-BBR
48 Example –tornado? 6th elevation (in altitudes of 0 m – 1000 m, roughly)Chapter 1 The Ku-BBR
49 Example –tornado? 7th elevation (in altitudes of 0 m – 1190 m, roughly)Chapter 1 The Ku-BBR
50 Example –tornado? 8th elevation (in altitudes of 0 m – 1370 m, roughly)Chapter 1 The Ku-BBR
51 Example –tornado? 9th elevation (in altitudes of 0 m – 1550 m, roughly)Chapter 1 The Ku-BBR
52 Example –tornado? 10th elevation (in altitudes of 0 m – 1610 m, roughly)Chapter 1 The Ku-BBR
53 Summary of Chapter 1 • We developed the Ku-BBR, a short-range and high-resolution   weather radar.      – Range resolut...
54 Ch.2: The Ku-BBR Network     1. The Ku-band BroadBand Radar (The Ku-BBR)          – Theory          – Observation accur...
55 Network Approach of the Ku-BBR     •     Single BBR Cross-range                          785 m              – Range res...
56 Network Approach of the Ku-BBR     •    The Ku-BBR Network                                              – CAFs of two r...
57 Equations     Single Radar Equation        P r0     r I r0 , r dV                          Composite Average...
58 Network Approach of the Ku-BBR     A Weighting Average of Received Signals     Obtained by Each Radar Node           ˆ ...
59 Evaluation of Spatial Resolution     •     Simulation in a two-BBR network           on base scan                      ...
60 Evaluation of Spatial Resolution     •    Normalized CAF (NCAF)          – Single radar                                ...
61 Evaluation of Spatial Resolution     •        Assumptions of Two BBR Simulation        – A Gaussian shape CAF          ...
62 Single BBR vs Two BBR Network                     NCAF                            Averaged NCAF            in Single BB...
63 Two BBR Network vs the Conventional                Averaged NCAF                   Radar Network             in Two BBR...
64 Resolution Map (in 2-D)         Resolution Square on Base Scan   A spatial resolution                                  ...
65 Deployment in Osaka Area, Japan Toyonaka radar (polarimetric)   135.455748E   34.804939N                               ...
66 Initial Observation Results                               13:54:05 09/14/10Chapter 2 The Ku-BBR Network
67 Initial Observation Results                               13:55:09 09/14/10Chapter 2 The Ku-BBR Network
68 Initial Observation Results                               13:56:13 09/14/10Chapter 2 The Ku-BBR Network
69 Initial Observation Results                               13:57:17 09/14/10Chapter 2 The Ku-BBR Network
70 Initial Observation Results                               13:58:21 09/14/10Chapter 2 The Ku-BBR Network
71 Summary of Chapter 2 • The Ku-BBR network accomplishes high spatial resolution in   2- or 3-D.      – Crossed pattern o...
72 Ch.3: Precipitation Attenuation Correction     1. The Ku-band BroadBand Radar (The Ku-BBR)           – Theory          ...
73 BackgroundHitschfeld – Bordan (HB) solution                         Path-IntegratedZ m r   Z r  exp 0.2 ln 10 r k...
74What is the problem?• Deterministic approach    ー Deterministic approaches (represented by HB solution) accumulate  esti...
75Proposal• Stochastic approach   ー A Kalman filter approach is applied.   ー Uncertainties of estimates are also obtained....
76Methodology                  – Basis<Observation (in dB)>                         Z e : (un-attenuated) reflectivity (dB...
77Methodology          – Processing (1/2)             BBR1                               BBR2 Step 1: Single-path retrieva...
78Methodology         – Processing (2/2) Step 3: Retrievals in each beam starting with traded cross points                ...
79Simulation Model       True Ze                    True Ze on BBR1   Zm on BBR1                    True Ze on BBR2   Zm o...
80Simulation Result                                 – Reflectivity                        Underestimateon BBR1 (dBZ)      ...
81Simulation Result                           – Comparison to HB solution                    OvershootZe on BBR1 (dBZ)    ...
82 Summary of Chapter 3  • A Kalman filter approach for precipitation attenuation    correction in the Ku-BBR network is d...
83Thank you for listening! End
84Back up
観測可能高度         ref) Houze (1993), Doviak (1993)          地形により更に          制限を受ける.                                         ...
Ku帯広帯域レーダの観測                          空間分解能• パルス圧縮レーダの距離分解能  r           c             2[m]    B  80[MHz]         2...
Cバンドレーダとの比較(旧ver.)       : Zm of C-band radar       : Zm of BBR       : Retrieved Ze of BBR (an old version)   87
Cバンドレーダとの比較(旧ver.)                     88
Precipitation Attenuation                                        –Mie solution  The solution for back scattering cross sec...
90 Precipitation Attenuation                                          –Equation     Measured Equivalent             Path-I...
91 Traditional Method for Correction     Assumptions                  • B.C.:        Z m r0   Z e r0                 ...
92 What is problem?• k-Z relation     – is dependent on DSD                                                               ...
93 Proposed Algorithm  • Assuming a Gamma DSD       N ( D)  N0 D  exp  D                                         n  ...
94 Simulation                               –in single BBR  • HB solution                     – overestimates in large are...
95 Algorithm in the BBR Network                        BBR1                                                   BBR2       S...
96 Algorithm in the BBR Network       Step 3: Retrieval with Traded Data       makes other correction data with Kalman    ...
97 Simulation                             –in the BBR network  • Errors remaining in Step 1 is properly                   ...
98                    Seminar in CSU           Luneburg Lens AntennaJun. 24, 2011Ei-ichi Yoshikawa
Lens Antenna FamilyDielectric (delay) lens          E-plane metal plate (fast) lens http://www.engineering-eye.com/rpt/c00...
What is Luneburg Lens?“Luneberg lens” is spherical lens for microwave. 1944 Proposal as optical lens        Principle     ...
Snell’s law             –excerpt from wikipediaSnells law states that the ratio of the sinesof the angles of incidence and...
How to focus?Simulation by a discretized functionof dielectric constant                                       102
How to focus?Simulation by a discretized functionof dielectric constant                                       103
How to focus?Simulation by a discretized functionof dielectric constant                                       104
Focused Beam               105
Many Focal Points                    106
Typical Application of Luneburg Lens                             Luneberg Lens                               Frequency Ran...
Construction     Ideal construction                                             SEI lens construction                     ...
Material                                                                   SEI materialUse special materials              ...
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INITIAL OBSERVATION RESULTS FOR PRECIPITATION ON THE KU-BAND BROADBAND RADAR NETWORK.pdf

  1. 1. 0 INITIAL OBSERVATION RESULTS FOR PRECIPITATIONON THE KU-BAND BROADBAND RADAR NETWORKEi-ichi Yoshikawa1, 2,Satoru Yoshida1, Takeshi Morimoto1, Tomoo Ushio1,Zen Kawasaki1, and Tomoaki Mega3Osaka University, Osaka, Japan1Colorado State University, CO., U.S.2Kyoto University, Kyoto, Japan3IGARSS 2011, Vancouver-Sendai, Jul. 29, 2011
  2. 2. 1Outline 1. Introduction 2. The Ku-band Broadband Radar (Ku-BBR) – Design concept – Observation accuracy – Observation result 3. The Ku-BBR Network – Deployment in Osaka – Initial observation Result 4. Conclusion
  3. 3. IntroductionResolution of Conventional Weather Radar ○Fronts, Hurricanes ×Cumulus, Tornadoes, ○Mesocyclones, Microbursts Supercells, Squall lines 10min △Thunderstorms, Macrobursts 100m 2
  4. 4. IntroductionResolution of the Ku-BBR Network ○Fronts, Hurricanes ○Cumulus, Tornadoes, ○Mesocyclones, Microbursts Supercells, Squall lines △Smaller phenomena? ○Thunderstorms, Macrobursts 1min Several meters 3
  5. 5. 4IntroductionThe Ku-BBR Network • High resolution ⇒ Range resolution : several meters 3-dB beam width : 3 deg ⇒ Temporal resolution : 1 min/VoS • Short range (15 km) radar ⇒ Min altitude : 14 m ⇒ Efficient for Troposphere (8 through 15 km altitude)<Conventional Radar> • Multi-Radar Network ⇒ Precipitation attenuation correction ⇒ High resolution grid retrieval ⇒ Wider area
  6. 6. 5Design Concept of the Ku-BBRHigh resolution Wide Band Widthsolution Pulse compression solution Ku-band Gain : BT ・Band width of 80 MHz Range resolution : c 2 B ⇒ Range resolution of 2 m (max)B :Band width (Hz), T :Pulse width (sec), ・In C- and X-band, it is difficult to c :Light speed (m/sec) obtain wide band width in Japan.Precipitation Attenuation Wide Area & High Accuracysolution Short range: 15 km solution Multi-radar network (Dual-poralization) ・Higher accuracy in overlapped area ・Polarimetric parameters are more sensitive in Ku-band than other ・Network approach for precipitation low frequency radars. attenuation correction
  7. 7. 6ConfigurationD/A Converter generates IQ signals Bistatic Lens Antennaarbitrarily. (170 MHz (max), 14 bit) to eliminate blind range Signal Processing Unit IF Unit Antenna Unit I Amp Mixer HPA Horn D/A IQ Memory QPC Modulator D/A 14bit Rotary 13.75 Luneberg 170MHz LO 2GHz LO Joint GHz Lens I A/D DSP IQ De- Board Q modulator A/D Amp Mixer LNA Horn Digital Baseband IF (2GHz) Ku (15.71-9 GHz) Real-time Digital Signal Processing for Pulse Compression and Doppler Spectrum Estimation
  8. 8. 7 Signal Processing Unit Pulse compression Signal Processing Unit Doppler estimation PC DSP boards A/D converter Former process Latter process I Received signal 32bit 32bit (I and Q) FFT integer floatQ (32768 points) 16bit integer Pulse Clutter Power, compression filter & Velocity, IFFT output FFT Memory (32768 poins) (64 points) Doppler Spectral width * (I and Q) moment 32bit float Reference signal 16bit integer estimation (I and Q) FFT 64 times 8192 times (32768 points) /segment /segment 16bit integer• First: Pulse compression, Second: Doppler spectrum estimation• Parallel calculation with DSPs – First: 32 DSPs (32 bit integer, 1 GHz), Second 3 DSPs (Float, 300 MHz) New FPGA system is in the process of production.
  9. 9. 8Fast Scanning System Luneburg Lens (φ450 mm) Azimuth rotation 40 RPM (max) 30 RPM (usual)Primary Feed Elevation rotation
  10. 10. Specifications Name Specifications Remarks System Operational Frequency [GHz] 15.75 Operational Mode Spiral, Conical, Fix Band Width[MHz] 80 (max) 15.71 - 15.79 GHz Coverage Az / EL [deg] 0-360 / 0-90 Coverage Range [km] 15 km (16 dBZ) variable Resolution Az / El [deg] 3/3 Range [m] 2 (min) variable Time/1scan [sec] 60 Power Consumption [kVA] 4kVA (max) Weight [kg] 500kg (max) Antenna Luneberg Lens Gain [dBi] 36 Beam Width [deg] 3 Polarization Linear Cross Polarization [dB] 25 (min) Antenna Noise Temp. [K] 75 (typ) Transmitter Transmission Power [W] 10(max) & Receiver Duty Ratio variable Noise figure [dB] 2 (max) Signal D/A 170MHz - 14bit IQ 2ch Processiong A/D 170MHz - 14bit IQ 2ch Range Gate [point] 32768 PRT [us] variable 9
  11. 11. 10Observation AccuracyCross-validation with Joss-Waldvogel Disdrometer (JWD)•Impact type disdrometer•Rain drop diameter: 0.3 - 5.0 mm•Ze is calculated by Mie theory 4   2 2   1  b Ze  5  ( D)  N ( D)dD N(D): DSD 2  b : Back Scattering coefficient 2  b    1 2l  1al  bl  l from mie scattering 4 l 1
  12. 12. 11 Observation Accuracy Scatter plot 40 HistogramZm of BBR (dBZ) 30 20 15 20 (%) 10 10 5 0 0 0 10 20 30 40 -10 -5 0 5 10 Ze of JWD (dBZ) Zm – Ze (dBZ) • The high resolution reflectivity measurements of the BBR show fairly good agreement compared with JWD. – Correlation coefficient: 0.95, Standard deviation: 1.59 dBZ
  13. 13. 12Examples of Ku-BBR ObservationVertical Pointing Mode Time-Height Cross Section (Reflectivity) 8Range resolutionof several meters 6 Height (km) 4 Minimum observation Reflectivity (dBZ) height of 50 m 50 2 0 0 5 10 15 20 0 Time (min)
  14. 14. 13Examples of Ku-BBR ObservationVolume Scanning Mode 3D construction of structure of strong vortex (Resolution of several meters, 1 min)
  15. 15. 142(3)-BBR Network in JapanToyonaka radar(Dual-pol) 135.455748E 34.804939N Nagisa radar 135.659029E 34.840145N SEI radar (from Aug., 2011) 135.435054E 34.676993N Osaka area, Japan
  16. 16. 15Initial Observation ResultsIntegration clearlyshows precipitation Integration by usingpatterns because of simple geometricthe high temporal weighting averageresolution 13:54:05 09/14/10
  17. 17. 16Initial Observation Results 13:55:09 09/14/10
  18. 18. 17Initial Observation Results 13:56:13 09/14/10
  19. 19. 18Initial Observation Results 13:57:17 09/14/10
  20. 20. 19Initial Observation Results 13:58:21 09/14/10
  21. 21. 20Initial Observation ResultsClear shapesof precipitationare retrieved Thin-plate shaped pattern of the BBR still remains
  22. 22. 21Conclusion• We developed the Ku-BBR Network, a short-range and high-resolution weather radar network. – Range resolution of several meters – Temporal resolution of 1 min per volume scan (30 elevations) – Coverage of 50 – 15000 m in range• Due to the high spatial resolution, reflectivity is accurately measured. – Reduction of the error from non-uniform beam filling – Very low Minimum detectable height• Precipitation patterns are clearly shown by data integration – With a use of a simple weighting average – Due to the high temporal resolution – Integrated products will be improved by a network radar signal processing
  23. 23. 22Thank you for listening! End
  24. 24. 23Initial Observation ResultsIntegration clearlyshows precipitation Integration by usingpatterns because of simple geometricthe high temporal weighting averageresolution 13:54:05 09/14/10
  25. 25. 24Initial Observation Results 13:55:09 09/14/10
  26. 26. 25Initial Observation Results 13:56:13 09/14/10
  27. 27. 26Initial Observation Results 13:57:17 09/14/10
  28. 28. 27Initial Observation Results 13:58:21 09/14/10
  29. 29. Introduction Resolution of the BBR ○Fronts, Hurricanes ○Cumulus, Tornadoes, ○Mesocyclones, Microbursts Supercells, Squall lines △Smaller phenomena?? ○Thunderstorms, Macrobursts 1min Severl metersIntroduction 28
  30. 30. 29 Introduction The BBR Network ○ Unobservable area in low altitudes ⇒ in 0 deg elevation 300 km range : 5 km altitude 100 km range : 0.5 km altitude 15 km range : 14 m altitude ○ Needless area to be observed ⇒ Troposphere is below 8 through 15 km altitude <Conventional Radar> ○ High resolution ⇒ Range resolution : several meters 3-dB beam width : 3 deg Temporal resolution : 1 min/VoS ○ Cover large area with multi radar × Precipitation attenuationIntroduction
  31. 31. 30 Outline 1. The Ku-band BroadBand Radar (The Ku-BBR) – Theory – Observation accuracy – Initial observation result 2. The Ku-BBR Network – Theory – Deployment in Osaka – Initial Observation Result 3. A Kalman Filter Approach for Precipitation Attenuation Correction in the Ku-BBR Network – Algorithm – Simulation ResultOutline
  32. 32. 31 Ch.1: The Ku-BBR 1. The Ku-band BroadBand Radar (The Ku-BBR) – Theory – Configuration – Initial observation result 2. The Ku-BBR Network – Theory – Deployment in Osaka – Initial Observation Result 3. A Kalman Filter Approach for Precipitation Attenuation Correction in the Ku-BBR Network – Algorithm – Simulation ResultChapter 1 The Ku-BBR
  33. 33. 32 Concept High resolution Wide Band Width solution Pulse compression solution Ku-band Gain : BT ・Band width of 80 MHz c ⇒ Range resolution of 2 m (max) Range Resolution : 2B B :Band width (Hz), T :Pulse width (sec), ・In C- and X-band, it is difficult to c :Light speed (m/sec) obtain wide band width in Japan. Precipitation Attenuation Wider Area & Higher Accuracy solution Designed as a short solution Radar network range polarimetric radar ・Higher accuracy in overlapped area ・Polarimetric parameters are more sensitive in Ku-band than other low ・Network approach for precipitation frequency radars. attenuation correctionChapter 1 The Ku-BBR
  34. 34. Pulse Compression for Distributed Particles • Pulse Compression – gives us sufficient energy on a target for detection with high range resolution and signal-to-noise ratio (SNR). – is equivalent to cross correlation between transmitted and received signals (almost equal to auto correlation). High power & Low power & short-duration pulse long-modulated pulseChapter 1 The Ku-BBR 33
  35. 35. Pulse Compression for Distributed Particles • Radar Equation for Precipitation Particles with Pulse Compression • Radar equation P G 2 h c 3  2  1 2 (received power) does Pr r   10 t Z 2 ln 2l r   2 2 2 2 NOT actually change. • High range resolution responding wide band width is accomplished. 2 2 c  3 Pt B G  h   2 • High SNR due to long B  1Pr r   pulse reduces to Z 2 ln 2 l r 10 2 2  2 2 integrate pulses and allows one to scan rapidly.Chapter 1 The Ku-BBR 34
  36. 36. 35 Configuration D/A Converter generates IQ signals Bistatic Lens Antenna arbitrarily. (170 MHz (max), 14 bit) (36 dBi, 3 deg) Signal Processing Unit IF Unit Antenna Unit I Amp Mixer HPA Horn D/A IQ Memory Q PC Modulator D/A 14bit Rotary 13.75 Luneberg 170MHz LO 2GHz LO Joint GHz Lens I A/D DSP IQ De- Board Q modulator A/D Amp Mixer LNA Horn Digital Baseband IF (2GHz) Ku (15.71-9 GHz) Real-time Digital Signal Processing for Pulse Compression and Doppler Spectrum EstimationChapter 1 The Ku-BBR
  37. 37. 36 Bistatic Lens Antenna System About 1450 mm Luneburg Lens (φ450 mm) Primary Feed Sumitomo Honeycomb Sandwich Redome High Power Transmitter Low Noise Amplifier • Direct Coupling level – Bistatic antenna (-70 dB) < Monostatic antenna with a duplexer – The nearest range is 50 m because we don’t need to turn the receiver off. • Simple Construction for Fast ScanningChapter 1 The Ku-BBR
  38. 38. 37 Bistatic Lens Antenna System Azimuth rotation 40 RPM (max) 30 RPM (usual) Elevation rotationChapter 1 The Ku-BBR
  39. 39. 38 Signal Processing Unit Pulse compression Signal Processing Unit Doppler estimation PC DSP boards A/D converter Former process Latter processI Received signal 32bit 32bit (I and Q) FFT integer floatQ (32768 points) 16bit integer Pulse Clutter Power, compression filter & Velocity, IFFT output FFT Memory (32768 poins) (64 points) Doppler Spectral width * (I and Q) moment 32bit float Reference signal 16bit integer estimation (I and Q) FFT 64 times 8192 times (32768 points) /segment /segment 16bit integer • First: Pulse compression, Second: Doppler spectrum estimation • Parallel calculation with DSPs – First: 32 DSPs (32 bit integer, 1 GHz), Second 3 DSPs (Float, 300 MHz) New FPGA system is in the process of production.Chapter 1 The Ku-BBR
  40. 40. Specifications Name Specifications Remarks System Operational Frequency [GHz] 15.75 Operational Mode Spiral, Conical, Fix Band Width[MHz] 80 (max) 15.71 - 15.79 GHz Coverage Az / EL [deg] 0-360 / 0-90 Coverage Range [km] 15 km (16 dBZ) variable Resolution Az / El [deg] 3/3 Range [m] 2 (min) variable Time/1scan [sec] 60 Power Consumption [kVA] 4kVA (max) Weight [kg] 500kg (max) Antenna Luneberg Lens Gain [dBi] 36 Beam Width [deg] 3 Polarization Linear Cross Polarization [dB] 25 (min) Antenna Noise Temp. [K] 75 (typ) Transmitter Transmission Power [W] 10(max) & Receiver Duty Ratio variable Noise figure [dB] 2 (max) Signal D/A 170MHz - 14bit IQ 2ch Processiong A/D 170MHz - 14bit IQ 2ch Range Gate [point] 32768 PRT [us] variableChapter 1 The Ku-BBR 39
  41. 41. 40 Observation Accuracy Cross-validation with Joss- Waldvogel Disdrometer (JWD) •Impact type disdrometer •Rain drop diameter: 0.3 - 5.0 mm •Ze is calculated by Mie theory 4   2 2   1  b Ze  5  ( D)  N ( D)dD N(D): DSD 2  b : Back Scattering coefficient 2  b    1 2l  1al  bl  l from mie scattering 4 l 1Chapter 1 The Ku-BBR
  42. 42. 41 Observation Accuracy Scatter plot 40 HistogramZm of BBR (dBZ) 30 20 15 20 (%) 10 10 5 0 0 0 10 20 30 40 -10 -5 0 5 10 Ze of JWD (dBZ) Zm – Ze (dBZ) • The high resolution reflectivity measurements of the BBR show fairly good agreement compared with JWD. – Correlation coefficient: 0.95, Standard deviation: 1.59 dBZChapter 1 The Ku-BBR
  43. 43. 42 Comparison to C-band Radar Highly attenuated Tanegashima, Kagoshima Fine structures are shown C-band radar (El = 0.09deg) The Ku-BBR (Base scan)Chapter 1 The Ku-BBR
  44. 44. 43 Observation Result (Time series example) Toyonaka, Osaka universityChapter 1 The Ku-BBR
  45. 45. 44 Example –tornado? 2nd elevation (in altitudes of 0 m – 270 m, roughly)Chapter 1 The Ku-BBR
  46. 46. 45 Example –tornado? 3rd elevation (in altitudes of 0 m – 450 m, roughly)Chapter 1 The Ku-BBR
  47. 47. 46 Example –tornado? 4th elevation (in altitudes of 0 m – 640 m, roughly)Chapter 1 The Ku-BBR
  48. 48. 47 Example –tornado? 5th elevation (in altitudes of 0 m – 820 m, roughly)Chapter 1 The Ku-BBR
  49. 49. 48 Example –tornado? 6th elevation (in altitudes of 0 m – 1000 m, roughly)Chapter 1 The Ku-BBR
  50. 50. 49 Example –tornado? 7th elevation (in altitudes of 0 m – 1190 m, roughly)Chapter 1 The Ku-BBR
  51. 51. 50 Example –tornado? 8th elevation (in altitudes of 0 m – 1370 m, roughly)Chapter 1 The Ku-BBR
  52. 52. 51 Example –tornado? 9th elevation (in altitudes of 0 m – 1550 m, roughly)Chapter 1 The Ku-BBR
  53. 53. 52 Example –tornado? 10th elevation (in altitudes of 0 m – 1610 m, roughly)Chapter 1 The Ku-BBR
  54. 54. 53 Summary of Chapter 1 • We developed the Ku-BBR, a short-range and high-resolution weather radar. – Range resolution of several meters – Temporal resolution of 1 min per volume scan (30 elevations) – Coverage of 50 – 15000 m in range • Due to the high spatial resolution, reflectivity is accurately measured. – Reduction of the error from non-uniform beam filling – Very low Minimum detectable height • The Ku-BBR finely detected a small phenomena not resolved by a conventional C-band radar. • The BBR is a strong tool to detect, analyze and predict these small scale phenomena.Chapter 1 The Ku-BBR
  55. 55. 54 Ch.2: The Ku-BBR Network 1. The Ku-band BroadBand Radar (The Ku-BBR) – Theory – Observation accuracy – Initial observation result 2. The Ku-BBR Network – Theory – Deployment in Osaka – Initial Observation Result 3. A Kalman Filter Approach for Precipitation Attenuation Correction in the Ku-BBR Network – Algorithm – Simulation ResultChapter 2 The Ku-BBR Network
  56. 56. 55 Network Approach of the Ku-BBR • Single BBR Cross-range 785 m – Range resolution is resolution in 15 km range very high. – On the other hand, 524 m in 10 km range cross-range resolution is 100 times poorer 262 m than range resolution in 5 km range in a range of 15 km. – Composite average 3 deg 7.5 m function (CAF) is a with 20 MHz BW thin-plate shape. Range resolutionChapter 2 The Ku-BBR Network
  57. 57. 56 Network Approach of the Ku-BBR • The Ku-BBR Network – CAFs of two radar nodes are overlapped. – A weighting average of the two received powers is equivalent to that of the two CAFs. – Overlapping two thin- plate shapes achieves several meter About 10 x 10 mNode 1 resolution in 2- or 3-D. spatial resolution Node 2Chapter 2 The Ku-BBR Network
  58. 58. 57 Equations Single Radar Equation P r0     r I r0 , r dV Composite Average Function (CAF) Pt g  f    0 ,   0 Ws r0 , r  where, 2 2 4 2 I r0 , r   4 3 l 2 r r 4 Equivalent Reflectivity Factor   r     b D N D, r dD 0 dV  r 2 dr sin ddChapter 2 The Ku-BBR Network
  59. 59. 58 Network Approach of the Ku-BBR A Weighting Average of Received Signals Obtained by Each Radar Node ˆ N A weighting average of P r0    wn Pn r0  received signals n 1 N   wn   r I n r0 , r dV n 1 N is translated to…    r  wn I n r0 , r dV n 1    r I r0 , r dV ˆ A weighting average of N CAFs where, w n 1 n 1Chapter 2 The Ku-BBR Network
  60. 60. 59 Evaluation of Spatial Resolution • Simulation in a two-BBR network on base scan Meridional range (km) Overlapped area Node 2 Node 1 Zonal (-7, 0) (7, 0) range (km) Non-overlapped areaChapter 2 The Ku-BBR Network
  61. 61. 60 Evaluation of Spatial Resolution • Normalized CAF (NCAF) – Single radar Pt g 2 2  2 P r0   Ws r0 , r  f 4  ,  r drdd r 4 3 r02l 2 r0  0 0 0 2 Pt g 2 2  4  r 3 2 2 l r   Ar , r  r dr 0 NCAF 0 0 – Radar network ˆ Pt g 2 2 N P r0    r  2n An r0 , r dr w 4 3 l 2 r0   n1 r0n Averaged Pt g 2 2 NCAF  r Ar0 , r dr ˆ 4  l r0    3 2Chapter 2 The Ku-BBR Network
  62. 62. 61 Evaluation of Spatial Resolution • Assumptions of Two BBR Simulation – A Gaussian shape CAF  r  r0 2  : A Gaussian pulse  Ws  exp  ln 2   (ζ = 7.5 m (corresponding to B = 20 MHz))  rh 2 2      0 2     0 2  : A Gaussian beam f    0 ,  0   exp  4   exp   ln 2     22 ln 2    h 2 (ζ = 3 deg) 2   h  – A weighting function rn2 wn  N :is equivalent to arithmetic average of reflectivity r n 1 n 2 factors in anti-log – Precipitation at a point is simultaneously observed by each radar nodeChapter 2 The Ku-BBR Network
  63. 63. 62 Single BBR vs Two BBR Network NCAF Averaged NCAF in Single BBR (Node 1) in Two BBR Network 518 m 10+2 m cross-range spatial resolution resolution 7.5 m range resolution Thin-plate shaped CAF About 10 x 10 m resolutionChapter 2 The Ku-BBR Network
  64. 64. 63 Two BBR Network vs the Conventional Averaged NCAF Radar Network in Two BBR Network with Conventional Radars Spatial resolution is NOT improved because the range and cross-range resolutions are comparable.Chapter 2 The Ku-BBR Network
  65. 65. 64 Resolution Map (in 2-D) Resolution Square on Base Scan A spatial resolution of 10+2 m2 is accomplished in Almost all region On the other hand, spatial resolution is NOT improved around the base-line because two CAF are not crossed.Chapter 2 The Ku-BBR Network
  66. 66. 65 Deployment in Osaka Area, Japan Toyonaka radar (polarimetric) 135.455748E 34.804939N Nagisa radar 135.659029E 34.840145N SEI radar (from Jun., 2011) 135.435054E 34.676993N Osaka area, JapanChapter 2 The Ku-BBR Network
  67. 67. 66 Initial Observation Results 13:54:05 09/14/10Chapter 2 The Ku-BBR Network
  68. 68. 67 Initial Observation Results 13:55:09 09/14/10Chapter 2 The Ku-BBR Network
  69. 69. 68 Initial Observation Results 13:56:13 09/14/10Chapter 2 The Ku-BBR Network
  70. 70. 69 Initial Observation Results 13:57:17 09/14/10Chapter 2 The Ku-BBR Network
  71. 71. 70 Initial Observation Results 13:58:21 09/14/10Chapter 2 The Ku-BBR Network
  72. 72. 71 Summary of Chapter 2 • The Ku-BBR network accomplishes high spatial resolution in 2- or 3-D. – Crossed pattern of several thin-plate shaped CAFs accomplishes high spatial resolution of tens of meters in 2- or 3-D. – But spatial resolution around the baseline between two Ku-BBRs is not improved. – Three or more-BBR network accomplishes high spatial resolution in 3- D anywhere except for around the ground. • Initial observation results showed high quality images. – Precipitation patterns are clearly shown. – Validation of the integrated data is one of our near future works.Chapter 2 The Ku-BBR Network
  73. 73. 72 Ch.3: Precipitation Attenuation Correction 1. The Ku-band BroadBand Radar (The Ku-BBR) – Theory – Observation accuracy – Initial observation result 2. The Ku-BBR Network – Theory – Deployment in Osaka – Initial Observation Result 3. A Kalman Filter Approach for Precipitation Attenuation Correction in the Ku-BBR Network – Algorithm – Simulation ResultChapter 3 A Kalman Filter Approach for Precipitation Attenuation Correction in the Ku-BBR Network
  74. 74. 73 BackgroundHitschfeld – Bordan (HB) solution Path-IntegratedZ m r   Z r  exp 0.2 ln 10 r k s ds    0 Attenuation (PIA)     When assuming a static k-Z relation, k  Z Z m r   Z r  exp  0.2 ln 10 r Z r  ds      0   The equation is solved as… Z r   Z m r exp C  0.2 ln 10S r   1   S r    Z m s  ds r  0 However, it is known that HB solution often outputs large errors… Z : (un-attenuated ) reflectivity, Z m : measured (attenuated) reflectivity, k : specific attenuation (dB/km), C : integral constant
  75. 75. 74What is the problem?• Deterministic approach ー Deterministic approaches (represented by HB solution) accumulate estimate errors in each range bin, as processing in range. ー The solution is frequently unstable and overshoots. ⇒ Stochastic approach is better [Haddad et al., 1996].• k-Z relation ー Using a constant k-Z relation generates estimate errors of k in each range bin.• Raindrop size distribution (DSD) ー DSD is the most important parameter in radar meteorology. ー DSD determines k, Z, and so on (including rainfall rate, liquid water content…). ー However, it is very difficult to estimate DSD in each range bin.
  76. 76. 75Proposal• Stochastic approach ー A Kalman filter approach is applied. ー Uncertainties of estimates are also obtained.• Network ー Assuming the same profile at cross points of beams from two radars, estimates of two BBRs are integrated with use of corresponding uncertainties. ー Uncertainty is also important for data assimilation into weather numerical models (Our future work).• DSD estimation ー In the BBR, DSD in the lowest altitude of 50 m is accurately estimated, compared with a ground-based equipment of disdrometer [Yoshikawa et al., 2010]. ー Thus, the BBR can always obtain the initial condition of DSD.
  77. 77. 76Methodology – Basis<Observation (in dB)> Z e : (un-attenuated) reflectivity (dBZ) Z m (r )  Z e (r )  PIAr  Z m : measured (attenuated) reflectivity (dB)  PIAr   2 k s ds r k : specific attenuation (dB/km) 0 PIA : Path-integrated attenuation (dB)<Gamma DSD [Ulbrich, 1983]> N ( D)  N0 D  exp  D  N0  6 103 exp 0.9 <Extended Kalman Filter> Prediction : r  r   r   w  exp   : 1 1  exp    r  r    r   w PIAr  r   PIAr   2rk  r , r  Filtering : Z m r   Ze  r , r   PIA(r )  v(r )
  78. 78. 77Methodology – Processing (1/2) BBR1 BBR2 Step 1: Single-path retrieval Step 2: Retrievals in each beam Trading estimate values starting with initial condition only at cross points
  79. 79. 78Methodology – Processing (2/2) Step 3: Retrievals in each beam starting with traded cross points Step 4: Network retrieval BBR1 Optimal estimation with use of all filtering
  80. 80. 79Simulation Model True Ze True Ze on BBR1 Zm on BBR1 True Ze on BBR2 Zm on BBR2BBR1 BBR2
  81. 81. 80Simulation Result – Reflectivity Underestimateon BBR1 (dBZ) Retrieved Ze Underestimate BBR2 (dBZ) Retrieved Ze onSingle-path retrieval Step 1 Retrieved Ze on BBR1 (dBZ) Retrieved Ze on BBR2 (dBZ)Network retrieval Unstable a little Corrected! Step 4
  82. 82. 81Simulation Result – Comparison to HB solution OvershootZe on BBR1 (dBZ) Retrieved Overshooton BBR2 (dBZ) Retrieved ZeHB solutionNetwork retrieval Retrieved Ze on BBR1 (dBZ) Retrieved Ze on BBR2 (dBZ)
  83. 83. 82 Summary of Chapter 3 • A Kalman filter approach for precipitation attenuation correction in the Ku-BBR network is developed. – Gamma DSD – Stochastic approach with a use of Kalman filter – Cyclic optimization in the radar network environment. • Simulation shown a high estimate accuracy. – Ze is accurately retrieved. – It is difficult to retrieve DSD parameters accurately. – Under consideration to use polarimetric parameters • Estimate accuracy depends on variances of DSD parameters (now under tuning). • Cross validation with disdrometer is our future work.Chapter 3 A Kalman Filter Approach for Precipitation Attenuation Correction in the Ku-BBR Network
  84. 84. 83Thank you for listening! End
  85. 85. 84Back up
  86. 86. 観測可能高度 ref) Houze (1993), Doviak (1993) 地形により更に 制限を受ける. 85
  87. 87. Ku帯広帯域レーダの観測 空間分解能• パルス圧縮レーダの距離分解能 r  c  2[m]  B  80[MHz] 2B• アンテナ指向性 2 h  3[deg] 86
  88. 88. Cバンドレーダとの比較(旧ver.) : Zm of C-band radar : Zm of BBR : Retrieved Ze of BBR (an old version) 87
  89. 89. Cバンドレーダとの比較(旧ver.) 88
  90. 90. Precipitation Attenuation –Mie solution The solution for back scattering cross section and absorption cross section for a water particle of dielectric sphere is due to Mie. s r 2 2    s (n,  )  2  (2l  1) Re al  bl    l 1 2 2   Function of Refractive index e 2  and rain drop   e (n,  )  2  (2l  1) Re{al  bl }  diameter r 2  l 1  s : scattering cross section,  e : extinction cross section, al , bl : recursion formula for Bessel function, n : refractive index,   2r Chapter 3 A Kalman Filter Approach for Precipitation Attenuation Correction in the Ku-BBR Network
  91. 91. 90 Precipitation Attenuation –Equation Measured Equivalent Path-Integrated Reflectivity Reflectivity Attenuation (PIA) Z m r   Z e r   2 10  k s ds r 3 k : Specific attenuation (dB/km) 0 range r (m) Ze Attenuation ZmChapter 3 A Kalman Filter Approach for Precipitation Attenuation Correction in the Ku-BBR Network
  92. 92. 91 Traditional Method for Correction Assumptions • B.C.: Z m r0   Z e r0  known • k-Z relation: k r   Z e r  unknown 0 (min range bin) 1 2 ・・・ Zm0 Zm1 Zm2 ・・・ B.C. Ze0=Zm0 Ze1=Zm1+PIA1 Ze2=Zm2+PIA2 ・・・ k-Z relation k0=αZe0β k1=αZe1β k2=αZe2β ・・・ PIA1=k0 PIA2=PIA1+k1 PIA3=PIA2+k2 ・・・Chapter 3 A Kalman Filter Approach for Precipitation Attenuation Correction in the Ku-BBR Network
  93. 93. 92 What is problem?• k-Z relation – is dependent on DSD k-Z relation Specific attenuation k (dB/km) 101 – yields errors in each Ku range bin if its coefficients X 100 are constant. C 10-1• Attenuation at Ku-band – 10 – 100 times more than 10-2 C-band 10-3 – 4 – 6 times more than X- band 10-4 10 20 30 40 50• Deterministic Approach Reflectivity Ze (dBZ) – Errors in k-Z relation are accumulated. ⇒Output is more unstable in longer ranges or with heavier precipitation.Chapter 3 A Kalman Filter Approach for Precipitation Attenuation Correction in the Ku-BBR Network
  94. 94. 93 Proposed Algorithm • Assuming a Gamma DSD N ( D)  N0 D  exp  D n n+1  N0  6 103 exp 0.9  Nn(D) Nn+1(D)=Nn(D) • No attenuation in the nearest range PIA(r0 )  0 PIAn PIAn+1=PIAn+kn Zmn+1 • Applying an extended Kalman filter Prediction :  1    w n n  exp   : 1 1  exp   n1  n  w PIAn1  PIAn  2rk n ,   n Filtering : Z m n  Z e n ,    PIAn  vn nChapter 3 A Kalman Filter Approach for Precipitation Attenuation Correction in the Ku-BBR Network
  95. 95. 94 Simulation –in single BBR • HB solution – overestimates in large area Truth – approaches infinity in strong precipitation • Proposed algorithm – underestimates behind strong precipitation HB solution Proposed algorithm 15 15 meridional (km) 10 10 5 5 Overestimation ⇒ Infinity Underestimation 0 0 behind strong precipitation -10 -5 0 5 10 -10 -5 0 5 10 zonal (km) zonal (km)Chapter 3 A Kalman Filter Approach for Precipitation Attenuation Correction in the Ku-BBR Network
  96. 96. 95 Algorithm in the BBR Network BBR1 BBR2 Step 1: Single-path retrieval Step 2: Data Trade corrects in each path with the exchanges corrected boundary condition. data at cross pointsChapter 3 A Kalman Filter Approach for Precipitation Attenuation Correction in the Ku-BBR Network
  97. 97. 96 Algorithm in the BBR Network Step 3: Retrieval with Traded Data makes other correction data with Kalman algorithm starting from each traded data Step 4: Network retrieval BBR1 optimally averages all the correction data with covariance informationChapter 3 A Kalman Filter Approach for Precipitation Attenuation Correction in the Ku-BBR Network
  98. 98. 97 Simulation –in the BBR network • Errors remaining in Step 1 is properly Truth re-corrected with a use of radar network environment. Step 1: Single-path retrieval Step 4: Network retrieval 15 15 meridional (km) 10 10 5 5 0 0 -10 -5 0 5 10 -10 -5 0 5 10 zonal (km) zonal (km)Chapter 3 A Kalman Filter Approach for Precipitation Attenuation Correction in the Ku-BBR Network
  99. 99. 98 Seminar in CSU Luneburg Lens AntennaJun. 24, 2011Ei-ichi Yoshikawa
  100. 100. Lens Antenna FamilyDielectric (delay) lens E-plane metal plate (fast) lens http://www.engineering-eye.com/rpt/c003_antenna/popup/04/image020.html 99
  101. 101. What is Luneburg Lens?“Luneberg lens” is spherical lens for microwave. 1944 Proposal as optical lens Principle Luneberg Lens Microwave Focal point Dielectric constant 2 2.0 r Er = 2 - 1.8 R 1.6 1.4 Er : Dielectric Constant R.K.Luneberg 1.2 r : Radius 1.0 1960~ Investigation as lens R : Radius of lens for microwave 0 0.2 0.4 0.6 0.8 1 (Center) (Surface) Relative radius of lens Hybrid Products Division 100
  102. 102. Snell’s law –excerpt from wikipediaSnells law states that the ratio of the sinesof the angles of incidence and refraction isequivalent to the ratio of phase velocities inthe two media, or equivalent to the oppositeratio of the indices of refraction: sin ������1 ������1 ������2 = = sin ������2 ������2 ������1 ������ : angle measured from the normal ������ : velocity of light in the respective medium ������ : refractive index of the respective medium 101
  103. 103. How to focus?Simulation by a discretized functionof dielectric constant 102
  104. 104. How to focus?Simulation by a discretized functionof dielectric constant 103
  105. 105. How to focus?Simulation by a discretized functionof dielectric constant 104
  106. 106. Focused Beam 105
  107. 107. Many Focal Points 106
  108. 108. Typical Application of Luneburg Lens Luneberg Lens Frequency Range : VHF - Ka-band (45MHz) (50GHz) Polarimetric capability isolation > 30dB Primary feed Several primary feeds are Move a primary feed installed in advance along to lens surface Multi-beam Antenna High Speed beam scanning Antenna Hybrid Products Division 107
  109. 109. Construction Ideal construction SEI lens construction High accuracy An exampleDielectric constant Dielectric constant original analysis tool 2.0 2.0 (FDTD method) 1.8 1.8 1.6 1.6 1.4 1.4 1.2 1.2 1.0 1.0 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 (Center) (Surface) Relative radius of lens Relative radius of lens Gradation of dielectric constant ∥ Difficult to form the lens Piling up several Layers Hybrid Products Division 108
  110. 110. Material SEI materialUse special materials Special PP+special Ceramic SEI lens Special ceramic = fiber shape High performance High productivity Able to high Low weight 2.5 Dielectric constant (Er) dielectric constant 2 General material (PS) εr≧1.7 Impossible to form Conventional lens 1.5 Center layers = High gravity bead foams are Impossible adhered by adhesives to form 1 0 0.2 0.4 0.6 0.8 1 Low performance (adhesives: high tanδ) Specific gravity Hand made →high cost High weight Hybrid Products Division 109

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