TC intensity estimation using Satellite data at JMA

Transcription

1 SECOND INTERNATIONAL WORKSHOP ON SATELLITE ANALYSIS OF TROPICAL CYCLONES (IWSATC-II) TC intensity estimation using Satellite data at JMA Topics: 1) Estimation of TC central pressure using Microwave Sounder 2) Research on Maximum Sustained Wind estimation using SSMIS Microwave Imager 3) Researches on TC intensity analysis using Atmospheric Motion Vectors from Himawari-8 4) Other work toward further improvement (Doppler radar) Ryo OYAMA and Koji KATO Japan Meteorological Agency

2 Estimation of TC central pressure using Microwave Sounder AMSU-based estimation(in operation since 2013) CONSENSUS between Dvorak and AMSU estimates ATMS-based estimation

3 Dvorak TC cloud pattern observed by infrared channel of GEO satellite AMSU (Oyama 2014) TC warm core intensity observed by AMSU-A of NOAA and MetOp satellites TB anomaly for Ch6(~400hPa) TB anomaly for Ch7 (~250hPa) TB anomaly for Ch8 (~180hPa) support Bolaven (1215), 18UTC 26August Central pressure = 940 hpa Max Max Max Max (Warm core intensity) AMSU added since May Hydrostatic equilibrium theory TC Central pressure 3

4 Error corrections of warm core intensity Error due to low spatial resolution (~48 km) of AMSU-A observation TC central pressure (MSLP) estimation MSLP = SLOPE (warm core intensity) + OFFSET Errors corrected underestimation of warm core intensity (K) TB attenuation error due to ice particles (c) Ch8 AMAX_DIFF(K) Ch8-3 y = x Number = SIW AMAX MW Scattering Index (SIW) MSLP estimation equation is derived using 365 observations with reference to JMA best track data in 2008.

5 CONSENSUS between Dvorak and AMSU TC central pressure estimates Validation result of MSLP estimates (hpa) to JMA best track for TCs in (Number of data =1413) Dvorak AMSU CONSENSUS (Dvorak + AMSU) RMSE BIAS RMSE BIAS RMSE BIAS CONSENSUS: Weighted average of two MSLP estimates using weights (defined as 1/RMSE) derived for each TC cloud pattern (hpa) BestTrack Dvorak AMSU CONSENSUS Bopha (1224) 11/ / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /10 00 Month/Day Hour (UTC) T1224, Number of data = 35 RMSE BIAS Dvorak AMSU CONSENSUS

6 Estimation of TC central pressure using ATMS/NPP (Future plan) AMSU method ATMS method Resolution (FOV) 48km 32km Swath 2200 km (30FOVs) 2600 km (96FOVs) Channels for warm core observation Ch8 (55.5GHz), 180hPa Ch9 (55.5GHz) Ch7 (54.94GHz), 250hPa Ch8 (54.94GHz) Ch6 (54.5GHz), 400hPa Ch7 (54.5GHz) Ch6 (53.596GHz) Scheme Oyama (2014) Based on AMSU method AMSU Max TB Anomaly (K) AMSU y = x R² = 0.79 Validation results to best track MSLP for 2015 To AMSU(NOAA-18) AMSU ATMS Bias RMSE Bias RMSE Number ALL MSLP < 950hPa hPa MSLP <970hPa hPa MSLP <990hPa hPa MSLP To AMSU(NOAA-19) AMSU ATMS Bias RMSE Bias RMSE Number ALL MSLP < 950hPa hPa MSLP <970hPa hPa MSLP <990hPa hPa MSLP ATMS Max TB Anomaly (K) Central Pressure (hpa) ATMS y = x R² = Central Pressure (hpa)

7 Research on Maximum Sustained Wind (MSW) estimation using SSMIS Microwave Imager Courtesy of Mr. T. Sakuragi, MRI/JMA

8 Data: Outline of MSW estimation using SSMIS Microwave Imager SSMIS TB data for 19 (H/V), 23 (V), 37(H/V), 91 (PCT) GHz JMA best track data (MSW (10-minute mean wind)) as reference To derive MSW estimation equation: 1. To derive TB parameters (maximum, mean, minimum, area) for several circles and annuli (within radius of 2 degrees) centered at TC center for TCs in To analyze principal components representing TB pattern using the TB parameters. 3. To derive a regression equation to explain MSW by using the most contributive 6 principal components obtained in Step 2.

9 MSW estimation equation (from SSMIS observations for ) : MSW= PC1+0.15PC2-0.31PC PC4+0.59PC5-0.13PC6 PC1~PC6: Principal component scores for 1 st ~6 th principal component Validation result for y=x Estimate(m/s) y = 0.65 x Number= 969 RMSE= 6.28 m/s Corr= 0.81 Bias= JMA best track data (m/s)

10 Vmax estimate (m/s) NEOGRI (1408) F16 F17 F18 0 7/2 7/4 7/6 7/8 7/10 7/12 7/14 Dependence of MSW estimate on TC life stage is under investigation. Quality of MSW estimate may be improved by optimizing TB parameters.

11 Researches on TC intensity analysis using Atmospheric Motion Vectors from Himawari-8

12 Himawari-8 Operation Status and Imagery Calibration/Navigation Monitoring from MSC Web The status of navigation and calibration is good! Himawari-8 Operation Status Imagery Calibration Imagery Navigation 12

13 Estimation of sea surface wind using low-level AMVs Courtesy of K. Shimojiand K. Nonaka, Meteorological Satellite Center / JMA

14 AMVs (New algorithm for Himawari-8) UTC MTSAT-2 AMV QI>0.85 Yellow : IR1-low Red : IR1-upper Blue : WV clear and cloudy Target box size : 5x5 pixel Method : Maximum Likelihood Estimation method Grid : 0.2 degree

15 ASCAT-Adjusted low-level AMVs to retrieve Sea Surface Winds Hourly AMVs are derived from 10 minute satellite imageries. B03(0.64µm), B07(3.9µm), B13(10.4µm) channels are used. Himawari-8 AMV data ASCAT 0.76 [Low-Level AMV] Arrows: Himawari-8 low-level AMVs

16 Statistical Result (AMV v.s. ASCAT) RMSE 1.25[m/s] BIAS -0.30[m/s] K.Nonaka and K.Shimoji ( Meteorological Satellite Center - Japan Meteorological Agency )

17 Good Agreement with ASCAT, T1516 ATSANI T1516 ATSANI AMV Adjusted- Sea Surface Wind ASCAT( >=30kt ) 30kt Wind Area Circle (Besttrack)

18 Disadvantages Less AMVs and poorer quality around the CSC. Less AMVs during nighttime.( B03 Visible Images are not available. ) Less AMVs around the CSC (T1516 ATSANI)

19 Disadvantages Less AMVs and poorer quality around the CSC. Less AMVs during nighttime.( B03 Visible Images are not available. ) T1525 CHAMPI 2016/10/17 00Z Daytime T1525 CHAMPI 2016/10/16 20Z Nighttime

20 Poorer quality in Higher Latitude. Problem T1523 CHOI-WAN 2016/10/08 00Z 39.5N 148.5E AMVs around 50[kt] ASCAT 45[kt] Not good agreement with ASCAT

21 Toward Operational Use Refine regressions to reduce differences between ASCAT and AMV. e.g. Dependence on Latitude and AMV heights More Frequent AMVs Feasibility of Rapid Scan Pictures 10 min Normal Scan AMV 2.5 min Rapid Scan AMV T1514 MOLAVE More AMVs with Rapid Scan

22 Diagnosis of TC intensity using upper-tropospheric AMVs

23 Purpose: To diagnose TC intensification by capturing uppertropospheric wind fields which are controlled by upward fluxes of mass and angular momentum by convection. Data: High-level IR-AMVs and WV-AMVs over cloudyregion (above 400 hpa level, QI>0.3) from imagery of geostationary satellites MTSAT and Himawari-8 (interval of imagery: 5-15 min) Cyclonic 台 Outflow Anti-Cyclonic Upward transport of angular momentum Method: Step 1: Integrated AMV dataset of high-level IR- AMV and WV-AMV (called high-level AMV ) prepared Step 2: Azimuthally averages of tangential and radial components of the AMVs for annuli of 6 radii (50, 100, 150, 200, 250 and 300km) derived Step 3: Max tangential wind (UMaxWind) and radial wind (UMaxOutflow) from azimuthally averaged wind components are used for diagnosing TC intensity and structure. R=50km R=100km R=150km R=200km R=250km R=300km

24 Preliminary results for 27 TCs in Using MTSAT-2 high-level AMVs Max Tangential Wind (UMaxWind) v.s. Best Track MSW Maximum Sustained Wind (m/s) UMaxWind (m/s) QC NoQC 多項式 (QC) y=0.03x x COR= 0.73 Upper-tropospheric and surface tangential winds are highly correlated. Max Radial Wind (UMaxOutflow) v.s. Max TC Developing Rate (each plot denotes TC) UMaxOutflow in Develooing stage (m/s) y = 0.29x COR = Max TC Developing rate (m/s/day) Rapid intensification tends to accompany with stronger upper-tropospheric outflow. Oyama, R., K. Shimoji, and M. Sawada (2015), AOMSUC6

25 Use of Himawari-8 AMVs Full disk Interval : 10 minutes (6 times per hour) Region 1 JAPAN (North-East) Interval : 2.5 minutes (4 times in 10 min) Dimension : EW x NS: 2000 x 1000 km Hourly high-level AMVs derived from Region3 imagery (B10+B13) at 5-min intervals, TC Goni(1515) Region 1 NE-JAPAN Region 2 SW-JAPAN Region 3 Target area Region 2 JAPAN (South-West) Interval : 2.5 minutes (4 times in 10 min) Dimension : EW x NS: 2000 x 1000 km Region 3 Target Area Interval : 2.5 minutes (4 times in 10 min) Dimension : EW x NS: 1000 x 1000 km Courtesy of Meteorological Satellite Center/JMA

26 Trial to estimate MSW using upper-tropospheric AMVs for Himawari-8 & MTSAT /1218 5/1300 5/1306 5/1312 5/1318 5/1400 5/1406 5/1412 5/1418 5/1500 5/1506 5/1512 5/1518 5/1600 5/1606 5/1612 5/1618 5/1700 5/1706 5/1712 5/1718 5/1800 5/1806 5/1812 5/1818 5/1900 5/1906 BestTrack, Estimation (m/s) Uppertropospheric max tangential wind (UMaxWind) -> MSW Dolphin (1507) BestTrack MSW Estimate (Himawari-8) MSW estimate (MTSAT-2) UTC Estimate = 0.03 x UMaxWind x UMaxWind : derived using MTSAT-2 AMV Himawari-8 High-level AMV(Band10+Band13) m/s Oyama, R., K. Shimoji, and M. Sawada (2015), AOMSUC6

27 Other work for further improvement: Doppler Radar TC Intensity Estimates Courtesy of U. Shimada, Meteorological Research Institute / JMA

28 Doppler Radar Intensity Estimates (1) Doppler Velocity TC Wind Speed Doppler Velocity [m/s] z=2km TC center z=2km (2) Wind Speed Central Pressure Wind Speed (i) Using gradient wind balance, isobars are deduced. Radar location Using the GBVTD technique TC Wind Speed [m/s] z=2km Surface Isobars Weather Station SLP observation=970 hpa (iii) Estimated MSLP=930 hpa

29 Doppler Radar Intensity Estimates Doppler Radar MSLPs vs. Best Track MSLPs N 5574 BIAS [hpa] R Estimated MSLPs [hpa] RMSD [hpa] Conventional Methods Methods Dvorak (Koba 1990) Dvorak (Martin and Gray 1993) RMSE [hpa] Dvorak (Velden et al. 2007) 11.7 AMSU (Oyama 2014) 10.1 AMSU with CIMSS (Velden et al. 2007) 7.5 AMSU with CIRA (Velden et al. 2007) 10.3 Characteristics of the DR method Y=0.904X Best Track MSLPs [hpa] The accuracy is comparable to or better than the accuracies of Dvorak and AMSU methods. For TCs with a RMW of km, the estimates had a RMSD (bias) of 5.55 hpa (0.69 hpa). The accuracy was higher when the distances between the TC center and the radar location was shorter.

30 Thank you!

31 Backup slides

32 Image Navigation for band 13 (10.4µm) Himawari-8 Image Navigation Estimated from Coast Line Analysis Image navigation accuracy is mostly less than 0.3 pixels Scale: one pixel 32

33 Validation of IR Bands Calibration based on GSICS inter-calibration Radiance Tb Bias Brightness Temp. (Tb) Tb Bias * Standard Radiance was calculated under clear sky condition over the ocean in nighttime by RTTOV 11.2 with US standard atmosphere (1976)