Derrick Herndon and Chris Velden University of Wisconsin - Madison Cooperative Institute for Meteorological Satellite Studies

Transcription

1 Derrick Herndon and Chris Velden University of Wisconsin - Madison Cooperative Institute for Meteorological Satellite Studies Presented at International Workshop on Satellite Analysis of Tropical Cyclones February, 2016 Honolulu, HI AMSU ARCHER SSMIS ATMS

2 Motivation Dvorak may not work well for all TC structures/phases Increasing the number of quality intensity estimates reduces uncertainty and increases current intensity confidence -> Better Forecast X T3.5 Morokat 2009 Bolaven 2012 Halong hours RI T5.5?

3 In order to account for storms with different structures an all the above approach is needed. Multiple satellite scanning strategies (Geo/LEO) Multiple channels to measure the various TC features that are related to intensity. (subjective/objective) Geostationary Intensity Position Structure MW Imager Position Structure Intensity? MW Sounder Intensity Structure

4 Derive Tb anomalies from mw temperature sounders. Channel 9 Channel 8 Channel 7 Channel 6 Vertical Cross Section of Tb Anomalies for Typhoon Lekima AMSU-A Weighting Functions for channels 3-10

5 Flown aboard NOAA 15-19, METOP A/B, Aqua, FY Series 2 Instruments: AMSU-A (temperature) AMSU-B/MHS (moisture) Primary channels of interest are 5-8 AMSU-A and channel 16 on AMSU-B Flown aboard DMSP F16-F19 Both sounder and imager channels Primary channels of interest are channels 3-5 (sounder) and channels (imager) ATMS is similar Pressure

6 Summary of Current Operational Temperature Sounder Resolution (km) Moisture Sounder /Imager Resolution (km) Swath Width (km) # of Sats Scan Type AMSU 48 (nadir) 79 x 149 (limb) 16 (nadir) 27 x 52 (limb) * Crosstrack SSMI S Conical ATMS 32 (nadir) 70 x 137 (limb) 16 (nadir) 30 x 68 (limb) Crosstrack * NOAA15 AMSU-B failed 2011, NOAA 19 AMSU-A CH8 noisy since 2009 METOP-A AMSU CH7 failed SSMIS F-18 and F-16 sounders failed in 2015 FY-3C MWTS-II failed in 2015

7 Initial estimate of TC center CIMSS Algorithm General Approach - Warning agency forecast - ARCHER Locate warmest pixel Estimate environmental temperature - filter out unrepresentative temps X Calculate temperature anomaly Use regressions for each channel to estimate pressure anomaly Use estimate of eye size to correct initial pressure anomaly estimate Estimate Vmax using pressure anomaly, latitude, storm size, Tb gradient and motion Sounder FOV

8 Eye Size Bias Correction: Account for Eyewall Slope Vertical Cross Section of Tb Anomalies for Typhoon Lekima Channel 6 Channel 5 Channel 4 Channel 3 Eye size bias correction for each channel accounts for Eyewall slope. Currently 45 degree eyewall slope is assumed however recent research suggests eyewall slope may change with TC eye size Smaller eye = steeper slope Channels for SSMIS

9 AMSU - Address loss of AMSU-B on N15 - Update limb corrections - Added Metop-B - ARCHER II-based eye size Developed and added SSMIS Added S-NPP ATMS Changes Since IWSATC 2011 Evaluation of upper level temperature observations From field campaigns -> improve sounder-based estimates

10 Parameters That Contribute to Vmax Estimates Inner core Tb gradient contribution to Vmax estimate. Two different inner core Tb gradients for these storms. Related to Holland B parameter (Holland 1980) Inner core organization. Energy transfer efficiency. Same MSLP for these two storms but different Vmax. Use ARCHER intensity score to adjust Vmax 91 Ghz Imagery Storm motion. Some portion of the motion is imparted on Vmax. The amount contributed varies with storm intensity and organization H*Wind for Hurricane Wilma 955 mb Vmax = 110 knots Storm Motion 35 knots Vmax exceeds expected value based on pressure/storm size by ~ 20 knots

11 TC Intensity and Structure from MIRS Retrievals One or two passes to fully cover TC and its environment Here two NOAA-19 passes over Hurricane Edouard, 0530 UTC and 0700 UTC 15 Sep 2014 Temperature retrieval at each yellow point forms the basis for the analysis GOES µm infrared image of Hurricane Edouard at 0515 UTC 15 Sep 2014, with location of NOAA-19 MIRS retrievals overlaid. Prepared by J. Dostalek, CIRA/CSU

12 The temperature retrievals are interpolated to an r-z grid Using the hydrostatic assumption, the gridded temperature profiles are used to calculate the pressure field on the r-z grid The pressure field is used to compute the gradient wind Various metrics from the r-z temperature and wind fields are used in a regression equation to estimate v max, p min, r 34, r 50, and r 64 Temperature anomaly (Top) and gradient wind (Bottom) from NOAA19 MIRS analysis, Hurricane Edouard, 0500 UTC, 15 Sep Prepared by J. Dostalek, CIRA/CSU

13 Examples of AMSU & ATMS Intensity Fixes CORENTIN STAN Prepared by J. Knaff NOAA/NESDIS

14 Validation Statistics AMSU ATMS These products are operational at NESDIS and fixes are available at: Values are MAE For AMSU: ftp://satepsanone.nesdis.noaa.gov/tcfp/amsutc/ For ATMS: ftp://satepsanone.nesdis.noaa.gov/tcfp/npptc/ Prepared by J. Dostalek, CIRA/CSU, J. Knaff NOAA/NESDIS

15 N = 876 CIMSS AMSU MSLP CIMSS AMSU Vmax Subj. Dvorak (Operational) BIAS AVG ERROR RMSE Homogenous sample with recon-aided Best Track estimates for ATL, EPAC, CPAC and WPAC. Subj. Dvorak is the average of subjective operational Dvorak estimates from TAFB and SAB (ATL/EPAC) or JTWC, SAB and JMA in WPAC. N=369 CIMSS SSMIS MSLP CIMSS SSMIS Vmax BIAS AVG ERROR RMSE SSMIS data verified with recon-aided Best Track estimates for ATL, EPAC, CPAC and WPAC

16 JPSS Risk Reduction Project: Evaluate Usefulness of ATMS to reduce improve TC Intensity estimates N=181 CIMSS ATMS MSLP CIMSS ATMS Vmax DVK Vmax BIAS AVG ERROR RMSE Dependent results CIMSS ATMS intensity estimates. DVK MSW is average of all available Dvorak estimates (no JMA). This sample is a combination of Atlantic, East Pacific, West Pacific and SHEM storms. Validation is aircraft data (N=103) when available and best track data when no aircraft data is available.

17 Example Cases

18 Tropical Transition: Hurricane Alex Hurricane Alex Jan 14, 2016 S-NPP ATMS Tb Cross Section

19 Monsoon Depressions Morokat (09W) 2009 Talas (15W) 2011 Nakri (12W) 2014 Dvorak 25 knots AMSU/SSMIS knots Best Track 20 knots Dvorak 35 knots AMSU/SSMIS knots Best Track 45 knots Dvorak knots AMSU/ATMS 60 knots Best Track 20 knots Obs 60 Gusts 75 knots

20 Monsoon Depressions Nakri (12W) 2014 Dvorak knots AMSU/ATMS 60 knots Best Track 20 knots Obs 60 gusts 75 knots 981 hpa ASCAT partial pass SSMIS warm core MSLP 981 hpa Vmax 56 knots

21 Halong (11W) 2014 Rapid Intensification/Weakening Dvorak knots AMSU/SSMIS knots Best Track 75 knots

22 Extratropical Transition Pabuk (19W) 2013 Aug 26 15Z Observation 965 mb Moving at 25 knots Aug 26 09Z

23 Wind Shear/CDO Structures Bolaven (16W) 2012 Aug 22 12Z Sounder estimates suggest much weaker system The CDO structure is one of the most difficult to characterize using the Dvorak Technique Aug 22 19Z RI? Aug 23 00Z Aug 23 12Z Aug 24 00Z Aug 24 12Z

24 Pinhole Eye Most problematic structure for nearly all methods. Intense scattering in the eyewall, very small eye compared to instrument resolution, TC position offset from instrument scan location. Largest sounder errors tend to occur with this structure. ATMS should help.

25 Future Work Evaluate high altitude observations from HS3/TCI campaigns to better understand microwave sounder bias characteristics Improve estimation of Vmax using improved inputs from ARCHER (SEF/ERC, asymmetries) Hydrometeor scattering correction for ATMS Address too strong bias during early developing stage Temperature cross section for Edouard 2014 using Global Hawk dropsondes

26 Questions?

27 REFERENCES Brueske K. and C. Velden 2003: Satellite-Based Tropical Cyclone Intensity Estimation Using the NOAA-KLM Series Advanced Microwave Sounding Unit (AMSU). Monthly Weather Review Volume 131, Issue 4 (April 2003) pp Demuth J. and M. DeMaria, 2004: Evaluation of Advanced Microwave Sounding Unit Tropical- Cyclone Intensity and Size Estimation Algorithms. Journal of Applied Meteorology Volume 43, Issue 2 (February 2004) pp Herndon D. and C. Velden, 2004: Upgrades to the UW-CIMSS AMSU-based TC intensity algorithm. Preprints, 26th Conference on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., Olander T. and C. Velden 2007: The Advanced Dvorak Technique: Continued Development of an Objective Scheme to Estimate Tropical Cyclone Intensity Using Geostationary Infrared Satellite Imagery. Wea. and Forecasting Volume 22, Issue 2 (April 2007) pp Velden C. et al., 2006: The Dvorak Tropical Cyclone Intensity Estimation Technique: A Satellite- Based Method that Has Endured for over 30 Years. Bulletin of the American Meteorological Society Volume 87, Issue 9 (September 2006) pp Wimmers, A., and C. Velden, 2010: Objectively Determining the Rotational Center of Tropical Cyclones in Passive Microwave Satellite Imagery. Submitted to JAMC.

28 REFERENCES Herndon, D., and C. Velden, J. D Hawkins 2012: Update on SATellite-based CONsensus (SATCON) Approach to TC Intensity Estimation. 30th Conference on Hurricanes and Tropical Meteorology. Ponte Vedra Beach, FL Herndon, D., and C. Velden, 2012: Estimating Tropical Cyclone Intensity Using the SSMIS and ATMS Sounders. 30th Conference on Hurricanes and Tropical Meteorology. Ponte Vedra Beach, FL Herndon, D., 2014: An Update on Tropical Cyclone Intensity Estimation from Satellite Microwave Sounders. 31st Conference on Hurricanes and Tropical Meteorology. San Diego, CA

29 CIMSS SSMIS Algorithm

30 CIMSS AMSU Algorithm Two scattering scenarios with different AMSU/TC geometry AMSU-B 89 GHz image with AMSU-A FOV closest to TC center AMSU-A scan FOV is located entirely in the large TC eye. AMSU FOV is near nadir. Scattering minimal to none. AMSU-B 89 GHz image with AMSU-A FOV closest to TC center AMSU-A scan FOV contains TC eyewall. Eye is small. AMSU-A FOV near scan edge. Significant scattering within the FOV

31 CIMSS AMSU Algorithm Compare to AMSU Footprint Adjust AMSU pressure if needed Get TC Eye Size AMSU MSLP Estimate Error vs Eye Size R 2 = MSLP Error (mb) Near Limb Footprint Difference Between Eye Size and FOV Resolution (km) Nadir Footprint

32 CIMSS AMSU Algorithm TC Presure Anomaly (mb) TC Pressure Anomaly (mb) AMSU Channel 6 vs Delta_P Chaneel 6 Tb Anomaly (K) AMSU Channel 8 vs Delta_P Channel 8 Tb Anomaly (K) TC Pressure Anomaly (mb) AMSU Channel 7 Tb vs Delta_P Channel 7 Tb Anomaly (K) Apply scattering correction to Tb s After removing under-sampled cases match Tb to MSLP anomaly for each channel Use regressions for initial estimates of MSLP anomaly

33 CIMSS AMSU Algorithm Storm center may fall between AMSU Footprints (FOV) Results in under-sampling of the warm core Use convolved AMSU-B moisture channel to adjust MSLP Only applied if initial MSLP estimate < 995 mb Proxy for TC position offset (bracketing factor) TC Center between FOV Cold AMSU-B 89 Ghz Tb used to adjust AMSU TC estimate TC Center is centered on FOV Warm AMSU-B 89 Ghz indicates no adjustment needed

34 CIMSS AMSU Algorithm Calculation of AMSU environmental Tb 8 FOV steps ~ 400 km nadir ~ 600 km limb A = Old Method B = New Method C = Noisy channel D = Topospheric anomaly in domain

35 CIMSS AMSU Algorithm Hydrometeors located at the levels of interest can mask the warm core signal through attenuation/scattering Result is a weak estimate. Especially for channels 6 and 7 This contamination can be corrected by comparing AMSU-A channels 2 and 15 - Use CH2 to predict CH15 in the absence of scattering. Difference between raw CH15 and predicted CH15 indicates level of scattering. Correlate difference with CHs 5-8 Hydrometeor scattering Corrected