Developing Guidance Product for Tropical Cyclogenesis Probabilities

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Explore the development of a guidance product for predicting tropical cyclogenesis probabilities using historical datasets of precipitation and environmental properties. Objectives include identifying predictors, quantifying genesis probability, and creating the Tropical Cyclogenesis Satellite Guidance Product. The study utilizes a 10-year dataset and focuses on differentiating between developing and nondeveloping systems. The ultimate goal is to improve tropical cyclone prediction accuracy based on satellite and model analyses.

  • Tropical Cyclogenesis
  • Guidance Product
  • Probabilities
  • Historical Dataset
  • Satellite Analysis
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  1. Towards Developing a Guidance Product for Tropical Cyclogenesis Probabilities Using a Comprehensive Historical Dataset of Precipitation and Environmental Properties Jonathan Zawislak1, Brandon Kerns2, Haiyan Jiang1, Shuyi Chen2, and Ed Zipser3 1 Florida International University, Miami, FL 2 Rosenstiel School of Marine and Atmospheric Science, Univ. of Miami, Miami, FL 3 University of Utah, Salt Lake City, UT 69th Interdepartmental Hurricane Conference, Tropical Cyclone Research Forum, March 3, 2015 1 S2_04

  2. Objectives Using a 10-year dataset of satellite and model analysis statistics (TC-PMW), IR cloud cluster longevity: o Identify the differences between developing and nondeveloping invests o Identifythe most important precipitation predictors o Identifythe most important environmental predictors For each predictor, quantify genesis probability for TC genesis in short- (0-48 hr) and medium-range (0-120 hr) Ultimate goal . Develop a genesis guidance product, the Tropical Cyclogenesis Satellite Guidance Product (TCGSatP) 2

  3. Tropical Cyclone - Passive Microwave (TC-PMW) Dataset Passes with 100% Data Coverage in 3deg Passes with 100% Data Coverage in 1deg % of Total Sat Passes % of Total Sat Passes % of Total Sat Passes % of Total Sat Passes % of Total Sat Passes Total Passes % of Total NONDEV Passes Satellite PRE Passes POST Passes 3506 1863 3457 2605 18.2 14.0 17.9 19.6 ATL 19295 9210 15030 12332 40.4 47.7 77.9 63.9 EPAC 13305 6221 10330 8837 27.9 46.8 77.6 66.4 CPAC 1565 729 1197 729 206 630 3.3 46.6 76.5 46.6 13.2 40.3 WPAC 11835 24.8 5806 9276 0 1670 10165 49.1 78.4 0.0 14.1 85.9 (N)OI 1710 3.6 801 1327 0 280 1430 46.8 77.6 0.0 16.4 83.6 47710 22767 37160 6098 8218 33394 100.0 47.7 77.9 12.8 17.2 70.0 2003-2012 Atlantic (ATL) & East Pacific (EPAC) - DEV: 497 (256 ATL; 241 EPAC) - NONDEV: 273 (172 ATL; 101 EPAC) - 5369 are NONDEV (16.5% of dataset) - 6062 are PREGENESIS (18.5% of dataset) - 47% full data coverage within a 3 of center - 78% full data coverage within a 1 of center - WP, SH, IO soon DEV: 1998-2012 (soon 2013) NONDEV: 2003-2012 (soon 2013) Centers adjusted to 1x1 NCEP FNL interpolated 3-hourly location for ENVIRONMENTAL PREDICTORS PMW SENSORS: PRECIPITATION PREDICTORS : Minimum PCT at 85-91, 37 GHz Fractional area of 250 K, 210 K, 160 K PCT at 85-91 GHz Fractional area of 270 and 235 K PCT at 37 GHz AMSR-E TMI SSMI-11, -13, -14, -15 SSMIS-16, -17, -18 3

  4. Environmental Predictors DEV & NONDEV DGP identical DEV slightly lower VWSH DEV higher midlevel RH 1.0 1.0 1.0 Radius=3 PREGEN 0.8 0.8 0.8 PREGEN PREGEN 0.6 0.6 0.6 CDF [%] CDF [%] CDF [%] NONDEV NONDEV NONDEV 0.4 0.4 0.4 NONDEV PREGEN (solid) POSTGEN (dash) 3666 3488 11411 0.2 0.2 0.2 0.0 0.0 0.0 0 4 8 12 16 20 20 36 52 68 84 100 -5 -1 mean DGP [x10 3 7 11 -1] 15 850-200 hPa 500 km VWSH [ms -1] 700-500 hPa mean RH [%] -5 s 1.0 1.0 1.0 NONDEV PREGEN POSTGEN 1160 1116 3189 Within 3 of center 0.8 0.8 0.8 PREGEN NONDEV 0.6 0.6 0.6 CDF [%] CDF [%] CDF [%] NONDEV 0.4 0.4 0.4 PREGEN NONDEV 0.2 0.2 0.2 PREGEN 0.0 0.0 0.0 0 2 4 6 8 10 -1] -5 mean 850 hPa RVOR [x10 -1 3 7 11 15 -1] 21 23 25 27 29 31 700-500 hPa mean MR [gkg -5 s mean SST [C] SST nearly identical RVOR nearly identical DEV slightly higher midlevel MR 4

  5. Environmental Predictors This figure separates by TWO short-range categorized genesis probability 1.0 1.0 1.0 Radius=3 Med/ High Midlevel RH increases w/ increasing probability No/ Low 0.8 0.8 0.8 VWSH decreases w/ increasing probability 0.6 0.6 0.6 CDF [%] CDF [%] CDF [%] 0.4 0.4 0.4 DGP increases w/ increasing probability No (dot) Low (dash) Medium (solid) High (solid) 452 963 624 371 0.2 0.2 0.2 0.0 0.0 0.0 0 4 8 12 16 20 20 36 52 68 84 100 -5 -1 mean DGP [x10 3 7 11 -1] 15 850-200 hPa 500 km VWSH [ms -1] 700-500 hPa mean RH [%] -5 s 1.0 1.0 1.0 No (dot) Low (dash) Medium (solid) High (solid) SST unchanged w/ probability 198 293 169 102 Midlevel MR increases w/ increasing probability RVOR increases w/ increasing probability 0.8 0.8 0.8 No/ Low 0.6 0.6 0.6 CDF [%] CDF [%] CDF [%] 0.4 0.4 0.4 Med/ High 0.2 0.2 0.2 0.0 0.0 0.0 0 2 4 6 8 10 -1] -5 mean 850 hPa RVOR [x10 -1 3 7 11 15 -1] 21 23 25 27 29 31 700-500 hPa mean MR [gkg -5 s mean SST [C] 5

  6. Precipitation Predictors Convective Intensity Convective Intensity Raining Fraction 1.0 1.0 1.0 NONDEV frac=0.75 Radius=3 0.8 0.8 0.8 PREGEN 0.6 0.6 0.6 CDF [%] CDF [%] NONDEV CDF NONDEV PREGEN 0.4 0.4 0.4 No difference in convective intensity PREGEN No difference in convective intensity DEV have more raining area 0.2 0.2 0.2 0.0 0.0 0.0 80 120 Min. 85-91GHz PCT [K] 160 200 240 280 150 180 Min. 37GHz PCT [K] 210 240 270 300 0.00 0.04 0.08 0.12 0.16 0.20 Fraction 85-91GHz PCT 250K 1.0 1.0 1.0 NONDEV 0.8 0.8 0.8 PREGEN NONDEV NONDEV PREGEN 0.6 0.6 0.6 CDF [%] CDF [%] CDF [%] PREGEN DEV have more raining area 0.4 0.4 0.4 DEV have more moderate convective area No difference in intense convective area NONDEV PREGEN (solid) POSTGEN (dashed) 3961 4508 15657 0.2 0.2 0.2 0.0 0.0 0.0 0.00 0.04 0.08 0.12 0.16 0.20 Fraction 37GHz PCT 270K Raining Fraction 0.0000 Fraction 85-91GHz PCT 210K Moderate Conv. Fraction 0.0033 0.0067 0.0100 0.000 Fraction 85-91GHz PCT 160K Intense Fraction 0.003 0.006 0.009 6

  7. Genesis Probabilities Genesis probability = *** number of cases that satisfy a given threshold combined number of PREGEN and NONDEV cases meeting the threshold criteria. ***i.e., the number of cases that, when the threshold is satisfied, form within 48 hours and within 120 hours threshold= mean value within +/- 12 hours of genesis Environmental Predictor Thresholds Mean threshold for within 12 hr of genesis midRH [%] 76 (84) midMR [g kg-1] 5.8 (6.8) VWSH [m s-1] 6.7 (5.2) DGP [s-1] 5.0 (4.9) RVOR850 [s-1] 4.9 (4.3) SST [C] 27.8 (28.1) AL (EP) Precipitation Predictor Thresholds Mean threshold for within 12 hr of genesis frac250 _hi [%] 7.4 (9.7) 0.85 AL (EP) frac210 _hi [%] frac270 _lo [%] 4.5 (5.3) frac160 _hi [%] 0.09 (0.08) 161 (162) minPCT _hi [K] minPCT _lo [K] 256 (256) (0.90) hi = 85-91 GHz lo = 37 GHz 7

  8. Genesis Probabilities Environmental Predictor Probabilities midRH [%] midMR [g kg-1] VWSH [m s-1] DGP [s-1] RVOR850 [s-1] Probability SST [C] 0-48 hr 36 (49) 36 (52) 36 (46) 37 (47) 40 (60) 30 (40) 0-120 hr 52 (67) 52 (67) 52 (62) 50 (54) 49 (68) 47 (63) DEV 56 (68) 57 (68) 55 (64) 51 (54) 51 (68) 50 (64) NONDEV 44 (32) 43 (32) 45 (36) 49 (46) 49 (32) 50 (36) 8

  9. Genesis Probabilities Precipitation Predictor Probabilities Frac250 _hi [%] Frac210 _hi [%] Frac270 _lo [%] Frac160 _hi [%] minPCT _hi [K] minPCT _lo [K] Probability 0-48 hr 43 (53) 38 (43) 42 (52) 34 (37) 35 (40) 34 (40) 0-120 hr 60 (67) 56 (57) 58 (67) 51 (51) 50 (55) 50 (55) DEV 64 (69) 58 (59) 61 (69) 54 (54) 53 (57) 52 (57) NONDEV 36 (31) 42 (41) 39 (31) 46 (46) 47 (43) 48 (43) 9

  10. Genesis Probabilities from Cloud Cluster Longevity o Kerns and Chen (2013) used an objective cloud cluster tracking algorithm based on Chen et al. (1996) o Cloud clusters are identified as contiguous areas of <208 K IR TB o Tracked forward and backward in time (e.g., time clusters ) when they overlap by 50% between consecutive hourly IR images NONDEV DEV Highest probabilities (yellow and orange shading) . accumulated cloud cluster longevity reaches 100 hours for invests tracked for less than 5 days, and 150 hours for invests tracked for longer than 5 days. 10

  11. Summary o Most important environmental predictors are midlevel RH and mixing ratio o Most important precipitation predictors are any proxy for raining area (fractional coverage of 250 K at 85-91 GHz) o Longer the cloud cluster longevity (single or multiple clusters), the higher probability of genesis o Dataset provides a useful tool to determine the probability of genesis in the short- and medium-range by placing an invest in a historical context - Additional real-time tool (TCGSatP) to be used in conjunction with current operational methods for genesis probabilities 11

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