IMPROVEMENTS TO ENSEMBLE TROPICAL RAINFALL POTENTIAL (eTRaP)IMPROVEMENTS TO ENSEMBLE TROPICAL RAINFALL POTENTIAL (eTRaP)

eTRaP Background

Robert J. Kuligowski, Center for Satellite Applications and Research, NOAA/NESDIS, College Park, MD Bob.Kuligowski@noaa.govStan Kidder, Colorado State University

Liqun Ma, Robert Glassberg, Clay Davenport, Rachel Hatteberg, Mike Turk, Sheldon Kusselson, NOAA/NESDIS/OSPO, College Park, MDBeth Ebert, Bureau of Meteorology, Melbourne, Victoria, Australia

Ongoing Improvements

Manual Point TRaP Forecasts• Topical Rainfall Potential (TRaP; Kidder et al. 2000) began in the early 1990s at the NESDIS

Satellite Analysis Branch (SAB) using a technique adapted from Spayd and Scofield (1984):• TRaP=RavD/V, where

• D is distance across the rain area of the storm along a vector parallel to the storm motion and intersecting the forecast point of interest;

• Rav is the average rain rate along D;

• V is the satellite-estimated cyclone speed.• Spayd and Scofield (1984) used rain rates from infrared cloud classification; TRaP used

microwave (MW)-derived rain rates.Additional Satellite-Based Ensemble Members

• Motivation: when the SSM/I was retired, eTRaP often did not run because the minimum required number of ensemble members was not available

• Solution: additional ensemble members from the Special Sensor Microwave Imager Sounder (SSMIS), infrared-based Global Hydro-Estimator (GHE), and Advanced Technology Microwave Sounder (ATMS). Sensor weights and bias adjustments were computed by validating the individual TRaPs against Stage IV over the CONUS, and all weights and adjustments were found to be 1 except for a multiplier of 0.7 for the GHE to remove a wet bias.

• Results: neutral impact (Fig. 6) but significant improvement eTRaP availability.

Probability Calibration• Motivation: as noted in Ebert et al. (2011), the eTRaP probabilities were overconfident.• Solution: correct the bias by inverting power-law fits to the reliability diagrams.• Caveat: the pixel-level exceedance probabilities are now unbiased but are always very low

for the highest rain rates, even when the deterministic forecast exceeds the threshold value (Fig. 5). This reflects uncertainty in the location of the heaviest rainfall due to forecast track errors and rain rate estimation errors.

ReferencesEbert, E. E., M. Turk, S. J. Kusselson, J. Yang, M. Seybold, P. R. Keehn, and R. J. Kuligowski, 20011: Ensemble Tropical Rainfall Potential

(eTRaP) forecasts. Wea. Forecasting, 26, 213-224.

Ferraro, R., P. Pellegrino, M. Turk, W. Chen, S. Qiu, R. Kuligowski, S. Kusselson, A. Irving, S. Kidder, and J. Knaff, 2005: The Tropical Rainfall Potential (TRaP) technique. Part II: Validation. Wea. Forecasting, 20, 465-475,

Kidder, S. Q., M. D. Goldberg, R. M. Zehr, M. DeMaria, J. F. W. Purdom, C. S. Velden, N. C. Grody, and S. J. Kusselson, 2000: Satellite analysis of tropical cyclones using the Advanced Microwave Sounding Unit (AMSU). Bull. Amer. Meteor. Soc., 81, 1241-1259.

-----, S. J. Kusselson, J. A. Knaff, R. Rl. Ferraro, R. J. Kuligowski, and M. Turk, 2005: The Tropical Rainfall Potential (TRaP) technique, Part I: Description and examples. Wea. Forecasting, 20, 456-464.

Lonfat, M., R. Rogers, T. Marchok, and F. D. Marks Jr., 2007: A parametric model for predicting hurricane rainfall. Mon. Wea. Rev., 135, 3086-3097.

Spayd. L. E. Jr., and R. A. Scofield, 1984: A tropical cyclone precipitation estimation technique using geostationary satellite data. NOAA Tech. Memo. NESDIS 5, 36 pp.

DISCLAIMER: The contents of this poster are solely the opinions of the author and do not constitute a statement of policy, decision, or position on behalf of the GOES-R Program Office, NOAA, or the U.S. Government.

Acknowledgment: This work was supported by the NESDIS Product System Development and Implementation (PSDI) and the Joint Polar Satellite System (JPSS).

Figure 1. NOAA-16 AMSU-B rain rates for TS Alison at 0812 UTC 5 June 2001 with the forecast storm track added; (b) resultingTRaP for the 24 h ending 1200 UTC 6 June 2001. Adapted from Fig. 2 of Kidder et al. (2005).

Ensembles• Ebert et al. (2011) extended the eTRaP framework by building ensembles of TRaPs with

different lead times, forecast storm tracks, and satellite rain rate estimates.• Motivation: ensembles generally have less error than individual members and provide

uncertainty information.• Methodology:

• The 6-h components of the 24-h TRaPs are arranged by valid time (Fig. 2);• The TRaP members in this simple ensemble are weighted by lead time (higher weights for

shorter lead times) and instrument, with all weights determined empirically;• The ensemble is used to produce both probability matched-ensemble means (deterministic

forecast) and exceedance probabilities for multiple thresholds (probabilistic forecast) for each 6-h segment and the 24-h forecast period (Fig. 3).

• Results: The resulting deterministic forecasts were superior to the individual TRaPs, but the probability forecasts were overconfident (wet bias).

Figure 2. The 6-h TRaP segments available for 4 consecutive 6-h periods (centered on 0600, 1200, 1800 24 Sep and 0000 25 Sep 2005) for Hurricane Rita. Adapted from Fig. 5 of Ebert et al. (2011).

Figure 3. eTRaP forecast for Hurricane Rita for the 24 ending 0000 UTC 25 Sep 2005: probability of exceeding 50, 100, 150, and 200 mm, followed by the probability-matched ensemble mean accumulation. Adapted from Fig. 7 of Ebert et al. (2011).

Automated Gridded TRaP Forecasts• Motivation: This manual technique was very labor-intensive and provided a forecast only for a

single point, so Kidder et al. (2005) developed an automated and gridded version that extrapolates each grid-box value of MW rain rate along the predicted storm track (Fig. 1).

• Results: TRaP outperformed the National Centers for Environmental Prediction Eta (now North American Model) forecasts (Ferraro et al. 2005).

• Assumptions: (Kidder et al. 2005):

1. The forecast storm track is correct;

2. The satellite-estimated rain rates are correct;

3. The spatial pattern of rain rates relative to the storm center does not change.• TRaP is produced in real time whenever new rain rates are available from a MW sensor and / or

a new forecast storm track is released.

Initial Operational eTRaP Configuration• Initialized every 6 h, centered on synoptic times (00, 06, 12, 18 UTC); issued ~4 h after

initialization time• Rain rate estimates from

• Special Sensor Microwave/Imager (SSM/I)• Advanced Microwave Sounding Unit-B (AMSU/B) / Microwave Humidity Sounder (MHS)• Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E); and• Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI).

• Forecast tracks from all major operational forecast centers• Real-time forecasts available at http://www.ssd.noaa.gov/PS/TROP/etrap.html (Fig. 4)

Adding R-CLIPER to the Ensemble• Motivation: Similarity of satellite-derived rain rate estimates reduces ensemble spread.• Solution: tested Rainfall Climatology-Persistence (R-CLIPER; Lonfat et al. 2007) to

increase the spread and improve the forecasts.• Results: neutral impact on performance (Fig.5).

Next Steps• Apply the R-CLIPER orographic adjustment (Lonfat et al. 2007) to all ensemble members.• Add Advanced Microwave Scanning Radiometer-2 (AMSR-2) and Global Precipitation

Measurement (GPM) Microwave Imager (GMI) rain rates to the ensemble.• To address the low probabilities for high rainfall amounts, we may pursue a neighborhood

probability similar to that used by the Storm Prediction Center where the probability of exceedance anywhere within a specified radius is given instead of at the pixel scale. Explore neighborhood probabilities.

Figure 6. Equitable Threat Score at 12.7 mm (6-h intervals) and 25.4 mm (24-h intervals) for eTRaP with only MW members, the GHE added, and the GHE and R-CLIPER added for all CONUS-landfalling tropical storms from 2004-11 .

Figure 4. Example eTRaP Web page for Cyclone Pam at 00 UTC 13 May 2015.

Figure 5. 24-h eTRaP forecast for Cyclone Pam at 00 UTC 13 May 2015 (top) and probability of exceeding 200 mm of rainfall during the same time period (bottom).