# Presentation: 200370

## Improving Intensity Forecasts via Microphysical Parameterization Methods in a Mesoscale Model for Hurricanes Erin (CAMEX-4, 2001) and Dennis (TCSP, 2005)Cerese Albers (FSU)

- Each hurricane forecast underwent rain rate initialization (Krishnamurti, et. al. 2007) for the first 24 hour period. Then a series of 25 microphysics experiments were performed on each storm for the following 24 hours (see Table 1). RMS error and anomaly correlation calculations were performed on the 25 experiments comparing them with the actual analysis and monthly means, respectively, to produce a “best” combination of microphysics options in the WRF model. Then this final best combination was run to see how much of an improved forecast could be generated.

- Image C.
- NASA TRMM IMAGERY OF
- H. ERIN on Sept. 10, 2001 01:08 UTC

- _Mesoscale Model_: NCAR Weather Research & Forecast Model **(WRF) ARW Version 2.1** (08/05)
- _Storms_: **Hurricane Erin (2001),** studied during the CAMEX-4 campaign
-              and **Hurricane Dennis (2005),** studied during the TCSP campaign
- _Model Parameters_: Runtime=48hours for final runs, Timestep= 60sec.
-              **Erin**: grid size: Outer domain-25km, Inner domain-5km.
-              **Dennis**: grid size: Single domain- 2.5 km
- _Options/parameters chosen_: WSM 6-class graupel scheme, LW Rad. rrtm scheme, SW Rad. Dudhia scheme, Sfc-layer Monin-Obukhov scheme, Land-Sfc thermal diffusion scheme, Boundary-layer YSU scheme, Cumulus option 3 for Outer domain (Grell-Devenyi ensemble) and 0 for Inner domain, ndown option between domains (for Erin), and inclusion of nested boundary conditions.
- _Variations_: **Microphysical parameterizations**: Melting of Snow, Melting of Graupel,
-                    Evaporation of Rain Water, Fall Speed of Snow, Fall Speed of Graupel,
-                    Intercept Parameter of Graupel.
-   ***Note***: These variations were chosen for the study based on the findings
- of prior studies, such as Pattnaik & Krishnamurti, 2006

- The resulting forecasts will be compared with the following observation datasets taken during the CAMEX-4 and TCSP field campaigns:
- -AMPR precip index               -EDOP reflectivity profiles
- -Dropsonde (winds,pressure) -TRMM (precip profiles)

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**Image A, the Control,  shows the WRF model standard reflectivity forecast for Hurricane Erin (2001).  **
**Image B, the Final Forecast, shows the marked improvement in simulated reflectivity that the optimal combination produced.**
**Image C is the same hour Real-Time TRMM and satellite overlay of precip.**

- Table 1.

- The results showed that the Rain-Rate Initialized only control run
- still performed inferiorly to the optimally microphysical parameterized
- forecast produced for Hurricane Erin. Skill score results for Hurricane
- Dennis (TCSP) are looking even better than Hurricane Erin’s did
- and so it is hoped that the more intense Dennis will also be positively
- affected by an optimized combination of microphysical parameterizations.
- These results are still being computed, and though this project is nearing
- completion, it is still a bit early to say definitively that Hurricane Dennis will
- follow Erin’s suit in the final forecast utilizing an optimal combination of
- microphysical parameterizations. One thing is certain, the overall structure
- of the storm is vastly improved over the original run.

- Image A. Sept 10, 2001 01:00 UTC

- Image B Sept 10, 2001 01:00 UTC