# Presentation: 919948

## Simulation and Forecast of Subseasonal Variability of Hurricane Activity

**Simulation and Forecast of Subseasonal Variability of Hurricane Activity**

- Kathy Pegion
- Center for Ocean-Land-Atmosphere Studies
- Philip Pegion
- NOAA/NCEP/CPC
- Mihai Sirbu
- Center for Ocean-Land-Atmosphere Studies

## Tropical cyclones tend to cluster in time and space on subseasonal timescales.  

- Tropical cyclones tend to cluster in time and space on subseasonal timescales.
- Is this predictable?
- Can it be forecasted with a dynamical model?

**Counts of Atlantic TS and Hurricanes every 6 hrs**

## Courtesy of the Clivar MJOWG

*Courtesy of the Clivar MJOWG*

## Questions

- How well does the CFS simulate the relationship?
- Can forecasts of these indices by the CFS be used to predict where tropical cyclone formation is more/less likely on 1-4 week timescales?

**Questions**

## Hurricane Indices

**Seasonal Genesis Parameter (Gray 1979)**
- SGP=vorticity*coriolis*shear*thermal energy*moist stability *RH
**Genesis Potential Index (Emanuel and Nolan 2004)**
- 	GPI=absolute vorticity*RH * Potential Intensity * shear

**Hurricane Indices**

## All tropical storm formations from the Hurricane Best Track Database 1979-2007

**All tropical storm formations from the Hurricane Best Track Database 1979-2007**

**Climatology and Standard Deviation of SGP & GPI **
**NCEP Reanalysis-2**

## MJO Index

**MJO Index**

- SQRT(RMM12+RMM22) > 1

- Real-time Multivariate MJO Index (Wheeler and Hendon 2004)
*(obtained from Matt Wheeler, BMRC; 1975-2000)*
- Based on combined EOFs of U200, U850, and OLR
- First two PC timeseries (RMM1 & RMM2) represent the MJO amplitude and phase

## Composite OLR (W/m2) anomalies (Jun-Nov)

**Composite OLR (W/m****2****) anomalies (Jun-Nov)**

-  Large-scale region of enhanced/suppressed convection
-  Eastward propagation
-  Note enhanced/supressed convection in Central America

**Observed**

**CFS03**

## Composite anomalies (Jun-Nov) Based on observed RMM

**Composite anomalies (Jun-Nov) Based on observed RMM**

- SGP & GPI vary with phase of MJO
- SGP & GPI are very similar

**R2 SGP**

**R2 GPI**

**Obs OLR**

## Composite SGP & GPI anomalies (Jun-Nov)

**Composite SGP & GPI anomalies (Jun-Nov)**
**Reanalysis-2 (1979-2007)**

- Location of tropical storm formations

*from Hurricane Best Track Database 1979-2007 obtained from Unisys*

-  Actual formations are generally consistent with regions of +/- SGP/GPI anomalies

**SGP**

**GPI**

## CFS

**CFS**

**NCEP/R2**

**SGP Climatology (Jun-Nov)**

**SGP**
**Standard Deviation**

## Composite SGP (Jun-Nov)

**Composite SGP (Jun-Nov)**

- CFS is generally able to capture the overall behavior of the relationship between the MJO and SGP

**NCEP/R2**

**CFS03/T62**

## NCEP/R2

**NCEP/R2**

**CFS03/T62**

**ERA40**

## Correlations between CFS forecasts and NCEP/R2

**Week-1**

**Week-2**

**Week-3**

**Week-4**

**Correlations between CFS forecasts and NCEP/R2**
**(Aug-Oct 2005-2007)**
**Full Fields of SGP, not anomalies, no MJO filter**

## Conclusions

- SGP & GPI indices capture the relationship between MJO and tropical cyclone development.
- The CFS is generally able to capture the relationship
- There are promising prospects for using SGP forecasts as guidance for forecasts for weeks 1-4 in some regions.