Using the Rossby Radius of Deformation as a Forecasting Tool for Tropical Cyclogenesis



Download 364.18 Kb.
Page1/2
Date28.03.2018
Size364.18 Kb.
#43831
  1   2


Using the Rossby Radius of Deformation as a Forecasting Tool for Tropical Cyclogenesis
Philippe Papin

Atmospheric Sciences

The University of North Carolina Asheville

One University Heights

Asheville, North Carolina 28804 USA
Faculty Advisor: Dr. Christopher Hennon
Abstract
Tropical cloud clusters (TCCs) are large areas of deep convection in the tropics that can develop into tropical cyclones, a process called “tropical cyclogenesis”. Theoretically, the Rossby Radius of Deformation (RROD) can be a useful parameter in predicting when this will happen. RROD is defined as the critical radius where latent heat is unable to be dispersed away from the TCC by gravity waves. Beyond this threshold value, gravity waves disperse the latent heat too far away to support the intensification of a vortex. In this study, RROD is used in conjunction with a new TCC dataset to investigate if RROD is a useful parameter in forecasting tropical cyclogenesis. The RROD was calculated from model analysis fields for Atlantic Basin TCCs that formed during the 2004-2008 hurricane seasons. The resultant RROD was divided by the radius of the TCC to yield the Rossby Radius Ratio (RRR). Results show that there is a significant difference in RRR between developing and non-developing TCCs, with roughly 95% of all developing TCCs becoming tropical cyclones with a RRR value of 47 or less. A RRR value of 47 or less was found in 60% of all non-developing cases. Additionally, using a threshold RRR value of 17 produces the highest Heidke Skill Score. Inputting this predictor into other studies using discriminate analysis could further improve the prediction of TCG.

Keywords: tropical cyclone, Rossby Radius of Deformation, cloud cluster, tropical cyclogenesis


  1. Introduction

The formation of tropical cyclones continues to be a subject of intense research within the meteorological community. Tropical cyclogenesis (TCG) is a process where deep organized thunderstorm activity, generally within the tropics, helps to generate enough latent heat to initiate both horizontal and vertical motion necessary to sustain an intense circulation4. The term tropical cloud cluster (TCC) is used to signify a grouping of thunderstorms in the tropics that form a unified cirrus shield12. These TCCs form from a variety of sources, such as easterly waves, monsoonal circulations, and interactions of these features within broader atmospheric disturbances such as the Madden-Julian Oscillation (MJO)9.

Despite the ability to identify TCCs visually using satellite imagery and passive microwave imagers, correctly forecasting TCG continues to be a challenge. In the Atlantic basin, less than one in five TCCs go on to become tropical cyclones1, 5. In addition, the lack of in-situ observations near the location of TCCs makes it difficult to properly assess the region3, 5. Previous studies suggested that there are mesoscale processes that occur within TCCs that are not yet fully understood, nor able to be modeled properly due to insufficient model resolution5, 6, 3. Thus, much research has been conducted in order to improve forecasting TCG. Oftentimes studies use datasets of TCCs in order to discriminate between non-developing and developing cases. McBride and Zehr examined TCCs in both the Atlantic and Pacific basins, finding that there was twice as much low-level vorticity in the vicinity of developing clusters than in non-developing cases. Additional work with TCCs was conducted in Perrone and Lowe14. Low level vorticity (defined at 950 hPa) was found to be one of the best predictors for TCG. The researchers also concluded that it was possible to make tropical cyclone formation forecasts based upon fields derived from observational data. DeMaria et al. developed a genesis parameter for TCG based upon zonal shear, instability, and mid-level moisture for the Atlantic basin2. While this parameter was useful in describing the limiting factors necessary for tropical cyclone development, it is not a sufficient condition. Thus, the study concluded it may be possible to develop a disturbance-centered parameter to better evaluate the genesis potential of individual TCCs2. Hennon and Hobgood and Hennon et al. used rigid methods for the identification of TCCs through satellite imagery5, 6. Results showed that the daily genesis potential (DGP), the different in relative vorticity between 900 and 200 hPa, was the most significant predictor for TCG, although other predictors were shown to be significant as well. Kerns and Zipser used vorticity maxima (VM) in tracking tropical disturbances, since the location of tropical disturbances such as easterly waves do not always have a direct correlation with the convection observed on satellite imagery8. Despite the difference in tropical disturbance identification, low-level vorticity at 925 hPa proved to be the best predictor of TCG in both the Atlantic and Pacific basin. Thus, in the studies noted above, the unifying theme is that vorticity plays a pivotal role in forecasting the development of a tropical cyclone from a TCC.

One potential predictor with a vorticity component is the rossby radius of deformation (RROD). RROD is defined as the radius at which rotation becomes as important for maintenance of a circulation as buoyancy18. The basic premise is that small circulations are typically dominated by buoyancy forcing, which results in gravity waves quickly dispersing energy in a stable environment. Thus, most of the energy is released as kinetic energy. Larger circulations are more rotational in character, and are dominated by rossby wave dynamics, allowing for additional persistence. In this scenario, potential energy is stored within the circulation. Simply put, if a disturbance is larger (smaller) than the environmentally derived RROD, it will persist (dissipate), all other forcings being equal.

Some literature has noted the potential usefulness of using RROD for TCG. Vitart et al. discussed how increased low-level vorticity helps to reduce the RROD, with a smaller RROD implying more favorable conditions for TCG19. Yeh et al. mentioned the RROD being reduced in the vicinity of a mei-yu front, which is largely enhanced by latent heat release in a mesoscale convective system (MCS)21. This same study is mentioned by Lee et al.; they implied that reduced RROD is a result of the increased latent heat release13. The authors also mention that such a process should be investigated further. In the following paper, RROD will be used in conjunction with a TCC dataset in order to determine the usefulness of incorporating such a value into a TCG prediction scheme.

Recent advances in unifying the global network of satellite imagery into the development of a global, gridded infrared (IR) dataset (GridSat) along with the unification of a global tropical cyclone best-track dataset (IBTrACS) have allowed for the objective automated identification of TCCs11, 10. Such a system has been devised by Hennon et al., where a global dataset of TCCs have been created to aid in the prediction of TCG4. The following study will use this dataset of TCCs to test how effective RROD is as a tool to forecast TCG. Section two of this paper will describe the methodology used to calculate the RROD value for each individual cluster, along with a brief description on how TCC cases were determined via Hennon et al.4. Section three will present the results of the study, while section four will provide concluding remarks discussing the usefulness of the results and future work that can be done to further improve the RROD dataset, including incorporation into other algorithms.


  1. Data and Methodology




    1. TCC database

As mentioned in the introduction, a dataset of TCCs was created by Hennon et al. using a simple automated algorithm4. This algorithm is described in Hennon et al.4. It is important to note that the following definitions and assumptions that were made in that study also apply to the TCCs used within this study. These definitions are now given below.



      1. Clusters are identified as areas of brightness temperature that meet or exceeds the top 2% threshold of brightness temperature (Tb) for each respective basin. As an example, this threshold is 224 K in the North Atlantic basin.

      2. A minimum radius of 1o is required for a cluster to be identified in the dataset. The threshold area of pixels must make an area of at least 34,800 km2 and be generally circular to be considered in the study.

      3. TCCs that exist beyond 30o north or south were not identified in the dataset. This is because many baroclinic systems may also inhabit latitudes beyond 30o and produce clusters that are not tropical in origin.

      4. Cluster independence was conducted in the algorithm. Any cluster that is further than 1200 km away from another cluster is considered to be independent.

      5. A given TCC must persist for at least 24 hours before it is considered a TCC.

      6. TCCs that are found must not already be a tropical cyclone.

For this particular study TCCs were obtained for a five year period from 2004-2008 in the Atlantic basin. The TCC dataset contains three-hour temporal resolution tracks with a multitude of characteristics such as latitude, longitude, size, brightness temperature, radius, and cloud top height. TCC data providing info on TCG is also given, allowing for the discrimination between developing and non-developing TCCs. Developing TCCs are defined as clusters that go on to become tropical cyclones, while non-developing TCCs do not. Developing and non-developing TCCs were separated at this point in order to better determine differences in RROD between each group. Table one shows yearly data of TCCs used in this study, including the number of TCCs in each given year, the number of developing cases, and the TCG ratio. It should be noted that some developing TCCs were not found by the algorithm, since some clusters existed above 30 degrees north, beyond the meridional range of the algorithm.
Table 1. TCCs data obtained from 2004-20084


Tropical Cloud Cluster Data for the Atlantic Basin

Year

Clusters

Developing Clusters

TCG Ratio

2004

214

13

6.07%

2005

266

22

8.27%

2006

238

8

3.36%

2007

222

10

4.50%

2008

253

12

4.74%




    1. GFS analysis field and RROD calculation

The RROD calculation also requires additional atmospheric data. The global forecast system (GFS) model from the National Center for Environmental Prediction (NCEP) was used for the 2004-2008 period. The GFS is a global operational atmospheric model that produces a multitude of different variables at 6 hour forecast intervals on a 1o by 1o horizontal grid. This temporal resolution allows for the introduction of key meteorological variables to be used in conjunction with individual cluster data obtained from the TCC dataset. Within this dataset, temperature, absolute vorticity, and pressure data at the model analysis time (no forecast fields were used) were obtained for use in the RROD algorithm.



The TCC and GFS datasets provides the necessary data to calculate the RROD value for each six hour time interval for each individual TCC. The Rossby radius of deformation () is defined as
 (1)
where, N is the Brunt Vaisala frequency, H is the depth of the system, ζ is the relative vorticity and is the planetary vorticity. The Brunt Vaisala frequency is calculated as:
 (2)
Where g is the local acceleration due to gravity,  is potential temperature at 1000 hPa, and z is geometric height.  represents the difference in potential temperature from 600 hPa to 1000 hPa. Brunt Vaisala frequency represents the frequency at which an air parcel will oscillate when displaced. This is critical since the frequency of internal gravity waves do not exceed the Brunt-Vaisala frequency within the local environment17. The importance of using 600 hPa as the upper height level of potential temperature is that this represents the general height where the maximum latent heat release occurs in convection associated with tropical cyclones7.

The denominator in equation 1 is also known as the absolute vorticity: it simply the sum of both relative and planetary vorticities. The absolute vorticity was obtained at 10 vertical levels between 925 hPa and 500 hPa and averaged together to provide a mean absolute vorticity number that could then be applied to the RROD equation. Simply using one level of vorticity (ie. 925 hPa) will not reveal the entire scope of a circulation within a TCC. Indeed, many clusters contain absolute vorticity maxima at higher heights than 925 hPa. Using an average value of absolute vorticity through 10 vertical levels provides a more realistic view of vorticity within a cluster. Vertical height (H) used to calculate RROD comes directly from the TCC database.



For each TCC time, RROD is calculated globally from the GFS and TCC data. However, the only relevant information needed is the area within each individual TCC, since the study is attempting to provide a reliable RROD value associated with each cluster. Thus, the algorithm checks to see if the grid point calculated is within the radius of the TCC. Figure 1 represents a flow chart used to reason how the algorithm works to obtain a proper RROD value for each cluster. A visual schematic is provided to show how the algorithm checks to make sure grid points are contained within a TCC. Each grid point within the cluster is represented by a blue dot and the center of the cluster is represented by a red x. The area of the cluster is represented as a black circle as defined by the radius.
Figure 1. Flow chart of RROD algorithm.


Download 364.18 Kb.

Share with your friends:
  1   2




The database is protected by copyright ©ininet.org 2022
send message

    Main page