Tropical Cyclone and Active Low Pressure Tracker. Maytee Mahayosananta Thai Meteorological Department (TMD) 2017 APEC Typhoon Symposium (APTS)

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1 Tropical Cyclone and Active Low Pressure Tracker Maytee Mahayosananta Thai Meteorological Department (TMD) 2017 APEC Typhoon Symposium (APTS)

2 Tropical Cyclone and Active Low Pressure Tracker - The tropical cyclone and active low pressure tracker system consists of a python group of functions based on the NCEP/EMC method: - Forecastools.py - Cyclone.py - Thaicyclones_op.py - The cyclone track module is a generalization of the NCEP/EMC method that can be applied to hindcasts and forecasts using different inputs and criteria.

3 Tropical Cyclone and Active Low Pressure Tracker Different combination of variables and detection criteria were tested The parameters used by NCEP/EMC are indicated inside the functions The python packages used by forecastools.py and cyclone.py are: numpy, pylab, matplotlib, netcdf4, basemap and ftplib. A python program was built to operationally download NOAA forecast and find and track cyclones A second code was adapted to apply the cyclone tracker on the reanalysis CFSR, for an example of the Typhoon Ketsana occurred in September of 2009.

4 Forecastools.py In order to facilitate the download of the operational forecast as well as plotting figures and uploading it to a website (via FTP), a python module was developed forecastools.getnoaaforecast forecastools.getnoaaforecastp forecastools.plotforecast forecastools.uploadit forecastools.list forecastools.distance to fetch NOAA forecast fields using nomads to fetch NOAA forecast on a single point using nomads to plot both forecast fields or grid point time-series to upload forecast figures to server/website via ftp list of the most important url and useful variables calculate distance in km between two points with lat1,lon1 and lat2,lon2

5 Input Data Global Forecast System (GFS)

6 Cyclone.py cyclone.findcandidates First identify cyclone candidates (independent in time) from meteorological input fields. cyclone.position Calculate cyclone positions using candidates previously identified and it also test events with additional imposed restrictions cyclone.linktime cyclone.cplot cyclone.ctable Take independent cyclone positions calculated with cyclone.position and link cyclones over time, creating tracks and cyclone evolution. Plot cyclone tracks using python Basemap. Create a cyclone table with min, max and mean values of an input variable(data) within each cyclone.

7 Cyclone.py The first three (prmslmsl, absvprs and hgtprs) are used by the first function cyclone.findcandidates to initially select cyclone candidates independent in time. The prmslmsl, ugrdprs, vgrdprs, ugrd10m, vgrd10m variables are used by cyclone.position to estimate the position initially calculated by cyclone.findcandidates and to impse additional minimum intensity criteria for the cyclone identification. cyclone.linktime finally link cyclones at different instants to calculate the tracks and the cyclone propagation. Some criteria as maximum cyclone propagation velocity and minimum duration can be determined too.

8 Thaicyclones_op.py For the cyclone identification loop over the model grid, a window sw is defined as the size (km) of the sub-grid that runs the entire grid/fields searching for cyclones. No more than one cyclone is identified within the same sub-grid. A set of thresholds (in cyclone.findcandidates) and restrictions (in cyclone.position) is defined to calibrate the system and select specific family of cyclones. In case of all cyclones and low-pressure system are intended to be identified, zero values can be entered. threshold[0] threshold[1] threshold[2] threshold[3] threshold[4] restriction[0] restriction[1] restriction[2] maximum mean sea level pressure minimum 850mb absolute vorticity minimum 700mb absolute vorticity maximum 850mb geopotential height maximum 700mb geopotential height minimum intensity of maximum wind inside the cyclone minimum pressure gradient (mb per km) present in all cyclonic directions minimum averaged cyclonic wind at each of the 4 rays of the cyclone

9 Example Python script Python script files

10 Process Thaicyclones_op.py Python script Cyclone.py Forecastools.py - fetch NOAA forecast fields -prmslmsl -ugrdprs -vgrdprs -ugrd10m -vgrd10m 3 days track forecasting Wave and FNL 0.25 degree from GFS OpenDAP-alt data

11 Case Study affect to Thailand - Mirinae (25-28 July 1026) - DIANMU (22-23 August 2016) - RAI (09-14 September 2016) - Active Low (25-30 October2016) - Active Low (05-10 January 2017) - Active Low (25-30 October2016) - Active Low (25-30 October2016)

12 1. Tropical storm Mirinae July 2016

19 2. Tropical storm DIANMU August 2016

26 3. Tropical storm RAI September 2016

36 4.Active Low October2016

41 5.Active Low January 2017

Use of the GFDL Vortex Tracker

Use of the GFDL Vortex Tracker Tim Marchok NOAA / Geophysical Fluid Dynamics Laboratory WRF Tutorial for Hurricanes January 24, 2018 Outline History & description of the GFDL vortex tracker Inputs & Outputs