UNITOV Catalogue of geoeffective CMEs

A database associating L1 Time of Arrival and Speed of an interplanetary CME to the kinematic characteristics of the corresponding CME, covering the period 1996-2020.

References

Dataset summary

internal_name:	unitov_icme_tableset
publisher:	University of Rome Tor Vergata Dept. of Physics
alph_code:	15
id_tmp:	44
short_name:	UNITOV-ICME
identifier:	aspis:/unitov/icme_tableset
dynamic:	+10kB updated every year
size:	100kB
format:	csv
type:	tableset
records_number:	213
time_start:	1997-01-01
time_stop:	2019-12-31
spatial:	IP
messenger:	Photons,Protons
spectral_min:	0
spectral_max:	0
spectral_units:
observable:	{CME, SW}
latest update:	2024-05-16 06:11:43

Columns specification

progr	column	units	type	description
1	time	UTC	datetime	datetime (1997-01-06T16:23:38) - time of the first detection of the CME in LASCO FoV
2	HPC_Tx	arcsec	float	arcsec (25.21) - Helioprojective Cartesian Longitutude: the angle relative to the plane containing the Sun-observer line and the Sun’s rotation axis, with positive values in the direction of the Sun’s west limb
3	HPC_Ty	arcsec	float	arcsec (-3.22) - Helioprojective Cartesian Latitude: the angle relative to the Sun’s equatorial plane, with positive values in the direction of the Sun’s north pole
4	HPC_distance	AU	float	AU (0.98) - the Sun-observer distance
5	CME_num	index	int	index (1) - progressive number
6	Start_Date	UTC	datetime	datetime (1997-01-06 16:23:38.000) - extimated time of the CME passage at 20 R_Sun
7	Arrival_Date	UTC	datetime	datetime (1997-01-06 16:23:38.000) - time of the CME passage at L1
8	PE_duration	hours	float	hours (22.0) - ICME Plasma Event duration at L1
9	Arrival_v	km/s	int	km/s (450) - measured velocity of the CME at L1
10	Transit_time	hours	float	hours (98.3) - time elapsed from the Start_Date and Arrival_Date
11	Transit_time_err	hours	float	hours (98.3) - error associated to the extimated transit time
12	LASCO_Date	UTC	datetime	datetime (1997-01-06 16:23:38.000) - time of the first detection of the CME in LASCO FoV
13	LASCO_v	km/s	int	km/s (459.5) - plane-of-sky velocity of the CME from LASCO images
14	LASCO_pa	deg	int	deg (263.0) - CME principal angle, counterclockwise from North
15	LASCO_da	deg	int	deg (165.0) - CME angular width
16	LASCO_halo	char	char	string (N/PH/HH/FH) - indicating halo/partial halo CME
17	v_r	km/s	float	km/s (705.35) - estimated radial velocity of the CME
18	v_r_err	km/s	float	km/s (104.89) - error associated to the radial velocity of the CME
19	Theta_source	arcsec	float	arcsec (14.92) - longitude of the most probable source of the CME on the solar disk
20	Phi_source	arcsec	float	arcsec (-25.97) - co-latitude of the most probable source of the CME on the solar disk
21	source_err	arcsec	float	arcsec (20.0) - uncertainty on the source location on the solar disk
22	POS_source_angle	deg	float	deg (64.60) - plane-of-sky angle of the source region of the CME
23	rel_wid	rad	float	rad (0.5585) - estimated deprojected CME angular width
24	Mass	g	float	g (1.8e+16) - Estimated mass of the CME
25	SW_type	char	char	char (F/S) - Solar wind type (Fast/Slow) for the ICME propagation
26	Bz	nT	int	nT (16) - z-component of the ICME magnetic field at L1
27	DST	nT	int	nT (-68.0) - minimum DST during ICME event at L1
28	v_r_stat	km/s	float	km/s (544.73) - statistical de-projection of the CME POS speed
29	Accel.	m/s^2	float	m/s^2 (8.9076) - CME acceleration between the lift-off and 20 solar radii
30	Analyitic_w	km/s	float	km/s (395.56) - solar wind value obtained via analytic inversion of the DBM equations
31	Analyitic_gamma	km^-1	float	km^-1 (3.5029e-06) -value of the drag parameter obtained via analytic inversion of the DBM equations
32	filename	char	char	string (CME_0111_param_inv.txt ) - name of the file containing PDBM information from the statistical inversion procedure

Code example

"""

This is an example script for accessing client-side ASPIS datasets.

The client is provided by the ASPISpy package and in particular the connection is resolved by the CaesarAPI class.

This example code allows the user to fetch a product from the ASPIS DB and

save it in a ready-to-use pandas.DataFrame structure.

To use this code to fetch another product, simply change

the product name and possibly the time range and columns of the dataset

(for the filtered_search method).

Notes:

- The CaesarAPI class provides a complete interface to the ASPIS DB through several methods,

each of which corresponds to a different API endpoint.

- Once CaesarAPI has been instantiated through authentication, the user can call the different methods

(e.g., CaesarAPI.download_data, CaesarAPI.filtered_search, etc. etc.).

- The import and initialization are the same for each code fragment.

"""

#### The following lines of code should be executed only once

#### during session initialization. If they are re-executed,

#### the session in ASPIS DB is recreated from scratch.

#### Once the session is initialized, the user can use of all

#### the methods provided by the CaesarAPI class.

import ASPISpy.aspis as aps

import pandas as pd

# The following lines to disable SSL certificate warning.

# Note: In this prototype version, the certificate is not available

import requests

from requests.packages.urllib3.exceptions import InsecureRequestWarning

requests.packages.urllib3.disable_warnings(InsecureRequestWarning)

# Initialize the CaesarAPI connector

api = aps.CaesarAPI(credentials={'email': '<your_email>','password': '<your_password>'})

# [INFO] user <your_email> authenticated

# Define the product to be fetched

product_name = "unitov_icme_tableset"

# FETCH THE FULL DATASET

r, df = api.get_product_detail(product_name)

# r is the full response of the request, df is the pandas.DataFrame

# which contains the dataset.

# > print(df.head())

#     id                 time     HPC_Tx     HPC_Ty  HPC_distance  CME_num  ...

# 0   0  1997-01-06T15:10:42  25.210143  -3.221796      0.983319        1   ...

# 1   1  1997-02-07T00:30:05  53.055793  -7.993757      0.986300        2   ...

# 2   2  1997-04-07T14:27:44  14.917489 -25.972924      1.001209        3   ...

# 3   3  1997-05-12T05:30:05   6.568649  21.393300      1.010308        4   ...

# 4   4  1997-05-21T21:00:53 -63.993601 -35.996400      1.012279        5   ...

# FETCH A SUBSET of <product_name>

df = api.filtered_search(prod_id=product_name,filters=['time < 2005-01-01','time > 2000-01-01'],cols=['time','bz','dst','v_r'],return_dataframe=True)

# [INFO] Executing the following query:

#     query MyQuery {

#       filtered_search(filters: [{field: "time", operator: "lt", value: "2005-01-01"}{field: "time", operator: "gt", value: "2000-01-01"}]) {

#         ... on UnitovIcmeTablesetType {

#           time

#           bz

#           dst

#           v_r

#         }

#       }

#     }

# > print(df.head())

#                   time  bz  dst      v_r

# 0  2000-01-18T17:54:05  16  -97  1132.60

# 1  2000-02-08T09:30:05   7  -25  1364.00

# 2  2000-02-10T02:30:05  13 -133  1097.20

# 3  2000-02-12T04:31:20   5  -67  1286.90

# 4  2000-02-17T19:31:23  15  -26   566.07

``