You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 bike_share_1
【Schema】
# Table: station
[
(id:INTEGER, Primary Key, Examples: [2, 3, 4]),
(name:TEXT, Examples: [San Jose Diridon Caltrain Station]),
(lat:REAL, Examples: [37.329732, 37.330698, 37.333988]),
(long:REAL, Examples: [-121.90178200000001, -121.888979, -121.894902]),
(dock_count:INTEGER, Examples: [27, 15, 11]),
(city:TEXT, Examples: [San Jose, Redwood City, Mountain View]),
(installation_date:TEXT, Examples: [8/6/2013, 8/5/2013, 8/7/2013])
]
# Table: status
[
(station_id:INTEGER, Examples: [2, 3, 4]),
(bikes_available:INTEGER, Examples: [2, 3, 4]),
(docks_available:INTEGER, Examples: [25, 24, 23]),
(time:TEXT, Examples: [2013/08/29 12:06:01, 2013/08/29 12:07:01, 2013/08/29 12:08:01])
]
# Table: trip
[
(id:INTEGER, Primary Key, Examples: [4069, 4073, 4074]),
(duration:INTEGER, Examples: [174, 1067, 1131]),
(start_date:TEXT, Examples: [8/29/2013 9:08, 8/29/2013 9:24, 8/29/2013 9:25]),
(start_station_name:TEXT, Examples: [2nd at South Park]),
(start_station_id:INTEGER, Examples: [64, 66, 22]),
(end_date:TEXT, Examples: [8/29/2013 9:11, 8/29/2013 9:42, 8/29/2013 9:43]),
(end_station_name:TEXT, Examples: [2nd at South Park]),
(end_station_id:INTEGER, Examples: [64, 69, 22]),
(bike_id:INTEGER, Examples: [288, 321, 317]),
(subscription_type:TEXT, Examples: [Subscriber, Customer]),
(zip_code:INTEGER, Examples: [94114, 94703, 94115])
]
# Table: weather
[
(date:TEXT, Examples: [8/29/2013, 8/30/2013, 8/31/2013]),
(max_temperature_f:INTEGER, Examples: [74, 78, 71]),
(mean_temperature_f:INTEGER, Examples: [68, 69, 64]),
(min_temperature_f:INTEGER, Examples: [61, 60, 57]),
(max_dew_point_f:INTEGER, Examples: [61, 57, 60]),
(mean_dew_point_f:INTEGER, Examples: [58, 56, 60]),
(min_dew_point_f:INTEGER, Examples: [56, 54, 53]),
(max_humidity:INTEGER, Examples: [93, 90, 87]),
(mean_humidity:INTEGER, Examples: [75, 70, 68]),
(min_humidity:INTEGER, Examples: [57, 50, 49]),
(max_sea_level_pressure_inches:REAL, Examples: [30.07, 30.05, 30.0]),
(mean_sea_level_pressure_inches:REAL, Examples: [30.02, 30.0, 29.96]),
(min_sea_level_pressure_inches:REAL, Examples: [29.97, 29.93, 29.92]),
(max_visibility_miles:INTEGER, Examples: [10, 9, 8]),
(mean_visibility_miles:INTEGER, Examples: [10, 9, 8]),
(min_visibility_miles:INTEGER, Examples: [10, 7, 6]),
(max_wind_Speed_mph:INTEGER, Examples: [23, 29, 26]),
(mean_wind_speed_mph:INTEGER, Examples: [11, 13, 15]),
(max_gust_speed_mph:INTEGER, Examples: [28, 35, 31]),
(precipitation_inches:TEXT, Examples: [0, 0.23, T]),
(cloud_cover:INTEGER, Examples: [4, 2, 6]),
(events:TEXT, Examples: [Fog, Rain, Fog-Rain]),
(wind_dir_degrees:INTEGER, Examples: [286, 291, 284]),
(zip_code:TEXT, Examples: [94107, 94063, 94301])
]

    【Question】
    Among the bike trips started on the days with a fog in 2013, how many of those trips started from the station "2nd at Townsend"? started on the days with a fog refers to start_date where events = 'fog'; in 2013 refers to date LIKE '%2013'; started from station refers to start_station_name; start_station_name = '2nd at Townsend';

    【Evidence】
    started on the days with a fog refers to start_date where events = 'fog'; in 2013 refers to date LIKE '%2013'; started from station refers to start_station_name; start_station_name = '2nd at Townsend';

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT COUNT(T1.start_station_name) FROM trip AS T1 INNER JOIN weather AS T2 ON T2.zip_code = T1.zip_code WHERE T2.date LIKE '%2013%' AND T2.events = 'Fog' AND T1.start_station_name = '2nd at Townsend' AND T2.zip_code = 94107