Reading a repetitive file with repetitive code
Well, you could find a "Pythonic" way to skip around a file, but that still wouldn't make it good code, in my opinion.  There are some fundamental issues with your approach that make it rather hackish:


f.seek(0) means that the file isn't being processed sequentially, and creates extra work.
You're hard-coding ano = 1977 when in fact ANO  1977 would be right there in the data file itself!  And you're deliberately skipping over that valuable information!  And then you have to hope that the file really does start with data for 1977!
This data format is human-friendly but machine-hostile.  You say that your ultimate goal is to import the data into pandas for analysis.  But PANDAS doesn't easily understand this IDEAM data format.  In fact, your output is just as difficult to parse as the original, but worse, since stripping out the year headers makes the data less self-documenting.  The work to implement a real parser seems unavoidable.  So, if you're going to transform the data, why not transform it into one of the formats that PANDAS knows how to read?
1, 10, 12, and 13 are "magic" numbers.  There is no way to understand why those numbers are there in the code.  Basically, you're blindly assuming that the input is in a certain format without any verification.  If your assumption is wrong, you could easily load data into your analysis incorrectly, and you would never know about the mistake.  You wouldn't want the wrong kind of publicity for your climate analysis, would you?


Therefore, I consider your approach to be a poor substitute for properly parsing the file.  Here, for example, would be one way to parse this data format:

import fileinput
import re

class IdeamParser:
    def __init__(self, *patterns):
        self.patterns = [(re.compile(regex), act) for regex, act in patterns]

    def parse(self, line_iter):
        for line in line_iter:
            for regex, action in self.patterns:
                matches = regex.search(line)
                if matches:
                    yield from action(self, matches)

    def start_meta(self, matches):
        self.meta = {}
        yield from []

    def read_meta(self, matches):
        self.meta.update(dict(zip(matches.groups()[::2], matches.groups()[1::2])))
        yield from []

    def months_header(self, matches):
        line = matches.group()
        self.month_cols = [m.span() for m in re.finditer(' *(\S+) *[*]', line)]
        yield from []

    def read_data(self, matches):
        day_of_month = int(matches.group(1))
        line = matches.group()
        values = [line[start:end] for start, end in self.month_cols]
        values = [float(val) if re.search(r'\d', val) else None for val in values]
        yield self.meta, day_of_month, values

parser = IdeamParser(
    (r'^\s*(\d{2})\s+(.*)', IdeamParser.read_data),
    (r'I D E A M', IdeamParser.start_meta),
    (r'(VALORES) (.*\(.*\))', IdeamParser.read_meta),
    (r'(FECHA DE PROCESO) : *(\d{4}/\d{2}/\d{2}) *'
        r'(ANO) +(\d{4}) *'
        r'(ESTACION) : *(.*?) *$', IdeamParser.read_meta),
    (r'(LATITUD) +(.*?) *'
        r'(TIPO EST) +(.*?) *'
        r'(DEPTO) +(.*?) *'
        r'(FECHA-INSTALACION) +(.*?) *$', IdeamParser.read_meta),
    …
    (r'\s*DIA +ENERO.*', IdeamParser.months_header),
)
for meta, day_of_month, values in parser.parse(fileinput.input()):
    print(meta, day_of_month, values)


Sample output:

{'DEPTO': 'ANTIOQUIA', 'FECHA DE PROCESO': '2016/09/08', 'LATITUD': '0553 N', 'FECHA-INSTALACION': '1970-JUN', 'ANO': '1986', 'TIPO EST': 'CO', 'ESTACION': '26185020  MESOPOTAMIA', 'VALORES': 'TOTALES DIARIOS DE RECORRIDO DEL VIENTO (Kms)'} 1 [144.0, 35.0, 10.0, 32.0, 52.0, 6.0, 81.0, 133.0, 44.0, 29.0, 7.0, 17.0]
…
{'DEPTO': 'ANTIOQUIA', 'FECHA DE PROCESO': '2016/09/08', 'LATITUD': '0553 N', 'FECHA-INSTALACION': '1970-JUN', 'ANO': '1986', 'TIPO EST': 'CO', 'ESTACION': '26185020  MESOPOTAMIA', 'VALORES': 'TOTALES DIARIOS DE RECORRIDO DEL VIENTO (Kms)'} 5 [35.0, None, 31.0, 18.0, 16.0, 15.0, 110.0, 160.0, 125.0, 12.0, 18.0, 56.0]


The print() statement at the end is just for demonstration.  You should replace it with code that generates a CSV file or SQL INSERT statements or something.  Or you could just load it into PANDAS directly.