示例#1
0
def compare_potential_temp(sounding, date):

    theta1 = thermo.theta1(K=sounding.TE, hPa=sounding.P)
    theta2 = thermo.theta2(K=sounding.TE,
                           hPa=sounding.P,
                           mixing_ratio=sounding.MR / 1000)

    thetaeq1 = thermo.theta_equiv1(K=sounding.TE, hPa=sounding.P)
    thetaeq2 = thermo.theta_equiv2(K=sounding.TE,
                                   hPa=sounding.P,
                                   relh=sounding.RH,
                                   mixing_ratio=sounding.MR / 1000)

    foo = pd.DataFrame({
        'theta1': theta1,
        'thetaeq1': thetaeq1,
        'theta2': theta2,
        'thetaeq2': thetaeq2
    })
    y = foo.index.values

    fig, ax = plt.subplots(figsize=(8.5, 11))
    ln1 = ax.plot(theta1, y, label='theta=f(temp,press) - W&H')
    ln2 = ax.plot(theta2, y, label='theta=f(temp,press, w) - Bolton 1980')
    ln3 = ax.plot(thetaeq1, y, label='thetaeq=f(temp,press,ws)  - W&H')
    ln4 = ax.plot(thetaeq2,
                  y,
                  label='thetaeq=f(temp,press, w, rh) - Bolton 1980')
    ax.set_xlim([280, 320])
    ax.set_ylim([0, 5000])
    ax.set_xlabel('Temperature [K]')
    ax.set_ylabel('Altitude [m]')

    ax2 = ax.twiny()
    ln5 = ax2.plot(sounding.MR, y, sns.xkcd_rgb["amber"], label='mixing ratio')
    ax2.set_xlim([0, 8])
    ax2.set_ylim([0, 5000])
    ax2.grid(False)
    ax2.set_xlabel('Mixing ratio [g kg-1]')

    ax3 = ax.twiny()
    ln6 = ax3.plot(sounding.RH,
                   y,
                   sns.xkcd_rgb["plum purple"],
                   label='relative humidity')
    ax3.set_xlim([0, 100])
    ax3.set_ylim([0, 5000])
    ax3.grid(False)
    ax3.set_xlabel('Relative humidity [%]')
    ax3.xaxis.set_label_coords(0.5, 1.07)
    ax3.tick_params(direction='out', pad=35)
    lns = ln1 + ln2 + ln3 + ln4 + ln5 + ln6
    labs = [l.get_label() for l in lns]
    ax3.legend(lns, labs, loc=0)

    plt.subplots_adjust(bottom=0.05, top=0.89)
    datestr = date.strftime('%Y%m%d_%H%M%S')
    plt.title('Comparison of Potential Temperature BBY sounding ' + datestr,
              y=0.99)
    plt.draw()
示例#2
0
def compare_potential_temp(sounding, date):

    theta1 = thermo.theta1(K=sounding.TE, hPa=sounding.P)
    theta2 = thermo.theta2(
        K=sounding.TE, hPa=sounding.P, mixing_ratio=sounding.MR/1000)

    thetaeq1 = thermo.theta_equiv1(K=sounding.TE, hPa=sounding.P)
    thetaeq2 = thermo.theta_equiv2(K=sounding.TE, hPa=sounding.P,
                                   relh=sounding.RH, mixing_ratio=sounding.MR/1000)

    foo = pd.DataFrame(
        {'theta1': theta1, 'thetaeq1': thetaeq1, 'theta2': theta2, 'thetaeq2': thetaeq2})
    y = foo.index.values

    fig, ax = plt.subplots(figsize=(8.5, 11))
    ln1 = ax.plot(theta1, y, label='theta=f(temp,press) - W&H')
    ln2 = ax.plot(theta2, y, label='theta=f(temp,press, w) - Bolton 1980')
    ln3 = ax.plot(thetaeq1, y, label='thetaeq=f(temp,press,ws)  - W&H')
    ln4 = ax.plot(
        thetaeq2, y, label='thetaeq=f(temp,press, w, rh) - Bolton 1980')
    ax.set_xlim([280, 320])
    ax.set_ylim([0, 5000])
    ax.set_xlabel('Temperature [K]')
    ax.set_ylabel('Altitude [m]')

    ax2 = ax.twiny()
    ln5 = ax2.plot(sounding.MR, y, sns.xkcd_rgb["amber"], label='mixing ratio')
    ax2.set_xlim([0, 8])
    ax2.set_ylim([0, 5000])
    ax2.grid(False)
    ax2.set_xlabel('Mixing ratio [g kg-1]')

    ax3 = ax.twiny()
    ln6 = ax3.plot(
        sounding.RH, y, sns.xkcd_rgb["plum purple"], label='relative humidity')
    ax3.set_xlim([0, 100])
    ax3.set_ylim([0, 5000])
    ax3.grid(False)
    ax3.set_xlabel('Relative humidity [%]')
    ax3.xaxis.set_label_coords(0.5, 1.07)
    ax3.tick_params(direction='out', pad=35)
    lns = ln1+ln2+ln3+ln4+ln5+ln6
    labs = [l.get_label() for l in lns]
    ax3.legend(lns, labs, loc=0)

    plt.subplots_adjust(bottom=0.05, top=0.89)
    datestr = date.strftime('%Y%m%d_%H%M%S')
    plt.title(
        'Comparison of Potential Temperature BBY sounding '+datestr, y=0.99)
    plt.draw()
示例#3
0
def parse_surface_met(file_met, index_field=None,
                  name_field=None, locelevation=None):

    from scipy import stats

    ''' 
        Process surface meteorological files 
        with .met extension

        Output dataframe assumes:
        - Each file is a 24h period observation
        - Frequency of observation is constant
    '''

    casename = file_met.split('/')[-2]
    station_id = file_met.split('/')[-1][:3]
    usr_case = str(int(casename[-2:]))

    if usr_case in ['1', '2']:
        index_field = {'bby': [3, 4, 10, 5, 6, 11, 13],
                       'czc': [3, 4, 10, 5, 6, 11, 13]}
    elif usr_case in ['3', '4', '5', '6', '7']:
        index_field = {'bby': [3, 5, 8, 10, 12, 17, 26],
                       'czc': [3, 4, 5, 6, 8, 13, 22]}
    else:
        index_field = {'bby': [3, 4, 5, 6, 8, 13, 15],
                       'czc': [3, 4, 5, 6, 8, 13, 15],
                       'frs': [3, 4, 5, 6, 8, 13, 15]}

    elev = {'bby': 15, 'czc': 462, 'frs': 45}
    name_field = ['press', 'temp', 'rh', 'wspd', 'wdir', 'precip', 'mixr']

    index_field = index_field[station_id]
    locelevation = elev[station_id]

    ''' read the csv file '''
    raw_dframe = pd.read_csv(file_met, header=None)

    ''' parse date columns into a single date col '''
    dates_col = [0, 1, 2]
    raw_dates = raw_dframe.ix[:, dates_col]
    raw_dates.columns = ['Y', 'j', 'HHMM']
    raw_dates['HHMM'] = raw_dates['HHMM'].apply(lambda x: '{0:0>4}'.format(x))
    raw_dates = raw_dates.apply(
        lambda x: '%s %s %s' % (x['Y'], x['j'], x['HHMM']), axis=1)
    dates_fmt = '%Y %j %H%M'
    dates = raw_dates.apply(lambda x: datetime.strptime(x, dates_fmt))

    ''' make meteo df, assign datetime index, and name columns '''
    meteo = raw_dframe.ix[:, index_field]
    meteo.index = dates
    meteo.columns = name_field

    ''' create a dataframe with regular and continuous 24h period time index
        (this works as a QC for time gaps)
    '''
    nano = 1000000000
    time_diff = np.diff(meteo.index)
    sample_freq = (stats.mode(time_diff)[0][0]/nano).astype(int)  # [seconds]
    fidx = meteo.index[0]  # (start date for dataframe)
    fidx_str = pd.to_datetime(
        fidx.year*10000 + fidx.month*100 + fidx.day, format='%Y%m%d')
    periods = (24*60*60)/sample_freq
    ts = pd.date_range(fidx_str, periods=periods, freq=str(sample_freq)+'s')
    nanarray = np.empty((periods, 1))
    nanarray[:] = np.nan
    df = pd.DataFrame(nanarray, index=ts)
    meteo = df.join(meteo, how='outer')
    meteo.drop(0, axis=1, inplace=True)

    ''' make field with hourly acum precip '''
    hour = pd.TimeGrouper('H')
    preciph = meteo.precip.groupby(hour).sum()
    meteo = meteo.join(preciph, how='outer', rsuffix='h')

    ''' add thermodynamics '''
    theta = tm.theta1(C=meteo.temp, hPa=meteo.press)
    thetaeq = tm.theta_equiv1(C=meteo.temp, hPa=meteo.press)
    meteo.loc[:, 'theta'] = pd.Series(theta, index=meteo.index)
    meteo.loc[:, 'thetaeq'] = pd.Series(thetaeq, index=meteo.index)

    ''' add sea level pressure '''
    Tv = tm.virtual_temperature(C=meteo.temp, mixing_ratio=meteo.mixr/1000.)
    slp = tm.sea_level_press(K=Tv+273.15, Pa=meteo.press*100, m=locelevation)
    meteo.loc[:, 'sea_levp'] = slp

    ''' assign metadata (prototype, not really used) '''
    units = {'press': 'mb', 'temp': 'C', 'rh': '%', 'wspd': 'm s-1',
             'wdir': 'deg', 'precip': 'mm', 'mixr': 'g kg-1'}
    agl = {'press': 'NaN', 'temp': '10 m', 'rh': '10 m',
           'wspd': 'NaN', 'wdir': 'NaN', 'precip': 'NaN', 'mixr': 'NaN'}
    for n in name_field:
        meteo[n].units = units[n]
        meteo[n].agl = agl[n]
        meteo[n].nan = -9999.999
        meteo[n].sampling_freq = '2 minute'

    return meteo
示例#4
0
def parse_surface_met(file_met,
                      index_field=None,
                      name_field=None,
                      locelevation=None):

    from scipy import stats
    ''' 
        Process surface meteorological files 
        with .met extension

        Output dataframe assumes:
        - Each file is a 24h period observation
        - Frequency of observation is constant
    '''

    casename = file_met.split('/')[-2]
    station_id = file_met.split('/')[-1][:3]
    usr_case = str(int(casename[-2:]))

    if usr_case in ['1', '2']:
        index_field = {
            'bby': [3, 4, 10, 5, 6, 11, 13],
            'czc': [3, 4, 10, 5, 6, 11, 13]
        }
    elif usr_case in ['3', '4', '5', '6', '7']:
        index_field = {
            'bby': [3, 5, 8, 10, 12, 17, 26],
            'czc': [3, 4, 5, 6, 8, 13, 22]
        }
    else:
        index_field = {
            'bby': [3, 4, 5, 6, 8, 13, 15],
            'czc': [3, 4, 5, 6, 8, 13, 15],
            'frs': [3, 4, 5, 6, 8, 13, 15]
        }

    elev = {'bby': 15, 'czc': 462, 'frs': 45}
    name_field = ['press', 'temp', 'rh', 'wspd', 'wdir', 'precip', 'mixr']

    index_field = index_field[station_id]
    locelevation = elev[station_id]
    ''' read the csv file '''
    raw_dframe = pd.read_csv(file_met, header=None)
    ''' parse date columns into a single date col '''
    dates_col = [0, 1, 2]
    raw_dates = raw_dframe.ix[:, dates_col]
    raw_dates.columns = ['Y', 'j', 'HHMM']
    raw_dates['HHMM'] = raw_dates['HHMM'].apply(lambda x: '{0:0>4}'.format(x))
    raw_dates = raw_dates.apply(lambda x: '%s %s %s' %
                                (x['Y'], x['j'], x['HHMM']),
                                axis=1)
    dates_fmt = '%Y %j %H%M'
    dates = raw_dates.apply(lambda x: datetime.strptime(x, dates_fmt))
    ''' make meteo df, assign datetime index, and name columns '''
    meteo = raw_dframe.ix[:, index_field]
    meteo.index = dates
    meteo.columns = name_field
    ''' create a dataframe with regular and continuous 24h period time index
        (this works as a QC for time gaps)
    '''
    nano = 1000000000
    time_diff = np.diff(meteo.index)
    sample_freq = (stats.mode(time_diff)[0][0] / nano).astype(int)  # [seconds]
    fidx = meteo.index[0]  # (start date for dataframe)
    fidx_str = pd.to_datetime(fidx.year * 10000 + fidx.month * 100 + fidx.day,
                              format='%Y%m%d')
    periods = (24 * 60 * 60) / sample_freq
    ts = pd.date_range(fidx_str, periods=periods, freq=str(sample_freq) + 's')
    nanarray = np.empty((periods, 1))
    nanarray[:] = np.nan
    df = pd.DataFrame(nanarray, index=ts)
    meteo = df.join(meteo, how='outer')
    meteo.drop(0, axis=1, inplace=True)
    ''' make field with hourly acum precip '''
    hour = pd.TimeGrouper('H')
    preciph = meteo.precip.groupby(hour).sum()
    meteo = meteo.join(preciph, how='outer', rsuffix='h')
    ''' add thermodynamics '''
    theta = tm.theta1(C=meteo.temp, hPa=meteo.press)
    thetaeq = tm.theta_equiv1(C=meteo.temp, hPa=meteo.press)
    meteo.loc[:, 'theta'] = pd.Series(theta, index=meteo.index)
    meteo.loc[:, 'thetaeq'] = pd.Series(thetaeq, index=meteo.index)
    ''' add sea level pressure '''
    Tv = tm.virtual_temperature(C=meteo.temp, mixing_ratio=meteo.mixr / 1000.)
    slp = tm.sea_level_press(K=Tv + 273.15,
                             Pa=meteo.press * 100,
                             m=locelevation)
    meteo.loc[:, 'sea_levp'] = slp
    ''' assign metadata (prototype, not really used) '''
    units = {
        'press': 'mb',
        'temp': 'C',
        'rh': '%',
        'wspd': 'm s-1',
        'wdir': 'deg',
        'precip': 'mm',
        'mixr': 'g kg-1'
    }
    agl = {
        'press': 'NaN',
        'temp': '10 m',
        'rh': '10 m',
        'wspd': 'NaN',
        'wdir': 'NaN',
        'precip': 'NaN',
        'mixr': 'NaN'
    }
    for n in name_field:
        meteo[n].units = units[n]
        meteo[n].agl = agl[n]
        meteo[n].nan = -9999.999
        meteo[n].sampling_freq = '2 minute'

    return meteo