Example #1
0
def mixing_ratio(_dewpoint):
    """Compute the mixing ratio

    Args:
      dewpoint (temperature): Dew Point temperature

    Returns:
      mixing ratio
    """
    dwpc = _dewpoint.value("C")
    e = 6.112 * np.exp((17.67 * dwpc) / (dwpc + 243.5))
    return dt.mixingratio(0.62197 * e / (1000.0 - e), "KG/KG")
Example #2
0
def mixing_ratio(dewpoint):
    """Compute the mixing ratio

    Args:
      dewpoint (temperature): Dew Point temperature

    Returns:
      mixing ratio
    """
    dwpc = dewpoint.value('C')
    e = 6.112 * np.exp((17.67 * dwpc) / (dwpc + 243.5))
    return dt.mixingratio(0.62197 * e / (1000.0 - e), 'KG/KG')
Example #3
0
def run_calcs(df):
    """Do our maths"""
    df['mixingratio'] = meteorology.mixing_ratio(
        temperature(df['dwpf'].values, 'F')).value('KG/KG')
    df['vapor_pressure'] = mcalc.vapor_pressure(
        1000. * units.mbar,
        df['mixingratio'].values * units('kg/kg')).to(units('kPa'))
    df['saturation_mixingratio'] = (meteorology.mixing_ratio(
        temperature(df['tmpf'].values, 'F')).value('KG/KG'))
    df['saturation_vapor_pressure'] = mcalc.vapor_pressure(
        1000. * units.mbar,
        df['saturation_mixingratio'].values * units('kg/kg')).to(units('kPa'))
    df['vpd'] = df['saturation_vapor_pressure'] - df['vapor_pressure']
    group = df.groupby('year')
    df = group.aggregate(np.average)

    df['dwpf'] = meteorology.dewpoint_from_pq(
        pressure(1000, 'MB'), mixingratio(df['mixingratio'].values,
                                          'KG/KG')).value('F')
    return df
Example #4
0
 def test_dewpoint_from_pq(self):
     """ See if we can produce dew point from pressure and mixing ratio """
     p = datatypes.pressure(1013.25, "MB")
     mr = datatypes.mixingratio(0.012, "kg/kg")
     dwpk = meteorology.dewpoint_from_pq(p, mr)
     self.assertAlmostEqual(dwpk.value("C"), 16.84, 2)
Example #5
0
def plotter(fdict):
    """ Go """
    import matplotlib
    matplotlib.use('agg')
    import matplotlib.pyplot as plt
    pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
    cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
    ctx = get_autoplot_context(fdict, get_description())

    station = ctx['zstation']
    network = ctx['network']
    month = ctx['month']

    nt = NetworkTable(network)

    if month == 'all':
        months = range(1, 13)
    elif month == 'fall':
        months = [9, 10, 11]
    elif month == 'winter':
        months = [12, 1, 2]
    elif month == 'spring':
        months = [3, 4, 5]
    elif month == 'summer':
        months = [6, 7, 8]
    else:
        ts = datetime.datetime.strptime("2000-" + month + "-01", '%Y-%b-%d')
        # make sure it is length two for the trick below in SQL
        months = [ts.month, 999]

    cursor.execute(
        """
        SELECT drct::int as t, dwpf from alldata where station = %s
        and drct is not null and dwpf is not null and dwpf <= tmpf
        and sknt > 3 and drct::int %% 10 = 0
        and extract(month from valid) in %s
        """, (station, tuple(months)))
    sums = np.zeros((361, ), 'f')
    counts = np.zeros((361, ), 'f')
    for row in cursor:
        r = mixing_ratio(temperature(row[1], 'F')).value('KG/KG')
        sums[row[0]] += r
        counts[row[0]] += 1

    sums[0] = sums[360]
    counts[0] = counts[360]

    rows = []
    for i in range(361):
        if counts[i] < 3:
            continue
        r = sums[i] / float(counts[i])
        d = dewpoint_from_pq(pressure(1000, 'MB'),
                             mixingratio(r, 'KG/KG')).value('F')
        rows.append(dict(drct=i, dwpf=d))

    df = pd.DataFrame(rows)
    drct = df['drct']
    dwpf = df['dwpf']

    (fig, ax) = plt.subplots(1, 1)
    ax.bar(drct, dwpf, ec='green', fc='green', width=10, align='center')
    ax.grid(True, zorder=11)
    ax.set_title(("%s [%s]\nAverage Dew Point by Wind Direction (month=%s) "
                  "(%s-%s)\n"
                  "(must have 3+ hourly obs > 3 knots at given direction)") %
                 (nt.sts[station]['name'], station, month.upper(),
                  max([1973, nt.sts[station]['archive_begin'].year
                       ]), datetime.datetime.now().year),
                 size=10)

    ax.set_ylabel("Dew Point [F]")
    ax.set_ylim(min(dwpf) - 5, max(dwpf) + 5)
    ax.set_xlim(-5, 365)
    ax.set_xticks([0, 45, 90, 135, 180, 225, 270, 315, 360])
    ax.set_xticklabels(['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW', 'N'])
    ax.set_xlabel("Wind Direction")

    return fig, df
Example #6
0
File: p76.py Project: akrherz/iem
 def to_dwpf(val):
     return meteorology.dewpoint_from_pq(pressure(1000, 'MB'),
                                         mixingratio(val, 'KG/KG')
                                         ).value('F')
Example #7
0
def plotter(fdict):
    """ Go """
    import matplotlib
    matplotlib.use('agg')
    import matplotlib.pyplot as plt
    ASOS = psycopg2.connect(database='asos', host='iemdb', user='******')
    cursor = ASOS.cursor(cursor_factory=psycopg2.extras.DictCursor)

    station = fdict.get('zstation', 'AMW')
    network = fdict.get('network', 'IA_ASOS')
    month = fdict.get('month', 'all')

    nt = NetworkTable(network)

    if month == 'all':
        months = range(1, 13)
    elif month == 'fall':
        months = [9, 10, 11]
    elif month == 'winter':
        months = [12, 1, 2]
    elif month == 'spring':
        months = [3, 4, 5]
    elif month == 'summer':
        months = [6, 7, 8]
    else:
        ts = datetime.datetime.strptime("2000-"+month+"-01", '%Y-%b-%d')
        # make sure it is length two for the trick below in SQL
        months = [ts.month, 999]

    cursor.execute("""
        SELECT drct::int as t, dwpf from alldata where station = %s
        and drct is not null and dwpf is not null and dwpf <= tmpf
        and sknt > 3 and drct::int %% 10 = 0
        and extract(month from valid) in %s
        """, (station,  tuple(months)))
    sums = np.zeros((361,), 'f')
    counts = np.zeros((361,), 'f')
    for row in cursor:
        r = mixing_ratio(temperature(row[1], 'F')).value('KG/KG')
        sums[row[0]] += r
        counts[row[0]] += 1

    sums[0] = sums[360]
    counts[0] = counts[360]

    rows = []
    for i in range(361):
        if counts[i] < 3:
            continue
        r = sums[i] / float(counts[i])
        d = dewpoint_from_pq(pressure(1000, 'MB'),
                             mixingratio(r, 'KG/KG')
                             ).value('F')
        rows.append(dict(drct=i, dwpf=d))

    df = pd.DataFrame(rows)
    drct = df['drct']
    dwpf = df['dwpf']

    (fig, ax) = plt.subplots(1, 1)
    ax.bar(drct-5, dwpf, ec='green', fc='green', width=10)
    ax.grid(True, zorder=11)
    ax.set_title(("%s [%s]\nAverage Dew Point by Wind Direction (month=%s) "
                  "(%s-%s)\n"
                  "(must have 3+ hourly obs > 3 knots at given direction)"
                  ) % (nt.sts[station]['name'], station, month.upper(),
                       max([1973, nt.sts[station]['archive_begin'].year]),
                       datetime.datetime.now().year), size=10)

    ax.set_ylabel("Dew Point [F]")
    ax.set_ylim(min(dwpf)-5, max(dwpf)+5)
    ax.set_xlim(-5, 365)
    ax.set_xticks([0, 45, 90, 135, 180, 225, 270, 315, 360])
    ax.set_xticklabels(['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW', 'N'])
    ax.set_xlabel("Wind Direction")

    return fig, df
Example #8
0
 def test_mixingratio(self):
     """ Mixing Ratio"""
     mixr = datatypes.mixingratio(10, 'KG/KG')
     self.assertEquals(mixr.value('KG/KG'), 10)
Example #9
0
def test_dewpoint_from_pq():
    """ See if we can produce dew point from pressure and mixing ratio """
    p = datatypes.pressure(1013.25, "MB")
    mr = datatypes.mixingratio(0.012, "kg/kg")
    dwpk = meteorology.dewpoint_from_pq(p, mr)
    assert abs(dwpk.value("C") - 16.84) < 0.01
Example #10
0
def run_model(nc, initts, ncout, oldncout):
    """ Actually run the model, please """
    t2 = nc.variables['t2']
    u10 = nc.variables['u10']
    v10 = nc.variables['v10']
    tm = nc.variables['time']
    lwdown = nc.variables['lwdown']
    swdown = nc.variables['swdown']
    q2 = nc.variables['q2']
    rc = nc.variables['rain_con']
    rn = nc.variables['rain_non']
    lats = nc.variables['latitcrs']
    lons = nc.variables['longicrs']

    # keep masking in-tact as we only write data below when we have it
    otmpk = ma.array(ncout.variables['tmpk'][:])
    otmpk._sharedmask = False
    owmps = ma.array(ncout.variables['wmps'][:])
    owmps._sharedmask = False
    oswout = ma.array(ncout.variables['swout'][:])
    oswout._sharedmask = False
    oh = ma.array(ncout.variables['h'][:])
    oh._sharedmask = False
    olf = ma.array(ncout.variables['lf'][:])
    olf._sharedmask = False
    obdeckt = ma.array(ncout.variables['bdeckt'][:])
    obdeckt._sharedmask = False
    osubsfct = ma.array(ncout.variables['subsfct'][:])
    osubsfct._sharedmask = False
    oifrost = ma.array(ncout.variables['ifrost'][:])
    oifrost._sharedmask = False
    odwpk = ma.array(ncout.variables['dwpk'][:])
    odwpk._sharedmask = False
    ofrostd = ma.array(ncout.variables['frostd'][:])
    ofrostd._sharedmask = False
    oicond = ma.array(ncout.variables['icond'][:])
    oicond._sharedmask = False
    # mini = 200
    # minj = 200
    # maxi = 0
    # maxj = 0
    errorcount = 0

    cmd = "/usr/bin/python model/usr/bin/metro "
    cmd += "--roadcast-start-date %s " % (initts.strftime("%Y-%m-%dT%H:%MZ"),)
    cmd += "--input-forecast isumm5.xml "
    cmd += "--input-observation rwis.xml "
    cmd += "--input-station station.xml "
    cmd += "--output-roadcast roadcast.xml "
    cmd += "--log-file /dev/null "
    # cmd += "--verbose-level 4 "
    cmd += "--use-solarflux-forecast --use-infrared-forecast"

    # We don't have pressure from MM5 (yet)
    pressure = dt.pressure(1000.0, 'MB')

    for i in range(len(nc.dimensions['i_cross'])):
        if errorcount > 100:
            print('Too many errors, aborting....')
            sys.exit()
        # loopstart = datetime.datetime.now()
        for j in range(len(nc.dimensions['j_cross'])):
            lat = lats[i, j]
            lon = lons[i, j]
            # Hey, we only care about Iowa data! -97 40 -90 43.75
            if lat < 40 or lat > 43.75 or lon < -97 or lon > -90:
                continue
            make_rwis(i, j, initts, oldncout)
            o = open('isumm5.xml', 'w')
            o.write("""<?xml version="1.0"?>
<forecast>
  <header>
      <production-date>%s</production-date>
      <version>1.1</version>
      <filetype>forecast</filetype>
      <station-id>ofr</station-id>
  </header>
  <prediction-list>""" % (
                datetime.datetime.utcnow().strftime("%Y-%m-%dT%H:%MZ"),))
            for t in range(1, len(nc.dimensions['time'])):
                ts = initts + datetime.timedelta(minutes=int(tm[t]))
                tmpk = dt.temperature(t2[t, i, j], 'K')
                mr = dt.mixingratio(q2[t, i, j], 'KG/KG')
                dwp = met.dewpoint_from_pq(pressure, mr)
                sped = dt.speed((u10[t, i, j]**2 + v10[t, i, j]**2)**.5, 'MPS')
                # sn - snow accumulation in cm
                # ap - surface pressure in mb
                o.write("""<prediction>
          <forecast-time>%s</forecast-time>
          <at>%.1f</at>
          <td>%.1f</td>
          <ra>%.1f</ra>
          <sn>0.0</sn>
          <ws>%.1f</ws>
          <ap>993.8</ap>
          <wd>300</wd>
          <cc>0</cc>
          <sf>%.1f</sf>
          <ir>%.1f</ir>
      </prediction>
                """ % (ts.strftime("%Y-%m-%dT%H:%MZ"),
                       tmpk.value("C"), dwp.value("C"),
                       (rn[t, i, j] + rc[t, i, j])*10.,
                       sped.value("KMH"),
                       swdown[t, i, j], lwdown[t, i, j])
                )

            o.write("</prediction-list></forecast>")
            o.close()

            proc = subprocess.Popen(cmd, shell=True,
                                    stdout=subprocess.PIPE,
                                    stderr=subprocess.PIPE)
            se = proc.stderr.read().decode("utf-8")
            if se != "":
                errorcount += 1
                print(('metro error i:%03i j:%03i stderr:|%s|'
                       ) % (i, j, se.strip()))
                continue

            # Starts at :20 minutes after start time
            tree = ET.parse('roadcast.xml')
            root = tree.getroot()
            tstep = 0
            for c in root.findall('./prediction-list/prediction'):
                tstep += 1

                # Road surface temperature    st    Celsius
                obdeckt[tstep, i, j] = float(c.find('./st').text) + 273.15
                # Road sub surface temperature* (40 cm)    sst    Celsius
                osubsfct[tstep, i, j] = float(c.find('./sst').text) + 273.15
                # Air temperature    at    Celsius
                otmpk[tstep, i, j] = float(c.find('./at').text) + 273.15
                # Dew point    td    Celsius
                odwpk[tstep, i, j] = float(c.find('./td').text) + 273.15
                # Wind speed    ws    km/h
                owmps[tstep, i, j] = float(c.find('./ws').text)
                # Quantity of snow or ice on the road    sn    cm
                # Quantity of rain on the road    ra    mm
                # Total (1 hr) snow precipitation    qp-sn    cm
                # Total (1 hr) rain precipitation    qp-ra    mm
                # Solar flux    sf    W/m2
                oswout[tstep, i, j] = float(c.find('./sf').text)
                # Incident infra-red flux    ir    W/m2
                # Vapor flux    fv    W/m2
                # Sensible heat    fc    W/m2
                # Anthropogenic flux    fa    W/m2
                # Ground exchange flux    fg    W/m2
                # Blackbody effect    bb    W/m2
                # Phase change    fp    W/m2
                # Road condition    rc    METRo code
                oicond[tstep, i, j] = int(c.find('./rc').text)
                # Octal cloud coverage**    cc    octal
    ncout.variables['tmpk'][:] = otmpk
    ncout.variables['wmps'][:] = dt.speed(owmps, 'KMH').value('MPS')
    ncout.variables['swout'][:] = oswout
    ncout.variables['h'][:] = oh
    ncout.variables['lf'][:] = olf
    ncout.variables['bdeckt'][:] = obdeckt
    ncout.variables['subsfct'][:] = osubsfct
    ncout.variables['ifrost'][:] = oifrost
    ncout.variables['frostd'][:] = ofrostd
    ncout.variables['dwpk'][:] = odwpk
    ncout.variables['icond'][:] = oicond
Example #11
0
File: p76.py Project: iny/iem
 def to_dwpf(val):
     """Unsure why I am doing this, like this"""
     return meteorology.dewpoint_from_pq(pressure(1000, 'MB'),
                                         mixingratio(val,
                                                     'KG/KG')).value('F')
Example #12
0
def test_dewpoint_from_pq():
    """ See if we can produce dew point from pressure and mixing ratio """
    p = datatypes.pressure(1013.25, "MB")
    mr = datatypes.mixingratio(0.012, "kg/kg")
    dwpk = meteorology.dewpoint_from_pq(p, mr)
    assert abs(dwpk.value("C") - 16.84) < 0.01
Example #13
0
 def test_mixingratio(self):
     """ Mixing Ratio"""
     mixr = datatypes.mixingratio(10, 'KG/KG')
     self.assertEquals(mixr.value('KG/KG'), 10)
Example #14
0
 def test_dewpoint_from_pq(self):
     """ See if we can produce dew point from pressure and mixing ratio """
     p = datatypes.pressure(1013.25, "MB")
     mr = datatypes.mixingratio(0.012, "kg/kg")
     dwpk = meteorology.dewpoint_from_pq(p, mr)
     self.assertAlmostEqual(dwpk.value("C"), 16.84, 2)
Example #15
0
def test_mixingratio():
    """ Mixing Ratio"""
    mixr = datatypes.mixingratio(10, "KG/KG")
    assert mixr.value("KG/KG") == 10
Example #16
0
 def to_dwpf(val):
     return meteorology.dewpoint_from_pq(pressure(1000, 'MB'),
                                         mixingratio(val,
                                                     'KG/KG')).value('F')
Example #17
0
def plotter(fdict):
    """ Go """
    import matplotlib
    matplotlib.use('agg')
    import matplotlib.pyplot as plt
    pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
    cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)

    station = fdict.get('zstation', 'AMW')
    network = fdict.get('network', 'IA_ASOS')
    month = fdict.get('month', 'all')

    nt = NetworkTable(network)

    if month == 'all':
        months = range(1, 13)
    elif month == 'fall':
        months = [9, 10, 11]
    elif month == 'winter':
        months = [12, 1, 2]
    elif month == 'spring':
        months = [3, 4, 5]
    elif month == 'summer':
        months = [6, 7, 8]
    else:
        ts = datetime.datetime.strptime("2000-"+month+"-01", '%Y-%b-%d')
        # make sure it is length two for the trick below in SQL
        months = [ts.month, 999]

    cursor.execute("""
        SELECT tmpf::int as t, dwpf from alldata where station = %s
        and tmpf is not null and dwpf is not null and dwpf <= tmpf
        and tmpf >= 0 and tmpf <= 140
        and extract(month from valid) in %s
        """, (station,  tuple(months)))
    sums = np.zeros((140,), 'f')
    counts = np.zeros((140,), 'f')
    for row in cursor:
        r = mixing_ratio(temperature(row[1], 'F')).value('KG/KG')
        sums[row[0]] += r
        counts[row[0]] += 1

    rows = []
    for i in range(140):
        if counts[i] < 3:
            continue
        r = sums[i] / float(counts[i])
        d = dewpoint_from_pq(pressure(1000, 'MB'),
                             mixingratio(r, 'KG/KG')
                             ).value('F')
        rh = relh(temperature(i, 'F'), temperature(d, 'F')).value('%')
        rows.append(dict(tmpf=i, dwpf=d, rh=rh))

    df = pd.DataFrame(rows)
    tmpf = df['tmpf']
    dwpf = df['dwpf']
    rh = df['rh']

    (fig, ax) = plt.subplots(1, 1)
    ax.bar(tmpf-0.5, dwpf, ec='green', fc='green', width=1)
    ax.grid(True, zorder=11)
    ax.set_title(("%s [%s]\nAverage Dew Point by Air Temperature (month=%s) "
                  "(%s-%s)\n"
                  "(must have 3+ hourly observations at the given temperature)"
                  ) % (nt.sts[station]['name'], station, month.upper(),
                       nt.sts[station]['archive_begin'].year,
                       datetime.datetime.now().year), size=10)

    ax.plot([0, 140], [0, 140], color='b')
    ax.set_ylabel("Dew Point [F]")
    y2 = ax.twinx()
    y2.plot(tmpf, rh, color='k')
    y2.set_ylabel("Relative Humidity [%] (black line)")
    y2.set_yticks([0, 5, 10, 25, 50, 75, 90, 95, 100])
    y2.set_ylim(0, 100)
    ax.set_ylim(0, max(tmpf)+2)
    ax.set_xlim(0, max(tmpf)+2)
    ax.set_xlabel("Air Temperature $^\circ$F")

    return fig, df
Example #18
0
def plotter(fdict):
    """ Go """
    import matplotlib
    matplotlib.use('agg')
    import matplotlib.pyplot as plt
    pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
    cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
    ctx = get_autoplot_context(fdict, get_description())
    station = ctx['zstation']
    network = ctx['network']
    month = ctx['month']

    nt = NetworkTable(network)

    if month == 'all':
        months = range(1, 13)
    elif month == 'fall':
        months = [9, 10, 11]
    elif month == 'winter':
        months = [12, 1, 2]
    elif month == 'spring':
        months = [3, 4, 5]
    elif month == 'summer':
        months = [6, 7, 8]
    else:
        ts = datetime.datetime.strptime("2000-"+month+"-01", '%Y-%b-%d')
        # make sure it is length two for the trick below in SQL
        months = [ts.month, 999]

    cursor.execute("""
        SELECT tmpf::int as t, dwpf from alldata where station = %s
        and tmpf is not null and dwpf is not null and dwpf <= tmpf
        and tmpf >= 0 and tmpf <= 140
        and extract(month from valid) in %s
        """, (station,  tuple(months)))
    sums = np.zeros((140,), 'f')
    counts = np.zeros((140,), 'f')
    for row in cursor:
        r = mixing_ratio(temperature(row[1], 'F')).value('KG/KG')
        sums[row[0]] += r
        counts[row[0]] += 1

    rows = []
    for i in range(140):
        if counts[i] < 3:
            continue
        r = sums[i] / float(counts[i])
        d = dewpoint_from_pq(pressure(1000, 'MB'),
                             mixingratio(r, 'KG/KG')
                             ).value('F')
        rh = relh(temperature(i, 'F'), temperature(d, 'F')).value('%')
        rows.append(dict(tmpf=i, dwpf=d, rh=rh))

    df = pd.DataFrame(rows)
    tmpf = df['tmpf']
    dwpf = df['dwpf']
    rh = df['rh']

    (fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
    ax.bar(tmpf-0.5, dwpf, ec='green', fc='green', width=1)
    ax.grid(True, zorder=11)
    ax.set_title(("%s [%s]\nAverage Dew Point by Air Temperature (month=%s) "
                  "(%s-%s)\n"
                  "(must have 3+ hourly observations at the given temperature)"
                  ) % (nt.sts[station]['name'], station, month.upper(),
                       nt.sts[station]['archive_begin'].year,
                       datetime.datetime.now().year), size=10)

    ax.plot([0, 140], [0, 140], color='b')
    ax.set_ylabel("Dew Point [F]")
    y2 = ax.twinx()
    y2.plot(tmpf, rh, color='k')
    y2.set_ylabel("Relative Humidity [%] (black line)")
    y2.set_yticks([0, 5, 10, 25, 50, 75, 90, 95, 100])
    y2.set_ylim(0, 100)
    ax.set_ylim(0, max(tmpf)+2)
    ax.set_xlim(0, max(tmpf)+2)
    ax.set_xlabel("Air Temperature $^\circ$F")

    return fig, df
Example #19
0
def test_mixingratio():
    """ Mixing Ratio"""
    mixr = datatypes.mixingratio(10, 'KG/KG')
    assert mixr.value('KG/KG') == 10