def absolute(u, v, lon, lat):
"""absolute(u,v,lon,lat):
Calculates the absolute vorticity (curl(u,v)) of a wind field using
a basic centred difference scheme. The centred differencing means
the returned array is reduced in size by 2 elements in each
dimension.
zeta((len(lat)-2,len(lon)-2)) = vorticity.absolute(u,v,lon,lat)
"""
dx = numpy.zeros((len(lat), len(lon)-2))
dy = numpy.zeros((len(lat)-2, len(lon)))
du = numpy.zeros((len(lat)-2, len(lon)))
dv = numpy.zeros((len(lat), len(lon)-2))
zeta = numpy.zeros((len(lat)-2, len(lon)-2))
for i in xrange(1, len(lon)-1):
for j in xrange(0, len(lat)):
dx[j,i-1] = metutils.convert((lon[i+1]-lon[i-1])*numpy.cos(pi*lat[j]/180.), "deg", "m")
dv[j,i-1] = v[i+1,j]-v[i-1,j]
for i in xrange(0, len(lon)):
for j in xrange(1, len(lat)-1):
dy[j-1,i] = metutils.convert((lat[j+1]-lat[j-1]), "deg", "m")
du[j-1,i] = u[i,j+1]-u[i,j-1]
for i in xrange(len(lat)-2):
for j in xrange(len(lon)-2):
zeta[i,j] = dv[i,j]/dx[i,j] - du[i,j]/dy[i,j] + metutils.coriolis(lat[i+1])
return zeta
def filterPressure(pressure,
inputPressureUnits='hPa',
missingValue=sys.maxint):
"""
Filter pressure values to remove any non-physical values.
:param pressure: input pressure values to check.
:param str inputPressureUnits: The units of the pressure values.
Can be one of ``hPa``, ``Pa``, ``kPa``,
``Pascals`` or ``mmHg``.
:param missingValue: replace all null values in the input data
with this value.
:type pressure: :class:`numpy.ndarray`
:type missingValue: int or float (default ``sys.maxint``)
:returns: :class:`numpy.ndarray` with only valid pressure values.
"""
novalue_index = np.where(pressure == missingValue)
pressure = metutils.convert(pressure, inputPressureUnits, "hPa")
pressure[novalue_index] = missingValue
# Convert any non-physical central pressure values to maximum integer
# This is required because IBTrACS has a mix of missing value codes
# (i.e. -999, 0, 9999) in the same global dataset.
pressure = np.where((pressure < 600) | (pressure > 1100), missingValue,
pressure)
return pressure
def filterPressure(pressure, inputPressureUnits='hPa',
missingValue=sys.maxint):
"""
Filter pressure values to remove any non-physical values.
:param pressure: input pressure values to check.
:param str inputPressureUnits: The units of the pressure values.
Can be one of ``hPa``, ``Pa``, ``kPa``,
``Pascals`` or ``mmHg``.
:param missingValue: replace all null values in the input data
with this value.
:type pressure: :class:`numpy.ndarray`
:type missingValue: int or float (default ``sys.maxint``)
:returns: :class:`numpy.ndarray` with only valid pressure values.
"""
novalue_index = np.where(pressure == missingValue)
pressure = metutils.convert(pressure, inputPressureUnits, "hPa")
pressure[novalue_index] = missingValue
# Convert any non-physical central pressure values to maximum integer
# This is required because IBTrACS has a mix of missing value codes
# (i.e. -999, 0, 9999) in the same global dataset.
pressure = np.where((pressure < 600) | (pressure > 1100),
missingValue, pressure)
return pressure
def grdSave(filename, data, lon, lat, delta, delimiter=' ', nodata=-9999,
fmt='%.10e', coords='latlon'):
"""
Save formatted data to an ascii grid format file.
The files have 6 header lines describing the data, followed by the
data in a gridded format.
Headers:
ncols
nrows
xllcorner
yllcorner
cellsize
NODATA_value
Usage:
grdSave(filename, data, lon, lat, delta, delimiter=' ',
nodata=-9999, fmt='%.10e, coords='latlon')
"""
if filename and os.path.isfile(filename):
if filename.endswith('.gz'):
import gzip
fh = gzip.open(filename, 'wb')
else:
fh = file(filename, 'w')
elif hasattr(filename, 'seek'):
fh = filename
else:
try:
fh = open(filename,'w')
except:
raise ValueError('Filename must be a string or file handle')
if coords == 'UTM':
zone, xllcorner, yllcorner = LLtoUTM(lat.min(),lon.min())
delta = metutils.convert(delta, "deg", "m")
else:
# Assume geographic coordinates
xllcorner = lon.min()
yllcorner = lat.min()
fh.write('ncols '+str(len(lon))+'\n')
fh.write('nrows '+str(len(lat))+'\n')
fh.write('xllcorner '+str(xllcorner)+'\n')
fh.write('yllcorner '+str(yllcorner)+'\n')
fh.write('cellsize '+str(delta)+'\n')
fh.write('NODATA_value '+str(nodata)+'\n')
X = numpy.array(data)
origShape = None
if len(X.shape) == 1:
origShape = X.shape
X.shape = len(X), 1
for row in X:
fh.write(delimiter.join([fmt%val for val in row]) + '\n')
fh.close()
if origShape is not None:
X.shape = origShape
def absolute(u, v, lon, lat):
"""
Calculates the absolute vorticity (f + curl(u,v)) of a wind field using
a basic centred difference scheme. The centred differencing means
the returned array is reduced in size by 2 elements in each
dimension.
:param u: 2-d array of eastward vector component.
:param v: 2-d array of northward vector component.
:param lon: 1-d array of longitudes of grid that defines the vector field.
:param lat: 1-d array of latitudes of grid that defines the vector field.
:type u: :class:`numpy.ndarray`
:type v: :class:`numpy.ndarray`
:type lon: :class:`numpy.ndarray`
:type lat: :class:`numpy.ndarray`
:return: 2-d :class:`numpy.ndarray` of absolute vorticity values.
"""
dx = np.zeros((len(lat), len(lon) - 2))
dy = np.zeros((len(lat) - 2, len(lon)))
du = np.zeros((len(lat) - 2, len(lon)))
dv = np.zeros((len(lat), len(lon) - 2))
zeta = np.zeros((len(lat) - 2, len(lon) - 2))
for i in xrange(1, len(lon) - 1):
for j in xrange(0, len(lat)):
dx[j, i-1] = metutils.convert((lon[i+1] - lon[i-1]) * \
np.cos(np.pi*lat[j]/180.),
"deg", "m")
dv[j, i - 1] = v[i + 1, j] - v[i - 1, j]
for i in xrange(0, len(lon)):
for j in xrange(1, len(lat) - 1):
dy[j - 1, i] = metutils.convert((lat[j + 1] - lat[j - 1]), "deg",
"m")
du[j - 1, i] = u[i, j + 1] - u[i, j - 1]
for i in xrange(len(lat) - 2):
for j in xrange(len(lon) - 2):
zeta[i, j] = dv[i, j]/dx[i, j] - du[i, j]/dy[i, j] + \
metutils.coriolis(lat[i+1])
return zeta
def absolute(u, v, lon, lat):
"""
Calculates the absolute vorticity (f + curl(u,v)) of a wind field using
a basic centred difference scheme. The centred differencing means
the returned array is reduced in size by 2 elements in each
dimension.
:param u: 2-d array of eastward vector component.
:param v: 2-d array of northward vector component.
:param lon: 1-d array of longitudes of grid that defines the vector field.
:param lat: 1-d array of latitudes of grid that defines the vector field.
:type u: :class:`numpy.ndarray`
:type v: :class:`numpy.ndarray`
:type lon: :class:`numpy.ndarray`
:type lat: :class:`numpy.ndarray`
:return: 2-d :class:`numpy.ndarray` of absolute vorticity values.
"""
dx = np.zeros((len(lat), len(lon) - 2))
dy = np.zeros((len(lat) - 2, len(lon)))
du = np.zeros((len(lat) - 2, len(lon)))
dv = np.zeros((len(lat), len(lon) - 2))
zeta = np.zeros((len(lat) - 2, len(lon) - 2))
for i in xrange(1, len(lon) - 1):
for j in xrange(0, len(lat)):
dx[j, i-1] = metutils.convert((lon[i+1] - lon[i-1]) * \
np.cos(np.pi*lat[j]/180.),
"deg", "m")
dv[j, i-1] = v[i+1, j] - v[i-1, j]
for i in xrange(0, len(lon)):
for j in xrange(1, len(lat) - 1):
dy[j-1, i] = metutils.convert((lat[j+1] - lat[j-1]), "deg", "m")
du[j-1, i] = u[i, j+1] - u[i, j-1]
for i in xrange(len(lat) - 2):
for j in xrange(len(lon) - 2):
zeta[i, j] = dv[i, j]/dx[i, j] - du[i, j]/dy[i, j] + \
metutils.coriolis(lat[i+1])
return zeta
def filterPressure(pressure, inputPressureUnits='hPa',
missingValue=sys.maxint):
"""
Filter pressure values to remove any non-physical values
"""
novalue_index = np.where(pressure == missingValue)
pressure = metutils.convert(pressure, inputPressureUnits, "hPa")
pressure[novalue_index] = missingValue
# Convert any non-physical central pressure values to maximum integer
# This is required because IBTrACS has a mix of missing value codes
# (i.e. -999, 0, 9999) in the same global dataset.
pressure = np.where((pressure < 600) | (pressure > 1100),
missingValue, pressure)
return pressure
def ltmPressure(jdays, time, lon, lat, ncfile):
"""
Extract pressure value from a daily long-term mean SLP dataset at the
given day of year and lon,lat position
To use this function (and hence some form of daily LTM SLP data) requires
knowledge of the day of year.
:param jdays: Julian day (day of year) values.
:param time: Time of day for each observation (fraction of a day).
:param lon: Longitude of TC position.
:param lat: Latitude of TC position.
:param str ncfile: Path to netCDF file containing daily long-term mean
sea level pressure data.
:type jdays: :class:`numpy.ndarray`
:type time: :class:`numpy.ndarray`
:type lon: :class:`numpy.ndarray`
:type lat: :class:`numpy.ndarray`
:returns: :class:`numpy.ndarray` of long-term mean sea level pressure
values at the day of year and positions given.
"""
jtime = jdays + np.modf(time)[0]
coords = np.array([jtime, lat, lon])
LOG.debug("Sampling data from MSLP data in {0}".format(ncfile))
ncobj = nctools.ncLoadFile(ncfile)
slpunits = getattr(ncobj.variables['slp'], 'units')
data = nctools.ncGetData(ncobj, 'slp')
# Get the MSLP by interpolating to the location of the TC:
penv = interp3d.interp3d(data,
coords,
scale=[365., 180., 360.],
offset=[0., -90., 0.])
penv = metutils.convert(penv, slpunits, 'hPa')
del data
ncobj.close()
del ncobj
return penv
def ltmPressure(jdays, time, lon, lat, ncfile):
"""
Extract pressure value from a daily long-term mean SLP dataset at the
given day of year and lon,lat position
To use this function (and hence some form of daily LTM SLP data) requires
knowledge of the day of year.
:param jdays: Julian day (day of year) values.
:param time: Time of day for each observation (fraction of a day).
:param lon: Longitude of TC position.
:param lat: Latitude of TC position.
:param str ncfile: Path to netCDF file containing daily long-term mean
sea level pressure data.
:type jdays: :class:`numpy.ndarray`
:type time: :class:`numpy.ndarray`
:type lon: :class:`numpy.ndarray`
:type lat: :class:`numpy.ndarray`
:returns: :class:`numpy.ndarray` of long-term mean sea level pressure
values at the day of year and positions given.
"""
jtime = jdays + np.modf(time)[0]
coords = np.array([jtime, lat, lon])
logger.debug("Sampling data from MSLP data in {0}".format(ncfile))
ncobj = nctools.ncLoadFile(ncfile)
slpunits = getattr(ncobj.variables['slp'], 'units')
data = nctools.ncGetData(ncobj, 'slp')
# Get the MSLP by interpolating to the location of the TC:
penv = interp3d.interp3d(data, coords, scale=[365., 180., 360.],
offset=[0., -90., 0.])
penv = metutils.convert(penv, slpunits, 'hPa')
del data
ncobj.close()
del ncobj
return penv
def ltmPressure(jdays, time, lon, lat, ncfile):
"""
Extract pressure value from a daily long-term mean SLP dataset at the
given day of year and lon,lat position
To use this function (and hence some form of daily LTM SLP data) requires
knowledge of the day of year.
"""
jtime = jdays + np.modf(time)[0]
coords = np.array([jtime, lat, lon])
logger.debug("Sampling data from MSLP data in {0}".format(ncfile))
ncobj = nctools.ncLoadFile(ncfile)
slpunits = getattr(ncobj.variables['slp'], 'units')
data = nctools.ncGetData(ncobj, 'slp')
# Get the MSLP by interpolating to the location of the TC:
penv = interp3d.interp3d(data, coords)
penv = metutils.convert(penv, slpunits, 'hPa')
del data
ncobj.close()
del ncobj
return penv
def dist2GC(cLon1, cLat1, cLon2, cLat2, lonArray, latArray, units="km"):
"""
Calculate the distance between an array of points and the great circle
joining two (other) points.
All input values are in degrees.
By default returns distance in km, other units specified by the
'units' kwarg.
Based on a cross-track error formulation from:
http://williams.best.vwh.net/avform.htm#XTE
"""
# Calculate distance and bearing from first point to array of points:
dist_ = gridLatLonDist(cLon1, cLat1, lonArray, latArray, units="rad")
bear_ = gridLatLonBear(cLon1, cLat1, lonArray, latArray)
#bearing of the cyclone:
cyc_bear_ = latLon2Azi([cLon1, cLon2], [cLat1, cLat2])
dist2GC_ = np.asin(np.sin(dist_) * np.sin(bear_ - cyc_bear_))
distance = metutils.convert(dist2GC_, "rad", units)
return distance
def dist2GC(cLon1, cLat1, cLon2, cLat2, lonArray, latArray, units="km"):
"""
Calculate the distance between an array of points and the great
circle joining two (other) points. All input values are in
degrees. By default returns distance in km, other units specified
by the 'units' kwarg.
Based on a cross-track error formulation from:
http://williams.best.vwh.net/avform.htm#XTE
:param float cLon1: Longitude of first point.
:param float cLat1: Latitude of first point.
:param float cLon2: Longitude of second point.
:param float cLat2: Latitude of second point.
:param lonArray: :class:`numpy.ndarray` of longitudes for which
the distance to the line joining the two points
will be calculated.
:param latArray: :class:`numpy.ndarray` of latitudes for which the
distance to the line joining the two points will
be calculated.
:returns: 2-d array of distances between the array points and the
line joining two points.
:rtype: :class:`numpy.ndarray`
"""
# Calculate distance and bearing from first point to array of points:
dist_ = gridLatLonDist(cLon1, cLat1, lonArray, latArray, units="rad")
bear_ = gridLatLonBear(cLon1, cLat1, lonArray, latArray)
#bearing of the cyclone:
cyc_bear_ = latLon2Azi([cLon1, cLon2], [cLat1, cLat2])
dist2GC_ = np.arcsin(np.sin(dist_) * np.sin(bear_ - cyc_bear_))
distance = metutils.convert(dist2GC_, "rad", units)
return distance
def dist2GC(cLon1, cLat1, cLon2, cLat2, lonArray, latArray, units="km"):
"""
Calculate the distance between an array of points and the great
circle joining two (other) points. All input values are in
degrees. By default returns distance in km, other units specified
by the 'units' kwarg.
Based on a cross-track error formulation from:
http://williams.best.vwh.net/avform.htm#XTE
:param float cLon1: Longitude of first point.
:param float cLat1: Latitude of first point.
:param float cLon2: Longitude of second point.
:param float cLat2: Latitude of second point.
:param lonArray: :class:`numpy.ndarray` of longitudes for which
the distance to the line joining the two points
will be calculated.
:param latArray: :class:`numpy.ndarray` of latitudes for which the
distance to the line joining the two points will
be calculated.
:returns: 2-d array of distances between the array points and the
line joining two points.
:rtype: :class:`numpy.ndarray`
"""
# Calculate distance and bearing from first point to array of points:
dist_ = gridLatLonDist(cLon1, cLat1, lonArray, latArray, units="rad")
bear_ = gridLatLonBear(cLon1, cLat1, lonArray, latArray)
#bearing of the cyclone:
cyc_bear_ = latLon2Azi([cLon1, cLon2], [cLat1, cLat2])
dist2GC_ = np.arcsin(np.sin(dist_) * np.sin(bear_ - cyc_bear_))
distance = metutils.convert(dist2GC_, "rad", units)
return distance
def grdSave(filename,
data,
lon,
lat,
delta,
delimiter=' ',
nodata=-9999,
fmt='%.10e',
coords='latlon'):
"""
Save formatted data to an ascii grid format file.
The files have 6 header lines describing the data, followed by the
data in a gridded format.
:param str filename: Path to the file to be written.
:param data: 2-d array of data values to store.
:param lon: Array of longitudes corresponding to data points.
:param lat: Array of latitudes corresponding to data points.
:param float delta: Spacing between grid points.
:param str delimiter: Delimiter to put between data points (default ' ').
:param float nodata: Value to indicate missing values (default -9999).
:param str fmt: String format statement.
:param str coords: Optionally store the data in UTM
coordinates. Default is to store the data in
geographic coordinates (``coords='latlon'``).
If ``coords='UTM'``, then the latitude &
longitudes are converted to the local UTM
coordinate system.
:raises ValueError: If the ``filename`` is not a string of file handle.
Usage::
>>> grdSave(filename, data, lon, lat, delta, delimiter=' ',
nodata=-9999, fmt='%.10e, coords='latlon')
"""
if filename and os.path.isfile(filename):
if filename.endswith('.gz'):
import gzip
fh = gzip.open(filename, 'wb')
else:
fh = file(filename, 'w')
elif hasattr(filename, 'seek'):
fh = filename
else:
try:
fh = open(filename, 'w')
except:
raise ValueError('Filename must be a string or file handle')
if coords == 'UTM':
zone, xllcorner, yllcorner = LLtoUTM(lat.min(), lon.min())
delta = metutils.convert(delta, "deg", "m")
else:
# Assume geographic coordinates
xllcorner = lon.min()
yllcorner = lat.min()
fh.write('ncols ' + str(len(lon)) + '\n')
fh.write('nrows ' + str(len(lat)) + '\n')
fh.write('xllcorner ' + str(xllcorner) + '\n')
fh.write('yllcorner ' + str(yllcorner) + '\n')
fh.write('cellsize ' + str(delta) + '\n')
fh.write('NODATA_value ' + str(nodata) + '\n')
X = numpy.array(data)
origShape = None
if len(X.shape) == 1:
origShape = X.shape
X.shape = len(X), 1
for row in X:
fh.write(delimiter.join([fmt % val for val in row]) + '\n')
fh.close()
if origShape is not None:
X.shape = origShape
def loadTrackFile(configFile, trackFile, source, missingValue=0,
calculateWindSpeed=True):
"""
Load TC track data from the given input file, from a specified source.
The configFile is a configuration file that contains a section called
'source' that describes the data.
This returns a collection of :class:`Track` objects that contains
the details of the TC tracks in the input file.
:param str configFile: Configuration file with a section ``source``.
:param str trackFile: Path to a csv-formatted file containing TC data.
:pararm str source: Name of the source format of the TC data. There
*must* be a section in ``configFile`` matching
this string, containing the details of the format
of the data.
:param missingValue: Replace all null values in the input data with
this value (default=0).
:param boolean calculateWindSpeed: Calculate maximum wind speed using
a pressure-wind relation described
in :func:`maxWindSpeed`
:returns: A collection of :class:`Track` objects.
If any of the variables are not present in the input
dataset, they are (where possible) calculated
(date/time/windspeed), sampled from default datasets
(e.g. environmental pressure) or set to the missing value.
Example::
>>> tracks = loadTrackFile('tcrm.ini', 'IBTRaCS.csv', 'IBTrACS' )
"""
logger.info("Loading %s" % trackFile)
inputData = colReadCSV(configFile, trackFile, source) #,
#nullValue=missingValue)
config = ConfigParser()
config.read(configFile)
inputSpeedUnits = config.get(source, 'SpeedUnits')
inputPressureUnits = config.get(source, 'PressureUnits')
inputLengthUnits = config.get(source, 'LengthUnits')
inputDateFormat = config.get(source, 'DateFormat')
if config.getboolean('DataProcess', 'FilterSeasons'):
startSeason = config.getint('DataProcess', 'StartSeason')
idx = np.where(inputData['season'] >= startSeason)[0]
inputData = inputData[idx]
# Determine the initial TC positions...
indicator = getInitialPositions(inputData)
# Sort date/time information
if 'age' in inputData.dtype.names:
year, month, day, hour, minute, datetimes = parseAge(inputData, indicator)
timeElapsed = inputData['age']
else:
year, month, day, hour, minute, datetimes = parseDates(inputData, indicator,
inputDateFormat)
timeElapsed = getTimeElapsed(indicator, year, month, day, hour, minute)
# Time between observations:
dt = getTimeDelta(year, month, day, hour, minute)
# Calculate julian days
jdays = julianDays(year, month, day, hour, minute)
lat = np.array(inputData['lat'], 'd')
lon = np.mod(np.array(inputData['lon'], 'd'), 360)
delta_lon = np.diff(lon)
delta_lat = np.diff(lat)
# Split into separate tracks if large jump occurs (delta_lon > 10 degrees
# or delta_lat > 5 degrees)
# This avoids two tracks being accidentally combined when seasons and track
# numbers match but basins are different as occurs in the IBTrACS dataset.
# This problem can also be prevented if the 'tcserialno' column is
# specified.
indicator[np.where(delta_lon > 10)[0] + 1] = 1
indicator[np.where(delta_lat > 5)[0] + 1] = 1
pressure = filterPressure(np.array(inputData['pressure'], 'd'),
inputPressureUnits, missingValue)
try:
windspeed = np.array(inputData['vmax'], 'd')
novalue_index = np.where(windspeed == sys.maxint)
windspeed = metutils.convert(windspeed, inputSpeedUnits, "mps")
windspeed[novalue_index] = missingValue
except (ValueError,KeyError):
logger.debug("No max wind speed data - all values will be zero")
windspeed = np.zeros(indicator.size, 'f')
assert lat.size == indicator.size
assert lon.size == indicator.size
assert pressure.size == indicator.size
try:
rmax = np.array(inputData['rmax'])
novalue_index = np.where(rmax == missingValue)
rmax = metutils.convert(rmax, inputLengthUnits, "km")
rmax[novalue_index] = missingValue
except (ValueError, KeyError):
logger.debug("No radius to max wind data - all values will be zero")
rmax = np.zeros(indicator.size, 'f')
if 'penv' in inputData.dtype.names:
penv = np.array(inputData['penv'], 'd')
else:
logger.debug("No ambient MSLP data in this input file")
logger.debug("Sampling data from MSLP data defined in "
"configuration file")
# Warning: using sampled data will likely lead to some odd behaviour
# near the boundary of the MSLP grid boundaries - higher resolution
# MSLP data will decrease this unusual behaviour.
try:
ncfile = cnfGetIniValue(configFile, 'Input', 'MSLPFile')
except:
logger.exception("No input MSLP file specified in configuration")
raise
time = getTime(year, month, day, hour, minute)
penv = ltmPressure(jdays, time, lon, lat, ncfile)
speed, bearing = getSpeedBearing(indicator, lon, lat, dt,
missingValue=missingValue)
if calculateWindSpeed:
windspeed = maxWindSpeed(indicator, dt, lon, lat, pressure, penv)
TCID = np.cumsum(indicator)
data = np.empty(len(indicator),
dtype={
'names': trackFields,
'formats': trackTypes
} )
for key, value in zip(trackFields, [indicator, TCID, year, month,
day, hour, minute, timeElapsed, datetimes,
lon, lat, speed, bearing,
pressure, windspeed, rmax, penv]):
data[key] = value
tracks = []
n = np.max(TCID)
for i in range(1, n + 1):
track = Track(data[TCID == i])
track.trackId = (i, n)
track.trackfile = trackFile
getMinPressure(track, missingValue)
getMaxWind(track, missingValue)
tracks.append(track)
return tracks
def maxWindSpeed(index, deltatime, lon, lat, pressure, penv,
gustfactor=0.9524):
"""
Calculate the 10-minute-mean maximum wind speed from the central
pressure deficit, using the method described in Holland et al. (2010).
:param indicator: Array (values of 1 or 0) indicating the beginning of
a new TC in the input dataset.
:param deltatime: Time difference (in hours) between each point in the
record.
:param lon: Longitudes of TC postions.
:param lat: Latitudes of TC positions.
:param pressure: Central pressure estimate of TCs (hPa).
:param penv: Environmental pressure estimates for each TC postion (hPa).
:param float gf: Gust factor - default value represents converting from a
1-minute sustained wind speed to a 10-minute mean wind
speed. Based on Harper et al. 2010, WMO-TD1555.
:type indicator: :class:`numpy.ndarray`
:type deltatime: :class:`numpy.ndarray`
:type lon: :class:`numpy.ndarray`
:type lat: :class:`numpy.ndarray`
:type pressure: :class:`numpy.ndarray`
:type penv: :class:`numpy.ndarray`
:returns: :class:`numpy.ndarray` of estimated wind speed based on
central pressure deficit.
Example::
>>> v = maxWindSpeed(indicator, dt, lon, lat, pressure, penv)
"""
# Speed and bearing:
speed, bearing = getSpeedBearing(index, lon, lat, deltatime)
speed = metutils.convert(speed, 'kmh', 'mps')
np.putmask(speed, speed > 10e+3, 0)
# Pressure deficit:
deltap = penv - pressure
# Pressure rate of change
dpt = np.zeros(index.size, 'f')
dpt[1:] = np.diff(pressure)
dpdt = dpt / deltatime
np.putmask(dpdt, index, 0)
np.putmask(dpdt, np.isnan(dpdt) |
np.isinf(dpdt) |
(np.abs(dpdt) > 5.), 0)
# Estimated pressure at the radius of maximum wind:
prmw = pressure + deltap / 3.7
# Calculate thermodynamic variables at RMW:
tsurf = 28.0 - 3 * (np.abs(lat) - 10.) / 20.
qmix = 0.9 * (3.802 / prmw) * np.exp(17.67 * tsurf / (243.5 + tsurf))
tvs = (tsurf + 273.15) * (1. + 0.81 * qmix)
rho = prmw * 100. / (tvs * 287.04)
chi = 0.6 * (1.0 - deltap / 215.)
beta = -0.000044 * np.power(deltap, 2.) + \
0.01 * deltap + 0.03 * dpdt - 0.014 * np.abs(lat) + \
0.15 * np.power(speed, chi) + 1.
# Holland's P-W relation derives a 1-minute mean wind speed, so we often
# need to convert to some other averaging period. I use the recommendations
# of Harper et al. (2010) WMO TD-1555:
# Common values are( Assuming "At-sea" conditions):
# 10-min mean: 0.95 (default)
# 3-second gust: 1.11
v = gustfactor * np.sqrt(deltap * 100 * beta / (rho * np.exp(1.)))
np.putmask(v, (np.isnan(v) |
np.isinf(v) |
(pressure >= 10e+7) |
(pressure <= 0) |
(speed >= 10e+7)), 0)
return v
def grdSave(filename, data, lon, lat, delta, delimiter=' ', nodata=-9999,
fmt='%.10e', coords='latlon'):
"""
Save formatted data to an ascii grid format file.
The files have 6 header lines describing the data, followed by the
data in a gridded format.
:param str filename: Path to the file to be written.
:param data: 2-d array of data values to store.
:param lon: Array of longitudes corresponding to data points.
:param lat: Array of latitudes corresponding to data points.
:param float delta: Spacing between grid points.
:param str delimiter: Delimiter to put between data points (default ' ').
:param float nodata: Value to indicate missing values (default -9999).
:param str fmt: String format statement.
:param str coords: Optionally store the data in UTM
coordinates. Default is to store the data in
geographic coordinates (``coords='latlon'``).
If ``coords='UTM'``, then the latitude &
longitudes are converted to the local UTM
coordinate system.
:raises ValueError: If the ``filename`` is not a string of file handle.
Usage::
>>> grdSave(filename, data, lon, lat, delta, delimiter=' ',
nodata=-9999, fmt='%.10e, coords='latlon')
"""
if filename and os.path.isfile(filename):
if filename.endswith('.gz'):
import gzip
fh = gzip.open(filename, 'wb')
else:
fh = file(filename, 'w')
elif hasattr(filename, 'seek'):
fh = filename
else:
try:
fh = open(filename,'w')
except:
raise ValueError('Filename must be a string or file handle')
if coords == 'UTM':
zone, xllcorner, yllcorner = LLtoUTM(lat.min(),lon.min())
delta = metutils.convert(delta, "deg", "m")
else:
# Assume geographic coordinates
xllcorner = lon.min()
yllcorner = lat.min()
fh.write('ncols '+str(len(lon))+'\n')
fh.write('nrows '+str(len(lat))+'\n')
fh.write('xllcorner '+str(xllcorner)+'\n')
fh.write('yllcorner '+str(yllcorner)+'\n')
fh.write('cellsize '+str(delta)+'\n')
fh.write('NODATA_value '+str(nodata)+'\n')
X = numpy.array(data)
origShape = None
if len(X.shape) == 1:
origShape = X.shape
X.shape = len(X), 1
for row in X:
fh.write(delimiter.join([fmt%val for val in row]) + '\n')
fh.close()
if origShape is not None:
X.shape = origShape
def main():
"""
Handle command line arguments and call processing functions
"""
p = argparse.ArgumentParser()
p.add_argument('-c', '--config_file', help="Configuration file")
p.add_argument('-v',
'--verbose',
help="Verbose output",
action='store_true')
p.add_argument('-y', '--year', help="Year to process (1979-2020)")
args = p.parse_args()
configFile = args.config_file
config = ConfigParser()
config.read(configFile)
logFile = config.get('Logging', 'LogFile')
logdir = dirname(realpath(logFile))
# if log file directory does not exist, create it
if not isdir(logdir):
try:
os.makedirs(logdir)
except OSError:
logFile = pjoin(os.getcwd(), 'pcmin.log')
logLevel = config.get('Logging', 'LogLevel')
verbose = config.getboolean('Logging', 'Verbose')
datestamp = config.getboolean('Logging', 'Datestamp')
if args.verbose:
verbose = True
if comm.size > 1 and comm.rank > 0:
logFile += '-' + str(comm.rank)
verbose = False
if datestamp:
base, ext = splitext(logFile)
curdate = datetime.datetime.now()
curdatestr = curdate.strftime('%Y%m%d%H%M')
logfile = f"{base}.{curdatestr}.{ext.lstrip('.')}"
logging.basicConfig(level=logLevel,
format="%(asctime)s: %(funcName)s: %(message)s",
filename=logfile,
filemode='w',
datefmt="%Y-%m-%d %H:%M:%S")
if verbose:
console = logging.StreamHandler(sys.stdout)
console.setLevel(getattr(logging, logLevel))
formatter = logging.Formatter(
'%(asctime)s: %(funcName)s: %(message)s',
datefmt='%H:%M:%S',
)
console.setFormatter(formatter)
LOGGER.addHandler(console)
LOGGER.info(f"Started {sys.argv[0]} (pid {os.getpid()})")
LOGGER.info(f"Log file: {logfile} (detail level {logLevel})")
LOGGER.info(f"Code version: f{COMMIT}")
tpath = config.get('Input', 'Temp')
rpath = config.get('Input', 'Humidity')
sstpath = config.get('Input', 'SST')
slppath = config.get('Input', 'SLP')
if args.year:
year = int(args.year)
else:
year = 2015
minLon = config.getfloat('Domain', 'MinLon')
maxLon = config.getfloat('Domain', 'MaxLon')
minLat = config.getfloat('Domain', 'MinLat')
maxLat = config.getfloat('Domain', 'MaxLat')
LOGGER.info(f"Domain: {minLon}-{maxLon}, {minLat}-{maxLat}")
for month in range(1, 13):
LOGGER.info(f"Processing {year}-{month}")
startdate = datetime.datetime(year, month, 1)
enddate = datetime.datetime(year, month, monthrange(year, month)[1])
filedatestr = f"{startdate.strftime('%Y%m%d')}-{enddate.strftime('%Y%m%d')}"
tfile = pjoin(tpath, f'{year}', f't_era5_oper_pl_{filedatestr}.nc')
try:
assert (os.path.isfile(tfile))
except AssertionError:
LOGGER.warning(f"Input file is missing: {tfile}")
LOGGER.warning(f"Skipping month {month}")
continue
tobj = nctools.ncLoadFile(tfile)
tvar = nctools.ncGetVar(tobj, 't')
tvar.set_auto_maskandscale(True)
rfile = pjoin(rpath, f'{year}', f'r_era5_oper_pl_{filedatestr}.nc')
try:
assert (os.path.isfile(rfile))
except AssertionError:
LOGGER.warning(f"Input file is missing: {rfile}")
LOGGER.warning(f"Skipping month {month}")
continue
robj = nctools.ncLoadFile(rfile)
rvar = nctools.ncGetVar(robj, 'r')
rvar.set_auto_maskandscale(True)
# This is actually relative humidity, we need to convert to mixing ratio
# Calculate mixing ratio - this function returns mixing ratio in g/kg
# Dimensions need to come from the pressure files
# These have been clipped to the Australian region, so contain
# a subset of the global data. The SST and MSLP data
# are then clipped to the same domain
tlon = nctools.ncGetDims(tobj, 'longitude')
tlat = nctools.ncGetDims(tobj, 'latitude')
LOGGER.debug(f"Latitude extents: {tlat.min()} - {tlat.max()}")
LOGGER.debug(f"Longitude extents: {tlon.min()} - {tlon.max()}")
varidx = np.where((tlon >= minLon) & (tlon <= maxLon))[0]
varidy = np.where((tlat >= minLat) & (tlat <= maxLat))[0]
templon = tlon[varidx]
templat = tlat[varidy]
LOGGER.info(f"Loading SST data")
sstfile = pjoin(sstpath, f'{year}',
f'sst_era5_oper_sfc_{filedatestr}.nc')
try:
assert (os.path.isfile(sstfile))
except AssertionError:
LOGGER.warning(f"Input file is missing: {sstfile}")
LOGGER.warning(f"Skipping month {month}")
continue
sstobj = nctools.ncLoadFile(sstfile)
sstvar = nctools.ncGetVar(sstobj, 'sst')
sstvar.set_auto_maskandscale(True)
sstlon = nctools.ncGetDims(sstobj, 'longitude')
sstlat = nctools.ncGetDims(sstobj, 'latitude')
LOGGER.debug(f"SST latitude extents: {sstlat.min()} - {sstlat.max()}")
LOGGER.debug(f"SST longitude extents: {sstlon.min()} - {sstlon.max()}")
LOGGER.info("Loading SLP data")
slpfile = pjoin(slppath, f'{year}',
f'msl_era5_oper_sfc_{filedatestr}.nc')
try:
assert (os.path.isfile(slpfile))
except AssertionError:
LOGGER.warning(f"Input file is missing: {slpfile}")
LOGGER.warning(f"Skipping month {month}")
continue
slpobj = nctools.ncLoadFile(slpfile)
slpvar = nctools.ncGetVar(slpobj, 'msl')
slpvar.set_auto_maskandscale(True)
# In the ERA5 data on NCI, surface variables are global,
# pressure variables are only over Australian region
LOGGER.info("Getting intersection of grids")
lonx, sstidx, varidxx = np.intersect1d(sstlon,
templon,
return_indices=True)
laty, sstidy, varidyy = np.intersect1d(sstlat,
templat[::-1],
return_indices=True)
nx = len(varidx)
ny = len(varidy)
LOGGER.info("Loading and converting SST and SLP data")
sst = metutils.convert(sstvar[:, sstidy, sstidx], sstvar.units, 'C')
slp = metutils.convert(slpvar[:, sstidy, sstidx], slpvar.units, 'hPa')
times = nctools.ncGetTimes(nctools.ncLoadFile(tfile))
nt = len(times)
LOGGER.debug(f"There are {nt} times in the data file")
levels = nctools.ncGetDims(nctools.ncLoadFile(tfile), 'level')
nz = len(levels)
LOGGER.debug(f"There are {nz} vertical levels in the data file")
# Create an array of the pressure variable that
# matches the shape of the temperature and mixing ratio
# variables.
LOGGER.info("Creating temporary pressure array")
pp = np.ones((nz, ny, nx))
ppT = pp.T
ppT *= levels
pmin = np.zeros(sst.shape)
vmax = np.zeros(sst.shape)
status = MPI.Status()
work_tag = 0
result_tag = 1
LOGGER.info("Calculating potential intensity")
if (comm.rank == 0) and (comm.size > 1):
w = 0
p = comm.size - 1
for d in range(1, comm.size):
if w < nt:
LOGGER.debug(f"Sending time {w} to node {d}")
comm.send(w, dest=d, tag=work_tag)
w += 1
else:
comm.send(None, dest=d, tag=work_tag)
p = w
terminated = 0
while (terminated < p):
result, tdx = comm.recv(source=MPI.ANY_SOURCE,
status=status,
tag=MPI.ANY_TAG)
pmin[tdx, :, :], vmax[tdx, :, :] = result
LOGGER.debug(f"Mean PI: {np.nanmean(vmax[tdx, :, :]):.2f} m/s")
d = status.source
if w < nt:
LOGGER.debug(f"Sending time {times[w]} to node {d}")
comm.send(w, dest=d, tag=status.tag)
w += 1
else:
# Exhausted all times, send empty packet:
comm.send(None, dest=d, tag=status.tag)
terminated += 1
elif (comm.size > 1) and (comm.rank != 0):
status = MPI.Status()
W = None
while (True):
W = comm.recv(source=0, tag=work_tag, status=status)
if W is None:
# Received an empty packet, so no work required
LOGGER.debug(
"No work to be done on this processor: {0}".format(
comm.rank))
break
LOGGER.debug(f"Processing time {times[W]} on node {comm.rank}")
t = metutils.convert(tvar[W, :, varidy, varidx], tvar.units,
'C')
r = metutils.rHToMixRat(rvar[W, :, varidy, varidx], t, pp, 'C')
r = np.where(r < 0, 0, r)
results = calculate(sst[W, :, :], slp[W, :, :], pp, t, r,
levels)
LOGGER.debug(f"Finished time {times[W]} on node {comm.rank}")
comm.send((results, W), dest=0, tag=status.tag)
elif (comm.size == 1) and (comm.rank == 0):
# We're working on a single processor:
for tdx in range(nt):
LOGGER.debug(f"Processing time {times[W]}")
t = metutils.convert(tvar[tdx, :, varidy, varidx], tvar.units,
'C')
r = metutils.rHToMixRat(rvar[tdx, :, varidy, varidx], t, pp,
'C')
r = np.where(r < 0, 0, r)
pmin[tdx, :, :], vmax[tdx, :, :] = calculate(
sst[tdx, :, :], slp[tdx, :, :], pp, t, r, levels)
if comm.rank == 0:
sleep(5)
comm.Barrier()
LOGGER.info(f"Saving data for month: {month}")
outputPath = config.get('Output', 'Path')
try:
os.makedirs(outputPath)
except:
pass
outputFile = pjoin(outputPath, f'pcmin.{filedatestr}.nc')
saveData(outputFile, pmin, vmax, lonx, laty, times)
LOGGER.info("Finished calculating potential intensity")
def gridLatLonDist(cLon, cLat, lonArray, latArray, units=None):
"""
Generate a grid containing the spherical earth distance
of the points defined by (lonarray, latarray) from the
point defined by (clon, clat).
(lonarray,latarray) and (clon,clat) are in degrees.
Returns distances in km by default, other units specified by the
'units' kwarg.
Based on m_lldist.m by Rich Pawlowicz ([email protected])
Modified by Craig Arthur 2006-11-13
Input:
cLon - longitude of the point to measure the distance from
cLat - latitude of the point to measure the distance from
lonArray - 1-d array of longitude values that will define the grid over
which distances will be calculated
latArray - 1-d array of latitude values that will define the grid over
which distances will be calculated
units - units of distance to be returned (default is kilometre)
Output:
dist - 2-d array containing the distance of the points defined in lonArray
and latArray from the point (cLon, cLat)
Example:
lonArray = np.arange(90.,100.,0.1)
latArray = np.arange(-20.,-10.,0.1)
dist = gridLatLonDist( 105., -15., lonArray, latArray,'km')
"""
# #CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC
# cLat_cos = 0.0
# cLat_sin = 0.0
# lat = empty(len(latArray), 'd')
# lon = empty(len(lonArray), 'd')
# dLon_sin = empty(len(lonArray), 'd')
# dLat_sin = empty(len(latArray), 'd')
# lat_cos = empty(len(latArray), 'd')
# dist = empty([len(latArray), len(lonArray)], 'd')
# code = """
# #include <math.h>
# double radius = 6367.0;
# double toRads = 0.017453292519943295;
# double cLon_ = cLon;
# double cLat_ = cLat;
# cLon_ = cLon_*toRads;
# cLat_ = cLat_*toRads;
# cLat_cos = cos(cLat_);
# for (int i = 0; i < NlonArray[0]; ++i)
# {
# lon(i) = lonArray(i)*toRads;
# double dLon = (lon(i) - cLon_)/2.0;
# dLon_sin(i) = sin(dLon);
# }
# for (int i = 0; i < NlatArray[0]; ++i)
# {
# lat(i) = latArray(i)*toRads;
# lat_cos(i) = cos(lat(i));
# double dLat = (lat(i) - cLat_)/2.0;
# dLat_sin(i) = sin(dLat);
# }
# for (int j = 0; j < NlatArray[0]; ++j)
# {
# for (int i = 0; i < NlonArray[0]; ++i)
# {
# double a = pow(dLat_sin(j), 2) + \
# cLat_cos*lat_cos(j)*pow(dLon_sin(i), 2);
# double c = 2.0*atan2(sqrt(fabs(a)), sqrt(1 - a));
# dist(j, i) = radius*c;
# }
# }
# """
# err = weave.inline(code,
# ['cLon', 'cLat', 'lonArray', 'latArray', 'lat', 'lon',
# 'dLon_sin', 'dLat_sin', 'lat_cos', 'dist', 'cLat_cos'],
# type_converters=converters.blitz,
# compiler = 'gcc')
# #CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC
radius = 6367.0
lat = np.radians(latArray)
lon = np.radians(lonArray)
cLon = math.radians(cLon)
cLat = math.radians(cLat)
lon_, lat_ = np.meshgrid(lon, lat)
dLon = lon_ - cLon
dLat = lat_ - cLat
a = np.square(np.sin(dLat / 2.0)) + \
np.cos(cLat) * np.cos(lat_) * np.square(np.sin(dLon / 2.0))
c = 2.0 * np.arctan2(np.sqrt(np.absolute(a)), np.sqrt(1 - a))
dist = radius * c
dist = metutils.convert(dist, "km", units)
return dist
def loadTrackFile(configFile,
trackFile,
source,
missingValue=0,
calculateWindSpeed=True):
"""
Load TC track data from the given input file, from a specified source.
The configFile is a configuration file that contains a section called
'source' that describes the data.
This returns a collection of :class:`Track` objects that contains
the details of the TC tracks in the input file.
:param str configFile: Configuration file with a section ``source``.
:param str trackFile: Path to a csv-formatted file containing TC data.
:pararm str source: Name of the source format of the TC data. There
*must* be a section in ``configFile`` matching
this string, containing the details of the format
of the data.
:param missingValue: Replace all null values in the input data with
this value (default=0).
:param boolean calculateWindSpeed: Calculate maximum wind speed using
a pressure-wind relation described
in :func:`maxWindSpeed`
:returns: A collection of :class:`Track` objects.
If any of the variables are not present in the input
dataset, they are (where possible) calculated
(date/time/windspeed), sampled from default datasets
(e.g. environmental pressure) or set to the missing value.
Example::
>>> tracks = loadTrackFile('tcrm.ini', 'IBTRaCS.csv', 'IBTrACS' )
"""
LOG.info("Loading %s" % trackFile)
inputData = colReadCSV(configFile, trackFile, source) #,
#nullValue=missingValue)
config = ConfigParser()
config.read(configFile)
inputSpeedUnits = config.get(source, 'SpeedUnits')
inputPressureUnits = config.get(source, 'PressureUnits')
inputLengthUnits = config.get(source, 'LengthUnits')
inputDateFormat = config.get(source, 'DateFormat')
if config.getboolean('DataProcess', 'FilterSeasons'):
startSeason = config.getint('DataProcess', 'StartSeason')
idx = np.where(inputData['season'] >= startSeason)[0]
inputData = inputData[idx]
# Determine the initial TC positions...
indicator = getInitialPositions(inputData)
# Sort date/time information
if 'age' in inputData.dtype.names:
year, month, day, hour, minute, datetimes = parseAge(
inputData, indicator)
timeElapsed = inputData['age']
else:
year, month, day, hour, minute, datetimes = parseDates(
inputData, indicator, inputDateFormat)
timeElapsed = getTimeElapsed(indicator, year, month, day, hour, minute)
# Time between observations:
dt = getTimeDelta(year, month, day, hour, minute)
# Calculate julian days
jdays = julianDays(year, month, day, hour, minute)
lat = np.array(inputData['lat'], 'd')
lon = np.mod(np.array(inputData['lon'], 'd'), 360)
delta_lon = np.diff(lon)
delta_lat = np.diff(lat)
# Split into separate tracks if large jump occurs (delta_lon > 10 degrees
# or delta_lat > 5 degrees)
# This avoids two tracks being accidentally combined when seasons and track
# numbers match but basins are different as occurs in the IBTrACS dataset.
# This problem can also be prevented if the 'tcserialno' column is
# specified.
indicator[np.where(delta_lon > 10)[0] + 1] = 1
indicator[np.where(delta_lat > 5)[0] + 1] = 1
pressure = filterPressure(np.array(inputData['pressure'], 'd'),
inputPressureUnits, missingValue)
try:
windspeed = np.array(inputData['vmax'], 'd')
novalue_index = np.where(windspeed == sys.maxint)
windspeed = metutils.convert(windspeed, inputSpeedUnits, "mps")
windspeed[novalue_index] = missingValue
except (ValueError, KeyError):
LOG.debug("No max wind speed data - all values will be zero")
windspeed = np.zeros(indicator.size, 'f')
assert lat.size == indicator.size
assert lon.size == indicator.size
assert pressure.size == indicator.size
try:
rmax = np.array(inputData['rmax'])
novalue_index = np.where(rmax == missingValue)
rmax = metutils.convert(rmax, inputLengthUnits, "km")
rmax[novalue_index] = missingValue
except (ValueError, KeyError):
LOG.debug("No radius to max wind data - all values will be zero")
rmax = np.zeros(indicator.size, 'f')
if 'penv' in inputData.dtype.names:
penv = np.array(inputData['penv'], 'd')
else:
LOG.debug("No ambient MSLP data in this input file")
LOG.debug("Sampling data from MSLP data defined in "
"configuration file")
# Warning: using sampled data will likely lead to some odd behaviour
# near the boundary of the MSLP grid boundaries - higher resolution
# MSLP data will decrease this unusual behaviour.
try:
ncfile = cnfGetIniValue(configFile, 'Input', 'MSLPFile')
except:
LOG.exception("No input MSLP file specified in configuration")
raise
time = getTime(year, month, day, hour, minute)
penv = ltmPressure(jdays, time, lon, lat, ncfile)
if 'poci' in inputData.dtype.names:
poci = np.array(inputData['poci'], 'd')
else:
LOG.debug("Determining poci")
eps = np.random.normal(0, scale=2.5717)
poci = getPoci(penv, pressure, lat, jdays, eps)
speed, bearing = getSpeedBearing(indicator,
lon,
lat,
dt,
missingValue=missingValue)
if calculateWindSpeed:
windspeed = maxWindSpeed(indicator, dt, lon, lat, pressure, poci)
TCID = np.cumsum(indicator)
data = np.empty(len(indicator),
dtype={
'names': trackFields,
'formats': trackTypes
})
for key, value in zip(trackFields, [
indicator, TCID, year, month, day, hour, minute, timeElapsed,
datetimes, lon, lat, speed, bearing, pressure, windspeed, rmax,
poci
]):
data[key] = value
tracks = []
n = np.max(TCID)
for i in range(1, n + 1):
track = Track(data[TCID == i])
track.trackId = (i, n)
track.trackfile = trackFile
getMinPressure(track, missingValue)
getMaxWind(track, missingValue)
tracks.append(track)
return tracks
def gridLatLonDist(cLon, cLat, lonArray, latArray, units=None):
"""
Generate a grid containing the spherical earth distance
of the points defined by (lonarray, latarray) from the
point defined by (clon, clat).
(lonarray,latarray) and (clon,clat) are in degrees.
Returns distances in km by default, other units specified by the
'units' kwarg.
Based on m_lldist.m by Rich Pawlowicz ([email protected])
Modified by Craig Arthur 2006-11-13
:param float cLon: Longitude of the point to measure the distance from.
:param float cLat: Latitude of the point to measure the distance from.
:param lonArray: 1-d array of longitude values that will define the
grid over which distances will be calculated.
:param latArray: 1-d array of latitude values that will define the
grid over which distances will be calculated.
:param str units: Units of distance to be returned (default is kilometre)
:returns: 2-d array containing the distance of the points defined in
``lonArray`` and ``latArray`` from the point
(``cLon``, ``cLat``).
Example::
>>> lonArray = np.arange(90.,100.,0.1)
>>> latArray = np.arange(-20.,-10.,0.1)
>>> dist = gridLatLonDist( 105., -15., lonArray, latArray, 'km')
"""
# #CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC
# cLat_cos = 0.0
# cLat_sin = 0.0
# lat = empty(len(latArray), 'd')
# lon = empty(len(lonArray), 'd')
# dLon_sin = empty(len(lonArray), 'd')
# dLat_sin = empty(len(latArray), 'd')
# lat_cos = empty(len(latArray), 'd')
# dist = empty([len(latArray), len(lonArray)], 'd')
# code = """
# #include <math.h>
# double radius = 6367.0;
# double toRads = 0.017453292519943295;
# double cLon_ = cLon;
# double cLat_ = cLat;
# cLon_ = cLon_*toRads;
# cLat_ = cLat_*toRads;
# cLat_cos = cos(cLat_);
# for (int i = 0; i < NlonArray[0]; ++i)
# {
# lon(i) = lonArray(i)*toRads;
# double dLon = (lon(i) - cLon_)/2.0;
# dLon_sin(i) = sin(dLon);
# }
# for (int i = 0; i < NlatArray[0]; ++i)
# {
# lat(i) = latArray(i)*toRads;
# lat_cos(i) = cos(lat(i));
# double dLat = (lat(i) - cLat_)/2.0;
# dLat_sin(i) = sin(dLat);
# }
# for (int j = 0; j < NlatArray[0]; ++j)
# {
# for (int i = 0; i < NlonArray[0]; ++i)
# {
# double a = pow(dLat_sin(j), 2) + \
# cLat_cos*lat_cos(j)*pow(dLon_sin(i), 2);
# double c = 2.0*atan2(sqrt(fabs(a)), sqrt(1 - a));
# dist(j, i) = radius*c;
# }
# }
# """
# err = weave.inline(code,
# ['cLon', 'cLat', 'lonArray', 'latArray', 'lat', 'lon',
# 'dLon_sin', 'dLat_sin', 'lat_cos', 'dist', 'cLat_cos'],
# type_converters=converters.blitz,
# compiler = 'gcc')
# #CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC
radius = 6367.0
lat = np.radians(latArray)
lon = np.radians(lonArray)
cLon = math.radians(cLon)
cLat = math.radians(cLat)
lon_, lat_ = np.meshgrid(lon, lat)
dLon = lon_ - cLon
dLat = lat_ - cLat
a = np.square(np.sin(dLat / 2.0)) + \
np.cos(cLat) * np.cos(lat_) * np.square(np.sin(dLon / 2.0))
c = 2.0 * np.arctan2(np.sqrt(np.absolute(a)), np.sqrt(1 - a))
dist = radius * c
dist = metutils.convert(dist, "km", units)
return dist
def maxWindSpeed(index,
deltatime,
lon,
lat,
pressure,
penv,
gustfactor=0.9524):
"""
Calculate the 10-minute-mean maximum wind speed from the central
pressure deficit, using the method described in Holland et al. (2010).
:param indicator: Array (values of 1 or 0) indicating the beginning of
a new TC in the input dataset.
:param deltatime: Time difference (in hours) between each point in the
record.
:param lon: Longitudes of TC postions.
:param lat: Latitudes of TC positions.
:param pressure: Central pressure estimate of TCs (hPa).
:param penv: Environmental pressure estimates for each TC postion (hPa).
:param float gf: Gust factor - default value represents converting from a
1-minute sustained wind speed to a 10-minute mean wind
speed. Based on Harper et al. 2010, WMO-TD1555.
:type indicator: :class:`numpy.ndarray`
:type deltatime: :class:`numpy.ndarray`
:type lon: :class:`numpy.ndarray`
:type lat: :class:`numpy.ndarray`
:type pressure: :class:`numpy.ndarray`
:type penv: :class:`numpy.ndarray`
:returns: :class:`numpy.ndarray` of estimated wind speed based on
central pressure deficit.
Example::
>>> v = maxWindSpeed(indicator, dt, lon, lat, pressure, penv)
"""
# Speed and bearing:
speed, bearing = getSpeedBearing(index, lon, lat, deltatime)
speed = metutils.convert(speed, 'kmh', 'mps')
np.putmask(speed, speed > 10e+3, 0)
# Pressure deficit:
deltap = penv - pressure
# Pressure rate of change
dpt = np.zeros(index.size, 'f')
dpt[1:] = np.diff(pressure)
dpdt = dpt / deltatime
np.putmask(dpdt, index, 0)
np.putmask(dpdt, np.isnan(dpdt) | np.isinf(dpdt) | (np.abs(dpdt) > 5.), 0)
# Estimated pressure at the radius of maximum wind:
prmw = pressure + deltap / 3.7
# Calculate thermodynamic variables at RMW:
tsurf = 28.0 - 3 * (np.abs(lat) - 10.) / 20.
qmix = 0.9 * (3.802 / prmw) * np.exp(17.67 * tsurf / (243.5 + tsurf))
tvs = (tsurf + 273.15) * (1. + 0.81 * qmix)
rho = prmw * 100. / (tvs * 287.04)
chi = 0.6 * (1.0 - deltap / 215.)
beta = -0.000044 * np.power(deltap, 2.) + \
0.01 * deltap + 0.03 * dpdt - 0.014 * np.abs(lat) + \
0.15 * np.power(speed, chi) + 1.
# Holland's P-W relation derives a 1-minute mean wind speed, so we often
# need to convert to some other averaging period. I use the recommendations
# of Harper et al. (2010) WMO TD-1555:
# Common values are( Assuming "At-sea" conditions):
# 10-min mean: 0.95 (default)
# 3-second gust: 1.11
v = gustfactor * np.sqrt(deltap * 100 * beta / (rho * np.exp(1.)))
np.putmask(v, (np.isnan(v) | np.isinf(v) | (pressure >= 10e+7) |
(pressure <= 0) | (speed >= 10e+7)), 0)
return v
