6d8adaccb2
Change-Id: I7ea007f0f919017e8212716cc15712c7ed611e69 Former-commit-id:8b23f32311[formerlyee11e5d13c] [formerly8b23f32311[formerlyee11e5d13c] [formerlyac33fb9463[formerly a8bcec4a2fa9ddd49dc300ebd63aebfdfb41723e]]] Former-commit-id:ac33fb9463Former-commit-id:b2b2136868[formerlybe163ebf2a] Former-commit-id:182a717b00
125 lines
No EOL
4.4 KiB
Python
125 lines
No EOL
4.4 KiB
Python
##
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# This software was developed and / or modified by Raytheon Company,
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# pursuant to Contract DG133W-05-CQ-1067 with the US Government.
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#
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# U.S. EXPORT CONTROLLED TECHNICAL DATA
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# This software product contains export-restricted data whose
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# export/transfer/disclosure is restricted by U.S. law. Dissemination
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# to non-U.S. persons whether in the United States or abroad requires
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# an export license or other authorization.
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#
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# Contractor Name: Raytheon Company
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# Contractor Address: 6825 Pine Street, Suite 340
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# Mail Stop B8
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# Omaha, NE 68106
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# 402.291.0100
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#
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# See the AWIPS II Master Rights File ("Master Rights File.pdf") for
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# further licensing information.
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###
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from numpy import log, exp
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import PartialDerivative as Partial
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import DgeoComps as DgeoComps
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import Vector
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##
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# Find Q vectors from upper and lower height and pressure.
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#
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# @param height_up: Height at each grid point for the top of the layer (m)
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# @type height_up: 2D numpy array
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# @param height_lo: Height at each grid point for the bottom of the layer (m)
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# @type height_lo: 2D numpy array
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# @param pressure_up: Pressure level corresponding to height_up (mb)
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# @type pressure_up: scalar or 2D numpy array
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# @param pressure_lo: Pressure level corresponding to height_lo (mb)
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# @type pressure_lo: scalar or 2D numpy array
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# @param dx: Spacing between data points in the X direction
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# @type dx: scalar or 2D numpy array
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# @param dy: Spacing between data points in the Y direction
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# @type dy: scalar or 2D numpy array
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# @param coriolis: Coriolis force (kg-m/s^2)
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# @type coriolis: 2D numpy array
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# @return: Q vectors
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# @rtype: tuple(U,V) of 2D numpy arrays
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def execute(height_up, height_lo, pressure_up, pressure_lo, dx, dy, coriolis):
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qx, qy, dtdx, dtdy = calculate(height_up, height_lo, pressure_up, pressure_lo, dx, dy, coriolis)
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# unmask the arrays we're interested in
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return Vector.componentsTo(qx, qy)
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##
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# Find Q vectors and dtdx and dtdy from upper and lower height and pressure.
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#
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# In qvector.f, comments described dtdx and dtdy as work arrays, but
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# frontogen.f was treating them as output arrays since they were filled
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# with the values frontogen.f needed during qvector.f's processing. This
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# method was created to return all 4 arrays, so fGen.py wouldn't have to
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# re-generate dtdx and dtdy.
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#
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# @attention: Returned vectors may contain NaN.
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#
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#
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# @param height_up: Height at each grid point for the top of the layer (m)
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# @type height_up: 2D numpy array
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# @param height_lo: Height at each grid point for the bottom of the layer (m)
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# @type height_lo: 2D numpy array
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# @param pressure_up: Pressure level corresponding to height_up (mb)
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# @type pressure_up: scalar or 2D numpy array
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# @param pressure_lo: Pressure level corresponding to height_lo (mb)
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# @type pressure_lo: scalar or 2D numpy array
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# @param dx: Spacing between data points in the X direction
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# @type dx: scalar or 2D numpy array
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# @param dy: Spacing between data points in the Y direction
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# @type dy: scalar or 2D numpy array
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# @param coriolis: Coriolis force (kg-m/s^2)
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# @type coriolis: 2D numpy array
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# @return: Q vectors
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# @rtype: tuple(U,V) of 2D numpy arrays
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def calculate(height_up, height_lo, pressure_up, pressure_lo, dx, dy, coriolis):
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"Find Q vectors from upper and lower height and pressure."
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# assume dx, dy, and coriolis don't need masked
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# Acceleration due to gravity (m/s**2)
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gravAcc = 9.806
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# magic numbers used in calculations.
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# Taken from original Fortran code; not sure what they are.
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magic_exp = 0.286
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magic_fac = 287
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height_mid = height_up + height_lo
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height_mid /= 2
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#******* Code to smooth height omitted *******
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# calculate components of geostrophic wind
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dugdx, dugdy, dvgdx, dvgdy = DgeoComps.execute(height_mid, dx, dy, coriolis)
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# get mean pressure
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# copied from Fortran; wouldn't sqrt(p_up * p_lo) be better?
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pavg = log(pressure_up) + log(pressure_lo)
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pavg /= 2
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pavg = exp( pavg )
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# derive cnvFac to convert potential temp to thickness
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pdiff = pressure_up - pressure_lo
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gravAcc = 9.806
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denom = (pavg/1000) ** magic_exp
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denom *= pdiff * magic_fac
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cnvFac = -gravAcc * pavg / denom
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temp = cnvFac * (height_up - height_lo)
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dtdx, dtdy = Partial.execute(temp, dx, dy)
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qx = -dugdx * dtdx
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qx -= dvgdx * dtdy
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qy = -dugdy * dtdx
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qy -= dvgdy * dtdy
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return (-qx, qy, dtdx, dtdy) |