Beispiel #1
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def gms_like_ratio(weights, tracer, dp):
    """Compute ratio of integrals in the style of gross moist stability."""
    # Integrate weights over lower tropospheric layer
    dp = to_pascal(dp)
    denominator = field_vert_int_max(weights, dp)
    # Integrate tracer*weights over whole column and divide.
    numerator = np.sum(weights*tracer*dp, axis=-3) / grav
    return numerator / denominator
Beispiel #2
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def gms_like_ratio(weights, tracer, dp):
    """Compute ratio of integrals in the style of gross moist stability."""
    # Integrate weights over lower tropospheric layer
    dp = to_pascal(dp)
    denominator = field_vert_int_max(weights, dp)
    # Integrate tracer*weights over whole column and divide.
    numerator = np.sum(weights * tracer * dp, axis=-3) / grav
    return numerator / denominator
Beispiel #3
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def field_vert_int_max(arr, dp):
    """Maximum magnitude of integral of a field from surface up."""
    dp = to_pascal(dp)
    # 2015-05-15: Problem: Sigma data indexing starts at TOA, while pressure
    #             data indexing starts at 1000 hPa.  So for now only do for
    #             sigma data and flip array direction to start from sfc.
    arr_dp_g = (arr*dp)[::-1] / grav
    # Input array dimensions are assumed ([time dims,] level, lat, lon).
    pos_max = np.amax(np.cumsum(arr_dp_g, axis=0), axis=-3)
    neg_max = np.amin(np.cumsum(arr_dp_g, axis=0), axis=-3)
    # Flip sign because integrating from p_sfc up, i.e. with dp negative.
    return -1*np.where(pos_max > -neg_max, pos_max, neg_max)
Beispiel #4
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def field_vert_int_max(arr, dp):
    """Maximum magnitude of integral of a field from surface up."""
    dp = to_pascal(dp)
    # 2015-05-15: Problem: Sigma data indexing starts at TOA, while pressure
    #             data indexing starts at 1000 hPa.  So for now only do for
    #             sigma data and flip array direction to start from sfc.
    arr_dp_g = (arr * dp)[::-1] / grav
    # Input array dimensions are assumed ([time dims,] level, lat, lon).
    pos_max = np.amax(np.cumsum(arr_dp_g, axis=0), axis=-3)
    neg_max = np.amin(np.cumsum(arr_dp_g, axis=0), axis=-3)
    # Flip sign because integrating from p_sfc up, i.e. with dp negative.
    return -1 * np.where(pos_max > -neg_max, pos_max, neg_max)
def msf(lats, levs, v):
    """Meridional mass streamfunction."""
    # Compute half level boundaries and widths.
    p_top = 5.
    p_bot = 1005.
    p_half = 0.5*(levs[1:] + levs[:-1])
    p_half = np.insert(np.append(p_half, p_top), 0, p_bot)
    dp = to_pascal(p_half[:-1] - p_half[1:])[np.newaxis, ::-1, np.newaxis]
    geom_factor = (2.*np.pi*r_e/grav *
                   np.cos(np.deg2rad(lats))[np.newaxis, np.newaxis, :])
    # Integrate from TOA down to surface.
    msf_ = geom_factor * np.cumsum(v.mean(axis=-1) * dp, axis=1)[::-1]
    # Average the values calculated at half levels; flip sign by convention.
    msf_[:,:-1] = -0.5*(msf_[:,1:] + msf_[:,:-1])
    # Uppermost level goes to 0 hPa (so divide by 2); surface value is zero.
    msf_[:,-1]*=0.5
    msf_[:,0] = 0.
    return msf_
Beispiel #6
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def msf(lats, levs, v):
    """Meridional mass streamfunction."""
    # Compute half level boundaries and widths.
    p_top = 5.
    p_bot = 1005.
    p_half = 0.5 * (levs[1:] + levs[:-1])
    p_half = np.insert(np.append(p_half, p_top), 0, p_bot)
    dp = to_pascal(p_half[:-1] - p_half[1:])[np.newaxis, ::-1, np.newaxis]
    geom_factor = (2. * np.pi * r_e / grav *
                   np.cos(np.deg2rad(lats))[np.newaxis, np.newaxis, :])
    # Integrate from TOA down to surface.
    msf_ = geom_factor * np.cumsum(v.mean(axis=-1) * dp, axis=1)[::-1]
    # Average the values calculated at half levels; flip sign by convention.
    msf_[:, :-1] = -0.5 * (msf_[:, 1:] + msf_[:, :-1])
    # Uppermost level goes to 0 hPa (so divide by 2); surface value is zero.
    msf_[:, -1] *= 0.5
    msf_[:, 0] = 0.
    return msf_