Post process geogrid output to compute GWD input fields directly from source data

util/OrographyStats.py

@@ -0,0 +1,95 @@
+import numpy as np
+
+
+class OrographyStats:
+  def __init__( self, box ):
+    self.box  = box
+
+    self.mean = np.mean( self.box )
+    # var (actulally stddev)
+    self.std  = np.std( self.box )
+    self.max = np.max( box )
+
+    # This currently does not use the landuse to average over only land and zero
+    # out on mostly water
+    # con (convexity)
+    if self.std < 1.0:
+      self.con = 0.0
+    else:
+      var4 = np.sum( [ ( h - self.mean )**4 for h in np.nditer( box ) ] )
+      self.con = var4 / ( self.box.size * self.std**4 )
+
+    # oa (orographic asymmetry)
+    self.oa = np.zeros( (4) )
+    self.calc_oa()
+
+    # ol (orographic effective length)
+    self.ol = np.zeros( (4) )
+    self.calc_ol()
+
+  def __repr__( self ):
+    return f"Mean : {self.mean} Std : {self.std} Max: {self.max} CON : {self.con} OA : {self.oa} OL : {self.ol}"
+
+  def calc_oa( self ):
+    # Note that right now the assumption is that the box is laid out in col major order
+    # with [y,x] indices
+
+    # oa1 is the orographic asymmetry in the West direction
+    nu = np.sum( self.box[:, :int((self.box.shape[1] + self.box.shape[1] % 2) / 2)] > self.mean )
+    nd = np.sum( self.box[:, int((self.box.shape[1] - self.box.shape[1] % 2) / 2):] > self.mean )
+    self.oa[0] = ( nu - nd ) / ( nu + nd ) if ( ( nu + nd ) > 0 ) else 0.0
+
+    # oa2 is the orographic asymmetry in the South direction
+    nu = np.sum( self.box[:int((self.box.shape[0] + self.box.shape[0] % 2) / 2), :] > self.mean )
+    nd = np.sum( self.box[int((self.box.shape[0] - self.box.shape[0] % 2) / 2):, :] > self.mean )
+    self.oa[1] = ( nu - nd ) / ( nu + nd ) if ( ( nu + nd ) > 0 ) else 0.0
+
+    # Pre-compute the geometric diagonal of the box
+    slope = self.box.shape[0] / self.box.shape[1]
+    j, i  = np.indices( self.box.shape )
+    # Corrected - all indices that lie on diagonal are counted in both upstream and downstream
+    vals = ( i + 1 ) * slope - ( self.box.shape[0] - j )
+    upstream   = ( vals <= slope )
+    downstream = ( vals >= -1.0 )
+
+    # MPAS calcs - slightly accounts for diagonal when i ~= j but increasingly off for larger discrepancies
+    # vals = np.rint( ( i + 1 ) * slope ) - ( self.box.shape[0] - j )
+    # upstream   = ( vals <= 0 )
+    # downstream = ( vals >= 0 )
+
+    # oa3 is the orographic asymmetry in the South-West direction
+    nu = np.sum( self.box[upstream] > self.mean )
+    nd = np.sum( self.box[downstream] > self.mean )
+    self.oa[2] = ( nu - nd ) / ( nu + nd ) if ( ( nu + nd ) > 0 ) else 0.0
+
+    # oa4 is the orographic asymmetry in the North-West direction
+    upstream   = np.flip( upstream, axis=0 )
+    downstream = np.flip( downstream, axis=0 )
+    nu = np.sum( self.box[upstream] > self.mean )
+    nd = np.sum( self.box[downstream] > self.mean )
+    self.oa[3] = ( nu - nd ) / ( nu + nd ) if ( ( nu + nd ) > 0 ) else 0.0
+
+  def calc_ol( self ):
+    # ol1 is the effective orographic length in the West direction
+    interior = self.box[int(np.floor(self.box.shape[0] * .25)):int(np.ceil(self.box.shape[0] * .75)), :]
+    self.ol[0] = np.sum( interior > self.mean ) / interior.size
+
+    # ol2 is the effective orographic length in the South direction
+    interior = self.box[:, int(np.floor(self.box.shape[1] * .25)):int(np.ceil(self.box.shape[1] * .75))]
+    self.ol[1] = np.sum( interior > self.mean ) / interior.size
+
+    # The prescribed methodology uses 4 quadrants to get the diagonals and effectively
+    # test half of the box, however this does not actually test the interior half
+    # of the area of the box in the wind direction...
+
+    # ol3 is the effective orographic length in the South-West direction
+    interiorA = self.box[:int(self.box.shape[0] / 2), :int(self.box.shape[1] / 2)]  # first half of x first half of y
+    interiorB = self.box[int(self.box.shape[0] / 2):, int(self.box.shape[1] / 2):]  # second half of x second half of y
+
+    self.ol[2] = ( np.sum( interiorA > self.mean ) + np.sum( interiorB > self.mean ) ) / ( interiorA.size + interiorB.size )
+
+    # ol4 is the effective orographic length in the North-West direction
+    interiorA = self.box[int(self.box.shape[0] / 2):, :int(self.box.shape[1] / 2)]  # first half of x second half of y
+    interiorB = self.box[:int(self.box.shape[0] / 2), int(self.box.shape[1] / 2):]  # second half of x first half of y
+
+    self.ol[3] = ( np.sum( interiorA > self.mean ) + np.sum( interiorB > self.mean ) ) / ( interiorA.size + interiorB.size )

util/SourceData.py

@@ -0,0 +1,257 @@
+import numpy as np
+import cartopy
+
+from TileData import TileData
+
+
+def get_kv_pair( line, delimiter="=", comment="#" ):
+  non_comment = line.split( comment, maxsplit=1 )[0]
+
+  if delimiter in non_comment:
+    key, _, value = non_comment.partition( delimiter )
+    return key.strip(), value.strip()
+  else:
+    return None, None
+
+
+class IndexData:
+  def __init__( self, path ):
+    self.source_path      = path
+    # Default values of index file from :
+    # https://www2.mmm.ucar.edu/wrf/users/wrf_users_guide/build/html/wps.html#index-options
+    self.projection       = "required"
+    self.source_type      = "required"
+    self.signed           = False
+    self.units            = "required"
+    self.description      = "required"
+    self.dx               = -1.0       # required
+    self.dy               = -1.0       # required
+    self.known_x          =  1.0
+    self.known_y          =  1.0
+    self.known_lat        = -1.0       # required
+    self.known_lon        = -1.0       # required
+    self.stdlon           = -2.0       # not required
+    self.truelat1         = -2.0       # not required
+    self.truelat2         = -2.0       # not required
+    self.wordsize         = -1         # required
+    self.tile_x           = -1         # required
+    self.tile_y           = -1         # required
+    self.tile_z           = -2         # not required
+    self.tile_z_start     = -2         # not required
+    self.tile_z_end       = -2         # not required
+    self.category_min     = -2         # not required
+    self.category_max     = -2         # not required
+    self.tile_bdr         =  0
+    self.missing_value    = -2.0       # not required
+    self.scale_factor     =  1.0
+    self.row_order        = "bottom_top"
+    self.endian           = "big"
+    self.iswater          = 16
+    self.islake           = -1         # i.e. no separate inland water category (does not mean required)
+    self.isice            = 24
+    self.isurban          =  1
+    self.isoilwater       = 14
+    self.mminlu           = "USGS"
+    self.filename_digits  =  5
+
+    self.read_file()
+
+  def read_file( self ):
+    with open( self.source_path + "/index" ) as f:
+      for line in f:
+        key, value = get_kv_pair( line )
+        if key is not None:
+          if   key == "projection":      self.projection      = value
+          elif key == "source_type":     self.source_type     = value
+          elif key == "signed":          self.signed          = ( value == "yes" )
+          elif key == "units":           self.units           = value
+          elif key == "description":     self.description     = value
+          elif key == "dx":              self.dx              = float( value )
+          elif key == "dy":              self.dy              = float( value )
+          elif key == "known_x":         self.known_x         = float( value )
+          elif key == "known_y":         self.known_y         = float( value )
+          elif key == "known_lat":       self.known_lat       = float( value )
+          elif key == "known_lon":       self.known_lon       = float( value )
+          elif key == "stdlon":          self.stdlon          = float( value )
+          elif key == "truelat1":        self.truelat1        = float( value )
+          elif key == "truelat2":        self.truelat2        = float( value )
+          elif key == "wordsize":        self.wordsize        = int( value )
+          elif key == "tile_x":          self.tile_x          = int( value )
+          elif key == "tile_y":          self.tile_y          = int( value )
+          elif key == "tile_z":          self.tile_z          = int( value )
+          elif key == "tile_z_start":    self.tile_z_start    = int( value )
+          elif key == "tile_z_end":      self.tile_z_end      = int( value )
+          elif key == "category_min":    self.category_min    = int( value )
+          elif key == "category_max":    self.category_max    = int( value )
+          elif key == "tile_bdr":        self.tile_bdr        = int( value )
+          elif key == "missing_value":   self.missing_value   = float( value )
+          elif key == "scale_factor":    self.scale_factor    = float( value )
+          elif key == "row_order":       self.row_order       = value
+          elif key == "endian":          self.endian          = value
+          elif key == "iswater":         self.iswater         = int( value )
+          elif key == "islake":          self.islake          = int( value )
+          elif key == "isice":           self.isice           = int( value )
+          elif key == "isurban":         self.isurban         = int( value )
+          elif key == "isoilwater":      self.isoilwater      = int( value )
+          elif key == "mminlu":          self.mminlu          = value
+          elif key == "filename_digits": self.filename_digits = int( value )
+
+  def get_dtype( self ):
+    endian = ">" if self.endian == "big" else "<"
+    signed = "i" if self.signed else "u"
+    return f"{endian}{signed}{self.wordsize}"
+
+
+class SourceData:
+  def __init__( self, name, path ):
+    self._earth_radius = 6371229.0
+    self._name         = name
+    self._source_path  = path
+    self._index        = IndexData( self._source_path )
+
+    self._projection   = None
+    self._npts_x       = int( 360.0 / self._index.dx )
+    self._pts_per_deg  = int( 1.0 / self._index.dx )
+    self._subgrid_m_dx = 2.0 * np.pi * self._earth_radius / self._npts_x
+
+    self._tile_data  = TileData(
+                                int( int( 360.0 / self._index.dx ) / self._index.tile_x ),
+                                int( int( 180.0 / self._index.dy ) / self._index.tile_y ),
+                                self._index.tile_x,
+                                self._index.tile_y,
+                                load_func=lambda i, j:
+                                  self.read_geogrid(
+                                                    # Limit the view to just the tile data for now, remove border
+                                                    self.get_tile_name_ij( i, j )[0] )[
+                                                                                        0,
+                                                                                        self._index.tile_bdr:self._index.tile_y + self._index.tile_bdr,
+                                                                                        self._index.tile_bdr:self._index.tile_x + self._index.tile_bdr
+                                                                                        ]
+                                )
+
+  def read_geogrid( self, file ):
+    """
+    Read in the geogrid raw data using the format provided here:
+    https://www2.mmm.ucar.edu/wrf/users/wrf_users_guide/build/html/wps.html#writing-static-data-to-the-geogrid-binary-format
+
+    Note that data passed out is assumed to be oriented as follows:
+      SW
+            <- -x               +x -> (increasing longitude)
+         ^  +---+---+---+---+---+---+
+         |  |   |   |   |   |   |   |
+        -y  +---+---+---+---+---+---+
+            |   |   |   |   |   |   |
+            +---+---+---+---+---+---+
+            |   |   |   |   |   |   |
+            +---+---+---+---+---+---+
+            |   |   |   |   |   |   |
+        +y  +---+---+---+---+---+---+
+         |  |   |   |   |   |   |   |
+         v  +---+---+---+---+---+---+
+        (increasing latitude)
+                                      NE
+    """
+    rawdata = np.fromfile(
+                          file,
+                          dtype=self._index.get_dtype()
+                          )
+    data = rawdata.astype( np.float32, casting="unsafe" ) * self._index.scale_factor
+    z_dim = 1
+    if self._index.tile_z_start > 0 and self._index.tile_z_end > 0:
+      z_dim = self._index.tile_z_end - self._index.tile_z_start
+    elif self._index.tile_z > 0:
+      z_dim = self._index.tile_z
+
+    data = data.reshape(
+                        (
+                          z_dim,
+                          self._index.tile_y + 2 * self._index.tile_bdr,
+                          self._index.tile_x + 2 * self._index.tile_bdr
+                        )
+                       )
+    if self._index.row_order == "top_bottom":
+      data = np.flip( data, axis=1 )
+
+    return data
+
+  def latlon_to_ij( self, lat, lon ):
+    # Ignore staggering for now
+    i = 0.0
+    j = 0.0
+    if self._index.projection == "regular_ll":
+      delta_lat = lat - self._index.known_lat
+      delta_lon = lon - self._index.known_lon
+
+      i = ( delta_lon / self._index.dx + self._index.known_x ) % int( 360.0 / self._index.dx )
+      j = ( delta_lat / self._index.dy + self._index.known_y )
+
+    return i, j
+
+  def ij_to_latlon( self, i, j ):
+    lat = 0.0
+    lon = 0.0
+    if self._index.projection == "regular_ll":
+      lon = ( i - self._index.known_x ) * self._index.dx + self._index.known_lon
+      lat = ( j - self._index.known_y ) * self._index.dy + self._index.known_lat
+
+    if lon > 180.0:
+      lon -= 360.0
+
+    return lat, lon
+
+  def get_tile_extent( self, starti, startj ):
+    start_lat, start_lon = self.ij_to_latlon( starti - self._index.tile_bdr, startj - self._index.tile_bdr )
+    stop_lat, stop_lon   = self.ij_to_latlon( starti + self._index.tile_x - 1 + self._index.tile_bdr, startj + self._index.tile_y - 1  + self._index.tile_bdr )
+    print( ( starti - self._index.tile_bdr, startj - self._index.tile_bdr ) )
+    print( ( starti + self._index.tile_x - 1 + self._index.tile_bdr, startj + self._index.tile_y - 1  + self._index.tile_bdr ) )
+    return ( start_lon, stop_lon, start_lat, stop_lat )
+
+  def get_tile_name_ll( self, lat, lon ):
+    i, j = self.latlon_to_ij( lat, lon )
+    return self.get_tile_name_ij( i, j )
+
+  def get_tile_name_ij( self, i, j ):
+    tile_i = self._index.tile_x * int( int( i ) / self._index.tile_x ) + 1
+    tile_j = self._index.tile_y * int( int( j ) / self._index.tile_y ) + 1
+
+    path  = f"{self._source_path}/"
+    path += f"{{xstart:0{self._index.filename_digits}d}}-{{xstop:0{self._index.filename_digits}d}}."
+    path += f"{{ystart:0{self._index.filename_digits}d}}-{{ystop:0{self._index.filename_digits}d}}"
+    return path.format(
+                        xstart=tile_i,
+                         xstop=tile_i + self._index.tile_x - 1,
+                        ystart=tile_j,
+                         ystop=tile_j + self._index.tile_y - 1
+                        ), tile_i, tile_j
+
+  def get_box( self, lat, lon, size_x, size_y=None ):
+    if size_y is None:
+      size_y = size_x
+
+    # X span in points
+    nx = 0
+    if (
+        np.cos( np.deg2rad( lat ) )
+        > ( 2.0 * self._pts_per_deg * size_x * 180.0 ) / ( self._npts_x * np.pi * self._earth_radius )
+        ):
+      nx = int(
+                np.ceil(
+                        ( 180.0 * size_x * self._pts_per_deg )
+                        / ( np.pi * self._earth_radius * np.cos( np.deg2rad( lat ) ) )
+                        )
+                )
+    else:
+      nx = int( self._npts_x / 2 )
+
+    # Y span in points
+    ny = int( np.ceil( ( 180.0 * size_y * self._pts_per_deg ) / ( np.pi * self._earth_radius ) ) )
+
+    true_i, true_j = self.latlon_to_ij( lat, lon )
+    # Generate the indices for this box regardless of tile periodicity, let the tile data handle that
+    indices = np.indices( ( ny, nx ) )
+    indices[0] += int( true_j - ny / 2 )
+    indices[1] += int( true_i - nx / 2 )
+
+    box = self._tile_data.get_box( indices )
+    # box is now a 2D box of data spanning size in meters centered on lat/lon
+    return box