diff --git a/modules/util/lon_lat_grid.py b/modules/util/lon_lat_grid.py
index 73cffdf3d19b837a75767483fbded46e9df923d6..9b910a0a4d628726e3fdeb3f787cd365ff02bc5b 100644
--- a/modules/util/lon_lat_grid.py
+++ b/modules/util/lon_lat_grid.py
@@ -21,13 +21,15 @@ class LonLatGrid:
     # Incoming longitude must be in range: 0 - 360 degrees
     # Can have NaN for off Earth grid points (these are handled internally).
     # closeness_threshold: if < distance of located_point to target, return off grid
-    def __init__(self, grd_lons, grd_lats, closeness_threshold=2000):
+    def __init__(self, grd_lons, grd_lats, closeness_threshold=5000, reduce=1, leaf_size=40):
         if grd_lons.shape != grd_lats.shape:
             raise MyGenericException('incoming lons,lats must have same shape')
 
         self.grd_lons = grd_lons
         self.grd_lats = grd_lats
-        self.shape = grd_lons.shape
+        self.grd_lons = grd_lons[::reduce, ::reduce]
+        self.grd_lats = grd_lats[::reduce, ::reduce]
+        self.shape = self.grd_lons.shape
         self.lenx = self.shape[1]
         self.leny = self.shape[0]
         self.fnc_dct = {}
@@ -49,8 +51,9 @@ class LonLatGrid:
 
         points = np.stack([flons, flats], axis=1)
 
-        self.kd = BallTree(np.deg2rad(points), leaf_size=500, metric='haversine')
+        self.kd = BallTree(np.deg2rad(points), leaf_size=leaf_size, metric='haversine')
         self.closeness_threshold = closeness_threshold
+        self.reduce = reduce
 
     # locate nearest neighbor for incoming target in earth coordinates (Should not have NaNs)
     # lons, lats can be flat or 2D
@@ -68,7 +71,10 @@ class LonLatGrid:
 
         xy = np.stack([lons, lats], axis=1)
 
-        dist, indices = self.kd.query(np.deg2rad(xy))
+        # Assumes nn for a query pont returned in order of increasing distance
+        dist, indices = self.kd.query(np.deg2rad(xy), k=1)
+        dist = dist[:, 0]
+        indices = indices[:, 0]
         dist *= 6370000  # convert unit radius to meters
 
         valid = (indices < self.map_indexes.size) & (dist < self.closeness_threshold)
@@ -90,9 +96,10 @@ class LonLatGrid:
 
         xy = np.stack([lons, lats], axis=1)
 
-        dist, indices = self.kd.query(np.deg2rad(xy))
-        dist = dist[0]
-        indices = indices[0]
+        # Assumes nn for a query pont returned in order of increasing distance
+        dist, indices = self.kd.query(np.deg2rad(xy), k=1)
+        dist = dist[:, 0]
+        indices = indices[:, 0]
         dist *= 6370000  # convert unit radius to meters
 
         valid = (indices < self.map_indexes.size) & (dist < self.closeness_threshold)
@@ -100,6 +107,9 @@ class LonLatGrid:
         ll = np.where(indices >= 0, (indices / self.lenx).astype(np.int32), indices)
         cc = np.where(indices >= 0, indices % self.lenx, indices)
 
+        cc *= self.reduce
+        ll *= self.reduce
+
         return cc, ll
 
     # bi-linear interpolation of a function on the grid, the range of which is grd_zvals for target lons,lats.