using GEOS-5 FP numeric weather prediction for default parameters

Author: gregory-halverson-jpl

FLiESANN/constants.py

@@ -3,6 +3,8 @@
 DEFAULT_WORKING_DIRECTORY = "."
 DEFAULT_FLIES_INTERMEDIATE = "FLiES_intermediate"
 
+GEOS5FP_DIRECTORY = "~/data/GEOS5FP_download"
+
 DEFAULT_MODEL_FILENAME = join(abspath(dirname(__file__)), "FLiESANN.h5")
 SPLIT_ATYPES_CTYPES = True
 

FLiESANN/prepare_FLiES_ANN_inputs.py

@@ -24,7 +24,7 @@ def prepare_FLiES_ANN_inputs(
     elevation_km_flat = np.array(elevation_km).flatten()
     SZA_flat = np.array(SZA).flatten()
 
-    inputs = pd.DataFrame({
+    inputs_dict = {
         "ctype": ctype_flat,
         "atype": atype_flat,
         "COT": COT_flat,
@@ -34,7 +34,22 @@ def prepare_FLiES_ANN_inputs(
         "albedo": albedo_flat,
         "elevation_km": elevation_km_flat,
         "SZA": SZA_flat
-    })
+    }
+
+    # check all values in inputs_dict are numpy arrays and throw an exception if any are not
+
+    for key, value in inputs_dict.items():
+        if not isinstance(value, np.ndarray):
+            raise TypeError(f"input {key} is not a numpy array: {type(value)}")
+
+    # check the sizes of the input arrays and throw an exception if they mis-match
+
+    input_sizes = {key: np.array(value).size for key, value in inputs_dict.items()}
+
+    if len(set(input_sizes.values())) != 1:
+        raise ValueError(f"FLiES input size mis-match: {input_sizes}")
+
+    inputs = pd.DataFrame(inputs_dict)
 
     if split_atypes_ctypes:
         inputs["ctype0"] = np.float32(inputs.ctype == 0)

FLiESANN/process_FLiES_ANN.py

@@ -1,10 +1,11 @@
 from typing import Union
 from time import process_time
-
+from datetime import datetime
 import numpy as np
 import rasters as rt
 from rasters import Raster, RasterGeometry
 from geos5fp import GEOS5FP
+from solar_apparent_time import solar_day_of_year_for_area, solar_hour_of_day_for_area
 from sun_angles import calculate_SZA_from_DOY_and_hour
 from koppengeiger import load_koppen_geiger
 
@@ -14,8 +15,6 @@
 from .run_FLiES_ANN_inference import run_FLiES_ANN_inference
 
 def process_FLiES_ANN(
-        day_of_year: Union[Raster, np.ndarray],
-        hour_of_day: Union[Raster, np.ndarray],
         albedo: Union[Raster, np.ndarray],
         COT: Union[Raster, np.ndarray] = None,
         AOT: Union[Raster, np.ndarray] = None,
@@ -25,8 +24,11 @@ def process_FLiES_ANN(
         SZA: Union[Raster, np.ndarray] = None,
         KG_climate: Union[Raster, np.ndarray] = None,
         geometry: RasterGeometry = None,
+        time_UTC: datetime = None,
+        day_of_year: Union[Raster, np.ndarray] = None,
+        hour_of_day: Union[Raster, np.ndarray] = None,
         GEOS5FP_connection: GEOS5FP = None,
-        GEOS5FP_directory: str = None,
+        resampling: str = "cubic",
         ANN_model=None,
         model_filename=DEFAULT_MODEL_FILENAME,
         split_atypes_ctypes=SPLIT_ATYPES_CTYPES) -> dict:
@@ -82,6 +84,16 @@ def process_FLiES_ANN(
     if geometry is None and isinstance(albedo, Raster):
         geometry = albedo.geometry
 
+    if (day_of_year is None or hour_of_day is None) and time_UTC is not None and geometry is not None:
+        day_of_year = solar_day_of_year_for_area(time_UTC=time_UTC, geometry=geometry)
+        hour_of_day = solar_hour_of_day_for_area(time_UTC=time_UTC, geometry=geometry)
+
+    if time_UTC is None and day_of_year is None and hour_of_day is None:
+        raise ValueError("no time given between time_UTC, day_of_year, and hour_of_day")
+
+    if GEOS5FP_connection is None:
+        GEOS5FP_connection = GEOS5FP(working_directory=DEFAULT_WORKING_DIRECTORY, download_directory=GEOS5FP_DIRECTORY)
+
     ## FIXME need to fetch default values for parameters: COT, AOT, vapor_gccm, ozone_cm, elevation_km, SZA, KG_climate 
 
     if SZA is None and geometry is not None:
@@ -101,6 +113,34 @@ def process_FLiES_ANN(
     if KG_climate is None:
         raise ValueError("Koppen Geieger climate classification or geometry must be given")
 
+    if COT is None and geometry is not None and time_UTC is not None:
+        COT = GEOS5FP_connection.COT(
+            time_UTC=time_UTC,
+            geometry=geometry,
+            resampling=resampling
+        )
+    
+    if AOT is None and geometry is not None and time_UTC is not None:
+        AOT = GEOS5FP_connection.AOT(
+            time_UTC=time_UTC,
+            geometry=geometry,
+            resampling=resampling
+        )
+
+    if vapor_gccm is None and geometry is not None and time_UTC is not None:
+        vapor_gccm = GEOS5FP_connection.vapor_gccm(
+            time_UTC=time_UTC,
+            geometry=geometry,
+            resampling=resampling
+        )
+
+    if ozone_cm is None and geometry is not None and time_UTC is not None:
+        ozone_cm = GEOS5FP_connection.ozone_cm(
+            time_UTC=time_UTC,
+            geometry=geometry,
+            resampling=resampling
+        )
+
     # Preprocess COT and determine aerosol/cloud types
     COT = np.clip(COT, 0, None)  # Ensure COT is non-negative
     COT = rt.where(COT < 0.001, 0, COT)  # Set very small COT values to 0

FLiESANN/version.txt

@@ -1 +1 @@
-1.2.1
+1.3.0

pyproject.toml

@@ -3,7 +3,7 @@ requires = ["setuptools>=60", "setuptools-scm>=8.0", "wheel"]
 
 [project]
 name = "FLiESANN"
-version = "1.2.1"
+version = "1.3.0"
 description = "Forest Light Environmental Simulator (FLiES) Radiative Transfer Model Artificial Neural Network (ANN) Implementation in Python"
 readme = "README.md"
 authors = [