diff --git a/ocf_datapipes/training/pvnet_site.py b/ocf_datapipes/training/pvnet_site.py
index 33af0256..a0b3e808 100644
--- a/ocf_datapipes/training/pvnet_site.py
+++ b/ocf_datapipes/training/pvnet_site.py
@@ -2,6 +2,7 @@
 
 import logging
 from datetime import datetime, timedelta
+from functools import partial
 from typing import List, Optional
 
 import xarray as xr
@@ -237,12 +238,18 @@ def construct_sliced_data_pipeline(
                 roi_width_pixels=conf_nwp[nwp_key].nwp_image_size_pixels_width,
             )
             # Coarsen the data, if it is separated by 0.05 degrees each
-            nwp_datapipe = nwp_datapipe.map(potentially_coarsen)
+            potentially_coarsen_partial = partial(
+                potentially_coarsen, coarsen_to_deg=conf_nwp[nwp_key].coarsen_to_degrees
+            )
+            nwp_datapipe = nwp_datapipe.map(potentially_coarsen_partial)
             # Somewhat hacky way for India specifically, need different mean/std for ECMWF data
             if conf_nwp[nwp_key].nwp_provider in ["ecmwf"]:
                 normalize_provider = "ecmwf_india"
+            elif conf_nwp[nwp_key].nwp_provider in ["gfs"]:
+                normalize_provider = "gfs_india"
             else:
                 normalize_provider = conf_nwp[nwp_key].nwp_provider
+
             nwp_datapipes_dict[nwp_key] = nwp_datapipe.normalize(
                 mean=NWP_MEANS[normalize_provider],
                 std=NWP_STDS[normalize_provider],
diff --git a/ocf_datapipes/utils/consts.py b/ocf_datapipes/utils/consts.py
index c868b37b..f4f02cd9 100644
--- a/ocf_datapipes/utils/consts.py
+++ b/ocf_datapipes/utils/consts.py
@@ -39,6 +39,7 @@ def __getitem__(self, key):
 NWP_PROVIDERS = [
     "ukv",
     "gfs",
+    "gfs_india",
     "icon-eu",
     "icon-global",
     "ecmwf",
@@ -132,17 +133,32 @@ def __getitem__(self, key):
 UKV_STD = _to_data_array(UKV_STD)
 UKV_MEAN = _to_data_array(UKV_MEAN)
 
-# These were calculated from 200 random init times (step 0s) from the MO global data
+# --- MO Global
+
 MO_GLOBAL_INDIA_MEAN = {
-    "temperature_sl": 298.2,
-    "wind_u_component_10m": 0.5732,
-    "wind_v_component_10m": -0.2831,
+    "temperature_sl": 295.34392488,
+    "wind_u_component_10m": 0.83223102,
+    "wind_v_component_10m": 0.0802083,
+    "downward_shortwave_radiation_flux_gl": 225.54222068,
+    "cloud_cover_high": 0.34935897,
+    "cloud_cover_low": 0.096081,
+    "cloud_cover_medium": 0.13878676,
+    "relative_humidity_sl": 69.59633137,
+    "snow_depth_gl": 3.45158744,
+    "visibility_sl": 23181.81547681,
 }
 
 MO_GLOBAL_INDIA_STD = {
-    "temperature_sl": 8.473,
-    "wind_u_component_10m": 2.599,
-    "wind_v_component_10m": 2.016,
+    "temperature_sl": 12.26983825,
+    "wind_u_component_10m": 3.45169835,
+    "wind_v_component_10m": 2.9825603,
+    "downward_shortwave_radiation_flux_gl": 303.85182864,
+    "cloud_cover_high": 0.40563507,
+    "cloud_cover_low": 0.18374192,
+    "cloud_cover_medium": 0.25972151,
+    "relative_humidity_sl": 21.00264399,
+    "snow_depth_gl": 30.19116501,
+    "visibility_sl": 5385.35839715,
 }
 
 
@@ -197,6 +213,48 @@ def __getitem__(self, key):
 GFS_MEAN = _to_data_array(GFS_MEAN)
 
 
+# ------ GFS
+GFS_INDIA_STD_DICT = {
+    "t": 14.93798,
+    "prate": 5.965701e-05,
+    "u10": 3.4826114,
+    "v10": 3.167296,
+    "u100": 4.140226,
+    "v100": 3.984121,
+    "dlwrf": 79.30329,
+    "dswrf": 325.58582,
+    "hcc": 39.91955,
+    "lcc": 23.208075,
+    "mcc": 33.283035,
+    "r": 25.545837,
+    "sde": 0.10192183,
+    "tcc": 42.583195,
+    "vis": 3491.437,
+}
+GFS_INDIA_MEAN_DICT = {
+    "t": 298.27713,
+    "prate": 1.7736e-05,
+    "u10": 1.5782778,
+    "v10": 0.09856875,
+    "u100": 1.4558668,
+    "v100": -0.28256148,
+    "dlwrf": 356.57776,
+    "dswrf": 284.358,
+    "hcc": 26.965801,
+    "lcc": 9.2288,
+    "mcc": 17.2132,
+    "r": 38.2474,
+    "sde": 0.02070413,
+    "tcc": 36.962795,
+    "vis": 23386.936,
+}
+
+
+GFS_INDIA_VARIABLE_NAMES = tuple(GFS_INDIA_MEAN_DICT.keys())
+GFS_INDIA_STD = _to_data_array(GFS_INDIA_STD_DICT)
+GFS_INDIA_MEAN = _to_data_array(GFS_INDIA_MEAN_DICT)
+
+
 # ------ ECMWF
 # These were calculated from 100 random init times of UK data from 2020-2023
 ECMWF_STD = {
@@ -369,6 +427,7 @@ def __getitem__(self, key):
 NWP_VARIABLE_NAMES = NWPStatDict(
     ukv=UKV_VARIABLE_NAMES,
     gfs=GFS_VARIABLE_NAMES,
+    gfs_india=GFS_INDIA_VARIABLE_NAMES,
     ecmwf=ECMWF_VARIABLE_NAMES,
     ecmwf_india=INDIA_ECMWF_VARIABLE_NAMES,
     excarta=EXCARTA_VARIABLE_NAMES,
@@ -379,6 +438,7 @@ def __getitem__(self, key):
 NWP_STDS = NWPStatDict(
     ukv=UKV_STD,
     gfs=GFS_STD,
+    gfs_india=GFS_INDIA_STD,
     ecmwf=ECMWF_STD,
     ecmwf_india=INDIA_ECMWF_STD,
     excarta=EXCARTA_STD,
@@ -389,6 +449,7 @@ def __getitem__(self, key):
 NWP_MEANS = NWPStatDict(
     ukv=UKV_MEAN,
     gfs=GFS_MEAN,
+    gfs_india=GFS_INDIA_MEAN,
     ecmwf=ECMWF_MEAN,
     ecmwf_india=INDIA_ECMWF_MEAN,
     excarta=EXCARTA_MEAN,