ENH: add AIGFS and HRRR Models to Forecast

docs/user/environment/1-atm-models/ensemble.rst

@@ -25,8 +25,8 @@ Global Ensemble Forecast System (GEFS)
 
 .. danger::
 
-    **GEFS shortcut unavailable**: ``file="GEFS"`` is currently disabled in
-    RocketPy because NOMADS OPeNDAP is deactivated for this endpoint.
+    **GEFS unavailable**: ``file="GEFS"`` is currently disabled in
+    RocketPy because NOMADS OPeNDAP has been deactivated.
 
 .. note::
 

docs/user/environment/1-atm-models/forecast.rst

@@ -6,8 +6,8 @@ Forecasts
 Weather forecasts can be used to set the atmospheric model in RocketPy.
 
 Here, we will showcase how to import global forecasts such as GFS, as well as
-local forecasts like NAM and RAP for North America, all available through
-OPeNDAP on the `NOAA's NCEP NOMADS <http://nomads.ncep.noaa.gov/>`_ website.
+local forecasts like NAM, RAP and HRRR for North America, all available through
+OPeNDAP on the `UCAR THREDDS <https://thredds.ucar.edu/>`_ server.
 Other generic forecasts can also be imported.
 
 .. important::
@@ -22,6 +22,10 @@ Other generic forecasts can also be imported.
 Global Forecast System (GFS)
 ----------------------------
 
+GFS is NOAA's global numerical weather prediction model. It provides worldwide
+atmospheric forecasts and is usually a good default choice when you need broad
+coverage, consistent availability, and launch planning several days ahead.
+
 Using the latest forecast from GFS is simple.
 Set the atmospheric model to ``forecast`` and specify that GFS is the file you want.
 Note that since data is downloaded from a remote OPeNDAP server, this line of code can
@@ -48,9 +52,34 @@ take longer than usual.
     `GFS overview page <https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php>`_.
 
 
+Artificial Intelligence Global Forecast System (AIGFS)
+------------------------------------------------------
+
+AIGFS is a global AI-based forecast product distributed through the same THREDDS
+ecosystem used by other RocketPy forecast inputs. It is useful when you want a
+global forecast alternative to traditional physics-only models.
+
+RocketPy supports the latest AIGFS global forecast through THREDDS.
+
+.. jupyter-execute::
+
+    env_aigfs = Environment(date=tomorrow)
+    env_aigfs.set_atmospheric_model(type="forecast", file="AIGFS")
+    env_aigfs.plots.atmospheric_model()
+
+.. note::
+
+    AIGFS is currently available as a global 0.25 degree forecast product on
+    UCAR THREDDS.
+
+
 North American Mesoscale Forecast System (NAM)
 ----------------------------------------------
 
+NAM is a regional forecast model focused on North America. It is best suited
+for launches inside its coverage area when you want finer regional detail than
+global models typically provide.
+
 You can also request the latest forecasts from NAM.
 Since this is a regional model for North America, you need to specify latitude
 and longitude points within North America.
@@ -78,6 +107,10 @@ We will use **SpacePort America** for this, represented by coordinates
 Rapid Refresh (RAP)
 -------------------
 
+RAP is a short-range, high-frequency regional model for North America. It is
+especially useful for near-term operations, where fast update cycles are more
+important than long forecast horizon.
+
 The Rapid Refresh (RAP) model is another regional model for North America.
 It is similar to NAM, but with a higher resolution and a shorter forecast range.
 The same coordinates for SpacePort America will be used.
@@ -111,6 +144,17 @@ The same coordinates for SpacePort America will be used.
 High Resolution Window (HIRESW)
 -------------------------------
 
+HIRESW is a convection-allowing, high-resolution regional system designed to
+resolve local weather structure better than coarser grids. It is most useful
+for short-range, local analysis where small-scale wind and weather features
+matter.
+
+The High Resolution Window (HIRESW) model is a sophisticated weather forecasting
+system that operates at a high spatial resolution of approximately 3 km.
+It utilizes two main dynamical cores: the Advanced Research WRF (WRF-ARW) and
+the Finite Volume Cubed Sphere (FV3), each designed to enhance the accuracy of
+weather predictions.
+
 .. danger::
 
     **HIRESW shortcut unavailable**: ``file="HIRESW"`` is currently disabled in
@@ -121,6 +165,33 @@ you can still load it explicitly by passing the path/URL in ``file`` and an
 appropriate mapping in ``dictionary``.
 
 
+High-Resolution Rapid Refresh (HRRR)
+------------------------------------
+
+HRRR is a high-resolution, short-range forecast model for North America with
+hourly updates. It is generally best for day-of-launch weather assessment and
+rapidly changing local conditions.
+
+RocketPy supports HRRR through a dedicated THREDDS shortcut.
+Like NAM and RAP, HRRR is a regional model over North America.
+
+If you have a HIRESW-compatible dataset from another provider (or a local copy),
+you can still load it explicitly by passing the path/URL in ``file`` and an
+appropriate mapping in ``dictionary``.
+
+    env_hrrr = Environment(
+        date=now_plus_twelve,
+        latitude=32.988528,
+        longitude=-106.975056,
+    )
+    env_hrrr.set_atmospheric_model(type="forecast", file="HRRR")
+    env_hrrr.plots.atmospheric_model()
+
+.. note::
+
+    HRRR is a high-resolution regional model with approximately 2.5 km grid
+    spacing over CONUS. Availability depends on upstream THREDDS data services.
+
 
 Using Windy Atmosphere
 ----------------------
@@ -154,6 +225,10 @@ to EuRoC's launch area in Portugal.
 ECMWF
 ^^^^^
 
+ECMWF (HRES) is a global, high-skill forecast model known for strong
+medium-range performance. It is often a good choice for mission planning when
+you need reliable synoptic-scale forecasts several days ahead.
+
 We can use the ``ECMWF`` model from Windy.com. 
 
 .. jupyter-execute::
@@ -173,6 +248,10 @@ We can use the ``ECMWF`` model from Windy.com.
 GFS
 ^^^
 
+Windy's GFS option provides NOAA's global model through Windy's interface. It
+is a practical baseline for global coverage and for comparing against other
+models when assessing forecast uncertainty.
+
 The ``GFS`` model is also available on Windy.com. This is the same model as
 described in the :ref:`global-forecast-system` section.
 
@@ -186,6 +265,10 @@ described in the :ref:`global-forecast-system` section.
 ICON
 ^^^^
 
+ICON is DWD's global weather model, available in Windy for broad-scale
+forecasting. It is useful as an independent global model source to cross-check
+wind and temperature trends against GFS or ECMWF.
+
 The ICON model is a global weather forecasting model already available on Windy.com.
 
 .. jupyter-execute::
@@ -203,6 +286,10 @@ The ICON model is a global weather forecasting model already available on Windy.
 ICON-EU
 ^^^^^^^
 
+ICON-EU is the regional European configuration of ICON, with higher spatial
+detail over Europe than ICON-Global. It is best for European launch sites when
+regional structure is important.
+
 The ICON-EU model is a regional weather forecasting model available on Windy.com.
 
 .. code-block:: python
@@ -228,4 +315,4 @@ Also, the servers may be down or may face high traffic.
 .. seealso::
 
     To browse available NCEP model collections on UCAR THREDDS, visit
-    `THREDDS NCEP Catalog <https://thredds.ucar.edu/thredds/catalog/grib/NCEP/GFS/Global_0p25deg/catalog.html>`_.
+    `THREDDS NCEP Catalog <https://thredds.ucar.edu/thredds/catalog/grib/NCEP/GFS/Global_0p25deg/catalog.html>`_.

docs/user/environment/1-atm-models/soundings.rst

@@ -59,7 +59,7 @@ Integrated Global Radiosonde Archive (IGRA).
 
 These options can be retrieved as a text file in GSD format. However,
 RocketPy no longer provides a dedicated ``set_atmospheric_model`` type for
-NOAA RUC Soundings.
+NOAA RUC Soundings, since NOAA has discontinued the OPENDAP service.
 
 .. note::
 

docs/user/environment/3-further/other_apis.rst

@@ -89,8 +89,10 @@ Instead of a custom dictionary, you can pass a built-in mapping name in the
 - ``"ECMWF_v0"``
 - ``"NOAA"``
 - ``"GFS"``
+- ``"AIGFS"``
 - ``"NAM"``
 - ``"RAP"``
+- ``"HRRR"``
 - ``"HIRESW"`` (mapping available; latest-model shortcut currently disabled)
 - ``"GEFS"`` (mapping available; latest-model shortcut currently disabled)
 - ``"MERRA2"``
@@ -116,10 +118,7 @@ legacy aliases:
 - ``"NAM_LEGACY"``
 - ``"NOAA_LEGACY"``
 - ``"RAP_LEGACY"``
-- ``"CMC_LEGACY"``
 - ``"GEFS_LEGACY"``
-- ``"HIRESW_LEGACY"``
-- ``"MERRA2_LEGACY"``
 
 Legacy aliases primarily cover older variable naming patterns such as
 ``lev``, ``tmpprs``, ``hgtprs``, ``ugrdprs`` and ``vgrdprs``.

rocketpy/environment/environment.py

@@ -11,10 +11,12 @@
 import pytz
 
 from rocketpy.environment.fetchers import (
+    fetch_aigfs_file_return_dataset,
     fetch_atmospheric_data_from_windy,
     fetch_gefs_ensemble,
     fetch_gfs_file_return_dataset,
     fetch_hiresw_file_return_dataset,
+    fetch_hrrr_file_return_dataset,
     fetch_nam_file_return_dataset,
     fetch_open_elevation,
     fetch_rap_file_return_dataset,
@@ -369,9 +371,11 @@ def __initialize_constants(self):
         self.__weather_model_map = WeatherModelMapping()
         self.__atm_type_file_to_function_map = {
             "forecast": {
+                "AIGFS": fetch_aigfs_file_return_dataset,
                 "GFS": fetch_gfs_file_return_dataset,
                 "NAM": fetch_nam_file_return_dataset,
                 "RAP": fetch_rap_file_return_dataset,
+                "HRRR": fetch_hrrr_file_return_dataset,
                 "HIRESW": fetch_hiresw_file_return_dataset,
             },
             "ensemble": {
@@ -665,9 +669,11 @@ def __resolve_dictionary_for_dataset(self, dictionary, dataset):
     def __validate_dictionary(self, file, dictionary):
         # removed CMC until it is fixed.
         available_models = [
+            "AIGFS",
             "GFS",
             "NAM",
             "RAP",
+            "HRRR",
             "HIRESW",
             "GEFS",
             "ERA5",
@@ -1132,8 +1138,9 @@ def set_atmospheric_model(  # pylint: disable=too-many-statements
             - ``"windy"``: one of ``"ECMWF"``, ``"GFS"``, ``"ICON"`` or
               ``"ICONEU"``.
             - ``"forecast"``: local path, OPeNDAP URL, open
-              ``netCDF4.Dataset``, or one of ``"GFS"``, ``"NAM"`` or ``"RAP"``
-              for the latest available forecast.
+              ``netCDF4.Dataset``, or one of ``"AIGFS"``, ``"GFS"``,
+              ``"NAM"``, ``"RAP"``, ``"HRRR"`` or ``"HIRESW"`` for the
+              latest available forecast.
             - ``"reanalysis"``: local path, OPeNDAP URL, or open
               ``netCDF4.Dataset``.
             - ``"ensemble"``: local path, OPeNDAP URL, open
@@ -1143,8 +1150,9 @@ def set_atmospheric_model(  # pylint: disable=too-many-statements
             Variable-name mapping for ``"forecast"``, ``"reanalysis"`` and
             ``"ensemble"``. It may be a custom dictionary or a built-in
             mapping name (for example: ``"ECMWF"``, ``"ECMWF_v0"``,
-            ``"NOAA"``, ``"GFS"``, ``"NAM"``, ``"RAP"``, ``"HIRESW"``,
-            ``"GEFS"``, ``"MERRA2"`` or ``"CMC"``).
+            ``"NOAA"``, ``"AIGFS"``, ``"GFS"``, ``"NAM"``, ``"RAP"``,
+            ``"HRRR"``, ``"HIRESW"``, ``"GEFS"``, ``"MERRA2"`` or
+            ``"CMC"``).
 
             If ``dictionary`` is omitted and ``file`` is one of RocketPy's
             latest-model shortcuts, the matching built-in mapping is selected
@@ -1761,13 +1769,17 @@ def process_forecast_reanalysis(self, file, dictionary):  # pylint: disable=too-
         # Some THREDDS datasets use projected x/y coordinates.
         if dictionary.get("projection") is not None:
             projection_variable = data.variables[dictionary["projection"]]
-            x_units = getattr(lon_array, "units", "m")
-            target_lon, target_lat = geodesic_to_lambert_conformal(
-                self.latitude,
-                self.longitude,
-                projection_variable,
-                x_units=x_units,
-            )
+            if dictionary.get("projection") == "LambertConformal_Projection":
+                x_units = getattr(lon_array, "units", "m")
+                target_lon, target_lat = geodesic_to_lambert_conformal(
+                    self.latitude,
+                    self.longitude,
+                    projection_variable,
+                    x_units=x_units,
+                )
+            else:
+                target_lon = self.longitude
+                target_lat = self.latitude
         else:
             target_lon = self.longitude
             target_lat = self.latitude
@@ -2065,13 +2077,17 @@ class for some dictionary examples.
         # coordinate system before locating the nearest grid cell.
         if dictionary.get("projection") is not None:
             projection_variable = data.variables[dictionary["projection"]]
-            x_units = getattr(lon_array, "units", "m")
-            target_lon, target_lat = geodesic_to_lambert_conformal(
-                self.latitude,
-                self.longitude,
-                projection_variable,
-                x_units=x_units,
-            )
+            if dictionary.get("projection") == "LambertConformal_Projection":
+                x_units = getattr(lon_array, "units", "m")
+                target_lon, target_lat = geodesic_to_lambert_conformal(
+                    self.latitude,
+                    self.longitude,
+                    projection_variable,
+                    x_units=x_units,
+                )
+            else:
+                target_lon = self.longitude
+                target_lat = self.latitude
         else:
             target_lon = self.longitude
             target_lat = self.latitude