diff --git a/README.md b/README.md
index ad2cde0..d9ee818 100644
--- a/README.md
+++ b/README.md
@@ -6,16 +6,14 @@ historical re-forecasts using either [PanguWeather](https://www.nature.com/artic
 [FourCastNet](https://arxiv.org/abs/2202.11214), or [GraphCast](https://www.science.org/doi/10.1126/science.adi2336),
 and save the outputs to their own cloud storage provider for further use.
 
-The initial release of this application is fully-featured, but it has a few limitations:
+The initial release of this application is fully-featured, with some limitations:
 
 - We only provide one storage adapter, for Google Cloud Storage. This can be generalized
   to support S3, Azure, or any other provider in the future.
-- We only enable access to the CDS-based archive of ERA-5 data to initialize the
-  models. To generate pseudo-operational forecasts, users should instead use the MARS
-  API, which offers access to very recent IFS analyses. However, given the licensing
-  restrictions of the AI models (PanguWeather and GraphCast are **forbidden** from being
-  used in commercial applications) and the cost/inaccessibility for most users, we defer
-  implementation of MARS for now.
+- By default, users may initialize a forecast from the CDS-based ERA-5 archive; we also
+  the option to initialize from a GFS forecast, retrieved from NOAA's archive of these
+  products on Google Cloud Storage. We do not provide a mechanism to initialize with IFS
+  operational forecasts from MARS.
 - The current application only runs on [Modal](https://www.modal.com); in the future, it
   would be great to port this to other serverless platforms, re-using as much of the
   core implementation as possible.
@@ -61,10 +59,11 @@ and may break or fail unexpectedly during normal use.
 4. Navigate to the repository on-disk and execute the command,
 
    ```shell
-   $ modal run ai-models-modal.main [\
+   $ modal run ai-models-modal.main \
          --model-name {panguweather,fourcastnetv2-small,graphcast} \
          --model-init 2023-07-01T00:00:00 \
-         --lead-time 12]
+         --lead-time 12 \
+         [--use-gfs]
    ```
 
    The first time you run this, it will take a few minutes to build an image and set up
@@ -74,6 +73,65 @@ and may break or fail unexpectedly during normal use.
 5. Download the model output from Google Cloud Storage at **gs://{GCS_BUCKET_NAME}** as
    provided via the `.env` file.
 
+## Using GFS/GDAS Initial Conditions
+
+We've implemented the ability for users to fetch initial conditions from an
+archived GFS forecast cycle. In the current implementation, we make some assumptions
+about how to process and map the GFS data to the ERA-5 data that the `ai-models`
+package typically tries to fetch:
+
+1. Some models require surface geopotential or orography fields as an input; we use the
+   GFS/GDAS version of this data instead of copying over from ERA-5. Similarly, when
+   needed we use the land-sea mask from GFS/GDAS instead of copying over ERA-5's.
+2. GraphCast is initialized with accumulated precipitation data that is not readily
+   available in the GFS/GDAS outputs; we currently approximate this very crudely by
+   looking at the 6-hr lagged precipitation rate fields from subsequent GFS/GDAS
+   analyses.
+3. The AI models are not fine-tuned (yet) on GFS data, so underlying differences in the
+   core distribution of atmospheric data between ERA-5 and GFS/GDAS could degrade
+   forecast quality in unexpected ways. Additionally, we apply the ERA-5 derived
+   Z-score or uniform distribution scaling from the parent AI models instead of providing
+   new ones for GFS/GDAS data.
+
+We use the `gfs.tHHz.pgrb2.0p25.f000` output files to pull the initial conditions. These
+are available in near-real-time (unlike the final GDAS analyses, which are lagged by
+about one model cycle). We may provide the option to use the `.anl` analysis files, too
+or hot-start initial conditions, based on feedback from the community/users. Converting
+to these files simply requires building a new set of mappers from the corresponding
+ERA-5 fields.
+
+### Running a Forecast from GFS/GDAS
+
+To tell `ai-models-for-all` to use GFS initial conditions, simply pass the command line
+flag "`--use-gfs`" and initialize a model run as usual.
+
+```shell
+$ modal run ai-models-modal.main \
+      --model-name {panguweather,fourcastnetv2-small,graphcast} \
+      --model-init 2023-07-01T00:00:00 \
+      --lead-time 12 \
+      --use-gfs \
+```
+
+The package will automatically download and process the GFS data to use for you, as well
+as archive it for future reference. Please note that the model setup process (where
+assets are downloaded and cached for future runs) may take much longer than usual as we
+also take the liberty of generating independent copies of the ERA-5 template files used
+to process the GFS data. Given the current quota restrictions on the CDS-API, this may
+take a very long time (luckily, the stub functions which perform this process are super
+cheap to run and will cost pennies even if they get stuck for several hours).
+
+For your convenience, we've saved pre-computed data templates for you to use; for a
+typical `.env` setup described below, you can locally run the following Google Cloud
+SDK command to copy over the input templates so that `ai-models-for-all` will
+automatically discover them:
+
+```shell
+$ gcloud storage cp \
+    gs://ai-models-for-all-input-templates/era5-to-gfs-f000 \
+    gs://${GCS_BUCKET_NAME}
+```
+
 ## More Detailed Setup Instructions
 
 To use this demo, you'll need accounts set up on [Google Cloud](https://cloud.google.com),