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nci · Sep 10, 2023 · 501add7 · 501add7
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diff --git a/docs/paper.bib b/docs/paper.bib
@@ -151,4 +151,14 @@ @Article{Taggart:2022c
 	pages		 = {201--231},
 	year		 = {2022},
 	publisher	 = {The Institute of Mathematical Statistics and the Bernoulli Society}
+}
+@Article{Gneiting:2011,
+	title		 = {Comparing density forecasts using threshold-and quantile-weighted scoring rules},
+	author		 = {Gneiting, Tilmann and Ranjan, Roopesh},
+	journal		 = {Journal of Business & Economic Statistics},
+	volume		 = {29},
+	number		 = {3},
+	pages		 = {411--422},
+	year		 = {2011},
+	publisher	 = {Taylor & Francis}
 }
diff --git a/docs/paper.md b/docs/paper.md
@@ -24,11 +24,11 @@ bibliography: paper.bib
 
 # Summary
 
-`scores` is a Python package containing mathematical functions for the verification, evaluation, and optimisation of model outputs and predictions. It primarily supports the geoscience and earth system science communities. `scores` is focused on supporting xarray datatypes for earth system data. It has wide potential application in machine learning, and domains other than meteorology, geoscience and weather. It also aims to be compatible with pandas, geopandas, pangeo and work with NetCDF4, Zarr, hdf5 and GRIB data sources among others. Scores is designed to utilise Dask for scaling and performance.
+`scores` is a Python package containing mathematical functions for the verification, evaluation, and optimisation of model outputs and predictions. It primarily supports the geoscience and earth system science communities. `scores` is focused on supporting xarray datatypes for earth system data. It has wide potential application in machine learning, and domains other than meteorology, geoscience and weather. It also aims to be compatible with xarray, pandas, geopandas and work with NetCDF4, Zarr, hdf5 and GRIB data sources among others. Scores is designed to utilise Dask for scaling and performance.
 
 All of the scores and metrics in this package have undergone a thorough statistical and scientific review. Every score has a companion Jupyter Notebook demonstrating its use in practise.
 
-At the time of writing, the scores contained in this package are: MSE, MAE, RMSE, FIRM [@Taggart:2022a], CRPS, the FlipFlop index [@Griffiths:2019] and the Murphy score [@Ehm:2016]. It also includes the Diebold-Mariano statistical test [@Diebold:1995] with both the [@Harvey:1997] and [@Hering:2011] modifications.
+At the time of writing, the scores contained in this package are: MSE, MAE, RMSE, FIRM [@Taggart:2022a], CRPS (including threshold-weighting, see [@Gneiting:2011], the FlipFlop index [@Griffiths:2019] and the Murphy score [@Ehm:2016]. It also includes the Diebold-Mariano statistical test [@Diebold:1995] with both the [@Harvey:1997] and [@Hering:2011] modifications.
 
 # Statement of need