paper edits

paper/paper.bib

@@ -1,5 +1,5 @@
 @ARTICLE{Thornton2021,
-  title     = "Gridded daily weather data for North America with comprehensive
+  title     = "Gridded daily weather data for {N}orth {A}merica with comprehensive
                uncertainty quantification",
   author    = "Thornton, Peter E and Shrestha, Rupesh and Thornton, Michele and
                Kao, Shih-Chieh and Wei, Yaxing and Wilson, Bruce E",
@@ -31,6 +31,7 @@ @ARTICLE{Thornton2021
   pages     = "190",
   month     =  jul,
   year      =  2021,
+  doi = {10.1038/s41597-021-00973-0},
   copyright = "https://creativecommons.org/licenses/by/4.0",
   language  = "en"
 }
@@ -56,6 +57,7 @@ @ARTICLE{Blankenau2020
   number    =  106376,
   pages     = "106376",
   month     =  dec,
+  doi       = {10.1016/j.agwat.2020.106376},
   year      =  2020,
   language  = "en"
 }

paper/paper.md

@@ -21,15 +21,15 @@ authors:
   - name: Justin L. Huntington
     affiliation: 1 
 affiliations:
-  - name: Desert Research Institute
+  - name: Desert Research Institute, Reno, USA
     index: 1
 date: April 2024
 bibliography: paper.bib
 ---
 
 # Introduction
 
-Gridded weather data has become increasingly accessible and accurate over the recent decades, and such data enables a variety of applications and research that require spatially continuous and high spatio-temporal resolution data [@Thornton2021;@Rasmussen2023;@MuozSabater2021]. Gridded weather data are often developed from an assimilation of data production and measurement techniques, including the incorporation of *in-situ* observational data networks, land surface modeling techniques, and remote sensing techniques. Although well-curated *in-situ* measurements of weather variables are typically more accurate than gridded products, they provide data at a single point in space, and they involve difficulties and expenses related to deployment and sensor calibration and maintenance, resulting in incomplete spatial and temporal coverage. Gaps in spatial and temporal coverage in *in-situ* weather data are often filled by gridded data products. The increased coverage provided by data filling in gridded datasets comes with the tradeoff of uncertainty and potential for bias [@Blankenau2020] that are introduced by gridded data development and assimilation techniques. 
+Gridded weather data have become increasingly accessible and accurate over the recent decades, and such data enable a variety of applications and research that require spatially continuous and high spatio-temporal resolution data [@Thornton2021;@Rasmussen2023;@MuozSabater2021]. Gridded weather data are often developed from an assimilation of data production and measurement techniques, including the incorporation of *in-situ* observational data networks, land surface modeling techniques, and remote sensing techniques. Although well-curated *in-situ* measurements of weather variables are typically more accurate than gridded products, they provide data at a single point in space, and they involve difficulties and expenses related to deployment and sensor calibration and maintenance, resulting in incomplete spatial and temporal coverage. Gaps in spatial and temporal coverage in *in-situ* weather data are often filled by gridded data products. The increased coverage provided by data filling in gridded datasets comes with the tradeoff of uncertainty and potential for bias [@Blankenau2020] that are introduced by gridded data development and assimilation techniques. 
 
 # Statement of Need
 
@@ -39,11 +39,11 @@ This package has the functionality to download point data from a variety of grid
 
 # Design and Features
 
-``gridwxcomp`` is a Python 3 package that consists of five core submodules and two utility submodules (\autoref{fig:fig1}). ``gridwxcomp`` can process the following meteorological variables: air temperature (minimum and maximum), dew point temperature, shortwave radiation, wind speed, vapor pressure, relative humidity (minimum, maximum, and average), and grass (short) and alfalfa (tall) reference evapotranspiration (ET). Daily gridded weather datasets hosted on Google Earth Engine can be accessed and compared against station data using ``gridwxcomp`` as long as the user has access to the dataset collection. Example public datasets include: CONUS404 [@Rasmussen2023], ERA5-Land [@MuozSabater2021], gridMET [@Abatzoglou2013], NLDAS [@Mitchell2004], RTMA [@DePondeca2011], and spatial CIMIS [@Hart2009]. 
+``gridwxcomp`` is a Python package that consists of five core submodules and two utility submodules (\autoref{fig:fig1}). ``gridwxcomp`` can process the following meteorological variables: air temperature (minimum and maximum), dew point temperature, shortwave radiation, wind speed, vapor pressure, relative humidity (minimum, maximum, and average), and grass (short) and alfalfa (tall) reference evapotranspiration (ET). Daily gridded weather datasets hosted on Google Earth Engine can be accessed and compared against station data using ``gridwxcomp`` as long as the user has access to the dataset collection. Example public datasets include: CONUS404 [@Rasmussen2023], ERA5-Land [@MuozSabater2021], gridMET [@Abatzoglou2013], NLDAS [@Mitchell2004], RTMA [@DePondeca2011], and spatial CIMIS [@Hart2009]. 
 
 ![Flowchart diagram of submodules and data processing pipeline of ``gridwxcomp``.\label{fig:fig1}](figure1.pdf)
 
-The ``prep_metadata`` submodule parses metadata of meteorological stations, including reprojection of station coordinates. The output file from ``prep_metadata`` is used by the ``ee_download`` submodule which queries Google Earth Engine for the gridded weather data specifed by the user and downloads time series data at the corresponding station coordinates. 
+The ``prep_metadata`` submodule parses metadata of meteorological stations, including reprojection of station coordinates. The output file from ``prep_metadata`` is used by the ``ee_download`` submodule which queries Google Earth Engine for the gridded weather data specified by the user and downloads time series data at the corresponding station coordinates. 
 
 The ``calc_bias_ratios`` submodule pairs the station and gridded daily data, performs unit conversions, and computes average monthly, seasonal, and annual bias ratios or differences (for temperature variables) between the station and gridded data for a specified variable. In addition to computing station-to-gridded biases, the ``calc_bias_ratios`` routine calculates the interannual variability (standard deviation and coefficient of variation) of those metrics and the number of paired data used in each metric. 
 
@@ -53,13 +53,13 @@ The ``plot`` submodule in ``gridwxcomp`` provides tools for generating interacti
 
 # Research Enabled by gridwxcomp
 
-The most significant application of ``gridwxcomp`` was the development of bias correction surfaces that are applied to gridded reference evapotransipiration (ETo) data which are key inputs to some of the remote sensing ET models that comprise the OpenET platform [@Volk2024;@Melton2021]. Daily data from approximately 800 weather stations located in irrigated agricultural sites were curated, and the American Society of Civil Engineers (ASCE) standardized Penman-Monteith reference ET equation [@allen2005] was used to estimate ETo at the stations. Then ``gridwxcomp`` was used to pair these data with the nearest ETo data from the gridMET [@Abatzoglou2013] dataset over temporally consistent periods. The long-term average monthly ratios for station ETo relative to the gridded ETo were calculated for each point and saved as georeferenced data by ``gridwxcomp`` and were subsequently spatially interpolated using a kriging approach. The interpolated monthly surfaces are used within the OpenET platform to correct gridMET ETo data before it is used by most of the remote sensing ET models as a major scaling flux. 
+The most significant application of ``gridwxcomp`` was the development of bias correction surfaces that are applied to gridded reference evapotranspiration (ETo) data which are key inputs to some of the remote sensing ET models that comprise the OpenET platform [@Volk2024;@Melton2021]. Daily data from approximately 800 weather stations located in irrigated agricultural sites were curated, and the American Society of Civil Engineers (ASCE) standardized Penman-Monteith reference ET equation [@allen2005] was used to estimate ETo at the stations. Then ``gridwxcomp`` was used to pair these data with the nearest ETo data from the gridMET [@Abatzoglou2013] dataset over temporally consistent periods. The long-term average monthly ratios for station ETo relative to the gridded ETo were calculated for each point and saved as georeferenced data by ``gridwxcomp`` and were subsequently spatially interpolated using a kriging approach. The interpolated monthly surfaces are used within the OpenET platform to correct gridMET ETo data before it is used by most of the remote sensing ET models as a major scaling flux. 
 
 # Co-author Roles
 
 ``gridwxcomp`` was developed through the following efforts:
 
-* John M. Volk: Conceptualization, Software, Validation, Writing & Editting
+* John M. Volk: Conceptualization, Software, Validation, Writing & Editing
 * Christian Dunkerly: Conceptualization, Software, Validation
 * Christopher Pearson: Conceptualization, Software, Validation
 * Charles Morton: Conceptualization, Software