Update paper.md

paper.md

@@ -1,5 +1,5 @@
 ---
-title: "SounderPy: A sounding visualization tool for severe-weather analysis and forecasting"
+title: "SounderPy: An atmospheric sounding visualization and analysis tool for Python"
 tags:
   - Python
   - meteorology
@@ -22,17 +22,14 @@ bibliography: paper.bib
 # Summary
 
 SounderPy is a simple, open-source Python package for retrieving, processing, & 
-plotting vertical profile (sounding) data. Built for simplicity and reliability for all 
-uses and users, this project’s goal is to provide a uniform method for sounding 
-analysis across multiple data types. Severe weather analysis and forecasting 
-requires a sound comprehension of thermodynamic and kinematic properties of the 
+plotting atmospheric vertical profile (sounding) data. Built for simplicity and
+reliability for all users and use cases, this project’s goal is to provide a 
+uniform method for sounding analysis across all data types. Severe weather analysis
+and forecasting requires a sound comprehension of thermodynamic and kinematic properties of the 
 environment. SounderPy makes this possible with robust access to data and custom 
 visualizations. The tool creates complex yet effective sounding and hodograph 
 plots with high readability which are designed specifically for severe weather 
-analysis and forecasting. SounderPy is capable of retrieving and plotting model 
-forecast data, observed radiosonde data, Aircraft Communications Addressing and 
-Reporting System (ACARS) observation data, and model reanalysis data. All of 
-this functionality can be completed in three simple lines of code or less, 
+analysis and forecasting. All of this functionality can be completed in three simple lines of code or less, 
 making SounderPy an accessible tool for both Python experts and novices. A number
 of scientific Python libraries build the base of SounderPy’s efficient and 
 durable functionality, such as NumPy, Matplotlib, xarray, Metpy, and SHARPpy. 
@@ -42,13 +39,26 @@ MIT license.
 # Statement of need
 
 Meteorological data from varying sources may be stored in a variety of file types 
-with a wide range data structures. Such diversity in data formats & availability 
+with a wide range of data structures. Such diversity in data formats & availability 
 can make consistent and effectient processing of complex meteorological data difficult.
 Additionally, thorough yet effectient meteorological analysis of atmospheric properties is 
 vital in describing the past, current, and future state of the atmosphere. Such statements 
 have been made since the dawn of "free-air" observations (soundings) by kites, balloons and 
-aircraft in the early 20th century [@Byers:1934]. 
+aircraft in the early 20th century [@Byers:1934]. Consistent calculations and displays of 
+meteorological data normalizes data analysis such that meaningful comparisons can be 
+drawn from different data types and sources. Reliable statisics and analogs can be developed
+from normalized data analysis which can aid forecasters and researchers with pattern recognition 
+and context. An example of such would be the comparison of numerical weather predicition output 
+to observations of past events. A forecaster may determine the similarities between 
+past and future events and factor those similarities into their forecast.
 
+SounderPy allows for simple access to multiple data sources, such as National Weather Service 
+observed radiosonde observations, Aircraft Communications Addressing and Reporting System 
+observations, numerical weather prediction forecast data, and numerical weather prediction 
+reanalysis data. Each data type has it's own source, file type, and data structure. 
+
+
+# Basics of the API