Added statement of need section to JOSS paper to be more explicit

paper.md

@@ -32,6 +32,7 @@ Much of atmospheric science is driven by the generation, interaction, and precip
 
 These distributions of particles are measured by sets of instruments called disdrometers, or particle probes, using a wide variety of methods with the most common being acoustic, laser-optical, or video. A primary difference between the different type of instruments is the amount of information they measure about the drops, ranging from just size, to size and velocity, up to images of individual drops. Additionally, the range of drop sizes that can be accurately recorded varies between devices and measurement modalities. Disdrometers can be deployed on ground- or ship-based platforms while particle probes are often deployed on aerial platforms. Ground and ship-based platforms most often focus on measuring the larger size distributions associated with precipitation at the ground. Aerial platforms more commonly include devices extending to a smaller range of particles. Hereafter we will use the term disdrometers for simplicity to refer to both classes of instruments unless specifically differentiated. 
 
+# Statement of Need
 The wide variety of disdrometers in existence, and their increasingly commonplace occurrence in field campaigns, has resulted in a wealth of DSD data from around the world. Unfortunately, working with the resulting data is not always so simple. File formats vary between instruments, and even between facilities releasing data from the same instruments. Commonly what is wanted is not the raw DSD measurements, but rather secondary, derived products such as various parameterized distributions, rain rates and other macro properties, and radar equivalent parameters. 
 
 PyDSD is an open source Python library that aims to simplify the process of working with this data with the express goal of both reading and processing of the data, as well as creating publication ready plots of the data. Its goal is to make many of the common tasks researchers face when dealing with these datasets easy to perform. PyDSD focuses on a robust set of algorithms and operations for liquid drop size distributions. PyDSD has formed the basis of DSD processing workflows in research and operations in the field