Skip to main content

Numerical simulation: unit system

Unit is always one of the most important factors in science. Most of the time, a number without unit is useless.
For numerical simulation, a good design of unit system can be critical.
Recently, I have written a short script/program to prepare climate data for my groundwater/surface water hydrology simulation. I downloaded some data from the National Climatic Data Center(NCDC) separately, and I did NOT notice the changes in unit system. In the end, I have to do everything over again.

So the question is: what kind of unit system should we use to improve our efficiency, and how do we actually implement it?

First of all, we need to identify the unit system used in the data obtained from third party. This is usually done through reading the meta data in the documentation. Never directly use the data without reading the documentation!
A lot of time we don't usually have choices of the unit system provided. In this case, the best practice might be stick with the original unit system.

However,  we should use the SI unit system whenever possible. They are apparently a few benefits from it. For example, NCDC climate data online usually provide standard unit and metric unit system. In this scenario, I would prefer to use metric. And you can always convert them into other units without much efforts.

When we implement related numerical simulation program, using SI unit system for data I/O is also very straightforward. For some type of data, a scale factor can easily preserve the precision without losing efficiency. In modern programming language, the double floating data type can handle almost all type of data without memory issues.

Even when you are dealing with old algorithms which use standard unit system, you can always convert the units before actual calculations, and convert them back to SI afterwards.

Below is the unit system used in one of my recent projects:
Data Unit Note
Temperature Fahrenheit It could be reproduced in Kelvin
Precipitation Millimeter
Dewpoint temperature Fahrenheit


Popular posts from this blog

Spatial datasets operations: mask raster using region of interest

Climate change related studies usually involve spatial datasets extraction from a larger domain.
In this article, I will briefly discuss some potential issues and solutions.

In the most common scenario, we need to extract a raster file using a polygon based shapefile. And I will focus as an example.

In a typical desktop application such as ArcMap or ENVI, this is usually done with a tool called clip or extract using mask or ROI.

Before any analysis can be done, it is the best practice to project all datasets into the same projection.

If you are lucky enough, you may find that the polygon you will use actually matches up with the raster grid perfectly. But it rarely happens unless you created the shapefile using "fishnet" or other approaches.

What if luck is not with you? The algorithm within these tool usually will make the best estimate of the value based on the location. The nearest re-sample, but not limited to, will be used to calculate the value. But what about the outp…

Numerical simulation: ode/pde solver and spin-up

For Earth Science model development, I inevitably have to deal with ODE and PDE equations. I also have come across some discussion related to this topic, i.e.,

In an attempt to answer this question, as well as redefine the problem I am dealing with, I decided to organize some materials to illustrate our current state on this topic.

Models are essentially equations. In Earth Science, these equations are usually ODE or PDE. So I want to discuss this from a mathematical perspective.

Ideally, we want to solve these ODE/PDE with initial condition (IC) and boundary condition (BC) using various numerical methods.

Because of the nature of geology, everything is similar to its neighbors. So we can construct a system of equations which may have multiple equation for each single grid cell. Now we have an array of equation…

Lessons I have learnt during E3SM development

I have been involved with the E3SM development since I joined PNNL as a postdoc. Over the course of time, I have learnt a lot from the E3SM model. I also found many issues within the model, which reflects lots of similar struggles in the lifespan of software engineering.

Here I list a few major ones that we all dislike but they are around in almost every project we have worked on.

Excessive usage of existing framework even it is not meant to Working in a large project means that you should NOT re-invent the wheels if they are already there. But more often, developers tend to use existing data types and functions even when they were not designed to do so. The reason is simple: it is easier to use existing ones than to create new ones. For example, in E3SM, there was not a data type to transfer data between river and land. Instead, developers use the data type designed for atmosphere and land to do the job. While it is ok to do so, it added unnecessary confusion for future development a…