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Introduction: (Initial Observation)

The sun shines on water in rivers, lakes, streams, wetlands and oceans and makes the water warmer. This turns the water into vapor or steam. The water vapor leaves the lake or ocean or river and goes into the air, where it becomes a cloud.
When the weather is colder, clouds or water vapors will bind together again and come back to the earth in the form of rain, hail, freezing rain, sleet, and snow. That’s what we call precipitation.

Precipitation in different areas of the world is constantly recorded, studied and reported in the news as a part of weather report. But why is the precipitation important. Why do we measure the amount of precipitation. How do we measure precipitation?

This project guide contains information that you need in order to start your project. If you have any questions or need more support about this project, click on the “Ask Question” button on the top of this page to send me a message.

If you are new in doing science project, click on “How to Start” in the main page. There you will find helpful links that describe different types of science projects, scientific method, variables, hypothesis, graph, abstract and all other general basics that you need to know.

Project advisor

Information Gathering:

Find out about different types of precipitation. Read books, magazines or ask professionals who might know in order to learn about the factors or conditions that causes precipitation. Keep track of where you got your information from.When cloud particles become too heavy to remain suspended in the air, they fall to the earth as precipitation. Precipitation occurs in a variety of forms; hail, rain, freezing rain, sleet or snow. This portion of the Clouds and Precipitation module focuses on precipitation and has been organized into the following sections. Click Here to see what conditions can make a precipitation to be rain or snow or any other frozen form of water.

More About Precipitation

Precipitation is recorded and reported by different organization such as National Drought Mitigation Center. Following is a sample precipitation map. SPI stands for Standard Precipitation Index.

Precipitation maps and reports are being used by agricultural producers, ranchers and municipal water suppliers.

Question/ Purpose:

What do you want to find out? Write a statement that describes what you want to do. Use your observations and questions to write the statement.

The purpose of this project is to find out what causes precipitation and experiment on the methods of measuring precipitation.

Identify Variables:

When you think you know what variables may be involved, think about ways to change one at a time. If you change more than one at a time, you will not know what variable is causing your observation. Sometimes variables are linked and work together to cause something. At first, try to choose variables that you think act independently of each other.


Independent variables that may affect precipitation are moisture (or cloud) and temperature. These variables will affect the amount and type of precipitation.


Based on your gathered information, make an educated guess about what types of things affect the system you are working with. Identifying variables is necessary before you can make a hypothesis.

Sample Hypothesis:

My hypothesis is that at low temperature the clouds will condense and become heavy so they drop to the earth in some form of precipitation.

Experiment Design:

Design an experiment to test each hypothesis. Make a step-by-step list of what you will do to answer each question. This list is called an experimental procedure. For an experiment to give answers you can trust, it must have a “control.” A control is an additional experimental trial or run. It is a separate experiment, done exactly like the others. The only difference is that no experimental variables are changed. A control is a neutral “reference point” for comparison that allows you to see what changing a variable does by comparing it to not changing anything. Dependable controls are sometimes very hard to develop. They can be the hardest part of a project. Without a control you cannot be sure that changing the variable causes your observations. A series of experiments that includes a control is called a “controlled experiment.”

Quick Experiment 1:

Be a Tropical Rain Maker!

Try this experiment, but only with adult supervision:

Boil  water in a tea kettle.  Watch the steam come out and go into the air. (Evaporation)Put ice in a metal pie pan.  Hold the pan over the steam and watch the drops form on the bottom of the pan. (Condensation)As the drops grow in size, they will get heavy and start to fall. (Precipitation)

Experiment 2: What causes precipitation?

What do you think might cause the condensed water (cloud) to turn into precipitation (rain). On your Lab Sheet record your hypothesis.


In this experiment you will be using the materials below to simulate one phase of Earth’s water cycle – precipitation. We have already seen how water vapor (evaporated water in the air) condenses onto tiny particles when the air is cooled and forms a cloud. Your task will be to conduct a “precipitation simulation” and to formulate a hypothesis explaining how the condensed water (cloud) turned into precipitation (rain). You will need:

  • a pan of water
  • a burner
  • a large cookie sheet
  • ice cubes
  • 2 metal trash cans
    The Experiment (Simulation) Procedure
    Place a pan of water on the burner.
    Place 2 metal trash cans on either side of the burner.
    Place the cookie sheet on the 2 trash cans so that it forms a “ceiling” over the pan of water.
    Place the ice cubes on top of the cookie sheet.
    Heat the water and watch as the steam accumulates and hits the cool surface.
    Record your observations on the Lab Sheet.
    Compare your simulation to Earth’s water cycle. Which stage of the simulation represented….
    Discuss in your report what happened to cause the condensation to turn into precipitation. Have you changed your hypothesis? Why or why not? Go to Hypothesis Revisited on your Lab Sheet and record your response.

Experiment 3: Snow Density


In many places, government water engineers and farmers depend on winter snow to provide an adequate water supply for cities and farms. These people watch and measure the amount of snow all winter long. It is not just the depth of the snow that is important, but the amount of precipitation or, in other words, the amount of water in the snow.

You can measure the amount of water in snow by collecting some of the snow, melting it, and measuring the water. Some snow is very dense, which means it contains a large quantity of water for its relative size. An example might be that one gallon of snow, when melted, becomes one quart of water. Other snow has a very low density. In this case, a gallon of snow makes only a cup of water.

Many things affect the density of snow including: temperature, air pressure, the nature of the storm, your geographic location and altitude, etc. In truth, each snowstorm brings snow of a different density.

In this experiment, you are the weather watchers. You must determine how much moisture is available in the snow in their area. This experiment does not require you to track precipitation for a complete season. The best time to do it, however, is during the biggest snow weeks when there is lots of activity in the weather.

DENSITY: The amount of space between molecules or particles of any substance is what determines its density. Lead is more dense than aluminum which is more dense than wood which is more dense than foam rubber which is more dense than Styrofoam. Density is equal to the mass (or weight) of a substance divided by its volume (d=m/V). The metric system was set up in such a way so that the density of water can be written as 1 gram per milliliter (1g/mL). One milliliter (mL) is also equal to one cubic centimeter.

PRECIPITATION: The word precipitation is defined as any form of water that falls from clouds and reaches the ground; the amount being expressed in inches of liquid water depth. For this activity, we will compare not the depth, but the volume of the snow or water. Water is “pure precipitation.” Snow, on the other hand, is mostly air. As you can see, the density of snow is related to the measure of the water in snow. The density of snow might be expressed as 1 cup of water per gallon of snow or 100 mL/L [density=V(water)/V(snow), where V=volume.]

Materials Needed:

  • Deep snow
  • Little Bear Snowshoes
  • Three large buckets or liquid containers the same size with lids
  • Measuring cups
  • Notebook

Procedure: Using snowshoes, walk into the deep snow. You have three buckets. Use one to collect snow from the surface. Avoid compressing the snow as you collect it. (This can be difficult, but the activity will work regardless of the collection methods used.) In the second bucket, collect snow from deep beneath the surface. The final bucket is to be used more like an actual forecaster might use it. Find a place where you can leave the bucket to collect falling snow. On the first day, you will not have data for this bucket, unless you make arrangements to leave it out the day before.

Cover the containers and take them inside to melt. Each day (or whatever time interval you choose) you will repeat this experiment. Be sure to plan ahead to guarantee that your collection methods can be repeated for at least 10 time intervals.

Recording Data: Once the snow is melted, measure the water in each container and  record the information in a table in your notebook .

You will draw a table in your notebook to record the following information for each sample that you test:

DATE = The day on which you took measurements.
SNOWFALL = Amount of water melted from the bucket left to collect falling snow.
SNOW SURFACE = Amount of water melted from the snow gathered from the surface.
SNOWPACK = Amount of water from the snow collected from deep beneath the surface.



Presenting The Data:

Using the chart guide and the completed Data table, make your own Data Presentation Chart.

Why Use Charts:

A page full of numbers is very hard to understand. But if you put those same numbers on the right kind of chart, they become very easy to understand. There are many kinds of charts. For this experiment, we will use a line graph. Think of a chart as your presentation or final report showing all the work you have done.


The first step is to select the units of measure. Along the bottom of your chart, write in the dates that correspond to the days on which you took measurements. Put the first day next to the word “TIME” at the left. Each new date should be an increase of equal amount. The amount of change from date to date is called the unit of measure.

To determine the best units for the water (percipitation), look at the measurements on your Data Record Handout. When you write the units on your chart, you want your smallest unit to be near the bottom, just above the word “WATER.” Your largest measure should be near the top. Choose the units that would make this possible. Example: 1/8th cup steps.
Plotting Data Points:

It is easiest to plot only one column from your Data Record at a time. Draw a dot above each date for the amount of “Snowfall” recorded. When you are finished, connect the dots with a colored marker. Next, draw dots for the surface density and use a different color of marker to connect the dots.

Do the same for the “Snowpack.” Choose a title or headline for your graph and write it along the top.


The point of this experiment is to have you collect data over time, but you could change it to measure data by location or some other variable. Two possible methods for measuring data over time that can be done in a single day are: (1) Fill a very tall container with snow and take depth measurements every 5-10 minutes and chart the melting speed. If you can find snow of different densities, you can compare the melt speed for each. (2) On a day when it is snowing, place a yard stick outside and measure snow depth every 30 minutes throughout the day. Again, you can chart this data over time. Adapt these ideas to fit your circumstances.

Make Your Own Rain Gauge (Additional Experiment)

You’ll need these materials:
a glass beaker (or any straight-sided glass that can be marked with a measuring scale)
a coat hanger or wire (bent to make a holding rack — see picture)
hammer and nails (to secure the rack)

Basically, any measuring glass left outside can serve as a rain gauge. However, since most rain showers are usually quite windy, you’ll want to fasten your rain gauge somewhere so that it doesn’t blow over. Locate a good place for your gauge. There should be nothing overhead, like trees, electric wires, or the edge of a roof. These obstructions can direct rainwater into or away from your gauge, creating a false reading. The edge of a fence, away from the building, is often a good place for your gauge.

Once you have found the spot, attach the holding rack (refer to picture). Then, slip your measuring glass into position. Wait for rain, then record your measurement, and empty the glass.

Materials and Equipment:

List of material can be extracted from the experiment section.
See a sample rain gauge available at MiniScience online store.

Results of Experiment (Observation):

Experiments are often done in series. A series of experiments can be done by changing one variable a different amount each time. A series of experiments is made up of separate experimental “runs.” During each run you make a measurement of how much the variable affected the system under study. For each run, a different amount of change in the variable is used. This produces a different amount of response in the system. You measure this response, or record data, in a table for this purpose. This is considered “raw data” since it has not been processed or interpreted yet. When raw data gets processed mathematically, for example, it becomes results.


You may need to calculate densities of snow as described above. Write your calculations in your reports.

Summary of Results:

Summarize what happened. This can be in the form of a table of processed numerical data, or graphs. It could also be a written statement of what occurred during experiments.

It is from calculations using recorded data that tables and graphs are made. Studying tables and graphs, we can see trends that tell us how different variables cause our observations. Based on these trends, we can draw conclusions about the system under study. These conclusions help us confirm or deny our original hypothesis. Often, mathematical equations can be made from graphs. These equations allow us to predict how a change will affect the system without the need to do additional experiments. Advanced levels of experimental science rely heavily on graphical and mathematical analysis of data. At this level, science becomes even more interesting and powerful.


Using the trends in your experimental data and your experimental observations, try to answer your original questions. Is your hypothesis correct? Now is the time to pull together what happened, and assess the experiments you did.

Related Questions & Answers:

What you have learned may allow you to answer other questions. Many questions are related. Several new questions may have occurred to you while doing experiments. You may now be able to understand or verify things that you discovered when gathering information for the project. Questions lead to more questions, which lead to additional hypothesis that need to be tested.

Possible Errors:

If you did not observe anything different than what happened with your control, the variable you changed may not affect the system you are investigating. If you did not observe a consistent, reproducible trend in your series of experimental runs there may be experimental errors affecting your results. The first thing to check is how you are making your measurements. Is the measurement method questionable or unreliable? Maybe you are reading a scale incorrectly, or maybe the measuring instrument is working erratically.

If you determine that experimental errors are influencing your results, carefully rethink the design of your experiments. Review each step of the procedure to find sources of potential errors. If possible, have a scientist review the procedure with you. Sometimes the designer of an experiment can miss the obvious.


List your printed or online references here.