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Raindrop size distribution

Raindrop size distribution is a physics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Raindrop size distribution rather than just read about it. In short: The raindrop size distribution (DSD), or granulometry of rain, is the distribution of the number of raindrops according to their diameter (D). Three processes account for the formation of drops: water vapor condensation, accumulation of small drops on large drops and collisions between sizes.

Raindrop size distribution — main illustration
Raindrop size distribution — illustration

Key takeaways

  • Raindrop size distribution belongs to physics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Raindrop size distribution to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Raindrop size distribution from memory before moving on to harder problems.

Reference excerpt

The raindrop size distribution (DSD), or granulometry of rain, is the distribution of the number of raindrops according to their diameter (D). Three processes account for the formation of drops: water vapor condensation, accumulation of small drops on large drops and collisions between sizes. According to the time spent in the cloud, the vertical movement in it and the ambient temperature, drops have a very varied history and a distribution of diameters from a few micrometers to a few millimeters.

Definition

In general, the drop size distribution is represented as a truncated gamma function for diameter zero to the maximum possible size of rain droplets. The number of drop with diameter D {\displaystyle D} is therefore :

N ( D ) = N 0 D μ e − Λ D {\displaystyle N(D)=N_{0}D^{\mu }e^{-\Lambda D}}

with N 0 {\displaystyle N_{0}} , μ {\displaystyle \mu } and Λ {\displaystyle \Lambda } as constants.

Marshall-Palmer distribution The most well-known study about raindrop size distribution is from Marshall and Palmer done at McGill University in Montréal in 1948. They used stratiform rain with μ = 0 {\displaystyle \mu =0} and concluded to an exponential drop size distribution. This Marshall-Palmer distribution is expressed as:

N ( D ) M P = N 0 e − Λ D {\displaystyle N(D)_{MP}=N_{0}e^{-\Lambda D}}

Where

N0 = 8000 m−3mm−1 ;

Λ {\displaystyle \scriptstyle \Lambda } = 4.1 R−0.21mm−1 (equivalent to 41 R−0.21cm−1 in the reference), R being the rainrate in stratiform precipitation in millimeters per hour; D = raindrop diameter in mm The units of N0 are sometimes simplified to cm −4 but this removes the information that this value is calculated per cubic meter of air. As the different precipitations (rain, snow, sleet, etc...), and the different types of clouds that produce them vary in time and space, the coefficients of the drop distribution function will vary with each situation. The Marshall-Palmer relationship is still the most quoted but it must be remembered that it is an average of many stratiform rain events in mid-latitudes. The upper figure shows mean distributions of stratiform and convective rainfall. The linear part of the distributions can be adjusted with particular Λ {\displaystyle \scriptstyle \Lambda } of the Marshall-Palmer distribution. The bottom one is a series of drop diameter distributions at several convective events in Florida with different precipitation rates. We can see that the experimental curves are more complex than the average ones, but the general appearance is the same. Many other forms of distribution functions are therefore found in the meteorological literature to more precisely adjust the particle size to particular events. Over time researchers have realized that the distribution of drops is more of a problem of probability of producing drops of different diameters depending on the type of precipitation than a deterministic relationship. So there is a continuum of families of curves for stratiform rain, and another for convective rain.

Ulbrich distribution The Marshall and Palmer distribution uses an exponential function that does not simulate properly drops of very small diameters (the curve in the top figure). Several experiments have shown that the actual number of these droplets is less than the theoretical curve. Carlton W. Ulbrich developed a more general formula in 1983 taking into account that a drop is spherical if D <1 mm and an ellipsoid whose horizontal axis gets flattened as D gets larger. It is mechanically impossible to exceed D = 10 mm as the drop breaks at large diameters. From the general distribution, the diameter spectrum changes, μ = 0 inside the cloud, where the evaporation of small drops is negligible due to saturation conditions and μ = 2 out of the cloud, where the small drops evaporate because they are in drier air. With the same notation as before, we have for the drizzle the distribution of Ulbrich:

… excerpt ends here. Continue reading the full article.

Illustrations

Raindrop size distribution: Example of distributions in convective rain in Florida with different rates of precipitation: logarithmic scale of number (N) versus linear scale of diameters (D)[1]
Example of distributions in convective rain in Florida with different rates of precipitation: logarithmic scale of number (N) versus linear scale of diameters (D)[1]

Worked examples

Example 1 — a first encounter with Raindrop size distribution

Start with the simplest possible case. Write down what Raindrop size distribution claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In physics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Raindrop size distribution before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Raindrop size distribution ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Raindrop size distribution

In research
Raindrop size distribution appears in physics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Raindrop size distribution in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Raindrop size distribution is common in secondary-school and first-year university syllabi. It links to neighbouring topics Cloud and fog physics, Radar meteorology, so understanding it makes those chapters shorter.
In everyday life
Look for Raindrop size distribution outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Raindrop size distribution in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Raindrop size distribution means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Raindrop size distribution out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Raindrop size distribution in simple terms?

The raindrop size distribution (DSD), or granulometry of rain, is the distribution of the number of raindrops according to their diameter (D). Three processes account for the formation of drops: water vapor condensation, accumulation of small drops on large drops and collisions between sizes.

Why does Raindrop size distribution matter?

Because it connects several physics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Raindrop size distribution?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Raindrop size distribution.

Tags

  • Cloud and fog physics
  • Radar meteorology

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