<p>It is also challenging to get clouds, rain etc. right in weather forecasts. Compared to climate applications the advantage in weather forecasts is that cloud information from the initial conditions can improve the cloud representation in the model. Unfortunately that information does not last that long in the model due to the limited lifetimes of clouds. Nevertheless, for short-term forecasts assimilation of satellite and radar data can favor clouds in numerical weather prediction, which is not possible in climate modeling.</p>

CC BY-SA 3.0
2014-05-02T21:07:20.560Z
2014-05-03T09:36:21.027Z
2014-05-03T09:36:21.027Z
BHF
<p>The answer to this question depends on what process one is interested in simulating well. As you know, short/mid-range weather prediction models and climate models have very different applications and goals.</p>

<p>Because the short range weather prediction model is typically of much higher resolution than climate models (~1-10 km versus ~50-200 km), it is almost always more skilled at simulating clouds forming at a specific location and time. Cloud and rain prediction skill tends to be greater in synoptic scale fronts and near topographic features, and smaller for sporadic, small-scale, tropical thunderstorms. In very high-resolution NWP models (2-3 km or smaller), convection and clouds may be simulated reasonably well without using any cloud parameterization scheme. Nevertheless, clouds and rainfall are still hard to simulate or forecast very accurately.</p>

<p>On the other hand, due to very low grid resolution, climate models can really aim only for correctly simulating the occurrence frequency and the amount of clouds and rain in a larger geographical region over a longer period of time. In climate, clouds play a significant role in radiative feedbacks. Climate models still rely on cloud parameterization schemes. </p>

CC BY-SA 3.0
2014-05-02T13:07:07.247Z
You are implicitly talking about global climate models.  A regional climate model *could* have a grid size sufficiently small for explicit simulation of clouds (and, indeed, some do try).
gerrit
CC BY-SA 3.0
2014-05-02T14:58:21.533Z
Yes, that is correct.
milancurcic
CC BY-SA 3.0
2014-05-02T04:53:46.267Z
2014-05-02T04:53:46.267Z
milancurcic
<p>Cloud forcing is still one of the major uncertainties of radiative forcing in climate modeling because there are so many uncertainties in it (and parametrizations). But also because the timescale of cloud development are much shorter than the timescale that climate models are run at.</p>

<p>So I'm curious: are clouds much easier to simulate in the short timespans of weather models? Or are they still a beast to simulate in them?</p>

<atmosphere><meteorology><climate><nwp><climate-models>
Are clouds significantly easier to simulate in weather models than climate models?
InquilineKea
# Are clouds significantly easier to simulate in weather models than climate models?

(Asked by: username_0 on 2014-05-02)

Cloud forcing is still one of the major uncertainties of radiative forcing in climate modeling because there are so many uncertainties in it (and parametrizations). But also because the timescale of cloud development are much shorter than the timescale that climate models are run at.

So I'm curious: are clouds much easier to simulate in the short timespans of weather models? Or are they still a beast to simulate in them?

**Tags:** atmosphere, meteorology, climate, nwp, climate-models


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## 2 answers

### Answer by username_2 on 2014-05-02. Score: 6 | Accepted answer

The answer to this question depends on what process one is interested in simulating well. As you know, short/mid-range weather prediction models and climate models have very different applications and goals.

Because the short range weather prediction model is typically of much higher resolution than climate models (~1-10 km versus ~50-200 km), it is almost always more skilled at simulating clouds forming at a specific location and time. Cloud and rain prediction skill tends to be greater in synoptic scale fronts and near topographic features, and smaller for sporadic, small-scale, tropical thunderstorms. In very high-resolution NWP models (2-3 km or smaller), convection and clouds may be simulated reasonably well without using any cloud parameterization scheme. Nevertheless, clouds and rainfall are still hard to simulate or forecast very accurately.

On the other hand, due to very low grid resolution, climate models can really aim only for correctly simulating the occurrence frequency and the amount of clouds and rain in a larger geographical region over a longer period of time. In climate, clouds play a significant role in radiative feedbacks. Climate models still rely on cloud parameterization schemes.

> **Comments:**
> **username_3**: You are implicitly talking about global climate models.  A regional climate model *could* have a grid size sufficiently small for explicit simulation of clouds (and, indeed, some do try).  
> **username_2**: Yes, that is correct.  

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### Answer by username_1 on 2014-05-02. Score: 4

It is also challenging to get clouds, rain etc. right in weather forecasts. Compared to climate applications the advantage in weather forecasts is that cloud information from the initial conditions can improve the cloud representation in the model. Unfortunately that information does not last that long in the model due to the limited lifetimes of clouds. Nevertheless, for short-term forecasts assimilation of satellite and radar data can favor clouds in numerical weather prediction, which is not possible in climate modeling.


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