Solar power plants get help from satellites to predict cloud cover

Posted: April 20, 2020 by oldbrew in Clouds, Energy, Forecasting, modelling, satellites, weather
Tags: ,

Image credit: MIT

At least they don’t need any help predicting hours of darkness.
– – –
The output of solar energy systems is highly dependent on cloud cover, says Science Daily.

While weather forecasting can be used to predict the amount of sunlight reaching ground-based solar collectors, cloud cover is often characterized in simple terms, such as cloudy, partly cloudy or clear.

This does not provide accurate information for estimating the amount of sunlight available for solar power plants.

In this week’s Journal of Renewable and Sustainable Energy, from AIP Publishing, a new method is reported for estimating cloud optical properties using data from recently launched satellites.

This new technique is known as Spectral Cloud Optical Property Estimation, or SCOPE.

In 2016, NASA began launching a new generation of Geostationary Operational Environmental Satellites, the GOES-R series.

These satellites occupy fixed positions above the Earth’s surface. Each is equipped with several sophisticated instruments, including the Advanced Baseline Imager, or ABI, which can detect radiation upwelling from the Earth at specific wavelengths.

The SCOPE method estimates three properties of clouds that determine the amount of sunlight reaching the Earth’s surface.

The first, cloud top height, is the altitude corresponding to the top of each cloud. The second, cloud thickness, is simply the difference in altitude between a cloud’s top and bottom. The third property is the cloud optical depth, a measure of how a cloud modifies light passing through it.

Clouds are, essentially, floating masses of condensed water. The water takes multiple forms as liquid droplets or ice crystals of varying sizes. These different forms of water absorb light in different amounts, affecting a cloud’s optical depth.

The amount of light absorbed also depends on the light’s wavelength. Absorption is especially variable for light in the wider infrared range of the spectrum but not so much for light in the narrower visible range.

The SCOPE method simultaneously estimates cloud thickness, top height and optical depth by coupling ABI sensor data from GOES-R satellites to an atmospheric model.

Two other inputs to the model come from ground-based weather stations: ambient temperature and relative humidity at the ground. These are used to adjust temperature and gas concentration vertical profiles in the model.

Full article here.

  1. tallbloke says:

    This should go both ways. Solar arrays could be feeding back empirical data telling us how cloudy it was. That could help improve models of how cloudy its going to be.

    While we’re on the subject. Here’s some Met-O data which should make some Met-O ears burn brightly.

  2. Chaswarnertoo says:

    The graphs show that big yellow H bomb in the sky influences temp.? Are you sure TB? 😎

  3. oldbrew says:

    Fewer low clouds in the tropics as Earth warms

    Date: August 16, 2016
    Source: ETH Zurich
    Summary: With the help of satellite data, scientists have shown that low-level cloud cover in the tropics thins out as the earth warms.
    – – –
    Or was it the other way round – Earth warms due to fewer low clouds? Satellite data doesn’t include logic.

  4. Gamecock says:

    ‘The output of solar energy systems is highly dependent on cloud cover, says Science Daily.’

    Is that copyrighted? Can I use that without acknowledging Science Daily?

  5. Gamecock says:

    Without an explanation of what possible good ‘solar forecasting applications’ provide, it seems academic. Improved accuracy of useless information.

  6. oldbrew says:

    Gamecock – SD were quoting this:

    Spectral cloud optical property estimation using real-time GOES-R longwave imagery
    Journal of Renewable and Sustainable Energy
    Published Online: 14 April 2020

    The output of ground-based, solar power generation systems is strongly dependent on cloud cover, which is the main contributor to solar power variability and uncertainty. Cloud optical properties are typically over-simplified in forecasting applications due to the lack of real-time, accurate estimates.

  7. Curious George says:

    The prediction can be very powerful. It gives solar farms enough time to move to a predicted cloudless area 🙂

  8. Gamecock says:

    You miss my point, oldbrew. Yes, I had read that.

    The unstated assumption is that there is something of value in knowing in more detail when and how thick coming clouds are. Nothing is stated. So SCOPE changes nothing.

  9. Paul Vaughan says:

    Mixing the facts with the wake up calls….

    When studying relation of cloud to temperature in your location, filter by DJFM vs. AMJJASON. Remember that from way back?

    Winter sunlight hours are short and there’s more cloud in winter when it’s warm (heat and moisture pump from equator). Regressions have opposite slope for summer (and additionally nights are much shorter).

    UK relationship with rest of N. hemisphere cleans up perfectly for AMJJASON. During DJFM AO & NAO are often out of phase with NPI & ALPI. AO/NAO can throw wild extremes when it’s clear and cold, masking core attractors to the untrained eye.

    Interannual climate variations were pure gold for the climate distortion artists that ran our civilization off a financial cliff while nose-diving western human rights to the depths of hell. America decided that something else was more important than the truth.

    Filter SOI & other ENSO indices by JA (July-August). The integral won’t indicate the true backbone attractor without the right filtering.

    We pay western technocrats to work for Russia and China destroying our freedom. They’re not free to accept that their “understanding” is fundamentally wrong. SatUN’s technocrats envision an easy way to deal with expression of climate truth: jail for a walk in the park.

    The backbone attractor has nothing to do with regional variations. On the contrary: it’s a global constraint that holds the regional integrals to a limit. All the moon (gravitational tides) can do is stir (global modes 3 & 4 total ~16% SST variance) in time, space, and material phase. The sun shapes the 2 major thermal tides (heat engine global modes 1 & 2 total ~84% variance).

    The truth won’t mean anything when we run out of money and become homeless, so we might as well relish it now.

    Thermal tide review is posted on Suggestions-42.
    New light on 27.03 day equatorial solar rotation is forthcoming.

  10. oldbrew says:

    Gamecock – re. something of value in knowing in more detail when and how thick coming clouds are.

    For running a large power grid for example, the various options have to be balanced and renewables get priority. Better forecasts of likely input should help with that?

  11. Gamecock says:

    “For running a large power grid for example, the various options have to be balanced and renewables get priority. Better forecasts of likely input should help with that?”

    The writers hoped you would assume that. They have to spell out how it could be used beneficially. It can’t be assumed.

  12. oldbrew says:

    2015 video says what the forecasting is about.

  13. hm says:

    it sounds like spin of some interesting science (that maybe they should have done long ago). How will solar power plants “get help”? Day to day?? Where to install them? Don’t we know the cloudy places already?

  14. oldbrew says:

    The BBC discovers the wavy jet stream can cause blocked high pressure zones with clear skies, then tags it ‘climate change’, code for *it’s your fault* 🙄

    Climate change: Blue skies pushed Greenland ‘into the red’

    The exact mechanism by which climate change affects the jet stream isn’t understood.

    BBC putting the cart before the horse as usual. Meteorology knows blocking well enough…

    New Studies Show Cloud Cover Changes Have Driven Greenland Warming And Ice Melt Trends Since The 1990s
    By Kenneth Richard on 20. April 2020

    Scientists now acknowledge cloud cover changes “control the Earth’s hydrological cycle”, “regulate the Earth’s climate”, and “dominate the melt signal” for the Greenland ice sheet via modulation of absorbed shortwave radiation. CO2 goes unmentioned as a contributing factor.

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