Satellite-Based Forecasts

DNICast Advances

- New methods haven been investigated and advances with respect to where clouds are, explanation of how to move clouds foreward in time and how to calculate the DNI.

- Variability indicator as an additional information to DNI values.

This page will give a short introduction into the methods of satellite based forecasts which are shown within the web-demonstrator. A detailed description of the methods can be found at: D3.6 - Report on satellite-based nowcasting methods

Meteotest method using GFS based Weather Research and Forecasting model for wind fields

The two main input sources of the nowcasting method used by Meteotest are satellite images from MSG/SEVIRI with 15 minute time resolution and numerical weather prediction (NWP) models. Satellite images were processed to a clearness index (CI) map. From the NWP model the wind vector field is taken and the CI map pixels are propagated forward along the wind trajectories for the next 4 hours. Ground measurements are included for post processing. The forecast scheme is visualized in figure 1:

Forecast scheme of Meteotest_demonstrator2.png
Figure 1: Forecast scheme of the Meteotest satellite based nowcasting method. Clearness index maps are derived from satellite images and each pixel is propagated forward by wind vectors from a numerical weather model. Measurements are included in the post processing step to account for offsets.

Satellite image processing to get the CI map is done using the empirical Heliosat 2 method (Rigollier et al., 2004). CI is used as the division of global horizontal radiation and clearsky global radiation. We use the HRV channel for the cloud detection with 1 km resolution at the sub-satellite point and around 3 km in the Mediterranean region. Furthermore a combination of the visible and infrared channels (VIS06, IR16, IR108) is used for the detection of snow cover at the ground. This implies clear sky conditions for satellite image pixels when snow cover is detected. For the NWP wind field we use the WRF output, from the operational WRF run at Meteotest, with GFS as boundary conditions every 6 hours. The outermost domain (domain 1) covers Western and Southern Europe as well as the Northern part of Africa.

DNI is calculated based on CI. For this step, we tested three approaches. These were using either a direct CI-to-DNI conversion or an indirect CI-to-DNI conversion method, different clear sky models and different aerosol sources.
     - The direct CI-to-DNI conversion follows the formulation of (Hammer, 2009) where DNI is calculated as a function of CI and direct beam radiation under clear sky conditions (Figure 7:).
     
     - With the indirect CI-to-DNI conversion, we calculate GHI first, followed by splitting GHI into the diffuse and direct beam radiation component using the BRL model (Ridley et al., 2010).
     
     - To model the clear sky conditions two clear sky models were tested. One is the clear sky model of the European Solar Radiation Atlas ESRA (Rigollier et al., 2000) and the second is the simplified version of the SOLIS clear sky model from (Ineichen, 2008).
   
     - Aerosols are a main component for modeling clear sky radiation. Two sources were evaluated. A climatology of Aerosol Optical Depth (AOD) and Linke turbidity (Remund et al., 2015) and AOD from the MACC (Monitoring Atmospheric Composition & Climate) project (https://www.gmes-atmosphere.eu/).

With the different models and data sources from the list above, three versions of model chains were defined.

model chains_demonstrator.png

If local DNI measurements are available, they can be included in the post processing scheme and the actual forecast is adapted. The forecasts are named with an additional ?PP? in the demonstrator.  We used a simple offset correction, which can be induced by e.g. incorrect aerosol input (M?ller et al., 2013). The post processing uses the last hour of measurements and the starting points of the last forecasts. If the forecasts are constantly lower or constantly higher than the measurements during that past hour, then the average difference in CI is calculated and added to the actual forecast. The adaption is not applied in case of variable conditions


DLR-PA method using Meteosat Rapid-Scan-Modus HRV channel

To overcome the limitations of current nowcasting methods using satellite data, which mostly evaluate cloud motion without discrimination of cloud type or cloud height based on a low image repetition rate, the potential of the Meteosat Rapid-Scan-Modus (5 instead of 15 minutes repetition rate) in combination with the HRV-channel (High Resolution Visible, 1km instead of 3km maximum spatial resolution) will be investigated. Using different channels of the satellite data additional information on the cloud type and the corresponding attenuation of the solar radiation can be provided. The work will be based on (Breitkreuz, 2009). A method using satellite data to provide forecasts of cloudiness or DNI maps up to several hours ahead is presented to allow for the consideration of differential cloud motion in separate cloud layers which improves the nowcast capabilities considerably.

For water clouds the APICS ("Algorithm for the Physical Investigation of Clouds with SEVIRI", Bugliaro et al., 2011) cloud retrieval is used. The cloud detection is based on two groups of threshold tests consisting in reflectance tests and spatial coherence tests applied to the solar SEVIRI channels. In a second step, cloud optical thickness and effective radius are derived from two solar channels based on the method by Nakajima and King (1990) and Nakajima and Nakajima (1995).

For the detection of convective clouds, detection methods of the Cb-TRAM algorithm (CumulonimBus TRacking And Monitoring, Zinner et al. (2008, 2013)) is used.

The forecast rests upon an optical flow method determining a motion vector field from two consecutive images (Zinner et al., 2008)). Unlike feature based approaches this method is area based: instead of vectors only for interesting cloud patterns a disparity vector field defined at each pixel position is derived. Movements in the atmosphere take place on different scales reaching from microscale (few centimeters) to global scale (10,000 km). These large-scale flows overlay the small scale movements, so that the determination of the disparity vector field for all scales is challenging. In order to take this into account the disparity vector fields are successively derived on different scales, starting from low resolution down to high resolution - a pyramidal scheme.

DLR-DFD method using a sectoral method based on Meteosat Second Generation imagery

This study applies a receptor-like approach tracking only clouds which are coming closer to the power plant (red color) and discriminating between thin cirrus and other cloud types (figure 2).

Detection of clouds_V2_Demonstrator.png
Figure 2: Detection of clouds which move towards the power plant (indicated by red color).

From the MSG/APOLLO-based cloud physical parameters especially the cloud mask and the cloud type discrimination into water/mixed phase and thin ice phase clouds are used in a pixel-wise resolution. Additionally, the cloud optical depth (COD) is used. The receptor model looks for the movement of a cloud towards the power plant. It performs the following steps:
Satellite pixels in a 29 x 29 pixel neighborhood are remapped from a latitude-longitude grid (SEVIRI satellite projection) into a x-y kilometer polar coordinate system with the location of interest in the center (see figure 3).

Sectoral approach_v2_Demonstrator.png
Figure 3: Sectoral approach distributed over an area of 150 x 150 km.

The algorithm uses two separate cloud masks - one consists of thin ice phase clouds only and the other consists of all pixels being classified as water or mixed phase cloud. Typically, the thin ice clouds are cirrus-like clouds in higher altitudes. They often have different directions of movement, are larger scale features with small COD and a large variability of COD.

As an additional parameter cloud variability is taken into account for modeling an ensemble of DNI values instead of a single value only.

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