The MSG Global Instability Indices Product and Its Use as a Nowcasting Tool (2024)

Keywords: Satellite observations; Instability; Indices; Nowcasting

1. Introduction

Since 2002, the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) has operated an advanced generation of a geostationary imager, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the Meteosat Second Generation (MSG) satellites. Two of the MSG satellites have been launched and are identified under their operational names: Meteosat-8 and Meteosat-9. The SEVIRI instrument was specially designed to provide enhanced nowcasting information by scanning the earth’s disk every 15 min in 11 spectral channels; an additional high spatial resolution broadband visible channel is also available (Schmetz et al. 2002). The channel selection is based on the well-known Advanced Very High Resolution Radiometer (AVHRR) imagers of polar orbiters, together with additional channels to allow for the detection of specific surface, cloud, and atmospheric features, like identification of fog, dust storms, fires, air masses, and cloud microphysical parameters. Table 1 gives a summary of the MSG channels.

Since the launch of MSG, a number of applications have been developed to make use of these new capabilities for nowcasting, especially for the detection and prediction of severe weather.

As a correct and timely prediction of convective processes—especially their severe stages—is an important area of nowcasting, many MSG-based observations of clouds and their temporal evolution have been used in both qualitative and quantitative aspects to identify the most severe parts of a convective cloud system (Setvák and Rabin 2005; Rosenfeld and Lensky 2006). The MSG infrared channel selection, however, makes it also possible to assess the air stability in preconvective, that is, still cloud-free, conditions. Air instability indices as single-valued numbers have a long history of evaluating the convective potential of the atmosphere. A comprehensive summary can be found in Peppler (1988). They are usually used such that some convective potential can be inferred if the respective index exceeds a certain threshold. Traditionally derived from radiosonde profiles, such indices typically combine measures of the thermal and moisture properties and often only use a small quantity of vertical profile parameters. Due to the limited spectral resolution, MSG-based temperature and moisture retrievals will only have coarse vertical resolution, which is, however, fully sufficient for the derivation of instability indices, as they typically utilize a lower quantity of observations within a vertical profile (Peppler 1988; Fuhrhop et al. 2000).

It should be noted that such indices are of a highly empirical nature and are often only applicable to certain geographic regions. Also, the above-mentioned threshold criteria may change between regions and seasons. Such indices can only assess the likelihood of convection within the next few hours, and should still be used in combination with other triggering/lifting mechanisms.

A satellite-derived map of instability parameters has the advantage of very good spatial and temporal resolutions—for example, 3-km pixel size and 15-min repeat cycle for MSG—which is a significant improvement over the sparse locations of radiosonde stations with at best two daily soundings.

In the following, we will first outline how the MSG-based instability indices are derived, and the general performance of the results will be shown by a number of case studies. The last section will focus on a more dedicated validation study in terms of real warning potential, where the satellite indices are compared to a later occurrence of lightning as a signature of severe convection.

2. Theoretical background

Atmospheric instability parameters are routinely extracted from the MSG imagery within the Meteorological Products Extraction Facility (MPEF) at EUMETSAT. Since these parameters are provided on a global scale (i.e., the entire MSG field of view), the product has been termed the Global Instability Index (GII). From the algorithm side, the GII parameters can be produced on any spatial scale raging from a single MSG pixel and as averages over n × n pixels. The current operational setup is such that the product is derived as 15 × 15 pixel averages, that is, over an area of approximately 50 × 50 km². Some of the case studies presented here, however, come from an offline test environment, where 3 × 3 or 5 × 5 MSG pixel averages are computed.

Instability indices are usually calculated from atmospheric profiles of temperature and humidity to provide some information concerning the vertical stability of the atmosphere. Various indices are used by forecasters for different applications and regions, and these indices are defined as the differences in profile parameters at different pressure levels (Galway 1956; Kurz 1993). Such data are usually derived from radio soundings, but a few satellite-derived indices also exist [e.g., derived from Geostationary Operational Environmental Satellites (GOES); Hayden (1988); Rao and Fuelberg (1997)]. The airmass parameters can be used to issue severe weather warnings if the corresponding index exceeds a certain threshold. These thresholds are usually determined empirically and should not be regarded as fixed values; they may vary from season to season and from region to region. A skilled local forecaster is absolutely necessary for a correct interpretation of the provided indices.

An operational satellite-based retrieval of the lifted index parameter and the total precipitable water has been performed since 1988 using first the GOES Visible and Infrared Spin Scan Radiometer (VISSR) Atmospheric Sounder (VAS) instrument and, later, the GOES Sounder (Hayden 1988; Huang et al. 1992; Menzel et al. 1998; Dostalek and Schmit 2001; Schmit et al. 2002). Studies have shown that the good spatial and temporal resolutions of the VAS instrument and of the derived parameters provide good potential for the identification of preconvective conditions (Kitzmiller and McGovern 1989). For the GOES data, these results are presented in a pictorial form with color-coded lifted index–precipitable water areas together with an overlay of the apparent cloud-top temperature as provided by the IR 11-μm channel. Animations of these images can be used to visually enhance the areas of high likelihood of strong convection and of cloud growth (information online at http://cimss.ssec.wisc.edu/goes/rt/). The MPEF GII retrieval algorithm is in its theory very similar to the physical retrievals developed for the GOES Sounder instrument, but was completely redeveloped, due to a number of differences in the channels and the details of the applied radiative transfer model. The algorithm only works for clear-sky conditions; that is, no instability information is inferred for cloudy pixels.

The GII retrieval is a “physical” method; that is, it tries to infer an actual temperature and humidity profile from the satellite-observed radiances in a given set of channels. The airmass parameters are then derived from this profile. Purely statistical methods exist, which use regression schemes between the satellite observations and the actual index, where the entire regression is based on a dataset such as radio soundings. The results of such statistical methods are often of poorer quality and have a number of other disadvantages, like the total dependence on a given instrument and training data.

The physical retrieval is often referred to as an optimal estimation or one-dimensional variational data assimilation (1DVAR) type of retrieval. An inversion algorithm is applied to find the atmospheric profile that best reproduces the observations (Rodgers 1976; Hayden 1988; Ma et al. 1999). In general, this is a multisolution problem: a wide range of temperature and moisture profiles as well as of surface conditions may exist that produce identical satellite measurements in such a limited number of spectral channels. A suitable “background” or “first guess” profile is used as a constraint to the solution. As this retrieval problem has an iterative solution, the first guess is fed into the iteration scheme as an initial proposal for a solution. The original first guess is then modified in a controlled manner until its radiative properties (the simulated radiances at the top of the atmosphere for the MSG channels) fit the satellite observations. A typical first-guess field is a short-term forecast. The major limitations of this method are the high computational effort and the fact that the retrieved profiles tend to retain features of the first guess, especially when coupled with the low-spectral resolution.

In its application to the SEVIRI instrument on board the MSG, the physical retrieval uses six channels: the three longwave radiation window channels (IR8.7, IR10.8, and IR12.0), the two water vapor channels (WV6.2 and WV7.3), and the CO₂ channel (IR13.4). Accordingly, the matrix 𝗦_ɛ contains the instrument’s temperature noise in these six channels with the uncertainty of the radiation model added.

The background profile is taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) global model forecasts, which are provided on a 1° latitude–longitude grid, from 6-hourly forecasts, which are interpolated in time to the actual image time. The covariance matrix 𝗦_x was produced from a global (up to 60°N/S) set of atmospheric profiles (Chevallier 2002) to cover a wide range of natural variability. As the full range of natural variability would leave the retrieval problem too unconstrained, that is, it would find highly unrealistic solutions, the original matrix 𝗦_x was downscaled to find a compromise between a realistic constraint and a possibility for the retrieval to find a different solution than the first guess. A further spectrally invariant uncertainty of 0.3 K is added to account for uncertainties within the radiative transfer model. The observation vector x contains the full vertical temperature and humidity profiles and the surface skin temperature as the important lower boundary condition for the IR window channels.

The Radiative Transfer for Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) model (RTTOVS; Eyre 1991; Saunders et al. 1999) is used as it provides a computationally fast method for deriving the Jacobians 𝗞. In addition, the forward model of RTTOV (version 8.7) is used to derive the simulated MSG temperatures for a given observation vector, or profile, x. The iteration is stopped if the root-mean-square (RMS) difference between the observed and simulated brightness temperatures in the six channels is less than a given threshold, usually set to 1.5 K. A spectral variation between the six individual channels is currently not considered.

Figure 1 shows a visualization of the operational product with its 15 × 15 MSG pixel resolution. The averaging process is done here on the brightness temperature level; that is, every box enters the iteration scheme with the average brightness temperatures. The product is derived for every 15-min MSG repeat cycle, for the full disk up to a satellite viewing angle of 70°. It should be noted that a GII value is assigned to a processing box if 50% or more of the box is cloud free, where the cloud information is taken from the MSG Cloud Mask product (Lutz 2007). The brightness temperatures are only averaged over the cloud-free fields of view.

There are always a number of pixels or processing boxes within each image where the physical method never converges to a solution, that is, where the final RMS difference against the observations is not reached within five iteration steps. This usually occurs over clouds, where the entire scheme is not applicable and thus naturally does not find a good solution. In the MPEF installation, as mentioned above, the GII retrieval actually only runs on the cloud-free pixels, and this information is taken from the MSG pixel-based cloud mask product. The necessary brightness temperatures for the retrieval are then only averaged over the cloud-free parts of the processing element. In principle, the GII can also run in a stand-alone mode, without the underlying cloud mask, as the presence of clouds will immediately lead to a “failed” GII retrieval and is thus automatically masked out from the result. Figure 2 shows an example of this stand-alone GII mode: one of the retrieved parameters (K index) is shown together with the infrared window channel (IR10.8) brightness temperatures for those areas where the retrieval never came to a good solution. The IR10.8 image is shown for reference as a visual conformation that indeed the retrieval failed over clouds; that is, clouds are also mostly directly detected within the retrieval. Only partially cloud-filled pixels will—depending on the degree of partial coverage—either not converge or show a too moist retrieval, a feature that is often observed near cloud edges in a pixel-based product.

A further important input parameter to the retrieval scheme is the correct knowledge of the spectral surface emissivity. This is especially important for the SEVIRI IR8.7 channel, which is centered at 8.7-μm wavelength. Nonvegetated, desert-type surfaces have a rather low surface emissivity of 0.75–0.80 in this channel, and this needs to be accounted for within the retrieval’s radiation model. Figure 3 shows an example of the total precipitable water product as one of the retrieval products. The left panel in Fig. 3 shows the retrieval results using a uniform spectral surface emissivity of 0.95 for all channels. Over most parts of the Saharan Desert the retrieval could not find a suitable solution; in the visualization, this is shown by the corresponding IR channel grayscale. However, using the spectral emissivities from the Global Infrared Land Surface Emissivity (IREMIS) database [information online at http://cimss.ssec.wisc.edu/iremis/; see also Seemann et al. (2008)], which are remapped to the MSG pixel locations, this problem was resolved, as the right panel in Fig. 3 shows. Depending on the location, the inclusion of the correct surface emissivity can also lead to slightly modified results.

As mentioned before, a typical feature of such an inversion scheme is its dependence on the first-guess field and the fact that the retrieved profiles tend to retain the general characteristics of the first-guess profiles. In many cases, the first guess already matches the observations so closely that the profile is not changed at all; that is, the RMS to the first-guess field is already below 1.5 K. However, there are many cases where the first-guess field is quite significantly changed within the retrieval; that is, the satellite adds information. This added information often modifies extreme values and local gradients. Figure 4 shows an example: the ECMWF 12-h forecast is used as the first guess for the retrieval. The forecast K index already shows a zone of moderate instability over Germany, extending to the southeast over Austria and neighboring countries. In the retrieval, this zone is not only slightly enhanced in values, but also shifted toward the east. This is actually supported by local radio soundings, as summarized in Table 2.

Figure 5 shows the typical behavior of the retrieval scheme, in terms of how many iterations of Eq. (2) it takes before a good match with the satellite observation is reached. The area of high K index changes with respect to the forecast over eastern Germany and Poland is well dominated by orange; that is, several iterations were needed here to change the not so appropriate first guess to the final result. Generally speaking, this means that more iterations usually imply a higher deviation from the first guess.

3. GII applications

Section 3a will show a few examples of the GII results, which demonstrate the area of application and possible operational usage. We will focus on a case of high instability, which was in this case a good predictor for strong convection, and we will also show an example of the GII results correctly identifying very stable air masses. Section 3b will then highlight a special application and verification environment that was set up during the convective season in the summer of 2006/07 in South Africa.

4. Summary and outlook

MSG offers the great opportunity to derive airmass parameters as instability indices and total precipitable water from the measurements in the infrared spectrum. As MSG SEVIRI is far from being an instrument with full sounding capabilities like modern hyperspectral sounders, detailed temperature and humidity profiles, taken from a global or local forecast model, are needed as a first guess or background field for the retrieval. It was shown that the MSG measurements can actually add information over the forecast in terms of local extremes and gradients. The MSG instability product is thus in its underlying algorithm and in the applicability of the results very similar to the respective GOES Sounder products (Menzel et al. 1998; Schmit et al. 2002).

Numerous test cases and the more quantitative verification process that was initiated by the South African Weather Service show the generally good warning potential of the derived instability fields.

Of high value is the added capability of a nearly continuous monitoring of the instability fields that is guaranteed by MSG’s 15-min repeat cycle. Over time, better spatial coverage can be achieved; as the chance of finding a cloud-free area for the retrieval process increases, the evolution of instability over time can be studied. A great benefit is also the fact that the fast repeat cycles allow the observations of very quick changes in time, where the temporal rate change itself can be of further nowcasting importance. A full evaluation of this latter aspect still needs to be performed and will be the subject of future work.

The current EUMETSAT GII product is aimed at helping forecasters to turn their attention to a certain region, which they can then monitor more closely with other means like satellite imagery and radar data over the next several hours. The MSG GII data have proven to provide lead times between 6 and 9 h. Within a fully developed convective nowcasting system, the GII can provide a first level of warning, usually covering a larger area. When time progresses, other satellite-derived parameters like cloud coverage, cloud-top cooling, and growth rates, together with the development of microphysical parameters, will then help to further indicate the exact location of the most severe parts of the clouds (Mecikalksi and Bedka 2006; Rosenfeld and Lensky 2006). These satellite-derived convective products are shown to still have some lead time over radar observations, that is, are even useful when good radar coverage is available, but will be especially beneficial for areas without any radar information. This is of special importance for large parts of Africa, where the availability of radar data is extremely limited. Providing useful nowcasting tools for use in African countries thus relies on satellite and numerical weather prediction model output. MSG with its spectral, spatial, and temporal resolution provides an ideal platform for the development and testing of such an automated system of a series of warning levels.

With respect to the GII product itself, the impact of the product resolution and the resolution of the underlying model are current areas of research between EUMETSAT and SAWS, where the above-mentioned local GII installation at SAWS plays a crucial role. South Africa also acts as a vehicle to the rest of Africa in providing forecasting and nowcasting tools. Through this product and the Software for the Utilization of Meteosat in Outlook Activities (SUMO) display system (which can be downloaded online free of charge at http://www.weathersa.co.za/SUMO/) to display it, African countries have access to tools to aid in their nowcasting procedures as well.

In addition to the here-described initial validation studies with lightning data, a long-term comparison with radiosondes is also being done at SAWS. It is expected that this will provide further insights to help in the interpretation of the product. It should also be mentioned that hyperspectral sounders like the Infrared Atmospheric Sounding Interferometer (IASI) and the Atmospheric Infrared Sounder (AIRS) might to some extent replace or amend the first-guess background fields, that is, may help to loosen the dependence of the product on the forecast fields. Studies in this respect are ongoing.