P3.33 ATMOSPHERIC INSTABILITY PARAMETERS DERIVED FROM MSG SEVIRI OBSERVATIONS Marianne König*, Stephen Tjemkes, Jochen Kerkmann EUMETSAT, Darmstadt, Germany 1. INTRODUCTION K-Index: Convective systems can develop in a thermo- (Tobs(850)–Tobs(500)) + TDobs(850) – (Tobs(700)–TDobs(700)) dynamically unstable atmosphere. Such systems may quickly reach high altitudes and can cause severe Lifted Index: storms. Meteorologists are thus especially interested to Tobs - Tlifted from surface at 500 hPa identify such storm potentials when the respective system is still in a preconvective state. A number of Maximum buoyancy: obs(max betw. surface and 850) obs(min betw. 700 and 300) instability indices have been defined to describe such Qe - Qe situations. Traditionally, these indices are taken from temperature and humidity soundings by radiosondes. As (T is temperature, TD is dewpoint temperature, and Qe radiosondes are only of very limited temporal and is equivalent potential temperature, numbers 850, 700, spatial resolution there is a demand for satellite-derived 300 indicate respective hPa level in the atmosphere) indices. The Meteorological Product Extraction Facility (MPEF) for the new European Meteosat Second and the precipitable water content as additionally Generation (MSG) satellite envisages the operational derived parameter. derivation of a number of instability indices from the brightness temperatures measured by certain SEVIRI 3. DESCRIPTION OF ALGORITHMS channels (Spinning Enhanced Visible and Infrared Imager, the radiometer onboard MSG). The traditional 3.1 The Physical Retrieval physical approach to this kind of retrieval problem is to infer the atmospheric profile via a constrained inversion An iterative solution to the inversion equation (e.g. and compute the indices then directly from the obtained Ma et al. (1999)) tries to infer the temperature and profile. As this algorithm would impose a too high humidity profile from the measured brightness computer load on the MPEF system, the operational temperatures, the back-ground atmospheric profile MPEF will use a statistical approach: The measured (usually referred to as first guess) and the associated brightness temperatures together with further predictors error/noise matrices. In each iteration step, the inversion are used to derive each instability index, where the needs knowledge about the change of brightness statistical relations between these parameters are temperature with the change of the atmosphere in each gained from a neural network and appropriate training level, which is described by the Jacobians of a radiation data. Both methods are currently installed in the model. It is the computation of these Jacobians together Eumetsat MPEF prototype environment and are tested with the inversion of large matrices which make this on GOES sounder data. This paper shortly describes method very CPU intensive. This method was chosen the two methods and shows a detailed comparison for the current operational retrieval of lifted index and between the two methods and to independent precipitable water a for the GOES sounder at CIMSS radiosonde data. It should be noted that both methods (Menzel et al., 1998), and the Eumetsat prototype only only allow the derivation of instability indices over differs from the CIMSS approach by using RTTOV as cloudfree areas. the radiation model (Saunders et al., 1999). RTTOV has the advantage of a much faster computation of the 2. DEFINITION OF INSTABILITY INDICES Jacobians. In application to the GOES sounder, the algorithm uses the sounder channels 5, 7, 8, 10, 11, and Various studies have been performed to relate certain 12, which are at about 13.4 mm, 12.0 mm, 10.8 mm, 7.3 instability measures taken from radio soundings to the mm, 6.5 mm, and 6.2 mm wavelength, resp.. In a possible occurrence of severe weather. It became soon clear that future application to SEVIRI, the method will also use 6 there is no unique index for all synoptic situations and channels, and as SEVIRI will not have a 6.5 mm for all locations. This paper focuses on 4 different channel, the 8.7 mm channel will be used instead. This parameters, three classical instability indices: channel selection ensures that sufficient information concerning the atmospheric state is passed to the retrieval algorithm, e.g. surface skin temperature and * Corresponding author’s address: Marianne König, low level moisture via the two window channels, higher EUMETSAT, Postfach 100555, 64205 Darmstadt, level humidity via the water vapour channels, and the Germany, phone (49) 6151 807344, email: higher level temperature information via the 13.4 mm [email protected] channel. Over clouds, the algorithm cannot find a solution, The real problem concerned with the neural net is i.e. the iteration scheme does not converge in these to have a good training dataset: This dataset must cases. Over clear skies, convergence is usually contain a wide range of possible observations of the achieved after one or two iterations, and each instability predictors and the output value. Otherwise the net will index can then easily be derived from the atmospheric perform badly if it is faced with real data which were not profile. properly reflected in the training dataset. This problem is Tests show that the physical retrieval substantially quite evident if radiosonde data are used for training: improves the first guess data concerning the resulting Although the input values can be obtained from the instability indices, where the improvements are clearly sounding rather easily – the brightness temperatures related to the better detection of the unstable cases. can be calculated with a forward model – and the Closer inspection of the retrieved actual profiles instability index is directly inferred from the sounding, demonstrates that the retrieval scheme mostly changes the radiosondes still give a very poor training dataset. the surface skin temperature and the low level humidity Due to the only twice daily (00 and 12 UTC) soundings, profile, while it leaves the temperature profile very near the diurnal cycle is not resolved, and also spatial to the first guess values. coverage is very poor. Locations are only a fixed set of For the MSG MPEF prototyping, the results of the certain values, and the ocean areas are practically not physical method are used as a kind of reference to covered at all. It was thus decided to use the results of assess the results of the statistical approach. the physical retrieval (taken from historical data) as training data for the neural net. This provides data for 3.2 The Statistical Method every possible location within the satellite’s field of view with good diurnal coverage. Initial results with training This method is based on a neural network based on several months of GOES data showed very approach: The neural net is used to identify linear and good agreement between the two methods. non-linear relationships between a number of input For this training phase, data were collected of values – the predictors – and one output value – the satellite measured brightness temperatures, of the pixel respective instability index. In general, a neural network locations and scan times and of the respective instability is a computer model of individual elements commonly index as provided by the physical method. This large referred to as neurons. The input parameters to the dataset (about 70,000 entries of heavily sampled GOES model make up the input neurons, the output value is sounder data over several months) was randomly split then the output neuron. There can be intermediate into three categories: One third of the data was used for layers which are called hidden layers of any number of the neural network training, one third was used as the neurons. The neurons of the individual layers are so-called generalisation data within the network training, connected by links, where each link is given a certain and the third section was used as an independent weight. The inputs are processed by a weighted dataset to test the network’s performance for data summation and the transfer function passes the result to unknown to the net during its learning phase. Figure 1 the neurons of the next layer, until the output is shows an example of this initial network test for the produced. During a training phase of the neural net, the precipitable water content. For the other indices, there is weights are optimised to fit the wanted output. A neural slightly more scatter with correlation coefficients network must thus be trained with input / output pairs, between 0.85 and 0.90. i.e. with some independent data. In our case, we use a simple three-layer backpropagation neural network (e.g. Chauvin and ) 80 2 Rumelhart, 1995) with 15 input neurons and one hidden 70 layer with 20 neurons. The transfer function is the hyperbolic tangent f(x) = tanh(x). 60 A backpropagation network is trained by learning 50 with clearly defined pairs of inputs and outputs. With 40 these ‘wanted’ outputs, the neural net successively 30 adjusts its weights in every learning cycle to minimise 20 the error between the ‘wanted’ output and the output produced with the weights and the transfer function from 10 the given inputs. This training cycle is repeated for a Precipitable Water (neural net, kg/m 0 large number of input / output patterns until a minimum 0 20 40 60 80 2 error is achieved. The criterion of a minimum error is Precipitable Water (reference, kg/m ) also used to determine the characteristics of the input Figure 1: Scatter plot of neural network derived values and the number of the hidden neurons. Obvious precipitable water compared to independent input values are of course the six brightness reference data from the physical method (correlation temperatures in the six channels (see section 3.1) and coefficient 0.97) the satellite viewing angle. Eight further parameters, which give some seasonal and diurnal time information and knowledge about the geographical location, slightly improved the performance of the net.