A multilayer perceptron and multiclass support vector machine based high accuracy technique for daily rainfall estimation from MSG SEVIRI data

The current paper introduces a new multilayer perceptron (MLP) and support vector machine (SVM) based approach to improve daily rainfall estimation from the Meteosat Second Generation (MSG) data. In this study, the precipitation is first detected and classified into convective and stratiform rain by two MLP models, and then four multi-class SVM algorithms were used for daily rainfall estimation. Relevant spectral and textural input features of the developed algorithms were derived from the spectral MSG SEVIRI radiometer channels. The models were trained using radar rainfall data set colected over north Algeria. Validation of the proposed daily rainfall estimation technique was performed by rain gauge network data set recorded over north Algeria. Thus, several statistical scores were calculated, such as correlation coefficient (r), root mean square error (RMSE), mean error (Bias), and mean absolute error (MAE). The findings given by: (r = 0.97, bias = 0.31 mm, RMSE = 2.20 mm and MAE = 1.07 mm), showed a quite satisfactory relationship between the estimation and the respective observed daily precipitation. Moreover, the comparison of the results with those of two advanced techniques based on random forests (RF) and weighted ‘k’ nearest neighbor (WkNN) showed higher accuracy obtained by the proposed model.     

Instantaneous rainfall estimation using neural network from multispectral observations of SEVIRI radiometer and its application in estimation of daily and monthly rainfall

In the present paper, an artificial neural network (ANN) based technique has been developed to estimate instantaneous rainfall by using brightness temperature from the IR sensors of SEVIRI radiometer, onboard Meteosat Second Generation (MSG) satellite. The study is carried out over north of Algeria. For estimation of rainfall, weight matrices of two ANNs namely MLP1 and MLP2 are developed. MLP1 is to identify raining or non-raining pixels. When rainy pixels are identified, then for those pixels, instantaneous rainfall is estimated by using MLP2. For identification of raining and non raining pixels, 7 input parameters from the IR sensors are utilized. Corresponding data of raining/non-raining pixels are taken from radar. For instantaneous rainfall estimation, 14 input parameters are utilized, where 7 parameters are information about raining pixels and 7 parameters are related with cloud features. The results obtained show the neural network performs reasonably well.     

Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem

With recent technological advances in remote sensing, very high-dimensional (hyperspectral) data are available for a better discrimination among different complex land-cover classes having similar spectral signatures. However, this large number of bands makes very complex the task of automatic data analysis. In the real application, it is difficult and expensive for the expert to acquire enough training samples to learn a classifier. This results in a classification problem with small-size training sample set. Recently, a regularization-based algorithm is usually proposed to handle such problem, such as Support Vector Machine (SVM), which usually are implemented in the dual form with Lagrange theory. However, it can be solved directly in primal formulation. In this paper, we introduces an alternative implementation technique for SVM to address the classification problem with small-size training sample set. It has been empirically proven that the effectiveness of the introduced implementation technique which has been evaluated by benchmark datasets.     

The impact of different background errors in the assimilation of satellite radiances and in-situ observational data using WRFDA for three rainfall events over Iran

Satellite radiances and in-situ observations are assimilated through Weather Research and Forecasting Data Assimilation (WRFDA) system into Advanced Research WRF (ARW) model over Iran and its neighboring area. Domain specific background error based on x and y components of wind speed (UV) control variables is calculated for WRFDA system and some sensitivity experiments are carried out to compare the impact of global background error and the domain specific background errors, both on the precipitation and 2-m temperature forecasts over Iran. Three precipitation events that occurred over the country during January, September and October 2014 are simulated in three different experiments and the results for precipitation and 2-m temperature are verified against the verifying surface observations. Results show that using domain specific background error improves 2-m temperature and 24-h accumulated precipitation forecasts consistently, while global background error may even degrade the forecasts compared to the experiments without data assimilation. The improvement in 2-m temperature is more evident during the first forecast hours and decreases significantly as the forecast length increases.     

An estimation of tropospheric corrections using GPS and synoptic data: Improving Urmia Lake water level time series from Jason-2 and SARAL/AltiKa satellite altimetry

Tropospheric correction is one of the most important corrections in satellite altimetry measurements. Tropospheric wet and dry path delays have strong dependence on temperature, pressure and humidity. Tropospheric layer has particularly high variability over coastal regions due to humidity, wind and temperature gradients. Depending on the extent of water body and wind conditions over an inland water, Wet Tropospheric Correction (WTC) is within the ranges from a few centimeters to tens of centimeters. Therefore, an extra care is needed to estimate tropospheric corrections on the altimetric measurements over inland waters. This study assesses the role of tropospheric correction on the altimetric measurements over the Urmia Lake in Iran. For this purpose, four types of tropospheric corrections have been used: (i) microwave radiometer (MWR) observations, (ii) tropospheric corrections computed from meteorological models, (iii) GPS observations and (iv) synoptic station data. They have been applied to Jason-2 track no. 133 and SARAL/AltiKa track no. 741 and 356 corresponding to 117–153 and the 23–34 cycles, respectively. In addition, the corresponding measurements of PISTACH and PEACHI, include new retracking method and an innovative wet tropospheric correction, have also been used. Our results show that GPS observation leads to the most accurate tropospheric correction. The results obtained from the PISTACH and PEACHI projects confirm those obtained with the standard SGDR, i.e., the role of GPS in improving the tropospheric corrections. It is inferred that the MWR data from Jason-2 mission is appropriate for the tropospheric corrections, however the SARAL/AltiKa one is not proper because Jason-2 possesses an enhanced WTC near the coast. Furthermore, virtual stations are defined for assessment of the results in terms of time series of Water Level Height (WLH). The results show that GPS tropospheric corrections lead to the most accurate WLH estimation for the selected virtual stations, which improves the accuracy of the obtained WLH time series by about 5%.     

Novel method for reconstruction of hyperspectral resolution images from multispectral data for complex coastal and inland waters

Hyperspectral resolution image products of a synthetic sensor featuring the high spatial resolution of the space-borne sensor can offer cost-effective means for enhancing our current capabilities in terms of providing an array of images in lieu of designing an expensive system for image acquisition, which can serve the expanding needs of the scientific and user communities for various critical water color applications. Despite several studies on enhancing the capability of land remote sensing sensors, full spectrum reconstruction of water color images with varying spectral bands is hampered by the lack of methods and accurate atmospheric correction procedures. In the present work, a novel method is developed for reconstruction of hyperspectral resolution images from high spatial-resolution Sentinel 2 Multispectral Instrument (MSI) data representative of many complex waters in coastal and inland zones. This method uses a deep neural network (DNN) with multiple blocks of deconvolution and dense layers. The spectral reconstruction of hyperspectral resolution images from multispectral data was based on rigorous training data from the atmospherically-corrected and validated HICO normalized water-leaving radiance products (with spectral resolution 438-868 nm sampled at 5.7 nm) of diverse water types. The generalizability and versatility of the DNN method was tested and evaluated systematically by means of various qualitative and quantitative analyses using concurrent space-borne (MSI and HICO) and in-situ measurements from different regional waters. Reconstructed hyperspectral resolution radiances obtained from the MSI images closely matched with independent HICO and MSI measurements within the desired accuracy. Successful reconstruction and validation of the hyperspectral radiances indicate that the proposed state-of-the-art method provides possible future directions for enhancing our current capabilities of space-borne sensors for various research purposes and societal applications at local, regional and global scales.     

Estimation of the Love and Shida numbers: h2, l2 using SLR data for the low satellites

In this paper we present results for the global elastic parameters: Love number h₂ and Shida number l₂ derived from the analysis of Satellite Laser Ranging (SLR) data. SLR data for the two low satellites STELLA (H = 800 km) and STARLETTE (H = 810 km) observed during 2.5 years from January 3, 2005 until July 1, 2007 with 18 globally distributed ground stations were analyzed. The analysis was done separately for the two satellites. We do a sequential analysis and study the stability and convergence of the estimates as a function of length of the data set used.     

The accuracy of data from ionosondes for the estimation of hmF2 and the identification of global change in the ionosphere

A long temporal series of simulated ionograms was generated with a superimposed secular variation of −14 km/century on the h_mF2 parameter. These ionograms were interpreted by the automatic scaling program Autoscala. By applying four different empirical formulas, four artificial series of h_mF2 were generated and then processed with the same methods used by other authors for real data sets. Data analysis of the simulated ionograms revealed the artificially imposed long-term trend. These results lead to the conclusion, that regardless of the empirical formula used, the accuracy of h_mF2 from ionosonde measurements would be adequate to observe a long-term trend of −14 km/century.