Space-Wise approach for airborne gravity data modelling

Regional gravity field modelling by means of remove-compute-restore procedure is nowadays widely applied in different contexts: it is the most used technique for regional gravimetric geoid determination, and it is also used in exploration geophysics to predict grids of gravity anomalies (Bouguer, free-air, isostatic, etc.), which are useful to understand and map geological structures in a specific region. Considering this last application, due to the required accuracy and resolution, airborne gravity observations are usually adopted. However, due to the relatively high acquisition velocity, presence of atmospheric turbulence, aircraft vibration, instrumental drift, etc., airborne data are usually contaminated by a very high observation error. For this reason, a proper procedure to filter the raw observations in both the low and high frequencies should be applied to recover valuable information. In this work, a software to filter and grid raw airborne observations is presented: the proposed solution consists in a combination of an along-track Wiener filter and a classical Least Squares Collocation technique. Basically, the proposed procedure is an adaptation to airborne gravimetry of the Space-Wise approach, developed by Politecnico di Milano to process data coming from the ESA satellite mission GOCE. Among the main differences with respect to the satellite application of this approach, there is the fact that, while in processing GOCE data the stochastic characteristics of the observation error can be considered a-priori well known, in airborne gravimetry, due to the complex environment in which the observations are acquired, these characteristics are unknown and should be retrieved from the dataset itself. The presented solution is suited for airborne data analysis in order to be able to quickly filter and grid gravity observations in an easy way. Some innovative theoretical aspects focusing in particular on the theoretical covariance modelling are presented too. In the end, the goodness of the procedure is evaluated by means of a test on real data retrieving the gravitational signal with a predicted accuracy of about 0.4 mGal.     

Alternative data-driven methods to estimate wind from waves by inverse modeling

An attempt is made to derive wind speed from wave measurements by carrying out an inverse modeling. This requirement arises out of difficulties occasionally encountered in collecting wave and wind data simultaneously. The wind speed at every 3-h interval is worked out from corresponding simultaneous measurements of significant wave height and average wave periods with the help of alternative data-driven methods such as program-based genetic programming, model trees, and locally weighted projection regression. Five different wave buoy locations in Arabian Sea, representing nearshore and offshore as well as shallow and deep water conditions, are considered. The duration of observations ranged from 15 months to 29 months for different sites. The testing performance of calibrated models has been evaluated with the help of eight alternative error statistics, and the best model for all locations is determined by averaging out the error measures into a single evaluation index. All the three methods satisfactorily estimated the wind speed from known wave parameters through inverse modeling. The genetic programming is found to be the most suitable tool in majority of the cases.     

Detection of temperature variability and trends in the lower troposphere over Delhi: A study of joint influence of ENSO and Land use/ Land cover during 1980–2015

A study of radiosonde observations for temperature at 850 hPa over Delhi for a period of 35 years was conducted. The influence of atmospheric oscillations and geophysical events like El Niño‐Southern Oscillation (ENSO) on tropospheric temperature variability showed indicative trends for changing urban climate in Delhi. The inter‐annual variation in surface temperature and its relationship with land use changes and land cover changes (LULCC) was also examined. LULCC and urban expansion into peripheral areas of Delhi (towards the West, North, North‐West and South) at the cost of agricultural and wasteland was found to be extensive. The upper range of the surface temperature has shifted by ~6°C. The post‐monsoon and winter months from November to February have experienced a considerable increase in the average temperature in the period examined. The monsoon months from June to September have undergone cooling of ~0.5°C–1°C at 850 hPa. An inverse relationship exists between the Southern Oscillation Index (SOI) and the monthly averaged temperature. The temperature of the atmosphere over Delhi at 850 hPa has increased only marginally (~ 0.3°C) for the time period 1980–2015. Bi‐modal peaks were the most peculiar features observed in mean monthly temperature variation during 2000–2009.     

Improving the computation of the gravitational terrain effect close to ground stations in the GTE software

The precise computation of the vertical gravitational attraction of the topographic masses (terrain correction) is still being studied both for geodetic and geophysical applications. In fact, it is essential in high precision geoid estimation by means of the well-known remove-compute-restore technique, which is used to isolate the gravitational effects of anomalous masses in exploration geophysics. The terrain correction can be evaluated exploiting a Digital Terrain Model (DTM) in different ways, such as classical numerical integration, prisms, tesseroids, polyhedrons, and/or Fast Fourier Transform techniques. The increasing resolution of recently developed DTMs, the increasing number of observation points, and the increasing accuracy of gravity data represent, nowadays, major challenges for the terrain correction computation. Classical point mass approximation and prism based-algorithms are indeed too slow, while Fourier-based algorithms are usually too much approximate when compared to the required accuracy. In this work, we improve the Gravity Terrain Effects (GTE) algorithm, the innovative tool that exploits a combined prism-Fast Fourier Transform approach especially developed for airborne gravimetry, to compute the terrain correction on the surface of the DTM (i.e. corresponding to the ground stations and/or its vicinity). This required development of a proper adjustment of the algorithms implemented within the GTE software and also to define and implement a procedure to overcome the problems of the computation of the gravitational effects due to the actual slope of the terrain close to the stations. The latter problem is thoroughly discussed and solved by testing different solutions like concentric cylindrical rings, triangulated polyhedrons, or ultra-high resolution squared prisms. Finally, numerical tests to prove the temporal efficiency and the computational performances of the improved GTE software to compute terrain correction for ground stations are also presented.     

Comparison of Satellite Altimetric Gravity and Global Geopotential Models with Shipborne Gravity in the Red Sea

The determination of high-resolution geoid for marine regions requires the integration of gravity data provided by different sources, e.g. global geopotential models, satellite altimetry, and shipborne gravimetric observations. Shipborne gravity data, acquired over a long time, comprises the short-wavelengths gravitation signal. This paper aims to produce a consistent gravity field over the Red Sea region to be used for geoid modelling. Both, the leave-one-out cross-validation and Kriging prediction techniques were chosen to ensure that the observed shipborne gravity data are consistent as well as free of gross-errors. A confidence level equivalent to 95.4% was decided to filter the observed shipborne data, while the cross-validation algorithm was repeatedly applied until the standard deviation of the residuals between the observed and estimated values are less than 1.5 mGal, which led to the elimination of about 17.7% of the shipborne gravity dataset. A comparison between the shipborne gravity data with DTU13 and SSv23.1 satellite altimetry-derived gravity models is done and reported. The corresponding results revealed that altimetry models almost have identical data content when compared one another, where the DTU13 gave better results with a mean and standard deviation of ?2.40 and 8.71 mGal, respectively. A statistical comparison has been made between different global geopotential models (GGMs) and shipborne gravity data. The Spectral Enhancement Method was applied to overcome the existing spectral gap between the GGMs and shipborne gravity data. EGM2008 manifested the best results with differences characterised with a mean of 1.35 mGal and a standard deviation of 11.11 mGal. Finally, the least-squares collocation (LSC) was implemented to combine the shipborne gravity data with DTU13 in order to create a unique and consistent gravity field over the Red Sea with no data voids. The combined data were independently tested using a total number of 95 randomly chosen shipborne gravity stations. The comparison between the extracted shipborne gravity data and DTU13 altimetry anomalies before and after applying the LSC revealed that a significant improvement is procurable from the combined dataset, in which the mean and standard deviation of the differences dropped from ?3.60 and 9.31 mGal to ?0.39 and 2.04 mGal, respectively.