Carrier phase bias estimation of geometry-free linear combination of GNSS signals for ionospheric TEC modeling

The ionosphere can be modeled and studied using multi-frequency GNSS signals and their geometry-free linear combination. Therefore, a number of GNSS-derived ionospheric models have been developed and applied in a broad range of applications. However, due to the complexity of estimating the carrier phase ambiguities, most of these models are based on low-accuracy carrier phase smoothed pseudorange data. This, in turn, critically limits their accuracy and applicability. Therefore, we present a new methodology of estimating the phase bias of the scaled L1 and L2 carrier phase difference which is a function of the ambiguities, the ionospheric delay, and hardware delays. This methodology is suitable for ionospheric modeling at regional and continental scales. In addition, we present its evaluation under varying ionospheric conditions. The test results show that the carrier phase bias of geometry-free linear combination can be estimated with a very high accuracy, which consequently allows for calculating ionospheric TEC with the uncertainty lower than 1.0 TECU. This high accuracy makes the resulting ionosphere model suitable for improving GNSS positioning for high-precision applications in geosciences.     

Characterization of diurnal cycles in ZTD from a decade of global GPS observations

The diurnal cycle of the tropospheric zenith total delay (ZTD) is one of the most obvious signals for the various physical processes relating to climate change on a short time scale. However, the observation of such ZTD oscillations on a global scale with traditional techniques (e.g. radiosondes) is restricted due to limitations in spatial and temporal resolution. Nowadays, the International GNSS Service (IGS) provides an important data source for investigating the diurnal and semidiurnal cycles of ZTD and related climatic signals. In this paper, 10 years of ZTD data from 1997 to 2007 with a 2-hour temporal resolution are derived from global positioning system (GPS) observations taken at 151 globally distributed IGS reference stations. These time series are used to investigate diurnal and semidiurnal oscillations. Significant diurnal and semidiurnal oscillations of ZTD are found for all GPS stations used in this study. The diurnal cycles (24 hours period) have amplitudes between 0.2 and 10.9 mm with an uncertainty of about 0.5 mm and the semidiurnal cycles (12 h period) have amplitudes between 0.1 and 4.3 mm with an uncertainty of about 0.2 mm. The larger amplitudes of the diurnal and semidiurnal ZTD cycles are observed in the low-latitude equatorial areas. The peak times of the diurnal cycles spread over the whole day, while the peak value of the semidiurnal cycles occurs typically about local noon. These GPS-derived diurnal and semidiurnal ZTD signals are similar with the surface pressure tides derived from surface synoptic pressure observations, indicating that atmospheric tides are the main driver of the diurnal and semidiurnal ZTD variations.     

A Stochastic Approach to Estimate Distribution of Built-Up Area in Regions with Thick Tree Cover

Buildings and other human-made constructions have been accepted as an indicator of human habitation and are identified as built-up area. Identification of built-up area in a region and its subsequent measurement is a key step in many fields of studies like urban planning, environmental studies, and population demography. Remote sensing techniques utilising medium resolution images (e.g. LISS III, Landsat) are extensively used for the extraction of the built-up area as high-resolution images are expensive, and its processing is difficult. Extraction of built land use from medium resolution images poses a challenge in regions like Western-Ghats, North-East regions of India, and countries in tropical region, due to the thick evergreen tree cover. The spectral signature of individual houses with a small footprint are easily overpowered by the overlapping tree canopy in a medium resolution image when the buildings are not clustered. Kerala is a typical case for this scenario. The research presented here proposes a stochastic-dasymetric process to aid in the built-up area recognition process by taking Kerala as a case study. The method utilises a set of ancillary information to derive a probability surface. The ancillary information used here includes distance from road junctions, distance from road network, population density, built-up space visible in the LISS III image, the population of the region, and the household size. The methodology employs logistic regression and Monte Carlo simulation in two sub processes. The algorithm estimates the built-up area expected in the region and distributes the estimated built-up area among pixels according to the probability estimated from the ancillary information. The output of the algorithm has two components. The first component is an example scenario of the built-up area distribution. The second component is a probability surface, where the value of each pixel denotes the probability of that pixel to have a significant built-up area within it. The algorithm is validated for regions in Kerala and found to be significant. The model correctly predicted the built-up pixel count count over a validation grid of 900 m in 95.2% of the cases. The algorithm is implemented using Python and ArcGIS.     

Image Pose Estimation Based on Procrustes Theory

This paper has established a high-precision hierarchical estimated pose parameters of image. Firstly, we select corresponding three image points of 3D points which constitute the largest area in image as a base, in order to estimate the depth and translate information; then based on the above method, we obtain the scale parameter of camera exterior information. And finally, the topic is transformed to a problem of estimating rotation relationship by vector, using Procrustes theory to obtain the best estimate of the angle elements of exterior parameters. The method can effectively solve problems which depth and coupling pose parameters cannot deal with. Experimental results show that this method of determining position and orientation parameter estimation model is of briefness, easy convergence and it can also achieve higher parameter estimation accuracy than the direct projection matrix factorization.     

Volume Height Estimation based on Fusion of Discrete Fourier Transform (DFT) and Least Square (LS) in a Tomographic SAR Application

Tomographic-SAR (Synthetic Aperture Radar) is a 3D Radar imaging technique, based on spectral estimation tools. This technique is used to estimate the distribution of the backscattering signal in the elevation axis, for each azimuth-range resolution cell of the SAR image. Spectral estimation algorithms belong to two families, non parametric estimation algorithms which include DFT (Discrete Fourier Transform), SVD (Single Value Decomposition), MUSIC (Multiple Signal Classification), CAPON and parametric estimation algorithms such as LS (Least Square) and ESPRIT (Estimation of signal parameters via rotation invariance techniques). In this paper we present an inversion algorithm based on the fusion of DFT and LS for the estimation of the reflectivity signal along the elevation axis. With an appropriate combination of these two algorithms and a realistic modeling of the signal distribution, we obtain a high resolution estimate of the reflectivity signal with medium computational effort. The inversion algorithm is tested on a forested area (Västerbotten in northern Sweden), with multibaseline data set acquired in L-band (BioSAR-2008 project). Results are promising with the proposed algorithm. We used MUSIC and RVoG (Random Volume over Ground) inversions for comparison and LIDAR (Laser Imaging Detection And Ranging) image as datasets for validation of the results.     

A hierarchical model for estimating methane emission from wetlands using MODIS data and ARIMA modeling

A three-step hierarchical Semi Automated Empirical Methane Emission Model (SEMEM) has been used to estimate methane emission from wetlands and waterlogged areas in India using Moderate Resolution Imagine Spectroradiometer (MODIS) sensor data onboard Terra satellite. Wetland Surface Temperature (WST), methane emission fluxes and wetland extent have been incorporated as parameters in order to model the methane emission. Analysis of monthly MODIS data covering the whole of India from November 2004 to April 2006 was carried out and monthly methane emissions have been estimated. Interpolation techniques were adopted to fill the data gaps due to cloudy conditions during the monsoon period. AutoRegressive Integrated Moving Average (ARIMA) model has been fitted to estimate the emitted methane for the months of May 2006 to August 2006 using SPSS software.     

Numerical investigation of internal waves in jet streams including nonlinear effects

Internal waves in an atmospheric jet stream are discussed, using both linear and non-linear theory. In the former case., the wave phase-speeds depend only on the total static stability in the jet stream layer. In the latter case, the temperature profile curvature plays the main role in the behaviour of gravity-type waves.     

Personality type differences between Ph.D. climate researchers and the general public: implications for effective communication

Effectively communicating the complexity of climate change to the public is an important goal for the climate change research community, particularly for those of us who receive public funds. The challenge of communicating the science of climate change will be reduced if climate change researchers consider the links between personality types, communication tendencies and learning preferences. Jungian personality type is one of many factors related to an individual’s preferred style of taking in and processing information, i.e., preferred communication style. In this paper, we demonstrate that the Jungian personality type profile of interdisciplinary, early career climate researchers is significantly different from that of the general population in the United States. In particular, Ph.D. climate researchers tend towards Intuition and focus on theories and the “big picture”, while the U.S. general population tends towards Sensing and focuses on concrete examples and experience. There are other differences as well in the way the general public as a group prefers to take in information, make decisions, and deal with the outer world, compared with the average interdisciplinary climate scientist. These differences have important implications for communication between these two groups. We suggest that climate researchers will be more effective in conveying their messages if they are aware of their own personality type and potential differences in preferred learning and communication styles between themselves and the general public (and other specific audiences), and use this knowledge to more effectively target their audience.     

First-Order Inconsistencies Caused by Rogue Trajectories

A theoretical requirement of the Interaction by Exchange with the Conditional Mean (IECM) micromixing model is that the mean concentration field produced by it must be consistent with the mean concentration field produced by a traditional Lagrangian stochastic (LS) marked particle model. We examine the violation of this requirement that occurs in a coupled LS–IECM model when unrealistically high particle velocities occur. No successful strategy was found to mitigate the effects of these rogue trajectories. It is our hope that this work will provide renewed impetus for investigation into rogue trajectories and methods to eliminate them from LS models.     

On the use of JMA intensity in earthquake early warning systems

The estimation of strength of shaking at a site from the initial P-wave portion of ground motion is the key problems for shortening the alert time of the earthquake Early Warning (EEW). The most of the techniques proposed for the purpose utilize (a) ground motion models based on the estimated magnitude and hypocentral distance, or (b) the interim proxies, such as initial vertical displacement P _d . We suggest the instrumental Japan Meteorological Agency (JMA) intensity (JMA_I) as a characteristic for fast estimation of damage potential in the EEW systems. We investigated the scaling relations between JMA_I measured using the whole earthquake recordings (overall intensity) and using particular time intervals of various duration (2.0–8.0 s) starting from the P-wave arrival (preliminary intensity). The dataset included 3,660 records (K-NET and the KiK-net networks) from 55 events (M _W 4.1–7.4) occurred in 1999–2008 in Japan. We showed that the time interval of 4–5 s from the P-wave arrival can be used for reliable estimations of the overall intensity with the average standard error of about 0.5 JMA units. The uncertainty in the prediction may be reduced by consideration of local site conditions or by development of the station-specific models.