scholarly journals Hydrogeophysical data integration through Bayesian Sequential Simulation with log-linear pooling

2020 ◽  
Vol 221 (3) ◽  
pp. 2184-2200
Author(s):  
Raphaël Nussbaumer ◽  
Grégoire Mariethoz ◽  
Erwan Gloaguen ◽  
Klaus Holliger
Keyword(s):  
Data Sets ◽  
Sequential Simulation ◽  
Data Set ◽  
Resistivity Tomography ◽  
Simulation Process ◽  
Spatial Coverage ◽  
Linear Pooling ◽  
Log Linear ◽  
Primary Variable ◽  
Secondary Variable

SUMMARY Bayesian sequential simulation (BSS) is a geostastistical technique, which uses a secondary variable to guide the stochastic simulation of a primary variable. As such, BSS has proven significant promise for the integration of disparate hydrogeophysical data sets characterized by vastly differing spatial coverage and resolution of the primary and secondary variables. An inherent limitation of BSS is its tendency to underestimate the variance of the simulated fields due to the smooth nature of the secondary variable. Indeed, in its classical form, the method is unable to account for this smoothness because it assumes independence of the secondary variable with regard to neighbouring values of the primary variable. To overcome this limitation, we have modified the Bayesian updating with a log-linear pooling approach, which allows us to account for the inherent interdependence between the primary and the secondary variables by adding exponential weights to the corresponding probabilities. The proposed method is tested on a pertinent synthetic hydrogeophysical data set consisting of surface-based electrical resistivity tomography (ERT) data and local borehole measurements of the hydraulic conductivity. Our results show that, compared to classical BSS, the proposed log-linear pooling method using equal constant weights for the primary and secondary variables enhances the reproduction of the spatial statistics of the stochastic realizations, while maintaining a faithful correspondence with the geophysical data. Significant additional improvements can be achieved by optimizing the choice of these constant weights. We also explore a dynamic adaptation of the weights during the course of the simulation process, which provides valuable insights into the optimal parametrization of the proposed log-linear pooling approach. The results corroborate the strategy of selectively emphasizing the probabilities of the secondary and primary variables at the very beginning and for the remainder of the simulation process, respectively.

scholarly journals The SPARC water vapour assessment II: comparison of stratospheric and lower mesospheric water vapour time series observed from satellites

2018 ◽  
Vol 11 (7) ◽  
pp. 4435-4463 ◽  
Author(s):  
Farahnaz Khosrawi ◽  
Stefan Lossow ◽  
Gabriele P. Stiller ◽  
Karen H. Rosenlof ◽  
Joachim Urban ◽  
...  
Keyword(s):  
Time Series ◽  
Water Vapour ◽  
Data Sets ◽  
Data Set ◽  
Spatial Coverage ◽  
Modelling Studies ◽  
The Difference ◽  
The Tropics ◽  
The Antarctic ◽  
Satellite Instruments

Abstract. Time series of stratospheric and lower mesospheric water vapour using 33 data sets from 15 different satellite instruments were compared in the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II). This comparison aimed to provide a comprehensive overview of the typical uncertainties in the observational database that can be considered in the future in observational and modelling studies, e.g addressing stratospheric water vapour trends. The time series comparisons are presented for the three latitude bands, the Antarctic (80∘–70∘ S), the tropics (15∘ S–15∘ N) and the Northern Hemisphere mid-latitudes (50∘–60∘ N) at four different altitudes (0.1, 3, 10 and 80 hPa) covering the stratosphere and lower mesosphere. The combined temporal coverage of observations from the 15 satellite instruments allowed the consideration of the time period 1986–2014. In addition to the qualitative comparison of the time series, the agreement of the data sets is assessed quantitatively in the form of the spread (i.e. the difference between the maximum and minimum volume mixing ratios among the data sets), the (Pearson) correlation coefficient and the drift (i.e. linear changes of the difference between time series over time). Generally, good agreement between the time series was found in the middle stratosphere while larger differences were found in the lower mesosphere and near the tropopause. Concerning the latitude bands, the largest differences were found in the Antarctic while the best agreement was found for the tropics. From our assessment we find that most data sets can be considered in future observational and modelling studies, e.g. addressing stratospheric and lower mesospheric water vapour variability and trends, if data set specific characteristics (e.g. drift) and restrictions (e.g. temporal and spatial coverage) are taken into account.

scholarly journals The Skewed-Elliptical Log-Linear Birnbaum–Saunders Alpha-Power Model

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1297
Author(s):  
Guillermo Martínez-Flórez ◽  
Heleno Bolfarine ◽  
Yolanda M. Gómez
Keyword(s):  
Maximum Likelihood ◽  
Power Distribution ◽  
Information Matrix ◽  
Likelihood Estimation ◽  
Data Sets ◽  
Alpha Power ◽  
Data Set ◽  
Special Cases ◽  
Standard Error Estimation ◽  
Log Linear

In this paper, the skew-elliptical sinh-alpha-power distribution is developed as a natural follow-up to the skew-elliptical log-linear Birnbaum–Saunders alpha-power distribution, previously studied in the literature. Special cases include the ordinary log-linear Birnbaum–Saunders and skewed log-linear Birnbaum–Saunders distributions. As shown, it is able to surpass the ordinary sinh-normal models when fitting data sets with high (above the expected with the sinh-normal) degrees of asymmetry. Maximum likelihood estimation is developed with the inverse of the observed information matrix used for standard error estimation. Large sample properties of the maximum likelihood estimators such as consistency and asymptotic normality are established. An application is reported for the data set previously analyzed in the literature, where performance of the new distribution is shown when compared with other proposed alternative models.

scholarly journals Total water vapour columns derived from Sentinel 5p using the AMC-DOAS method

2021 ◽  
Author(s):  
Tobias Küchler ◽  
Stefan Noël ◽  
Heinrich Bovensmann ◽  
John Philip Burrows ◽  
Thomas Wagner ◽  
...  
Keyword(s):  
Water Vapour ◽  
Optical Absorption Spectroscopy ◽  
Data Sets ◽  
Independent Data ◽  
Total Water ◽  
Max Planck ◽  
Ozone Monitoring Instrument ◽  
Data Set ◽  
Medium Range Weather Forecast ◽  
Spatial Coverage

Abstract. Water vapour is the most abundant natural greenhouse gas in the Earth's atmosphere and global data sets are required for meteorological applications and climate research. The Tropospheric Ozone Monitoring Instrument (TROPOMI) onboard Sentinel 5 Precursor (S5P) launched on 13 October 2017 has a very high spatial resolution of around 5 km and a daily global coverage. Currently, there is no operational total water vapour product for S5P measurements. Here, we present first results of a new scientific total column water vapour (TCWV) product for S5P using the so-called Air Mass Corrected Differential Optical Absorption Spectroscopy (AMC-DOAS) scheme. This method analyses spectral data between 688 and 700 nm and has already been successfully applied to measurements from the Global Monitoring Experiment (GOME) on ERS-2, the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) on Envisat and GOME-2 on MetOp. The adaptation of the AMC-DOAS method to S5P data especially includes an additional post-processing procedure to correct the influences of surface albedo, cloud height and cloud fraction. The quality of the new S5P AMC-DOAS water vapour product is assessed by comparisons with data from GOME-2 on MetOp-B retrieved also with the AMC-DOAS algorithm and with four completely independent data sets, namely re-analysis data from the European Centre for Medium range Weather Forecast (ECMWF ERA5), data obtained by the Special Sensor Microwave Imager and Sounder (SSMIS) flown on the Defense Meteorological Satellite Program (DMSP) platform 16 and two scientific S5P TCWV products derived from TROPOMI measurements. Both are recently published TCWV products for S5P provided by the Max Planck Institute for Chemistry (MPIC) in Mainz and the Netherlands Institute for Space Research (SRON), Utrecht. The SRON TCWV is limited to clear sky scenes over land. These comparisons reveal a good agreement between the various data sets but also some systematic deviations between all of them. On average, the derived offset between AMC-DOAS S5P TCWV and AMC-DOAS GOME-2B TCWV is negative (around −1.5 kg m−2) over land and positive over ocean surfaces (more than 1.5 kg m−2). In contrast, SSMIS TCWV is on average lower than AMC-DOAS S5P TCWV by about 3 kg m−2. TCWV from ERA5 and S5P AMC-DOAS TCWV comparison shows spatial differences over both land and water surface. Over land there are systematical spatial structures with enhanced discrepancies between S5P AMC-DOAS TCWV and ERA5 TCWV in tropical regions. Over sea, S5P AMC-DOAS TCWV is slightly lower than ERA5 TCWV by around 2 kg m−2. The S5P AMC-DOAS TCWV and S5P TCWV from MPIC agree on average within 1 kg m−2 over both land and ocean. TCWV from SRON shows differences to AMC-DOAS S5P TCWV of around 1.2 kg m−2. All of these deviations are in line with the accuracy of these products and with the typical range of deviations of 5 kg m−2 obtained when comparing different TCWV data sets. The AMC-DOAS TCWV product for S5P provides therefore a valuable new and independent data set for atmospheric applications which also shows a better spatial coverage than the other S5P TCWV products.

scholarly journals Application of time-lapse ERT imaging to watershed characterization

Geophysics ◽  
2008 ◽  
Vol 73 (3) ◽  
pp. G7-G17 ◽  
Author(s):  
Carlyle R. Miller ◽  
Partha S. Routh ◽  
Troy R. Brosten ◽  
James P. McNamara
Keyword(s):  
Reference Model ◽  
Time Lapse ◽  
Data Sets ◽  
Data Set ◽  
Resistivity Tomography ◽  
Practical Applications ◽  
Data Inversion ◽  
Time Periods ◽  
The Difference ◽  
Base Data

Time-lapse electrical resistivity tomography (ERT) has many practical applications to the study of subsurface properties and processes. When inverting time-lapse ERT data, it is useful to proceed beyond straightforward inversion of data differences and take advantage of the time-lapse nature of the data. We assess various approaches for inverting and interpreting time-lapse ERT data and determine that two approaches work well. The first approach is model subtraction after separate inversion of the data from two time periods, and the second approach is to use the inverted model from a base data set as the reference model or prior information for subsequent time periods. We prefer this second approach. Data inversion methodology should be consideredwhen designing data acquisition; i.e., to utilize the second approach, it is important to collect one or more data sets for which the bulk of the subsurface is in a background or relatively unperturbed state. A third and commonly used approach to time-lapse inversion, inverting the difference between two data sets, localizes the regions of the model in which change has occurred; however, varying noise levels between the two data sets can be problematic. To further assess the various time-lapse inversion approaches, we acquired field data from a catchment within the Dry Creek Experimental Watershed near Boise, Idaho, U.S.A. We combined the complimentary information from individual static ERT inversions, time-lapse ERT images, and available hydrologic data in a robust interpretation scheme to aid in quantifying seasonal variations in subsurface moisture content.

Application and evaluation of electromagnetic methods for imaging saltwater intrusion in coastal aquifers: Seaside Groundwater Basin, California

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. B77-B88 ◽  
Author(s):  
Vanessa Nenna ◽  
Daan Herckenrath ◽  
Rosemary Knight ◽  
Nick Odlum ◽  
Darcy McPhee
Keyword(s):  
Coastal Aquifers ◽  
Saltwater Intrusion ◽  
Groundwater Resources ◽  
Management Strategies ◽  
Geophysical Methods ◽  
Unconfined Aquifer ◽  
Data Sets ◽  
Data Set ◽  
Aquifer System ◽  
Spatial Coverage

Developing effective resource management strategies to limit or prevent saltwater intrusion as a result of increasing demands on coastal groundwater resources requires reliable information about the geologic structure and hydrologic state of an aquifer system. A common strategy for acquiring such information is to drill sentinel wells near the coast to monitor changes in water salinity with time. However, installation and operation of sentinel wells is costly and provides limited spatial coverage. We studied the use of noninvasive electromagnetic (EM) geophysical methods as an alternative to installation of monitoring wells for characterizing coastal aquifers. We tested the feasibility of using EM methods at a field site in northern California to identify the potential for and/or presence of hydraulic communication between an unconfined saline aquifer and a confined freshwater aquifer. One-dimensional soundings were acquired using the time-domain electromagnetic (TDEM) and audiomagnetotelluric (AMT) methods. We compared inverted resistivity models of TDEM and AMT data obtained from several inversion algorithms. We found that multiple interpretations of inverted models can be supported by the same data set, but that there were consistencies between all data sets and inversion algorithms. Results from all collected data sets suggested that EM methods are capable of reliably identifying a saltwater-saturated zone in the unconfined aquifer. Geophysical data indicated that the impermeable clay between aquifers may be more continuous than is supported by current models.

scholarly journals A new data set for estimating organic carbon storage to 3 m depth in soils of the northern circumpolar permafrost region

2013 ◽  
Vol 5 (2) ◽  
pp. 393-402 ◽  
Author(s):  
G. Hugelius ◽  
J. G. Bockheim ◽  
P. Camill ◽  
B. Elberling ◽  
G. Grosse ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Mineral Soil ◽  
Geographical Information ◽  
Gis Mapping ◽  
Permafrost Region ◽  
Data Sets ◽  
Depth Range ◽  
Data Set ◽  
Spatial Coverage ◽  
Gis Data

Abstract. High-latitude terrestrial ecosystems are key components in the global carbon cycle. The Northern Circumpolar Soil Carbon Database (NCSCD) was developed to quantify stocks of soil organic carbon (SOC) in the northern circumpolar permafrost region (a total area of 18.7 × 106 km2). The NCSCD is a geographical information system (GIS) data set that has been constructed using harmonized regional soil classification maps together with pedon data from the northern permafrost region. Previously, the NCSCD has been used to calculate SOC storage to the reference depths 0–30 cm and 0–100 cm (based on 1778 pedons). It has been shown that soils of the northern circumpolar permafrost region also contain significant quantities of SOC in the 100–300 cm depth range, but there has been no circumpolar compilation of pedon data to quantify this deeper SOC pool and there are no spatially distributed estimates of SOC storage below 100 cm depth in this region. Here we describe the synthesis of an updated pedon data set for SOC storage (kg C m−2) in deep soils of the northern circumpolar permafrost regions, with separate data sets for the 100–200 cm (524 pedons) and 200–300 cm (356 pedons) depth ranges. These pedons have been grouped into the North American and Eurasian sectors and the mean SOC storage for different soil taxa (subdivided into Gelisols including the sub-orders Histels, Turbels, Orthels, permafrost-free Histosols, and permafrost-free mineral soil orders) has been added to the updated NCSCDv2. The updated version of the data set is freely available online in different file formats and spatial resolutions that enable spatially explicit applications in GIS mapping and terrestrial ecosystem models. While this newly compiled data set adds to our knowledge of SOC in the 100–300 cm depth range, it also reveals that large uncertainties remain. Identified data gaps include spatial coverage of deep (> 100 cm) pedons in many regions as well as the spatial extent of areas with thin soils overlying bedrock and the quantity and distribution of massive ground ice. An open access data-portal for the pedon data set and the GIS-data sets is available online at http://bolin.su.se/data/ncscd/. The NCSCDv2 data set has a digital object identifier (doi:10.5879/ECDS/00000002).

Deep Ensemble Neural Network Approach for Federal Highway Administration Axle-Based Vehicle Classification Using Advanced Single Inductive Loops

2021 ◽  
pp. 036119812110491
Author(s):  
Yiqiao Li ◽  
Andre Tok ◽  
Stephen G. Ritchie
Keyword(s):  
Neural Network ◽  
Model Performance ◽  
Imbalanced Data ◽  
Vehicle Classification ◽  
Data Sets ◽  
Neural Network Approach ◽  
Data Set ◽  
Inductive Loop ◽  
Spatial Coverage ◽  
Federal Highway

The Federal Highway Administration (FHWA) vehicle classification scheme is designed to serve various transportation needs such as pavement design, emission estimation, and transportation planning. Many transportation agencies rely on Weigh-In-Motion and Automatic Vehicle Classification sites to collect these essential vehicle classification counts. However, the spatial coverage of these detection sites across the highway network is limited by high installation and maintenance costs. One cost-effective approach has been the use of single inductive loop sensors as an alternative to obtaining FHWA vehicle classification data. However, most data sets used to develop such models are skewed since many classes associated with larger truck configurations are less commonly observed in the roadway network. This makes it more difficult to accurately classify under-represented classes, even though many of these minority classes may have disproportionately adverse effects on pavement infrastructure and the environment. Therefore, previous models have been unable to adequately classify under-represented classes, and the overall performance of the models is often masked by excellent classification accuracy of majority classes, such as passenger vehicles and five-axle tractor-trailers. To resolve the challenge of imbalanced data sets in the FHWA vehicle classification, this paper constructed a bootstrap aggregating deep neural network model on a truck-focused data set using single inductive loop signatures. The proposed method significantly improved the model performance on several truck classes, especially minority classes such as Classes 7 and 11 which were overlooked in previous research. The model was tested on a distinct data set obtained from four spatially independent sites and achieved an accuracy of 0.87 and an average F1 score of 0.72.

scholarly journals Effects of temporal resolution of input precipitation on the performance of hydrological forecasting

2011 ◽  
Vol 29 ◽  
pp. 21-25 ◽  
Author(s):  
F. Wetterhall ◽  
Y. He ◽  
H. Cloke ◽  
F. Pappenberger
Keyword(s):  
Flood Forecasting ◽  
Lead Times ◽  
Data Sets ◽  
Precipitation Data ◽  
Hydrological Models ◽  
Data Set ◽  
Flood Prediction ◽  
Time Period ◽  
Spatial Coverage ◽  
Prediction Systems

Abstract. Flood prediction systems rely on good quality precipitation input data and forecasts to drive hydrological models. Most precipitation data comes from daily stations with a good spatial coverage. However, some flood events occur on sub-daily time scales and flood prediction systems could benefit from using models calibrated on the same time scale. This study compares precipitation data aggregated from hourly stations (HP) and data disaggregated from daily stations (DP) with 6-hourly forecasts from ECMWF over the time period 1 October 2006–31 December 2009. The HP and DP data sets were then used to calibrate two hydrological models, LISFLOOD-RR and HBV, and the latter was used in a flood case study. The HP scored better than the DP when evaluated against the forecast for lead times up to 4 days. However, this was not translated in the same way to the hydrological modelling, where the models gave similar scores for simulated runoff with the two datasets. The flood forecasting study showed that both datasets gave similar hit rates whereas the HP data set gave much smaller false alarm rates (FAR). This indicates that using sub-daily precipitation in the calibration and initiation of hydrological models can improve flood forecasting.

scholarly journals Global carbon monoxide products from combined AIRS, TES and MLS measurements on A-train satellites

2014 ◽  
Vol 14 (1) ◽  
pp. 103-114 ◽  
Author(s):  
J. X. Warner ◽  
R. Yang ◽  
Z. Wei ◽  
F. Carminati ◽  
A. Tangborn ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Data Fusion ◽  
Satellite Data ◽  
Lower Troposphere ◽  
Data Sets ◽  
Data Set ◽  
Upper Troposphere ◽  
In Situ Data ◽  
Spatial Coverage

Abstract. This study tests a novel methodology to add value to satellite data sets. This methodology, data fusion, is similar to data assimilation, except that the background model-based field is replaced by a satellite data set, in this case AIRS (Atmospheric Infrared Sounder) carbon monoxide (CO) measurements. The observational information comes from CO measurements with lower spatial coverage than AIRS, namely, from TES (Tropospheric Emission Spectrometer) and MLS (Microwave Limb Sounder). We show that combining these data sets with data fusion uses the higher spectral resolution of TES to extend AIRS CO observational sensitivity to the lower troposphere, a region especially important for air quality studies. We also show that combined CO measurements from AIRS and MLS provide enhanced information in the UTLS (upper troposphere/lower stratosphere) region compared to each product individually. The combined AIRS–TES and AIRS–MLS CO products are validated against DACOM (differential absorption mid-IR diode laser spectrometer) in situ CO measurements from the INTEX-B (Intercontinental Chemical Transport Experiment: MILAGRO and Pacific phases) field campaign and in situ data from HIPPO (HIAPER Pole-to-Pole Observations) flights. The data fusion results show improved sensitivities in the lower and upper troposphere (20–30% and above 20%, respectively) as compared with AIRS-only version 5 CO retrievals, and improved daily coverage compared with TES and MLS CO data.

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