Tower Network Research Articles

Spatio-Temporal Trends of Surface Energy Budget in Tibet from Satellite Remote Sensing Observations and Reanalysis Data

Being the highest and largest land mass of the earth, the Tibetan Plateau has a strong impact on the Asian climate especially on the Asian monsoon. With high downward solar radiation, the Tibetan Plateau is a climate sensitive region and the main water source for many rivers in South and East Asia. Although many studies have analyzed energy fluxes in the Tibetan Plateau, a long-term detailed spatio-temporal variability of all energy budget parameters is not clear for understanding the dynamics of the regional climate change. In this paper, satellite remote sensing and reanalysis data are used to quantify spatio-temporal trends of energy budget parameters, net radiation, latent heat flux, and sensible heat flux over the Tibetan Plateau from 2001 to 2019. The validity of both data sources is analyzed from in situ ground measurements of the FluxNet micrometeorological tower network, which verifies that both datasets are valid and reliable. It is found that the trend of net radiation shows a slight increase. The latent heat flux increases continuously, while the sensible heat flux decreases continuously throughout the study period over the Tibetan Plateau. Varying energy fluxes in the Tibetan plateau will affect the regional hydrological cycle. Satellite LE product observation is limited to certain land covers. Thus, for larger spatial areas, reanalysis data is a more appropriate choice. Normalized difference vegetation index proves a useful indicator to explain the latent heat flux trend. Despite the reduction of sensible heat, the atmospheric temperature increases continuously resulting in the warming of the Tibetan Plateau. The opposite trend of sensible heat flux and air temperature is an interesting and explainable phenomenon. It is also concluded that the surface evaporative cooling is not the indicator of atmospheric cooling/warming. In the future, more work shall be done to explain the mechanism which involves the complete heat cycle in the Tibetan Plateau.

Estimating the Net Ecosystem Exchange at Global FLUXNET Sites Using a Random Forest Model

Despite considerable progress in scaling carbon fluxes from eddy covariance sites to globe, significant uncertainties still exist when estimating the global net ecosystem exchange (NEE). In this study, the site-level NEE was estimated from FLUXNET, a global network of eddy covariance towers, using a random forest (RF) model based on remote sensing products and precipitation data. The plant function type (PFT) had the highest relative explanatory power in predicting the global site-level NEE. However, within PFTs, water-related variables (i.e., the total precipitation, remotely sensed evapotranspiration, land surface water index, and the difference between daytime and nighttime land surface temperature) and soil respiration ( R s) were strong predictors of NEE variability. Cross-validation analyses revealed the good performance of RF in predicting the spatiotemporal variability of monthly NEE at 168 global FLUXNET sites, with R 2 of 0.72 and RMSE of 0.96 g·C·m−2·day−1. The performance was also good when predicting across-site ( R 2 = 0.75) and seasonal patterns ( R 2 = 0.92) over the 58 sites with available data being longer than two years and the 12-month value being present for each year. The RF-estimated NEE showed better relationships with the tower-measured NEE than a global NEE product from FLUXCOM across all PFTs. The difference between the RF-estimated NEE and FLUXCOM NEE was likely linked to the different predictor sets, such as those with more water-related variables and R s. This study indicates the importance of considering the influence of water-related variables and R s in the estimation of NEE at the global scale.

On tower and checkerboard neural network architectures for gene expression inference

BackgroundOne possible approach how to economically facilitate gene expression profiling is to use the L1000 platform which measures the expression of ∼1,000 landmark genes and uses a computational method to infer the expression of another ∼10,000 genes. One such method for the gene expression inference is a D–GEX which employs neural networks.ResultsWe propose two novel D–GEX architectures that significantly improve the quality of the inference by increasing the capacity of a network without any increase in the number of trained parameters. The architectures partition the network into individual towers. Our best proposed architecture — a checkerboard architecture with a skip connection and five towers — together with minor changes in the training protocol improves the average mean absolute error of the inference from 0.134 to 0.128.ConclusionsOur proposed approach increases the gene expression inference accuracy without increasing the number of weights of the model and thus without increasing the memory footprint of the model that is limiting its usage.

Diagnosis of environmental controls on daily actual evapotranspiration across a global flux tower network: the roles of water and energy

Relative contributions from environmental factors to daily actual evapotranspiration (ETa) across a variety of climate zones is a widely open research question, especially regarding the roles played by soil water content ((SWC); water supply) and net radiation ((Rn); energy supply) in controlling ETa. Here, the boosted regression tree method scheme was employed to quantify environmental controls on daily ETa using the global FLUXNET dataset. Similar to the general trend suggested by the Budyko theory at annual scales, the results showed that the relative control of SWC on daily ETa increased with increasing aridity index (Φ); however, Rn played a major role at most FLUXNET sites (roughly Φ < 4), indicating that Rn could be a leading control on daily ETa even at water-limited sites. The variability in the relative controls of SWC and Rn also partly depended on factors affecting water availability for daily ETa (e.g. vegetation characteristics and groundwater depth). Our study showed that other than SWC and Rn, the net effect of environmental controls (particularly leaf area index) on daily ETa was more important at drier sites than at relatively humid sites. This suggests that near-surface hydrological processes are more sensitive to vegetation variations due to their ability to extract deep soil water and enhance ETa, especially under arid and semi-arid climatic conditions. Our findings illustrate how environmental controls on daily ETa change as the climate dries, which has important implications for many scientific disciplines including hydrological, climatic, and agricultural studies.

Multi-TALK: Multi-Microphone Cross-Tower Network for Jointly Suppressing Acoustic Echo and Background Noise.

In this paper, we propose a multi-channel cross-tower with attention mechanisms in latent domain network (Multi-TALK) that suppresses both the acoustic echo and background noise. The proposed approach consists of the cross-tower network, a parallel encoder with an auxiliary encoder, and a decoder. For the multi-channel processing, a parallel encoder is used to extract latent features of each microphone, and the latent features including the spatial information are compressed by a 1D convolution operation. In addition, the latent features of the far-end are extracted by the auxiliary encoder, and they are effectively provided to the cross-tower network by using the attention mechanism. The cross tower network iteratively estimates the latent features of acoustic echo and background noise in each tower. To improve the performance at each iteration, the outputs of each tower are transmitted as the input for the next iteration of the neighboring tower. Before passing through the decoder, to estimate the near-end speech, attention mechanisms are further applied to remove the estimated acoustic echo and background noise from the compressed mixture to prevent speech distortion by over-suppression. Compared to the conventional algorithms, the proposed algorithm effectively suppresses the acoustic echo and background noise and significantly lowers the speech distortion.

Probabilistic positioning in mobile phone network and its consequences for the privacy of mobility data

The traditional approach to mobile phone positioning is based on the assumption that the geographical location of a cell tower recorded in a Call Details Record (CDR) is a proxy for a device's location. A Voronoi tessellation is then constructed based on the entire network of cell towers and this tessellation is considered as a coordinate system, with the device located in a Voronoi polygon of a cell tower that is recorded in the CDR. If Voronoi-based positioning is correct, the uniqueness of the device trajectory is very high, and the device can be identified based on 3–5 of its recorded locations.We investigate a probabilistic approach to device positioning that is based on knowledge of each antennas' parameters and number of connections, as dependent on the distance to the antenna. The critical difference between the Voronoi-based and the real world layout is in the essential overlap of the antennas' service areas: The device that is located in a cell tower's polygon can be served by a more distant antenna that is chosen by the network system to balance the network load. Combining data on the distance distribution of the number of connections available for each antenna in the network, we resolve the overlap problem by applying Bayesian inference and construct a realistic distribution of the device location. Probabilistic device positioning demands a full revision of mobile phone privacy and new full set of tools for data analysis.

Vegetation-heatwave correlations and contrasting energy exchange responses of different vegetation types to summer heatwaves in the Northern Hemisphere during the 1982–2011 period

Heatwave is a serious threat to society and can lead to grave consequences. Although it is well known that persistent large-scale circulation anomalies are the key to generate heatwaves, land-atmosphere interactions have also been suggested to intensify and propagate heatwaves. Vegetation plays a vital role in land-atmosphere interactions, modulating energy and water exchange through various pathways. However, vegetation impacts on surface energy exchange during extreme events such as heatwaves, and the attribution of effects to different vegetation types, is complex and poorly understood. In this study, we found strong interannual correlations between summer heatwaves and various plant function types (PFTs), based on the Global Heatwave and Warm-spell Record (GHWR) and leaf area index (LAI) products from satellites during 1982–2011. In Central Europe and the southern and southeastern part of North America, where the land cover is dominated by grasslands, temperate deciduous forests or temperate needleleaf forests, heatwaves tend to occur more frequently in years with lower LAI. In contrast, in the northwestern and northeastern part of North America, where boreal evergreen forests dominate, higher-than-normal LAI is associated with an increase in heatwave occurrence. These findings are in general supported by composite analyses of extreme LAI years in these PFT regions and heatwave characteristics therein.We speculate that the different surface heat flux responses over different PFTs during heatwaves may explain the above relationships. Focusing on North America, and using various datasets including those generated by the Global Land Data Assimilation System (GLDAS) with three different land surface models (CLM, MOS, NOAH), three reanalysis datasets (MERRA-2, NOAA-CIRES-DOS, NCEP/NCAR), and also observations from an extensive network of flux towers, we found that when temperate and boreal evergreen forests are greener, positive sensible heat anomalies increase significantly during heatwaves. Meanwhile, over boreal evergreen forests, changes in latent heat anomalies are much smaller than the positive sensible heat anomalies, suggesting that a greener boreal evergreen forest may prolong and amplify heatwaves significantly. This generates a positive feedback mechanism that begins with higher LAI in generally warmer years in these temperature-limited regions, thus sustaining the strong positive heatwave-LAI correlation. In contrast, for temperate needleleaf forests, temperate deciduous forests and grasslands, strong positive latent heat anomalies with cloud, precipitation and evaporative cooling feedback during high-LAI years appear to suppress heatwaves regionally. Our study revealed the interannual relationships between heatwaves and vegetation as well as the underlying energy exchange processes for different vegetation types and background climate conditions, with implications for the management of forest resources in view of worsening heatwave severity under the future climate.

CROSSINN: A Field Experiment to Study the Three-Dimensional Flow Structure in the Inn Valley, Austria

AbstractWhile the exchange of mass, momentum, moisture, and energy over horizontally homogeneous, flat terrain is mostly driven by vertical turbulent mixing, thermally and dynamically driven mesoscale flows substantially contribute to the Earth–atmosphere exchange in the atmospheric boundary layer over mountainous terrain (MoBL). The interaction of these processes acting on multiple scales leads to a large spatial variability in the MoBL, whose observational detection requires comprehensive instrumentation and a sophisticated measurement strategy. We designed a field campaign that targets the three-dimensional flow structure and its impact on the MoBL in a major Alpine valley. Taking advantage of an existing network of surface flux towers and remote sensing instrumentation in the Inn Valley, Austria, we added a set of ground-based remote sensing instruments, consisting of Doppler lidars, a ceilometer, a Raman lidar, and a microwave radiometer, and performed radio soundings and aircraft measurements. The objective of the Cross-Valley Flow in the Inn Valley Investigated by Dual-Doppler Lidar Measurements (CROSSINN) experiment is to determine the mean and turbulent characteristics of the flow in the MoBL under different synoptic conditions and to provide an intensive dataset for the future validation of mesoscale and large-eddy simulations. A particular challenge is capturing the two-dimensional kinematic flow in a vertical plane across the whole valley using coplanar synchronized Doppler lidar scans, which allows the detection of cross-valley circulation cells. This article outlines the scientific objectives, instrument setup, measurement strategy, and available data; summarizes the synoptic conditions during the measurement period of 2.5 months; and presents first results.

Ambient Vibration as a Basis for Determining the Structural Behaviour of Watchtowers against Horizontal Loads in Southeast Spain

The Kingdom of Granada occupied the southeast of the Iberian Peninsula, what today would be the current Spanish provinces of Granada, Malaga and Almeria. Having succeeded the Nasrid kingdom of Granada (1238–1492), it remained a geographical and administrative unit until 1834, defended from the advancement of Castilian troops by means of a large network of watchtowers located principally along its inland border. Following the Castilian conquest of Granada, the extensive coastline was also strengthened with a network of towers and fortifications that were progressively adapted to house artillery. A technical survey focusing on the characteristics of their geometrics and construction, as well as their performance in a series of non-destructive tests, such as ambient vibration testing, was undertaken to study the towers’ mechanical strength against both gravitational and seismic loads. The results propose a numerical estimate that defines the fundamental frequency of this type of structure, which in turn can be used to approximate the mechanical properties of the masonry. Such a precise definition based on objective data enables accurate and rigorous numerical analysis of this defensive architecture, thus reducing uncertainties. Furthermore, slenderness is found to be a relevant parameter for adjusting fundamental frequency and for analysing the towers’ historical evolution, enabling their initial height and number of levels to be estimated.

Development of a Mesoscale Inversion System for Estimating Continental‐Scale CO2 Fluxes

AbstractComputational requirements often impose limitations on the spatial and temporal resolutions of atmospheric CO2 inversions, increasing aggregation and representation errors. This study enables higher spatial and temporal resolution inversions with spatial and temporal error structures similar to those used in other published inversions by representing the prior flux error covariances as a Kronecker product of spatial and temporal covariances and by using spectral methods for the spatial correlations. Compared to existing inversion systems that are forced to degrade the resolution of the problem in order to bring the dimensionality down to computationally tractable levels, this inversion framework is able to take advantage of mesoscale transport simulations and more of the complexity of spatial and temporal covariances in the surface CO2 fluxes. This approach was successfully implemented over one month with an identical‐twin observing system simulation experiment (OSSE) using a set of assumptions about the prior flux uncertainties compatible both with continental‐scale uncertainty estimates and with comparisons of vegetation models to flux towers. The demonstration illustrates the potential of the newly developed inversion system to use high‐temporal‐resolution information from the North American tower network, to extract high‐resolution information about CO2 fluxes that is inaccessible to coarser resolution inversion systems, and to simultaneously optimize an ensemble of prior estimates. This demonstration sets the stage for regional flux inversions that can take full advantage of the high‐resolution data available in tower CO2 records and mesoscale atmospheric transport reanalyses, include more realistic prior error structures, and explore specifying prior fluxes with ensembles.

Development of Land Surface Albedo Algorithm for the GK-2A/AMI Instrument

The Korea Meteorological Administration successfully launched Korea’s next-generation meteorological satellite, Geo-KOMPSAT-2A (GK-2A), on 5 December 2018. It belongs to the new generation of GEO (Geostationary Elevation Orbit) satellite which offers capabilities to disseminate high spatial- (0.5–2 km) and high temporal-resolution (10 min) observations over a broad area, herein a geographic disk encompassing the Asia–Oceania region. The targeted objective is to enhance our understanding of climate change, owing to a bulk of coherent observations. For such, we developed an algorithm to map the land surface albedo (LSA), which is a major Essential Climate Variable (ECV). The retrieval algorithm devoted to GK-2A/Advanced Meteorological Imager (AMI) data considered Japan’s Himawari-8/Advanced Himawari Imager (AHI) data for prototyping, as this latter owns similar specifications to AMI. Our proposed algorithm is decomposed in three major steps: atmospheric correction, bidirectional reflectance distribution function (BRDF) modeling and angular integration, and narrow-to-broadband conversion. To perform BRDF modeling, the optimization method using normalized reflectance was applied, which improved the quality of BRDF modeling results, particularly when the number of observations was less than 15. A quality assessment was performed to compare our results to those of Moderate Resolution Imaging Spectroradiometer (MODIS) LSA products and ground measurement from Aerosol Robotic Network (AERONET) sites, Australian and New Zealand flux tower network (OzFlux) site and the Korea Flux Network (KoFlux) site from throughout 2017. Our results show dependable spatial and temporal consistency with MODIS broadband LSA data, and rapid changes in LSA due to snowfall and snow melting were well expressed in the temporal profile of our results. Our outcomes also show good agreement with the ground measurements from AERONET, OzFlux and KoFlux ground-based network with root mean square errors (RMSE) of 0.0223 and 0.0306, respectively, which is close to the accuracy of MODIS broadband LSA. Moreover, our results reveal still more reliable LSA products even when clouds are frequently present, such as during the summer monsoon season. It shows that our results are useful for continuous LSA monitoring.

Spatial Dependence of Stably Stratified Nocturnal Boundary-Layer Regimes in Complex Terrain

The stably stratified atmospheric boundary layer (SBL) has been found in previous studies to display distinct regimes of behaviour. In particular, a contrast is often drawn between the weakly (wSBL) and very (vSBL) stable boundary layers. Time series of SBL regime affiliation obtained from hidden Markov model analyses of data from three different towers at the Los Alamos National Laboratory are used to investigate the spatial dependence of SBL regime occupation and SBL transitions. The local topography influences the flow such that south-west to north-east flow prevails, for which wSBL and vSBL conditions respectively are more likely to occur. Joint probabilities of shared regime occupation at the three towers (with and without conditioning on wind direction) are much larger than would be expected from statistically independent regime sequences at the different locations. Very persistent wSBL nights (without any transitions to the vSBL) have a higher probability of occurring across the entire tower network domain than very persistent vSBL nights. Many regime transitions occur within a narrow time window between the different towers; occurrence probabilities of such events are much higher than would be expected from statistically independent regime transitions. Of such events, transitions occurring at exactly the same time across the tower network occur most often. Many co-occurring turbulence recovery events can be associated with high-intensity intermittent turbulence events. Our results imply that the scale on which the SBL regime occupation and transitions are dependent exceeds 10 km in this region of complex terrain.

Greenhouse gas observations from the Northeast Corridor tower network

We present the organization, structure, instrumentation, and measurements of the Northeast Corridor greenhouse gas observation network. This network of tower-based in situ carbon dioxide and methane observation stations was established in 2015 with the goal of quantifying emissions of these gases in urban areas in the northeastern United States. A specific focus of the network is the cities of Baltimore, MD, and Washington, DC, USA, with a high density of observation stations in these two urban areas. Additional observation stations are scattered throughout the northeastern US, established to complement other existing urban and regional networks and to investigate emissions throughout this complex region with a high population density and multiple metropolitan areas. Data described in this paper are archived at the National Institute of Standards and Technology and can be found at https://doi.org/10.18434/M32126 (Karion et al., 2019).

Analisis Determinan Persaingan Industri Telekomunikasi (PT. Indosat Ooredoo, PT. Telkomsel, PT. XL Axiata) di Indonesia Dalam Pasar Oligopoli

Nowadays, cellular phones are highly needed and become the necessities of life. The formulation of the problem was whether the factors of price/tariff, promotion, the number of customers, the number of employees, and tower networks influenced the competition (income) in telecommunication in Indonesia. The theory used in the research was the theory of micro economy and management which were related to income, competition, employees, price, and customers. Secondary data were analyzed quantitatively in the numerical form and time series by using multiple regression models. They were gathered from books, journals, research results, and other sources which were relevant to this research. Dependent variable was competition (income of telecommunication industry), while mindependent variables were tariff/price, the number of customers, the number of employees, promotion, and network. The result and the conclusion of the research showed that the number of customers and tariff had the most dominant influence on competition, while employees, promotion, and network/bts had insignificant influence on competition even though these three variables were the facility to increase company’s income. Therefore, the management of the company should be careful in identifying competitors and in making policies.

FLUXNET-CH4 Synthesis Activity: Objectives, Observations, and Future Directions

AbstractThis paper describes the formation of, and initial results for, a new FLUXNET coordination network for ecosystem-scale methane (CH4) measurements at 60 sites globally, organized by the Global Carbon Project in partnership with other initiatives and regional flux tower networks. The objectives of the effort are presented along with an overview of the coverage of eddy covariance (EC) CH4 flux measurements globally, initial results comparing CH4 fluxes across the sites, and future research directions and needs. Annual estimates of net CH4 fluxes across sites ranged from −0.2 ± 0.02 g C m–2 yr–1 for an upland forest site to 114.9 ± 13.4 g C m–2 yr–1 for an estuarine freshwater marsh, with fluxes exceeding 40 g C m–2 yr–1 at multiple sites. Average annual soil and air temperatures were found to be the strongest predictor of annual CH4 flux across wetland sites globally. Water table position was positively correlated with annual CH4 emissions, although only for wetland sites that were not consistently inundated throughout the year. The ratio of annual CH4 fluxes to ecosystem respiration increased significantly with mean site temperature. Uncertainties in annual CH4 estimates due to gap-filling and random errors were on average ±1.6 g C m–2 yr–1 at 95% confidence, with the relative error decreasing exponentially with increasing flux magnitude across sites. Through the analysis and synthesis of a growing EC CH4 flux database, the controls on ecosystem CH4 fluxes can be better understood, used to inform and validate Earth system models, and reconcile differences between land surface model- and atmospheric-based estimates of CH4 emissions.

Analysis of the Diurnal, Weekly, and Seasonal Cycles and Annual Trends in Atmospheric CO2 and CH4 at Tower Network in Siberia from 2005 to 2016

We analyzed 12 years (2005–2016) of continuous measurements of atmospheric CO 2 and CH 4 concentrations made at nine tower observation sites in the Japan–Russia Siberian Tall Tower Inland Observation Network (JR-STATION), located in Siberia. Since the data are very noisy and have a low temporal resolution due to gaps in instrument operation, we used the recently developed Prophet model, which was designed to handle the common features of time series (multiple strong seasonalities, trend changes, outliers) and has a robust performance in the presence of missing data and trend shifts. By decomposing each sampled time-series into its major components (i.e., annual trend and seasonal, weekly, and hourly variation), we observed periodically changing patterns of tracer concentrations. Specifically, we detected multi-year variability of tracers and identified high-concentration events. The frequency of such events was found to vary throughout the year, reaching up to 20% of days for some months, while the number of such events was found to be different for CO 2 and CH 4 . An analysis of weather conditions showed that, in most cases, high-concentration events were caused by a temperature inversion and low wind speed. Additionally, wind directions were found to be different for high- and low-concentration events. For some sites, the wind direction indicated the location of strong local sources of CO 2 and CH 4 . As well as elucidating the seasonality of greenhouse gas concentrations, this study confirmed the potential of the Prophet model for detecting periodicity in environmental phenomena.

From NEON Field Sites to Data Portal: A Community Resource for Surface–Atmosphere Research Comes Online

AbstractThe National Ecological Observatory Network (NEON) is a multidecadal and continental-scale observatory with sites across the United States. Having entered its operational phase in 2018, NEON data products, software, and services become available to facilitate research on the impacts of climate change, land-use change, and invasive species. An essential component of NEON are its 47 tower sites, where eddy-covariance (EC) sensors are operated to determine the surface–atmosphere exchange of momentum, heat, water, and CO2. EC tower networks such as AmeriFlux, the Integrated Carbon Observation System (ICOS), and NEON are vital for providing the distributed observations to address interactions at the soil–vegetation–atmosphere interface. NEON represents the largest single-provider EC network globally, with standardized observations and data processing explicitly designed for intersite comparability and analysis of feedbacks across multiple spatial and temporal scales. Furthermore, EC is tightly integrated with soil, meteorology, atmospheric chemistry, isotope, phenology, and rich contextual observations such as airborne remote sensing and in situ sampling bouts. Here, we present an overview of NEON’s observational design, field operation, and data processing that yield community resources for the study of surface–atmosphere interactions. Near-real-time data products become available from the NEON Data Portal, and EC and meteorological data are ingested into AmeriFlux and FLUXNET globally harmonized data releases. Open-source software for reproducible, extensible, and portable data analysis includes the eddy4R family of R packages underlying the EC data product generation. These resources strive to integrate with existing infrastructures and networks, to suggest novel systemic solutions, and to synergize ongoing research efforts across science communities.

Influence of shield wires on geomagnetically induced currents in power systems

There is increasing interest in assessing the GIC risk to power systems with GIC disturbing events to power systems observed in many parts of the world. An essential step in GIC risk assessment is modeling of GIC. Many factors have been considered in the GIC network model, but the effect of shield wires is still not clear. In this paper, we investigate multiple ways in which shield wires can influence the GIC in the transmission lines, including: mutual inductance between the shield wire and the transmission line, voltage change in substation grounding resistance due to GIC flowing to/from the shield wire, the geoelectric field distortion due to GIC flowing to/from the shield wire through tower footings and the influence of shield wires on the effective substation grounding resistance. Typical values for each effect are calculated to determine the significance of the shield wire effect on GIC. For the situations considered, the effect of shield wires on the effective substation grounding resistance is the only one that significantly affects GIC in transformers. To include this effect in GIC modeling a simple formula is given for the resistance of a shield wire and tower network.

NEGRO TOWER: DOCUMENTATION, CONSERVATION, AND RESTORATION

Abstract. The Negro Tower or Arráez Tower, in El Algar (Cartagena, Spain), dates from 1585. This tower was part of an extensive network of watch and defense towers built along the coastline of Murcia between the sixteenth and seventeenth centuries. It is an inland rural tower, in visual contact with the coastal towers. Its objective was to receive and transfer warnings from onshore towers to protect the population, especially farmers. The tower is protected as a monument but is currently in a dilapidated state of conservation. Therefore, this building has been chosen as the object of an integral study, which can serve as a reliable basis for its correct conservation and restoration. Through historical analysis, light has been shed upon the evolution of the Tower from the social, cultural, and architectural points of view. The graphic documentation has been made using digital photogrammetry and a 3D laser scanner, compatible with classical topography. Based on all this information, an integral project of consolidation and volumetric recomposition of the tower has been proposed. As a result of this research, we intend to contribute to the conservation and recovery of the heritage value of the Tower, as well as the integration of the historic monument in its natural environment.

A Multiplatform Inversion Estimation of Statewide and Regional Methane Emissions in California during 2014-2016.

California methane (CH4) emissions are quantified for three years from two tower networks and one aircraft campaign. We used backward trajectory simulations and a mesoscale Bayesian inverse model, initialized by three inventories, to achieve the emission quantification. Results show total statewide CH4 emissions of 2.05 ± 0.26 (at 95% confidence) Tg/yr, which is 1.14 to 1.47 times greater than the anthropogenic emission estimates by California Air Resource Board (CARB). Some of differences could be biogenic emissions, superemitter point sources, and other episodic emissions which may not be completely included in the CARB inventory. San Joaquin Valley (SJV) has the largest CH4 emissions (0.94 ± 0.18 Tg/yr), followed by the South Coast Air Basin, the Sacramento Valley, and the San Francisco Bay Area at 0.39 ± 0.18, 0.21 ± 0.04, and 0.16 ± 0.05 Tg/yr, respectively. The dairy and oil/gas production sources in the SJV contribute 0.44 ± 0.36 and 0.22 ± 0.23 Tg CH4/yr, respectively. This study has important policy implications for regulatory programs, as it provides a thorough multiyear evaluation of the emissions inventory using independent atmospheric measurements and investigates the utility of a complementary multiplatform approach in understanding the spatial and temporal patterns of CH4 emissions in the state and identifies opportunities for the expansion and applications of the monitoring network.