scholarly journals GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses

2019 ◽  
Vol 12 (11) ◽  
pp. 4661-4679 ◽  
Author(s):  
Bin Cao ◽  
Xiaojing Quan ◽  
Nicholas Brown ◽  
Emilie Stewart-Jones ◽  
Stephan Gruber
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Ground Surface ◽  
Reanalysis Data ◽  
Meteorological Variables ◽  
Ensemble Simulations ◽  
Driving Time ◽  
Wide Range ◽  
Global Coverage ◽  
Uncertain Input

Abstract. Simulations of land-surface processes and phenomena often require driving time series of meteorological variables. Corresponding observations, however, are unavailable in most locations, even more so, when considering the duration, continuity and data quality required. Atmospheric reanalyses provide global coverage of relevant meteorological variables, but their use is largely restricted to grid-based studies. This is because technical challenges limit the ease with which reanalysis data can be applied to models at the site scale. We present the software toolkit GlobSim, which automates the downloading, interpolation and scaling of different reanalyses – currently ERA5, ERA-Interim, JRA-55 and MERRA-2 – to produce meteorological time series for user-defined point locations. The resulting data have consistent structure and units to efficiently support ensemble simulation. The utility of GlobSim is demonstrated using an application in permafrost research. We perform ensemble simulations of ground-surface temperature for 10 terrain types in a remote tundra area in northern Canada and compare the results with observations. Simulation results reproduced seasonal cycles and variation between terrain types well, demonstrating that GlobSim can support efficient land-surface simulations. Ensemble means often yielded better accuracy than individual simulations and ensemble ranges additionally provide indications of uncertainty arising from uncertain input. By improving the usability of reanalyses for research requiring time series of climate variables for point locations, GlobSim can enable a wide range of simulation studies and model evaluations that previously were impeded by technical hurdles in obtaining suitable data.

scholarly journals GlobSim (v1.0): Deriving meteorological time series for point locations from multiple global reanalyses

2019 ◽  
Author(s):  
Bin Cao ◽  
Xiaojing Quan ◽  
Nicholas Brown ◽  
Emilie Stewart-Jones ◽  
Stephan Gruber
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Ground Surface ◽  
Reanalysis Data ◽  
Meteorological Variables ◽  
Ensemble Simulations ◽  
Driving Time ◽  
Wide Range ◽  
Global Coverage ◽  
Uncertain Input

Abstract. Simulations of land-surface processes and phenomena often require driving time series of meteorological variables. Corresponding observations, however, are unavailable in most locations, even more so, when considering the duration, continuity and data quality required. Atmospheric reanalyses provide global coverage of relevant meteorological variables, but their use is largely restricted to grid-based studies. This is because technical challenges limit the ease with which reanalysis data can be applied to models at the site scale. We present the software toolkit GlobSim, which automates the downloading, interpolation and scaling of different reanalyses – currently ERA-5, ERA-Interim, JRA-55 and MERRA-2 – to produce meteorological time series for user-defined point locations. The resulting data have consistent structure and units to efficiently support ensemble simulation. The utility of GlobSim is demonstrated using an application in permafrost research. We perform ensemble simulations of ground-surface temperature for ten terrain types in a remote tundra area in northern Canada and compare the results with observations. Simulation results reproduced seasonal cycles and variation between terrain types well, demonstrating that GlobSim can support efficient land-surface simulations. Ensemble means often yielded better accuracy than individual simulation and ensemble ranges additionally provide indications of uncertainty arising from uncertain input. By improving the usability of reanalyses for research requiring time-series of climate variables for point locations, GlobSim can enable a wide range of simulation studies and model evaluations that previously were impeded by technical hurdles in obtaining suitable data.

scholarly journals LAND SUBSIDENCE MONITORING USING PS-InSAR TECHNIQUE FOR L-BAND SAR DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 995-997 ◽  
Author(s):  
S. Thapa ◽  
R. S. Chatterjee ◽  
K. B. Singh ◽  
D. Kumar
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Land Surface ◽  
Surface Deformation ◽  
Underground Mining ◽  
Ground Surface ◽  
Sar Interferometry ◽  
Persistent Scatterer Interferometry ◽  
Long Time ◽  
Subsidence Monitoring

Differential SAR-Interferometry (D-InSAR) is one of the potential source to measure land surface motion induced due to underground coal mining. However, this technique has many limitation such as atmospheric in homogeneities, spatial de-correlation, and temporal decorrelation. Persistent Scatterer Interferometry synthetic aperture radar (PS-InSAR) belongs to a family of time series InSAR technique, which utilizes the properties of some of the stable natural and anthropogenic targets which remain coherent over long time period. In this study PS-InSAR technique has been used to monitor land subsidence over selected location of Jharia Coal field which has been correlated with the ground levelling measurement. This time series deformation observed using PS InSAR helped us to understand the nature of the ground surface deformation due to underground mining activity.

scholarly journals Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products

2019 ◽  
Vol 11 (22) ◽  
pp. 2616 ◽  
Author(s):  
Stefan Mayr ◽  
Claudia Kuenzer ◽  
Ursula Gessner ◽  
Igor Klein ◽  
Martin Rutzinger
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Optical Sensors ◽  
Land Surface ◽  
Global Scale ◽  
Observation Time ◽  
Large Area ◽  
Validation Data ◽  
Information Products ◽  
Global Coverage

Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided.

scholarly journals Investigating Ground Subsidence and the Causes over the Whole Jiangsu Province, China Using Sentinel-1 SAR Data

2021 ◽  
Vol 13 (2) ◽  
pp. 179
Author(s):  
Yonghong Zhang ◽  
Hongan Wu ◽  
Mingju Li ◽  
Yonghui Kang ◽  
Zhong Lu
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Surface Deformation ◽  
Underground Mining ◽  
Ground Deformation ◽  
Ground Surface ◽  
Data Retrieval ◽  
Field Work ◽  
Jiangsu Province ◽  
Ground Subsidence

Interferometric synthetic aperture radar (InSAR) mapping of localized ground surface deformation has become an important tool to manage subsidence-related geohazards. However, monitoring land surface deformation using InSAR at high spatial resolution over a large region is still a formidable task. In this paper, we report a research on investigating ground subsidence and the causes over the entire 107, 200 km2 province of Jiangsu, China, using time-series InSAR. The Sentinel-1 Interferometric Wide-swath (IW) images of 6 frames are used to map ground subsidence over the whole province for the period 2016–2018. We present processing methodology in detail, with emphasis on the three-level co-registration scheme of S-1 data, retrieval of mean subsidence velocity (MSV) and subsidence time series, and mosaicking of multiple frames of results. The MSV and subsidence time series are generated for 9,276,214 selected coherent pixels (CPs) over the Jiangsu territory. Using 688 leveling measurements in evaluation, the derived MSV map of Jiangsu province shows an accuracy of 3.9 mm/year. Moreover, subsidence causes of the province are analyzed based on InSAR-derived subsidence characteristics, historical optical images, and field-work findings. Main factors accounting for the observed subsidence include: underground mining, groundwater withdrawal, soil consolidations of marine reclamation, and land-use transition from agricultural (paddy) to industrial land. This research demonstrates not only the capability of S-1 data in mapping ground deformation over wide areas in coastal and heavily vegetated region of China, but also the potential of inferring valuable knowledge from InSAR-derived results.

scholarly journals On the Predictability of Land Surface Fluxes from Meteorological Variables

2017 ◽  
Author(s):  
Ned Haughton ◽  
Gab Abramowitz ◽  
Andy J. Pitman
Keyword(s):  
Land Surface ◽  
Meteorological Data ◽  
A Priori ◽  
Predictive Performance ◽  
Empirical Models ◽  
Surface Fluxes ◽  
Meteorological Variables ◽  
Meteorological Forcing ◽  
Wide Range ◽  
Surface Models

Abstract. Previous research has shown that Land Surface Models (LSMs) are performing poorly when compared with rela- tively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appears to provide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that is used by LSMs for predicting land surface fluxes, by interrogating Fluxnet data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce, and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We re-analyse previously published LSM simulations, and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.

scholarly journals On the predictability of land surface fluxes from meteorological variables

2018 ◽  
Vol 11 (1) ◽  
pp. 195-212 ◽  
Author(s):  
Ned Haughton ◽  
Gab Abramowitz ◽  
Andy J. Pitman
Keyword(s):  
Land Surface ◽  
Meteorological Data ◽  
A Priori ◽  
Predictive Performance ◽  
Empirical Models ◽  
Surface Fluxes ◽  
Meteorological Variables ◽  
Meteorological Forcing ◽  
Wide Range ◽  
Surface Models

Abstract. Previous research has shown that land surface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear to provide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting land surface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.

Estimating time series of aerosol particle number concentrations in the five HEAPSS cities on the basis of measured air pollution and meteorological variables

2005 ◽  
Vol 39 (12) ◽  
pp. 2261-2273 ◽  
Author(s):  
Pentti Paatero ◽  
Pasi Aalto ◽  
Sally Picciotto ◽  
Tom Bellander ◽  
Gemma Castaño ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Aerosol Particle ◽  
Particle Number ◽  
Meteorological Variables

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

scholarly journals Quantification of the Environmental Impacts of Highway Construction Using Remote Sensing Approach

2021 ◽  
Vol 13 (7) ◽  
pp. 1340
Author(s):  
Shuailong Feng ◽  
Shuguang Liu ◽  
Lei Jing ◽  
Yu Zhu ◽  
Wende Yan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Environmental Impacts ◽  
Land Surface ◽  
Vegetation Index ◽  
Highway Construction ◽  
Temporal Changes ◽  
Moisture Index ◽  
Buffer Zones ◽  
Wide Range ◽  
Spatial And Temporal Changes

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.

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