scholarly journals Spatio-temporal approach to moving window block kriging of satellite data v1.0

2017 ◽  
Vol 10 (2) ◽  
pp. 709-720 ◽  
Author(s):  
Jovan M. Tadić ◽  
Xuemei Qiu ◽  
Scot Miller ◽  
Anna M. Michalak
Keyword(s):  
Global Scale ◽  
Variable Density ◽  
Satellite Observations ◽  
Moving Window ◽  
Block Kriging ◽  
Temporal Correlations ◽  
Environmental Properties ◽  
Level 3 ◽  
Spatio Temporal ◽  
The Difference

Abstract. Numerous existing satellites observe physical or environmental properties of the Earth system. Many of these satellites provide global-scale observations, but these observations are often sparse and noisy. By contrast, contiguous, global maps are often most useful to the scientific community (i.e., Level 3 products). We develop a spatio-temporal moving window block kriging method to create contiguous maps from sparse and/or noisy satellite observations. This approach exhibits several advantages over existing methods: (1) it allows for flexibility in setting the spatial resolution of the Level 3 map, (2) it is applicable to observations with variable density, (3) it produces a rigorous uncertainty estimate, (4) it exploits both spatial and temporal correlations in the data, and (5) it facilitates estimation in real time. Moreover, this approach only requires the assumption that the observable quantity exhibits spatial and temporal correlations that are inferable from the data. We test this method by creating Level 3 products from satellite observations of CO2 (XCO2) from the Greenhouse Gases Observing Satellite (GOSAT), CH4 (XCH4) from the Infrared Atmospheric Sounding Interferometer (IASI) and solar-induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2). We evaluate and analyze the difference in performance of spatio-temporal vs. recently developed spatial kriging methods.

scholarly journals Spatio-temporal approach to moving window block kriging of satellite data

2016 ◽  
Author(s):  
Jovan M. Tadić ◽  
Xuemei Qiu ◽  
Scot Miller ◽  
Anna M. Michalak
Keyword(s):  
Global Scale ◽  
Variable Density ◽  
Satellite Observations ◽  
Moving Window ◽  
Block Kriging ◽  
Temporal Correlations ◽  
Environmental Properties ◽  
Level 3 ◽  
Spatio Temporal ◽  
The Difference

Abstract. Numerous existing satellites observe physical or environmental properties of the Earth system. Many of these satellites provide global-scale observations, but these observations are often sparse and noisy. By contrast, contiguous, global maps are often most useful to the scientific community (i.e., level 3 products). We develop a spatiotemporal moving window block kriging method to create contiguous maps from sparse and/or noisy satellite observations. This approach exhibits several advantages over existing methods: 1) it allows for flexibility in setting the spatial resolution of the level 3 map, 2) it is applicable to observations with variable density, 3) it produces a rigorous uncertainty estimate, 4) it exploits both spatial and temporal correlations in the data, and 5) it facilitates estimation in real time. Moreover, this approach only requires a limited number of assumptions – that the observable quantity exhibits spatial and temporal correlations that are inferable from the data. We test this method by creating Level 3 products from satellite observations of CO2 (XCO2) from GOSAT, CH4 (XCH4) from IASI and solar-induced chlorophyll fluorescence (SIF) from GOME-2. We evaluate and analyze the difference in performance of spatio-temporal vs. recently developed spatial kriging methods.

scholarly journals Spatio-Temporal Validation of AIRS CO2 Observations Using GAW, HIPPO and TCCON

2020 ◽  
Vol 12 (21) ◽  
pp. 3583
Author(s):  
Hui Yang ◽  
Gefei Feng ◽  
Ru Xiang ◽  
Yunjing Xu ◽  
Yong Qin ◽  
...  
Keyword(s):  
Co2 Concentration ◽  
Satellite Observations ◽  
Total Carbon ◽  
Near Surface ◽  
Temporal Validation ◽  
Satellite Sensors ◽  
Spatio Temporal ◽  
The Difference ◽  
Carbon Dioxide Co2

Carbon dioxide (CO2) is a significant atmospheric greenhouse gas and its concentrations can be observed by in situ surface stations, aircraft flights and satellite sensors. This paper investigated the ability of the CO2 satellite observations to monitor, analyze and predict the horizontal and vertical distribution of atmospheric CO2 concentration at global scales. CO2 observations retrieved by an Atmospheric Infrared Sounder (AIRS) were inter-compared with the Global Atmosphere Watch Program (GAW) and HIAPER Pole-to-Pole Observations (HIPPOs), with reference to the measurements obtained using high-resolution ground-based Fourier Transform Spectrometers (FTS) in the Total Carbon Column Observing Network (TCCON) from near-surface level to the mid-to-high troposphere. After vertically integrating the AIRS-retrieved values with the column averaging kernels of TCCON measurements, the AIRS observations are spatio-temporally compared with HIPPO-integrated profiles in the mid-to-high troposphere. Five selected GAW stations are used for comparisons with TCCON sites near the surface of the Earth. The results of AIRS, TCCON (5–6 km), GAW and TCCON (1 km) CO2 measurements from 2007 to 2013 are compared, analyzed and discussed at their respective altitudes. The outcomes indicate that the difference of about 3.0 ppmv between AIRS and GAW or other highly accurate in situ surface measurements is mainly due to the different vertical altitudes, rather than the errors in the AIRS. The study reported here also explores the potential of AIRS satellite observations for analyzing the spatial distribution and seasonal variation of CO2 concentration at global scales.

scholarly journals Mapping of satellite Earth observations using moving window block kriging

2015 ◽  
Vol 8 (10) ◽  
pp. 3311-3319 ◽  
Author(s):  
J. M. Tadić ◽  
X. Qiu ◽  
V. Yadav ◽  
A. M. Michalak
Keyword(s):  
Quantitative Measure ◽  
Spatiotemporal Variability ◽  
Moving Window ◽  
Data Sampling ◽  
Block Kriging ◽  
Spatiotemporal Resolution ◽  
Earth Observations ◽  
Biogeochemical Models ◽  
Level 3 ◽  
System Properties

Abstract. Global gridded maps (a.k.a. Level 3 products) of Earth system properties observed by satellites are central to understanding the spatiotemporal variability of these properties. They also typically serve either as inputs into biogeochemical models or as independent data for evaluating such models. Spatial binning is a common method for generating contiguous maps, but this approach results in a loss of information, especially when the measurement noise is low relative to the degree of spatiotemporal variability. Such "binned" fields typically also lack a quantitative measure of uncertainty. Geostatistical mapping has previously been shown to make higher spatiotemporal resolution maps possible, and also provides a measure uncertainty associated with the gridded products. This study proposes a flexible moving window block kriging method that can be used as a tool for creating high spatiotemporal resolution maps from satellite data. It relies only on the assumption that the observed physical quantity exhibits spatial correlation that can be inferred from the observations. The method has several innovations relative to previously applied methods: (1) it provides flexibility in the spatial resolution of the contiguous maps, (2) it is applicable for physical quantities with varying spatiotemporal coverage (i.e., density of measurements) by utilizing a more general and versatile data sampling approach, and (3) it provides rigorous assessments of the uncertainty associated with the gridded products. The method is demonstrated by creating Level 3 products from observations of column-integrated carbon dioxide (XCO2) from the GOSAT (Greenhouse Gases Observing Satellite) satellite, and solar induced fluorescence (SIF) from the GOME-2 (Global Ozone Monitoring Experiment-2) instrument.

scholarly journals Analysing spatio-temporal patterns of the global NO<sub>2</sub>-distribution retrieved from GOME satellite observations using a generalized additive model

2009 ◽  
Vol 9 (2) ◽  
pp. 9367-9398
Author(s):  
M. Hayn ◽  
S. Beirle ◽  
F. A. Hamprecht ◽  
U. Platt ◽  
B. H. Menze ◽  
...  
Keyword(s):  
Temporal Trends ◽  
Temporal Patterns ◽  
Global Scale ◽  
Transport Processes ◽  
Additive Models ◽  
Satellite Observations ◽  
Nonparametric Model ◽  
Local Wind ◽  
Wind Fields ◽  
Spatio Temporal

Abstract. With the increasing availability of observations from different space-borne sensors, the joint analysis of observational data from multiple sources becomes more and more attractive. For such an analysis – oftentimes with little prior knowledge about local and global interactions between the different observational variables available – an explorative data-driven analysis of the remote sensing data may be of particular relevance. In the present work we used generalized additive models (GAM) in this task, in an exemplary study of spatio-temporal patterns in the tropospheric NO2-distribution derived from GOME satellite observations (1996 to 2001) at global scale. We modelled different temporal trends in the time series of the observed NO2, but focused on identifying correlations between NO2 and local wind fields. Here, our nonparametric modelling approach had several advantages over standard parametric models: While the model-based analysis allowed to test predefined hypotheses (assuming, for example, sinusoidal seasonal trends) only, the GAM allowed to learn functional relations between different observational variables directly from the data. This was of particular interest in the present task, as little was known about relations between the observed NO2 distribution and transport processes by local wind fields, and the formulation of general functional relationships to be tested remained difficult. We found the observed temporal trends – weekly, seasonal and linear changes – to be in overall good agreement with previous studies and alternative ways of data analysis. However, NO2 observations showed to be affected by wind-dominated processes over several areas, world wide. Here we were able to estimate the extent of areas affected by specific NO2 emission sources, and to highlight likely atmospheric transport pathways. Overall, using a nonparametric model provided favourable means for a rapid inspection of this large spatio-temporal data set,with less bias than parametric approaches, and allowing to visualize dynamical processes of the NO2 distribution at a global scale.

scholarly journals Analysing spatio-temporal patterns of the global NO<sub>2</sub>-distribution retrieved from GOME satellite observations using a generalized additive model

2009 ◽  
Vol 9 (17) ◽  
pp. 6459-6477 ◽  
Author(s):  
M. Hayn ◽  
S. Beirle ◽  
F. A. Hamprecht ◽  
U. Platt ◽  
B. H. Menze ◽  
...  
Keyword(s):  
Temporal Patterns ◽  
Global Scale ◽  
Parametric Model ◽  
Additive Models ◽  
Satellite Observations ◽  
Local Wind ◽  
Wind Fields ◽  
Data Set ◽  
Spatio Temporal ◽  
Non Parametric

Abstract. With the increasing availability of observational data from different sources at a global level, joint analysis of these data is becoming especially attractive. For such an analysis – oftentimes with little prior knowledge about local and global interactions between the different observational variables at hand – an exploratory, data-driven analysis of the data may be of particular relevance. In the present work we used generalized additive models (GAM) in an exemplary study of spatio-temporal patterns in the tropospheric NO2-distribution derived from GOME satellite observations (1996 to 2001) at global scale. We focused on identifying correlations between NO2 and local wind fields, a quantity which is of particular interest in the analysis of spatio-temporal interactions. Formulating general functional, parametric relationships between the observed NO2 distribution and local wind fields, however, is difficult – if not impossible. So, rather than following a model-based analysis testing the data for predefined hypotheses (assuming, for example, sinusoidal seasonal trends), we used a GAM with non-parametric model terms to learn this functional relationship between NO2 and wind directly from the data. The NO2 observations showed to be affected by wind-dominated processes over large areas. We estimated the extent of areas affected by specific NO2 emission sources, and were able to highlight likely atmospheric transport "pathways". General temporal trends which were also part of our model – weekly, seasonal and linear changes – showed to be in good agreement with previous studies and alternative ways of analysing the time series. Overall, using a non-parametric model provided favorable means for a rapid inspection of this large spatio-temporal NO2 data set, with less bias than parametric approaches, and allowing to visualize dynamical processes of the NO2 distribution at a global scale.

scholarly journals Amazonian terrestrial water balance inferred from satellite-derived water vapor isotopes

2021 ◽  
Author(s):  
Mingjie Shi ◽  
John Worden ◽  
Adriana Bailey ◽  
David Noone ◽  
Camille Risi ◽  
...  
Keyword(s):  
Water Vapor ◽  
Water Balance ◽  
Temporal Distribution ◽  
Satellite Observations ◽  
Amazon Forest ◽  
Tightly Coupled ◽  
Terrestrial Water ◽  
Spatio Temporal ◽  
The Difference ◽  
Variable Bias

Abstract The evolution of the Amazon forest is tightly coupled to its terrestrial water balance (evapotranspiration minus precipitation, or ET-P), as an increase in ET-P reduces soil moisture, increasing water stress. However, large differences of ~ 50% between current monthly estimates of ET-P make it challenging to confidently quantify its spatio-temporal distribution and evolution. Here, we show that new satellite observations of the HDO/H2O ratio of water vapor, spanning 2003 to 2020, constrain estimates of the Amazon water balance with monthly precision of ~ 20%. The HDO/H2O ratio of water vapor is sensitive to the difference between ET and P, rather than to either flux alone, because lighter isotopes preferentially evaporate and heavier isotopes preferentially condense. Consequently, variable bias and sensitivity errors that result from combining different ET and precipitation products are minimized with this proxy. Our analysis demonstrates these data can quantify the spatial patterns of Amazon water balance from monthly to interannual time scales.

scholarly journals Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling

2017 ◽  
Vol 21 (12) ◽  
pp. 6201-6217 ◽  
Author(s):  
Hylke E. Beck ◽  
Noemi Vergopolan ◽  
Ming Pan ◽  
Vincenzo Levizzani ◽  
Albert I. J. M. van Dijk ◽  
...  
Keyword(s):  
Hydrological Modeling ◽  
Comprehensive Evaluation ◽  
Microwave Remote Sensing ◽  
Global Scale ◽  
Estimation Accuracy ◽  
Near Surface ◽  
Temporal Correlations ◽  
Gauge Data ◽  
Spatio Temporal ◽  
Thermal Infrared Imagery

Abstract. We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( <  50 000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates.

scholarly journals Mapping of satellite Earth observations using moving window block kriging

2014 ◽  
Vol 7 (4) ◽  
pp. 5381-5405 ◽  
Author(s):  
J. M. Tadić ◽  
X. Qiu ◽  
V. Yadav ◽  
A. M. Michalak
Keyword(s):  
Quantitative Measure ◽  
Spatiotemporal Variability ◽  
Moving Window ◽  
Data Sampling ◽  
Block Kriging ◽  
Spatiotemporal Resolution ◽  
Earth Observations ◽  
Biogeochemical Models ◽  
Level 3 ◽  
System Properties

Abstract. Global gridded maps (a.k.a. Level 3 products) of Earth system properties observed by satellites are central to understanding the spatiotemporal variability of these properties. They also typically serve either as inputs into biogeochemical models, or as independent data for evaluating such models. Spatial binning is a common method for generating contiguous maps, but this approach results in a loss of information, especially when the measurement noise is low relative to the degree of spatiotemporal variability. Such "binned" fields typically also lack a quantitative measure of uncertainty. Geostatistical mapping has previously been shown to make higher spatiotemporal resolution maps possible, and also provides a measure of the uncertainty associated with the gridded products. This study proposes a flexible moving window block kriging method that can be used as a tool for creating high spatiotemporal resolution maps from satellite data. It relies only on the assumption that the observed physical quantity exhibits spatial correlation that can be inferred from the observations. The method has several innovations relative to previously applied methods: (1) it provides flexibility in the spatial resolution of the contiguous maps (2) it is applicable for physical quantities with varying spatiotemporal coverage (i.e., density of measurements) by utilizing a more general and versatile data sampling approach, and (3) it provides rigorous assessments of the uncertainty associated with the gridded products. The method is demonstrated by creating Level 3 products from observations of column-integrated carbon dioxide (XCO2) from the GOSAT satellite, and solar induced fluorescence (SIF) from the GOME-2 instrument.

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