Basic Statistics of Spatio-Temporal Variations of sea Surface Height Derived From Topex Altimeter Data
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Topex altimeter Sea Surface Height↗
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Statistical Analysis of Spatio-temporal Variations of Sea Surface Height Observed by Topex Altimeter
Using non-gridded Topex altimeter data, high resolution 2-d power spectra and spatio-temporal autocorrelation functions of sea surface height (SSH) variations are estimated and employed for studying anisotropic SSH fields varying in a broad range of scales.
SSH fields spatio-temporal Rossby waves North Atla↗
A Statistical Perspective on Climate Informatics
No abstract available
spatio-temporal statistical modeling↗
Space-Time Data fusion for Remote Sensing Applications
NASA has been collecting massive amounts of remote sensing data about Earth's systems for more than a decade. Missions are selected to be complementary in quantities measured, retrieval techniques, and sampling characteristics, so these datasets are highly synergistic. To fully exploit this, a rigorous methodology for combining data with heterogeneous sampling characteristics is required. For scientific purposes, the methodology must also provide quantitative measures of uncertainty that propagate input-data uncertainty appropriately. We view this as a statistical inference problem. The true but notdirectly- observed quantities form a vector-valued field continuous in space and time. Our goal is to infer those true values or some function of them, and provide to uncertainty quantification for those inferences. We use a spatiotemporal statistical model that relates the unobserved quantities of interest at point-level to the spatially aggregated, observed data. We describe and illustrate our method using CO2 data from two NASA data sets.
carbon dioxide↗
Global-scale modes of surface temperature variability on interannual to century timescales
Using 100 years of global temperature anomaly data, we have performed a singluar value decomposition of temperature variations in narrow frequency bands to isolate coherent spatio-temporal modes of global climate variability. Statistical significance is determined from confidence limits obtained by Monte Carlo simulations. Secular variance is dominated by a globally coherent trend; with nearly all grid points warming in phase at varying amplitude. A smaller, but significant, share of the secular variance corresponds to a pattern dominated by warming and subsequent cooling in the high latitude North Atlantic with a roughly centennial timescale. Spatial patterns associated with significant peaks in variance within a broad period range from 2.8 to 5.7 years exhibit characteristic El Nino-Southern Oscillation (ENSO) patterns. A recent transition to a regime of higher ENSO frequency is suggested by our analysis. An interdecadal mode in the 15-to-18 years period and a mode centered at 7-to-8 years period both exhibit predominantly a North Atlantic Oscillation (NAO) temperature pattern. A potentially significant decadal mode centered on 11-to-12 years period also exhibits an NAO temperature pattern and may be modulated by the century-scale North Atlantic variability.
Mann, Michael E.↗
A Spatio-Temporal Approach for Global Validation and Analysis of MODIS Aerosol Products
With the launch of the MODIS sensor on the Terra spacecraft, new data sets of the global distribution and properties of aerosol are being retrieved, and need to be validated and analyzed. A system has been put in place to generate spatial statistics (mean, standard deviation, direction and rate of spatial variation, and spatial correlation coefficient) of the MODIS aerosol parameters over more than 100 validation sites spread around the globe. Corresponding statistics are also computed from temporal subsets of AERONET-derived aerosol data. The means and standard deviations of identical parameters from MOMS and AERONET are compared. Although, their means compare favorably, their standard deviations reveal some influence of surface effects on the MODIS aerosol retrievals over land, especially at low aerosol loading. The direction and rate of spatial variation from MODIS are used to study the spatial distribution of aerosols at various locations either individually or comparatively. This paper introduces the methodology for generating and analyzing the data sets used by the two MODIS aerosol validation papers in this issue.
Ichoku, Charles↗
Continental Spatio-Temporal Data Analysis with Linear Spectral Mixture Model Using FOSS
This work demonstrates the development and implementation of a Fully Constrained Least Squares (FCLS) unmixing model developed in C++ programming language with OpenCV package and boost C++ libraries in the NASA Earth Exchange (NEX). Visualization of the results is supported by GRASS GIS and statistical analysis is carried in R in a Linux system environment. FCLS was first tested on computer simulated data with Gaussian noise of various signal-to-noise ratio, and Landsat data of an agricultural scenario and an urban environment using a set of global end members of substrate (soils, sediments, rocks, and non-photosynthetic vegetation), vegetation that includes green photosynthetic plants and dark objects which encompasses absorptive substrate materials, clear water, deep shadows, etc. For the agricultural scenario, a spectrally diverse collection of 11 scenes of Level 1 terrain corrected, cloud free Landsat-5 TM data of Fresno, California, USA were unmixed and the results were validated with the corresponding ground data. To study an urbanized landscape, a clear sky Landsat-5 TM data were unmixed and validated with coincident World View-2 abundance maps (of 2 m spatial resolution) for an area of San Francisco, California, USA. The results were evaluated using descriptive statistics, correlation coefficient, RMSE, probability of success, boxplot and bivariate distribution function. Finally, FCLS was used for sub-pixel land cover analysis of the monthly WELD (Wen-enabled Landsat data) repository from 2008 to 2011 of North America. The abundance maps in conjunction with DMSP-OLS nighttime lights data were used to extract the urban land cover features and analyze their spatial-temporal growth.
Landsat Satellites↗
Evaluation of the Relative Contribution of Observing Systems in Reanalyses: Aircraft Temperature Bias and Analysis Innovations
Reanalyses have become important sources of data in weather and climate research. While observations are the most crucial component of the systems, few research projects consider carefully the multitudes of assimilated observations and their impact on the results. This is partly due to the diversity of observations and their individual complexity, but also due to the unfriendly nature of the data formats. Here, we discuss the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) and a companion dataset, the Gridded Innovations and Observations (GIO). GIO is simply a post-processing of the assimilated observations and their innovations (forecast error and analysis error) to a common spatio-temporal grid, following that of the MERRA analysis fields. This data includes in situ, retrieved and radiance observations that are assimilated and used in the reanalysis. While all these disparate observations and statistics are in a uniform easily accessible format, there are some limitations. Similar observations are binned to the grid, so that multiple observations are combined in the gridding process. The data is then implicitly thinned. Some details in the meta data may also be lost (e.g. aircraft or station ID). Nonetheless, the gridded observations should provide easy access to all the observations input to the reanalysis. To provide an example of the GIO data, a case study evaluating observing systems over the United States and statistics is presented, and demonstrates the evaluation of the observations and the data assimilation. The GIO data is used to collocate 200mb Radiosonde and Aircraft temperature measurements from 1979-2009. A known warm bias of the aircraft measurements is apparent compared to the radiosonde data. However, when larger quantities of aircraft data are available, they dominate the analysis and the radiosonde data become biased against the forecast. When AMSU radiances become available the radiosonde and aircraft analysis and forecast error take on an annual cycle. While this supports results of previous work that recommend bias corrections for the aircraft measurements, the interactions with AMSU radiances will also require further investigation. This also provides an example for reanalysis users in examining the available observations and their impact on the analysis. GIO data is presently available alongside the MERRA reanalysis.
Bosilovich, Michael G.↗
Spatial Statistical Data Fusion for Remote Sensing Applications
No abstract available
remote sensing data↗
Air Pollution Scenario over Pakistan: Characterization and Ranking of Extremely Polluted Cities using Long-Term Concentrations of Aerosols and Trace Gases
Pakistan ranks third in the world in terms of mortality attributable to air pollution, with aerosol mass concentrations (PM2.5) consistently well above WHO (World Health Organization) air quality guidelines (AQG). However, regulation is dependent on a sparse network of air quality monitoring stations and insufficient ground data. This study utilizes long-term observations of aerosols and trace gases to characterize and rank the air pollution scenarios and pollution characteristics of 80 selected cities in Pakistan. Datasets used include (1) the Aqua and Terra (AquaTerra) MODIS (Moderate Resolution Imaging Spectroradiometer) Level 2 Collection 6.1 merged Dark Target and Deep Blue (DTB) aerosol optical depth (AOD) retrieval products; (2) the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis PM1, PM2.5, and PM10 data; (3) the MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) reanalysis PM2.5 data, (4) the OMI (Ozone Monitoring Instrument) tropospheric vertical column density (TVCD) of nitrogen dioxide (NO2), and VCD of sulfur dioxide (SO2) in the Planetary Boundary Layer (PBL), (5) the VIIRS (Visible Infrared Imaging Radiometer Suite) Nighttime Lights data, (6) MODIS Collection 6 Version 2 global monthly fire location data (MCD14ML), (7) population density, (8) MODIS Level 3 Collection 6 land cover types, (9) AERONET (AErosol RObotic NETwork) Version 3 Level 2.0 data, and (10) ground-based PM2.5 concentrations from air quality monitoring stations. Potential Source Contribution Function (PSCF) analyses were performed by integrating with ground-based PM2.5 concentrations and the NOAA (National Oceanic and Atmospheric Administration) HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) air parcel back trajectories to identify potential pollution source areas which are responsible for extreme air pollution in Pakistan. Results show that the ranking of the top polluted cities depends on the type of pollutant considered and the metric used. For example, Jhang, Multan, and Vehari were characterized as the top three polluted cities in Pakistan when considering AquaTerra DTB AOD products; for PM1, PM2.5, and PM10 Lahore, Gujranwala, and Okara were the top three; for tropospheric NO2 VCD Lahore, Rawalpindi, and Islamabad and for PBL SO2 VCD Lahore, Mirpur, and Gujranwala. The results demonstrate that Pakistan’s entire population has been exposed to high PM2.5 concentrations for many years, with a mean annual value of 54.7 μg/cu. m, over all Pakistan from 2003 to 2020. This value exceeds Pakistan’s National Environmental Quality Standards (Pak-NEQS, i.e., <15 μg/cu. m annual mean) for ambient air defined by the Pakistan Environmental Protection Agency (Pak-EPA) as well as the WHO Interim Target-1 (i.e., mean annual PM2.5 <35 μg/cu. m). The spatial analyses of the concentrations of aerosols and trace gases in terms of population density, nighttime lights, land cover types, and fire location data, and the PSCF analysis indicate that Pakistan’s air quality is strongly affected by anthropogenic sources inside of Pakistan, with contributions from surrounding countries. Statistically significant positive (increasing) trends in PM1, PM2.5, PM10, tropospheric NO2 VCD, and SO2 VCD were observed in ~89%, ~67%, ~48%, 91%, and ~88% of the Pakistani cities (80 cities), respectively. This comprehensive analysis of aerosol and trace gas levels, their characteristics in spatio-temporal domains, and their trends over Pakistan, is the first of its kind. Results will be helpful to the Ministry of Climate Change (Government of Pakistan), Pak-EPA, SUPARCO (Pakistan Space and Upper Atmosphere Research Commission), policymakers, and the local research community to mitigate air pollution and its effects on human health.
Muhammad Bilal↗
Data Assimilation Into a Coupled Ocean Atmosphere Model: Application to the 1997-1998 El Nino
As part of JPL's ocean data assimilation effort to study ocean circulation and seasonal-interannual climate variability, sea level anomaly observed by TOPEX altimeter, together with sea surface temperature and wind stress data, are assimilated into a simple coupled ocean atmosphere model of the tropical Pacific. Model-data consistency is examined. Impact of the assimilation (as initialization) on El Nino Southern Oscillation (ENSO) forecasts is evaluated. The coupled model consists of a shallow water component with two baroclinic modes, an Ekman shear layer, a simplified mixed-layer temperature equation, and a statistical atmosphere based on dominant correlations between historical surface temperature and wind stress anomaly data. The adjoins method is used to fit the coupled model to the data over various six-month periods from late 1996 to early 1998 by optimally adjusting the initial state, model parameters, and basis functions of the statistical atmosphere. On average, the coupled model can be fitted to the data to approximately within the data and representation errors (5 cm, 0.5 C, and 10 sq m/sq m for sea level, surface temperature, and pseudo wind stress anomalies, respectively). The estimated fields resemble observed spatio-temporal structure reasonably well. Hindcasts/forecasts of the 1997/1998 El Nino initialized from forced estimated ocean states and parameters are much more realistic than those simply initialized from ocean states (see figure below). In particular, the ability of the model to produce significant warming beyond the initial state is dramatically improved. Parameter estimation, which compensates for some model errors, is found to be important to obtaining better fits of the model to data and to improving forecasts.
Lee, Tong↗
Hybrid phenology matching model for robust crop phenological retrieval
Crop phenology regulates seasonal agroecosystem carbon, water, and energy exchanges, and is a key component in empirical and process-based crop models for simulating biogeochemical cycles of farmlands, assessing gross and net primary production, and forecasting the crop yield. The advances in phenology matching models provide a feasible means to monitor crop phenological progress using remote sensing observations, with a priori information of reference shapes and reference phenological transition dates. Yet the underlying geometrical scaling assumption of models, together with the challenge in defining phenological references, hinders the applicability of phenology matching in crop phenological studies. The objective of this study is to develop a novel hybrid phenology matching model to robustly retrieve a diverse spectrum of crop phenological stages using satellite time series. The devised hybrid model leverages the complementary strengths of phenometric extraction methods and phenology matching models. It relaxes the geometrical scaling assumption and can characterize key phenological stages of crop cycles, ranging from farming practice-relevant stages (e.g., planted and harvested) to crop development stages (e.g., emerged and mature). To systematically evaluate the influence of phenological references on phenology matching, four representative phenological reference scenarios under varying levels of phenological calibrations in terms of time and space are further designed with publicly accessible phenological information. The results indicate that the hybrid phenology matching model can achieve high accuracies for estimating corn and soybean phenological growth stages in Illinois, particularly with the year- and region-adjusted phenological reference (R-squared higher than 0.9 and RMSE less than 5 days for most phenological stages). The inter-annual and regional phenological patterns characterized by the hybrid model correspond well with those in the crop progress reports (CPRs) from the USDA National Agricultural Statistics Service (NASS). Compared to the benchmark phenology matching model, the hybrid model is more robust to the decreasing levels of phenological reference calibrations, and is particularly advantageous in retrieving crop early phenological stages (e.g., planted and emerged stages) when the phenological reference information is limited. This innovative hybrid phenology matching model, together with CPR-enabled phenological reference calibrations, holds 3 considerable promise in revealing spatio-temporal patterns of crop phenology over extended geographical regions.
Phenology↗
Satellite Remote Sensing of Snow/Ice Albedo over the Himalayas
The Himalayan glaciers and snowpacks play an important role in the hydrological cycle over Asia. The seasonal snow melt from the Himalayan glaciers and snowpacks is one of the key elements to the livelihood of the downstream densely populated regions of South Asia. During the pre-monsoon season (April-May-June), South Asia not only experiences the reversal of the regional meridional tropospheric temperature gradient (i.e., the onset of the summer monsoon), but also is being bombarded by dry westerly airmass that transports mineral dust from various Southwest Asian desert and arid regions into the Indo-Gangetic Plains in northern India. Mixed with heavy anthropogenic pollution, mineral dust constitutes the bulk of regional aerosol loading and forms an extensive and vertically extended brown haze lapping against the southern slopes of the Himalayas. Episodic dust plumes are advected over the Himalayas, and are discernible in satellite imagery, resulting in dust-capped snow surface. Motivated by the potential implications of accelerated snowmelt, we examine the changes in radiative energetics induced by aerosol transport over the Himalayan snow cover by utilizing space borne observations. Our objective lies in the investigation of potential impacts of aerosol solar absorption on the Top-of-Atmosphere (TOA) spectral reflectivity and the broadband albedo, and hence the accelerated snowmelt, particularly in the western Himalayas. Lambertian Equivalent Reflectivity (LER) in the visible and near-infrared wavelengths, derived from Moderate Resolution Imaging Spectroradiometer radiances, is used to generate statistics for determining perturbation caused due to dust layer over snow surface in over ten years of continuous observations. Case studies indicate significant reduction of LER ranging from 5 to 8% in the 412-860nm spectra. Broadband flux observations, from the Clouds and the Earth's Radiant Energy System, are also used to investigate changes in shortwave TOA flux over dust-laden and dust-free snow covered regions. Additionally, spatio-temporal and intra-seasonal variations of LER, along with snow cover information, are used to characterize the seasonal melt pattern and thus to distinguish the outstanding aerosol-induced snowmelt signal. Results from this observational work are expected to provide better understanding of the radiative impact of aerosols over snow surface, especially its role in the Himalayan hydro-glacialogical variability.
Hsu, N. Christina↗
OMMYDCLD: a New A-train Cloud Product that Co-locates OMI and MODIS Cloud and Radiance Parameters onto the OMI Footprint
Clouds cover approximately 60% of the earth's surface. When obscuring the satellite's field of view (FOV), clouds complicate the retrieval of ozone, trace gases and aerosols from data collected by earth observing satellites. Cloud properties associated with optical thickness, cloud pressure, water phase, drop size distribution (DSD), cloud fraction, vertical and areal extent can also change significantly over short spatio-temporal scales. The radiative transfer models used to retrieve column estimates of atmospheric constituents typically do not account for all these properties and their variations. The OMI science team is preparing to release a new data product, OMMYDCLD, which combines the cloud information from sensors on board two earth observing satellites in the NASA A-Train: Aura/OMI and Aqua/MODIS. OMMYDCLD co-locates high resolution cloud and radiance information from MODIS onto the much larger OMI pixel and combines it with parameters derived from the two other OMI cloud products: OMCLDRR and OMCLDO2. The product includes histograms for MODIS scientific data sets (SDS) provided at 1 km resolution. The statistics of key data fields - such as effective particle radius, cloud optical thickness and cloud water path - are further separated into liquid and ice categories using the optical and IR phase information. OMMYDCLD offers users of OMI data cloud information that will be useful for carrying out OMI calibration work, multi-year studies of cloud vertical structure and in the identification and classification of multi-layer clouds.
OMMYDCLD↗
Constraints on Determining the Eruption Style and Composition of Terrestrial Lavas from Space
The surface temperatures of active lavas relate to cooling rates, chemistry, and eruption style. We analyzed 61 hyperspectral satellite images acquired by the National Aeronautics and Space Administration s Earth Observing-1 (EO-1) Hyperion imaging spectrometer to document the surface temperature distributions of active lavas erupted at 13 volcanoes. Images were selected to encompass the range of common lava eruption styles, specifically, lava fountains, flows, lakes, and domes. Our results reveal temperature distributions for terrestrial lavas that correlate with composition (i.e., a statistically significant difference in the highest temperatures retrieved for mafic lavas and intermediate and felsic lavas) and eruption style. Maximum temperatures observed for mafi c lavas are approx.200 C higher than for intermediate and felsic lavas. All eruption styles exhibit a low-temperature mode at approx.300 C; lava fountains and 'a' a flows also exhibit a higher-temperature mode at approx.700 C. The observed differences between the temperatures are consistent with the contrasting rates at which the lava surfaces are thermally renewed. Eruption styles that allow persistent and pervasive thermal renewal of the lava surface (e.g., fractured crusts on channel-fed 'a' a flows) exhibit a bimodal temperature distribution; eruption styles that do not (e.g., the continuous skin of pahoehoe lavas) exhibit a single mode. We conclude that insights into composition and eruption style can only be gained remotely by analyzing a large spatio-temporal sample of data. This has implications for determining composition and eruption style at the Jovian moon Io, for which no in situ validation is available.
Wright, Robert↗
How Well do Multisatellite Products Capture the Space-Time Dynamics of Precipitation? Part II: Building an Error Model Through Spectral System Identification
Satellite precipitation products, as all quantitative estimates, come with some inherent degree of uncertainty. To associate a quantitative value of the uncertainty to each individual estimate, error modeling is necessary. Most of the error models proposed so far compute the uncertainty as a function of precipitation intensity only, and only at one specific spatio-temporal scale. We propose a spectral error model which accounts for the neighboring space-time dynamics of precipitation into the uncertainty quantification. Systematic distortions of the precipitation signal and random errors are characterized distinctively in every frequency-wavenumber band in the Fourier domain, to accurately characterize error across scales. The systematic distortions are represented as a deterministic space-time linear filtering term. The random errors are represented as a non-stationary additive noise. The spectral error model is applied to the IMERG multi satellite precipitation product and its parameters are estimated empirically through a system identification approach using the GV-MRMS gauge-radar measurements as reference (“truth”) over the eastern United States. The filtering term is found to be essentially low-pass. While traditional error models attribute most of the error variance to random errors, it is found here that the systematic filtering term explains 48% of the error variance at the native resolution of IMERG. This fact confirms that, at high resolution, filtering effects in satellite precipitation products cannot be ignored, and that the error cannot be represented as a purely random additive or multiplicative term. An important consequence is that precipitation estimates derived from totally different sources shall not be expected to automatically have statistically independent errors.
Precipitation↗