Satellite Observation Data Research Articles

Stability of the Optimal Solution to a Problem of Variational Assimilation with Error Covariance Matrices of Observational Data for a Sea Thermodynamics Model

A mathematical model of sea thermodynamics developed at the Institute of Numerical Mathematics of the Russian Academy of Sciences is considered. A problem of variational assimilation of daily-averaged sea surface temperature (SST) data with observational error covariance matrices is formulated and investigated. On the basis of variational assimilation of satellite observation data, an inverse problem of restoring the heat flux on the sea surface is solved. The stability of the optimal solution to the problem of variational data assimilation is studied, and the results of numerical experiments with the model for Baltic Sea dynamics are presented.

A global space-based stratospheric aerosol climatology: 1979–2016

Abstract. We describe the construction of a continuous 38-year record of stratospheric aerosol optical properties. The Global Space-based Stratospheric Aerosol Climatology, or GloSSAC, provided the input data to the construction of the Climate Model Intercomparison Project stratospheric aerosol forcing data set (1979–2014) and we have extended it through 2016 following an identical process. GloSSAC focuses on the Stratospheric Aerosol and Gas Experiment (SAGE) series of instruments through mid-2005, and on the Optical Spectrograph and InfraRed Imager System (OSIRIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data thereafter. We also use data from other space instruments and from ground-based, air, and balloon borne instruments to fill in key gaps in the data set. The end result is a global and gap-free data set focused on aerosol extinction coefficient at 525 and 1020 nm and other parameters on an “as available” basis. For the primary data sets, we developed a new method for filling the post-Pinatubo eruption data gap for 1991–1993 based on data from the Cryogenic Limb Array Etalon Spectrometer. In addition, we developed a new method for populating wintertime high latitudes during the SAGE period employing a latitude-equivalent latitude conversion process that greatly improves the depiction of aerosol at high latitudes compared to earlier similar efforts. We report data in the troposphere only when and where it is available. This is primarily during the SAGE II period except for the most enhanced part of the Pinatubo period. It is likely that the upper troposphere during Pinatubo was greatly enhanced over non-volcanic periods and that domain remains substantially under-characterized. We note that aerosol levels during the OSIRIS/CALIPSO period in the lower stratosphere at mid- and high latitudes is routinely higher than what we observed during the SAGE II period. While this period had nearly continuous low-level volcanic activity, it is possible that the enhancement in part reflects deficiencies in the data set. We also expended substantial effort to quality assess the data set and the product is by far the best we have produced. GloSSAC version 1.0 is available in netCDF format at the NASA Atmospheric Data Center at https://eosweb.larc.nasa.gov/. GloSSAC users should cite this paper and the data set DOI (https://doi.org/10.5067/GloSSAC-L3-V1.0).

Object‐oriented soil erosion modelling: A possible paradigm shift from potential to actual risk assessments in agricultural environments

AbstractOver the last 2 decades, geospatial technologies such as Geographic Information System and spatial interpolation methods have facilitated the development of increasingly accurate spatially explicit assessments of soil erosion. Despite these advances, current modelling approaches rest on (a) an insufficient definition of the proportion of arable land that is exploited for crop production and (b) a neglect of the intra‐annual variability of soil cover conditions in arable land. To overcome these inaccuracies, this study introduces a novel spatio‐temporal approach to compute an enhanced cover‐management factor (C) for revised universal soil loss equation‐based models. It combines highly accurate agricultural parcel information contained in the Land Parcel Identification System with an object‐oriented Landsat imagery classification technique to assess spatial conditions and interannual variability of soil cover conditions at field scale. With its strong link to Land Parcel Identification System and Earth observation satellite data, the approach documents an unprecedented representation of farming operations. This opens the door for the transition from the currently used potential soil erosion risk assessments towards the assessment of the actual soil erosion risk. Testing this method in a medium‐size catchment located in the Swiss Plateau (Upper Enziwigger River Catchment), this study lays an important foundation for the application of the very same methods for large‐scale or even pan‐European applications. Soil loss rates modelled in this study were compared with the insights gained from emerging techniques to differentiate sediment source contribution through compound‐specific isotope analysis on river sediments. The presented technique is adaptable beyond revised universal soil loss equation‐type soil erosion models.

Differences in the fractions of ice clouds between eastern and western parts of Eurasian continent using CALIPSO in January 2007

We investigated the difference in the fraction of ice (water) cloud bins between eastern and western parts of Eurasia using Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data in January 2007. The fraction of ice cloud bins between −25 and 0 °C was clearly larger in eastern Eurasia than in western Eurasia. A significantly increased (about 20–30%) fraction of ice‐containing clouds is observed in eastern Eurasia in the cloud top temperatures range from −30 to −15 °C. The difference in ice cloud bin fractions between eastern and western Eurasia was remarkable between −20 and −5 °C, being about 20% greater in eastern Eurasia. The fraction of ice cloud bins in the lower troposphere (below 3 km) was larger in eastern Eurasia than in western Eurasia. These results indicate that the ice formation process was more promoted in the lower troposphere in eastern Eurasia than in western Eurasia. This is the first time such results have been obtained from cloud internal structure observations using the CALIPSO active sensor.

Origins of Eddy Kinetic Energy in the Bay of Bengal

AbstractBy analyzing satellite observational data and ocean general circulation model experiments, this study investigates the key processes that determine the spatial distribution and seasonality of intraseasonal eddy kinetic energy (EKE) within the Bay of Bengal (BOB). It is revealed that a complicated mechanism involving both local and remote wind forcing and ocean internal instability is responsible for the generation and modulation of EKE in this region. High‐level EKE mainly resides in four regions: east of Sri Lanka (Region 1), the western BOB (Region 2), northwest of Sumatra (Region 3), and the coastal rim of the BOB (Region 4). The high EKE levels in Regions 1 and 2 are predominantly produced by ocean internal instability, which contributes 90% and 79%, respectively. Prominent seasonality is also observed in these two regions, with higher EKE levels in boreal spring and fall due to enhanced instability of the East Indian Coast Current and the Southwest Monsoon Current, respectively. In contrast, ocean internal instability contributes 49% and 52% of the total EKE in Regions 3 and 4, respectively, whereas the atmospheric forcing of intraseasonal oscillations (ISOs) also plays an important role. ISOs produce EKE mainly through wind stress, involving both the remote effect of equatorial winds and the local effect of monsoonal winds. Equatorial‐origin wave signals significantly enhance the EKE levels in Regions 3 and 4, in the form of reflected Rossby waves and coastal Kelvin waves, respectively. The local wind forcing effect through Ekman pumping also has a significant contribution in Regions 3 and 4 (24% and 22%, respectively).

Investigating slow-moving landslides in the Zhouqu region of China using InSAR time series

In the Zhouqu region (Gansu, China), landslide distribution and activity exploits geological weaknesses in the fault-controlled belt of low-grade metamorphic rocks of the Bailong valley and severely impacts lives and livelihoods in this region. Landslides reactivated by the Wenchuan 2008 earthquake and debris flows triggered by rainfall, such as the 2010 Zhouqu debris flow, have caused more than 1700 casualties and estimated economic losses of some US$0.4 billion. Earthflows presently cover some 79% of the total landslide area and have exerted a strong influence on landscape dynamics and evolution in this region. In this study, we use multi-temporal Advanced Land Observing Satellite and Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data and time series interferometric synthetic aperture radar to investigate slow-moving landslides in a mountainous region with steep topography for the period December 2007–August 2010 using the Small Baseline Subsets (SBAS) technique. This enabled the identification of 11 active earthflows, 19 active landslides with deformation rates exceeding 100 mm/year and 20 new instabilities added into the pre-existing landslide inventory map. The activity of these earthflows and landslides exhibits seasonal variations and accelerated deformation following the Wenchuan earthquake. Time series analysis of the Suoertou earthflow reveals that seasonal velocity changes are characterized by comparatively rapid acceleration and gradual deceleration with distinct kinematic zones with different mean velocities, although velocity changes appear to occur synchronously along the landslide body over seasonal timescales. The observations suggest that the post-seismic effects (acceleration period) on landslide deformation last some 6–7 months.

AIS-based Ship Drift Estimation in Kuroshio Coastal Area

Ship Drift Estimation has been widely used for weather routing navigation. Improvement of accuracy of the estimation makes energy-saving navigation more efficient. Ocean current estimation database, which is being used as ship drift speed in weather routing services, is generated by solving ocean dynamic models and by being assimilated with the satellite observation data. However, there are some differences between estimated current speed and ship drift speed which can be calculated from GPS, Log speed, Gyrocompass measurement values. This study proposes the method for ship drift estimation by means of ships' navigation data collected using AIS. In this results, the estimation error was decreased to 79% of that of ocean current estimation (JCOPE-T) in Kuroshio watershed area.

Favourable and Unfavourable Scenarii of Radiative Fog Formation Defined by Ground-Based and Satellite Observation Data

An innovative set of six observed predictors of fog formation by radiative cooling is proposed, which describes: 1) one of the main processes of fog formation that is the water uptake by aerosols at surface level; 2) the fog development along the vertical up to 30 meters, as well as the cloud cover; and 3) the temporal evolution of the parameters describing the water uptake by aerosols and the cloud cover. The visibility is used as a signal of atmospheric and surface processes affecting the water uptake by aerosols. The vertical fog development is described by the vertical thermal gradient along a 30-m meteorological mast. The cloud cover is described using a ceilometer for low and middle clouds above the instrument, and satellite data for clouds in a pixel and in a larger 9 × 9-pixel region around the site. The set of predictors can be observed in operational conditions, such as on airport fields. Data acquired at the SIRTA platform (Paris, France) during two autumns and two winters were analysed, and the cloud cover classification derived from the SEVIRI instrument on the METEOSAT Second Generation satellite by the EUMETSAT/NWCSAF program was also utilized. The training data set was acquired in November 2011 while the validation data set extended over 12 months. All situations were discriminated between favourable and unfavourable scenarii of radiative cooling fog formation, with a factor of 10 in probability. 246 moderate visibility events (with visibility between 5 and 10 km) were observed under a clear sky in 12 months at SIRTA. While developed fog formed according to one scenario, thin fog formed according to six scenarii. On the contrary to thin fog, developed fog did not form when cirrus clouds were observed above moderate visibility situations. The fog formation probability varied from 12 to 43%, and even reached 100% for one thin fog. As the training data set was acquired in autumn, the predictor set seems more appropriate to nowcast fog in humid autumn conditions, when the fog formation probability was multiplied by ~2. The predictive set could be enriched by predictors proposed in literature, and tests suggest that relative humidity would improve the fog formation probability in winter. Five predictors identify 65 of the 217 no-fog events counted in 12 months, with ~3 h anticipation time. If visibility decreased below 5 km, 88 additional no-fog events were identified. In this case, the formation probability was 35% for developed fog, and 24–60% for thin fog, but with an anticipation time reduced to ~1.5 h. Only one fog, which represents 3% of the fog events, would be missed with such a predictive scheme.

PyTroll: An Open-Source, Community-Driven Python Framework to Process Earth Observation Satellite Data

AbstractPyTroll (http://pytroll.org) is a suite of open-source easy-to-use Python packages to facilitate processing and efficient sharing of Earth Observation (EO) satellite data. The PyTroll software is intended for both 24/7 real-time operations as well as research and development. PyTroll grew out of the need to provide a resilient and agile platform that can respond quickly to new user needs and new data sources. PyTroll, being open source, stimulates international collaboration, which is vital with the rapid increase of satellite information availability. The PyTroll software development is strongly user driven and has grown over the past eight years from a collaborative effort between the Danish and Swedish national meteorological services to encompass a worldwide community with active contributors. PyTroll is being used at least operationally in the national meteorological services of Denmark, Norway, Sweden, Finland, Germany, Switzerland, Italy, Estonia, and Latvia. However, given its simplicity, minimal demand on user resources, and community-driven approach, it also encourages and facilitates usage of EO data for individual applications. While PyTroll was originally developed to cater to the needs of the atmospheric remote sensing community, it could be equally useful for land and ocean applications and within hydrology. This article provides an overview of PyTroll, with examples showing the capability of some of the core packages.

ASCAT Wind Superobbing Based on Feature Box

Redundant observations impose a computational burden on an operational data assimilation system, and assimilation using high-resolution satellite observation data sets at full resolution leads to poorer analyses and forecasts than lower resolution data sets, since high-resolution data may introduce correlated error in the assimilation. Thus, it is essential to thin the observations to alleviate these problems. Superobbing like other data thinning methods lowers the effect of correlated error by reducing the data density. Besides, it has the added advantage of reducing the uncorrelated error through averaging. However, thinning method using averaging could lead to the loss of some meteorological features, especially in extreme weather conditions. In this paper, we offer a new superobbing method which takes into consideration the meteorological features. The new method shows very good error characteristic, and the numerical simulation experiment of typhoon “Lionrock” (2016) shows that it has a positive impact on the analysis and forecast compared to the traditional superobbing.

Coastal wetland change detection using high spatial resolution KOMPSAT-2 imagery

Coastal wetlands store terrestrial carbon and conserve biodiversity, thus playing an essential ecological role. Reliable regional-scale assessments of wetland dynamics, such as analysis of land use and land cover (LULC), and coastal monitoring, provide important hydrographical and socio-geographical information. Remote sensing images, such as Earth Observation satellite data, are useful for examining temporal LULC changes and providing environmental monitoring data. This study presents a method of mapping and monitoring changes in coastal LULC using classified multispectral images acquired by the Korea Multi-Purpose Satellite-2 (KOMPSAT-2). Wetland changes are monitored in three different protected tidal flats areas on the coastal boundary of the Korean Peninsula with the Yellow Sea, for the period of 2008 - 2015. High overall accuracy and Kappa coefficient values for the accuracy assessment indicate the suitability of LULC classification using high spatial resolution KOMPSAT imagery, even when an unsupervised classification approach is adopted. The LULC maps were analyzed and evaluated using post-classification change detection methods. Results showed spatial decreases of 6 and 20% for mixed forest and wetlands in the Gyeonggi area, respectively, but no significant changes over time for Jeonbuk and Jeonnam. There was a 12% increase in developed areas for Gyeonggi but only 1.9 and 6% for Jeonbuk and Jeonnam, respectively. LULC change is thus easily identified through a pixel-based analysis of multispectral KOMPSAT-2 images over time. Such data are useful for environmental and policy managers when developing advanced coastal management strategies.

Effectiveness and limitations of parameter tuning in reducing biases of top-of-atmosphere radiation and clouds in MIROC version 5

Abstract. This study discusses how much of the biases in top-of-atmosphere (TOA) radiation and clouds can be removed by parameter tuning in the present-day simulation of a climate model in the Coupled Model Inter-comparison Project phase 5 (CMIP5) generation. We used output of a perturbed parameter ensemble (PPE) experiment conducted with an atmosphere–ocean general circulation model (AOGCM) without flux adjustment. The Model for Interdisciplinary Research on Climate version 5 (MIROC5) was used for the PPE experiment. Output of the PPE was compared with satellite observation data to evaluate the model biases and the parametric uncertainty of the biases with respect to TOA radiation and clouds. The results indicate that removing or changing the sign of the biases by parameter tuning alone is difficult. In particular, the cooling bias of the shortwave cloud radiative effect at low latitudes could not be removed, neither in the zonal mean nor at each latitude–longitude grid point. The bias was related to the overestimation of both cloud amount and cloud optical thickness, which could not be removed by the parameter tuning either. However, they could be alleviated by tuning parameters such as the maximum cumulus updraft velocity at the cloud base. On the other hand, the bias of the shortwave cloud radiative effect in the Arctic was sensitive to parameter tuning. It could be removed by tuning such parameters as albedo of ice and snow both in the zonal mean and at each grid point. The obtained results illustrate the benefit of PPE experiments which provide useful information regarding effectiveness and limitations of parameter tuning. Implementing a shallow convection parameterization is suggested as a potential measure to alleviate the biases in radiation and clouds.

Chislennoe issledovanie protsessov evolyutsii zagryazneniy v Azovskom more s ispol'zovaniem dannykh sputnikovykh nablyudeniy

Chislennoe issledovanie protsessov evolyutsii zagryazneniy v Azovskom more s ispol'zovaniem dannykh sputnikovykh nablyudeniy

Tendencies in Coccolithophorid Blooms in Some Marine Environments of the Northern Hemisphere according to the Data of Satellite Observations in 1998–2013

Based on the method developed for the delineation of E. huxleyi blooms, a new technique is achieved for (1) the automated detection of E. huxleyi blooms among coexisting massive blooms of microalgae species of other phytoplankton groups and (2) quantifying the boom surface of this type of coccolithophores. As a result, according to the data of the Climate Change Initiative Ocean Colour (OC CCI) for 1998–2013, we have obtained multiyear time series of variability in both the incidence of E. huxleyi bloom and its area in the North, Norwegian, Greenland, Barents, and Bering seas. It is found that E. huxleyi blooms propagate within the intra-annual cycle from the studied middle-latitude marine areas towards the northern areas of the Northern Atlantic Ocean (NAO) and the Arctic Ocean (AO) following the pathways of the main Gulfstream and its branches. It is also found that E. huxleyi blooms are formed annually, initially in the vicinity of the British Islands; then they successively emerge in the northward direction following the western coast of the Great Britain, turn over its northern extremity to reach, firstly, the North Sea (in May), the Norwegian Sea, and finally the Greenland Sea (in June). Then they burst out in the Barents Sea, where the typical period of blooming lasts until late August and, in some years, even to mid-September. We determine the patterns of maximal rates and duration of blooms for each of the seas studied in the Atlantic and Arctic Oceans. As for the Bering Sea, the temporal and spatial variability in the growth of E. huxleyi has an irregular pattern: after a period of remarkably high expression of this phenomenon in 1998–2001, there was an abrupt decrease in both the number and, especially, extent of bloom areas.

The fractal analysis of the gravitational field and topography of the Mars

The aim of this paper is to represent the results of a structured fractal analysis of gravitational and topographical parameters of Mars on the basis of data obtained from the space missions. To analyze Martian fractal structures the observations from the data received from boards of the space missions including «Mars Global Surveyor» has been used. The models of relief and Mars’ gravitational field have been constructed on the basis of harmonic analysis of the expansion in spherical functions of the satellite observations data. As a result, fractal dimensions of Martian relief’s anomalies and Mars’ gravitational potential by longitude and latitude have been determined. Mean values of the fractal dimensions have been obtained as well: mean fractal dimensions of Martian topographic model by latitude , by longitude ; mean fractal dimensions of Martian gravitational potential anomalies by latitude , by longitude .

RAPT: Rare Class Prediction in Absence of True Labels

Many real-world problems involve learning models for rare classes in situations where there are no gold standard labels for training samples but imperfect labels are available for all instances. In this paper, we present RAPT, a three step predictive modeling framework for classifying rare class in such problem settings. The first step of the proposed framework learns a classifier that jointly optimizes precision and recall by only using imperfectly labeled training samples. We also show that, under certain assumptions on the imperfect labels, the quality of this classifier is almost as good as the one constructed using perfect labels. The second and third steps of the framework make use of the fact that imperfect labels are available for all instances to further improve the precision and recall of the rare class. We evaluate the RAPT framework on two real-world applications of mapping forest fires and urban extent from earth observing satellite data. The experimental results indicate that RAPT can be used to identify forest fires and urban areas with high precision and recall by using imperfect labels, even though obtaining expert annotated samples on a global scale is infeasible in these applications.

The Orbiting Carbon Observatory-2 early science investigations of regional carbon dioxide fluxes.

NASA's Orbiting Carbon Observatory-2 (OCO-2) mission was motivated by the need to diagnose how the increasing concentration of atmospheric carbon dioxide (CO2) is altering the productivity of the biosphere and the uptake of CO2 by the oceans. Launched on 2 July 2014, OCO-2 provides retrievals of the column-averaged CO2 dry-air mole fraction ([Formula: see text]) as well as the fluorescence from chlorophyll in terrestrial plants. The seasonal pattern of uptake by the terrestrial biosphere is recorded in fluorescence and the drawdown of [Formula: see text] during summer. Launched just before one of the most intense El Niños of the past century, OCO-2 measurements of [Formula: see text] and fluorescence record the impact of the large change in ocean temperature and rainfall on uptake and release of CO2 by the oceans and biosphere.

The effect of relative humidity on the tropospheric aerosol extinction coefficient with typical underlying surfaces based on CALIPSO data

ABSTRACTWater vapour content greatly influences the aerosol extinction coefficient in the atmosphere. Based on 27 months of the Cloud-aerosol lidar and Infrared Pathfinder Satellite Observation data, the effect of humidity on the aerosol extinction coefficient at five typical underlying surface areas across China was studied. The results show the following: (1) The extinction coefficient in higher humidity conditions in the plateau (R2), plain (R3, R4), and coastal areas (R5) is greater than that in lower humidity conditions at equal altitudes. The influence of humidity on the extinction coefficient reaches a maximum in the tropospheric layer 1–2 km above all five regions. (2) The extinction coefficient and relative humidity have a positive correlation in R2–R5 but not in the Taklimakan Desert region (R1). The correlation coefficients at altitudes of 0–4 km are stronger than those at altitudes of 4–8 km. (3) The hygroscopic growth factors (HGFs) are positively correlated with the relative humidity. The average values of the HGFs are ordered, from high to low, as R2, R4 and R3, R5 and R1. The change in HGF in the 0–2 km layer is the smallest in R1 and R3–R5, but greatest in R2.

인공위성 관측 자료를 이용한 해양-대기 DMS flux 추정 및 장기 추세 분석

The long-term linear trend of global sea-to-air dimethyl sulfide (DMS) flux was analyzed over a 16-year time span (2000~2015), based on satellite observation data. The emission rates of DMS (i.e. DMS flux) in the global ocean were estimated from sea surface DMS concentrations, which were constructed with chlorophyll a (Chl-a) concentrations and mixed layer depths (MLD), and transfer velocity from sea to air, which was parameterized with sea surface wind (SSW) and sea surface temperature (SST). In general, the DMS flux in the global ocean exhibited a gradual decreasing pattern from 2000 (a total of 12.1 Tg/yr) to 2015 (10.7 Tg/yr). For the latitude band (<TEX>$10^{\circ}$</TEX> interval between <TEX>$0^{\circ}$</TEX> and <TEX>$60^{\circ}$</TEX>), the DMS flux at the low latitude of the Northern (NH) and Southern hemisphere (SH) was significantly higher than that at the middle latitude. The seasonal mean DMS flux was highest in winter followed by in summer in both hemispheres. From the long-term analysis with the Mann-Kendall (MK) statistical test, a clear downward trend of DMS flux was predicted to be broad over the global ocean during the study period (NH: <TEX>$-0.001{\sim}-0.036{\mu}mol/m^2/day\;per\;year$</TEX>, SH: <TEX>$-0.011{\sim}-0.051{\mu}mol/m^2/day\;per\;year$</TEX>). These trend values were statistically significant (p < 0.05) for most of the latitude bands. The magnitude of the downward trend of DMS flux at the low latitude in the NH was somewhat higher than that at the middle latitude during most seasons, and vice versa for the SH. The spatio-temporal characteristics of DMS flux and its long-term trend were likely to be primarily affected not only by the SSW (high positive correlation of r = 0.687) but also in part by the SST (r = 0.685).

Estimation of chlorophyll concentration in waters near Hokkaido using the linear combination method.

An inversion algorithm is implemented to retrieve the surface chlorophyll a (Chl) concentration using satellite observation data from the MODIS instrument. The algorithm employs a simple and flexible index (LCI) to combine with the Chl without explicit correction for aerosol scattering. To investigate the sensitivity of LCI to Chl and other influence factors, an oceanic radiative transfer model coupled with a comprehensive bio-optical module is developed. It is studied that the LCI is significantly linear to Chl and not sensitive to other influence factors, except in very low oceanic salinity or scattering angle conditions, where over a 12% relative difference of derived Chl exists. Inversion results show the retrieved Chl are highly consistent with the MODIS operational data products in waters near Hokkaido, with the correlation coefficient, root mean square deviation, and average percentage difference of 0.9702, 0.3756 mg m-3, and 13.89%, respectively. Investigation of the validity of this algorithm with a variety of atmospheric conditions indicates that the residual influence of atmosphere on the LCI index, after Rayleigh scattering correction, is generally within ± 0.001, allowing the retrieval error of Chl at less than 25% in most cases. A good comparison between retrieval and in situ measurements is also identified and implies that the retrieval accuracy via the LCI method depends on the linear combination coefficients used and the bio-optical module selected, while effects of polarization can be ignored.