Satellite Swath Research Articles

Preselection of the reference orbit for an Earth remote sensing satellite

Low near-circular orbits of Earth remote sensing (ERS) satellites are considered. The objective is to select the orbits most suitable for a particular satellite mission. In particular, the problem of an approximate determination of the orbit parameters that allow a satisfactory satellite survey of the target surface of the Earth is considered. The main desires of observation system developers regarding the conditions of the Earth's surface survey are considered. To reconcile these desires with the regularities of satellite motion in low Earth orbits, use may be made of simple models that describe these regularities. In doing so, it is desirable to visualize viewing swaths on the Earth's surface. A compromise between the desires of observation system developers and the satellite motion regularities is the selection of orbits that best meet the characteristics of a particular satellite and its observation system. This article presents a simple model and algorithm that make it possible to preselect ERS satellite orbits. The proposed model is based on familiar relationships, and the novelty of the article lies in a compact and generalized presentation of the model for ERS satellite orbit preselection. The article presents models that make it possible to estimate the satellite swath width and choose the orbit inclination angle, a stable orbit shape, the orbit altitude, and the orbital period. The advantages and disadvantages of solar synchronous orbits are considered. Analytical expressions are constructed to fairly simply estimate the excursion of a satellite from its operational orbit under the action of the aerodynamic drag, estimate the rate of recovery of the orbit parameters under the action of a constant transversal control acceleration, and determine allowable time intervals between engine starts and engine operation intervals. The advantages of repeat ground track orbits are shown. The simplest model for calculating and visualizing satellite viewing swathes of the Earth's surface is constructed. Thus, the article proposes a simple algorithm for the preselection of low Earth orbits for ERS satellites with a satisfactory observation of the target surface of the Earth.

SMOS Brightness Temperature Monitoring Quality Control Review and Enhancements

Brightness temperature (Tb) observations from the European Space Agency (ESA) Soil Moisture Ocean Salinity (SMOS) instrument are passively monitored in the European Centre for Medium-range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). Several quality control procedures are performed to screen out poor quality data and/or data that cannot accurately be simulated from the numerical weather prediction (NWP) model output. In this paper, these quality control procedures are reviewed, and enhancements are proposed, tested, and evaluated. The enhancements presented include improved sea ice screening, coastal and ambiguous land-ocean screening, improved radio frequency interference (RFI) screening, and increased usage of observation at the edge of the satellite swath. Each of the screening changes results in improved agreement between the observations and model equivalent values. This is an important step in advance of future experiments to test the direct assimilation of SMOS Tbs into the ECMWF land data assimilation system.

Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS

Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from multispectral data from the Advanced Baseline Imager (ABI) onboard the GOES-16 platform. The DeepCTC model is trained and tested using coincident data from both CloudSat and ABI over the CONUS region. Evaluations of DeepCTC indicate that the model performs well for a variety of cloud types including Altostratus, Altocumulus, Cumulus, Nimbostratus, Deep Convective and High clouds. However, capturing low-level clouds remains a challenge for the model. Results from simulated GOES-16 ABI imageries of the Hurricane Harvey event show a large-scale perspective of the rapid and consistent cloud-type monitoring is possible using the DeepCTC model. Additionally, assessments using half-hourly Multi-Radar/Multi-Sensor (MRMS) precipitation rate data (for Hurricane Harvey as a case study) show the ability of DeepCTC in identifying rainy clouds, including Deep Convective and Nimbostratus and their precipitation potential. We also use DeepCTC to evaluate the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) product over different cloud types with respect to MRMS referenced at a half-hourly time scale for July 2018. Our analysis suggests that DeepCTC provides supplementary insights into the variability of cloud types to diagnose the weakness and strength of near real-time GEO-based precipitation retrievals. With additional training and testing, we believe DeepCTC has the potential to augment the widely used PERSIANN-CCS algorithm for estimating precipitation.

Methodology and software for estimating target efficiency of land remote sensing satellites at the stage of design

The paper aims to present the software developed with the purpose of estimating land remote sensing satellites and space monitoring system target efficiency indices. The essence of the approach is step-by-step simulation of the satellite orbital motion with permanent determination of the flight conditions and estimation of the satellite and the space monitoring system target parameters. Main factors affecting target efficiency are taken into consideration; they include orbital elements and the satellite coordinates variation while its orbital movement, the satellite manoeuvres in the process of its target operation, conditions of survey objects taking, characteristics of survey equipment, etc. The algorithm used is based on a number of mathematical models such as models for generating initial data on survey objects, models for choice of survey objects within the satellite swath, models for constructing land remote sensing satellites survey path, models for estimating orientation of solar cell battery panels, etc. The software has been developed in Delphi programming support environment. It enables calculation of satellites target parameters such as ground resolution, operational efficiency, surveillance frequency, productivity.

Comparing level-2 and level-3 satellite ocean color retrieval validation methodologies.

Many ocean color data applications leverage global spatially composited level-3 (L3) satellite data because of their regular Earth-grid frame of reference. However, ocean color satellite retrieval performance is routinely evaluated on level-2 (L2) data at the native satellite swath resolution and geometries. This study assesses how accurately binned and gridded L3 data represent L2 satellite data products via satellite-to-in situ match-up activities. L2 and L3 satellite data retrievals of the photosynthetic pigment chlorophyll-a are compared with a common in situ dataset, revealing similar L2 and L3 satellite-to-in situ performance for both MODIS-Aqua and VIIRS-SNPP. This agreement implies that L2 validation results are generally applicable to L3 data. However, uncertainties are introduced during the generation of L3 data from L2 data. L3 data comparisons introduce a wider temporal window between the time of in situ measurement and the time of the satellite observation, which can unintentionally reflect on the quality of the satellite retrieval or algorithm performance. The choice of L3 map projection may introduce additional uncertainty by spatially distorting the true location of the satellite retrievals. Each manipulation of satellite data beyond the instrument's native spatiotemporal reference (L2) reduces the applicability of L2 validation results to higher data processing levels.

Microphysical Properties of Convective Clouds in Summer over the Tibetan Plateau from SNPP/VIIRS Satellite Data

The Tibetan Plateau (TP) plays an important role in formation and development of the East Asian atmospheric circulation, climate variability, and disastrous weathers in China. Among the many topics on TP meteorology, it is critical to understand the microphysical characteristics of clouds over the TP; however, observations of the cloud micro-physics in this area are insufficient mainly due to sparse stations and limited cloud physical data. The Visible Infrared Imaging Radiometer Suite (VIIRS), onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite, has an improved imaging spectroradiometer with 17 channels of 750-m moderate resolution and 5 channels of 375-m image resolution. The high-resolution instrument has an advantage for observing the small or initial convective clouds. Based on the methodologies that we proposed before for retrieving cloud microphysical properties from SNPP, an automated mapping software package named Automatic Mapping of Convective Clouds (AMCC) has been developed at the scale of satellite swath. The properties of convective clouds are retrieved by AMCC and their values are averaged over 0.33° × 0.33° grids based on the SNPP/VIIRS satellite data over the TP during the summers of 2013–17. The results show that: (1) the temperature of lifting condensation level (TLCL) at Naqu meteorological station and the cloud base temperature (Tb) retrieved from VIIRS are linearly correlated, with a correlation coefficient of 0.87 and standard deviation (STD) of 3.0°C; (2) convective clouds over the TP have the following macro- and micro-physical properties. First, the cloud base temperature (Tb) is about −5°C, the cloud base height above the ground (Hb) ranges between 1800 and 2200 m, and the cloud water content is low. Second, the cloud condensation nuclei concentration (NCCN) is between 200 and 400 mg−1 with 0.7% in maximum supersaturation (Smax); consequently, the condensation growth of water cloud droplet with less NCCN and higher Smax is fast. Third, because the precipitation initiation depth (D14) varies within 1500–2000 m and 500–1000 m at the Yarlung Zangbo River basin and southern Tibet, respectively, the clouds over these areas are more prone to precipitation. Fourth, mean height of the cloud top above sea level (Htop) is between 10 and 13 km, but the cloud depth (Dcld) is rather small, which is about 5000 m in southern TP and gradually reduces to 2500 m in northern TP. Fifth, the glaciation temperature (Tg) ranges from −30°C in central and southern TP to −25°C in northern TP, which, combined with the warmer Tg and the Tb less than 0°C, leads to the domination of ice process in the clouds; (3) the macro- and microphysical properties of convective clouds over the TP explain why rainfall there is frequent and lasts over a short time with small amount and large rain drops.

Scatterometer estimates of the tropical sea‐breeze circulation near Darwin, with comparison to regional models

In tropical coastal environments, simulating the diurnal cycle of wind and precipitation in numerical weather and climate models presents unique challenges due to the interaction of intraseasonal and mesoscale dynamics. This can lead not only to incorrect short‐term weather forecasts but also to unphysical energy and momentum transport by convective processes. In particular, the sea/land‐breeze circulation and its role in initiating convection has been identified as a possible source of errors in the timing and offshore extent of coastal precipitation in the Tropics.In this study, the offshore land/sea breeze around Darwin, Australia, is examined using scatterometer wind observations and two regional atmospheric models. Although the comparison is limited by satellite swath times which cluster around two times of day, useful results are obtained by sub‐sampling the simulated data to match the coverage of the scatterometer data.We find that offshore surface sea‐breeze characteristics (intensity and horizontal spatial extent) from the models and satellite estimates are generally in good agreement, with intensity differences less than 2 m s−1, and offshore extents not varying by more than approximately 150 km. The variation in offshore extent and amplitude of the land/sea‐breeze wind perturbations with monsoon regime is well simulated. Furthermore, despite the simplifying assumptions of linear sea‐breeze theory, the model and scatterometer results are in broad agreement with theoretical values, particularly in the presence of the weak background winds during the monsoon break period.

Fundamentals of the route theory for satellite constellation design for Earth discontinuous coverage. Part 3: Low-cost Earth observation with minimal satellite swath

Continuing the series of papers with description of the fundamentals of the Route Theory for satellite constellation design, the general method for minimization of the satellite swath width required under given constraint on the maximum revisit time (MRT), the main quality characteristic of the satellite constellation discontinuous coverage, is presented. The interrelation between MRT and multiplicity of the periodic coverage – the minimum number of the observation sessions realized for the points of observation region during the satellite tracks’ repetition period – is revealed and described. In particular, it is shown that a change of MRT can occur only at points of coverage multiplicity changing. Basic elements of multifold Earth coverage theory are presented and used for obtaining analytical relations for the minimum swath width providing given multifold coverage. The satellite swath width calculation procedure for the multifold coverage of rotating Earth using the iterations on the sphere of stationary coverage is developed. The numerical results for discontinuous coverage with minimal satellite swath, including comparison with some known particular cases and implementations of the method, are presented.

Optimal Reorganization of NASA Earth Science Data for Enhanced Accessibility and Usability for the Hydrology Community

AbstractA long‐standing “Digital Divide” in data representation exists between the preferred way of data access by the hydrology community and the common way of data archival by earth science data centers. Typically, in hydrology, earth surface features are expressed as discrete spatial objects (e.g., watersheds), and time‐varying data are contained in associated time series. Data in earth science archives, although stored as discrete values (of satellite swath pixels or geographical grids), represent continuous spatial fields, one file per time step. This Divide has been an obstacle, specifically, between the Consortium of Universities for the Advancement of Hydrologic Science, Inc. and NASA earth science data systems. In essence, the way data are archived is conceptually orthogonal to the desired method of access. Our recent work has shown an optimal method of bridging the Divide, by enabling operational access to long‐time series (e.g., 36 years of hourly data) of selected NASA datasets. These time series, which we have termed “data rods,” are pre‐generated or generated on‐the‐fly. This optimal solution was arrived at after extensive investigations of various approaches, including one based on “data curtains.” The on‐the‐fly generation of data rods uses “data cubes,” NASA Giovanni, and parallel processing. The optimal reorganization of NASA earth science data has significantly enhanced the access to and use of the data for the hydrology user community.

Mitigation of systematic errors in SMOS sea surface salinity

Sea Surface Salinity (SSS) acquired by the Soil Moisture and Ocean Salinity (SMOS) satellite mission are subject to systematic errors originating from various non-geophysical contaminations such as land contamination. These systematic errors reach more than 2 pss in some regions close to the land with very strong spatial gradients according to the coast orientation and the across-track position within the satellite swath. An empirical method to estimate and correct the time independent systematic errors from resampled quasi L2 SMOS SSS is presented. The method is based on self-consistency hypothesis of long term (July 2010–June 2014) variability of SMOS SSS among SMOS dwell-lines and orbit orientation (ascending and descending). The bias correction is performed by first adjusting SSS relative variations among dwell-lines and orbits orientation and then by determining a mean correction over four years. A reference time series of SSS and the associated relative systematic error for each dwell-line and orbit orientation is estimated using a least square approach. Then, the 4-year mean value of corrected SMOS SSS is adjusted relative to a 4-year mean in situ data climatology. Four years of salinity maps mitigated from systematic errors are presented. Independent validation using in situ thermosalinograph SSS from ships of opportunity is presented. The new SMOS bias corrected SSS over the global ocean shows an improvement of 32% of the RMSD.

Distribution of sulfur aerosol precursors in the SPCZ released by continuous volcanic degassing at Ambrym, Vanuatu

The Melanesian Volcanic Arc (MVA) emits about 12kTd−1 of sulfur dioxide (SO2) to the atmosphere from continuous passive (non-explosive) volcanic degassing, which contributes 20% of the global SO2 emission from volcanoes. Here we assess, from up-to-date and long-term observations, the SO2 emission of the Ambrym volcano, one of the dominant volcanoes in the MVA, and we investigate its role as sulfate precursor on the regional distribution of aerosols, using both satellite observations and model results at 1°×1° spatial resolution from WRF-Chem/GOCART. Without considering aerosol forcing on clouds, our model parameterizations for convection, vertical mixing and cloud properties provide a reliable chemical weather representation, making possible a cross-examination of model solution and observations. This preliminary work enables the identification of biases and limitations affecting both the model (missing sources) and satellite sensors and algorithms (for aerosol detection and classification) and leads to the implementation of improved transport and aerosol processes in the modeling system. On the one hand, the model confirms a 50% underestimation of SO2 emissions due to satellite swath sampling of the Ozone Monitoring Instrument (OMI), consistent with field studies. The OMI irregular sampling also produces a level of noise that impairs its monitoring capacity during short-term volcanic events. On the other hand, the model reveals a large sensitivity on aerosol composition and Aerosol Optical Depth (AOD) due to choices of both the source function in WRF-Chem and size parameters for sea-salt in FlexAOD, the post-processor used to compute offline the simulated AOD. We then proceed to diagnosing the role of SO2 volcanic emission in the regional aerosol composition. The model shows that both dynamics and cloud properties associated with the South Pacific Convergence Zone (SPCZ) have a large influence on the oxidation of SO2 and on the transport pathways of volcanic species across the South Pacific atmosphere. For example, in the tropical cloudy air, the sulfate production in the aqueous phase is very efficient, resulting in the formation of a large cloud of highly scattering sulfate aerosols advected horizontally to Eastern Indonesia, in agreement with the AOD feature captured by MODIS/Aqua, but missed in CALIOP/CALIPSO (lidar) products. Model sensitivity experiments indicate that aerosol re-suspension due to evaporating droplets is a significant pathway for the supply of volcanic sulfur species in the remote marine boundary layer. By strongly modulating the irreversible loss due to wet scavenging, this aerosol process has a similar influence on the sulfur burden as natural emission from volcanoes or biogenic sources like dimethyl sulfate (DMS). The results emphasize the importance of MVA passive degassing and SPCZ dynamics on the aerosol background, and raise questions about potential impacts on the local climate and marine ecosystems.

해상육상통합 무선통신환경에서 오브컴 저궤도위성 AIS시스템 성능평가

본 논문에서는 AIS의 기본 개념과 위성 AIS을 위한 고려사항, 위성 AIS의 적용에 따른 문제점과 성능 분석을 위한 예상 링크 값을 바탕으로 해상에서의 오브컴(ORBCOMM) 위성의 AIS 성능분석을 수행하였다. 제안한 방식은 현재 제공되고 있는 오브컴 저궤도 위성을 이용하여 두 개의 swath width과 선박의 수에 띠론 메시지 슬롯 할당과 reporting 간격을 고려한 메시지 충돌을 시뮬레이션 하였고, 해상 영역은 육상기지국 몇 해안에서 가까운 영역부터 먼 해상 영역으로 구분하여, 각 해상 영역에는 선박 A(속력 0-14 knots), 선박 B(속력 14-23 knots), 선박 C(속력 23 knots 이상)의 트래픽이 모두 존재 한다는 가정하에 시뮬레이션을 통해 성능 평가하였다. 그 결과 선박 수가 증가할수록 슬롯에 겹치는 메시지 수가 많았으며, 운항 속도별 reporting 간격의 경우 보고주기가 길수록, 선박 A의 트래픽이 높은 영역일수록 검출확률이 높아짐을 보였다. In this paper, the feasibility of ORBCOMM satellite-based automatic identification system (SAT-AIS) is evaluated in the context of ship AIS slot collision probability depending on reporting rate, We evaluate detection probability evaluation of ORBCOMM satellite-based AIS considering link budget, SOTDMA protocol and satellite's swath width. The simulation determines the total number of vessels served by ORBCOMM satellite according to satellite's swath width, AIS slot allocation and reporting rate. By simulation results, the ORBCOMM satellite-based AIS slot collision is increased directly proportional to the total number of vessels and the more detection probability evaluation of ORBCOMM satellite-based AIS degrader, the more sip AIS reporting rate shorter.

Evaluation of Data Reduction Algorithms for Real-Time Analysis

Abstract Data reduction tools are developed and evaluated using a data analysis framework. Simple (nonadaptive) and intelligent (adaptive) thinning algorithms are applied to both synthetic and real data and the thinned datasets are ingested into an analysis system. The approach is motivated by the desire to better represent high-impact weather features (e.g., fronts, jets, cyclones, etc.) that are often poorly resolved in coarse-resolution forecast models and to efficiently generate a set of initial conditions that best describes the current state of the atmosphere. As a precursor to real-data applications, the algorithms are applied to one- and two-dimensional synthetic datasets. Information gleaned from the synthetic experiments is used to create a thinning algorithm that combines the best aspects of the intelligent methods (i.e., their ability to detect regions of interest) while reducing the impacts of spatial irregularities in the data. Both simple and intelligent thinning algorithms are then applied to Atmospheric Infrared Sounder (AIRS) temperature and moisture profiles. For a given retention rate, background, and observation error, the optimal 1D analyses (i.e., lowest MSE) tend to have observations that are near regions of large curvature and gradients. Observation error leads to the selection of spurious data in homogeneous regions of the intelligent algorithms. In the 2D experiments, simple thinning tends to perform better within the homogeneous data regions. Analyses produced using AIRS data demonstrate that observations selected via a combination of the simple and intelligent approaches reduce clustering, provide a more even distribution along the satellite swath edges, and, in general, have lower error and comparable computational requirements compared to standard operational thinning methodologies.

Note on the effect of horizontal gradients for nadir-viewing microwave and infrared sounders

Passive microwave and infrared nadir sounders such as the Advanced Microwave Sounding Unit A (AMSU-A) and the Atmospheric InfraRed Sounder (AIRS), both flying on NASA s EOS Aqua satellite, provide information about vertical temperature and humidity structure that is used in data assimilation systems for numerical weather prediction and climate applications. These instruments scan cross track so that at the satellite swath edges, the satellite zenith angles can reach approx. 60 deg. The emission path through the atmosphere as observed by the satellite is therefore slanted with respect to the satellite footprint s zenith. Although radiative transfer codes currently in use at operational centers use the appropriate satellite zenith angle to compute brightness temperature, the input atmospheric fields are those from the vertical profile above the center of the satellite footprint. If horizontal gradients are present in the atmospheric fields, the use of a vertical atmospheric profile may produce an error. This note attempts to quantify the effects of horizontal gradients on AIRS and AMSU-A channels by computing brightness temperatures with accurate slanted atmospheric profiles. We use slanted temperature, water vapor, and ozone fields from data assimilation systems. We compare the calculated slanted and vertical brightness temperatures with AIRS and AMSU-A observations. We show that the effects of horizontal gradients on these sounders are generally small and below instrument noise. However, there are cases where the effects are greater than the instrument noise and may produce erroneous increments in an assimilation system. The majority of the affected channels have weighting functions that peak in the upper troposphere (water vapor sensitive channels) and above (temperature sensitive channels) and are unlikely t o significantly impact tropospheric numerical weather prediction. However, the errors could be significant for other applications such as stratospheric analysis. Gradients in ozone and tropospheric temperature appear to be well captured by the analyses. In contrast, gradients in upper stratospheric and mesospheric temperature as well as upper tropospheric humidity are less well captured. This is likely due in part to a lack of data to specify these fields accurately in the analyses. Advanced new sounders, like AIRS, may help to better specify these fields in the future.

Variability in coastal zone color scanner (CZCS) Chlorophyll imagery of ocean margin waters off the US East Coast

The purpose of our study was to use the 7.5-year coastal zone color scanner (CZCS) image time series (Oct. 1978 to July, 1986) to study general patterns in near-surface phytoplankton chlorophyll concentrations in ocean margin waters off the US East Coast. We defined 21 relatively large study areas (>100 km 2) within the MAB and SAB to set boundaries for averaging and subsequent analyses. Our objective was to partition the observed CZCS-derived chlorophyll concentration (CSAT, mg m −3) variability of these 21 study areas within three general categories based on time scale: daily (i.e. day–week), seasonal and interannual. An additional objective was to determine relations between the temporal patterns in the 21 study areas. All available CZCS imagery (more than 3500 scenes of Level 1 imagery, i.e. top-of-the-atmosphere radiance in satellite swath coordinates) covering some or all of our area of interest (northwest Atlantic off the US East Coast) were obtained at full resolution, processed to Level 2 (water-leaving radiance, chlorophyll concentration and other derived products in satellite swath coordinates) and mapped to two different study regions located off the southeast and northeast coasts of the US. Satellite-derived estimates of near-surface chlorophyll concentrations (CSAT) were extracted on a pixel-by-pixel basis from each of the 21 study areas (chosen based on oceanographic criteria) from each of the daily composite CSAT images. For each image and when satellite coverage permitted, CSAT values were averaged to yield a time series of daily mean values for each of the 21 study areas. We used three basic approaches to quantify temporal and spatial patterns in the 21 time series: (1) multiple linear correlation, (2) structure functions (semi-variance calculations) and (3) empirical orthogonal functions (EOF). Our results show: (1) a simple annual CSAT cycle common to all ocean margin waters along the entire US East Coast, consisting of a broad peak in CSAT concentration during winter and minimum concentrations during the summer; (2) relatively subtle across- and along-shelf changes to the timing and relative magnitude of the winter CSAT maxima and summer minima, as well as the presence of secondary seasonal peaks in some regions; (3) high variability at time scales of days to weeks superimposed on the seasonal pattern; (4) high spatial coherence of the seasonal component between all 21 study areas; (5) high coherence of the days-to-weeks component between adjacent study areas, but generally low or no coherence for study areas not adjacent or near each other; and (6) detectable, but low interannual variability.

Sampling Errors in Rainfall Estimates by Multiple Satellites

This paper examines the sampling characteristics of combining data collected by several low-orbiting satellites attempting to estimate the space-time average of rain rates. The several satellites can have different orbital and swath-width parameters. The satellite overpasses are allowed to make partial coverage snapshots of the grid box with each overpass. Such partial visits are considered in an approximate way, letting each intersection area fraction of the grid box by a particular satellite swath be a random variable with mean and variance parameters computed from exact orbit calculations. The derivation procedure is based upon the spectral minimum mean-square error formalism introduced by North and Nakamoto. By using a simple parametric form for the spacetime spectral density, simple formulas are derived for a large number of examples, including the combination of the Tropical Rainfall Measuring Mission with an operational sun-synchronous orbiter. The approximations and results are discussed and directions for future research are summarized.