Understanding the Imaging Capability of Tundra Orbits Compared to Other Orbits

Today most operational Earth observing satellites reside in low Earth orbits (LEO) at less than 1,000 km altitude, and in geostationary Earth orbits (GEO) at ~35,800 km altitude. These orbits have been the venues of choice for observations, albeit for very different reasons. LEO provides high spatial resolution with low temporal resolution while GEO provides for low spatial resolution, but high temporal resolution. NOAA utilizes both venues for their environmental satellites. The NOAA Polar-orbiting Operational Environmental Satellites (POES) reside in LEO Sun synchronous orbits at approximately 830 km in altitude, as do the Defense Meteorological Satellite Program (DMSP) satellites of the Department of Defense. In the near future the POES and DMSP satellites will be merged into a new satellite system referred to as the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The NOAA Geostationary Operational Environmental Satellite (GOES) system, as the name specifies, resides at the other preferred observational venue of GEO. The Jet Propulsion Laboratory (JPL), under contract to NOAA, has been studying the characteristics of medium Earth orbits (MEO), at altitudes between 1000 and 35,800 km, as an observation venue to answer the question as to whether MEO might capture the attributes of the two traditional venues. This on-going study initially focused on determining the optimal altitude for MEO observations, through numerous trade studies involving altitude, instrument complexity, coverage, radiation environment, data temporality, revisit time, data rates, downlink requirements and other parameters including cost and launch complexity. Once the optimal altitude of 10,400 km had been determined the study proceeded to explore single through multiple MEO satellite constellation performance capabilities using two instrument types, a visible through infrared (IR) imager and IR sounder as the satellites’ payload. The MEO performance capabilities were compared to comparable LEO and GEO satellite constellation capabilities. This portion of the study concluded that indeed for global coverage a constellation of satellites operating in the MEO venue could capture the attributes of those operating in the LEO and GEO venues. Three 8-satellite constellations configurations - Walker, ICO, and Equatorial-Polar (EP) - then were studied to develop more constellation coverage statistics including robustness to individual satellite failure. That study phase concluded that the EP constellation was superior to both the ICO and Walker configurations. The study is presently examining if, and to what extent, the equatorial portion of the EP constellation might provide substantive supplemental data to that collected by the NPOESS and GOES satellite constellations.

For more than four decades, geostationary weather satellites have been used successfully by NOAA's National Weather Service for daily weather forecasting and for tracking storm events. At the same time, polar orbit systems, such as NOAA/AVHRR, SeaWIFS and Landsat TM, have provided valuable information about slowly varying features and processes in the open ocean and on land. However, coastal and estuarine applications of remote sensing require significantly finer temporal, spatial, and spectral resolution than open ocean studies. Current polar orbit satellites lack the temporal resolution, while present geostationary orbit satellites have limited spatial and spectral resolution. Geostationary satellites can provide images of clouds, ocean and land surfaces very frequently, since they are positioned above a selected point on the equator and continuously view the same one third of the Earth. However, in order to have the same angular velocity as the Earth, they must be located in an orbit 23,000 miles above the equator, which limits their spatial resolution. NOAA, with support from NASA and the Navy, is developing GOES-R, a geostationary satellite. GOES-R will include a Hyperspectral Environmental Suite for Coastal Waters (HES-CW) and atmospheric sounding sensors which should provide the resolution levels required for coastal and estuarine observations. To achieve this, full use will be made of the fact that a geostationary platform offers a very high sampling frequency and long integration times, allowing for high signal-to-noise (S/N) ratios. Thus, useful trade-offs can be made between S/N, number of spectral bands, and sampling frequency, even though the spatial resolution is constrained to 150-300 m at the equator by system design considerations, such as mirror size, stability and pointing of the spacecraft. The objective of this paper is to illustrate coastal and estuarine resolution requirements for typical coastal features and processes such as: 1) chlorophyll concentration and coastal productivity, 2) harmful algal blooms and eutrophication, 3) current measurement by feature tracking, 4) oil slick drift and capture by estuarine fronts, 5) dynamic behavior of turbidity plumes and ocean-dumped waste, 6) propagation of internal waves on the continental shelf, 7) ice cover for coastal shipping, 8) coastal flooding and other catastrophic events, and 9) coastal ecosystem and wetland habitat changes. These resolution requirements are then compared to current GOES-R specifications.

Enhanced algorithm for estimating snow depth from geostationary satellites

International comparison of satellite winds — An update

This article provides a description of a land surface temperature (LST) data set generated (and provided in near-real-time or offline) based on infrared data from sensors onboard different geostationary (GEO) satellites: Meteosat Second Generation (MSG), Geostationary Operational Environmental Satellite (GOES), and Multifunction Transport Satellite (MTSAT). Given the different characteristics of the imagers onboard each GEO platform, different algorithmic methodologies for the retrieval of LST are presented and implemented – namely the Generalized Split-Window (GSW) algorithm and the Dual Algorithm (DA) in its mono- and dual-channel forms – using semi-empirical functions that relate LST to top-of-atmosphere brightness temperatures in infrared window channels. The assumptions and physics underlying each methodology, as well as the uncertainties of LST estimates, are discussed. The formulations are trained using a data set of radiative transfer simulations for a wide range of atmospheric and surface conditions. The performance of each algorithm is then assessed by comparing its output against an independent set of simulations, suggesting that product uncertainties range from 2°C (for GSW and the two-channel algorithm) to 4°C (for the one-channel algorithm). Finally, LST retrievals from different GEO satellites are merged into a single field. In overlapping areas, the average discrepancies between LST products derived from GOES and from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard MSG are within 1°C during night-time, but may reach 3°C during daytime. Over those areas, the merged LST field is obtained as a weighted average of available LST retrievals for the same time slot, taking into account the respective error bar.

The United States is embarking on the implementation of an Integrated Ocean Observing System (IOOS), as part of a broader global environmental observation effort, in order to increase the understanding of the ocean environment to derive societal benefits. These benefits will be achieved by making timely, accurate observations of the environment leading to quality predictions and forecasts of the environment and the consequences on the public. To this end, IOOS will consist of an observing subsystem, a modeling and analysis subsystem, and a data management and communications subsystem that ties it all together. The observing subsystem will provide sustained in situ and remotely-sensed measurements of the coastal and global ocean. Initially, IOOS will derive observations from existing operational and pre-operational programs and subsequently incorporate observations from novel programs as they attain operational status. The Geostationary Operational Environmental Satellite (GOES) program, and its follow-on, GOES-R, will play a key role in IOOS. The IOOS Development Plan calls for the GOES program to support the coastal and global components of the observing subsystem. Coordination of technology and applications across the various international geostationary satellite programs (GOES, MTSAT, METEOSAT, INSAT) will achieve a truly global observation system. In the near-term, GOES will be able to provide global and coastal zone sea surface temperature, sea ice, and heat flux observations that will impact some of the IOOS societal goals. As GOES-R comes online in the next decade, the planned sensor suite will permit the measurement of a variety of new core parameters including ocean color, optical properties, surface currents, and possibly bathymetry. Observing those new parameters in a frequency, spatial extent, and spatial resolution never before achieved will open up a new horizon of potential impacts on the societal benefits of IOOS

A proposed instrument for deployment on next-generation Geostationary Operational Environmental Satellite (GOES) platforms is the Geostationary Synthetic Thinned Aperture Radiometer (GeoSTAR). A high-resolution full-Earth disk model has been developed to aid in the design of the instrument and to characterize sensor performance. A number of ancillary geophysical data fields are used as inputs into a radiative-transfer model that also accounts for the propagation and viewing geometries from a geostationary Earth orbit (GEO). The model produces high-resolution (10 km times 10 km) simulated full-Earth disk microwave images from GEO. The model is used as a tool to examine several critical aspects of GeoSTAR performance and design. Differential image processing is assessed as a means of mitigating the effects of the Gibbs phenomenon; its performance is found to be excellent, even with nonideal a priori information. The spatial resolution and precision of images generated at 50 GHz are evaluated. The magnitude of the highest spatial-frequency components sampled by GeoSTAR is found to be well above its minimum detectable signal. However, the differential image processing removes most of the high-frequency content, which is due to static high-contrast boundaries in the scene. Most of the residual high-frequency content lies at or below the instrument noise floor.

Advanced infrared (IR) sounders such as the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) provide atmospheric temperature and moisture profiles with high vertical resolution and high accuracy in preconvection environments. The derived atmospheric stability indices such as convective available potential energy (CAPE) and lifted index (LI) from advanced IR soundings can provide critical information 1 ~ 6 h before the development of severe convective storms. Three convective storms are selected for the evaluation of applying AIRS full spatial resolution soundings and the derived products on providing warning information in the preconvection environments. In the first case, the AIRS full spatial resolution soundings revealed local extremely high atmospheric instability 3 h ahead of the convection on the leading edge of a frontal system, while the second case demonstrates that the extremely high atmospheric instability is associated with the local development of severe thunderstorm in the following hours. The third case is a local severe storm that occurred on 7–8 August 2010 in Zhou Qu, China, which caused more than 1400 deaths and left another 300 or more people missing. The AIRS full spatial resolution LI product shows the atmospheric instability 3.5 h before the storm genesis. The CAPE and LI from AIRS full spatial resolution and operational AIRS/AMSU soundings along with Geostationary Operational Environmental Satellite (GOES) Sounder derived product image (DPI) products were analyzed and compared. Case studies show that full spatial resolution AIRS retrievals provide more useful warning information in the preconvection environments for determining favorable locations for convective initiation (CI) than do the coarser spatial resolution operational soundings and lower spectral resolution GOES Sounder retrievals.

The blurring from diffraction for the infrared (IR) radiances on a geostationary satellite (GEO) e.g., the next generation of Geostationary Operational Environmental Satellite (GOES-R) was simulated by using Moderate Resolution Imaging Spectroradiometer Airborne Simulator data and the point spread function (PSF) model for an unobscured telescope. The portion of the total radiance contributed from each nearby geometrical field of view (GFOV) was calculated. For 90% ensquared energy (EE) (equivalent to 10% of the energy coming from outside the footprint), the closest GFOVs contribute 7%; the contribution from the closest GFOVs increases to 22% for 70% EE. The increased portion from the nearby GFOVs causes larger blurring and degrades the pixel-based retrieval product accuracy. Radiance contamination from the nearby field for the GEO IR radiances with 90%, 80%, and 70% EE causes 0.2-, 0.3-, and 0.4-K blurring errors, respectively, in the 12-mum IR longwave window band in clear 300-K scenes. The blurring error is doubled in cloudy 230-K scenes. For the 13.8-mum absorption band, the blurring error will be smaller than that of the 12-mum band because the atmospheric layer where the temperature sensitivity peaks for the 13.8 mum is more uniform than the surface where the 12 mum is most sensitive. This indicates that the PSF has a greater impact on a heterogeneous surface. Similar blurring errors occur at both 4- and 10-km spatial resolution IR sensors. The blurring error is not random, and it varies spectrally. These conclusions are very relevant to the design of a cost-effective GEO IR sounder that meets the science requirements

The impacts of special Geostationary Operational Environmental Satellite (GOES) rapid-scan (RS) wind observations on numerical model 24–120-h track forecasts of Hurricane Katrina are examined in a series of data assimilation and forecast experiments. The RS wind vectors are derived from geostationary satellites by tracking cloud motions through successive 5-min images. In these experiments, RS wind observations are added over the area 15°–60°N, 60°–110°W, and they supplement the observations used in operational forecasts. The inclusion of RS wind observations reduces errors in numerical forecasts of the Katrina landfall position at 1200 UTC 29 August 2005 by an average of 12% compared to control cases that include “targeted” dropsonde observations in the Katrina environment. The largest average improvements are made to the 84- to 120-h Katrina track forecasts, rather than to the short-range track forecasts. These results suggest that RS wind observations can potentially be used in future cases to improve track forecasts of tropical cyclones.

Abstract. The ability to detect convective regions and to add latent heating to drive convection is one of the most important additions to short-term forecast models such as National Oceanic and Atmospheric Administration's (NOAA's) High-Resolution Rapid Refresh (HRRR) model. Since radars are most directly related to precipitation and are available in high temporal resolution, their data are often used for both detecting convection and estimating latent heating. However, radar data are limited to land areas, largely in developed nations, and early convection is not detectable from radars until drops become large enough to produce significant echoes. Visible and infrared sensors on a geostationary satellite can provide data that are more sensitive to small droplets, but they also have shortcomings: their information is almost exclusively from the cloud top. Relatively new geostationary satellites, Geostationary Operational Environmental Satellite-16 and Satellite-17 (GOES-16 and GOES-17), along with Himawari-8, can make up for this lack of vertical information through the use of very high spatial and temporal resolutions, allowing better observation of bubbling features on convective cloud tops. This study develops two algorithms to detect convection at vertically growing clouds and mature convective clouds using 1 min GOES-16 Advanced Baseline Imager (ABI) data. Two case studies are used to explain the two methods, followed by results applied to 1 month of data over the contiguous United States. Vertically growing clouds in early stages are detected using decreases in brightness temperatures over 10 min. For mature convective clouds which no longer show much of a decrease in brightness temperature, the lumpy texture from rapid development can be observed using 1 min high spatial resolution reflectance data. The detection skills of the two methods are validated against Multi-Radar/Multi-Sensor System (MRMS), a ground-based radar product. With the contingency table, results applying both methods to 1-month data show a relatively low false alarm rate of 14.4 % but missed 54.7 % of convective clouds detected by the radar product. These convective clouds were missed largely due to less lumpy texture, which is mostly caused by optically thick cloud shields above.

Destriping GOES images by matching empirical distribution functions

Meteorological satellites have become an indispensable tool for weather and land observation. Traditionally, geostationary (GEO) satellites have been used in operational meteorological services due to their high temporal resolution, while polar-orbiting satellites, with their high spatial resolution, are applied more to monitor environmental change and natural disasters. The development of China’s next-generation geostationary meteorological satellites (the FY-4 series) represents an exciting expansion of Chinese non-meteorological remote sensing capabilities. The first satellite (FY-4A) of the FY-4 series was launched on 11 December 2016. The Advanced Geosynchronous Radiation Imager (AGRI) on board FY-4A has 14 spectral bands (increased from the 5 bands of FY-2) that are quantized with 12 bits per pixel (up from 10 bits for FY-2) and sampled at 1 km at nadir in the visible (VIS), 2 km in the near-infrared (NIR), and 4 km in the remaining IR spectral bands (compared with 1.25 km for VIS, no NIR, and 5 km for IR of FY-2). In later satellites in the FY-4A series, the AGRI channel number will be gradually increased from 14 to 18 with IR spatial resolution of 2 km, and the full-disk temporal resolution will be enhanced from 15 to 5 min. With their improved spectral, spatial, and temporal resolution properties, the FY-4 series will gradually approach low earth orbiting (LEO) sensors in spatial and spectral resolution, which will offer greater opportunity and capability for observing small objects and rapid changes in land, ocean, and atmosphere. This review paper provides an introduction to the Chinese FY-4 observation capabilities, a comparison of FY-4 with other new-generation GEO and LEO weather satellites, and associated non-meteorological applications. A series of typical examples based on recent and on-going operational work in National Satellite Meteorological Center of China Meteorological Administration (NSMC/CMA) that use FY-4A data for non-meteorological applications are demonstrated and discussed, including (i) aerosol monitoring, (ii) dust monitoring, (iii) volcanic ash detection and aviation applications, (iv) fire detection and dynamical evaluation, (v) water body detection, and (vi) floating algae monitoring. The paper concludes with a synthesis of these application areas and the challenges that CMA has to address for future research, technological innovation, and in-depth applications.

The Chinese Area Positioning System (CAPS) is a new type of satellite navigation systems. The geostationary satellites for normal communication with 2 or 3 sets of inclined geostationary orbit (GEO) satellites are employed to construct a satellite navigation system. The navigation signals are constructed and disseminated on the ground, and the satellites only transfer the signals to the ground by transponders. Such a system is easy to construct. But for such a positioning system, it needs precise and accurate orbit determination of geostationary satellites. A GEO satellite is placed at an altitude of 36000km above the equator and its movement with respect to the station on the ground is very small, so it is very difficult to determine the orbit precisely. An original method for determination of satellite orbits based on pairs of combined observations is developed at the National Time Service Center, Chinese Academy of Sciences. A special ranging observation with the distance between two stations via a satellite, called pairs of combined observations, is proposed. The satellite behaves with such an observation is not a ball, but an ellipse ball. The Dilution of Precision (DOP) is much less than those methods such as the SLR observation. The advantage of pairs of combined observations is that clock offsets for all observing stations and on-bound are cancelled by the method itself, so, the accuracy and precision of orbit determination is not affected by the clock offsets and the DOP is greatly improved. The ranging signals are generated and measured on the ground, and the instrumentation errors can be determined in real time with a high accuracy. Therefore, such a system is very stable. A very high rate of the PN code is employed, a disseminated power is very low, and it can observe signals from remote stations with a lower power less than 1w. With the spread-spectrum technique, multi-stations can work at the same time and same carrier frequency. It can reach the accuracy of raging about 1 cm. The accuracy and precision of the orbit determination are very high, up to a few centimeters. The observing system with the model proposed above for the GEO satellites including 5 stations (one main station and 4 slave stations) placed in China has been set up. The combined observations were made between the main station and the slave stations. The number of combined observations for the 5 stations is 9. The observations are made per second. The observations for 8 sets of satellites are continuous, lasting for about 5 years. The results for the GEO observations show that the observation residuals of the satellite orbit determination for a 3-day solution are 4.0 cm. With the development of the method to determine the orbit based on the discussion above, it is very easy to correct the orbit with observations nearly in real time. Obviously, the method proposed by the National Time Service Center is suitable for precise orbit determination of GEO satellites in a satellite navigation system, and also it is suitable for satellite management and deep space tracking.

The Wide Area Augmentation System(WAAS) is the most mature satellite-based augmentation system currently available to provide higher performance navigation and positioning services for civil aviation users. WAAS broadcasts a wide range of differential correction data and integrity information to users via Geostationary Orbit(GEO) satellites to achieve enhanced navigation performance of GNSS. This paper selects the enhanced messages broadcast by different GEO satellites from February 1st to February 7th, 2022, decodes and compares the data of the messages MT2-5 and MT25, and analyzes the differences of the messages broadcast by different GEOs.