Evaluating Consistency of Snow Water Equivalent Retrievals from Passive Microwave Sensors over the North Central U. S.: SSM/I vs. SSMIS and AMSR-E vs. AMSR2

For four decades, satellite-based passive microwave sensors have provided valuable snow water equivalent (SWE) monitoring at a global scale. Before continuous long-term SWE records can be used for scientific or applied purposes, consistency of SWE measurements among different sensors is required. SWE retrievals from two passive sensors currently operating, the Special Sensor Microwave Imager Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), have not been fully evaluated in comparison to each other and previous instruments. Here, we evaluated consistency between the Special Sensor Microwave/Imager (SSM/I) onboard the F13 Defense Meteorological Satellite Program (DMSP) and SSMIS onboard the F17 DMSP, from November 2002 to April 2011 using the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) for continuity. Likewise, we evaluated consistency between AMSR-E and AMSR2 SWE retrievals from November 2007 to April 2016, using SSMIS for continuity. The analysis is conducted for 1176 watersheds in the North Central U.S. with consideration of difference among three snow classifications (Warm forest, Prairie, and Maritime). There are notable SWE differences between the SSM/I and SSMIS sensors in the Warm forest class, likely due to the different interpolation methods for brightness temperature (Tb) between the F13 SSM/I and F17 SSMIS sensors. The SWE differences between AMSR2 and AMSR-E are generally smaller than the differences between SSM/I and SSMIS SWE, based on time series comparisons and yearly mean bias. Finally, the spatial bias patterns between AMSR-E and AMSR2 versus SSMIS indicate sufficient spatial consistency to treat the AMSR-E and AMSR2 datasets as one continuous record. Our results provide useful information on systematic differences between recent satellite-based SWE retrievals and suggest subsequent studies to ensure reconciliation between different sensors in long-term SWE records

Selection of the key earth observation sensors and platforms focusing on applications for Polar Regions in the scope of Copernicus system 2020-2030

An optimal payload selection conducted in the frame of the H2020 ONION project (id 687490) is presented based on the ability to cover the observation needs of the Copernicus system in the time period 2020–2030. Payload selection is constrained by the variables that can be measured, the power consumption, and weight of the instrument, and the required accuracy and spatial resolution (horizontal or vertical). It involved 20 measurements with observation gaps according to the user requirements that were detected in the top 10 use cases in the scope of Copernicus space infrastructure, 9 potential applied technologies, and 39 available commercial platforms. Additional Earth Observation (EO) infrastructures are proposed to reduce measurements gaps, based on a weighting system that assigned high relevance for measurements associated to Marine for Weather Forecast over Polar Regions. This study concludes with a rank and mapping of the potential technologies and the suitable commercial platforms to cover most of the requirements of the top ten use cases, analyzing the Marine for Weather Forecast, Sea Ice Monitoring, Fishing Pressure, and Agriculture and Forestry: Hydric stress as the priority use cases.Peer ReviewedPostprint (published version

Exploring the Trade-offs of Aggregated versus Disaggregated Architectures for Environmental Monitoring in Low-Earth Orbit

Traditionally, government space agencies have developed aggregated systems that co-host multiple capabilities on shared spacecraft buses. However, in response to cost growth and schedule delays on past programs, leaders in the government space community have expressed an interest in disaggregation, or distributing their capabilities across multiple spacecraft. Since their aggregated National Polar-orbiting Operational Satellite System (NPOESS) program was cancelled in 2010, both the National Oceanic and Atmospheric Administration (NOAA) and the Department of Defense (DoD) have investigated opportunities to reduce program costs through disaggregation. This paper expands their initial investigation and explores the cost impacts of aggregation and disaggregation across a large trade space of candidate architectures for environmental monitoring in low-Earth orbit. We find that on average, aggregated architectures are less costly than fully disaggregated ones but also find opportunities for cost savings by developing semi-aggregated systems, or systems with one or two satellites per orbital plane. Finally, we investigate several trades that are currently under consideration by NOAA and the DoD and make recommendations for future environmental monitoring systems in low-Earth orbit.Massachusetts Institute of Technology (Sandia Corporation Excellence in Engineering Graduate Fellowship)Skolkovo Institute of Science and Technolog

EOS Data and Information System (EOSDIS)

In the past decade, science and technology have reached levels that permit assessments of global environmental change. Scientific success in understanding global environmental change depends on integration and management of numerous data sources. The Global Change Data and Information System (GCDIS) must provide for the management of data, information dissemination, and technology transfer. The Earth Observing System Data and Information System (EOSDIS) is NASA's portion of this global change information system

The status of environmental satellites and availability of their data products

The latest available information about the status of unclassified environmental satellite (flown by the United States) and their data products is presented. The type of environmental satellites discussed include unmanned earth resource and meteorological satellites, and manned satellites which can act as a combination platform for instruments. The capabilities and data products of projected satellites are discussed along with those of currently operating systems

Three Dimensional Radiative Effects in Passive Millimeter/Sub-Millimeter All-sky Observations

This study was conducted to quantify the errors prompted by neglecting three-dimensional (3D) effects, i.e., beam-filling and horizontal photon transport effects, at millimeter/sub-millimeter wavelengths. This paper gives an overview of the 3D effects that impact ice cloud retrievals of both current and proposed (Ice Cloud Imager) satellite instruments operating at frequencies of approximate to 186.3 and approximate to 668 GHz. The 3D synthetic scenes were generated from two-dimensional (2D) CloudSat (Cloud Satellite) observations over the tropics and mid-latitudes using a stochastic approach. By means of the Atmospheric Radiative Transfer Simulator (ARTS), three radiative transfer simulations were carried out: one 3D, one independent beam approximation (IBA), and a one-dimensional (1D). The comparison between the 3D and IBA simulations revealed a small horizontal photon transport effect, with IBA simulations introducing mostly random errors and a slight overestimation (below 1 K). However, performing 1D radiative transfer simulations results in a significant beam-filling effect that increases primarily with frequency, and secondly, with footprint size. For a sensor footprint size of 15 km, the errors induced by neglecting domain heterogeneities yield root mean square errors of up to approximate to 4 K and approximate to 13 K at 186.3 GHz and 668 GHz, respectively. However, an instrument operating at the same frequencies, but with a much smaller footprint size, i.e., 6 km, is subject to smaller uncertainties, with a root mean square error of approximate to 2 K at 186.3 GHz and approximate to 7.1 K at 668 GHz. When designing future satellite instruments, this effect of footprint size on modeling uncertainties should be considered in the overall error budget. The smallest possible footprint size should be a priority for future sub-millimeter observations in light of these results

Abbreviations and acronyms

This booklet provides a partial list of acronyms, abbreviations, and other short word forms, including their definitions, used in documents at the Goddard Space Flight Center (GSFC). This list does not preclude the use of other short forms of less general usage, as long as these short forms are identified the first time they appear in a document and are defined in a glossary in the document in which they are used. This document supplements information in the GSFC Scientific and Technical Information Handbook (GHB 2200.2/April 1989). It is not intended to contain all short word forms used in GSFC documents; however, it was compiled of actual short forms used in recent GSFC documents. The entries are listed first, alphabetically by the short form, and then again alphabetically by definition

Global Precipitation Measurement (GPM): Unified Precipitation Estimation From Space

Global Precipitation Measurement (GPM) is an international satellite mission that uses measurements from an advanced radar/radiometer system on a Core Observatory as reference standards to unify and advance precipitation estimates through a constellation of research and operational microwave sensors. GPM is a science mission focusing on a key component of the Earth's water and energy cycle, delivering near real-time observations of precipitation for monitoring severe weather events, freshwater resources, and other societal applications. This work presents the GPM mission design, together with descriptions of sensor characteristics, inter-satellite calibration, retrieval methodologies, ground validation activities, and societal applications

RETRIEVAL OF ICE CLOUD PARAMETERS USING DMSP SPECIAL SENSOR MICROWAVE IMAGER/SOUNDER

Clouds exert a profound influence on both the water balance of the atmosphere and the earth's radiation budget (Stephens 2005; Stephens and Kummerow 2007). Among the global distribution, 30% of them are ice clouds (Riedi et al. 2000). It is important to improve our knowledge of the ice cloud properties in order to determine their influence to the global ecosystem. For ice clouds with millimeter-size ice particles, which are generally found in anvil cirrus and deep convections, microwave and millimeter wave length satellite measurements are suitable for the ice cloud microphysical property retrieval because of its strong ability to penetrate deeper into dense ice clouds. For these types of ice clouds, brightness temperatures at the top of the atmosphere are analytically derived as a function of vertically integrated ice water content (i.e. ice water path), effective particle diameter, and bulk volume density. In general, three brightness temperature measurements are needed to retrieve the three ice cloud microphysical parameters. A two-stream radiative transfer theory was applied to data from the Advanced Microwave Sounding Unit (AMSU) and the Moisture Humidity Sensor (MHS) in order to generate global ice water paths operationally. This research further applied the model and theory to derive ice water path (IWP) from the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite Program (DMSP) F-16 satellite. Compared to AMSU/MHS, which have field of views (FOV) varying with scan position, SSMIS scans the Earth's atmosphere at a constant viewing angle of 53o and therefore offers a uniform FOV within each scan. This unique feature allows for improved global mapping and monitoring of ice clouds so that a more accurate and realistic IWP and ice particle effective diameter distribution is expected. A direct application of SSMIS-derived ice water path is its relationship with surface rain rate as derived previously for AMSU and MHS instruments. Here, SSMIS-derived rain rate was compared to the AMSU and MHS rainfall products and hourly synthetic precipitation observations from rain gauges and surface radar. Results show that SSMIS surface precipitation distribution is spatially consistent and does not have apparent artificial boundary near coastal zones as previously seen in other algorithms. Also, the ice water path associated with a severe storm reasonably delineates the strong convective precipitation areas and has a spatial variation consistent with surface precipitation. From retrieved instantaneous surface precipitation, a tropical and subtropical oceanic precipitation anomaly time series is constructed from 5 year's worth (2005-2009) of SSMIS data. This data record is also linked to the previous constructed SSM/I 15-year (1992-2006) data record to provide a longer term climate study by satellite observations. In future studies, refined algorithms for the estimate of ice cloud base temperature and ice particle bulk volume density are going to be developed to improve the accuracy of IWP retrieval under various cloud vertical distributions. Meanwhile, a better inter-sensor cross calibration scheme is the key to make satellite measurements more useful in climate change study