Coastal land can be lost at rapid rates due to relative sea-level rise (RSLR) resulting from local land subsidence. However, the comparative severity of local land subsidence is unknown due to high spatial variabilities and difficulties reconciling observations across localities. Here we provide self-consistent, high spatial resolution relative local land subsidence (RLLS) velocities derived from Interferometric Synthetic Aperture Radar for the 48 largest coastal cities, which represent 20% of the global urban population. We show that cities experiencing the fastest RLLS are concentrated in Asia. RLLS is also more variable across the 48 cities (−16.2 to 1.1 mm per year) than the Intergovernmental Panel on Climate Change estimations of vertical land motion (−5.2 to 4.9 mm per year). With our standardized method, the identification of relative vulnerabilities to RLLS and comparisons of RSLR effects accounting for RLLS are now possible across cities worldwide. These will better inform sustainable urban planning and future adaptation strategies in coastal cities. Coastal cities face a compound threat from relative sea-level rise and land subsidence; however, local land subsidence rates are spatially variable and can be difficult to quantify. Remote interferometric radar observations allow high-resolution estimations of local land subsidence to better inform the future of major coastal cities.

Observations of the Earth from remote sensing satellites are downloaded to various data systems on the Earth for processing, storage, and analysis. But with increasing complexity of the instruments, computer systems, science requirements, and algorithms, the amount of data to process has become so large that traditional science data systems and how data are handled can no longer be used. These forcing functions now require novel ways to handle the increased data such as moving all end-to-end data systems to be collocated in common cloud computing regions. The science data systems must also be rearchitected to be cloud enabled to be able to support petabyte-scale data processing and elastic processing demands and to utilize cloud-native services to be able to scale up efficiently to support the new data processing requirements. Science Data Systems (SDSes) provide the capability to develop, test, process, and analyze instrument observational data efficiently, systematically, and at large scales. SDSes ingest the raw satellite instrument observations and process them from low-level instrument values into higher level observational measurement values that compose the science data products. Before the advent of the cloud paradigm, the machinery for the SDS had mostly been hosted and operated on-premise at data centers owned and operated by the respective stakeholders. However, technological advancements in the satellite platform, resource availability, instrument sensitivities, communications downlink bandwidth, and improved science processing algorithms have resulted in increasingly larger mission science data volumes and data rates. These larger requirements and cost constraints are forcing functions for moving science data systems as well as other associated data systems in the enterprise onto the cloud. However, at big data scales, anomalous system behaviors such as resiliency and stability of the data system must also be addressed.

Coastal land is being lost worldwide at an alarming rate due to relative sea-level rise (RSLR) resulting from vertical land motion (VLM). This problem is understudied at a global scale, due to high spatial variability and difficulties reconciling VLM between regions. Here we provide self-consistent, high spatial resolution VLM observations derived from Interferometric Synthetic Aperture Radar for the 51 largest coastal cities, representing 22% of the global urban population. We show that peak subsidence rates are faster than current global mean sea-level rise rates and VLM contributions to RSLR are greater than IPCC projections in 90% and 53% of the cities respectively. Localized VLM worsens RSLR impacts on land and population in 73-75% of the cities, with Chittagong (Bangladesh), Yangon (Myanmar) and Jakarta (Indonesia) at greatest risk. With this dataset, accurate projections and comparisons of RSLR effects accounting for VLM are now possible for urban areas at a global scale.

A method is described to characterize the scale dependence of cloud chord length using cloud-type classification reported with the 94-GHz CloudSat radar. The cloud length along the CloudSat track is quantified using horizontal and vertical structures of cloud classification separately for each cloud type and for all clouds independent of cloud type. While the individual cloud types do not follow a clear power-law behavior as a function of horizontal or vertical scale, a robust power-law scaling of cloud chord length is observed when cloud type is not considered. The exponent of horizontal length is approximated by beta approximate to 1.66 + 0.00 across two orders of magnitude (similar to 10-1000km). The exponent of vertical thickness is approximated by beta approximate to 2.23 +/- 0.03 in excess of one order of magnitude (similar to 1-14 km). These exponents are in agreement with previous studies using numerical models, satellites, dropsondes, and in situ aircraft observations. These differences in horizontal and vertical cloud scaling are consistent with scaling of temperature and horizontal wind in the horizontal dimension and with scaling of buoyancy flux in the vertical dimension. The observed scale dependence should serve as a guide to test and evaluate scale-cognizant climate and weather numerical prediction models.

We use the Atmospheric Infrared Sounder (AIRS) version 6 ice cloud property and thermodynamic phase retrievals to quantify variability and 14-year trends in ice cloud frequency, ice cloud top temperature (Tci), ice optical thickness (τi) and ice effective radius (rei). The trends in ice cloud properties are shown to be independent of trends in information content and χ2. Statistically significant decreases in ice frequency, τi, and ice water path (IWP) are found in the SH and NH extratropics, but trends are of much smaller magnitude and statistically insignificant in the tropics. However, statistically significant increases in rei are found in all three latitude bands. Perturbation experiments consistent with estimates of AIRS radiometric stability fall significantly short of explaining the observed trends in ice properties, averaging kernels, and χ2 trends. Values of rei are larger at the tops of opaque clouds and exhibit dependence on surface wind speed, column water vapour (CWV) and surface temperature (Tsfc) with changes up to 4–5 µm but are only 1.9 % of all ice clouds. Non-opaque clouds exhibit a much smaller change in rei with respect to CWV and Tsfc. Comparisons between DARDAR and AIRS suggest that rei is smallest for single-layer cirrus, larger for cirrus above weak convection, and largest for cirrus above strong convection at the same cloud top temperature. This behaviour is consistent with enhanced particle growth from radiative cooling above convection or large particle lofting from strong convection.

The April 25, 2015 M7.8 Gorkha earthquake caused more than 8,000 fatalities and widespread building damage in central Nepal. Four days after the earthquake, the Italian Space Agency's (ASI's) COSMO-SkyMed Synthetic Aperture Radar (SAR) satellite acquired data over Kathmandu area. Nine days after the earthquake, the Japan Aerospace Exploration Agency's (JAXA's) ALOS-2 SAR satellite covered larger area. Using these radar observations, we rapidly produced damage proxy maps derived from temporal changes in Interferometric SAR (InSAR) coherence. These maps were qualitatively validated through comparison with independent damage analyses by National Geospatial-Intelligence Agency (NGA) and the UNITAR's (United Nations Institute for Training and Research's) Operational Satellite Applications Programme (UNOSAT), and based on our own visual inspection of DigitalGlobe's WorldView optical pre- vs. post-event imagery. Our maps were quickly released to responding agencies and the public, and used for damage assessment, determining inspection/imaging priorities, and reconnaissance fieldwork.

The 25 April 2015 M-w 7.8 Gorkha earthquake caused more than 8000 fatalities and widespread building damage in central Nepal. The Italian Space Agency's COSMO-SkyMed Synthetic Aperture Radar (SAR) satellite acquired data over Kathmandu area four days after the earthquake and the Japan Aerospace Exploration Agency's Advanced Land Observing Satellite-2 SAR satellite for larger area nine days after the main-shock. We used these radar observations and rapidly produced damage proxy maps (DPMs) derived from temporal changes in Interferometric SAR coherence. Our DPMs were qualitatively validated through comparison with independent damage analyses by the National Geospatial-Intelligence Agency and the United Nations Institute for Training and Research's United Nations Operational Satellite Applications Programme, and based on our own visual inspection of DigitalGlobe's World-View optical pre- versus postevent imagery. Our maps were quickly released to responding agencies and the public, and used for damage assessment, determining inspection/imaging priorities, and reconnaissance fieldwork.

The uncertainties of the Atmospheric Infrared Sounder (AIRS) Level 2 version 6 specific humidity (q) and temperature (T) retrievals are quantified as functions of cloud types by comparison against Integrated Global Radiosonde Archive radiosonde measurements. The cloud types contained in an AIRS/Advanced Microwave Sounding Unit footprint are identified by collocated Moderate Resolution Imaging Spectroradiometer retrieved cloud optical depth (COD) and cloud top pressure. We also report results of similar validation of q and T from European Centre for Medium-Range Weather Forecasts (ECMWF) forecasts (EC) and retrievals from the AIRS Neural Network (NNW), which are used as the initial state for AIRS V6 physical retrievals. Differences caused by the variation in the measurement locations and times are estimated using EC, and all the comparisons of data sets against radiosonde measurements are corrected by these estimated differences. We report in detail the validation results for AIRS GOOD quality control, which is used for the AIRS Level 3 climate products. AIRS GOOD quality q reduces the dry biases inherited from the NNW in the middle troposphere under thin clouds but enhances dry biases in thick clouds throughout the troposphere (reaching -30% at 850hPa near deep convective clouds), likely because the information contained in AIRS retrievals is obtained in cloud-cleared areas or above clouds within the field of regard. EC has small moist biases (similar to 5-10%), which are within the uncertainty of radiosonde measurements, in thin and high clouds. Temperature biases of all data are within 1K at altitudes above the 700hPa level but increase with decreasing altitude. Cloud-cleared retrievals lead to large AIRS cold biases (reaching about -2K) in the lower troposphere for large COD, enhancing the cold biases inherited from the NNW. Consequently, AIRS GOOD quality T root-mean-squared errors (RMSEs) are slightly smaller than the NNW errors in thin clouds (1.5-2.5K) but slightly larger than the NNW errors for thick COD (reaching 3.5K near the surface). The AIRS BEST quality control retains retrievals with uncertainties closer to those of the NNW. The AIRS error estimates reported in the L2 product tend to underestimate the precision (RMSE) implied by comparisons to the radiosonde measurements and do not reflect the observed cloud dependency of uncertainties.

The precision, accuracy, and potential sampling biases of temperature T and water vapor q vertical profiles obtained by satellite infrared sounding instruments are highly cloud-state dependent and poorly quantified. The authors describe progress toward a comprehensive T and q climatology derived from the Atmospheric Infrared Sounder (AIRS) suite that is a function of cloud state based on collocated CloudSat observations. The AIRS sampling rates, biases, and center root-mean-square differences (CRMSD) are determined through comparisons of pixel-scale collocated ECMWF model analysis data. The results show that AIRS provides a realistic representation of most meteorological regimes in most geographical regions, including those dominated by high thin cirrus and shallow boundary layer clouds. The mean AIRS observational biases relative to the ECMWF analysis between the surface and 200 hPa are within +/- 1 K in T and from -1 to +0.5 g kg(-1) in q. Biases because of cloud-state-dependent sampling dominate the total biases in the AIRS data and are largest in the presence of deep convective (DC) and nimbostratus (Ns) clouds. Systematic cold and dry biases are found throughout the free troposphere for DC and Ns. Somewhat larger biases are found over land and in the midlatitudes than over the oceans and in the tropics, respectively. Tropical and oceanic regions generally have a smaller CRMSD than the midlatitudes and over land, suggesting agreement of T and q variability between AIRS and ECMWF in these regions. The magnitude of CRMSD is also strongly dependent on cloud type.

Multi-decadal climate data records are critical to studying climate variability and change. These often also require merging data from multiple instruments such as those from NASA's A-Train that contain measurements covering a wide range of atmospheric conditions and phenomena. Multi-decadal climate data record of water vapor measurements from sensors on A-Train, operational weather, and other satellites are being assembled from existing data sources, or produced from well-established methods published in peer-reviewed literature. However, the immense volume and inhomogeneity of data often requires an "exploratory computing" approach to product generation where data is processed in a variety of different ways with varying algorithms, parameters, and code changes until an acceptable product is generated. Furthermore, the data product information associated with source data, processing methods, parameters used, intermediate & final product outputs, and associated materials are often hidden in each of the trials and scattered throughout the processing system(s). We will present methods to help users better capture and explore the production legacy of the data, metadata, ancillary files, code, and computing environment changes used during the production of these merged and multi-sensor data products. By building provenance services on semantic and provenance technologies, we show how to leverage provenance-as-a-service to capture sufficient information to enable users to track processing, perform faceted searches on the provenance record, and visualize the provenance of the products and processing lineage. We will also present services for capturing sufficient provenance information and the associated artifacts to enable some reproducibility of these climate data records.

With its high accuracy, stability, and worldwide coverage GPS radio occultation offers an attractive means of independently validating and calibrating the world's premier weather and climate sensors. These include such instruments as AIRS, AMSU, and MODIS on NASA's EOS platforms, and similar systems on operational weather satellites. GPSRO also offers a valuable comparison standard for global weather analyses, such as those produced by NOAA's National Center for Environmental Predictions (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF). We have studied the performance of GPSRO temperature profiles through comparisons of coincident data from CHAMP and SAC-C, as well as from COSMIC. We have also compared GPSRO temperature profiles with nearby profiles from AIRS (Atmospheric Infrared Sounder), carried on NASA's Aqua platform, and with the ECMWF analyses. Our principal findings are: •AIRS and ECMWF temperature profiles depart in systematic ways from GPSRO profiles. These departures are highly repeatable and vary by geographical region. •There is significant correlation between the AIRS and ECMWF departures from GPSRO, not explainable by GPSRO error. This may arise because AIRS retrievals are initialized with estimates derived from ECMWF training samples. •ECMWF single-profile RMS temperature deviations range between 0.6 and 1.8 K and are at a maximum near the tropopause. Biases are typically below 0.5 K. •AIRS single-profile RMS temperature deviations range between 0.9 and 2.2 K and are also at a maximum near the tropopause. Biases are typically below 0.5 K but reach 1 K near the tropopause in the Antarctic.

GENESIS/SciFlo is a Grid-based automated workflow execution system for conducting atmospheric and climate studies that integrate data sets from EOS and other instruments. Many components of SciFlo are in experimental operation and several scientific studies making use of these tools are underway. These include validation studies of AIRS, MODIS, and GPS-based atmospheric temperature and moisture data, atmospheric aerosol studies with MISR data, and cloud model validation with multiple datasets. The first public version of SciFlo will be rolled out in late 2006 as part of the ESIP Federation's new portal , the Earth Information Exchange (EIE). The EIE will be a " community " within the government-wide Geospatial One-Stop (GOS). SciFlo will enable the chaining of data services from multiple providers into an automated end-to-end investigation. Services will include data discovery, subsetting, co-registration, data mining, and analysis. At present its functions are restricted to the developmental scenarios of the GENESIS project.

SciFlo is a system for Scientific Knowledge Creation on the Grid using a Semantically-Enabled Dataflow Execution Environment. SciFlo leverages Simple Object Access Protocol (SOAP) Web Services and the Grid Computing standards (WS-* & Globus Alliance toolkits), and enables scientists to do multi-instrument Earth Science by assembling reusable Web Services and native executables into a distributed computing flow (operator graph). SciFlo's XML dataflow documents can be a mixture of concrete operators (fully bound operations) and abstract template operators (late binding via semantic lookup). All data objects and operators can be both simply typed (simple and complex types in XML schema) and semantically typed (linked to OWL ontologies). We will demonstrate a version of the SciFlo workflow engine executing a variety of workflow documents. [Note: This document is a quick description of the proposed demo, not a fully referenced and defended paper.]