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This presentation documents the progress made and challenges remaining in the development of NASA's 4D space-time mapping CAA code.
This presentation documents the progress made and challenges remaining in the development of NASA's 4D space-time mapping CAA code.
As a preliminary study for the future worldwide direct mapping of the open ocean tide with satellites equipped with precision altimeters we conducted a simulated study using sets of artificially generated altimeter data constructed from a realistic geoid and four pairs of major tides in the northeastern Pacific Ocean. Recovery of the original geoid and eight tidal maps is accomplished by a space-time, least squares harmonic analysis scheme. The resultant maps appear fairly satisfactory even when random noises up to + or - 100 cm are added to the altimeter data of sufficient space-time density. The method also produces a refined geoid which is rigorously corrected for the dynamic tides.
Satellite measurements of global aerosol properties are very useful in constraining aerosol parameterization in climate models. The reliability of different data sets in representing global and regional aerosol variability becomes an essential question. In this study, we present the results of a comparison using combined principal component analysis (CPCA), applied to monthly mean, mapped (Level 3) aerosol optical depth (AOD) product from Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Ozone Monitoring Instrument (OMI). This technique effectively finds the common space-time variability in the multiple data sets by decomposing the combined AOD field. The results suggest that all of the sensors capture the globally important aerosol regimes, including dust, biomass burning, pollution, and mixed aerosol types. Nonetheless, differences are also noted. Specifically, compared with MISR and OMI, MODIS variability is significantly higher over South America, India, and the Sahel. MODIS deep blue AOD has a lower seasonal variability in North Africa, accompanied by a decreasing trend that is not found in either MISR or OMI AOD data. The narrow swath of MISR results in an underestimation of dust variability over the Taklamakan Desert. The MISR AOD data also exhibit overall lower variability in South America and the Sahel. OMI does not capture the Russian wild fire in 2010 nor the phase shift in biomass burning over East South America compared to Central South America, likely due to cloud contamination and the OMI row anomaly. OMI also indicates a much stronger (boreal) winter peak in South Africa compared with MODIS and MISR.
Lightning is being studied relative to storm structure using a VHF space-time discharge mapping system, radar, a cloud-to-ground flash locator, acoustic reconstruction of thunder, and other instrumentation. The horizontal discharge processes within the cloud generally propagate at speeds of 10,000-100,000 m/s. Horizontal extents of lightning were found up to 90 km. In an analysis of a limited number of flashes, lightning occurred in or near regions of high cyclonic shear. Positive cloud-to-ground flashes have been observed emanating from several identifiable regions of severe storms. Lightning echoes observed with 10-cm radar generally are 10-25 dB greater than the largest precipitation echo in the storm.
Land surface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the stocks and fluxes of water (including soil moisture, snow, evaporation, and runoff) and energy (including the temperature of and sensible heat released from the soil) after they arrive on the land surface as precipitation and sunlight. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy and space-time resolution. Hence LSMs have been developed to integrate the available observations with our understanding of the physical processes involved, using powerful computers, in order to map these stocks and fluxes as they change in time. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth's water cycle and climate variability. NASA's Global Land Data Assimilation System (GLDAS) project facilitates testing of several different LSMs with a variety of input datasets (e.g., precipitation, plant type). Precipitation is arguably the most important input to LSMs. Many precipitation datasets have been produced using satellite and rain gauge observations and weather forecast models. In this study, seven different global precipitation datasets were evaluated over the United States, where dense rain gauge networks contribute to reliable precipitation maps. We then used the seven datasets as inputs to GLDAS simulations, so that we could diagnose their impacts on output stocks and fluxes of water. In terms of totals, the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) had the closest agreement with the US rain gauge dataset for all seasons except winter. The CMAP precipitation was also the most closely correlated in time with the rain gauge data during spring, fall, and winter, while the satellitebased estimates performed best in summer. The GLDAS simulations revealed that modeled soil moisture is highly sensitive to precipitation, with differences in spring and summer as large as 45% depending on the choice of precipitation input.
Topex/Poseidon altimetry data are used to estimate the non-tidal mass redistribution as a function of space-time. The goal is to study the contribution of ocean circulations in the geodynamic effects including Earth's rotational and gravitational variations. We examine the non-seasonal anomalies at monthly sampling rate over the T/P span of eight years, concentrating especially on interannual variabilities. Since the sea-surface height data obtained from altimetry is the combined effect of steric change (primarily thermal effect) and the mass flux, and because the former has zero contribution to the geodynamic effects, one needs to do a so-called steric correction by removing from the altimetry data the steric contributions. We achieve it using multiyear monthly sea-surface temperature maps, together with monthly "climatology" for mixed-layer depth maps for mean-months of the year. We analyze both sets of the altimetry data and the steric correction data using the empirical orthogonal function/principal component analysis (in which we take care of issues associated with the area-weighting and non-zero mean), and examine the most important modes, either globally or regionally. In particular, the ENSO in the tropical Pacific/Indian Oceans exhibits the most prominent pattern. The net mass transport after the steric correction can then be compared with: (1) ocean general circulation model outputs for the same period of time (such as from POCM-4B); (2) non-atmospheric Earth rotation variations obtained from space geodesy data and atmospheric angular momentum data; (3) non-atmospheric low-degree gravitational variations from satellite-laser-ranging observations and global atmospheric data.
Work under this grant has used information on precipitation and water vapor fluxes in the area of the Mexican Monsoon to analyze the regional precipitation climatology, to understand the nature of water vapor transport during the monsoon using model and observational data, and to analyze the ability of the TRMM remote sensing algorithm to characterize precipitation. An algorithm for estimating daily surface rain volumes from hourly GOES infrared images was developed and compared to radar data. Estimates were usually within a factor of two, but different linear relations between satellite reflectances and rainfall rate were obtained for each day, storm type and storm development stage. This result suggests that using TRMM sensors to calibrate other satellite IR will need to be a complex process taking into account all three of the above factors. Another study, this one of the space-time variability of the Mexican Monsoon, indicate that TRMM will have a difficult time, over the course of its expected three year lifetime, identifying the diurnal cycle of precipitation over monsoon region. Even when considering monthly rainfalls, projected satellite estimates of August rainfall show a root mean square error of 38 percent. A related examination of spatial variability of mean monthly rainfall using a novel method for removing the effects of elevation from gridded gauge data, show wide variation from a satellite-based rainfall estimates for the same time and space resolution. One issue addressed by our research, relating to the basic character of the monsoon circulation, is the determination of the source region for moisture. The monthly maps produced from our study of monsoon variability show the presence of two rainfall maxima in the analysis normalized to sea level, one in south-central Arizona associated with the Mexican monsoon maximum and one in southeastern New Mexico associated with the Gulf of Mexico. From the point of view of vertically-integrated fluxes and flux divergence of water vapor from ECMWF data, most moisture at upper levels arrives from the Gulf of Mexico, while low level moisture comes from the northern Gulf of California. Composites of ECMWF analyses for wet and dry periods (classified by rain gauge data) show that both regimes show low level moisture arriving from northern and central Gulf of California. Above 700 MB, moisture comes from both source regions and the Sierra Madre Occidental. During wet periods a longer fetch through the moist air mass above western Mexico results in a greater moisture flux into the Sonoran Desert region, while there is less moisture from the Gulf of Mexico both above and below 700 mb. Work on the grant subcontract at the University of Colorado concentrated on the development of a technique useful to TRMM combining visible, infrared and passive microwave data for measuring precipitation. Two established techniques using either visible or infrared data applied over the US Southwest correlated with gauges at the 0.58 to 0.70 level. The application of some established passive microwave techniques were less successful for a variety of reason, including problems in both the gauge and satellite data quality, sampling problems and weaknesses inherent in the algorithms themselves. A more promising solution for accurate rainfall estimation was explored using visible and infrared data to perform a cloud classification, which when combined with information about the background (e.g. Iand/ocean), was used to select the most appropriate microwave algorithm from a suite of possibilities.
Scale is an "innate" concept in geographic information systems. It is recognized as something that is intrinsic to the ingestion, storage, manipulation, analysis, modeling, and output of space and time data within a GIS purview, yet the relative meaning and ramifications of scaling spatial and temporal data from this perspective remain enigmatic. As GISs become more sophisticated as a product of more robust software and more powerful computer systems, there is an urgent need to examine the issue of scale, and its relationship to the whole body of spatiotemporal data, as imparted in GISS. Scale is fundamental to the characterization of geo-spatial data as represented in GISS, but we have relatively little insight on the effects of, or how to measure the effects of, scale in representing multiscaled data; i.e., data that are acquired in different formats (e.g., map, digital) and exist in varying spatial, temporal, and in the case of remote sensing data, radiometric, configurations. This is particularly true in the emerging era of Integrated GISs (IGIS), wherein spatial data in a variety of formats (e.g., raster, vector) are combined with multiscaled remote sensing data, capable of performing highly sophisticated space-time data analyses and modeling. Moreover, the complexities associated with the integration of multiscaled data sets in a multitude of formats are exacerbated by the confusion of what the term "scale" is from a multidisciplinary perspective; i.e., "scale" takes on significantly different meanings depending upon one's disciplinary background and spatial perspective which can lead to substantive confusion in the input, manipulation, analyses, and output of IGISs (Quattrochi, 1993). Hence, we must begin to look at the universality of scale and begin to develop the theory, methods, and techniques necessary to advance knowledge on the "Science of Scale" across a wide number of spatial disciplines that use GISs.
During the past year the ACS science team has concentrated on analyzing ACS observations, writing papers, and disseminating our results to the astronomy community at conferences and workshops around the world. We also have put considerable effort in getting our results to the public via public lectures and through press releases. Taking a very broad view of our program, we are investigating the evolution of galaxies and clusters of galaxies from their birth, approximately one billion years after the beginning of the Universe, to the present. We have found and characterized a population of galaxies that are no more than 1.4 billion years old. These may well be the Universe s first generation of infant galaxies. Looking at the Universe 500,000 years later, we see what appears to be a cluster of galaxies just beginning to form (a proto-cluster) around a luminous radio galaxy. Moving forward in time and closer to the present, we are studying clusters of galaxies that are less than half the age of the Universe. Our observations and analysis lead us to the important conclusion that the elliptical galaxies in these clusters must have had their last significant star formation some three billion years earlier, which is about the time when the proto-cluster was forming. Coming still closer to home, we are observing nearby massive clusters of galaxies that are approximately 12 billion years old. The gravity from these large aggregates of dark and luminous matter is so strong it warps space-time itself, and makes the cluster act as a cosmic telescope that magnifies the distant galaxies behind the cluster. We used the magnified (or lensed) galaxies to map the distribution of the dominant matter within the clusters, which is the so-called dark matter (the matter is invisible, and its nature is unknown). We also are using these cosmic telescopes to study the distant lensed galaxies that would otherwise be too small and too faint to be seen even by Hubble and the ACS.