Bruce Vollmer

Estimating Sampling Biases and Measurement Uncertainties of AIRS-AMSU-A Temperature and Water Vapor Observations Using MERRA Reanalysis

We use MERRA (Modern Era Retrospective-Analysis for Research Applications) temperature and water vapor data to estimate the sampling biases of climatologies derived from the AIRS/AMSU-A (Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A) suite of instruments. We separate the total sampling bias into temporal and instrumental components. The temporal component is caused by the AIRS/AMSU-A orbit and swath that are not able to sample all of time and space. The instrumental component is caused by scenes that prevent successful retrievals. The temporal sampling biases are generally smaller than the instrumental sampling biases except in regions with large diurnal variations, such as the boundary layer, where the temporal sampling biases of temperature can be ± 2 K and water vapor can be 10% wet. The instrumental sampling biases are the main contributor to the total sampling biases and are mainly caused by clouds. They are up to 2 K cold and > 30% dry over midlatitude storm tracks and tropical deep convective cloudy regions and up to 20% wet over stratus regions. However, other factors such as surface emissivity and temperature can also influence the instrumental sampling bias over deserts where the biases can be up to 1 K cold and 10% wet. Some instrumental sampling biases can vary seasonally and/or diurnally. We also estimate the combined measurement uncertainties of temperature and water vapor from AIRS/AMSU-A and MERRA by comparing similarly sampled climatologies from both data sets. The measurement differences are often larger than the sampling biases and have longitudinal variations.

Evolution of Information Management at the GSFC Earth Sciences (GES) Data and Information Services Center (DISC): 2006–2007

Increasingly sophisticated National Aeronautics and Space Administration (NASA) Earth science missions have driven their associated data and data management systems from providing simple point-to-point archiving and retrieval to performing user-responsive distributed multisensor information extraction. To fully maximize the use of remote-sensor-generated Earth science data, NASA recognized the need for data systems that provide data access and manipulation capabilities responsive to research brought forth by advancing scientific analysis and the need to maximize the use and usability of the data. The decision by NASA to purposely evolve the Earth Observing System Data and Information System (EOSDIS) at the Goddard Space Flight Center (GSFC) Earth Sciences (GES) Data and Information Services Center (DISC) and other information management facilities was timely and appropriate. The GES DISC evolution was focused on replacing the EOSDIS Core System (ECS) by reusing the in-house developed disk-based Simple, Scalable, Script-based Science Product Archive (S4PA) data management system and migrating data to the disk archives. Transition was completed in December 2007.

Assessment of Precipitation Anomalies in California Using TRMM and MERRA Data

Using modern satellite (Tropical Rainfall Measuring Mission, TRMM, 1998–2014) and reanalysis (Modern-Era Retrospective Analysis for Research and Applications, MERRA, 1979–2015) data, we reassess certain aspects of the precipitation climate in California from the past decades. California has a well-pronounced rain season that peaks in December–February. However, the 95% confidence interval around the climatological precipitation during these months imply that deviations on the order of 60% of the expected amounts are very likely during the most important period of the rain season. While these positive and negative anomalies alternate almost every year and tend to cancel each other, severe multiyear declines of precipitation in California appear on decadal scales. The 1986–1994 decline of precipitation was similar to the current one that started in 2011 and is apparent in the reanalysis data. In terms of accumulated deficits of precipitation, that episode was no less severe than the current one. While El Nino (the warm phase of the El Nino–Southern Oscillation, ENSO) is frequently cited as the natural forcing expected to bring a relief from drought, our assessment is that ENSO has been driving at best only 6% of precipitation variability in California in the past three decades. Using fractional risk analysis of precipitation during typical versus drying periods, we show that the likelihood of losing the most intensive precipitation events drastically increases during the multiyear drying events. Storms delivering up to 50% of the precipitation in California are driven by atmospheric rivers making landfall. However, these phenomena can be suppressed and even blocked by persistent ridges of atmospheric pressure in the northeast Pacific. The reanalysis and satellite data are proven to be reliable to the extent where they yield information on developing conditions and observed precipitation anomalies.

Advances in

NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) archives and distributes pioneering collections of data on atmospheric greenhouse gases. In September of 2012, the Atmospheric Infrared Sounder (AIRS) marked a decade of tropospheric observations of carbon dioxide (CO2). Most recently, the Atmospheric CO2 Observations from Space (ACOS) project and GES DISC released CO2 retrievals derived from radiances observed by the Japanese Greenhouse gases Observing SATellite (GOSAT) satellite, launched in 2009. In this letter, we present the most recent estimates of decadal mid-tropospheric trends of CO2 from AIRS, as well as the most recent status of the total column-average distribution of CO2 from ACOS. We also demonstrate that significant discrepancies still exist in the global distribution of observed and modeled column amounts of CO2 using the CO2 retrievals from the ACOS project.

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The past decade has seen a rapid increase in availability and usage of near real-time data from satellite sensors. Applications have demonstrated the utility of timely data in a number of areas ranging from numerical weather prediction and forecasting, to monitoring of natural hazards, disaster relief, agriculture and homeland security. As applications mature, the need to transition from prototypes to operational capabilities presents an opportunity to improve current near real-time systems and inform future capabilities. This paper presents NASAs effort to implement a near real-time capability for land and atmosphere data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS), Atmospheric Infrared Sounder (AIRS), Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), Microwave Limb Sounder (MLS) and Ozone Monitoring Instrument (OMI) instruments on the Terra, Aqua, and Aura satellites.

Observations From AIRS and ACOS

Advances in satellite technologies, computing and web mapping have led to a huge increase in the number of people accessing satellite data, or satellite-derived information. Satellite images are routinely used in media reports, virtual globes and interactive maps. The increased exposure, and familiarization, of the general public to satellite data and products is leading to greater expectations about what data should be available and how they should be packaged. To meet these expectations, NASAs Land Atmosphere Near-real time Capability for EOS (LANCE) has refined the way in which users can browse, filter and retrieve satellite imagery for a particular area of interest. The developments on LANCE, part of the NASA Earth Data website, are largely user-driven based on interviews and interactions with end users. This paper describes the tools available at LANCE, key application areas supported and examples of how LANCE data are being used. All of the LANCE tools can be accessed through http://earthdata.nasa.gov/lance.

Implementation of the Land, Atmosphere Near Real-time Capability for EOS (LANCE)

NASA provides a wide variety of Earth-observing satellite data products to a diverse community. These data are annotated with quality information in a variety of ways, with the result that many users struggle to understand how to properly account for quality when dealing with satellite data. To address this issue, a Data Quality Screening Service (DQSS) is being implemented for a number of datasets. The DQSS will enable users to obtain data files in which low-quality pixels have been filtered out, based either on quality criteria recommended by the science team or on the users particular quality criteria. The objective is to increase proper utilization of this critical quality data in science data analysis of satellite data products.

The use of NASA LANCE imagery and data for Near real-time applications

The development and deployment of data processing systems to process Earth Observing System (EOS) data has proven to be costly and prone to technical and schedule risk. Integration of science algorithms into a robust operational system has been difficult. The core processing system, based on commercial tools, has demonstrated limitations at the rates needed to produce the several terabytes per day for EOS, primarily due to job management overhead. This has motivated an evolution in the EOS Data Information System toward a more distributed one incorporating Science Investigator-Led Processing Systems (SIPS). As part of this evolution, the Goddard Earth Sciences Distributed Active Archive Center (GES DAAC) has developed a simplified processing system to accommodate the increased load expected with the advent of reprocessing and launch of a second satellite. This system, the Simple, Scalable, Script-Based Science Processor (S4P) could also serve as a resource for future SIPS.

A quality screening service for remote sensing data

NASA’s Land, Atmosphere Near real-time Capability for EOS (Earth Observing System) (LANCE) provides global data and imagery from the Terra, Aqua and Aura satellites in less than 3 h from satellite observation to meet the needs of the near real-time (NRT) applications community. Science quality, or higher-level “standard” products are made available within 8–40 h of observation but application users, operational agencies, and even researchers often need data much sooner than what routine science processing offers. This chapter describes the architecture of LANCE and modifications made to achieve the nominal 3-h latency requirement.

Simple, Scalable, Script-Based Science Processor (S4P)

We introduce a precise, efficient, and flexible spatial prescreening technique developed to support Moderate Resolution Imaging Spectroradiometer (MODIS) data subsetting in response to the requests of Aerosol Robotic Network researchers. The technique is capable of handling all MODIS data granules uniformly regardless of their locations on the Earth, with no special treatment required for dateline and pole crossing regions. It is easily adaptable to other problems where spatial prescreening is desired