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WCRP surface radiation budget shortwave data product description, version 1.1
Shortwave radiative fluxes which reach the Earth's surface are key elements that influence both atmospheric and oceanic circulation. The World Climate Research Program has established the Surface Radiation Budget climatology project with the ultimate goal of determining the various components of the surface radiation budget from satellite data on a global scale. This report describes the first global product that is being produced and archived as part of that effort. The interested user can obtain the monthly global data sets free of charge using e-mail procedures.
A New NASA Data Product: Tropospheric and Stratospheric Column Ozone in the Tropics Derived from TOMS Measurements
Tropospheric column ozone (TCO) and stratospheric column ozone (SCO) gridded data in the tropics for 1979-present are now available from NASA Goddard Space Flight Center via either direct ftp, world-NN,ide-NN,eb, or electronic mail. This note provides a brief overview of the method used to derive the data set including validation and adjustments.
Micro-kinetic modeling of temporal analysis of products data using kinetics-informed neural networks
Kinetics-informed neural networks improve fit quality for multi-pulse and noisy temporal analysis of products datasets.
Soil Moisture Active Passive (SMAP) Project Algorithm Theoretical Basis Document SMAP L1B Radiometer Data Product: L1B_TB
The purpose of the Soil Moisture Active Passive (SMAP) radiometer calibration algorithm is to convert Level 0 (L0) radiometer digital counts data into calibrated estimates of brightness temperatures referenced to the Earth's surface within the main beam. The algorithm theory in most respects is similar to what has been developed and implemented for decades for other satellite radiometers; however, SMAP includes two key features heretofore absent from most satellite borne radiometers: radio frequency interference (RFI) detection and mitigation, and measurement of the third and fourth Stokes parameters using digital correlation. The purpose of this document is to describe the SMAP radiometer and forward model, explain the SMAP calibration algorithm, including approximations, errors, and biases, provide all necessary equations for implementing the calibration algorithm and detail the RFI detection and mitigation process. Section 2 provides a summary of algorithm objectives and driving requirements. Section 3 is a description of the instrument and Section 4 covers the forward models, upon which the algorithm is based. Section 5 gives the retrieval algorithm and theory. Section 6 describes the orbit simulator, which implements the forward model and is the key for deriving antenna pattern correction coefficients and testing the overall algorithm.
Earth science system of the future: observing, processing, and delivering data products directly to the users
Advances in our understanding and ability to predict changes in our environment will require more comprehensive and coordinated measurements, data delivery systems, and modeling tools.
Biophysical, morphological, canopy optical property, and productivity data from the Superior National Forest
Described here are the results of a NASA field experiment conducted in the Superior National Forest near Ely, Minnesota, during the summers of 1983 and 1984. The purpose of the experiment was to examine the use of remote sensing to provide measurements of biophysical parameters in the boreal forests. Leaf area index, biomass, net primary productivity, canopy coverage, overstory and understory species composition data are reported for about 60 sites, representing a range of stand density and age for aspen and spruce. Leaf, needle, and bark high-resolution spectral reflectance and transmittance data are reported for the major boreal forest species. Canopy bidirectional reflectance measurements are provided from a helicopter-mounted Barnes Multiband Modular Radiometer (MMR) and the Thematic Mapper Simulator (TMS) on the NASA C-130 aircraft.
MINERvA open-data product
MINERvA is THE neutrino cross section experiment Scintillator tracker/calorimeter ran in the NuMI beam at Fermilab same beam as the MINOS and NOvA oscillation experiments With our data, we are solving systematic shortcomings in neutrino interaction rate/spectra that are the largest part of the systematic uncertainty in today s (and tomorrow s) measurements. Some aspects have NO equivalent in the neutrino program future. Scientific scope includes both particle and nuclear physics GeV scale cross sections, A dependence, MeV scale effects most published measurements for a neutrino experiments ever (well, tied with T2K) with 25% more papers in the pipeline.
An Assessment of New Satellite Data Products for the Development of a Long-Term Global Solar Resource at 10-100 km
A project representing an effort to reprocess the NASA based solar resource data sets is reviewed. The effort represented a collaboration between NASA, NOAA, NREL and the SUNY-Albany and aimed to deliver a 10 km resolution, 3-hourly data set spanning from 1983 through near-present. Part of the project was to transition project capability to NREL for annual processing to extend data set. Due to delays in the key input project called ISCCP, we evaluate only Beta versions of this data set and also introduce the potential use of another NASA Langley based cloud data set for the CERES project. The CERES project uses these cloud properties to compute global top-of-atmosphere and surface fluxes at the 1x1 degree resolution. Here, we also briefly discuss these data sets in potential usage for solar resource benchmarking.
NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research
The lack of observations over vast tropical oceans is a major challenge for tropical cyclone research. Satellite observations and model reanalysis data play an important role in filling these- gaps. Established in the mid-1980's, the Goddard Earth Sciences Data and Information Services Center (GES DISC), as one of the 12 NASA data centers, archives and distributes data from several Earth science disciplines such as precipitation, atmospheric dynamics, atmospheric composition, hydrology, including well-known NASA satellite missions (e.g. TRMM, GPM) and model assimilation projects (MERRA-2). Acquiring datasets suitable for tropical cyclone research in a large data archive is a challenge for many, especially for those who are not familiar with satellite or model data. Over the years, the GES DISC has developed user-friendly data services. For example, Giovanni is an online visualization and analysis tool, allowing users to visualize and analyze over 2000 satellite- and model-based variables with a Web browser, without downloading data and software. In this chapter, we will describe data and services at the GES DISC with emphasis on tropical cyclone research. We will also present two case studies and discuss future plans.
SAGE III/ISS Data Product Improvements
In 2024 the Stratospheric Aerosol and Gas Experiment on the International Space Station (SAGE III/ISS) entered its eighth year of operations. Using a CCD spectrometer to observe solar and lunar occultations through Earth’s limb, the mission has retrieved tens of thousands of vertical profiles for ozone, aerosol, water vapor, and nitrogen dioxide. Retrieval algorithms are constantly under development to enhance these products. The next product version will improve upon previous versions by addressing problems with the aerosol extinction spectrum and recovering hundreds of profiles impeded by the recent proliferation of sunspots. Users of SAGE III/ISS data can also expect improvements to uncertainty estimates and a continued expansion of ancillary products.
Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) data products user's guide
Two tape products from the Total Ozone Mapping Spectrometer (TOMS) aboard the Nimbus-7 have been archived at the National Space Science Data Center. The instrument measures backscattered Earth radiance and incoming solar irradiance; their ratio -- the albedo -- is used in ozone retrievals. In-flight measurements are used to monitor changes in the instrument sensitivity. The algorithm to retrieve total column ozone compares the observed ratios of albedos at pairs of wavelengths with pair ratios calculated for different ozone values, solar zenith angles, and optical paths. The initial error in the absolute scale for TOMS total ozone is 3 percent, the one standard-deviation random error is 2 percent, and the drift is +/- 1.5 percent over 14.5 years. The High Density TOMS (HDTOMS) tape contains the measured albedos, the derived total ozone amount, reflectivity, and cloud-height information for each scan position. It also contains an index of SO2 contamination for each position. The Gridded TOMS (GRIDTOMS) tape contains daily total ozone and reflectivity in roughly equal area grids (110 km in latitude by about 100-150 km in longitude). Detailed descriptions of the tape structure and record formats are provided.
Small experiment, Big Data: the data production of the Muon $g-2$ Experiment
The Muon $g-2$ Experiment at Fermilab aims to measure the muon anomalous magnetic moment with the unprecedented precision of 140 parts per billion (ppb). In April 2021 the collaboration published the first measurement, based on the first year of data taking. The result confirmed the previous experiment at Brookhaven National Laboratory (BNL), and increased the long-standing tension with the Standard Model prediction to 4.2 $\sigma$. The experiment is now running the sixth year of data acquisition of positive muon data, having accumulated a total of $\sim$19 times the statistics of the BNL experiment. A collaboration-wide effort is now in place to help produce the multi-petabyte-sized data sets, a challenge typically faced by much bigger experiments. Having a quick production turnaround time is of critical importance in order to achieve a timely analysis and publication schedule. In this paper I will describe the production workflow, the former and current challenges, the resources, tools, and the future prospects of the Muon $g-2$ Experiment.
Application of a Machine Learning Algorithm in Generating an Evapotranspiration Data Product From Coupled Thermal Infrared and Microwave Satellite Observations
Land surface evapotranspiration (ET) is one of the main energy sources for atmospheric dynamics and a critical component of the local, regional, and global water cycles. Consequently, accurate measurement or estimation of ET is one of the most active topics in hydro-climatology research. With massive and spatially distributed observational data sets of land surface properties and environmental conditions being collected from the ground, airborne or space-borne platforms daily over the past few decades, many research teams have started to use big data science to advance the ET estimation methods. The Geostationary satellite Evapotranspiration and Drought (GET-D) product system was developed at the National Oceanic and Atmospheric Administration (NOAA) in 2016 to generate daily ET and drought maps operationally. The primary inputs of the current GET-D system are the thermal infrared (TIR) observations from NOAA GOES satellite series. Because of the cloud contamination to the TIR observations, the spatial coverage of the daily GET-D ET product has been severely impacted. Based on the most recent advances, we have tested a machine learning algorithm to estimate all-weather land surface temperature (LST) from TIR and microwave (MW) combined satellite observations. With the regression tree machine learning approach, we can combine the high accuracy and high spatial resolution of GOES TIR data with the better spatial coverage of passive microwave observations and LST simulations from a land surface model (LSM). The regression tree model combines the three LST data sources for both clear and cloudy days, which enables the GET-D system to derive an all-weather ET product. This paper reports how the all-weather LST and ET are generated in the upgraded GET-D system and provides an evaluation of these LST and ET estimates with ground measurements. The results demonstrate that the regression tree machine learning method is feasible and effective for generating daily ET under all weather conditions with satisfactory accuracy from the big volume of satellite observations.
Simulated NASA Satellite Data Products for the NOAA Integrated Coral Reef Observation Network/Coral Reef Early Warning System
This RPC (Rapid Prototyping Capability) experiment will demonstrate the use of VIIRS (Visible/Infrared Imager/Radiometer Suite) and LDCM (Landsat Data Continuity Mission) sensor data as significant input to the NOAA (National Oceanic and Atmospheric Administration) ICON/ CREWS (Integrated Coral Reef Observation System/Coral Reef Early Warning System). The project affects the Coastal Management Program Element of the Applied Sciences Program.
Oceanographic and meteorological research based on the data products of SEASAT
Reservations were expressed concerning the sum of squares wind recovery algorithm and the power law model function. The SAS sum of squares (SOS) method for recovering winds from backscatter data leads to inconsistent results when V pol and H pol winds are compared. A model function that does not use a power law and that accounts for sea surface temperature is needed and is under study both theoretically and by means of the SASS mode 4 data. Aspects of the determination of winds by means of scatterometry and of the utilization of vector wind data for meteorological forecasts are elaborated. The operational aspect of an intermittent assimilation scheme currently utilized for the specification of the initial value field is considered with focus on quantifying the absolute 12-hour linear displacement error of the movement of low centers.
Contrail Tracking and ARM Data Product Development
A contrail tracking system was developed to help in the assessment of the effect of commercial jet contrails on the Earth's radiative budget. The tracking system was built by combining meteorological data from the Rapid Update Cycle (RUC) numerical weather prediction model with commercial air traffic flight track data and satellite imagery. A statistical contrail-forecasting model was created a combination of surface-based contrail observations and numerical weather analyses and forecasts. This model allows predictions of widespread contrail occurrences for contrail research on either a real-time basis or for long-term time scales. Satellite-derived cirrus cloud properties in polluted and unpolluted regions were compared to determine the impact of air traffic on cirrus.