Thermosphere conditions deduced from satellite observations.
Atmospheric composition data from Explorer XVII SATELLITE, obtaining expression for thermal diffusion factor of He
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Atmospheric composition data from Explorer XVII SATELLITE, obtaining expression for thermal diffusion factor of He
Low-cloud feedbacks are thought to be the greatest source of uncertainty in climate projections on the centennial scale. Past research has mainly focused on the global low-cloud feedback as a whole whereas only a few studies have examined separated contributions from the two main low cloud categories: stratocumulus (Sc) and shallow cumulus (Cu). Here we aim to evaluate and constrain Sc and Cu cloud feedbacks in models using satellite observations to ultimately reduce their contribution to the spread in equilibrium climate sensitivity. We utilize the Cumulus and Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD) recently developed by Cesana et al. (2019), which discriminates Sc from Cu clouds. This algorithm – based on cloud morphological characteristics at the orbital level – outperforms previous cloud-type classifications stemming from passive-sensor measurements. We investigate the observed relationship between the Sc and Cu cloud cover in tropical subsidence oceanic regions and their link to the primary low-cloud controlling factors (sea surface temperature, estimated inversion strength). To gain further insight into the Sc-Cu cloud partitioning, we explore the Sc-Cu relationship during the four meteorological seasons (DJF, MAM, JJA, SON) as well as on monthly timescale. We find a substantial negative correlation between the Sc and Cu cloud cover on annual, seasonal and monthly timescales. We finally evaluate how well climate models of the Coupled Model Intercomparison Project phase 5 (CMIP5) and phase 6 (CMIP6) agree with the observations. These results establish a framework to advance model parameterizations of the underlying physical processes driving the stratocumulus and shallow cumulus clouds (turbulence and convection).
An algorithm for generating deep-layer mean temperatures from satellite-observed microwave observations is presented. Unlike traditional temperature retrieval methods, this algorithm does not require a first guess temperature of the ambient atmosphere. By eliminating the first guess a potentially systematic source of error has been removed. The algorithm is expected to yield long-term records that are suitable for detecting small changes in climate. The atmospheric contribution to the deep-layer mean temperature is given by the averaging kernel. The algorithm computes the coefficients that will best approximate a desired averaging kernel from a linear combination of the satellite radiometer's weighting functions. The coefficients are then applied to the measurements to yield the deep-layer mean temperature. Three constraints were used in deriving the algorithm: (1) the sum of the coefficients must be one, (2) the noise of the product is minimized, and (3) the shape of the approximated averaging kernel is well-behaved. Note that a trade-off between constraints 2 and 3 is unavoidable. The algorithm can also be used to combine measurements from a future sensor (i.e., the 20-channel Advanced Microwave Sounding Unit (AMSU)) to yield the same averaging kernel as that based on an earlier sensor (i.e., the 4-channel Microwave Sounding Unit (MSU)). This will allow a time series of deep-layer mean temperatures based on MSU measurements to be continued with AMSU measurements. The AMSU is expected to replace the MSU in 1996.
Subroutines used in computer programs for least squares adjustment of satellite observations for simultaneous directions or ranges
Computer programs for least squares adjustment of satellite observations for simultaneous ranges or directions
This work explores on-board planning for the single spacecraft, multiple ground station Earth-observing satellite scheduling problem through artificial neural network function approximation of state–action value estimates generated by Monte Carlo tree search (MCTS). An extensive hyperparameter search is conducted for MCTS on the basis of performance, safety, and downlink opportunity utilization to determine the best hyperparameter combination for data generation. A hyperparameter search is also conducted on neural network architectures. The learned behavior of each network is explored, and each network architecture’s robustness to orbits and epochs outside of the training distributions is investigated. Furthermore, each algorithm is compared with a genetic algorithm, which serves to provide a baseline for optimality. MCTS is shown to compute near-optimal solutions in comparison to the genetic algorithm. The state–action value networks are shown to match or exceed the performance of MCTS in six orders of magnitude less execution time, showing promise for execution on board spacecraft.
Deep convection gives rise to large upper level clouds that strongly interact with radiation and are important to the climate energy budget. From an object-oriented perspective, these individual deep cloud systems are characterized by a well depicted cloud shield life cycle, starting with small cloud extents that grow at varying rates before decaying and vanishing. A simple formulation of the growth rate of the cloud shield has been proposed that links together the growth rate on the convective part of the cloud, the mass flux of both the convective and stratiform parts of the cluster and a simple removal sink term (Elsaesser et al., 2022). In this presentation we first show using a suite of satellite observations (infrared from geostationary satellites, GPM radar, etc.) that the functional form of the proposed equation is well suited to quantify the shield growth rate. We then focus on RCE simulations, with deep cloud system objects post processed, to explore the relative role of each term of the growth rate budget. Three different models are used in the same RCEMIP-like configurations. The results show that the budget equation works very well for each model, although the time constants require model-dependent adjustments. We will further show in Vienna the commonalities and the specificities of each model.
The results of analytical and simulation studies of the stellar-inertial measurement system (SIMS) for an earth observation satellite are presented. Subsystem design analyses and sensor design trades are reported. Three candidate systems are considered: (1) structure-mounted gyros with structure-mounted star mapper, (2) structure-mounted gyros with gimbaled star tracker, and (3) gimbaled gyros with structure-mounted star mapper. The purpose of the study is to facilitate the decisions pertaining to gimbaled versus structure-mounted gyros and star sensors, and combinations of systems suitable for the EOS satellite.
This is the final report for "Using satellite observations to quantify biomass burning emissions of NOx and hydrocarbons in the Tropics", funded through the New Investigator Program between March 2001 and March 2005. This period includes a 1-year no-cost extension of the original award. This report summarizes our accomplishments during the duration of the grant. Section 2 focuses on the research component of this work, while section 3 describes the education component. The personnel supported under this project is given in section 4. Section 5 lists publications resulting from NASA support and section 6 provides a list of conferences and seminars where the results were presented.
We present a low-cost (~$10K per instrument), portable solution to ground-based validation of satellite observations for difficult to reach locations with precisions of 1 ppm XCO2 and 10 ppb XCH4 for hourly data products. While Total Carbon Column Observing Network (TCCON) is the gold-standard for ground validation, there are locations where a ground column validation data point would be useful but conditions are not conducive to a permanent station. Examples include wetlands, thawing permafrost, the tropics, the Amazon, sub-Saharan Africa, as well as locations without a power grid or with geopolitical conflict. In addition, the low-cost and portability mean a geographical region can be studied in depth with multiple instruments. This passive, sun-pointing instrument is a miniaturized, laser heterodyne radiometer (mini-LHR) that has been under development by our team since 2009. It can be operated either in tandem with AERONET (a global network of 500 instruments that measure aerosol optical depth), or as a stand-alone instrument with a low-cost (~$3K), light-weight sun tracker. One of the main benefits of the mini-LHR is that it can quickly reach remote locations and provide a validation measurement even if there is limited or no infrastructure at the site. The instrument weighs ~10 lbs, fits into a backpack, and is powered by two folding solar panels and a battery pack. In clear conditions, the instrument can be set-up in under an hour. Portability means that mini-LHRs can be easily moved for side-by-side comparisons with other mini-LHRs and with TCCON which simplifies assessing instrument bias as well as accuracy. Like TCCON, the mini-LHR points directly at the sun with a narrow field-of-view and is its insensitivity to cloud and aerosol scattering that is common to nadir-pointing passive satellite approaches. Here we present a collection of sample data sets to demonstrate performance from locations that vary in climate, altitude, solar zenith angle, hours of sun per day, etc., as well as data from side-by-side TCCON comparisons. Retrievals of CO2 and CH4 were completed using the NASA/Goddard's Planetary Spectrum Generator (PSG) that incorporates meteorological inputs from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data set.
A series of plots that describe the state of the stratosphere and to some degree, the mesosphere as revealed by satellite observations are shown. The pertinent instrument features, spatial and temporal coverage, and details of accuracy and precision for the experiments providing the data were described. The main features of zonal mean cross sections and polar stereographic projections were noted and intercomparisons were discussed where a parameter was measured by more than one experiment. The main purpose was to collect the available data in one place and provide enough inforamation on limitations or cautions about the data so that they could be used in model comparisons and science studies.
Solar wind upstream waves ahead of earth bow shock by dual satellite observations
The single scattering albedo and optical depth of typical savanna vegetation in Botswana (Africa) have been determined by inverse modelling using satellite observed microwave signatures and surface soil moisture. Soil emissivity was modelled using a multi-layer radiative transfer model. The study is based on large scale surface moisture data and Nimbus/SMMR 6-6 GHz and 37 GHz dual polarized brightness temperatures over a 3-year period. As compared to the optical depths, the derived single scattering albedos displayed only minor seasonal variations, whereas the values fit well within the range reported in the literature from laboratory and field experiments. Both 6-6 and 37GHz optical depths were found to be significantly related to NDVI-values derived from NOAA/AVHRR.
There are significant gaps between the observations, models, and decision support tools that make use of new data. These challenges include: 1) Decreasing the time to incorporate new satellite data into operational forecast assimilation systems, 2) Blending in-situ and satellite observing systems to produce the most accurate and comprehensive data products and assessments, 3) Accelerating the transition from research to applications through national test beds, field campaigns, and pilot demonstrations, and 4) Developing the partnerships and organizational structures to effectively transition new technology into operations. At the Short-term Prediction Research and Transition (SPORT) Center in Huntsville, Alabama, a NASA-NOAA-University collaboration has been developed to accelerate the infusion of NASA Earth science observations, data assimilation and modeling research into NWS forecast operations and decision-making. The SPoRT Center research focus is to improve forecasts through new observation capability and the regional prediction objectives of the US Weather Research Program dealing with 0-1 day forecast issues such as convective initiation and 24-hr quantitative precipitation forecasting. The near real-time availability of high-resolution experimental products of the atmosphere, land, and ocean from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Infrared Spectroradiometer (AIRS), and lightning mapping systems provide an opportunity for science and algorithm risk reduction, and for application assessment prior to planned observations from the next generation of operational low Earth orbiting and geostationary Earth orbiting satellites. This paper describes the process for the transition of experimental products into forecast operations, current products undergoing assessment by forecasters, and plans for the future. The SPoRT Web page is at (http://www.ghcc.msfc.nasa.gov/sport).
As we enter the new era of satellite remote sensing for CO2 and other carbon cyclerelated quantities, advanced modeling and analysis capabilities are required to fully capitalize on the new observations. Model estimates of CO2 surface flux and atmospheric transport are required for initial constraints on inverse analyses, to connect atmospheric observations to the location of surface sources and sinks, and ultimately for future projections of carbon-climate interactions. For application to current, planned, and future remotely sensed CO2 data, it is desirable that these models are accurate and unbiased at time scales from less than daily to multi-annual and at spatial scales from several kilometers or finer to global. Here we focus on simulated CO2 fluxes from terrestrial vegetation and atmospheric transport mutually constrained by analyzed meteorological fields from the Goddard Modeling and Assimilation Office for the period 1998 through 2006. Use of assimilated meteorological data enables direct model comparison to observations across a wide range of scales of variability. The biospheric fluxes are produced by the CASA model at lxi degrees on a monthly mean basis, modulated hourly with analyzed temperature and sunlight. Both physiological and biomass burning fluxes are derived using satellite observations of vegetation, burned area (as in GFED-2), and analyzed meteorology. For the purposes of comparison to CO2 data, fossil fuel and ocean fluxes are also included in the transport simulations. In this presentation we evaluate the model's ability to simulate CO2 flux and mixing ratio variability in comparison to in situ observations at sites in Northern mid latitudes and the continental tropics. The influence of key process representations is inferred. We find that the model can resolve much of the hourly to synoptic variability in the observations, although there are limits imposed by vertical resolution of boundary layer processes. The seasonal cycle and its interannual variations generally respond adequately, but discrepancies in the tropics suggest the need for a refinement of the soil moisture dependence of the respiration flux in CASA. Examples and inferences for interpretation of satellite data will be discussed. In general, the fidelity of these simulations leads us to anticipate incorporation of real-time, highly resolved remote sensing and other observations into quantitative analyses that will reduce uncertainty in the terrestrial CO2 sink and revolutionize our understanding of the key processes controlling atmospheric CO2 and its evolution with time.
Intensity and occurrence of VLF emissions and charged particle fluxes, using Injun 3 satellite observations
Results are presented on eight years of satellite observations of the polar mesospheric clouds (PMCs) by the SBUV spectral radiometer, showing that PMCs occur in the summertime polar cap regions of both hemispheres and that they exhibit year-to-year variability. It was also found that the increase in the PMC occurrence frequency was inversely correlated with solar activity. Two kinds of hemispherical asymmetries could be identified: (1) PMCs in the Northern Hemisphere were significantly brighter than in the Southern Hemisphere, in accordance with previous results derived from SME data; and (2) the solar cycle response in the south is more pronounced than in the north. The paper also describes the cloud detection algorithm.
One of the challenges facing atmospheric scientists is to interpret trends in multi-decadal data records. Although data records from satellite instruments are not as long as some ground-based records, global coverage and resolved vertical profiles provide unique information for identifying signatures of climate change. For example, the Halogen Occultation Experiment (HALOE) on the Upper Atmosphere Research Satellite provided profiles of O3, H2O, HC1, HF, CH4 from October 1991 until November 2005. There are also multi-annual ground based measurements of the column HCl. Middle latitude ground-based measurements show a seasonal cycle, and the HALOE profiles show that this is driven by the seasonal change in the composition and mass of the region between the tropopause and 380K surface (the lowermost stratosphere). Understanding the processes that produce the seasonal cycle makes it possible to interpret a future change in the seasonal cycle as a marker of a change in the stratospheric residual circulation. Satellite observations have also provided key information for improving the physical basis of models used to predict future composition and climate circulation. An example is the "tape recorder" signature in tropical stratospheric water vapor, i.e., the slow ascent of high and low water vapor anomalies roughly corresponding to the tropopause temperature at the time air entered the stratosphere. This signature has become a key diagnostic of performance for climate models.