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Optimal estimation of atmospheric temperature and composition from limb sounding observations is extended to the direct retrieval of line-of-sight atmospheric structure that can be obtained in certain limb viewing geometries.
In many current studies conducted to enhance the usefulness of meteorological satellite radiance data, one common objective is to infer conventional weather variables. The present investigation, on the other hand, is mainly concerned with the efficient retrieval (minimization of errors) of a nonstandard atmospheric descriptor. The atmosphere's Vertical Infrared Radiative Emitting Structure (VIRES) is retrieved. VIRES is described by the broadband infrared weighting function curve. The shapes of these weighting curves are primarily a function of the three-dimensional cloud structure. The weighting curves are retrieved by a method which uses satellite spectral radiance data. The basic theory involved in the VIRES retrieval procedure parallels the technique used to retrieve temperature soundings.
If the aerosol composition and size distribution below cloud are uniform, the vertical profile of cloud condensation nuclei (CCN) concentration can be retrieved entirely from surface measurements of CCN concentration and particle humidification function and surface-based retrievals of relative humidity and aerosol extinction or backscatter. This provides the potential for long-term measurements of CCN concentrations near cloud base. We have used a combination of aircraft, surface in situ, and surface remote sensing measurements to test various aspects of the retrieval scheme. Our analysis leads us to the following conclusions. The retrieval works better for supersaturations of 0.1% than for 1% because CCN concentrations at 0.1% are controlled by the same particles that control extinction and backscatter. If in situ measurements of extinction are used, the retrieval explains a majority of the CCN variance at high supersaturation for at least two and perhaps five of the eight flights examined. The retrieval of the vertical profile of the humidification factor is not the major limitation of the CCN retrieval scheme. Vertical structure in the aerosol size distribution and composition is the dominant source of error in the CCN retrieval, but this vertical structure is difficult to measure from remote sensing at visible wavelengths.
Conceptual information retrieval systems use structured document indices, domain knowledge and a set of heuristic retrieval strategies to match user queries with a set of indices describing the document's content. Such retrieval strategies increase the set of relevant documents retrieved (increase recall), but at the expense of returning additional irrelevant documents (decrease precision). Usually in conceptual information retrieval systems this tradeoff is managed by hand and with difficulty. This paper discusses ways of managing this tradeoff by the application of standard induction algorithms to refine the retrieval strategies in an engineering design domain. We gathered examples of query/retrieval pairs during the system's operation using feedback from a user on the retrieved information. We then fed these examples to the induction algorithm and generated decision trees that refine the existing set of retrieval strategies. We found that (1) induction improved the precision on a set of queries generated by another user, without a significant loss in recall, and (2) in an interactive mode, the decision trees pointed out flaws in the retrieval and indexing knowledge and suggested ways to refine the retrieval strategies.
Estimates of surface precipitation and the associated vertical latent heating structure can be retrieved from space-borne passive microwave sensors such as the TRMM Microwave Imager (TMI) and Special Sensor Microwave/Imager (SSM/I). Studies have shown that assimilation of microwave rain rates can improve the quality of global analysis and forecast. Numerical weather prediction (NWP) centers are beginning to make operational use of these data. In the next few years, there will be a gradual increase in microwave rain products available from operational and research satellites, culminating to a target constellation of 9 satellites to provide global rain measurements every 3 hours with the proposed Global Precipitation Measurement (GPM) mission to be launched around 2007. Realizing the full potential of these observations in data assimilation will require continued advances in retrieval algorithms, assimilation techniques, and model physics. I present results from two research efforts at NASA GSFC. The first is on the assimilation of tropical precipitation using temperature/moisture tendency corrections within a continuous variational assimilation framework to improve analyses and forecasts produced by the Goddard Earth Observing System (GEOS) global data assimilation system. Results show that assimilation of TMI and SSM/I rain rates improves not only precipitation and moisture, but also the related clouds, radiation energy fluxes, and large-scale circulations in GEOS analyses. The improved analyses also yield better short-range forecasts, quantitative precipitation forecast (QPF) threat scores, and storm track predictions. The second part consists of results from exploratory experiments on variational assimilation of convective and stratiform latent heating profiles within the general framework of model parameter estimation as a way to identify systematic model errors and optimize physical paremeterization schemes in global forecast and climate models.
The Advanced Microwave Sounding Unit (AMSU) and the Microwave Humidity Sounder (MHS) constitute the advanced microwave sounding system to be flown on the EOS-PM platform. Similar instruments (the AMSU-A corresponding to the AMSU and the AMSU-B corresponding to the MHS) are scheduled to become operational on the NOAA polar orbiting satellites beginning with NOAA-K. The unique characteristics of the AMSU-MHS instruments, as compared to the capabilities of their infrared and microwave predecessors, introduce new opportunities, and challenges, for operational retrievals of atmospheric structure. Not only will these new data improve present capabilities for the retrieval of atmospheric profiles of temperature and moisture, but they will provide the only opportunity for successfully retrieving atmospheric temperature and humidity profiles in the presence of modest amounts of cloud and precipitation. A complementary opportunity is presented by the potential of the AMSU-MHS to obtain information about the structure of clouds and precipitation. The data sets obtained will contribute to the current knowledge of global water and energy budgets, and provide critical information on the horizontal and vertical distribution of tropospheric water vapor, the spatial and temporal distribution of rain, and the relationship of cloud formation and dissipation to atmospheric dynamics and thermodynamics.
A procedure for the retrieval of cloud vertical structure from passive microwave radiometry is demonstrated by using passive microwave radiometry observations made during the Tropical Rainfall Measuring Mission. The procedure uses a set of cloud radiative models, with each model consisting of five vertical layers, specifying a distinct cloud vertical structure in terms of the near-surface parameters. The retrieval procedure is separated into two tasks (1) retrieving a set of geophysical parameters for each cloud radiative model and (2) finding which of the cloud radiative models and its associated retrieved parameters best fit the observed geophysical conditions. It is shown that this retrieval technique can detect differences and similarities between precipitating systems.
The present investigation is concerned with the vertical resolving power of satellite borne temperature sounding instruments. Specifically, information is presented on the capabilities of the High Resolution Infra-red Radiation Sounder (HIRS) and a proposed sounding instrument called the Advanced Meteorological Temperature Sounder (AMTS). Two quite different methods for assessing the vertical resolving power of satellite sounders are discussed, taking into account the theoretical method of Conrath (1972), and an independent criterion for vertical resolving power which is based on actual retrievals of signal structure in the temperature field. Some discrepancies are found between theory and retrieval tests. The results indicate that the theoretical Backus and Gilbert spread function does not represent the shortest resolvable length scale for a retrieval system.
The information content of the VAS radiances can be converted to meteorological parameters useful for analyzing a severe weather environment. The method by which the VAS variances are converted to vertical profiles of temperature, dewpoints, and equivalent potential temperature involves a basic regression technique using the most local radiosonde data available for establishing a correlation matrix. The results indicate that mesoscale features apparent within images of the radiances can be converted to usable temperature and moisture fields using regression when surface temperature and dewpoint observations are included within the total data base. In addition, results indicate that surface data are very important for better defining lower tropospheric structure that the VAS radiances alone cannot properly resolve. Analyses of these retrievals distinctly show mesoscale structure in the temperature and moisture fields derived with VAS radiances collected every 3 hours, and 0000 GMT. The retrievals capture the moisture structure. More important, convective instability is clearly detected immediately before the onset of convection. The results indicate that the VAS is capable of providing valuable mesoscale information suitable for analyzing a preconvective environment that is generally clear.
As part of the process to determine whether it is possible to retrieve boundary layer structure with the current sounding techniques, temperature retievals were performed for radiosonde profiles that showed temperature inversions. It was found that when temperature inversions exceed 8 to 10 C a retrieval will indeed show a temperature increase with height over a limited vertical distance. For weaker inversions retrieved temperatures are generally smoothly decreasing with height. It is, however, impossible to determine the actual mixed layer height from the retrievals. Whether the water vapor channels could be used in observing mixed layer structure was investigated. Temperature inversions are accompanied by significant drops in relative humidity. While this effect is very pronounced in parts of the trade wind regimes with relative humidity drops of up to 60%, it is widespread in other areas of the ocean as well. A simulation experiment was performed in which brightness temperatures were computed for smooth temperature and humidity profiles and compared with those computed from inversion profiles.
The release of latent heat in clouds is an essential part of the formation and I intensification o~hurricanes. The community knows very little about the intensity and structure of latent heating due largely to inadequate observations. In this paper, a new method for retrieving the latent heating field in hurricanes from airborne Dopple~ radar is presented and fields from rapidly intensifying Hurricane Guillermo (1997) are shown.
In recent years, methods for estimating atmospheric latent heating vertical structure from both passive and active microwave remote sensing have matured to the point where quantitative evaluation of these methods is the next logical step. Two approaches for heating algorithm evaluation are proposed: First, application of heating algorithms to synthetic data, based upon cloud-resolving model simulations, can be used to test the internal consistency of heating estimates in the absence of systematic errors in physical assumptions. Second, comparisons of satellite-retrieved vertical heating structures to independent ground-based estimates, such as rawinsonde-derived analyses of heating, provide an additional test. The two approaches are complementary, since systematic errors in heating indicated by the second approach may be confirmed by the first. A passive microwave and combined passive/active microwave heating retrieval algorithm are evaluated using the described approaches. In general, the passive microwave algorithm heating profile estimates are subject to biases due to the limited vertical heating structure information contained in the passive microwave observations. These biases may be partly overcome by including more environment-specific a priori information into the algorithm s database of candidate solution profiles. The combined passive/active microwave algorithm utilizes the much higher-resolution vertical structure information provided by spaceborne radar data to produce less biased estimates; however, the global spatio-temporal sampling by spaceborne radar is limited. In the present study, the passive/active microwave algorithm is used to construct a more physically-consistent and environment-specific set of candidate solution profiles for the passive microwave algorithm and to help evaluate errors in the passive algorithm s heating estimates. Although satellite estimates of latent heating are based upon instantaneous, footprint- scale data, suppression of random errors requires averaging to at least half-degree resolution. Analysis of mesoscale and larger space-time scale phenomena based upon passive and passive/active microwave heating estimates from TRMM, SSMI, and AMSR data will be presented at the conference.
MOOSEenger is an open-source, terminal-first chat application for the MOOSE ecosystem that couples specialized parsing of MOOSE documentation and “.i” input files with retrieval-augmented generation to deliver grounded answers about multiphysics modeling and workflows. It includes dedicated readers for MOOSE-style HTML and a pyhit-based parser that uses the MOOSE syntax tree to preserve block structure and attach retrieval metadata. A data-ingestion pipeline performs semantic chunking into atomic facts and stores them hierarchically in a local Chroma vector database that maintains parent–child relationships across documents; the system can ingest directories, individual files, and single-page web content, and it provides CRUD operations (insert, update, delete) to manage the corpus. At query time, relevant chunks are embedded, retrieved, and fused into the model context, with interactive features such as token streaming, persistent chat history, and dynamic RAG (retrieval triggered by user input or intermediate model output). Deployment is flexible: MOOSEenger runs with local Ollama models or remote Hugging Face/OpenAI backends—typically coordinating generation, lightweight tagging/summarization, and embeddings across three models—and it also supports a server mode and integration with the VS Code Continue interface.
Infrared spectra returned by the Thermal Emission Spectrometer (TES) are well suited for retrieval of the thermal structure and the distribution of aerosols in the Martian atmosphere. Combined nadir- and limb-viewing spectra allow global monitoring of the atmosphere up to 0.01 mbar (65 km). We report here on the atmospheric thermal structure and the distribution of aerosols as observed thus far during the mapping phase of the Mars Global Surveyor mission. Zonal and temporal mean cross sections are used to examine the seasonal evolution of atmospheric temperatures and zonal winds during a period extending from northern hemisphere mid-summer through vernal equinox (L(sub s) = 104-360 deg). Temperature maps at selected pressure levels provide a characterization of planetary-scale waves. Retrieved atmospheric infrared dust opacity maps show the formation and evolution of regional dust storms during southern hemisphere summer. Response of the atmospheric thermal structure to the changing dust loading is observed. Maps of water-ice clouds as viewed in the thermal infrared are presented along with seasonal trends of infrared water-ice opacity. Uses of these observations for diagnostic studies of the dynamics of the atmosphere are discussed.
The retrieval of vertical structure from joint passive microwave and lightning observations is demonstrated. Three years of data from the TRMM (Tropical Rainfall Measuring Mission) are used as a training dataset for regression and classification neural networks; the TMI (TRMM Microwave Imager) and LIS (Lightning Imaging Sensor) provide the inputs, the PR (Precipitation Radar) provides the training targets. Both vertical reflectivity profile categorization (into 9 convective, 7 stratiform, 2 mixed and 6 anvil types) and geophysical parameters (surface rainfall, vertically integrated liquid (VIL), ice water content (IWC) and echo tops) are retrieved. Retrievals are successful over both land and ocean surfaces. The benefit of using lightning observations as inputs to these retrievals is quantitatively demonstrated; lightning essentially provides an additional convective/stratiform discriminator, and is most important for isolation of midlevel (tops in the mixed phase region) convective profile types (this is because high frequency passive microwave observations already provide good convective/stratiform discrimination for deep convective profiles). This is highly relevant as midlevel convective profiles account for an extremely large fraction of tropical rainfall, and yet are most difficult to discriminate from comparable-depth stratiform profile types using passive microwave observations alone.
A brief example of the use of formal methods techniques in the specification of a software system is presented. The report is part of a larger effort targeted at defining a formal methods pilot project for NASA. One possible application domain that may be used to demonstrate the effective use of formal methods techniques within the NASA environment is presented. It is not intended to provide a tutorial on either formal methods techniques or the application being addressed. It should, however, provide an indication that the application being considered is suitable for a formal methods by showing how such a task may be started. The particular system being addressed is the Structured File Services (SFS), which is a part of the Data Storage and Retrieval Subsystem (DSAR), which in turn is part of the Data Management System (DMS) onboard Spacestation Freedom. This is a software system that is currently under development for NASA. An informal mathematical development is presented. Section 3 contains the same development using Penelope (23), an Ada specification and verification system. The complete text of the English version Software Requirements Specification (SRS) is reproduced in Appendix A.
Obtaining accurate and widespread measurements of the vertical structure of the Earth’s forests has been a longsought goal for the ecological community. Such observations are critical for accurately assessing the existing biomass of forests, and how changes in this biomass caused by human activities or variations in climate may impact atmospheric CO2 concentrations. Additionally, the three-dimensional structure of forests is a key component of habitat quality and biodiversity at local to regional scales. The Global Ecosystem Dynamics Investigation (GEDI) was launched to the International Space Station in late 2018 to provide high-quality measurements of forest vertical structure in temperate and tropical forests between 51.6 N & S latitude. The GEDI instrument is a geodetic-class laser altimeter/waveform lidar comprised of 3 lasers that produce 8 transects of structural information. Over its two-year nominal lifetime GEDI is anticipated to provide over 10 billion waveforms at a footprint resolution of 25 m. These data will be used to derive a variety of footprint and gridded products, including canopy height, canopy foliar profiles, Leaf Area Index (LAI), sub-canopy topography and biomass. Additionally, data from GEDI are used to demonstrate the efficacy of its measurements for prognostic ecosystem modeling, habit and biodiversity studies, and for fusion using radar and other remote sensing instruments. GEDI science and technology are unique: no other space-based mission has been created that is specifically optimized for retrieving vegetation vertical structure. As such, GEDI promises to advance our understanding of the importance of canopy vertical variations within an ecological paradigm based on structure, composition and function.