Determination of Atmospheric and Surface Parameters From Simulated AIRS/AMSU Sounding Data: Retrieval and Cloud Clearing Methodology
Explore the source record for details and available documents.
AIRS Atmosphere Infrared Sounder AMSU Advanced Mic↗
SEARCH · Search NASA
Results for “Retrieval methodology”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A Methodology for Surface Soil Moisture and Vegetation Optical Depth Retrieval Using the Microwave Polarization Difference Index
A methodology for retrieving surface soil moisture and vegetation optical depth from satellite microwave radiometer data is presented. The procedure is tested with historical 6.6 GHz brightness temperature observations from the Scanning Multichannel Microwave Radiometer over several test sites in Illinois. Results using only nighttime data are presented at this time, due to the greater stability of nighttime surface temperature estimation. The methodology uses a radiative transfer model to solve for surface soil moisture and vegetation optical depth simultaneously using a non-linear iterative optimization procedure. It assumes known constant values for the scattering albedo and roughness. Surface temperature is derived by a procedure using high frequency vertically polarized brightness temperatures. The methodology does not require any field observations of soil moisture or canopy biophysical properties for calibration purposes and is totally independent of wavelength. Results compare well with field observations of soil moisture and satellite-derived vegetation index data from optical sensors.
Owe, Manfred↗
A Vegetation Correction Methodology for Time Series Based Soil Moisture Retrieval From C-band Radar Observations
A methodology is presented to correct backscatter (sigma(sup 0)) observations for the effect of vegetation. The proposed methodology is based on the concept that the ratio of the surface scattering over the total amount of scattering (sigma(sup 0)(sub soil)/sigma(sup 0)) is only affected by the vegetation and can be described as a function of the vegetation water content. Backscatter observations sigma(sup 0) from the soil are not influenced by vegetation. Under bare soil conditions (sigma(sup 0)(sub soil)/sigma(sup 0)) equals 1. Under low to moderate biomass and soil moisture conditions, vegetation affects the observed sigma(sup 0) through absorption of the surface scattering and contribution of direct scattering by the vegetation itself. Therefore, the contribution of the surface scattering is smaller than the observed total amount of scattering and decreases as the biomass increases. For dense canopies scattering interactions between the soil surface and vegetation elements (e.g. leaves and stems) also become significant. Because these higher order scattering mechanisms are influenced by the soil surface, an increase in (sigma(sup 0)(sub soil)/sigma(sup 0)) may be observed as the biomass increases under densely vegetated conditions. This methodology is applied within the framework of time series based approach for the retrieval of soil moisture. The data set used for this investigation has been collected during a campaign conducted at USDA's Optimizing Production Inputs for Economic and Environmental Enhancement OPE-3) experimental site in Beltsville, Maryland (USA). This campaign took place during the corn growth cycle from May 10th to 0ctober 2nd, 2002. In this period the corn crops reached a vegetation water content of 5.1 kg m(exp -2) at peak biomass and a soil moisture range varying between 0.00 to 0.26 cubic cm/cubic cm. One of the deployed microwave instruments operated was a multi-frequency (C-band (4.75 GHz) and L-band (1.6 GHz)) quad-polarized (HH, HV, VV, VH) radar which was mounted on a 20 meter long boom. In the OPE-3 field campaign, radar observations were collected once a week at nominal times of 8 am, 10 am, 12 noon and 2 pm. During each data run the radar acquired sixty independent measurements within an azimuth of 120 degrees from a boom height of 12.2 m and at three different incidence angles (15,35, and 55 degrees). The sixty observations were averaged to provide one backscatter value for the study area and its accuracy is estimated to be 51.0 dB. For this investigation the C-band observations have been used. Application of the proposed methodology to the selected data set showed a well-defined relationship between (sigma(sup 0)(sub soil)/sigma(sup 0)) and the vegetation water content. It is found that this relationship can be described with two experimentally determined parameters, which depend on the sensing configuration (e.g. incidence angle and polarization). Through application of the proposed vegetation correction methodology and the obtained parameterizations, the soil moisture retrieval accuracy within the framework of a time series based approach is improved from 0.033 to 0.032 cubic cm/cubic cm, from 0.049 to 0.033 cubic cm/cubic cm and from 0.079 to 0.047 cubic cm/cubic cm for incidence angles of 15,35 and 55 degrees, respectively. Improvement in soil moisture retrieval due to vegetation correction is greater at larger incidence angles (due to the increased path length and larger vegetation effects on the surface signal at the larger angles).
Joseph, Alicia T.↗
Goddard Latent Heating (LH) Retrieval Algorithm
Rainfall production is a fundamental process within the Earth's hydrological cycle because it represents both a principal forcing term in surface water budgets, and its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations within the Tropics - as well as modify the energetic efficiencies of mid-latitude weather systems. This paper highlights the retrieval of latent heat release from satellite measurements generated by the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) satellite observatory, which were launched in November 1997 and February 2014, respectively. Both TRMM and GPM measurements have been providing an accurate four-dimensional account of rainfall over the global Tropics and mid-latitudes: information that can be used to estimate the space-time structure of latent heating. Two sets of latent heating retrieval algorithm methodologies (Goddard Convective-Stratiform or CSH, and Japan Spectral Latent Heating or SLH) have been developed to estimate latent heating based on rain rate profile retrievals obtained from TRMM and GPM measurements. The CSH algorithm will be described followed by a discussion its improvements and performance against the combined derived surface ran rates. We will also present the CSH retrieved LH for TRMM and GPM.
Tao, Wei-Kuo↗
Latent Heating Algorithm
Rainfall production is a fundamental process within the Earth's hydrological cycle because it represents both a principal forcing term in surface water budgets, and its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations within the Tropics - as well as modify the energetic efficiencies of mid-latitude weather systems. This paper highlights the retrieval of latent heat release from satellite measurements generated by the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) Mission satellite observatory, which were launched in November 1997 and February 2014, respectively. Both TRMM and GPM measurements have been providing an accurate four-dimensional account of rainfall over the global Tropics and mid-latitudes: information that can be used to estimate the space-time structure of latent heating.Two sets of latent heating (LH) retrieval algorithm methodologies (Goddard Convective-Stratiform Heating or CSH, and Japan's Spectral Latent Heating or SLH) have been developed to estimate latent heating based on rain rate profile retrievals obtained from TRMM and GPM measurements. The differences and similarities between the CSH and SLH algorithm designs will be presented. The presentation then will provide the results of LH structures derived from the TRMM and GPM. At the end of presentation, the further research on latent heating retrieval from satellites will be discussed.
Tao, Wei-Kuo↗
Retrieval of Atmospheric and Surface Parameters from AIRS/AMSU/HSB Data Under Cloudy Conditions
New state of the art methodology is described to analyze AIRS/AMSU/HSB data in the presence of multiple cloud formations. The methodology forms the basis for the AIRS Science Team algorithm which will be used to analyze AIRS/AMSU/HSB data on EOS Aqua. The cloud clearing methodology requires no knowledge of the spectral properties of the clouds. The basic retrieval methodology is general and extracts the maximum information from the radiances, consistent with the channel noise covariance matrix. The retrieval methodology minimizes the dependence of the solution on the first guess field and the first guess error characteristics. Results are shown for AIRS Science Team simulation studies with multiple cloud formations. These simulation studies imply that clear column radiances can be reconstructed under partial cloud cover with an accuracy comparable to single spot channel noise in the temperature and water vapor sounding regions, temperature soundings can be produced under partial cloud cover with RMS errors on the order of, or better than, 1deg K in 1 km thick layers from the surface to 700 mb, 1 km layers from 700 mb to 300 mb, 3 km layers from 300 mb to 30 mb, and 5 km layers from 30 mb to 1 mb, and moisture profiles can be obtained with an accuracy better than 20% absolute errors in 1 km layers from the surface to nearly 200 mb.
Susskind, Joel↗
Combined Radar and Radiometer Analysis of Precipitation Profiles for a Parametric Retrieval Algorithm
A methodology to analyze precipitation profiles using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) is proposed. Rainfall profiles are retrieved from PR measurements, defined as the best-fit solution selected from precalculated profiles by cloud-resolving models (CRMs), under explicitly defined assumptions of drop size distribution (DSD) and ice hydrometeor models. The PR path-integrated attenuation (PIA), where available, is further used to adjust DSD in a manner that is similar to the PR operational algorithm. Combined with the TMI-retrieved nonraining geophysical parameters, the three-dimensional structure of the geophysical parameters is obtained across the satellite-observed domains. Microwave brightness temperatures are then computed for a comparison with TMI observations to examine if the radar-retrieved rainfall is consistent in the radiometric measurement space. The inconsistency in microwave brightness temperatures is reduced by iterating the retrieval procedure with updated assumptions of the DSD and ice-density models. The proposed methodology is expected to refine the a priori rain profile database and error models for use by parametric passive microwave algorithms, aimed at the Global Precipitation Measurement (GPM) mission, as well as a future TRMM algorithms.
Masunaga, Hirohiko↗
Comparison of Dust Optical Depth From Multi-Sensor Products and MONARCH (Multiscale Online Non-hydrostatic AtmospheRe CHemistry) Dust Reanalysis Over North Africa, the Middle East, and Europe
Aerosol reanalysis datasets are model-based, observationally constrained, continuous 3D aerosol fields with a relatively high temporal frequency that can be used to assess aerosol variations and trends, climate effects, and impacts on socioeconomic sectors, such as health. Here we compare and assess the recently published MONARCH (Multiscale Online Non-hydrostatic AtmospheRe CHemistry) high-resolution regional desert dust reanalysis over northern Africa, the Middle East, and Europe (NAMEE) with a combination of ground-based observations and space-based dust retrievals and products. In particular, we compare the total and coarse dust optical depth (DOD) from the new reanalysis with DOD products derived from MODIS (MODerate resolution Imaging Spectroradiometer), MISR (Multi-angle Imaging SpectroRadiometer), and IASI (Infrared Atmospheric Sounding Interferometer) spaceborne instruments. Despite the larger uncertainties, satellite-based datasets provide a better geographical coverage than ground-based observations, and the use of different retrievals and products allows at least partially overcoming some single-product weaknesses in the comparison. Nevertheless, limitations and uncertainties due to the type of sensor, its operating principle, its sensitivity, its temporal and spatial resolution, and the methodology for retrieving or further deriving dust products are factors that bias the reanalysis assessment. We, therefore, also use ground-based DOD observations provided by 238 stations of the AERONET (AErosol RObotic NETwork) located within the NAMEE region as a reference evaluation dataset. In particular, prior to the reanalysis assessment, the satellite datasets were evaluated against AERONET, showing moderate underestimations in the vicinities of dust sources and downwind regions, whereas small or significant overestimations, depending on the dataset, can be found in the remote regions. Taking these results into consideration, the MONARCH reanalysis assessment shows that total and coarse-DOD simulations are consistent with satellite- and ground-based data, qualitatively capturing the major dust sources in the area in addition to the dust transport patterns. Moreover, the MONARCH reanalysis reproduces the seasonal dust cycle, identifying the increased dust activity that occurred in the NAMEE region during spring and summer. The quantitative comparison between the MONARCH reanalysis DOD and satellite multi-sensor products shows that the reanalysis tends to slightly overestimate the desert dust that is emitted from the source regions and underestimate the transported dust over the outflow regions, implying that the model's removal of dust particles from the atmosphere, through deposition processes, is too effective. More specifically, small positive biases are found over the Sahara desert (0.04) and negative biases over the Atlantic Ocean and the Arabian Sea (−0.04), which constitute the main pathways of the long-range dust transport. Considering the DOD values recorded on average there, such discrepancies can be considered low, as the low relative bias in the Sahara desert (< 50 %) and over the adjacent maritime regions (< 100 %) certifies. Similarly, over areas with intense dust activity, the linear correlation coefficient between the MONARCH reanalysis simulations and the ensemble of the satellite products is significantly high for both total and coarse DOD, reaching 0.8 over the Middle East, the Atlantic Ocean, and the Arabian Sea and exceeding it over the African continent. Moreover, the low relative biases and high correlations are associated with regions for which large numbers of observations are available, thus allowing for robust reanalysis assessment.
Michail Mytilinaios↗
Data Fusion for Earth Science Remote Sensing
Beginning in 2004, NASA has supported the development of an international network of ground-based remote sensing installations for the measurement of greenhouse gas columns. This collaboration has been successful and is currently used in both carbon cycle investigations and in the efforts to validate the GOSAT space-based column observations of CO2 and CH4. With the support of a grant, this research group has established a network of ground-based column observations that provide an essential link between the satellite observations of CO2, CO, and CH4 and the extensive global in situ surface network. The Total Carbon Column Observing Network (TCCON) was established in 2004. At the time of this report seven sites, employing modern instrumentation, were operational or were expected to be shortly. TCCON is expected to expand. In addition to providing the most direct means of tying the in situ and remote sensing data sets together, TCCON provides a means of testing the retrieval algorithms of SCIAMACHY and GOSAT over the broadest variation in atmospheric state. TCCON provides a critically maintained and long timescale record for identification of temporal drift and spatial bias in the calibration of the space-based sensors. Finally, the global observations from TCCON are improving our understanding of how to use column observations to provide robust estimates of surface exchange of C02 and CH4 in advance of the launch of OCO and GOSAT. TCCON data are being used to better understand the impact of both regional fluxes and long-range transport on gradients in the C02 column. Such knowledge is essential for identifying the tools required to best use the space-based observations. The technical approach and methodology of retrieving greenhouse gas columns from near-IR solar spectra, data quality and process control are described. Additionally, the impact of and relevance to NASA of TCCON and satellite validation and carbon science are addressed.
data fusion↗
Methodology for interpretation of SST retrievals using the AVHRR split window algorithm
Intercomparisons of sea surface temperature (SST) products derived from the operational NOAA-7 AVHRR-II algorithm and in situ observations are made. The 1982 data sets consist of ship survey data during the winter from the Mid-Atlantic Bight (MAB), ship and buoy measurements during April and September in the Gulf of Mexico and shipboard observations during April off the N.W. Spanish coast. The analyses included single pixel comparisons and the warmest pixel technique for 2 x 2 pixel and 10 x 10 pixel areas. The reason for using multi-pixel areas was for avoiding cloud contaminated pixels in the vicinity of the field measurements. Care must be taken when applying the warmest pixel technique near oceanic fronts. The Gulf of Mexico results clearly indicate a persistent degradation in algorithm accuracy due to El Chichon aerosols. The MAB and Spanish data sets indicate that very accurate estimates can be achieved if care is taken to avoid clouds and oceanic fronts.
Barbieri, R. W.↗
A method for remote sensing the emissivity, fractional cloud cover and cloud top temperature of high-level, thin clouds
A methodology for retrieving the emissivity, cloud cover and cloud top temperature of high-level, thin clouds is developed and described. In the thermal infrared windows, the outgoing radiances from the earth's atmosphere contain information about cloud emissivity and cloud top temperature. This information is clearly demonstrated in the brightness temperature difference curves of two window channels. For the purpose of illustration, two window channels centered at 810 and 930 cm are chosen to construct the brightness temperature difference curves for a range of cloud top temperatures. These curves vary for different cloud top temperatures, and along each of these curves the emissivity changes. The brightness temperature difference method is used in a simulation study to demonstrate the feasibility of retrieving the cloud top temperature and emissivity by the utilization of measurements in two window channels. As expected, a perfect retrieval is found if perfect measurements and ideal atmospheric conditions are assumed. If a random error, which has a normal distribution with a mean of zero and standard deviation of + or - 0.5 C, is imposed to the measurements, a reasonable retrieval is found for emissivity greater than 0.3. The algorithm has been applied to a limited amount of HIRS2 data, which has 3.7, 3.98 and 11 micron channels. The cloud top temperature, emissivity and cloud cover are determined by using these channels.
Wu, Man-Li C.↗
Ice water path estimation and characterization using passive microwave radiometry
Model computations of top-of-atmospheric microwave brightness temperatures T(B) from layers of precipitation-sized ice of variable bulk density and ice water content (IWC) are presented. It is shown that the 85-GHz T(B) depends essentially on the ice optical thickness. The results demonstrate the potential usefulness of scattering-based channels for characterizing the ice phase and suggest a top-down methodology for retrieval of cloud vertical structure and precipitation estimation from multifrequency passive microwave measurements. Attention is also given to radiative transfer model results based on the multiparameter radar data initialization from the Cooperative Huntsville Meteorological Experiment (COHMEX) in northern Alabama. It is shown that brightness temperature warming effects due to the inclusion of a cloud liquid water profile are especially significant at 85 GHz during later stages of cloud evolution.
Vivekanandan, J.↗
4-D Cloud Water Content Fields Derived from Operational Satellite Data
In order to improve operational safety and efficiency, the transportation industry, including aviation, has an urgent need for accurate diagnoses and predictions of clouds and associated weather conditions. Adverse weather accounts for 70% of all air traffic delays within the U.S. National Airspace System. The Federal Aviation Administration has determined that as much as two thirds of weather-related delays are potentially avoidable with better weather information and roughly 20% of all aviation accidents are weather related. Thus, it is recognized that an important factor in meeting the goals of the Next Generation Transportation System (NexGen) vision is the improved integration of weather information. The concept of a 4-D weather cube is being developed to address that need by integrating observed and forecasted weather information into a shared 4-D database, providing an integrated and nationally consistent weather picture for a variety of users and to support operational decision support systems. Weather analyses and forecasts derived using Numerical Weather Prediction (NWP) models are a critical tool that forecasters rely on for guidance and also an important element in current and future decision support systems. For example, the Rapid Update Cycle (RUC) and the recently implemented Rapid Refresh (RR) Weather Research and Forecast (WRF) models provide high frequency forecasts and are key elements of the FAA Aviation Weather Research Program. Because clouds play a crucial role in the dynamics and thermodynamics of the atmosphere, they must be adequately accounted for in NWP models. The RUC, for example, cycles at full resolution five cloud microphysical species (cloud water, cloud ice, rain, snow, and graupel) and has the capability of updating these fields from observations. In order to improve the models initial state and subsequent forecasts, cloud top altitude (or temperature, T(sub c)) derived from operational satellite data, surface observations of cloud base altitude, radar reflectivity, and lightning data are used to help build and remove clouds in the models assimilation system. Despite this advance and the many recent advances made in our understanding of cloud physical processes and radiative effects, many problems remain in adequately representing clouds in models. While the assimilation of cloud top information derived from operational satellite data has merit, other information is available that has not yet been exploited. For example, the vertically integrated cloud water content (CWC) or cloud water path (CWP) and cloud geometric thickness (delta Z) are standard products being derived routinely from operational satellite data. These and other cloud products have been validated under a variety of conditions. Since the uncertainties have generally been found to be less than those found in model analyses and forecasts, the satellite products should be suitable for data assimilation, provided an appropriate strategy can be developed that links the satellite-derived cloud parameters with cloud parameters specified in the model. In this paper, we briefly outline such a strategy and describe a methodology to retrieve cloud water content profiles from operational satellite data. Initial results and future plans are presented. It is expected that the direct assimilation of this new product will provide the most accurate depiction of the vertical distribution of cloud water ever produced at the high spatial and temporal resolution needed for short term weather analyses and forecasts.
Smith, William L., Jr.↗
Water Planetary and Cometary Atmospheres: H2O/HDO Transmittance and Fluorescence Models
We developed a modern methodology to retrieve water (H2O) and deuterated water (HDO) in planetary and cometary atmospheres, and constructed an accurate spectral database that combines theoretical and empirical results. Based on a greatly expanded set of spectroscopic parameters, we built a full non-resonance cascade fluorescence model and computed fluorescence efficiencies for H2O (500 million lines) and HDO (700 million lines). The new line list was also integrated into an advanced terrestrial radiative transfer code (LBLRTM) and adapted to the CO2 rich atmosphere of Mars, for which we adopted the complex Robert-Bonamy formalism for line shapes. We then retrieved water and D/H in the atmospheres of Mars, comet C/2007 WI, and Earth by applying the new formalism to spectra obtained with the high-resolution spectrograph NIRSPEC/Keck II atop Mauna Kea (Hawaii). The new model accurately describes the complex morphology of the water bands and greatly increases the accuracy of the retrieved abundances (and the D/H ratio in water) with respect to previously available models. The new model provides improved agreement of predicted and measured intensities for many H2O lines already identified in comets, and it identifies several unassigned cometary emission lines as new emission lines of H2O. The improved spectral accuracy permits retrieval of more accurate rotational temperatures and production rates for cometary water.
Villanueva, G. L.↗
Variable Sampling Mapping
The performance of an optical system (for example, a telescope) is limited by the misalignments and manufacturing imperfections of the optical elements in the system. The impact of these misalignments and imperfections can be quantified by the phase variations imparted on light traveling through the system. Phase retrieval is a methodology for determining these variations. Phase retrieval uses images taken with the optical system and using a light source of known shape and characteristics. Unlike interferometric methods, which require an optical reference for comparison, and unlike Shack-Hartmann wavefront sensors that require special optical hardware at the optical system's exit pupil, phase retrieval is an in situ, image-based method for determining the phase variations of light at the system s exit pupil. Phase retrieval can be used both as an optical metrology tool (during fabrication of optical surfaces and assembly of optical systems) and as a sensor used in active, closed-loop control of an optical system, to optimize performance. One class of phase-retrieval algorithms is the iterative transform algorithm (ITA). ITAs estimate the phase variations by iteratively enforcing known constraints in the exit pupil and at the detector, determined from modeled or measured data. The Variable Sampling Mapping (VSM) technique is a new method for enforcing these constraints in ITAs. VSM is an open framework for addressing a wide range of issues that have previously been considered detrimental to high-accuracy phase retrieval, including undersampled images, broadband illumination, images taken at or near best focus, chromatic aberrations, jitter or vibration of the optical system or detector, and dead or noisy detector pixels. The VSM is a model-to-data mapping procedure. In VSM, fully sampled electric fields at multiple wavelengths are modeled inside the phase-retrieval algorithm, and then these fields are mapped to intensities on the light detector, using the properties of the detector and optical system, for comparison with measured data. Ultimately, this model-to-data mapping procedure enables a more robust and accurate way of incorporating the exit-pupil and image detector constraints, which are fundamental to the general class of ITA phase retrieval algorithms.
Smith, Jeffrey, S.↗
Results from CrIS/ATMS Obtained Using an AIRS "Version-6 Like" Retrieval Algorithm
We have tested and evaluated Version-6.22 AIRS and Version-6.22 CrIS products on a single day, December 4, 2013, and compared results to those derived using AIRS Version-6. AIRS and CrIS Version-6.22 O3(p) and q(p) products are both superior to those of AIRS Version-6All AIRS and CrIS products agree reasonably well with each other CrIS Version-6.22 T(p) and q(p) results are slightly poorer than AIRS under very cloudy conditions. Both AIRS and CrIS Version-6.22 run now at JPL. Our short term plans are to analyze many common months at JPL in the near future using Version-6.22 or a further improved algorithm to assess the compatibility of AIRS and CrIS monthly mean products and their interannual differencesUpdates to the calibration of both CrIS and ATMS are still being finalized. JPL plans, in collaboration with the Goddard DISC, to reprocess all AIRS data using a still to be finalized Version-7 retrieval algorithm, and to reprocess all recalibrated CrISATMS data using Version-7 as well.
AIRS↗
Results from CrIS/ATMS Obtained Using an "AIRS Version-6 Like" Retrieval Algorithm
A main objective of AIRS/AMSU on EOS is to provide accurate sounding products that are used to generate climate data sets. Suomi NPP carries CrIS/ATMS that were designed as follow-ons to AIRS/AMSU. Our objective is to generate a long term climate data set of products derived from CrIS/ATMS to serve as a continuation of the AIRS/AMSU products. We have modified an improved version of the operational AIRS Version-6 retrieval algorithm for use with CrIS/ATMS. CrIS/ATMS products are of very good quality, and are comparable to, and consistent with, those of AIRS.
Retrieval methodology↗
Water in Planetary and Cometary Atmospheres: H2O/HDO Transmittance and Fluorescence Models
We developed a modern methodology to retrieve water (H2O) and deuterated water (HDO) in planetary and cometary atmospheres, and constructed an accurate spectral database that combines theoretical and empirical results. On the basis of a greatly expanded set of spectroscopic parameters, we built a full non-resonance cascade fluorescence model and computed fluorescence efficiencies for H2O (500 million lines) and HDO (700 million lines). The new line list was also integrated into an advanced terrestrial radiative transfer code (LBLRTM) and adapted to the CO2 rich atmosphere of Mars, for which we adopted the complex Robert-Bonamy formalism for line shapes. We retrieved water and D/H in the atmospheres of Mars, comet C/2007 W1 (Boattini), and Earth by applying the new formalism to spectra obtained with the high-resolution spectrograph NIRSPEC/Keck II atop Mauna Kea (Hawaii). The new model accurately describes the complex morphology of the water bands and greatly increases the accuracy of the retrieved abundances (and the D/H ratio in water) with respect to previously available models. The new model provides improved agreement of predicted and measured intensities for many H2O lines already identified in comets, and it identifies several unassigned cometary emission lines as new emission lines of H2O. The improved spectral accuracy permits retrieval of more accurate rotational temperatures and production rates for cometary water.
comets↗