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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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

The Effect of Clouds Upon Limb Scattered Radiances and the Retrieval of Ozone Profiles Using These Radiances

The use of limb scattered radiance profiles to retrieve ozone profiles is currently being investigated. The goal is to produce ozone profiles with better vertical resolution than is available with the Backscattered Ultraviolet (BUV) technique and with much greater spatial and temporal coverage than with the solar occultation method (i.e. SAGE II). This method, which uses UV and visible light scattered from the earth's limb, has recently been proven to work for clear sky cases with data from the STS-87 flight of the Shuttle Ozone Sounding Limb Experiment/Limb Ozone Retrieval Experiment (SOLSE/LORE). As to be expected, clouds have a substantial impact upon the limb radiance (increasing the radiance as much as 80-100% is some cases). Here we use a variety of radiative transfer models and limited SOLSE/LORE data to investigate the effect of clouds upon the limb radiance at wavelengths used in the ozone retrieval (approximately 600 nm). Though the presence of clouds can greatly increase the limb radiance, they have a minimal effect upon the retrieved ozone profile, since the retrieval uses a differential absorption technique.

Flittner, D. E.↗
NASA NTRSDate not supplied

Deriving Broadband Radiances from Geostationary Satellite Visible Radiance Channels for Use in the CERES SYN1deg Product

The NASA CERES project was designed to monitor the Earth’s radiation budget, both at the regional and global scale. The CERES SSF1deg single satellite product provides TOA flux observations from Aqua, Terra, NPP, and NOAA-20. The SYN1deg product incorporates observations from Terra, Aqua, and geostationary (GEO) satellites. The GEO observations are included in the SYN1deg product to infer the regional diurnal cycle between CERES observations. GEO satellites throughout the CERES record have different narrowband spectral response functions and do not directly observe radiances over the broadband spectra of CERES instruments. Hence the emphasis of this study is improving the process of deriving broadband radiances from the myriad GEO visible channel observations. We present a new method for converting the narrowband radiances observed from GEO operational satellites. This study relies on radiative transfer model hyper-spectral radiances for many cloud and surface conditions under all angular conditions observed by GEO sensors. First, the hyper-spectral radiances from a radiative transfer model must be convolved with the spectral response function of the visible imager for each GEO sensor in the CERES record. For each angular bin and surface type, the clear-sky and multiple optical depth and phase cloudy-sky narrowband and broadband reflectance pairs are linearly regressed. This study found that, no matter how the scene conditions are stratified, for example the all-sky, clear-sky, ice or water clouds, they all provide similar linear regressions. This includes ocean glint conditions, where the clear-sky reflectance exceeds cloudy conditions and yet the relationship was found to be linear in the given angular bin. This infers that no GEO retrieved cloud information is necessary in the narrowband to broadband conversion process, which relies only on visible channel reflectance. Over land, most of the GEO visible channels do not observe the NIR vegetation reflectance. We intend to use land adjustment factors developed using the TOA reflectance difference between the real-time MODIS .86um and the .64um channel. We validate this approach by using coincident observational data between CERES and MODIS aboard Aqua and Terra as well as GEO and CERES observations .

CERES↗
NASA NTRS2006

The Goddard Snow Radiance Assimilation Project: An Integrated Snow Radiance and Snow Physics Modeling Framework for Snow/cold Land Surface Modeling

Microwave-based retrievals of snow parameters from satellite observations have a long heritage and have so far been generated primarily by regression-based empirical "inversion" methods based on snapshots in time. Direct assimilation of microwave radiance into physical land surface models can be used to avoid errors associated with such retrieval/inversion methods, instead utilizing more straightforward forward models and temporal information. This approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success. Recent developments in forward radiative transfer modeling, physical land surface modeling, and land data assimilation are converging to allow the assembly of an integrated framework for snow/cold lands modeling and radiance assimilation. The objective of the Goddard snow radiance assimilation project is to develop such a framework and explore its capabilities. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. In fact, multiple models are available for each element enabling optimization to match the needs of a particular study. Together these form a modular and flexible framework for self-consistent, physically-based remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster. Capabilities for assimilation of snow retrieval products are already under development for LIS. We will describe plans to add radiance-based assimilation capabilities. Plans for validation activities using field measurements will also be discussed.

Kim, E.↗
NASA NTRS1976

Wheat signature modeling and analysis for improved training statistics: Supplement. Simulated LANDSAT wheat radiances and radiance components

Simulated scanner system data values generated in support of LACIE (Large Area Crop Inventory Experiment) research and development efforts are presented. Synthetic inband (LANDSAT) wheat radiances and radiance components were computed and are presented for various wheat canopy and atmospheric conditions and scanner view geometries. Values include: (1) inband bidirectional reflectances for seven stages of wheat crop growth; (2) inband atmospheric features; and (3) inband radiances corresponding to the various combinations of wheat canopy and atmospheric conditions. Analyses of these data values are presented in the main report.

Malila, W. A.↗
NASA NTRS1993

Inference of cirrus cloud properties using satellite-observed visible and infrared radiances. I - Parameterization of radiance fields

The impact of using phase functions for spherical droplets and hexagonal ice crystals to analyze radiances from cirrus is examined. Adding-doubling radiative transfer calculations are employed to compute radiances for different cloud thicknesses and heights over various backgrounds. These radiances are used to develop parameterizations of top-of-the-atmosphere visible reflectance and IR emittance using tables of reflectances as a function of cloud optical depth, viewing and illumination angles, and microphysics. This parameterization, which includes Rayleigh scattering, ozone absorption, variable cloud height, and an anisotropic surface reflectance, reproduces the computed top-of-the-atmosphere reflectances with an accruacy of +/- 6 percent for four microphysical models: 10-micron water droplet, small symmetric crystal, cirrostratus, and cirrus uncinus. The accuracy is twice that of previous models.

Minnis, Patrick↗
NASA NTRS2013

Equivalent Sensor Radiance Generation and Remote Sensing from Model Parameters: Equivalent Sensor Radiance Formulation - Part 1

In this paper we describe a general procedure for calculating equivalent sensor radiances from variables output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint the algorithm takes explicit account of the model subgrid variability, in particular its description of the probably density function of total water (vapor and cloud condensate.) The equivalent sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies. We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products.) We focus on clouds and cloud/aerosol interactions, because they are very important to model development and improvement.

Simulations↗
NASA NTRS2008

Evaluation and Assimilation of Cloud Cleared Radiances for AIRS in GEOS-5

The use of clear (cloud-free) channels for AIRS in GEOS-5 had shown positive impact on forecast skills in both hemispheres. However, improvements in forecast skills due to the assimilation of AIRS data are less impressive since the number of assimilated channels from AIRS is much larger than that from other Infrared sounders such as HIRS-3 onboard NOAA 15-17 satellites. This limitation of AIRS radiance data to improve the forecast skill is mainly due to the fact that channels capable of peaking below clouds are not used in the assimilation and yet those have highest vertical resolving capability of AIRS instrument are concentrated in the lower troposphere. On average, the percentage of AIRS footprints completely clear for all channels is less than 10%. The percentage of assimilated AIRS channel radiances however ranges from 100% for channels peaking in the upper stratosphere, above the cloud, to no more that 5% in the lower atmosphere due to cloud contamination. Our current ability to model and predict clouds accurately in global model, and to fully characterize and parameterize optical properties of cloud particles in radiative transfer model are the two major obstacles prohibiting us to use cloudy radiance directly in the assimilation. To further improve forecast skill using AIRS data, we ought to use the channels peaking below the clouds in the troposphere, which can be accomplished by assimilating cloud-cleared radiance. The cloud-cleared radiance data for AIRS used in this study were obtained from optimal cloud clearing procedures developed by researchers at CIMSS of University of Wisconsin at Madison to retrieve clear column radiances for all AIRS channels by collocating multi-band MODIS IR clear radiance observations with the AIRS cloudy radiances on a single footprint basis. Two adjacent AIRS cloudy footprints are used to retrieve one AIRS cloud-cleared radiance spectrum and no background information (first guess) is needed. To assimilate the cloud-cleared radiance data, the errors of the cloud-cleared radiances need to be addressed. The details of convolving AIRS radiances with MODIS spectral response function and comparison with MODIS-measured cloud-free radiance will be presented. The range of errors of cloud-cleared radiances for AIRS using collocated MODIS clear and near-by AIRS clear data will be shown. The NASA. global data assimilation model, GEOS-5, is used to evaluate and assimilate the cloud-cleared radiance for AIRS. The residues between the cloud-cleared brightness temperature and the simulated brightness temperature from background (i.e., OMFs) will be investigated. The quality control procedures will be documented based on error estimation and the OMFs. Finally, the impacts between assimilation of clear channel radiances and cloud-cleared radiances will be addressed.

Liu, Hui-chun↗
NASA NTRSDate not supplied

MISR Level 1B1 Local Mode Radiance Data (MIB1LM_V2)

The results of two types of processing are included in this product. First, the Radiance Scaling operation converts the camera's digital number output to a measure of energy incident on the front optical surface. The measurement is expressed in units called radiance (energy per unit area, wavelength, and solid angle) as defined by the International Standard (SI). Second, Radiance Conditioning modifies the radiances to remove instrument-dependent effects. Specifically, image sharpening is applied, and focal-plane scattering is removed. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the 1504 detector elements of each spectral band and each camera. In addition to the Level 1B1 radiometric product for MISR's Global Mode imagery, there is a separate Level 1B1 product for each high-resolution Local Mode scene. The Radiometric Product contains spectral radiances for all MISR channels (four spectral bands and nine cameras). Each radiance value represents the incident radiance averaged over the sensor's total band response. Processing includes both radiance scaling and conditioning steps. Radiance scaling converts the Level 1A data from digital counts to radiances using coefficients derived in combination with the On-Board Calibrator (OBC) and vicarious calibrations. The OBC contains Spectralon calibration panels which are deployed monthly and reflect sunlight into the cameras. The OBC detector standards then measure this reflected light to provide the calibration. Vicarious field campaigns are conducted less frequently but provide an independent methodology useful for reducing systematic errors. Radiance conditioning removes undesirable instrument effects. Image enhancement is provided by deconvolving the scene with the sensor's point-spread-function. Additionally, in-band scaling adjusts the reported radiances to correspond to a nominal band response profile. This frees the Level 2 software from the need to correct for detector element non-uniformities. No out-of-band correction is done for this product, nor are the data geometrically corrected or resampled at this point. In summary, the Level 1B1 Product contains the Data Numbers (DNs) radiometrically-scaled to radiances with no geometric resampling. [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=275 m for red band only; Longitude_Resolution=275 m for red band only; Temporal_Resolution=about 15 orbits/day].

AM-1↗
NASA NTRSDate not supplied

MISR Level 1B1 Radiance Data (MI1B1_V1)

The results of two types of processing are included in this product. First, the Radiance Scaling operation converts the camera's digital number output to a measure of energy incident on the front optical surface. The measurement is expressed in units called radiance (energy per unit area, wavelength, and solid angle) as defined by the International Standard (SI). Second, Radiance Conditioning modifies the radiances to remove instrument-dependent effects. Specifically, image sharpening is applied, and focal-plane scattering is removed. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the 1504 detector elements of each spectral band and each camera. The Radiometric Product contains spectral radiances for all MISR channels (four spectral bands and nine cameras). Each radiance value represents the incident radiance averaged over the sensor's total band response. Processing includes both radiance scaling and conditioning steps. Radiance scaling converts the Level 1A data from digital counts to radiances using coefficients derived in combination with the On-Board Calibrator (OBC) and vicarious calibrations. The OBC contains Spectralon calibration panels which are deployed monthly and reflect sunlight into the cameras. The OBC detector standards then measure this reflected light to provide the calibration. Vicarious field campaigns are conducted less frequently but provide an independent methodology useful for reducing systematic errors. Radiance conditioning removes undesirable instrument effects. Image enhancement is provided by deconvolving the scene with the sensor's point-spread-function. Additionally, in-band scaling adjusts the reported radiances to correspond to a nominal band response profile. This frees the Level 2 software from the need to correct for detector element non-uniformities. No out-of-band correction is done for this product, nor are the data geometrically corrected or resampled at this point. In summary, the Level 1B1 Product contains the Data Numbers (DNs) radiometrically-scaled to radiances with no geometric resampling. [Location=GLOBAL] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=275 m for red band only; Longitude_Resolution=275 m for red band only; Horizontal_Resolution_Range=250 meters - < 500 meters; Temporal_Resolution=about 15 orbits/day; Temporal_Resolution_Range=Daily - < Weekly, about 15 orbits/day, Daily - < Weekly].

EOSDIS↗
NASA NTRS2003

Extraction of Profile Information from Cloud Contaminated Radiances

Clouds act to reduce the signal level and may produce noise dependence on the complexity of the cloud properties and the manner in which they are treated in the profile retrieval process. There are essentially three ways to extract profile information from cloud contaminated radiances: (1) cloud-clearing using spatially adjacent cloud contaminated radiance measurements, (2) retrieval based upon the assumption of opaque cloud conditions, and (3) retrieval or radiance assimilation using a physically correct cloud radiative transfer model which accounts for the absorption and scattering of the radiance observed. Cloud clearing extracts the radiance arising from the clear air portion of partly clouded fields of view permitting soundings to the surface or the assimilation of radiances as in the clear field of view case. However, the accuracy of the clear air radiance signal depends upon the cloud height and optical property uniformity across the two fields of view used in the cloud clearing process. The assumption of opaque clouds within the field of view permits relatively accurate profiles to be retrieved down to near cloud top levels, the accuracy near the cloud top level being dependent upon the actual microphysical properties of the cloud. The use of a physically correct cloud radiative transfer model enables accurate retrievals down to cloud top levels and below semi-transparent cloud layers (e.g., cirrus). It should also be possible to assimilate cloudy radiances directly into the model given a physically correct cloud radiative transfer model using geometric and microphysical cloud parameters retrieved from the radiance spectra as initial cloud variables in the radiance assimilation process. This presentation reviews the above three ways to extract profile information from cloud contaminated radiances. NPOESS Airborne Sounder Testbed-Interferometer radiance spectra and Aqua satellite AIRS radiance spectra are used to illustrate how cloudy radiances can be used in the profile retrieval process.

Smith, W. L.↗
NASA NTRSDate not supplied

MISR Level 1B1 Local Mode Radiance Data (MIB1LM_V1)

The results of two types of processing are included in this product. First, the Radiance Scaling operation converts the camera's digital number output to a measure of energy incident on the front optical surface. The measurement is expressed in units called radiance (energy per unit area, wavelength, and solid angle) as defined by the International Standard (SI). Second, Radiance Conditioning modifies the radiances to remove instrument-dependent effects. Specifically, image sharpening is applied, and focal-plane scattering is removed. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the 1504 detector elements of each spectral band and each camera. In addition to the Level 1B1 radiometric product for MISR's Global Mode imagery, there is a separate Level 1B1 product for each high-resolution Local Mode scene. The Radiometric Product contains spectral radiances for all MISR channels (four spectral bands and nine cameras). Each radiance value represents the incident radiance averaged over the sensor's total band response. Processing includes both radiance scaling and conditioning steps. Radiance scaling converts the Level 1A data from digital counts to radiances using coefficients derived in combination with the On-Board Calibrator (OBC) and vicarious calibrations. The OBC contains Spectralon calibration panels which are deployed monthly and reflect sunlight into the cameras. The OBC detector standards then measure this reflected light to provide the calibration. Vicarious field campaigns are conducted less frequently but provide an independent methodology useful for reducing systematic errors. Radiance conditioning removes undesirable instrument effects. Image enhancement is provided by deconvolving the scene with the sensor's point-spread-function. Additionally, in-band scaling adjusts the reported radiances to correspond to a nominal band response profile. This frees the Level 2 software from the need to correct for detector element non-uniformities. No out-of-band correction is done for this product, nor are the data geometrically corrected or resampled at this point. In summary, the Level 1B1 Product contains the Data Numbers (DNs) radiometrically-scaled to radiances with no geometric resampling. [Location=GLOBAL] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=275 m for red band only; Longitude_Resolution=275 m for red band only; Horizontal_Resolution_Range=250 km - < 500 km or approximately 2.5 degrees - < 5.0 degrees; Temporal_Resolution=about 15 orbits/day].

NEAR IR IMAGERY↗
NASA NTRSDate not supplied

MISR Level 1B1 Radiance Data (MI1B1_V2)

The results of two types of processing are included in this product. First, the Radiance Scaling operation converts the camera's digital number output to a measure of energy incident on the front optical surface. The measurement is expressed in units called radiance (energy per unit area, wavelength, and solid angle) as defined by the International Standard (SI). Second, Radiance Conditioning modifies the radiances to remove instrument-dependent effects. Specifically, image sharpening is applied, and focal-plane scattering is removed. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the 1504 detector elements of each spectral band and each camera. The Radiometric Product contains spectral radiances for all MISR channels (four spectral bands and nine cameras). Each radiance value represents the incident radiance averaged over the sensor's total band response. Processing includes both radiance scaling and conditioning steps. Radiance scaling converts the Level 1A data from digital counts to radiances using coefficients derived in combination with the On-Board Calibrator (OBC) and vicarious calibrations. The OBC contains Spectralon calibration panels which are deployed monthly and reflect sunlight into the cameras. The OBC detector standards then measure this reflected light to provide the calibration. Vicarious field campaigns are conducted less frequently but provide an independent methodology useful for reducing systematic errors. Radiance conditioning removes undesirable instrument effects. Image enhancement is provided by deconvolving the scene with the sensor's point-spread-function. Additionally, in-band scaling adjusts the reported radiances to correspond to a nominal band response profile. This frees the Level 2 software from the need to correct for detector element non-uniformities. No out-of-band correction is done for this product, nor are the data geometrically corrected or resampled at this point. In summary, the Level 1B1 Product contains the Data Numbers (DNs) radiometrically-scaled to radiances with no geometric resampling. [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1.1 km; Longitude_Resolution=1.1 km; Temporal_Resolution=about 15 orbits/day; Temporal_Resolution_Range=about 15 orbits/day].

AM-1↗
NASA NTRS2017

Simulating PACE Global Ocean Radiances

The NASA PACE mission is a hyper-spectral radiometer planned for launch in the next decade. It is intended to provide new information on ocean biogeochemical constituents by parsing the details of high resolution spectral absorption and scattering. It is the first of its kind for global applications and as such, poses challenges for design and operation. To support pre-launch mission development and assess on-orbit capabilities, the NASA Global Modeling and Assimilation Office has developed a dynamic simulation of global water-leaving radiances, using an ocean model containing multiple ocean phytoplankton groups, particulate detritus, particulate inorganic carbon (PIC), and chromophoric dissolved organic carbon (CDOC) along with optical absorption and scattering processes at 1 nm spectral resolution. The purpose here is to assess the skill of the dynamic model and derived global radiances. Global bias, uncertainty, and correlation are derived using available modern satellite radiances at moderate spectral resolution. Total chlorophyll, PIC, and the absorption coefficient of CDOC (aCDOC), are simultaneously assimilated to improve the fidelity of the optical constituent fields. A 5-year simulation showed statistically significant (P < 0.05) comparisons of chlorophyll (r = 0.869), PIC (r = 0.868), and a CDOC (r =0.890) with satellite data. Additionally, diatoms (r = 0.890), cyanobacteria (r = 0.732), and coccolithophores (r = 0.716) were significantly correlated with in situ data. Global assimilated distributions of optical constituents were coupled with a radiative transfer model (Ocean-Atmosphere Spectral Irradiance Model, OASIM) to estimate normalized water-leaving radiances at 1 nm for the spectral range 250-800 nm. These unassimilated radiances were within 0.074 mW/sq cm/micron/sr of MODIS-Aqua radiances at 412, 443, 488, 531, 547, and 667 nm. This difference represented a bias of 10.4% (model low). A mean correlation of 0.706 (P < 0.05) was found with global distributions of MODIS radiances. These results suggest skill in the global assimilated model and resulting radiances. The reported error characterization suggests that the global dynamical simulation can support some aspects of mission design and analysis. For example, the high spectral resolution of the simulation supports investigations of band selection. The global nature of the radiance representations supports investigations of satellite observing scenarios. Global radiances at bands not available in current and past missions support investigations of mission capability. PACE, ocean color, water-leaving radiances, biogeochemical model, radiative transfer model

PIC↗
NASA NTRS2015

Effects of Nighttime Light Radiance on the Sleep of the General Population

The objectives of this study is to verify if the exposure to greater nighttime radiance is associated with changes in the sleep/wake schedule and with greater sleep disturbances. Methods: The target population was the adults (18 years and older) living in California, USA. This represents 24 million of inhabitants. A total of 3,104 subjects participated in the survey (participation rate 85.6%). The participants were interviewed by telephone using the Sleep-EVAL system. The interviews covered several topics including sleeping habits, sleep quality, sleep disturbances, physical symptoms related to menopause. Chronic insomnia was defined as difficulty initiating or maintaining sleep for at least 3 months. Global nighttime light emissions have been collected by the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) sensors. We extracted the radiance calibrated nighttime lights corresponding to the date of the interviews for a three by three window centered on each coordinate corresponding to an interview address. Results: Dissatisfaction with sleep quantity and/or quality was associated with an increased nighttime radiance (p=0.02). Similarly, excessive sleepiness accompanied with impaired functioning was significantly associated with an increased nighttime radiance (p (is) less than 0.0001). The association remained significant after controlling for age, gender and use of a night lamp in the bedroom. Confusional arousals were also significantly associated with an increased nighttime radiance (p (is) less than 0.0001). Bedtime hour was linearly increasing with the intensity of nighttime radiance: the later the bedtime, the greater the nighttime radiance (p (is) less than 0.0001). Similarly, wakeup time became progressively later as the nighttime radiance increased (p (is) less than 0.0001). Both associations remained significant after controlling for age, gender and use of a night lamp in the bedroom. Circadian Rhythm Disorders were the only sleep disorder significantly associated with increased nighttime radiance (p (is) less than 0.0001). Exposure to increased nighttime light radiance appeared to cause a shift in the sleep/wake schedule, excessive sleepiness and Circadian Rhythm Disorders.

Excessive Sleepiness↗
NASA NTRS2018

Examining Dense Data Usage near the Regions with Severe Storms in All-Sky Microwave Radiance Data Assimilation and Impacts on GEOS Hurricane Analyses

Many numerical weather prediction (NWP) centers assimilate radiances affected by clouds and precipitation from microwave sensors, with the expectation that these data can provide critical constraints on meteorological parameters in dynamically sensitive regions to make significant impacts on forecast accuracy for precipitation. The Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center assimilates all-sky microwave radiance data from various microwave sensors such as all-sky GPM Microwave Imager (GMI) radiance in the Goddard Earth Observing System (GEOS) atmospheric data assimilation system (ADAS), which includes the GEOS atmospheric model, the Gridpoint Statistical Interpolation (GSI) atmospheric analysis system, and the Goddard Aerosol Assimilation System (GAAS). So far, most of NWP centers apply same large data thinning distances, that are used in clear-sky radiance data to avoid correlated observation errors, to all-sky microwave radiance data. For example, NASA GMAO is applying 145 km thinning distances for most of satellite radiance data including microwave radiance data in which all-sky approach is implemented. Even with these coarse observation data usage in all-sky assimilation approach, noticeable positive impacts from all-sky microwave data on hurricane track forecasts were identified in GEOS-5 system. The motivation of this study is based on the dynamic thinning distance method developed in our all-sky framework to use of denser data in cloudy and precipitating regions due to relatively small spatial correlations of observation errors. To investigate the benefits of all-sky microwave radiance on hurricane forecasts, several hurricane cases selected between 2016-2017 are examined. The dynamic thinning distance method is utilized in our all-sky approach to understand the sources and mechanisms to explain the benefits of all-sky microwave radiance data from various microwave radiance sensors like Advanced Microwave Sounder Unit (AMSU-A), Microwave Humidity Sounder (MHS), and GMI on GEOS-5 analyses and forecasts of various hurricanes.

Kim, Min-Jeong↗