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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 NTRS2003

Using Cloud Top Pressures Derived from Raman Scattering in the UV for the OMI Total Column Ozone Retrievals

The OMI cloud pressure product is necessary for accounting for cloud effects on the mission- critical total ozone product. One of the OM1 cloud pressure algorithms uses UV measurements to derive cloud pressures from the high frequency structure of top- of-atmosphere reflectance caused by rotational Raman scattering (RRS) in the atmosphere. RRS results in filling-in of Fraunhofer lines in the backscatter UV spectra (also known as the Ring effect). The magnitude of filling-in of the Fraunhofer lines is roughly proportional to the average number of solar photon scatterings in the atmosphere above the clouds. This property of RRS is used to deduce an effective cloud pressure. The cloud pressure algorithm retrieves the cloud pressure and cloud fraction using a concept of the Mixed Lambert Equivalent Reflectivity (MLER) also used for the TOMS-V8 OM1 total column ozone algorithm. Currently, this OMI total column ozone algorithm utilizes information about cloud top pressures from a climatology based on IR measurements. The IR-derived cloud top pressure is known to be lower than UV-derived cloud top pressure because UV radiation penetrates clouds deeper than IR radiation. That is why the UV-derived cloud pressure may be more consistent withthe total ozone algorithm. We estimate total column ozone differences caused by replacing the cloud pressure climatology with cloud pressures retrieved from GOME data same as used for retrieval of ozone.

Joiner, J.↗
NASA NTRS2023

The CHROMA Cloud-Top Pressure Retrieval Algorithm for the Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) Satellite Mission

This paper provides the theoretical basis and simulated retrievals for the Cloud Height Retrieval from O2 Molecular Absorption (CHROMA) algorithm. Simulations are performed for the Ocean Color Instrument (OCI), which is the primary payload on the forthcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, and the Ocean Land Colour Instrument (OLCI) currently flying on the Sentinel 3 satellites. CHROMA is a Bayesian approach which simultaneously retrieves cloud optical thickness (COT), cloud-top pressure and height (CTP and CTH respectively), and (with a significant prior constraint) surface albedo. Simulated retrievals suggest that the sensor and algorithm should be able to meet the PACE mission goal for CTP error, which is ±60 mb for 65 % of opaque (COT ≥3) single-layer clouds on global average. CHROMA will provide pixel-level uncertainty estimates, which are demonstrated to have skill at telling low-error situations from high-error ones. CTP uncertainty estimates are well-calibrated in magnitude, although COT uncertainty is overestimated relative to observed errors. OLCI performance is found to be slightly better than OCI overall, demonstrating that it is a suitable proxy for the latter in advance of PACE's launch. CTP error is only weakly sensitive to correct cloud phase identification or assumed ice crystal habit/roughness. As with other similar algorithms, for simulated retrievals of multi-layer systems consisting of optically thin cirrus clouds above liquid clouds, retrieved height tends to be underestimated because the satellite signal is dominated by the optically thicker lower layer. Total (liquid plus ice) COT also becomes underestimated in these situations. However, retrieved CTP becomes closer to that of the upper ice layer for ice COT ≈3 or higher.

PACE↗
NASA NTRS2020

Cloud-top pressure retrieval with DSCOVR EPIC oxygen A- and B-band observations

An analytic transfer inverse model for Earth Polychromatic Imaging Camera (EPIC) observations is proposed to retrieve the cloud-top pressure (CTP) with the consideration of in-cloud photon penetration. In this model, an analytic equation was developed to represent the reflection at the top of the atmosphere from above cloud, in cloud, and below cloud. The coefficients of this analytic equation can be derived from a series of EPIC simulations under different atmospheric conditions using a nonlinear regression algorithm. With estimated cloud pressure thickness, the CTP can be retrieved from EPIC observation data by solving the analytic equation. To simulate the EPIC measurements, a program package using the double-k approach was developed. Compared to line-by-line calculation, this approach can calculate high-accuracy results with a 100-fold computation time reduction. During the retrieval processes, two kinds of retrieval results, i.e., baseline CTP and retrieved CTP, are provided. The baseline CTP is derived without considering in-cloud photon penetration, and the retrieved CTP is derived by solving the analytic equation, taking into consideration in-cloud and below-cloud interactions. The retrieved CTPs for the oxygen A and B bands are smaller than their related baseline CTP. At the same time, both baseline CTP and retrieved CTP at the oxygen B band are larger than those at the oxygen A band. Compared to the difference in baseline CTP between the B band and A band, the difference in retrieved CTP between these two bands is generally reduced. Out of around 10 000 cases, in retrieved CTP between the A and B bands we found an average bias of 93 mb with a standard deviation of 81 mb. The cloud layer top pressure from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements is used for validation. Under single-layer cloud situations, the retrieved CTPs for the oxygen A band agree well with the CTPs from CALIPSO, the mean difference of which within 5 mb in the case study. Under multiple-layer cloud situations, the CTPs derived from EPIC measurements may be larger than the CTPs of high-level thin clouds due to the effect of photon penetration.

DSCOVR↗
NASA NTRS1985

Remote sensing of cloud-top pressure using reflected solar radiation in the oxygen A-band

An algorithm has been developed for using the reflection of solar radiation in the oxygen A-band to determine cloud-top altitude. Because of multiple scattering and molecular absorption inside the cloud, the reflection of clouds is substantially modified in comparison with a mirror cloud, which is assumed to have a 100 percent reflection. To infer true cloud-top altitude, therefore, it is necessary to accurately estimate the amount of 'photon penetration'. Theoretical calculations indicate that the amount of photon penetration depends on the altitude, the scaled volume scattering coefficient, and the scaled optical thickness of the cloud. Algorithms using the reflection in the oxygen A-band to determine the cloud-top pressure have been applied to an aircraft field experiment in conjunction with CCOPE, 1981. Results of this study are very encouraging, especially for extended clouds.

Wu, M.-L. C.↗
NASA NTRS2014

Use of MODIS Cloud Top Pressure to Improve Assimilation Yields of AIRS Radiances in GSI

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA's Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Previously, it has been shown that cloud top designation associated with quality control procedures within the Gridpoint Statistical Interpolation (GSI) system used operationally by a number of Joint Center for Satellite Data Assimilation (JCSDA) partners may not provide the best representation of cloud top pressure (CTP). Because this designated CTP determines which channels are cloud-free and, thus, available for assimilation, ensuring the most accurate representation of this value is imperative to obtaining the greatest impact from satellite radiances. This paper examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing analysis increments and numerical forecasts generated using operational techniques with a research technique that swaps CTP from the Moderate-resolution Imaging Spectroradiometer (MODIS) for the value of CTP calculated from the radiances within GSI.

Zavodsky, Bradley↗
NASA NTRS2014

Use of MODIS Cloud Top Pressure to Improve Assimilation Yields of AIRS Radiances in GSI

Radiances from hyperspectral sounders such as the Atmospheric Infrared Sounder (AIRS) are routinely assimilated both globally and regionally in operational numerical weather prediction (NWP) systems using the Gridpoint Statistical Interpolation (GSI) data assimilation system. However, only thinned, cloud-free radiances from a 281-channel subset are used, so the overall percentage of these observations that are assimilated is somewhere on the order of 5%. Cloud checks are performed within GSI to determine which channels peak above cloud top; inaccuracies may lead to less assimilated radiances or introduction of biases from cloud-contaminated radiances.Relatively large footprint from AIRS may not optimally represent small-scale cloud features that might be better resolved by higher-resolution imagers like the Moderate Resolution Imaging Spectroradiometer (MODIS). Objective of this project is to "swap" the MODIS-derived cloud top pressure (CTP) for that designated by the AIRS-only quality control within GSI to test the hypothesis that better representation of cloud features will result in higher assimilated radiance yields and improved forecasts.

Zavodsky, Bradley↗
NASA NTRS2005

A Comparison of High Spectral Resolution Infrared Cloud-Top Pressure Altitude Algorithms Using S-HIS Measurements

This paper presents a comparison of cloud-top altitude retrieval methods applied to S-HIS (Scanning High Resolution Interferometer Sounder) measurements. Included in this comparison is an improvement to the traditional CO2 Slicing method. The new method, CO2 Sorting, determines optimal channel pairs to apply the CO2 Slicing. Measurements from collocated samples of the Cloud Physics Lidar (CPL) and Modis Airborne Simulator (MAS) instruments assist in the comparison. For optically thick clouds good correlation between the S-HIS and lidar cloud-top retrievals are found. For tenuous ice clouds there can be large differences between lidar (CPL) and S-HIS retrieved cloud-tops. It is found that CO2 Sorting significantly reduces the cloud height biases for the optically thin cloud (total optical depths less then 1.0). For geometrically thick but optically thin cirrus clouds large differences between the S-HIS infrared cloud top retrievals and the CPL detected cloud top where found. For these cases the cloud height retrieved by the S-HIS cloud retrievals correlated closely with the level the CPL integrated cloud optical depth was approximately 1.0.

Holz, Robert E.↗
NASA NTRSDate not supplied

Surface Radiation Budget (SRB) Release 2.5 Longwave cloud props 3 hourly Data in Native Format (SRB_REL2.5_LW_CLDPROPS_3HRLY)

The Surface Radiation Budget (SRB) data set contain 47 surface and cloud properties including cloud fraction, cloud visible optical depth, cloud top temperature, cloud top pressure, cloud base pressure, and cloud water and ice content for each of 5 cloud types and in total. In addition to these cloud properties, the parameters also include surface pressure, total column ozone, day/night flag, and precipitable water. All perameters were measured at three hourly intervals for each day for the entire globe between 07/01/1983 and 06/30/2005 and serve as inputs to the Global Energy and Water-Cycle Experiment (GEWEX) longwave algorithm (GLW). [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-01-01; Stop_Date=2005-06-30] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1 degree; Longitude_Resolution=Ranges from 1 degree (tropics and subtropics) to 120 degrees (the poles).; Temporal_Resolution=3 hourly; Temporal_Resolution_Range=3 hourly].

CLOUD TOP TEMPERATURE↗
NASA NTRS1995

Comparison between SAGE II and ISCCP high-level clouds. 2: Locating clouds tops

A comparison is made of the vertical distribution of high-level cloud tops derived from the Stratospheric Aerosol and Gas Experiment II (SAGE II) occultation measurements and from the International Satellite Cloud Climatology Project (ISCCP) for all Julys and Januarys in 1985 to 1990. The results suggest that ISCCP overestimates the pressure of high-level clouds by up to 50-150 mbar, particularly at low latitudes. This is caused by the frequent presence of clouds with diffuse tops (greater than 50% time when cloudy events are observed). The averaged vertical extent of the diffuse top is about 1.5 km. At midlatitudes where the SAGE II and ISCCP cloud top pressure agree best, clouds with distinct tops reach a maximum relative proportion of the total level cloud amount (about 30-40%), and diffuse-topped clouds are reduced to their minimum (30-40%). The ISCCP-defined cloud top pressure should be regarded not as the material physical height of the clouds but as the level which emits the same infrared radiance as observed. SAGE II and ISCCP cloud top pressures agree for clouds with distinct tops. There is also an indication that the cloud top pressures of optically thin clouds not overlying thicker clouds are poorly estimated by ISCCP at middle latitudes. The average vertical extent of these thin clouds is about 2.5 km.

Liao, Xiaohan↗
DOE OSTI2024

Parallax-corrected VISST-derived pixel-level products from satellite GOES-16

The NASA Langley group led by William Smith produced GOES-16 satellite cloud retrievals over an approximate 10 by 10 degree region over the CACTI field campaign location. These retrievals are described here: https://www.arm.gov/capabilities/vaps/visst and are available for download here . They use algorithms historically called VISST that are now referred to as SatCORPS. More information can be found in Trepte et al. (2019), Minnis et al. (2021), and Yost et al. (2021). If using this dataset, please cite these references, the CACTI VISST dataset DOI found at the download link above, and this dataset’s DOI. The CACTI VISST pixel-level retrievals are on a 2 km spatial grid and available every 15 minutes (every 10 minutes late in the campaign), producing 21,765 files for the entire field campaign between October 2018 and April 2019. They are not corrected for parallax error, which is an offset in the actual geographical location of a cloud above the surface due to the satellite viewing the cloud partly from the side off nadir. This dataset applies a correction for parallax using the location relative to the satellite and the retrieved cloud top height above the surface, which allows the dataset to be geo-located with surface-based observations. The parallax correction for each location depends on the longitude, latitude and cloud top height above ground level (AGL) for that longitude and latitude in the original VISST files. The cloud top height AGL requires first computing the surface elevation at each VISST grid point. Data from the Advanced Spaceborne Thermal Emission and Reflection (ASTER) Global Digital Elevation Map Version 3 at 30-m resolution is projected onto the VISST grid using conservative coarsening (conserving surface elevation) in the xESMF Python package. The surface elevation is then subtracted from the VISST-retrieved cloud top height above mean sea level. These cloud top heights AGL are then combined with longitude and latitude to estimate the latitude and longitude corrections. Due to variability in cloud top height, the parallax shifts produce an irregular grid of values since higher cloud tops are shifted further than lower cloud tops. A ball tree-based neighbor search with Haversine distance is performed using the Python-based scikit-learn library to find the nearest VISST grid point to each parallax correction-shifted point. The data value of the shifted point is then assigned to that VISST grid point. In this manner, the irregular geographical shifts to correct for parallax are projected back to the rectilinear VISST grid. Because relatively higher clouds should obscure lower clouds, the variable values for the highest cloud top are preferentially chosen if two or more values are assigned to a grid point. The parallax correction should be viewed as an improved but still imperfect estimation of the cloud top locations, largely because the cloud top height is an imperfect retrieval. Please see the attached README document for further information. Users are encouraged to contact the authors with any additional questions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

TPSAS-NF1676L-18357-DND

High-spectral resolution measurements (in the IR region) from space born infrared sensors can provide sufficient spectral information about cloud properties like cloud top pressure, cloud particle size and cloud optical thickness. Empirical orthogonal function (EOF) based regression scheme was used to retrieve cloud properties along with atmospheric state profiles. A channel based physical inversion scheme was also tried on cloud-contaminated radiances to improve retrieval accuracy achieved by regression scheme. Recently developed principle component (PC) based physical retrieval algorithm utilized the EOF transformation to compress both hyperspectral radiance spectra and atmospherical profiles into PC scores to greatly boost the computational speed while maintaining the information content. The accuracy of the (PC) based radiation transfer model (PCRTM) to simulate top-of the atmosphere(TOA) high-spectral resolution radiance spectra for both clear and cloudy sky conditions has been well tested by comparing with the other line-by-line radiation transfer model and using the observation data. The application of the PCRTM based physical inversion method on simulated IASI radiance under 'cloudy' conditions will be a good integrity test for the new algorithm.

X Liu↗
NASA NTRS2004

The Geostationary Operational Environmental Satellite (GOES) Product Generation System

The Geostationary Operational Environmental Satellite (GOES) Product Generation System (GPGS) is introduced and described. GPGS is a set of computer programs developed and maintained at the Global Hydrology and Climate Center and is designed to generate meteorological data products using visible and infrared measurements from the GOES-East Imager and Sounder instruments. The products that are produced by GPGS are skin temperature, total precipitable water, cloud top pressure, cloud albedo, surface albedo, and surface insolation. A robust cloud mask is also generated. The retrieval methodology for each product is described to include algorithm descriptions and required inputs and outputs for the programs. Validation is supplied where applicable.

Haines, S. L.↗
NASA NTRS2022

Assessment of Two Stochastic Cloud Subcolumn Generators Using Observed Fields of Vertically Resolved Cloud Extinction

We evaluate two stochastic subcolumn generators used in GCMs to emulate subgrid cloud variability enabling comparisons with satellite observations and simulations of certain physical processes. Our evaluation necessitated the creation of a reference observational dataset that resolves horizontal and vertical cloud variability. The dataset combines two CloudSat cloud products that resolve two-dimensional cloud optical depth variability of liquid, ice, and mixed phase clouds when blended at ~200 m vertical and ~ 2 km horizontal scales. Upon segmenting the dataset to individual “scenes”, mean profiles of the cloud fields are passed as input to generators that produce scene-level cloud subgrid variability. The assessment of generator performance at the scale of individual scenes and in a mean sense is largely based on inferred joint histograms that partition cloud fraction within predetermined combinations of cloud top pressure –cloud optical thickness ranges. Our main finding is that both generators tend to underestimate optically thin clouds, while one of them also tends to overestimate some cloud types of moderate and high optical thickness. Associated radiative flux errors are also calculated by applying a simple transformation to the cloud fraction histogram errors, and are found to approach values almost as high as 3 Wm-2 for the cloud radiative effect in the shortwave part of the spectrum.

cloud variability↗
NASA NTRS2012

Spatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites

The Moderate Resolution Imaging Spectroradiometer (MODIS) was developed by NASA and launched aboard the Terra spacecraft on December 18, 1999 and Aqua spacecraft on May 4, 2002. A comprehensive set of remote sensing algorithms for the retrieval of cloud physical and optical properties have enabled over twelve years of continuous observations of cloud properties from Terra and over nine years from Aqua. The archived products from these algorithms include 1 km pixel-level (Level-2) and global gridded Level-3 products. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. Results include the latitudinal distribution of cloud optical and radiative properties for both liquid water and ice clouds, as well as latitudinal distributions of cloud top pressure and cloud top temperature. MODIS finds the cloud fraction, as derived by the cloud mask, is nearly identical during the day and night, with only modest diurnal variation. Globally, the cloud fraction derived by the MODIS cloud mask is approx.67%, with somewhat more clouds over land during the afternoon and less clouds over ocean in the afternoon, with very little difference in global cloud cover between Terra and Aqua. Overall, cloud fraction over land is approx.55%, with a distinctive seasonal cycle, whereas the ocean cloudiness is much higher, around 72%, with much reduced seasonal variation. Cloud top pressure and temperature have distinct spatial and temporal patterns, and clearly reflect our understanding of the global cloud distribution. High clouds are especially prevalent over the northern hemisphere continents between 30 and 50 . Aqua and Terra have comparable zonal cloud top pressures, with Aqua having somewhat higher clouds (cloud top pressures lower by 100 hPa) over land due to afternoon deep convection. The coldest cloud tops (colder than 230 K) generally occur over Antarctica and the high clouds in the tropics (ITCZ and the deep convective clouds over the western tropical Pacific and Indian sub-continent).

King, Michael D.↗
NASA NTRS2002

Retrieval Of Cloud Pressure And Chlorophyll Content Using Raman Scattering In GOME Ultraviolet Spectra

Reliable cloud pressure estimates are needed for accurate retrieval of ozone and other trace gases using satellite-borne backscatter ultraviolet (buv) instruments such as the global ozone monitoring experiment (GOME). Cloud pressure can be derived from buv instruments by utilizing the properties of rotational-Raman scattering (RRS) and absorption by O2-O2. In this paper we estimate cloud pressure from GOME observations in the 355-400 nm spectral range using the concept of a Lambertian-equivalent reflectivity (LER) surface. GOME has full spectral coverage in this range at relatively high spectral resolution with a very high signal-to-noise ratio. This allows for much more accurate estimates of cloud pressure than were possible with its predecessors SBUV and TOMS. We also demonstrate the potential capability to retrieve chlorophyll content with full-spectral buv instruments. We compare our retrieved LER cloud pressure with cloud top pressures derived from the infrared ATSR instrument on the same satellite. The findings confirm results from previous studies that showed retrieved LER cloud pressures from buv observations are systematically higher than IR-derived cloud-top pressure. Simulations using Mie-scattering radiative transfer algorithms that include O2-O2 absorption and RRS show that these differences can be explained by increased photon path length within and below cloud.

Atlas, Robert↗
NASA NTRS1994

Cirrus cloud retrieval using infrared sounding data: Multilevel cloud errors

In this study we perform an error analysis for cloud-top pressure retrieval using the High-Resolution Infrared Radiometric Sounder (HIRS/2) 15-microns CO2 channels for the two-layer case of transmissive cirrus overlying an overcast, opaque stratiform cloud. This analysis includes standard deviation and bias error due to instrument noise and the presence of two cloud layers, the lower of which is opaque. Instantaneous cloud pressure retrieval errors are determined for a range of cloud amounts (0.1-1.0) and cloud-top pressures (850-250 mb). Large cloud-top pressure retrieval errors are found to occur when a lower opaque layer is present underneath an upper transmissive cloud layer in the satellite field of view (FOV). Errors tend to increase with decreasing upper-cloud effective cloud amount and with decreasing cloud height (increasing pressure). Errors in retrieved upper-cloud pressure result in corresponding errors in derived effective cloud amount. For the case in which a HIRS FOV has two distinct cloud layers, the difference between the retrieved and actual cloud-top pressure is positive in all cases, meaning that the retrieved upper-cloud height is lower than the actual upper-cloud height. In addition, errors in retrieved cloud pressure are found to depend upon the lapse rate between the low-level cloud top and the surface. We examined which sounder channel combinations would minimize the total errors in derived cirrus cloud height caused by instrument noise and by the presence of a lower-level cloud. We find that while the sounding channels that peak between 700 and 1000 mb minimize random errors, the sounding channels that peak at 300-500 mb minimize bias errors. For a cloud climatology, the bias errors are most critical.

Baum, Bryan A.↗