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Fred G Rose

Publications and source records attributed to Fred G Rose.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

An Algorithm to Derive Temperature and Humidity Profile Changes Using Spatially and Temporally Averaged Spectral Radiance Differences

A linear inversion algorithm to derive changes of surface skin temperature and atmospheric temperature and specific humidity vertical profiles using spatially and temporally averaged spectral radiance differences is developed. The algorithm uses spectral radiative kernels, which is the top-of-atmosphere spectral radiance change caused by perturbations of skin temperature and air temperature and specific humidity in the atmosphere, and is an improved version of the algorithm used in earlier studies. Two improvements are the inclusion of the residual and cloud spectral kernels in the form of eigenvectors of principal components. Three and six eigenvectors are used for, respectively, the residual and cloud spectral kernels. An underlying assumption is that the spectral shape of the principal components is constant and their magnitude varies temporally and spatially. The algorithm is tested using synthetic spectral radiances with the spectral range of the Atmospheric Infrared Sounder averaged over 16 days and over a 10° × 10° grid box. Changes of skin temperature, air temperature, and specific humidity vertical profiles are derived from the difference of nadir-view all-sky spectral radiances. The root-mean-square difference of retrieved and true skin temperature differences is 0.59 K. The median of absolute errors in the air temperature change is less than 0.5 K above 925 hPa. The median of absolute errors in the relative specific humidity changes is less than 10% above 825 hPa.

Fang Pan↗
NASA NTRS2020

Effects of electromagnetic wave interference on observations of the Earth radiation budget

This paper investigates conditions necessary to match the irradiance derived by integrating radiances measured by a narrow field of view scanning radiometer with the irradiance measured by a hemispherical radiometer, both placed at a satellite altitude for Earth radiation budget estimates. When all sources are similar and they are spatially distributed randomly, then integrating radiance for the irradiance does not introduce a bias. Although the exact magnitude of the bias in other conditions is unknown, a finite area of the aperture that is much larger than the coherence area of radiation contributing to the Earth radiation budget, and a finite time to take a single measurement that is longer than the coherence time are likely to make the difference of the irradiance integrated from radiances and the irradiance measured by a hemispherical instrument insignificant. This conclusion does not contradict the existence of spatial coherence of light from incoherent sources. Therefore, electromagnetic energy absorbed by Earth is derivable from radiances measured by a scanning radiometer integrated over the Earth-viewing hemisphere and then averaging across all locations on the satellite orbital sphere when combined with solar irradiance measurements. Comparisons made in earlier studies show that the difference is less than 1%. In addition, when surface irradiances computed by a radiative transfer model constrained by top-of-atmosphere irradiances derived from radiance measurements are compared with downward shortwave irradiances taken by combinations of a pyreheliometer and a shaded pyranometer, or pyranometers, and with longwave ir- radiances taken by pyrgeometers, the biases in monthly mean irradiances are less than the uncertainties in the surface observations.

Seiji Kato↗
NASA NTRS2020

Global and Regional Entropy Production by Radiation Estimated from Satellite Observations

Vertical profiles of shortwave and longwave irradiances computed with satellite-derived cloud properties and temperature and humidity profiles from reanalysis are used to estimate entropy production. Entropy production by shortwave radiation is computed by the absorbed irradiance within layers in the atmosphere and by the surface divided by their temperatures. Similarly, entropy production by longwave radiation is computed by emitted irradiance to space from layers in the atmosphere and surface divided by their temperatures. Global annual mean entropy production by shortwave absorption and longwave emission to space are, respectively, 0.852 and 0.928 W/sq.mK. With a steady-state assumption, entropy production by irreversible processes within the Earth system is estimated to be 0.076 W/sq.mK and by nonradiative irreversible processes to be 0.049 W/sq.mK. Both global annual mean entropy productions by shortwave absorption and longwave emission to space increase with increasing shortwave absorption (i.e., with decreasing the planetary albedo). The increase of entropy production by shortwave absorption is, however, larger than the increase of entropy production by longwave emission to space. The result implies that global annual mean entropy production by irreversible processes decreases with increasing shortwave absorption. Input and output temperatures derived by dividing the absorbed shortwave irradiance and emitted longwave irradiance to space by respective entropy production are, respectively, 282 and 259 K, which give the Carnot efficiency of the Earth system of 8.5%.

Seiji Kato↗
NASA NTRS2020

Toward a Consistent Definition between Satellite and Model Clear-Sky Radiative Fluxes

A new method of determining clear-sky radiative fluxes from satellite observations for climate model evaluation is presented. The method consists of applying adjustment factors to existing satellite clear-sky broadband radiative fluxes that make the observed and simulated clear-sky flux definitions more consistent. The adjustment factors are determined from the difference between observation-based radiative transfer model calculations of monthly mean clear-sky fluxes obtained by ignoring clouds in the atmospheric column and by weighting hourly mean clear-sky fluxes with imager-based clear-area fractions. The global mean longwave (LW) adjustment factor is −2.2 W m−2 at the top of the atmosphere and 2.7 W m−2 at the surface. The LW adjustment factors are pronounced at high latitudes during winter and in regions with high upper-tropospheric humidity and cirrus cloud cover, such as over the west tropical Pacific, and the South Pacific and intertropical convergence zones. In the shortwave (SW), global mean adjustment is 0.5 W m−2 at TOA and −1.9 W m−2 at the surface. It is most pronounced over sea ice off of Antarctica and over heavy aerosol regions, such as eastern China. However, interannual variations in the regional SW and LW adjustment factors are small compared to those in cloud radiative effect. After applying the LW adjustment factors, differences in zonal mean cloud radiative effect between observations and climate models decrease markedly between 60°S and 60°N and poleward of 65°N. The largest regional improvements occur over the west tropical Pacific and Indian Oceans. In contrast, the impact of the SW adjustment factors is much smaller.

Norman G Loeb↗
NASA NTRS2018

TPSAS-NF1676L-29978-DND

Energy within an atmospheric column changes due to absorption of shortwave irradiance, emission of longwave irradiance, enthalpy released by precipitation, convergence of dry static and kinetic energy flux, and surface sensible heat flux. When satellite derived irradiance, precipitation, and surface sensible heat flux data products, as well as tendency and horizontal advection of dry static and kinetic energy fluxes from reanalysis are used to compute the atmospheric energy balance, energy fluxes does not balance exactly, resulting in an energy balance residual. A study by Kato et al. (2016) shows that large residuals exist over tropical oceans. In this study, we examine how the energy balance residual changes with time. If the energy balance residual for a given region is constant every year, this implies that annual anomalies of energy flux components balance over the region. If, however, the energy balance residual changes with time, an analysis of the dependence on atmospheric or cloud properties may provide information to understand the reason for the residual. A preliminary result indicates that significant interannual variabilities in energy balance residual exist when annual energy balance residuals over the entire ocean is examined. Temporal variability of regional energy budget residual and covariability of the energy budget residual with the multivariate ENSO index and the occurrence of specific cloud types will further be investigated.

Seiji Kato↗
NASA NTRS2017

TPSAS-NF1676L-26925-DND

Observationally-based atmospheric energy balance is analyzed using Clouds and the Earth's Radiant Energy System (CERES)-derived TOA and surface irradiance, Global Precipitation Climatology Project (GPCP)-derived precipitation, dry static and kinetic energy tendency and divergence estimated from ERA-Interim, and surface sensible heat flux from SeaFlux. The residual tends to be negative over tropics and positive over mid-latitudes. A negative residual implies that precipitation rate is too small, divergence is too large, or radiative cooling is too large. The residual of atmospheric energy is spatially and temporally correlated with cloud objects to identify cloud types associated with the residual. Spatially, shallow cumulus, cirrostratus, and deep convective cloud object occurrence are positively correlated with the absolute value of the residual. The temporal correlation coefficient between the number of deep convective cloud objects and individual energy components, net atmospheric irradiance, precipitation rate, and the sum of dry static and kinetic energy divergence and their tendency over western Pacific are, respectively, 0.84, 0.95, and 0.93. However, when all energy components are added, the atmospheric energy residual over tropical Pacific temporally correlates well with the number of shallow cumulus cloud objects over tropical Pacific. Because shallow cumulus does not alter enough atmospheric energy compared to the residual, these suggest 1) if retrieval errors associated with deep convective clouds are causing the column integrated atmospheric energy residual, the errors vary among individual deep convective clouds, 2) it is possible that the residual is associated with processes in which shallow cumulus clouds affect deep convective clouds and hence atmospheric energy budget over tropical western Pacific, and 3) it is also possible that cumulus congestus present cirrostratus that missed in massive sensor retrieval contribute the residual. Cloud objects used in the study that leads to the above conclusions are derived from Moderate Resolution Imaging Spectroradiometer (MODIS). In this investigation, we use Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat data to evaluate and verify above findings. Specifically, we focus on 3) above and analyze frequency of occurrence of cumulus congestus present under cirrostratus and correlation to the energy balance residual.

Seiji Kato↗
NASA NTRSDate not supplied

Ed 4 EBAF-Surface

Explore the source record for details and available documents.

Seiji Kato↗
NASA NTRSDate not supplied

TPSAS-NF1676L-27246-DND

Work done after the last CERES meeting: evaluation of Ed4 SYN (with ship, buoy, and land sites data), start revising C3M, Production of Ed2.8 EBAF-surface (available through August 2016), development of the algorithm of Ed4 EBAF-surface, start evaluating GMAO products, MERRA2 and FP (Ham’s presentation), and the evaluation of the effect of multilayer cloud on surface radiation.

Seiji Kato↗
NASA NTRSDate not supplied

Diagnosing Changes In Earth's Top-Of-Atmosphere Radiation Budget Since 2000

Earth’s radiation budget (ERB) is a key indicator of the climate system as it describes the delicate balance between how much of the sun’s energy reaching Earth is absorbed and how much thermal infrared radiation is emitted back to space. It is now well established that there is a positive net imbalance in ERB of 0.5-1 Wm-2. This imbalance, referred to as Earth’s Energy Imbalance (EEI), is shaped by natural and anthropogenic climate forcings and the climate system’s response to the forcing.When averaged over a long period, a positive EEI leads to an increase in surface temperature, sea levelrise, ocean heat content, melting of snow and sea-ice, and an acceleration of the hydrological cycle.EEI is also influenced by internal climate variability at interannual (e.g., El Niño/Southern Oscillation(ENSO)) and longer timescales (e.g., Pacific Decadal Oscillation (PDO)). Here we use CERES data toshow that since 2000, EEI has been increasing at a rate of 0.04+-0.03 Wm-2 per yr, corresponding to 0.8 Wm-1 over the 20-year CERES record. To understand the underlying causes for this increase, we analyze the CERES data in the context of internal climate variability indices and regional pattern changes. We also perform a partial radiative perturbation analysis that decomposes the TOA SW and LW radiation variations in terms of contributions by clouds, aerosols, surface albedo, temperature,water vapor and other gases (e.g., ozone, carbon dioxide, methane, etc.).

Norman G Loeb↗
NASA NTRSDate not supplied

Towards a More Realistic Representation of Surface Albedo in NASA CERES Satellite Products: A Comparison with MOSAiC Field Campaign

Observing the Arctic from space is one of the most challenging tasks in climate science. Uncertainty in the NASA Clouds and the Earth’s Radiant Energy System (CERES)-derived irradiances is larger over sea ice than any other scene type and comes from several sources. The one-year long MOSAiC expedition in the central Arctic provides a rare opportunity to explore uncertainty in CERES-derived radiative fluxes. First, a systematic and statistically robust assessment of surface shortwave and longwave fluxes has been conducted using in-situ measurements from MOSAiC flux stations. The CERES SYN1deg product overestimates the SW_down flux by 11.40 Wm-2 and underestimates the SW_up flux by −15.70 Wm-2 and LW_down fluxes by −13.30 Wm-2 at the surface during summertime. In addition, large differences are found in the LW_up flux (~320 Wm-2) when the surface reaches melting point (~0℃). The large negative bias in upwelling shortwave flux can be attributed to the underestimation of surface albedo (−0.15) in SYN1deg. In addition to direct comparison, a series of perturbation experiments with a radiative transfer model are performed to estimate the contributions to the differences. By correcting both cloud and albedo inputs, the biases in SW_net flux and LW_net flux can be reduced to less than half of the control run biases to +19.90% and −10.53%, respectively. Furthermore, a compensating effect between underestimation of broadband albedo and overestimation of spectral albedo in visible and mid-infrared bands in SYN1deg datasets is found and contributes to the shortwave flux differences. The difference in CERES broadband albedo (~20 Wm 2) contributes to larger uncertainty in SW_up flux than spectral albedo shape (~3 Wm 2). The results of this study inform the future development of CERES products and will ultimately reduce uncertainties in Arctic surface radiation budget derived from satellite measurements.

Yiyi Huang↗