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At least 1,351 records · Page 75
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NASA NTRS2006

On the Relation Between Sunspot Area and Sunspot Number

Often, the relation between monthly or yearly averages of total sunspot area, A, and sunspot number, R, has been described using the formula A = 16.7 R. Such a simple relation, however, is erroneous. The yearly ratio of A/R has varied between 5.3 in 1964 to 19.7 in 1926, having a mean of 13.1 with a standard deviation of 3.5. For 1875-1976 (corresponding to the Royal Greenwich Observatory timeframe), the yearly ratio of A/R has a mean of 14.1 with a standard deviation of 3.2, and it is found to differ significantly from the mean for 1977-2004 (corresponding to the United States Air Force/National Oceanic and Atmospheric Administration Solar Optical Observing Network timeframe), which equals 9.8 with a standard deviation of 2.1. Scatterplots of yearly values of A versus R are highly correlated for both timeframes and they suggest that a value of R = 100 implies A=1,538 +/- 174 during the first timeframe, but only A=1,076 +/- 123 for the second timeframe. Comparison of the yearly ratios adjusted for same day coverage against yearly ratios using Rome Observatory measures for the interval 1958-1998 indicates that sunspot areas during the second timeframe are inherently too low.

Wilson, Robert M.↗
NASA NTRS2002

Ocean state estimation for climate studies

Climate variabilities, which are of interest to CLIVAR, involve a broad range of spatial and temporal scales. Ocean state estimation (often referred to as ocean data assimilation), by optimally combining observations and models, becomes an important element of CLIVAR.

oceanography climate↗
NASA NTRS1995

Measurement characteristics and Kinematics of Sea Ice Using Microwave Satellite Data

Changes in sea ice conditions have major effects on the ocean and on weather, but visual observations miss periods of cloudiness and darkness. Microwave radar satellites are the only uninterrupted source of high resolution (30 m) data. Recent developments will allow high-resolution global sea-ice tracking at intervals of a few days.

remote sensing sea ice synthetic aperture radar SA↗
NASA NTRS2009

Feature Detection Systems Enhance Satellite Imagery

In 1963, during the ninth orbit of the Faith 7 capsule, astronaut Gordon Cooper skipped his nap and took some photos of the Earth below using a Hasselblad camera. The sole flier on the Mercury-Atlas 9 mission, Cooper took 24 photos - never-before-seen images including the Tibetan plateau, the crinkled heights of the Himalayas, and the jagged coast of Burma. From his lofty perch over 100 miles above the Earth, Cooper noted villages, roads, rivers, and even, on occasion, individual houses. In 1965, encouraged by the effectiveness of NASA s orbital photography experiments during the Mercury and subsequent Gemini manned space flight missions, U.S. Geological Survey (USGS) director William Pecora put forward a plan for a remote sensing satellite program that would collect information about the planet never before attainable. By 1972, NASA had built and launched Landsat 1, the first in a series of Landsat sensors that have combined to provide the longest continuous collection of space-based Earth imagery. The archived Landsat data - 37 years worth and counting - has provided a vast library of information allowing not only the extensive mapping of Earth s surface but also the study of its environmental changes, from receding glaciers and tropical deforestation to urban growth and crop harvests. Developed and launched by NASA with data collection operated at various times by the Agency, the National Oceanic and Atmospheric Administration (NOAA), Earth Observation Satellite Company (EOSAT, a private sector partnership that became Space Imaging Corporation in 1996), and USGS, Landsat sensors have recorded flooding from Hurricane Katrina, the building boom in Dubai, and the extinction of the Aral Sea, offering scientists invaluable insights into the natural and manmade changes that shape the world. Of the seven Landsat sensors launched since 1972, Landsat 5 and Landsat 7 are still operational. Though both are in use well beyond their intended lifespans, the mid-resolution satellites, which provide the benefit of images detailed enough to reveal large features like highways while still broad enough for global coverage, continue to scan the entirety of the Earth s surface. In 2012, NASA plans to launch the Landsat Data Continuity Mission (LDCM), or Landsat 8, to extend the Landsat program s contributions to cartography, water management, natural disaster relief planning, and more.

Source record↗
NASA NTRS2011

Improvement of the GEOS-5 AGCM upon Updating the Air-Sea Roughness Parameterization

The impact of an air-sea roughness parameterization over the ocean that more closely matches recent observations of air-sea exchange is examined in the NASA Goddard Earth Observing System, version 5 (GEOS-5) atmospheric general circulation model. Surface wind biases in the GEOS-5 AGCM are decreased by up to 1.2m/s. The new parameterization also has implications aloft as improvements extend into the stratosphere. Many other GCMs (both for operational weather forecasting and climate) use a similar class of parameterization for their air-sea roughness scheme. We therefore expect that results from GEOS-5 are relevant to other models as well.

Garfinkel, C. I.↗
NASA NTRS2012

Evaluation of Cloud Microphysics Simulated using a Meso-Scale Model Coupled with a Spectral Bin Microphysical Scheme through Comparison with Observation Data by Ship-Borne Doppler and Space-Borne W-Band Radars

Equivalent radar reflectivity factors (Ze) measured by W-band radars are directly compared with the corresponding values calculated from a three-dimensional non-hydrostatic meso-scale model coupled with a spectral-bin-microphysical (SBM) scheme for cloud. Three case studies are the objects of this research: one targets a part of ship-borne observation using 95 GHz Doppler radar over the Pacific Ocean near Japan in May 2001; other two are aimed at two short segments of space-borne observation by the cloud profiling radar on CloudSat in November 2006. The numerical weather prediction (NWP) simulations reproduce general features of vertical structures of Ze and Doppler velocity. A main problem in the reproducibility is an overestimation of Ze in ice cloud layers. A frequency analysis shows a strong correlation between ice water contents (IWC) and Ze in the simulation; this characteristic is similar to those shown in prior on-site studies. From comparing with the empirical correlations by the prior studies, the simulated Ze is overestimated than the corresponding values in the studies at the same IWC. Whereas the comparison of Doppler velocities suggests that large-size snowflakes are necessary for producing large velocities under the freezing level and hence rules out the possibility that an overestimation of snow size causes the overestimation of Ze. Based on the results of several sensitivity tests, we conclude that the source of the overestimation is a bias in the microphysical calculation of Ze or an overestimation of IWC. To identify the source of the problems needs further validation research with other follow-up observations.

Iguchi, T.↗
NASA NTRS2012

Validation of the Radiometric Stability of the Atmospheric Infrared Sounder

It has been widely accepted that an infrared sounder in low polar orbit is capable of producing climate quality data, if the spectral brightness temperatures have instrumental trends of less than 10 mK/yr. Achieving measurement stability at this level is not only very demanding of the design of the instrument, it is also pushes the state of art of measuring on orbit what stability is actually achieved. We discuss this using Atmospheric Infrared Sounder (AIRS) L1B data collected between 2002 and 2011. We compare the L1B brightness temperature observed in cloud filtered night tropical ocean spectra (obs) to the brightness temperature calculated based on the known surface emissivity, temperature and water vapor profiles from the ECMWF ReAnalysis (ERA) and the growth rates of CO2, N2O and Ozone. The trend in (obs-calc) is a powerful tool for the evaluation of the stability of the 2378 AIRS channels. We divided the channels into seven classes: All channels which sound in the stratosphere (at pressure levels below 150 hPa), 14 micron CO2 sounding, 4 micron CO2 P-branch sounding, 4 micron CO2 R-branch sounding, water vapor sounding, shortwave surface sounding and longwave surface sounding. The peak in the weighting function at 1050 hPa separates sounding and surface channels. The boundary between shortwave and longwave is 5 microns. Except for the stratosphere sounding channels, the remaining six groups have (obs-calc) trends of less than 20 mK/yr. The longwave surface channels have trends of 2 mK/yr, significantly less than the 8 mK/yr trend seem in the shortwave window channels. Based on the design of the instrument, trends within a group of channels should be the same. While the longwave and shortwave trends are less than the canonical 10 mK/yr, the larger trend in the shortwave channels could be an artifact of using the pre-launch determined calibration coefficients. This is currently under evaluation. The trend in (obs-calc) for the non-surface sounding channels, in particular for stratosphere sounding and upper tropospheric water channels, is dominated by artifacts created in calc, most likely due to changes in the ERA Ozone and water vapor. Based on this argument the best estimate of the trend for the channels within a channel group is given by the surface sensitive channels within the group. Based on this consideration we estimated the trend of all AIRS longwave channels as 2 mK/yr, while the shortwave channels have a trend of 8 mK/yr.

CO2↗
NASA NTRS2015

NASA CYGNSS Mission Applications Workshop

NASA's Cyclone Global Navigation Satellite System, (CYGNSS), mission is a constellation of eight microsatellites that will measure surface winds in and near the inner cores of hurricanes, including regions beneath the eyewall and intense inner rainbands that could not previously be measured from space. The CYGNSS-measured wind fields, when combined with precipitation fields (e.g., produced by the Global Precipitation Measurement [GPM] core satellite and its constellation of precipitation imagers), will provide coupled observations of moist atmospheric thermodynamics and ocean surface response, enabling new insights into hurricane inner core dynamics and energetics. The outcomes of this workshop, which are detailed in this report, comprise two primary elements: (1) A report of workshop proceedings, and; (2) Detailed Applications Traceability Matrices with requirements and operational considerations to serve broadly for development of value-added tools, applications, and products.

Amin, Aimee V.↗
NASA NTRS2017

Spaceborne Lidar in the Study of Marine Systems

Satellite passive ocean color instruments have provided an unbroken ~20-year record of global ocean plankton properties, but this measurement approach has inherent limitations in terms of spatial-temporal sampling and ability to resolve vertical structure within the water column. These limitations can be addressed by coupling ocean color data with measurements from a spaceborne lidar. Airborne lidars have been used for decades to study ocean subsurface properties, but recent breakthroughs have now demonstrated that plankton properties can be measured with a satellite lidar. The satellite lidar era in oceanography has arrived. Here we present a review of the lidar technique, its applications in marine systems, a prospective on what can be accomplished in the near future with an ocean- and atmosphere-optimized satellite lidar, and a vision for a multi-platform ‘virtual constellation’ of observational assets enabling a 3-dimensional reconstruction of global ocean ecosystems.

Chris A Hostetler↗
NASA NTRS1986

Convection beneath young oceanic lithosphere - Implications for thermal structure and gravity

Small-scale convection under the oceanic lithosphere which begins in the first 5 m.y. of cooling can produce a gravity signal with the amplitude and wavelength observed for large areas of the central Pacific and southern Indian oceans using Seasat altimeter data. The trend of the observed anomalies is parallel to the direction of plate motion as might be expected if they were produced by small-scale convection. Models predict that the wavelengths of gravity anomalies increase more rapidly with age than is observed. The persistence of short relatively uniform wavelength anomalies (less than 200 km) to crustal ages of 50 Ma may indicate that they were produced when the lithosphere was very young and thin and were 'frozen in' as cooling thickened the elastic lithosphere. Small-scale convection which begins under very young lithosphere does not violate other geophysical data such as the rate of seafloor subsidence and variations of geoid height with age. After convection has begun, the subsidence due to thermal contraction within the lithosphere varies linearly with age, in the absence of mantle heat sources, although the rate of change of these quantities is affected by convection. Much of the variation of the geoid height across fracture zones can be fit by a model which includes small-scale convection.

Buck, W. R.↗
NASA NTRS2022

Understanding Model-Observation Discrepancies in Satellite Retrievals of Atmospheric Temperature Using GISS ModelE

We examine multiple factors in the representation of satellite-retrieved atmospheric temperature diagnostics in historical simulations of climate change during the satellite era (specifically 1979–2021) using GISS ModelE contributions to the Coupled Model Intercomparison Project (Phase 6) (CMIP6). The tropospheric and stratospheric trends in these diagnostics are affected by greenhouse gases (notably carbon dioxide and ozone), coupling with the ocean, volcanic aerosols, solar activity and compositional and dynamic feedbacks. We explore the impacts of internal variability, changing forcing specifications, composition interactivity, the quality of the stratospheric circulation, vertical resolution, and possible impacts of the mis-specification of volcanic aerosol optical depths. Overall temperature trends throughout the satellite period are well captured, but discrepancies at all levels exist and have multiple distinct causes. We find that stratospheric comparisons (using Stratospheric Sounding Unit (SSU) retrievals and successor instruments) are most affected by variations in the representation of ozone depletion and feedbacks, followed by the volcanic signals. Tropospheric skill (using the Microwave Sounding Unit (MSU) retrievals) is affected by the trends in ocean heat uptake and tropospheric aerosols, but also by the representation of stratospheric processes through the impact of the Brewer-Dobson circulation on the height of the tropical tropopause. We demonstrate that no single factor is the dominant cause of the discrepancies and that almost all observations lie within the broad envelope of structural uncertainty.

satellite retrievals of atmospheric temperature↗
NASA NTRS2020

CMIP6 Historical Simulations (1850‐2014) with GISS‐E2.1

Simulations of the CMIP6 historical period 1850‐‐2014, characterized by the emergence of anthropogenic climate drivers like greenhouse gases, are presented for different configurations of the NASA Goddard Institute for Space Studies (GISS) Earth System ModelE2.1. The GISS‐E2.1 ensembles are more sensitive to forcing than their CMIP5 predecessors (GISS‐E2), but warm less during recent decades due to reduced total forcing. This forcing reduction is attributed to an increase in longwave opacity in pre‐industrial simulations, resulting in an atmosphere less sensitive to further increases in opacity that result from rising greenhouse gas concentrations. This demonstrates the importance of the base climatology to forcing and forced climate trends. Most model versions match observed temperature trends since 1979 from the ocean to the stratosphere. The choice of ocean model is important to the transient climate response, as found in CMIP5 GISS‐E2: the model that more efficiently exports heat to the deep ocean shows a smaller rise in tropospheric temperature. Model sea level rise over the historical period is traced to excessive drawdown of aquifers to meet irrigation demand with a smaller contribution from thermal expansion. This shows how fully coupled models can provide indirect observational constraints upon forcing, in this case, constraining irrigation rates with observed sea level changes. The overall agreement of GISS‐E2.1 with observed trends is familiar from evaluation of its predecessors, as is the conclusion that these trends are almost entirely anthropogenic in origin.

NASA Goddard Institute for Space Studies (GISS) Ea↗
NASA NTRS2002

Non-Spherical Aerosol Phase Functions Derived from MODIS and AERONET Observations

We compare MODIS (Moderate Resolution Imaging Spectroradiometer) satellite aerosol retrievals of spectral optical thickness and size parameters over ocean with the same quantities derived from AERONET (Aerosol Robotic Network) observations made at island and coastal sites. Over much of the globe, the satellite-derived quantities agree well with the AERONET quantities. However, in regimes dominated by desert dust aerosol, the agreement is less robust. In the dust regimes, the MODIS retrievals show greater spectral dependence and report smaller particle sizes than do the AERONET derivations. We suggest that the reason for this discrepancy is the nonspherical nature of desert dust particles, which the initial MODIS algorithm is not able to handle. Using the discrepancy between MODIS and AERONET derived spectral optical thickness as an asset, instead of a detriment, we reconstruct the aerosol phase functions that the MODIS algorithm would have needed in order to match the AERONET retrievals. No assumptions of particle shape are used in the derivation of these functions and the results are empirical total column, ambient phase functions. We compare the empirically derived phase functions with phase functions calculated from spheres and spheroids, both situations in which assumptions about particle shape must be made. The resulting empirical nonspherical phase functions will be included in future updates of the MODIS algorithm.

Remer, L. A.↗
NASA NTRS2022

Assimilation of Sea Ice Thickness Derived from Cryosat-2 Along-Track Freeboard Measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)

The feasibility of assimilating sea ice thickness (SIT) observations derived from CryoSat-2 along-track measurements of sea ice freeboard is successfully demonstrated using a 3D-Var assimilation scheme, NEMOVAR, within the Met Office's global, coupled ocean–sea-ice model, Forecast Ocean Assimilation Model (FOAM). The CryoSat-2 Arctic freeboard measurements are produced by the Centre for Polar Observation and Modelling (CPOM) and are converted to SIT within FOAM using modelled snow depth. This is the first time along-track observations of SIT have been used in this way, with other centres assimilating gridded and temporally averaged observations. The assimilation leads to improvements in the SIT analysis and forecast fields generated by FOAM, particularly in the Canadian Arctic. Arctic-wide observation-minus-background assimilation statistics for 2015–2017 show improvements of 0.75 m mean difference and 0.41 m root-mean-square difference (RMSD) in the freeze-up period and 0.46 m mean difference and 0.33 m RMSD in the ice break-up period. Validation of the SIT analysis against independent springtime in situ SIT observations from NASA Operation IceBridge (OIB) shows improvement in the SIT analysis of 0.61 m mean difference (0.42 m RMSD) compared to a control without SIT assimilation. Similar improvements are seen in the FOAM 5 d SIT forecast. Validation of the SIT assimilation with independent Beaufort Gyre Exploration Project (BGEP) sea ice draft observations does not show an improvement, since the assimilated CryoSat-2 observations compare similarly to the model without assimilation in this region. Comparison with airborne electromagnetic induction (Air-EM) combined measurements of SIT and snow depth shows poorer results for the assimilation compared to the control, despite covering similar locations to the OIB and BGEP datasets. This may be evidence of sampling uncertainty in the matchups with the Air-EM validation dataset, owing to the limited number of observations available over the time period of interest. This may also be evidence of noise in the SIT analysis or uncertainties in the modelled snow depth, in the assimilated SIT observations, or in the data used for validation. The SIT analysis could be improved by upgrading the observation uncertainties used in the assimilation. Despite the lack of CryoSat-2 SIT observations available for assimilation over the summer due to the detrimental effect of melt ponds on retrievals, it is shown that the model is able to retain improvements to the SIT field throughout the summer months due to prior, wintertime SIT assimilation. This also results in regional improvements to the July modelled sea ice concentration (SIC) of 5 % RMSD in the European sector, due to slower melt of the thicker sea ice.

Emma K. Fiedler↗
NASA NTRS2012

Comparative Analysis of Upper Ocean Heat Content Variability from Ensemble Operational Ocean Analyses

Upper ocean heat content (HC) is one of the key indicators of climate variability on many time-scales extending from seasonal to interannual to long-term climate trends. For example, HC in the tropical Pacific provides information on thermocline anomalies that is critical for the longlead forecast skill of ENSO. Since HC variability is also associated with SST variability, a better understanding and monitoring of HC variability can help us understand and forecast SST variability associated with ENSO and other modes such as Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO), Tropical Atlantic Variability (TAV) and Atlantic Multidecadal Oscillation (AMO). An accurate ocean initialization of HC anomalies in coupled climate models could also contribute to skill in decadal climate prediction. Errors, and/or uncertainties, in the estimation of HC variability can be affected by many factors including uncertainties in surface forcings, ocean model biases, and deficiencies in data assimilation schemes. Changes in observing systems can also leave an imprint on the estimated variability. The availability of multiple operational ocean analyses (ORA) that are routinely produced by operational and research centers around the world provides an opportunity to assess uncertainties in HC analyses, to help identify gaps in observing systems as they impact the quality of ORAs and therefore climate model forecasts. A comparison of ORAs also gives an opportunity to identify deficiencies in data assimilation schemes, and can be used as a basis for development of real-time multi-model ensemble HC monitoring products. The OceanObs09 Conference called for an intercomparison of ORAs and use of ORAs for global ocean monitoring. As a follow up, we intercompared HC variations from ten ORAs -- two objective analyses based on in-situ data only and eight model analyses based on ocean data assimilation systems. The mean, annual cycle, interannual variability and longterm trend of HC have been analyzed

Xue, Yan↗