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All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

Effects of atmospheric moisture on rock resistivity.

This study examines the changes in resistivity of rock samples as induced by atmospheric moisture. Experiments were performed on samples of hematitic sandstone, pyrite, and galena. The sandstone underwent a change in resistivity of four orders of magnitude when it was measured in a vacuum of 500 ntorr and in air of 37% relative humidity. Pyrite and galena showed no variations in resistivity when they were measured under the same conditions. These results, plus others obtained elsewhere, indicate that rocks of the resistive type are affected in their electrical properties by atmospheric moisture, whereas rocks of the conductive type are not. The experimental evidence obtained is difficult to reconcile with a model of aqueous electrolytic conduction on the sample surface. It is instead suggested that adsorbed water molecules alter the surface resistivity in a manner similar to that observed in semiconductors and insulators.

Alvarez, R.↗
NASA NTRS1989

Atmospheric moisture and cloud structure determined from SSM/I and global gridpoint analyses

Data from the Special Sensor Microwave Imager/I on the DMSP satellite are used to study atmospheric moisture and cloud structure. Column-integrated water vapor and total liquid water retrievals are obtained using an algorithm based on a radiative model for brightness temperature (Wentz, 1983). The results from analyzing microwave and IR measurements are combined with independent global gridpoint analyses to study the distribution and structure of atmospheric moisture over oceanic regions.

Robertson, Franklin R.↗
NASA NTRS1989

Global atmospheric moisture variability

Research efforts during FY-88 have focused on completion of several projects relating to analysis of FGGE data during SOP-1 and on expanded studies of global atmospheric moisture. In particular, a revised paper on the relationship between diabatic heating and baroclinicity in the South Pacific Convergence Zone (SPCZ) was submitted. A summary of completed studies on diagnostic convective parameterization was presented at the Satellite Meteorology and Oceanography Convergence last February. These investigations of diabatic heating in the SPCZ have demonstrated the requirement for a more quantitative description of atmospheric moisture. As a result, efforts were directed toward use of passive remote microwave measurements from the Nimbus-7 SMMR and the DOD's Special Sensor Microwave Imager (SSMI/I) as critical sources of moisture data. Activities this year are summarized.

Robertson, Franklin R.↗
NASA NTRS2007

Identification of Human-Induced Changes in Atmospheric Moisture Content

Data from the satellite-based Special Sensor Microwave Imager (SSM/I) show that the total atmospheric moisture content over oceans has increased by 0.41 kg/sq m per decade since 1988. Results from current climate models indicate that water vapor increases of this magnitude cannot be explained by climate noise alone. In a formal detection and attribution analysis using the pooled results from 22 different climate models, the simulated "fingerprint" pattern of anthropogenically caused changes in water vapor is identifiable with high statistical confidence in the SSM/I data. Experiments in which forcing factors are varied individually suggest that this fingerprint "match" is primarily due to human-caused increases in greenhouse gases and not to solar forcing or recovery from the eruption of Mount Pinatubo. Our findings provide preliminary evidence of an emerging anthropogenic signal in the moisture content of earth's atmosphere.

Santer, B.D.↗
NASA NTRS2000

A Method for Assessing the Quality of Model-Based Estimates of Ground Temperature and Atmospheric Moisture Using Satellite Data

A method is developed for validating model-based estimates of atmospheric moisture and ground temperature using satellite data. The approach relates errors in estimates of clear-sky longwave fluxes at the top of the Earth-atmosphere system to errors in geophysical parameters. The fluxes include clear-sky outgoing longwave radiation (CLR) and radiative flux in the window region between 8 and 12 microns (RadWn). The approach capitalizes on the availability of satellite estimates of CLR and RadWn and other auxiliary satellite data, and multiple global four-dimensional data assimilation (4-DDA) products. The basic methodology employs off-line forward radiative transfer calculations to generate synthetic clear-sky longwave fluxes from two different 4-DDA data sets. Simple linear regression is used to relate the clear-sky longwave flux discrepancies to discrepancies in ground temperature ((delta)T(sub g)) and broad-layer integrated atmospheric precipitable water ((delta)pw). The slopes of the regression lines define sensitivity parameters which can be exploited to help interpret mismatches between satellite observations and model-based estimates of clear-sky longwave fluxes. For illustration we analyze the discrepancies in the clear-sky longwave fluxes between an early implementation of the Goddard Earth Observing System Data Assimilation System (GEOS2) and a recent operational version of the European Centre for Medium-Range Weather Forecasts data assimilation system. The analysis of the synthetic clear-sky flux data shows that simple linear regression employing (delta)T(sub g)) and broad layer (delta)pw provides a good approximation to the full radiative transfer calculations, typically explaining more thin 90% of the 6 hourly variance in the flux differences. These simple regression relations can be inverted to "retrieve" the errors in the geophysical parameters, Uncertainties (normalized by standard deviation) in the monthly mean retrieved parameters range from 7% for (delta)T(sub g) to approx. 20% for the lower tropospheric moisture between 500 hPa and surface. The regression relationships developed from the synthetic flux data, together with CLR and RadWn observed with the Clouds and Earth Radiant Energy System instrument, ire used to assess the quality of the GEOS2 T(sub g) and pw. Results showed that the GEOS2 T(sub g) is too cold over land, and pw in upper layers is too high over the tropical oceans and too low in the lower atmosphere.

Wu, Man Li C.↗
NASA NTRS2000

Estimates of Ground Temperature and Atmospheric Moisture from CERES Observations

A method is developed to retrieve surface ground temperature (Tg) and atmospheric moisture using clear sky fluxes (CSF) from CERES-TRMM observations. In general, the clear sky outgoing long-wave radiation (CLR) is sensitive to upper level moisture (q(sub h)) over wet regions and Tg over dry regions The clear sky window flux from 800 to 1200 /cm (RadWn) is sensitive to low level moisture (q(sub j)) and Tg. Combining these two measurements (CLR and RadWn), Tg and q(sub h) can be estimated over land, while q(sub h) and q(sub t) can be estimated over the oceans. The approach capitalizes on the availability of satellite estimates of CLR and RadWn and other auxiliary satellite data. The basic methodology employs off-line forward radiative transfer calculations to generate synthetic CSF data from two different global 4-dimensional data assimilation products. Simple linear regression is used to relate discrepancies in CSF to discrepancies in Tg, q(sub h) and q(sub t). The slopes of the regression lines define sensitivity parameters that can be exploited to help interpret mismatches between satellite observations and model-based estimates of CSF. For illustration, we analyze the discrepancies in the CSF between an early implementation of the Goddard Earth Observing System Data Assimilation System (GEOS-DAS) and a recent operational version of the European Center for Medium-Range Weather Prediction data assimilation system. In particular, our analysis of synthetic total and window region SCF differences (computed from two different assimilated data sets) shows that simple linear regression employing (Delta)Tg and broad layer (Delta)q(sub l) from 500 hPa to surface and (Delta)q(sub h) from 200 to 500 hPa provides a good approximation to the full radiative transfer calculations, typically explaining more than 90% of the 6-hourly variance in the flux differences. These simple regression relations can be inverted to "retrieve" the errors in the geophysical parameters. Uncertainties (normalized by standard deviation) in the monthly mean retrieved parameters range from 7% for (Delta)T to about 20% for (Delta)q(sub t). Our initial application of the methodology employed an early CERES-TRMM data set (CLR and Radwn) to assess the quality of the GEOS2 data. The results showed that over the tropical and subtropical oceans GEOS2 is, in general, too wet in the upper troposphere (mean bias of 0.99 mm) and too dry in the lower troposphere (mean bias of -4.7 mm). We note that these errors, as well as a cold bias in the Tg, have largely been corrected in the current version of GEOS-2 with the introduction of a land surface model, a moist turbulence scheme and the assimilation of SSTM/I total precipitable water.

Wu, Man Li C.↗
NASA NTRS2021

Soil moisture-atmosphere feedbacks mitigate declining water availability in drylands

Global warming alters surface water availability (precipitation minus evapotranspiration, P–E) and hence freshwater resources. However, the influence of land–atmosphere feedbacks on future P–E changes and the underlying mechanisms remain unclear. Here we demonstrate that soil moisture (SM) strongly impacts future P–E changes, especially in drylands, by regulating evapotranspiration and atmospheric moisture inflow. Using modelling and empirical approaches, we find a consistent negative SM feedback on P–E, which may offset ~60% of the decline in dryland P–E otherwise expected in the absence of SM feedbacks. The negative feedback is not caused by atmospheric thermodynamic responses to declining SM; rather, reduced SM, in addition to limiting evapotranspiration, regulates atmospheric circulation and vertical ascent to enhance moisture transport into drylands. This SM effect is a large source of uncertainty in projected dryland P–E changes, underscoring the need to better constrain future SM changes and improve the representation of SM–atmosphere processes in models.

precipitation↗
NASA NTRS2000

Estimates of Ground Temperature and Atmospheric Moisture from CERES Observations

A method is developed to retrieve surface ground temperature (T(sub g)) and atmospheric moisture using clear sky fluxes (CSF) from CERES-TRMM observations. In general, the clear sky outgoing longwave radiation (CLR) is sensitive to upper level moisture (q(sub l)) over wet regions and (T(sub g)) over dry regions The clear sky window flux from 800 to 1200/cm (RadWn) is sensitive to low level moisture (q(sub t)) and T(sub g). Combining these two measurements (CLR and RadWn), Tg and q(sub h) can be estimated over land, while q(sub h) and q(sub l) can be estimated over the oceans. The approach capitalizes on the availability of satellite estimates of CLR and RadWn and other auxiliary satellite data. The basic methodology employs off-line forward radiative transfer calculations to generate synthetic CSF data from two different global 4-dimensional data assimilation products. Simple linear regression is used to relate discrepancies in CSF to discrepancies in T(sub g), q(sub h) and q(sub l). The slopes of the regression lines define sensitivity parameters that can be exploited to help interpret mismatches between satellite observations and model-based estimates of CSF. For illustration, we analyze the discrepancies in the CSF between an early implementation of the Goddard Earth Observing System Data Assimilation System (GEOS-DAS) and a recent operational version of the European Center for Medium-Range Weather Prediction data assimilation system. In particular, our analysis of synthetic total and window region SCF differences (computed from two different assimilated data sets) shows that simple linear regression employing Delta(T(sub g)) and broad layer Delta(q(sub l) from .500 hPa to surface and Delta(q(sub h)) from 200 to .300 hPa provides a good approximation to the full radiative transfer calculations. typically explaining more than 90% of the 6-hourly variance in the flux differences. These simple regression relations can be inverted to "retrieve" the errors in the geophysical parameters. Uncertainties (normalized by standard deviation) in the monthly mean retrieved parameters range from 7% for Delta(T(sub g)) to about 20% for Delta(q(sub l)). Our initial application of the methodology employed an early CERES-TRMM data set (CLR and Radwn) to assess the quality of the GEOS2 data. The results showed that over the tropical and subtropical oceans GEOS2 is, in general, too wet in the upper troposphere (mean bias of 0.99 mm) and too dry in the lower troposphere (mean bias of -4.7 min). We note that these errors, as well as a cold bias in the T(sub g). have largely been corrected in the current version of GEOS-2 with the introduction of a land surface model, a moist turbulence scheme and the assimilation of SSM/I total precipitable water.

Wu, Man Li C.↗
NASA NTRS1993

High resolution depiction of atmospheric moisture, stability and surface temperature from combined MAMS and VAS radiances

In order to elucidate mesoscale variability of the earth-atmosphere system, aircraft-borne Multi-spectral Atmospheric Mapping Sensor (MAMS), 100-meter-resolution radiometric data, and geostationary-borne VISSR Atmospheric Sounder (VAS) 8-km-resolution radiometric data are used together in a physical retrieval method to produce 100-m-resolution depictions of atmospheric moisture, stability, and skin temperature. The VAS, with its IR-sounding capability, provides the vertical information to the retrieval while the MAMS, with its 100-m resolution, provides the horizontal information. The retrievals show mesoscale features, including a moist tongue intrusion and an urban heat island. Mesogamma-scale gradients are found to exceed mesobeta-scale gradients, and significant mesogamma-scale variability is not captured in current geostationary sounding data. It is suggested that improvements to the spatial resolution of operating sounding data will yield improved information on atmospheric and surface gradients, especially at the mesogamma scale.

Moeller, C. C.↗
NASA NTRS1985

Estimation of atmospheric moisture content from microwave radiometric measurements during CCOPE

A multiple linear regression technique has been applied to retrieve continuous sequences of atmospheric moisture profiles from a set of measured data. In this method the selection of an optimal subset of sensor channels plays a crucial role, in order to reduce the impact of data noise and redundancy. The data were obtained with a 4-channel remote-sensing microwave instrument carried aboard an aircraft. In contrast, most previously reported moisture profile retrievals from microwave radiometry have been used on simulated, discontinuous data. Although the data were obtained over a land surface with only a limited amount of correlative data, the retrievals were quite successful.

Lambrigtsen, B. H.↗
NASA NTRS2012

Constraining Middle Atmospheric Moisture in GEOS-5 Using EOS-MLS Observations

Middle atmospheric water vapor plays an important role in climate and atmospheric chemistry. In the middle atmosphere, water vapor, after ozone and carbon dioxide, is an important radiatively active gas that impacts climate forcing and the energy balance. It is also the source of the hydroxyl radical (OH) whose abundances affect ozone and other constituents. The abundance of water vapor in the middle atmosphere is determined by upward transport of dehydrated air through the tropical tropopause layer, by the middle atmospheric circulation, production by the photolysis of methane (CH4), and other physical and chemical processes in the stratosphere and mesosphere. The Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis with GEOS-5 did not assimilate any moisture observations in the middle atmosphere. The plan is to use such observations, available sporadically from research satellites, in future GEOS-5 reanalyses. An overview will be provided of the progress to date with assimilating the EOS-Aura Microwave Limb Sounder (MLS) moisture retrievals, alongside ozone and temperature, into GEOS-5. Initial results demonstrate that the MLS observations can significantly improve the middle atmospheric moisture field in GEOS-5, although this result depends on introducing a physically meaningful representation of background error covariances for middle atmospheric moisture into the system. High-resolution features in the new moisture field will be examined, and their relationships with ozone, in a two-year assimilation experiment with GEOS-5. Discussion will focus on how Aura MLS moisture observations benefit the analyses.

Jin, Jianjun↗
NASA NTRS1991

Determination of atmospheric moisture structure and infrared cooling rates from high resolution MAMS radiance data

This program has applied Multispectral Atmospheric Mapping Sensor (MAMS) high resolution data to the problem of monitoring atmospheric quantities of moisture and radiative flux at small spatial scales. MAMS, with 100-m horizontal resolution in its four infrared channels, was developed to study small scale atmospheric moisture and surface thermal variability, especially as related to the development of clouds, precipitation, and severe storms. High-resolution Interferometer Sounder (HIS) data has been used to develop a high spectral resolution retrieval algorithm for producing vertical profiles of atmospheric temperature and moisture. The results of this program are summarized and a list of publications resulting from this contract is presented. Selected publications are attached as an appendix.

Menzel, W. Paul↗
NASA NTRS2021

Soil Moisture–Atmosphere Feedbacks Dominate Land Carbon Uptake Variability

Year-to-year changes in carbon uptake by terrestrial ecosystems play an essential role in determining atmospheric carbon dioxide concentrations. It remains uncertain to what extent temperature and water availability can explain these variations at the global scale. Here we use factorial climate model simulations and show that variability in soil moisture drives 90% of the inter-annual variability in global land carbon uptake, mainly through its impact on photosynthesis. We find that most of this ecosystem response occurs indirectly as soil moisture–atmosphere feedbacks amplify temperature and humidity anomalies, and enhance the direct effects of soil water stress. The strength of this feedback mechanism explains why coupled climate models indicate a dominant role of soil moisture which is not readily apparent in land surface model simulations and observational analyses. These findings highlight the need to account for feedbacks between soil and atmospheric dryness when estimating the carbon cycle’s response to climatic change globally, as well as when conducting field-scale investigations of the ecosystem response to droughts. Our results show that most of the global variability in modelled land carbon uptake is driven by temperature and vapour pressure deficit effects which are controlled by soil moisture.

Atmospheric dynamics↗
NASA NTRS1985

Assessing the capability of EOS sensors in measuring ocean-atmosphere moisture exchange

As part of the Science Synergism Studies to identify interdisciplinary Scientific studies, which could be addressed by the Environmental Observing System (EOS), the techniques being developed to measure ocean-atmosphere moisture exchanges using satellite sensors were evaluated. Studies required to use sensors proposed for EOS were examined. A method has been developed to compute the moisture flux using the wind speed, sea surface temperature, and preciptable water measured by satellite sensors. It relies on a statistical model which predicts surface-level humidity from precipitable water. The Scanning Multichannel Microwave Radiometer (SMMR) measures all 3 parameters and was found to be sensitive to the annual cycle and large interannual variations such as the 1982 to 1983 El Nino. There are systematic differences between geophysical parameters measured by Nimbus/SMMR and in situ measurements. After quadratic trends and crosstalks were removed from the parameters through multivariate regressions, the latent heat fluxes computed from SMMR agree with those computed from ship reports to within 30 W/sq m. The poor quality of ship reports may be the cause of a portion of this scatter. Similar results are found using SEASAT/SMMR data. When the scatterometer winds were used instead of the SMMR winds, the difference between the satellite fluxes and the ship fluxes was reduced.

Liu, W. T.↗
NASA NTRS2002

On the Choice of Variable for Atmospheric Moisture Analysis

The implications of using different control variables for the analysis of moisture observations in a global atmospheric data assimilation system are investigated. A moisture analysis based on either mixing ratio or specific humidity is prone to large extrapolation errors, due to the high variability in space and time of these parameters and to the difficulties in modeling their error covariances. Using the logarithm of specific humidity does not alleviate these problems, and has the further disadvantage that very dry background estimates cannot be effectively corrected by observations. Relative humidity is a better choice from a statistical point of view, because this field is spatially and temporally more coherent and error statistics are therefore easier to obtain. If, however, the analysis is designed to preserve relative humidity in the absence of moisture observations, then the analyzed specific humidity field depends entirely on analyzed temperature changes. If the model has a cool bias in the stratosphere this will lead to an unstable accumulation of excess moisture there. A pseudo-relative humidity can be defined by scaling the mixing ratio by the background saturation mixing ratio. A univariate pseudo-relative humidity analysis will preserve the specific humidity field in the absence of moisture observations. A pseudorelative humidity analysis is shown to be equivalent to a mixing ratio analysis with flow-dependent covariances. In the presence of multivariate (temperature-moisture) observations it produces analyzed relative humidity values that are nearly identical to those produced by a relative humidity analysis. Based on a time series analysis of radiosonde observed-minus-background differences it appears to be more justifiable to neglect specific humidity-temperature correlations (in a univariate pseudo-relative humidity analysis) than to neglect relative humidity-temperature correlations (in a univariate relative humidity analysis). A pseudo-relative humidity analysis is easily implemented in an existing moisture analysis system, by simply scaling observed-minus background moisture residuals prior to solving the analysis equation, and rescaling the analyzed increments afterward.

Dee, Dick P.↗
NASA NTRS1985

Observation of lower-atmospheric moisture structure and its evolution using a Raman lidar

Observations of the water-vapor mixing ratio in the lower atmosphere and its temporal evolution have been made with a Raman lidar. Comparison with an independent radiosonde measurement indicated excellent agreement. The moisture structure, observed up to an altitude of 5 km and over an 80-min period during the early morning of April 30, 1985 (the present lidar is limited to night operation), showed temporal variations of several atmospheric features which could not be resolved by balloon soundings. Application of the lidar should provide the opportunity to study details of atmospheric moisture, its structure, and its evolution in a manner never before realized.

Melfi, S. H.↗
NASA NTRS1984

A statistical correlation method for the retrieval of atmospheric moisture profiles by microwave radiometry

A statistical correlation technique is applied to the retrieval of vertical moisture profiles under clear-sky conditions from down-looking radiometric measurements of atmospheric radiation at microwave wavelengths. For a given set of channels, the method selects the optimum radiometric channels for estimating water vapor at specific pressure levels between the surface and 300 mb. The water vapor mixing ratio at these pressure levels is then calculated from a linear combination of the selected channel brightness temperatures. To test its validity the algorithm was applied, in a numerical experiment, to fifty independent tropical radiosondes. The rms absolute deviation of the estimated moisture profiles from the actual profiles was comparable to that obtained using an iterative retrieval method reported earlier. The statistical method, however, requires several orders of magnitude less computer time than the iterative method; it is suitable for high speed processing of large amounts of data.

Kakar, R. K.↗