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NASA NTRS1992

Hydrologic applications of SAR derived soil moisture

The MACHYDRO-90 was a multi-sensor aircraft campaign conducted to study drainage basin hydrology and the role of soil moisture in defining hydrologic characteristics and patterns. The results from the synthetic aperture radar (SAR) are presented. Data were collected over a period in which the soil conditions changed from dry to wet and then through a drying period which was close to ideal. Radar backscatter data are compared to detailed soil moisture samples taken to define soil moisture gradients within a watershed. The analysis also includes 40-MHz bandwidth SAR data, which provide very high spatial resolution. It is shown these data can be interpreted for hydrology and their application to hydrologic modeling is discussed.

Engman, Edwin T.↗

NASA NTRS1993

Moisture and heat budgets of a cirrus cloud from aircraft measurements during FIRE

This study is based on the NCAR King Air aircraft and radiosonde observations on 31 October 1986 during the FIRE in Wisconsin over Oshkosh. Aircraft step-up and spiral descent flights are used to obtain kinematic and thermodynamic data. In the step-up maneuver, six different penetrations were made between 1528 and 1616 UTC. Each penetration was about 30 km long separated in the vertical by about 300 m. The time difference between the two spiral soundings was about 43 min. The aircraft descended at a rate of 1.5 m/s during these spiral soundings. Kinematic, cloud physical, and radiometric observations from various instruments are used to estimate the different terms in the moisture- and heat-budget equations. The results show that the advection terms, estimated using the mean longitudinal wind and vertical velocities, and radiative fluxes are important in forming budgets for the cirrus layers. Ice-crystal growth is significant in the upper layers. The maintenance of cirrus can be attributed to relatively warm and moist air advection, radiative cooling at upper levels, and moisture advection in the vertical. Turbulent heat and moisture fluxes are found to be significant in the low levels of cirrus.

Gultepe, Ismail↗

NASA NTRS1993

Design of a global soil moisture initialization procedure for the simple biosphere model

Global soil moisture and land-surface evapotranspiration fields are computed using an analysis scheme based on the Simple Biosphere (SiB) soil-vegetation-atmosphere interaction model. The scheme is driven with observed precipitation, and potential evapotranspiration, where the potential evapotranspiration is computed following the surface air temperature-potential evapotranspiration regression of Thomthwaite (1948). The observed surface air temperature is corrected to reflect potential (zero soil moisture stress) conditions by letting the ratio of actual transpiration to potential transpiration be a function of normalized difference vegetation index (NDVI). Soil moisture, evapotranspiration, and runoff data are generated on a daily basis for a 10-year period, January 1979 through December 1988, using observed precipitation gridded at a 4 deg by 5 deg resolution.

Liston, G. E.↗

NASA NTRS1994

Large area mapping of soil moisture using the ESTAR passive microwave radiometer

Investigations designed to study land surface hydrologic-atmospheric interactions, showing the potential of L band passive microwave radiometry for measuring surface soil moisture over large areas, are discussed. Satisfying the data needs of these investigations requires the ability to map large areas rapidly. With aircraft systems this means a need for more beam positions over a wider swath on each flightline. For satellite systems the essential problem is resolution. Both of these needs are currently being addressed through the development and verification of Electronically Scanned Thinned Array Radiometer (ESTAR) technology. The ESTAR L band radiometer was evaluated for soil moisture mapping applications in two studies. The first was conducted over the semiarid rangeland Walnut Gulch watershed located in south eastern Arizona (U.S.). The second was performed in the subhumid Little Washita watershed in south west Oklahoma (U.S.). Both tests showed that the ESTAR is capable of providing soil moisture with the same level of accuracy as existing systems.

Jackson, T. J.↗

NASA NTRS1994

The scale and persistence of soil moisture anomalies as simulated in a global model

Short term variability of climate is intimately connected with soil moisture variability. Soil moisture provides the storage and subsequent return to the atmosphere, through evaporation and transpiration, of precipitation anomalies over land. Global Circulation Model (GCM) simulations enable consistent identification of correlations and dynamical connections between the hydrologic variables, many of which are incompletely observed. One way to facilitate understanding with these increasingly intricate models is to perform sensitivity studies in which a boundary condition or process is prescribed. In this study we will report on a sensitivity study in which a GCM with a sophisticated land surface representation is used to investigate soil moisture variability in the model climate. The simulations to be used in this study were made at R15 resolution (approximately 4.5 deg latitude x 7.5 deg longitude) with prescribed sea surface temperatures (SST) in the GENESIS model (Thompson and Pollard, 1994), which is coupled to a Land Surface Transfer model (LSX) at 2 deg x 2 deg resolution (Pollard and Thompson, 1994). All the results represented here were taken from the monthly averages of the model results. The LSX model accounts for the physical effects of vegetation with two layers specified at each grid point. Vegetation attributes such as leaf area indices, fractional cover, leaf albedos, etc., were taken from the global dataset in Dorman and Sellers (1989). A six-layer soil model extends from the surface to 4.25 m depth. SST's were prescribed in two ten year experiments using monthly SST values with the daily value being interpolated from the nearest two months. In the first experiment monthly climatological values were used, and in the second, the Atmospheric Model Intercomparison Project (AMIP) observed SST's for the years 1979 through 1988 were used (Gates, 1992). Thus, the former experiment gives a measure of the intrinsic model variability, to be compared with that of the latter experiment, which includes month-to-month variability due to ocean forcing.

Fitzjarrald, D.↗

NASA NTRS1993

Inflatable Antenna Microwave Radiometer for Soil Moisture Measurement

Microwave measurements of soil moisture are not being obtained at the required spatial Earth resolution with current technology. Recently, new novel designs for lightweight reflector systems have been developed using deployable inflatable antenna structures which could enable lightweight real-aperture radiometers. In consideration of this, a study was conducted at the NASA Langley Research Center (LaRC) to determine the feasibility of developing a microwave radiometer system using inflatable reflector antenna technology to obtain high spatial resolution radiometric measurements of soil moisture from low Earth orbit and which could be used with a small and cost effective launch vehicle. The required high resolution with reasonable swath width coupled with the L-band measurement frequency for soil moisture dictated the use of a large (30 meter class) real aperture antenna in conjunction with a pushbroom antenna beam configuration and noise-injection type radiometer designs at 1.4 and 4.3 GHz to produce a 370 kilometer cross-track swath with a 10 kilometer resolution that could be packaged for launch with a Titan 2 class vehicle. This study includes design of the inflatable structure, control analysis, structural and thermal analysis, antenna and feed design, radiometer design, payload packaging, orbital analysis, and electromagnetic losses in the thin membrane inflatable materials.

Bailey, M. C.↗

NASA NTRS1991

High-Moisture Diet for Laboratory Rats: Nutrient Analysis, Growth, and Organ Weights

A diet (KSC-25) to be sterilized by irradiation was formulated to contain 66% moisture and to provide the required nutrients for growing rats. Analyses of the irradiated dry diet provided data to evaluate its nutrient content. The diet was evaluated for its ability to supply all nutrients, including water, required by immature rats. Sixteen Sprague-Dawley rats were fed the high-moisture diet with or without access to a water bottle. Rats (n = 16) fed an irradiated purified diet in a meal form with access to a water bottle were the control animals. Feed efficiency, food and water consumption, and growth rate data were collected during the 28-day study. Organ weights were collected on day 28. The test diet met or exceeded the National Research Council (NRC) estimated nutritional requirements for immature laboratory rats. The 66% moisture KSC-25 diet provided all nutrients, including water, required by weanling male Sprague-Dawley rats for growth equivalent to the established purified diet.

Battles, August H.↗

NASA NTRS1993

Comparison of Gavage, Water Bottle, and a High-Moisture Diet Bolus as Dosing Methods for Quantitative D-xylose Administration to B6D2F1 (Mus musculus) Mice

Gavage, water bottle, and diet incorporation are 3 dosing methods used orally to administer test compounds to rodents. These 3 methods were compared in mice to determine which represented the most quantitative delivery system. For dietary incorporation, a high-moisture bolus form of NIH-31 rodent meal was developed using hydroxypropyl methylcellulose as an autoclave-stable binding agent. A high-moisture bolus were selected to increase the acceptability of the dosed diet and to promote quantitative consumption through reduced wastage. The test compound used was D-xylose, a pentose sugar that may be quantitatively detected, colorimetrically, in urine following oral dosing. Six male and 6 female B6D2FI mice were placed in metabolism cages and dosed with a known quantity of D-xylose by each of the 3 methods. Urine was collected before and after each method of administration and analysed for total D-xylose; the per cent recovery was based upon the amount of D-xylose consumed. Quantitative consumption was apparently greatest for water bottle dosing with an average recovery of 56.0% of the original D-xylose dose. High-moisture bolus incorporation ranked second with 50.0% D-xylose recovery, and gavage was third with 41.0% D-xylose recovery.

Zimmer, J. Paul↗

NASA NTRS1999

Statistical Quality Control of Moisture Data in GEOS DAS

A new statistical quality control algorithm was recently implemented in the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The final step in the algorithm consists of an adaptive buddy check that either accepts or rejects outlier observations based on a local statistical analysis of nearby data. A basic assumption in any such test is that the observed field is spatially coherent, in the sense that nearby data can be expected to confirm each other. However, the buddy check resulted in excessive rejection of moisture data, especially during the Northern Hemisphere summer. The analysis moisture variable in GEOS DAS is water vapor mixing ratio. Observational evidence shows that the distribution of mixing ratio errors is far from normal. Furthermore, spatial correlations among mixing ratio errors are highly anisotropic and difficult to identify. Both factors contribute to the poor performance of the statistical quality control algorithm. To alleviate the problem, we applied the buddy check to relative humidity data instead. This variable explicitly depends on temperature and therefore exhibits a much greater spatial coherence. As a result, reject rates of moisture data are much more reasonable and homogeneous in time and space.

Dee, D. P.↗

NASA NTRS1999

Moisture Forecast Bias Correction in GEOS DAS

Data assimilation methods rely on numerous assumptions about the errors involved in measuring and forecasting atmospheric fields. One of the more disturbing of these is that short-term model forecasts are assumed to be unbiased. In case of atmospheric moisture, for example, observational evidence shows that the systematic component of errors in forecasts and analyses is often of the same order of magnitude as the random component. we have implemented a sequential algorithm for estimating forecast moisture bias from rawinsonde data in the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The algorithm is designed to remove the systematic component of analysis errors and can be easily incorporated in an existing statistical data assimilation system. We will present results of initial experiments that show a significant reduction of bias in the GEOS DAS moisture analyses.

Dee, D.↗

NASA NTRS1999

Improving Assimilated Global Climate Data Using TRMM and SSM/I Rainfall and Moisture Data

Current global analyses contain significant errors in primary hydrological fields such as precipitation, evaporation, and related cloud and moisture in the tropics. Work has been underway at NASA's Data Assimilation Office to explore the use of TRMM and SSM/I-derived rainfall and total precipitable water (TPW) data in global data assimilation to directly constrain these hydrological parameters. We found that assimilating these data types improves not only the precipitation and moisture estimates but also key climate parameters directly linked to convection such as the outgoing longwave radiation, clouds, and the large-scale circulation in the tropics. We will present results showing that assimilating TRMM and SSM/I 6-hour averaged rain rates and TPW estimates significantly reduces the state-dependent systematic errors in assimilated products. Specifically, rainfall assimilation improves cloud and latent heating distributions, which, in turn, improves the cloudy-sky radiation and the large-scale circulation, while TPW assimilation reduces moisture biases to improve radiation in clear-sky regions. Rainfall and TPW assimilation also improves tropical forecasts beyond 1 day.

Hou, Arthur Y.↗

NASA NTRS1999

Data Assimilation in the Presence of Forecast Bias: The GEOS Moisture Analysis

We describe the application of the unbiased sequential analysis algorithm developed by Dee and da Silva (1998) to the GEOS DAS moisture analysis. The algorithm estimates the persistent component of model error using rawinsonde observations and adjusts the first-guess moisture field accordingly. Results of two seasonal data assimilation cycles show that moisture analysis bias is almost completely eliminated in all observed regions. The improved analyses cause a sizable reduction in the 6h-forecast bias and a marginal improvement in the error standard deviations.

Dee, Dick P.↗

NASA NTRS1999

Improving Global Reanalyses and Short-Range Forecast Using TRMM and SSM/I-Derived Precipitation and Moisture Observations

Global reanalyses currently contain significant errors in the primary fields of the hydrological cycle such as precipitation, evaporation, moisture, and the related cloud fields, especially in the tropics. The Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center has been exploring the use of tropical rainfall and total precipitable water (TPW) observations from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments to improve short-range forecast and reanalyses. We describe a 1+1D procedure for assimilating 6-hr averaged rainfall and TPW in the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The algorithm is based on a 6-hr time integration of a column version of the GEOS DAS, hence the 1+1D designation. The scheme minimizes the least-square differences between the observed TPW and rain rates and those produced by the column model over the 6-hr analysis window. This 1+1D scheme, in its generalization to four dimensions, is related to the standard 4D variational assimilation but uses analysis increments instead of the initial condition as the control variable. Results show that assimilating the TMI and SSW rainfall and TPW observations improves not only the precipitation and moisture fields but also key climate parameters such as clouds, the radiation, the upper-tropospheric moisture, and the large-scale circulation in the tropics. In particular, assimilating these data reduce the state-dependent systematic errors in the assimilated products. The improved analysis also provides better initial conditions for short-range forecasts, but the improvements in forecast are less than improvements in the time-averaged assimilation fields, indicating that using these data types is effective in correcting biases and other errors of the forecast model in data assimilation.

Hou, Arthur Y.↗

NASA NTRS1998

Ultrasonic Velocity Variations with Soil Composition for Moisture Measurement

Soil moisture content may be measured by many methods, but the presently available techniques all have drawbacks when used in ground truth measurements for remote sensing. Ultrasonic velocity varies with soil moisture content, and may be used as the basis of a new measurement technique. In order to characterize a sensor capable of field use, soil particle size distribution data are compared to ultrasonic velocity in a variety of soils over a wide moisture range.

Metzl, R.↗

NASA NTRS2000

Evaluation of GE-167 Silicone Rubber (RTV) For Possible Service As A Moisture-Barrier For Certain Strain Gage Applications

The Langley Research Center uses strain gages in a wide variety of demanding test environments. Strain gage installations, depending on the testing scenario, may see high temperatures, cryogenic temperature, moisture accumulation, mechanical abuse, or any combination of these conditions. At Langley, there is often a need to provide protection for strain gages against moisture and mechanical abuse, especially when large-scale, harsh environment testing is to be encountered. This technical memorandum discusses the evaluation of a room temperature curing silicone rubber sealant manufactured by the General Electric Company for consideration as a moisture-barrier for certain strain gage installations.

Hare, David A.↗

NASA NTRS2000

Improving Global Reanalyses and Short Range Forecast Using TRMM and SSM/I-Derived Precipitation and Moisture Observations

Global reanalyses currently contain significant errors in the primary fields of the hydrological cycle such as precipitation, evaporation, moisture, and the related cloud fields, especially in the tropics. The Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center has been exploring the use of tropical rainfall and total precipitable water (TPW) observations from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments to improve short-range forecast and reanalyses. We describe a "1+1"D procedure for assimilating 6-hr averaged rainfall and TPW in the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The algorithm is based on a 6-hr time integration of a column version of the GEOS DAS, hence the "1+1"D designation. The scheme minimizes the least-square differences between the observed TPW and rain rates and those produced by the column model over the 6-hr analysis window. This 1+lD scheme, in its generalization to four dimensions, is related to the standard 4D variational assimilation but uses analysis increments instead of the initial condition as the control variable. Results show that assimilating the TMI and SSM/I rainfall and TPW observations improves not only the precipitation and moisture fields but also key climate parameters such as clouds, the radiation, the upper-tropospheric moisture, and the large-scale circulation in the tropics. In particular, assimilating these data reduce the state-dependent systematic errors in the assimilated products. The improved analysis also provides better initial conditions for short-range forecasts, but the improvements in forecast are less than improvements in the time-averaged assimilation fields, indicating that using these data types is effective in correcting biases and other errors of the forecast model in data assimilation.

Hou, Arthur Y.↗

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 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.↗