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At least 451 records · Page 25
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NASA NTRS2019

Soil Moisture in the Climate System

The moisture held within the top meter or two of soil is a very tiny fraction (less than 0.01) of the Earthstotal water (Eagleson, 1970). Nevertheless, its presence at the interface of the land and atmosphere gives it inordinateimportance in the context of climate variability. Simply put, soil moisture variations can help determine meteorologicalvariations. Consider, for example, an anomalously high evapotranspiration rate induced by a high soil moisture content.The high evapotranspiration can lead to an anomalously cooled land surface and thus cooler air temperatures(Seneviratne et al. 2010), and it can also lead to modifications in the evolution of the boundary layer, with concomitantimpacts on the generation of convective rainfall (Betts et al., 1994).Given this potential for feedback on the atmosphere,soil moisture is particularly important in the context of prediction. Atmospheric physics and dynamics are fast andcontribute little to the lifetime of an atmospheric anomaly, as reflected in the well-known time scale of typical weatherforecaststhese rely heavily on atmospheric initialization and are valid for only a week or so. Soil moisture processes, incontrast, are relatively slow. A soil moisture anomaly may persist for a month or more and is thus predictable at such timescales. As a result, and because (as noted above) the atmosphere may respond in a predictable way to a given soilmoisture anomaly, aspects of the atmosphere may also be predictable at the monthly time scale (NRC, 2010).

Koster, Randal↗
NASA NTRS2021

Passive Microwave Brightness Temperature Assimilation to Improve Snow Mass Estimation across Complex Terrain in Pakistan, Afghanistan, and Tajikistan

An ensemble Kalman filter is used to assimilate Advanced Microwave Scanning Radiometer-2 (AMSR2) observations of passive microwave (PMW) brightness temperatures (spectral differences, ΔT b ) into land surface model estimates of snow mass over northwestern high mountain Asia (HMA). Trained support vector machines serve as the observation operator and map the geophysical modeled variables into ΔT b space within the assimilation framework. Evaluation of the assimilation routine is carried out through comparison of assimilated snow mass estimates with an in situ dataset. The assimilation framework helps improve the land surface model estimates through PMW ΔT b assimilation, particularly in terms of decreasing the domain-wide bias. The assimilation framework proved more effective during the (dry) snow accumulation season and decreased the bias and root-mean-square error (RMSE) in snow mass estimates at 76% and 58% of the comparative pixels, respectively. During the snow ablation season, the PMW brightness temperature signal contained less information related to snow mass due to the presence of other concurrent geophysical features that effectively serve as noise during the snow mass update. The utilization of PMW ΔT b for accurate snow mass estimation in complex terrain such as HMA is dependent on a multitude of factors for optimal results; however, it does add utility to the land surface model if the relevant pitfalls are taken into consideration prior to the state variable update.

Jawairia Ahmad↗
NASA NTRS2011

NASA Giovanni Portals for NLDAS/GLDAS Online Visualization, Analysis, and Intercomparison

The North American Land Data Assimilation System (NLDAS) and Global Land Data Assimilation System (GLDAS) are generating a series of land surface forcing (e.g., precipitation, surface meteorology, and radiation), state (e.g., soil moisture and temperature, and snow), and flux (e.g., evaporation and sensible heat flux) products, simulated by several land surface models. To date, NLDAS and GLDAS have generated more than 30 (1979 - present) and 60 (1948 - present) years of data, respectively. To further facilitate data accessibility and utilization, three new portals in the NASA Giovanni system have been made available for NLDAS and GLDAS online visualization, analysis, and intercomparison.

Rui, Hualan↗
NASA NTRS2012

Diagnosing the Nature of Land-Atmosphere Coupling: A Case Study of Dry/Wet Extremes in the U. S. Southern Great Plains

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of land surface and planetary boundary layer (PBL) temperature and moisture states and fluxes. In turn, these interactions regulate the strength of the connection between surface moisture and precipitation in a coupled system. To address model deficiencies, recent studies have focused on development of diagnostics to quantify the strength and accuracy of the land- PBL coupling at the process-level. In this paper, a diagnosis of the nature and impacts of local land-atmosphere coupling (LoCo) during dry and wet extreme conditions is presented using a combination of models and observations during the summers of 2006 and 2007 in the U.S. Southern Great Plains. A range of diagnostics exploring the links and feedbacks between soil moisture and precipitation are applied to the dry/wet regimes exhibited in this region, and in the process a thorough evaluation of nine different land-PBL scheme couplings is conducted under the umbrella of a high-resolution regional modeling testbed. Results show that the sign and magnitude of errors in land surface energy balance components are sensitive to the choice of land surface model, regime type, and running mode. In addition, LoCo diagnostics show that the sensitivity of L-A coupling is stronger towards the land during dry conditions, while the PBL scheme coupling becomes more important during the wet regime. Results also demonstrate how LoCo diagnostics can be applied to any modeling system (e.g. reanalysis products) in the context of their integrated impacts on the process-chain connecting the land surface to the PBL and in support of hydrological anomalies.

Santanello, Joseph A. Jr.↗
NASA NTRS2012

Diagnosing the Nature of Land-Atmosphere Coupling: A Case Study of Dry/Wet Extremes in the U.S. Southern Great Plains

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of land surface and planetary boundary layer (PBL) temperature and moisture states and fluxes. In turn, these interactions regulate the strength of the connection between surface moisture and precipitation in a coupled system. To address model deficiencies, recent studies have focused on development of diagnostics to quantify the strength and accuracy of the land-PBL coupling at the process level. In this paper, a diagnosis of the nature and impacts of local land-atmosphere coupling (LoCo) during dry and wet extreme conditions is presented using a combination of models and observations during the summers of 2006 and 2007 in the U.S. southern Great Plains. A range of diagnostics exploring the links and feedbacks between soil moisture and precipitation is applied to the dry/wet regimes exhibited in this region, and in the process, a thorough evaluation of nine different land-PBL scheme couplings is conducted under the umbrella of a high-resolution regional modeling test bed. Results show that the sign and magnitude of errors in land surface energy balance components are sensitive to the choice of land surface model, regime type, and running mode. In addition, LoCo diagnostics show that the sensitivity of L-A coupling is stronger toward the land during dry conditions, while the PBL scheme coupling becomes more important during the wet regime. Results also demonstrate how LoCo diagnostics can be applied to any modeling system (e.g., reanalysis products) in the context of their integrated impacts on the process chain connecting the land surface to the PBL and in support of hydrological anomalies.

southern plains↗
NASA NTRS1992

Multispectral satellite observations for arid land studies

Multispectral satellite data when properly calibrated and standardized can be used synergistically for a quantitative analysis of processes and surface characteristics, and for quantifying land surface change. Relationships among multispectral satellite data (visible reflectance, surface temperature and polarization difference of microwave emission at 37 GHz frequency) have been used to develop hypotheses concerning the relative sensitivity of these data to varied land surface characteristics, which needs to be verified by field observations. Radiative transfer models have also been developed to understand these multispectral data. Interannual variations of visible reflectance and polarization difference for the period 1982-1986 over the Sahel and the Sudan zones of Africa show a lagged response with respect to the rainfall deficit during recovery from drought, which needs to be understood in terms of biophysical parameters.

Choudhury, Bhaskar J.↗
NASA NTRSDate not supplied

Large Vehicle Lunar Landing Surface Interaction and In-Situ Resource Based Risk Mitigation: Landing & Launch Pads

A key capability required for the exploration of planetary bodies is the ability to land on the surface. Previous work performed by NASA and other institutions has primarily focused on landing small spacecraft on planetary surfaces and the associated small-to-medium thrusters required for the soft landing. In the case of human exploration—particularly the establishment of long duration exploration and habitation outposts—the ability to land large landers, such as the SpaceX Starship, is necessary. These larger landing systems require the use of more powerful engines, with higher engine exhaust temperatures and higher landing loads. Understanding the excavation of material by the engines, as well as the potential for the landing legs to sink into the subsurface, is key in ensuring reliable and safe landings. A further improvement in landing reliability can be achieved by constructing landing / launch pads, especially with in-situ resources. Some material excavation by the plume is inevitable, leaving at least a portion of the surface scoured and uneven under the lander and ejecting regolith particles and rocks at very high velocities. One possible solution would be to robotically build landing / launch pads (ideally autonomously) at the destination using in-situ materials. In this case, the first one or few landers will need to land on unimproved surfaces at higher risk; however, they would bring the required equipment to build the landing pads with mostly local resources, thus increasing the reliability of safe landing for subsequent larger landers. A number of methods to build in-situ landing and launch pads have already been developed. These methods include no or some addition of required binder additives to the local regolith material, different processing approaches and result in varying landing pad strengths. A sub-scale rocket engine plume, was used to simulate some of the conditions of a landing on the Moon to assess the effectiveness of various materials for an in-situ built landing pad, The GO2/GCH4 rocket engine fired on 1m2 area coupons of representative pad materials. The results will allow continued development towards materials that satisfy the landing pad properties required for the effective risk reduction and increased reliability for landing people and equipment on the lunar surface. This work contained two parts: (1) computer modeling of a large rocket engine plume interacting with regolith on the Moon, using the Granular Gas Flow Solver (GGFS) provided by CFD Research Corporation as well as other computational fluid dynamics codes (CFD) such as Loci/CHEM. (2) Developing landing/launch pad materials that could be used for in-situ construction on the lunar surface in the future, to mitigate the calculated effects of a large vehicle rocket engine landing and launching on the Moon. The resulting computed values of plume impingement surface temperature, stagnation pressure, gas velocity, shear stress and heat flux were then matched as closely as possible in the Earth’s atmosphere in a sub-scale rocket engine GO2/CH4 test which was provided by Masten Space Systems in Mojave, California. The rocket engine was mounted on a test stand with vertical translation capabilities so that the landing operations of a lander could be simulated (Figure 1).The pad materials test coupons were placed at the surface of a large bin containing simulated lunar basalt regolith and subjected to a test firing as shown in Figure 2. The results of this testing will be presented with related findings, analysis and discussion.

Plume Surface Interaction↗
NASA NTRS2011

Forecasting Fire Season Severity in South America Using Sea Surface Temperature Anomalies

Fires in South America cause forest degradation and contribute to carbon emissions associated with land use change. We investigated the relationship between year-to-year changes in fire activity in South America and sea surface temperatures. We found that the Oceanic Ni o Index was correlated with interannual fire activity in the eastern Amazon, whereas the Atlantic Multidecadal Oscillation index was more closely linked with fires in the southern and southwestern Amazon. Combining these two climate indices, we developed an empirical model to forecast regional fire season severity with lead times of 3 to 5 months. Our approach may contribute to the development of an early warning system for anticipating the vulnerability of Amazon forests to fires, thus enabling more effective management with benefits for climate and air quality.

Chen, Yang↗
NASA NTRS2021

The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis

The Soil Moisture Active Passive (SMAP) Level-4 (L4) product provides global estimates of surface soil moisture (SSM) and root-zone soil moisture (RZSM) via the assimilation of SMAP brightness temperature (Tb) observations into the NASA Catchment Land Surface Model (CLSM). Here, using in situ measurements from 2474 sites in China, we evaluate the performance of soil moisture estimates from the L4 data assimilation (DA) system and from a baseline “open-loop” (OL) simulation of CLSM without Tb assimilation. Using random forest regression, the efficiency of the L4 DA system (i.e., the performance improvement in DA relative to OL) is attributed to eight control factors related to the CLSM as well as τ–ω radiative transfer model (RTM) components of the L4 system. Results show that the Spearman rank correlation (R) for L4 SSM with in situ measurements increases for 77 % of the in situ measurement locations (relative to that of OL), with an average R increase of approximately 14 % (ΔR=0.056). RZSM skill is improved for about 74 % of the in situ measurement locations, but the average R increase for RZSM is only 7 % (ΔR=0.034). Results further show that the SSM DA skill improvement is most strongly related to the difference between the RTM-simulated Tb and the SMAP Tb observation, followed by the error in precipitation forcing data and estimated microwave soil roughness parameter h. For the RZSM DA skill improvement, these three dominant control factors remain the same, although the importance of soil roughness exceeds that of the Tb simulation error, as the soil roughness strongly affects the ingestion of DA increments and further propagation to the subsurface. For the skill of the L4 and OL estimates themselves, the top two control factors are the precipitation error and the SSM–RZSM coupling strength error, both of which are related to the CLSM component of the L4 system. Finally, we find that the L4 system can effectively filter out errors in precipitation. Therefore, future development of the L4 system should focus on improving the characterization of the SSM–RZSM coupling strength.

Jianxiu Qiu↗
NASA NTRS2012

Diagnosing the Nature of Land-Atmosphere Coupling: A Case Study of Dry/Wet Extremes

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of land surface and planetary boundary layer (PBL) temperature and moisture states and fluxes. In turn, these interactions regulate the strength of the connection between surface moisture and precipitation in a coupled system. To address deficiencies in numerical weather prediction and climate models due to improper treatment of L-A interactions, recent studies have focused on development of diagnostics to quantify the strength and accuracy of the land-PBL coupling at the process-level. In this study, a diagnosis of the nature and impacts oflocalland-atmosphere coupling (LoCo) during dry and wet extreme conditions is presented using a combination of models and observations during the summers of2006-7 in the U.S. Southern Great Plains. Specifically, the Weather Research and Forecasting (WRF) model has been coupled to NASA's Land Information System (LIS), which provides a flexible and high-resolution representation and initialization of land surface physics and states. A range of diagnostics exploring the links and feedbacks between soil moisture and precipitation are examined for the dry/wet regimes of this region, along with the behavior and accuracy of different land-PBL scheme couplings under these conditions. In addition, we examine the impact of improved specification ofland surface states, anomalies, and fluxes that are obtained through the use of a hew optimization and uncertainty module in LIS, on the L-A coupling in WRF forecasts. Results demonstrate how LoCo diagnostics can be applied to coupled model components in the context of their integrated impacts on the process-chain connecting the land surface to the PBL and support of hydrological anomalies.

Santanello, Joseph A., Jr.↗
NASA NTRS2011

Diagnosing the Nature of Land-Atmosphere Coupling During the 2006-7 Dry/Wet Extremes in the U. S. Southern Great Plains

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of land surface and planetary boundary layer (PBL) temperature and moisture states and fluxes. In turn, these interactions regulate the strength of the connection between surface moisture and precipitation in a coupled system. To address deficiencies in numerical weather prediction and climate models due to improper treatment of L-A interactions, recent studies have focused on development of diagnostics to quantify the strength and accuracy of the land-PBL coupling at the process-level. In this study, a diagnosis of the nature and impacts of local land-atmosphere coupling (LoCo) during dry and wet extreme conditions is presented using a combination of models and observations during the summers of2006-7 in the U.S. Southern Great Plains. Specifically, the Weather Research and Forecasting (WRF) model has been coupled to NASA's Land Information System (LIS), which provides a flexible and high resolution representation and initialization of land surface physics and states. A range of diagnostics exploring the links and feedbacks between soil moisture and precipitation are examined for the dry/wet regimes of this region, along with the behavior and accuracy of different land-PBL scheme couplings under these conditions. Results demonstrate how LoCo diagnostics can be applied to coupled model components in the context of their integrated impacts on the process-chain connecting the land surface to the PBL and support of hydrological anomalies.

Santanello, Joseph A.↗
NASA NTRS1982

Introduction: Historical perspective on the HCMM program

When some thermal radiation sensitive devices on U.S. meteorological satellites generated low resolution images showing temperature variations on Earth's land and sea surfaces during the middle 1960's, interest was aroused in the potential of thermal sensing in geology, agriculture, soil moisture, ground water, water temperature, and vegetation applications. The concept of using the property of thermal inertia to identify materials, particularly those of a geologic nature can be traced to the same time period that marks the flight of the first LANDSAT - Spacecraft systems and their performance, and capabilities are reviewed as well as achievements in hydrology, geology, agriculture, pedology, and urban climate effects applications.

Source record↗
NASA NTRSDate not supplied

Improved Peatland Hydrology and L-Band Microwave Radiative Transfer Modeling in Version 7 of the SMAP Level-4 Soil Moisture Data Assimilation Product

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface (0-5 cm) and root-zone (0-100 cm) soil moisture from April 2015 to present with a mean latency of 2.5 days from the time of observation. The L4_SM estimates are derived from the assimilation of SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model. The recently released Version 7 of L4_SM comprises two key advances. First, the Catchment model now includes the recently developed PEATCLSM hydrology module for peatlands and uses an updated global map of peatlands. Second, revised parameters are used in the L-band radiative transfer model that converts the simulated soil moisture and temperature estimates into Tb predictions for use in the radiance-based L4_SM analysis. Specifically, the L-band scattering albedo, soil roughness, and vegetation opacity climatology are obtained from the SMAP Level-2 dual-channel soil moisture retrieval product. In this presentation, we examine the improvements gained with the model and parameter changes. In peatlands, the dynamics of water table depth, surface soil moisture and evapotranspiration are considerably improved in L4_SM Version 7 over Version 6 when evaluated against in situ measurements. These improvements are also manifested in smaller Tb observation-minus-forecast residuals.

SMAP↗
NASA NTRS1997

Internal Physical Features of a Land Surface Model Employing a Tangent Linear Model

The Earth's land surface, including its biomass, is an integral part of the Earth's weather and climate system. Land surface heterogeneity, such as the type and amount of vegetative covering., has a profound effect on local weather variability and therefore on regional variations of the global climate. Surface conditions affect local weather and climate through a number of mechanisms. First, they determine the re-distribution of the net radiative energy received at the surface, through the atmosphere, from the sun. A certain fraction of this energy increases the surface ground temperature, another warms the near-surface atmosphere, and the rest evaporates surface water, which in turn creates clouds and causes precipitation. Second, they determine how much rainfall and snowmelt can be stored in the soil and how much instead runs off into waterways. Finally, surface conditions influence the near-surface concentration and distribution of greenhouse gases such as carbon dioxide. The processes through which these mechanisms interact with the atmosphere can be modeled mathematically, to within some degree of uncertainty, on the basis of underlying physical principles. Such a land surface model provides predictive capability for surface variables including ground temperature, surface humidity, and soil moisture and temperature. This information is important for agriculture and industry, as well as for addressing fundamental scientific questions concerning global and local climate change. In this study we apply a methodology known as tangent linear modeling to help us understand more deeply, the behavior of the Mosaic land surface model, a model that has been developed over the past several years at NASA/GSFC. This methodology allows us to examine, directly and quantitatively, the dependence of prediction errors in land surface variables upon different vegetation conditions. The work also highlights the importance of accurate soil moisture information. Although surface variables are predicted imperfectly due to inherent uncertainties in the modeling process, our study suggests how satellite observations can be combined with the model, through land surface data assimilation, to improve their prediction.

Yang, Runhua↗
NASA NTRS2017

El Nino-Induced Tropical Ocean/Land Energy Exchange in MERRA-2 and M2AMIP

Studies have shown the correlation and connection of surface temperatures across the globe, ocean and land, related to Tropical SSTs especially El Nino. This climate variability greatly influences regional weather and hydroclimate extremes (e.g. drought and flood). In this paper, we evaluate the relationship of temperatures across the tropical oceans and continents in MERRA-2, and also in a newly developed MERRA-2 AMIP ensemble simulation (M2AMIP). M2AMIP uses the same model and spatial resolution as MERRA-2, producing the same output diagnostics over 10 ensemble members. Composite El Nino temperature data are compared with observations to evaluate the land/sea contrast, variations and phase relationship. The temperature variations are related to surface heat fluxes and the atmospheric temperatures and transport, to identify the processes that lead to the lagged redistribution of heat in the tropics and beyond. Discernable cloud, radiation and data assimilation changes accompany the onset of El Nino affecting continental regions through the progression to and following the peak values. While the model represents these variations in general, regional strengths and weaknesses can be identified.

Bosilovich, Michael G.↗
NASA NTRS1967

Report 2: First Application of the Successive Transformation Method

Report 1 of these studies described a basic method for predicting lunar surface temperatures, using readings recorded by Surveyor I and extrapolating to obtain surface temperatures in other selected areas. The areas selected were those considered as potential Apollo landing sites. This second report indicates that the temperature isotherms are directly related to the topographical profile of the surface areas they cover. If, after further observations by Surveyor 11, it becomes evident that the "Successive Transformation Method" provides accurate temperature data, this technique would become a valuable tool for charting the profile of any remote surface where manned spacecraft could be landed.

Rojas, Hector R.↗
NASA NTRS2018

Improving the SMAP Level-4 Soil Moisture Product

The NASA Soil Moisture Active Passive (SMAP) mission generates, among other data sets, the Level 4 Soil Moisture (L4 SM) product. The L4 SM algorithm is based on the assimilation of SMAP radiometer brightness temperature observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. The L4 SM data are published with a mean latency of approx. 2.5 days from the time of observation and provide global, three-hourly, 9 km resolution estimates of surface and root-zone soil moisture and related land surface states and fluxes. In 2018, the product was upgraded from Version 3 to Version 4. Underlying the new version is a revised modeling system that includes improved input parameter datasets for land cover, topography, and vegetation height that are based on recent, high quality space-borne remote sensing observations. Land cover inputs were updated to the GlobCover2009 product, which is based on satellite observations from the Medium Resolution Imaging Spectrometer. Topographic statistics now rely on observations from the Shuttle Radar Topography Mission. Finally, vegetation height inputs are derived from space-borne Lidar measurements. Additionally, SMAP Level-2 soil moisture retrievals and in situ soil moisture measurements were used to calibrate a particular Catchment model parameter that governs the recharge of soil moisture from the models root-zone excess reservoir into the surface excess reservoir. Specifically, the replenishment of soil moisture near the surface from below under non-equilibrium conditions was substantially reduced, which brings the models surface soil moisture more in line with the SMAP Level 2 and in situ soil moisture. Finally, the calibration of the assimilated SMAP brightness temperatures changed substantially from Version 3 to Version 4. Considerable effort went into the version upgrade, creating an expectation that the new version is improved over the old version. Indeed, some aspects of the new version are clearly better. However, other aspects are not, and on balance, the overall improvement is modest at best. In this presentation we summarize the skill of the new and old versions vs. independent in situ measurements and in terms of data assimilation diagnostics, including, for example, the statistics of the (soil moisture) analysis increments and the observation minus forecast (brightness temperatures) residuals. We share our experience with trying to improve to the L4 SM product and the lessons learned from the effort.

Reichle, Rolf↗