Response of Fungi to Diurnal Temperature Extremes
Response of fungi to diurnal temperature extremes, describing soil, simulated Martian temperature regime and fungi growth
MARS ATMOSPHERE↗
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Systematic Errors in Simulated L-Band Brightness Temperature in the SMAP Level-4 Soil Moisture Analysis
The NASA Soil Moisture Active Passive (SMAP) mission has been providing L-band (1.4 GHz) brightness temperature (Tb) observations since April 2015. By assimilating the Tb observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter (EnKF), the SMAP Level-4 Soil Moisture (L4_SM) product provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with ~2.5-day latency for use in research and applications. The EnKF-based L4_SM analysis assumes unbiased forecast errors. Consequently, the seasonally varying bias between the model forecast Tb and the observed values is removed prior to the assimilation of the SMAP Tb observations. The L4_SM system is thus designed to only correct errors in synoptic-scale and interannual variations from the long-term mean seasonal cycle while maintaining the model’s (possibly wrong) climatology. In this paper, we examine the Tb observation-minus-forecast (O-F) residuals from the L4_SM Version 7 product (computed after rescaling the Tb observations to the mean seasonal cycle of the simulated Tb). The long-term average of the Tb O-F residuals has a global mean of only 0.13 K and locally small values, ranging from -1 to 3 K. The model forecast Tb, however, still exhibits undesirable systematic errors relative to the (rescaled) Tb observations. At some locations, the time-average Tb O-F values strongly depend on surface soil moisture (SM). At the Yanco SMAP core validation site, for example, the Tb O-F residuals typically range from 5 to 15 K under dry soil moisture conditions (SM < 0.15 m3 m-3) yet are predominantly negative under wet soil moisture conditions (SM > 0.25 m3 m-3), with values ranging from 0 to -40 K. This results in soil moisture analysis increments that persistently make the soil drier under dry SM conditions and persistently make the soil wetter under wet SM conditions, suggesting an error in the dynamic range of the simulated Tb, soil moisture or soil temperature. In this paper, we describe the higher-order systematic Tb forecast errors in more detail, examine their impact on the L4_SM product quality, and explore potential avenues to improve the L4_SM algorithm.
Rolf Reichle↗
Estimation of soil hydraulic properties with microwave techniques
Useful quantitative information about soil properties may be obtained by calibrating energy and moisture balance models with remotely sensed data. A soil physics model solves heat and moisture flux equations in the soil profile and is driven by the surface energy balance. Model generated surface temperature and soil moisture and temperature profiles are then used in a microwave emission model to predict the soil brightness temperature. The model hydraulic parameters are varied until the predicted temperatures agree with the remotely sensed values. This method is used to estimate values for saturated hydraulic conductivity, saturated matrix potential, and a soil texture parameter. The conductivity agreed well with a value measured with an infiltration ring and the other parameters agreed with values in the literature.
Oneill, P. E.↗
CONSERVB: A numerical method to compute soil water content and temperature profiles under a bare surface
A comprehensive, yet fairly simple model of water disposition in a bare soil profile under the sequential impact of rain storms and other atmospheric influences, as they occur from hour to hour is presented. This model is intended mostly to support field studies of soil moisture dynamics by our current team, to serve as a background for the microwave measurements, and, eventually, to serve as a point of departure for soil moisture predictions for estimates based in part upon airborne measurements. The main distinction of the current model is that it accounts not only for the moisture flow in the soil-atmosphere system, but also for the energy flow and, hence, calculates system temperatures. Also, the model is of a dynamic nature, capable of supporting any required degree of resolution in time and space. Much critical testing of the sample is needed before the complexities of the hydrology of a vegetated surface can be related meaningfully to microwave observations.
Vanbavel, C. H. M.↗
Electrical properties of lunar soil dependence on frequency, temperature and moisture.
It was found that the dielectric constant and loss tangent of lunar soil samples in the range from 100 Hz to 1 MHz are not strongly dependent on frequency provided care is taken to avoid exposure of the sample to atmospheric air containing moisture. The loss tangent value obtained is lower by nearly a factor 10 than any previously reported value. The measurement data imply that the surface layers of the moon are probably extremely transparent to radiowaves.
Strangway, D. W.↗
Lunar surface temperatures at Tranquillity Base
Tranquillity Base lunar soil diurnal temperature calculation from one dimensional energy equation, using temperature dependent thermodynamic properties
Birkebak, R. C.↗
Detection of moisture and moisture related phenomena from Skylab
The author has identified the following significant results. Skylab 2 data for June 5, 1973 (Texas site) relates favorably with previously calculated aircraft data when correlating brightness temperature to soil moisture. However, more detailed work is needed to determine the corrected surface temperature. In addition, correlations between the S194 antenna temperature and soil moisture have been obtained for five sets of Skylab data. The best correlations were obtained for the surface to one inch depth in four cases and for surface to two inches depth for the fifth case. Correlation coefficients for the surface to one inch depth were -0.98, -0.95, -0.90, -0.82, and -0.80.
Eagleman, J. R.↗
Soil Moisture Active Passive (SMAP) Project Assessment Report for Version 4 of the L4_SM Data Product
This report provides an assessment of Version 4 of the SMAP Level 4 Surface and Root Zone Soil Moisture (L4_SM) product, released on 14 June 2018. The assessment includes comparisons of L4_SM soil moisture and temperature estimates with in situ measurements from core validation sites and sparse networks. The assessment further includes a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product, including observation-minus-forecast (O-F) brightness temperature residuals and soil moisture analysis increments.Together, the core validation site comparisons and the statistics of the assimilation diagnostics areconsidered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to upscaling errors from the point-scale to the grid-cell scale of the data product.The Version 4 L4_SM product benefits from an improved land surface modeling system and from retrospective surface meteorological forcing data that are as consistent as possible with the present-day datain terms of their climatology. Specifically, the model changes include revised parameters and parameterizations for (i) the surface energy balance, (ii) recharge from below of the model's surface excess reservoir, and (iii) the snow depletion curve. Updated ancillary inputs include improved datasets for landcover, topography, and vegetation height. The Version 4 algorithm further includes a revised approach to precipitation corrections that improves the precipitation climatology in Africa and the high-latitudes. Moreover, for system calibration the model is forced retrospectively with MERRA-2 reanalysis data, which are more consistent with the near-real time GEOS forward processing (FP) data used during the SMAP period than the retrospective GEOS data that were available for previous L4_SM versions. An analysis of the time-average surface and root zone soil moisture shows that the global pattern ofarid and humid regions is captured by the Version 4 L4_SM estimates. Owing to the changes in the landsurface modeling system, surface soil moisture is typically drier by several volumetric percent in Version 4 compared to Version 3, whereas root zone soil moisture is wetter in Version 4 in some regions and drierin others. Because of these climatological differences, the Version 3 and Version 4 products should not be combined into a single dataset for use in applications.Results from the core validation site comparisons indicate that Version 4 of the L4_SM data product meets the self-imposed L4_SM accuracy requirement, which is formulated in terms of the RMSE after removal of the long-term mean difference (ubRMSE). The overall ubRMSE of the 3-hourly L4_SM dataat the 9 km scale is 0.039 m3 m-3 for surface soil moisture and 0.029 m3 m-3 for root zone soil moisture,below the 0.04 m3 m-3 requirement. The L4_SM estimates are an improvement over estimates from a model-only Nature Run version 7.2 (NRv7.2), which demonstrates the beneficial impact of the SMAP brightness temperature data. Overall, L4_SM surface and root zone soil moisture estimates are more skillful than NRv7.2 estimates, with statistically significant improvements at the 5% level for surface soil moisture R and anomaly R values. Results from comparisons of the L4_SM product to i
Reichle, Rolf H.↗
Reflectivity, Emissivity, and Brightness from Multilayered Soil with Linearly Varying Permittivity at P- and L- bands
Passive sensing of root zone soil moisture is theoretically investigated using P and L band frequencies. The soil is considered to be a stratified medium. The reflectivity from the soil surface is found by considering the soil to be composed of a finely layered structure with layer widths that are small compared to a wavelength. A well-known transmission line algorithm is then used to compute the reflectivity at the surface. Emissivity is obtained as (1 – reflectivity). It assumed that the soil is at a constant temperature of 300o K when determining the brightness temperature. A soil moisture profile consisting of two homogeneous layers separated by a layer of linearly varying soil moisture is investigated. The study is made at an incident angle of 40O for horizontal polarization at 0.8 and 1.4 GHz. Two variations of the basic profile are considered: a transition from a layer of lesser moisture to a layer of greater moisture and the opposite. In both cases the P band signal shows greater penetration than the L band case. There is greater penetration in the transition to drier soils. In both cases the L band signals show sensitivity to only the upper surface of the soil as expected.
Ming Li↗
Calculations of the spectral nature of the microwave emission from soils
The brightness temperatures for a set of soil profiles observed at USDA facilities in Arizona and Georgia were calculated at the wavelengths of 2.8, 6, 11, 21, and 49 cm using a coherent radiative transfer model. The soil moisture sampling depth is found to be a function of wavelength and is in the range 0.06 to 0.1 of a wavelength. The thermal sampling depth also depends on wavelength and is approximately equal to one wavelength at dry soil condition and 0.1 - 0.5 wavelengths at wet soil conditions. Calculated values of emissivity show strong diurnal variations when the soils are wet, while there is little diurnal change when the soil is dry. The soil moistures within the four depth intervals of 0-2, 0-5, 0-9, and 0-15 cm were parameterized as function of the calculated emissivity and brightness temperature. Best-fit parameters and correlation coefficients are presented for five wavelengths. Interrelationships among the effective temperature, surface temperature, and emissivity are displayed.
Mo, T.↗
Measuring surface fluxes in CAPE
Two stations (site 1612 and site 2008) were operated by the University of Georgia group from 6 July 1991 to 18 August 1991. The following data were collected continuously: surface energy fluxes (i.e., net radiation, soil heat fluxes, sensible heat flux and latent heat flux), air temperature, vapor pressure, soil temperature (at 1 cm depth), and precipitation. Canopy reflectance and light interception data were taken three times at each site between 6 July and 18 August. Soil moisture content was measured twice at each site.
Kanemasu, E. T.↗
Passive microwave sensing of soil moisture content - The effects of soil bulk density and surface roughness
Microwave radiometric measurements over bare fields of different surface roughness were made at frequencies of 1.4 GHz, 5 GHz, and 10.7 GHz to study the frequency dependence, as well as the possible time variation, of surface roughness. An increase in surface roughness was found to increase the brightness temperature of soils and reduce the slope of regression between brightness temperature and soil moisture content. The frequency dependence of the surface roughness effect was relatively weak when compared with that of the vegetation effect. Radiometric time-series observations over a given field indicate that field surface roughness might gradually diminish with time, especially after a rainfall or irrigation. The variation of surface roughness increases the uncertainty of remote soil moisture estimates by microwave radiometry. Three years of radiometric measurements over a test site revealed a possible inconsistency in the soil bulk density determination, which is an important factor in the interpretation of radiometric data.
Wang, J. R.↗
Passive microwave sensing of soil moisture content: Soil bulk density and surface roughness
Microwave radiometric measurements over bare fields of different surface roughnesses were made at the frequencies of 1.4 GHz, 5 GHz, and 10.7 GHz to study the frequency dependence as well as the possible time variation of surface roughness. The presence of surface roughness was found to increase the brightness temperature of soils and reduce the slope of regression between brightness temperature and soil moisture content. The frequency dependence of the surface roughness effect was relatively weak when compared with that of the vegetation effect. Radiometric time series observation over a given field indicated that field surface roughness might gradually diminish with time, especially after a rainfall or irrigation. This time variation of surface roughness served to enhance the uncertainty in remote soil moisture estimate by microwave radiometry. Three years of radiometric measurements over a test site revealed a possible inconsistency in the soil bulk density determination, which turned out to be an important factor in the interpretation of radiometric data.
Wang, J. R.↗
COMBINED EFFECTS OF ULTRAHIGH VACUUM AND TEMPERATURE ON THE VIABILITY OF SOME SPORES AND SOIL ORGANISMS
Ultrahigh vacuum and temperature effects on viability of spore and soil organisms
ULTRAHIGH VACUUM↗
Integration of SMAP and SMOS L-Band Observations
Soil Moisture Active Passive (SMAP) mission and the ESA Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2-3 days. SMAP brightness temperature observations were compared with SMOS observations at 40 Degrees incidence angle. The brightness temperatures from the two missions are not consistent and have a bias of about 2.7K over land with respect to each other. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between the soil moisture products. The reprocessed constant-angle SMOS brightness temperatures were used in the SMAP soil moisture retrieval algorithm to develop a consistent multi-satellite product. The integrated product will have an increased global revisit frequency (1 day) and period of record that would be unattainable by either one of the satellites alone. Results from the development and validation of the integrated product will be presented.
SMOS↗
Integration of SMAP and SMOS Observations
Soil Moisture Active Passive (SMAP) mission and the Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2-3 days. SMAP brightness temperature observations were compared with SMOS observations at 40o incidence angle. The brightness temperatures from the two missions are not consistent. SMAP observations show a warmer TB bias (about 1.27 K: V pol and 0.62 K: H pol) as compared to SMOS. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between their soil moisture products. The reprocessed constant-angle SMOS brightness temperatures were used in the SMAP soil moisture retrieval algorithm to develop a consistent multi-satellite product. The integrated product has an increased global revisit frequency (1 day) and period of record that is unattainable by either one of the satellites alone. Results from the development and validation of the integrated soil moisture product will be presented.
passive microwave↗
Integrated SMAP and SMOS Soil Moisture Observations
Soil Moisture Active Passive (SMAP) mission and the Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2-3 days. SMAP brightness temperature observations were compared with SMOS observations at 40o incidence angle. The brightness temperatures from the two missions are close to each other but SMAP observations show a warmer TB bias (about 0.64 K: V pol and 1.14 K: H pol) as compared to SMOS. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between their soil moisture products. The reprocessed constant-angle SMOS brightness temperatures (SMOS-SMAP) were used in the SMAP soil moisture retrieval algorithm to develop a consistent multi-satellite product. The integrated product has an increased global revisit frequency (1 day) and period of record that is unattainable by either one of the satellites alone. The SMOS-SMAP soil moisture retrievals compared with in situ observations show a retrieval accuracy of less than 0.04 m3/m3. Results from the development and validation of the integrated soil moisture product will be presented.
SMOS↗
Interactive Vegetation Phenology, Soil Moisture, and Monthly Temperature Forecasts
The time scales that characterize the variations of vegetation phenology are generally much longer than those that characterize atmospheric processes. The explicit modeling of phenological processes in an atmospheric forecast system thus has the potential to provide skill to subseasonal or seasonal forecasts. We examine this possibility here using a forecast system fitted with a dynamic vegetation phenology model. We perform three experiments, each consisting of 128 independent warm-season monthly forecasts: 1) an experiment in which both soil moisture states and carbon states (e.g., those determining leaf area index) are initialized realistically, 2) an experiment in which the carbon states are prescribed to climatology throughout the forecasts, and 3) an experiment in which both the carbon and soil moisture states are prescribed to climatology throughout the forecasts. Evaluating the monthly forecasts of air temperature in each ensemble against observations, as well as quantifying the inherent predictability of temperature within each ensemble, shows that dynamic phenology can indeed contribute positively to subseasonal forecasts, though only to a small extent, with an impact dwarfed by that of soil moisture.
Seasonal Forecasting↗