Gas filter correlation radiometry: Report of panel

To measure the concentration of a gas in the troposphere, the gas filter radiometer correlates the pattern of the spectral lines of a sample of gas contained within the instrument with the pattern of the spectral lines in the upwelling radiation. A schematic diagram of a generalized gas filter radiometer is shown. Three instruments (the Gas Filter Radiometer, GFR; the Halogen Occultation Experiment, HALOE; and the Gas Filter Correlation Spectrometer, GASCOFIL) that have application to remotely measuring tropospheric constituents are described. A set of preliminary calculations to determine the feasibility of performing a multiple-layer, tropospheric carbon monoxide measurement experiment was performed. It can be seen that a three-layer measurement in the troposphere is possible

Assessment and enhancement of MERRA land surface hydrology estimates

The Modern-Era Retrospective Analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides, in addition to atmospheric fields, global estimates of soil moisture, latent heat flux, snow, and runoff for 1979 present. This study introduces a supplemental and improved set of land surface hydrological fields ("MERRA-Land") generated by rerunning a revised version of the land component of the MERRA system. Specifically, the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameter values in the rainfall interception model, changes that effectively correct for known limitations in the MERRA surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ECMWF Re-Analysis-Interim (ERA-I). MERRA-Land and ERA-I root zone soil moisture skills (against in situ observations at 85 U.S. stations) are comparable and significantly greater than that of MERRA. Throughout the Northern Hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 18 U.S. basins) of MERRA and MERRA-Land is typically higher than that of ERA-I. With a few exceptions, the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using MERRA output for land surface hydrological studies

Assimilation of Terrestrial Water Storage from GRACE in a Snow-Dominated Basin

Terrestrial water storage (TWS) information derived from Gravity Recovery and Climate Experiment (GRACE) measurements is assimilated into a land surface model over the Mackenzie River basin located in northwest Canada. Assimilation is conducted using an ensemble Kalman smoother (EnKS). Model estimates with and without assimilation are compared against independent observational data sets of snow water equivalent (SWE) and runoff. For SWE, modest improvements in mean difference (MD) and root mean squared difference (RMSD) are achieved as a result of the assimilation. No significant differences in temporal correlations of SWE resulted. Runoff statistics of MD remain relatively unchanged while RMSD statistics, in general, are improved in most of the sub-basins. Temporal correlations are degraded within the most upstream sub-basin, but are, in general, improved at the downstream locations, which are more representative of an integrated basin response. GRACE assimilation using an EnKS offers improvements in hydrologic state/flux estimation, though comparisons with observed runoff would be enhanced by the use of river routing and lake storage routines within the prognostic land surface model. Further, GRACE hydrology products would benefit from the inclusion of better constrained models of post-glacial rebound, which significantly affects GRACE estimates of interannual hydrologic variability in the Mackenzie River basin

Uncertainty Quantification of GEOS-5 L-band Radiative Transfer Model Parameters Using Bayesian Inference and SMOS Observations

Uncertainties in L-band (1.4 GHz) radiative transfer modeling (RTM) affect the simulation of brightness temperatures (Tb) over land and the inversion of satellite-observed Tb into soil moisture retrievals. In particular, accurate estimates of the microwave soil roughness, vegetation opacity and scattering albedo for large-scale applications are difficult to obtain from field studies and often lack an uncertainty estimate. Here, a Markov Chain Monte Carlo (MCMC) simulation method is used to determine satellite-scale estimates of RTM parameters and their posterior uncertainty by minimizing the misfit between long-term averages and standard deviations of simulated and observed Tb at a range of incidence angles, at horizontal and vertical polarization, and for morning and evening overpasses. Tb simulations are generated with the Goddard Earth Observing System (GEOS-5) and confronted with Tb observations from the Soil Moisture Ocean Salinity (SMOS) mission. The MCMC algorithm suggests that the relative uncertainty of the RTM parameter estimates is typically less than 25 of the maximum a posteriori density (MAP) parameter value. Furthermore, the actual root-mean-square-differences in long-term Tb averages and standard deviations are found consistent with the respective estimated total simulation and observation error standard deviations of m3.1K and s2.4K. It is also shown that the MAP parameter values estimated through MCMC simulation are in close agreement with those obtained with Particle Swarm Optimization (PSO)

Evaluation of MERRA Land Surface Estimates in Preparation for the Soil Moisture Active Passive Mission

The authors evaluated several land surface variables from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) product that are important for global ecological and hydrological studies, including daily maximum (Tmax) and minimum (Tmin) surface air temperatures, atmosphere vapor pressure deficit (VPD), incident solar radiation (SWrad), and surface soil moisture. The MERRA results were evaluated against in situ measurements, similar global products derived from satellite microwave [the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E)] remote sensing and earlier generation atmospheric analysis [Goddard Earth Observing System version 4 (GEOS-4)] products. Relative to GEOS-4, MERRA is generally warmer (~0.5°C for Tmin and Tmax) and drier (~50 Pa for VPD) for low- and middle-latitude regions (\u3c50°N) associated with reduced cloudiness and increased SWrad. MERRA and AMSR-E temperatures show relatively large differences (\u3e3°C) in mountainous areas, tropical forest, and desert regions. Surface soil moisture estimates from MERRA (0–2-cm depth) and two AMSR-E products (~0–1-cm depth) are moderately correlated (R ~ 0.4) for middle-latitude regions with low to moderate vegetation biomass. The MERRA derived surface soil moisture also corresponds favorably with in situ observations (R = 0.53 ± 0.01, p \u3c 0.001) in the midlatitudes, where its accuracy is directly proportional to the quality of MERRA precipitation. In the high latitudes, MERRA shows inconsistent soil moisture seasonal dynamics relative to in situ observations. The study’s results suggest that satellite microwave remote sensing may contribute to improved reanalysis accuracy where surface meteorological observations are sparse and in cold land regions subject to seasonal freeze–thaw transitions. The upcoming NASA Soil Moisture Active Passive (SMAP) mission is expected to improve MERRA-type reanalysis accuracy by providing accurate global mapping of freeze–thaw state and surface soil moisture with 2–3-day temporal fidelity and enhanced (≤9 km) spatial resolution

Renal function during rofecoxib therapy in patients with metastatic cancer: retrospective analysis of a prospective phase II trial

German Clinical Trials Register DRKS: DRKS0000011

Plant Water Uptake Thresholds Inferred From Satellite Soil Moisture

Empirical functions are widely used in hydrological, agricultural, and Earth system models to parameterize plant water uptake. We infer soil water potentials at which uptake is downregulated from its well‐watered rate and at which uptake ceases, in biomes with <60% woody vegetation at 36‐km grid resolution. We estimate thresholds through Bayesian inference using a stochastic soil water balance framework to construct theoretical soil moisture probability distributions consistent with empirical distributions derived from satellite soil moisture observations. The global median Nash–Sutcliffe efficiency between empirical soil moisture distributions and theoretical distributions using reference constants, inferred median parameters per biome, and spatially variable inferred parameters are 0.38, 0.59, and 0.8, respectively. Spatially variable thresholds capture location‐specific vegetation and climate characteristics and can be connected to biome‐level water uptake strategies. Results demonstrate that satellite soil moisture probability distributions encode information, valuable to understanding biome‐level ecohydrological adaptation and resistance to climate variability

Recent climate and fire disturbance impacts on boreal and arctic ecosystem productivity estimated using a satellite-based terrestrial carbon flux model

Warming and changing fire regimes in the northern (≥45°N) latitudes have consequences for land-atmosphere carbon feedbacks to climate change. A terrestrial carbon flux model integrating satellite Normalized Difference Vegetation Index and burned area records with global meteorology data was used to quantify daily vegetation gross primary productivity (GPP) and net ecosystem CO2 exchange (NEE) over a pan-boreal/Arctic domain and their sensitivity to climate variability, drought, and fire from 2000 to 2010. Model validation against regional tower carbon flux measurements showed overall good agreement for GPP (47 sites: R = 0.83, root mean square difference (RMSD) = 1.93 g C m−2 d−1) and consistency for NEE (22 sites: R = 0.56, RMSD = 1.46 g C m−2 d−1). The model simulations also tracked post-fire NEE recovery indicated from three boreal tower fire chronosequence networks but with larger model uncertainty during early succession. Annual GPP was significantly (p \u3c 0.005) larger in warmer years than in colder years, except for Eurasian boreal forest, which showed greater drought sensitivity due to characteristic warmer, drier growing seasons relative to other areas. The NEE response to climate variability and fire was mitigated by compensating changes in GPP and respiration, though NEE carbon losses were generally observed in areas with severe drought or burning. Drought and temperature variations also had larger regional impacts on GPP and NEE than fire during the study period, though fire disturbances were heterogeneous, with larger impacts on carbon fluxes for some areas and years. These results are being used to inform development of similar operational carbon products for the NASA Soil Moisture Active Passive (SMAP) mission

Evaluation and Enhancement of Permafrost Modeling With the NASA Catchment Land Surface Model

Besides soil hydrology and snow processes, the NASA Catchment Land Surface Model (CLSM) simulates soil temperature in six layers from the surface down to 13m depth. In this study, to examine CLSM's treatment of subsurface thermodynamics, a baseline simulation produced subsurface temperatures for 1980-2014 across Alaska at 9-km resolution. The results were evaluated using in situ observations from permafrost sites across Alaska. The baseline simulation was found to capture the broad features of inter- and intra-annual variations in soil temperature. Additional model experiments revealed that: (i) the representativeness of local meteorological forcing limits the model's ability to accurately reproduce soil temperature, and (ii) vegetation heterogeneity has a profound influence on subsurface thermodynamics via impacts on the snow physics and energy exchange at surface. Specifically, the profile-average RMSE for soil temperature was reduced from 2.96 C to 2.10 C at one site and from 2.38 C to 2.25 C at another by using local forcing and land cover, respectively. Moreover, accounting for the influence of soil organic carbon on the soil thermal properties in CLSM leads to further improvements in profile-average soil temperature RMSE, with reductions of 16% to 56% across the different study sites. The mean bias of climatological ALT is reduced by 36% to 89%, and the RMSE is reduced by 11% to 47%. Finally, results reveal that at some sites it may be essential to include a purely organic soil layer to obtain, in conjunction with vegetation and snow effects, a realistic "buffer zone" between the atmospheric forcing and soil thermal processes