Effects of foam on ocean surface microwave emission inferred from radiometric observations of reproducible breaking waves

Includes bibliographical references.WindSat, the first satellite polarimetric microwave radiometer, and the NPOESS Conical Microwave Imager/Sounder both have as a key objective the retrieval of the ocean surface wind vector from radiometric brightness temperatures. Available observations and models to date show that the wind direction signal is only 1-3 K peak-to-peak at 19 and 37 GHz, much smaller than the wind speed signal. In order to obtain sufficient accuracy for reliable wind direction retrieval, uncertainties in geophysical modeling of the sea surface emission on the order of 0.2 K need to be removed. The surface roughness spectrum has been addressed by many studies, but the azimuthal signature of the microwave emission from breaking waves and foam has not been adequately addressed. RECENtly, a number of experiments have been conducted to quantify the increase in sea surface microwave emission due to foam. Measurements from the Floating Instrumentation Platform indicated that the increase in ocean surface emission due to breaking waves may depend on the incidence and azimuth angles of observation. The need to quantify this dependence motivated systematic measurement of the microwave emission from reproducible breaking waves as a function of incidence and azimuth angles. A number of empirical parameterizations of whitecap coverage with wind speed were used to estimate the increase in brightness temperatures measured by a satellite microwave radiometer due to wave breaking in the field of view. These results provide the first empirically based parameterization with wind speed of the effect of breaking waves and foam on satellite brightness temperatures at 10.8, 19, and 37 GHz.This work was supported in part by the Department of the Navy, Office of Naval Research under Awards N00014-00-1-0615 (ONR/YIP) and N00014-03-1-0044 (Space and Remote Sensing) to the University of Massachusetts Amherst, and N00014-00-1-0152 (Space and Remote Sensing) to the University of Washington. The National Polar-orbiting Operational environmental Satellite System Integrated Program Office supported the Naval Research Laboratory's participation through Award NA02AANEG0338 and supported data analysis at Colorado State University and the University of Washington through Award NA05AANEG0153

Controls on Ecosystem Carbon Dioxide Exchange in Short- and Long-Hydroperiod Florida Everglades Freshwater Marshes

Although freshwater wetlands are among the most productive ecosystems on Earth, little is known of carbon dioxide (CO2) exchange in low latitude wetlands. The Everglades is an extensive, oligotrophic wetland in south Florida characterized by short- and long-hydroperiod marshes. Chamber-based CO2 exchange measurements were made to compare the marshes and examine the roles of primary producers, seasonality, and environmental drivers in determining exchange rates. Low rates of CO2 exchange were observed in both marshes with net ecosystem production reaching maxima of 3.77 and 4.28 μmol CO2 m−2 s−1 in short- and long-hydroperiod marshes, respectively. Fluxes of CO2 were affected by seasonality only in the short-hydroperiod marsh, where flux rates were significantly lower in the wet season than in the dry season. Emergent macrophytes dominated fluxes at both sites, though this was not the case for the short-hydroperiod marsh in the wet season. Water depth, a factor partly under human control, significantly affected gross ecosystem production at the short-hydroperiod marsh. As Everglades ecosystem restoration proceeds, leading to deeper water and longer hydroperiods, productivity in short-hydroperiod marshes will likely be more negatively affected than in long-hydroperiod marshes. The Everglades stand in contrast to many freshwater wetlands because of ecosystem-wide low productivity rates

Multidecadal climate oscillations detected in a transparency record from a subtropical Florida lake

Synchronous interannual variability in water transparency observed in neighboring lakes has been linked to regional precipitation and resultant runoff of dissolved organic material, but many climate forcings oscillate over time scales longer than most limnological records can detect. A strong relationship (R2 5 0.86) between transparency and the previous two years’ rainfall and lake stage in a 25-yr record from a Florida lake enabled us to hindcast transparency from a longer 75-yr record of rainfall and lake stage. Predictions revealed a ,30-yr cycle in transparency linked to the Atlantic Multidecadal Oscillation (AMO). Transparency was greatest (4–8 m) in the cool phase of the AMO (,1962–1993) associated with below-average rainfall in south Florida and lowest (0.1– 3.0 m) during two warm phases (,1932–1961, 1994–present) associated with above-average, but more variable, annual rainfall. Models that predict effects of large-scale hydrologic restoration projects on solute export from South Florida’s expansive wetlands need to account for recent entry into a warm AMO phase, where teleconnections between the AMO phases and runoff are opposite of those shown for the U.S. interior

Winter wheat yield prediction using convolutional neural networks from environmental and phenological data

Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an extensive dataset of weather, soil, and crop phenology variables in 271 counties across Germany from 1999 to 2019. We proposed a Convolutional Neural Network (CNN) model, which uses a 1-dimensional convolution operation to capture the time dependencies of environmental variables. We used eight supervised machine learning models as baselines and evaluated their predictive performance using RMSE, MAE, and correlation coefficient metrics to benchmark the yield prediction results. Our findings suggested that nonlinear models such as the proposed CNN, Deep Neural Network (DNN), and XGBoost were more effective in understanding the relationship between the crop yield and input data compared to the linear models. Our proposed CNN model outperformed all other baseline models used for winter wheat yield prediction (7 to 14% lower RMSE, 3 to 15% lower MAE, and 4 to 50% higher correlation coefficient than the best performing baseline across test data). We aggregated soil moisture and meteorological features at the weekly resolution to address the seasonality of the data. We also moved beyond prediction and interpreted the outputs of our proposed CNN model using SHAP and force plots which provided key insights in explaining the yield prediction results (importance of variables by time). We found DUL, wind speed at week ten, and radiation amount at week seven as the most critical features in winter wheat yield prediction

On the Particle Data Group evaluation of Psi' and chi_c Branching Ratios

I propose a new evaluation of $\psi'(2S)$ and $\chi_c(1P)$ branching ratios which avoids the correlations affecting the current Particle Data Group evaluation. These correlations explain the apparent technique-dependent discrepancies between the available determinations of the ${\cal B}(\chi_c(1P)\to p\bar p)$ and $\Gamma(\chi_c(1P)\to \gamma\gamma)$ under the hypotesis that the current values of the $\psi'(2S)\to\chi_c(1P)\gamma$ branching ratios are overestimated. In the process I also noticed that Particle Data Group has not restated many of the older measurements, when necessary, for the new value of ${\cal B}(J/\psi\to l^+l^-)$, which significantly affects the evaluation of some relevant $\psi'(2S)$ and $\chi_c(1P)$ exclusive branching ratios.Comment: 13 pages. Revised version. Submitted to Phys. Rev.

A Global lake ecological observatory network (GLEON) for synthesising high-frequency sensor data for validation of deterministic ecological models

A Global Lake Ecological Observatory Network (GLEON; www.gleon.org) has formed to provide a coordinated response to the need for scientific understanding of lake processes, utilising technological advances available from autonomous sensors. The organisation embraces a grassroots approach to engage researchers from varying disciplines, sites spanning geographic and ecological gradients, and novel sensor and cyberinfrastructure to synthesise high-frequency lake data at scales ranging from local to global. The high-frequency data provide a platform to rigorously validate processbased ecological models because model simulation time steps are better aligned with sensor measurements than with lower-frequency, manual samples. Two case studies from Trout Bog, Wisconsin, USA, and Lake Rotoehu, North Island, New Zealand, are presented to demonstrate that in the past, ecological model outputs (e.g., temperature, chlorophyll) have been relatively poorly validated based on a limited number of directly comparable measurements, both in time and space. The case studies demonstrate some of the difficulties of mapping sensor measurements directly to model state variable outputs as well as the opportunities to use deviations between sensor measurements and model simulations to better inform process understanding. Well-validated ecological models provide a mechanism to extrapolate high-frequency sensor data in space and time, thereby potentially creating a fully 3-dimensional simulation of key variables of interest

Radiometric measurements of the microwave emissivity of foam

Includes bibliographical references.Radiometric measurements of the microwave emissivity of foam were conducted during May 2000 at the Naval Research Laboratory's Chesapeake Bay Detachment using radiometers operating at 10.8 and 36.5 GHz. Horizontal and vertical polarization measurements were performed at 36.5 GHz; horizontal, vertical, +45°, ­45°, left-circular, and right-circular polarization measurements were obtained at 10.8 GHz. These measurements were carried out over a range of incidence angles from 30° to 60°. Surface foam was generated by blowing compressed air through a matrix of gas-permeable tubing supported by an aluminum frame and floats. Video micrographs of the foam were used to measure bubble size distribution and foam layer thickness. A video camera was boresighted with the radiometers to determine the beam-fill fraction of the foam generator. Results show emissivities that were greater than 0.9 and approximately constant in value over the range of incidence angles for vertically polarized radiation at both 10.8 and 36.5 GHz, while emissivities of horizontally polarized radiation showed a gradual decrease in value as incidence angle increased. Emissivities at +45°, ­45°, left-circular, and right-circular polarizations were all very nearly equal to each other and were in turn approximately equal to the average values of the horizontal and vertical emissivities in each case.This work was sponsored by the Department of the Navy, Office of Naval Research under Award N0014-00-1-280 to the University of Massachusetts, Award N00014-00-0152 to the University of Washington, and Award N0001400WX21032 to the Naval Research Laboratory