Sandra L. Castro

On scaling exponents of spatial peak flows from rainfall and river network geometry

Abstract The hypothesis of statistical self-similarity, or scaling invariance, in the spatial variability of rainfall, channel network structures and floods has been supported by recent advances in data analyses. This hypothesis is used here to calculate the statistical scaling exponents of peak river flows using a random cascade model of spatial rainfall intensities and the Peano basin as an idealized model of a river basin. The ‘maximum contributing set’ approximately determines the magnitudes of peak flows in a self-similar manner in different subbasins of the Peano basin. For an instantaneously applied random, spatially uniform rainfall, the Hausdorff dimension of the maximum contributing set appear, as the statistical simple scaling exponent of peak flows. This result is generalized to an instantaneously applied cascade rainfall, and it is shown to give rise to statistical multiscaling in peak flows. The multiscaling exponent of peak flows is computed and interpreted as a Hausdorff dimension of a fractal set supporting rainfall intensity on the maximum contributing set of the Peano basin. Potential implications of this interpretation are illustrated using the regional food frequency analysis of the Appalachian flood data in the United States and a rainfall data set from the tropical Atlantic ocean. It is argued that the hypothesis of self-similarity identifies a powerful theoretical framework which can unify a statistical theory of regional flood frequency with important empirical features of topographic, rainfall and flood data sets and distributed rainfall-landform-runoff relationships.

Estimating Sea Surface Temperature from Infrared Satellite and In Situ Temperature Data

Sea surface temperature (SST) is a critical quantity in the study of both the ocean and the atmosphere as it is directly related to and often dictates the exchanges of heat, momentum, and gases between the ocean and the atmosphere. As the most widely observed variable in oceanography, SST is used in many different studies of the ocean and its coupling with the atmosphere. The history of this measurement and how this history led to todays practice of computing SST by regressing satellite infrared measurements against in situ SST observations made by drifting/moored buoys and ships are examined. The fundamental differences between satellite and in situ SST are discussed and recommendations are made for how both data streams should be handled. A comprehensive in situ validation/calibration plan is proposed for the satellite SSTs and consequences of the suggested measurements are discussed with respect to the role of SST as an integral part of the fluxes between the ocean and the atmosphere.

Skin and bulk sea surface temperature estimates from passive microwave and thermal infrared satellite imagery and their relationships to atmospheric forcing

Infrared and microwave SST retrievals are highly complementary but are found to have significant differences that must be addressed if the products are to be combined. Individual products are evaluated using buoy observations to identify any dependence of the retrieval uncertainty on atmospheric forcing. The infrared products are seen to be affected by aerosols, water vapor, and SST while the microwave product is affected by atmospheric stability, wind speed, SST, and water vapor. Applying bias adjustments based on these results reduces the differences between the products

Production of an enhanced blended infrared and microwave sea surface temperature product

The simultaneous availability of infrared and passive microwave satellite sensors provides highly complementary information enabling the creation of improved all-weather, high-resolution sea surface temperature (SST) products. Existing SST products from the infrared advanced very high resolution radiometer and TRMM microwave imager are blended to produce daily, pre-dawn, 0.25deg resolution SST grids representative of the temperature at 1-m depth. Complex spatial and temporal differences between the original products resulting from different retrieval errors and measurement times are first addressed using derived bias adjustments and diurnal warming corrections. The products are then combined using an optimal interpolation approach that accounts for differing uncertainties in the products. Evaluation of the resulting analyzed SSTs with buoy observations demonstrates that the bias corrections improve the accuracy of the products making them comparable to single-sensor products but with improved sampling. Diurnal corrections based on limited forcing data reduce bias in the analysis but add scatter, suggesting further improvements are required

SKIN AND BULK SEA SURFACE TEMPERATURE ESTIMATES FROM PASSIVE MICROWAVE AND THERMAL INFARED SATELLITE IMAGERY AND THEIR RELATIONSHIPS TO ATMOSPHERIC FORCING

Infrared and microwave SST retrievals are highly complementary but are found to have significant differences that must be addressed if the products are to be combined. Individual products are evaluated using buoy observations to identify any dependence of the retrieval uncertainty on atmospheric forcing. The infrared products are seen to be affected by aerosols, water vapor, and SST while the microwave product is affected by atmospheric stability, wind speed, SST, and water vapor. Applying bias adjustments based on these results reduces the differences between the products

Submesoscale Sea Surface Temperature Variability from UAV and Satellite Measurements

Earlier studies of spatial variability in sea surface temperature (SST) using ship-based radiometric data suggested that variability at scales smaller than 1 km is significant and affects the perceived uncertainty of satellite-derived SSTs. Here, we compare data from the Ball Experimental Sea Surface Temperature (BESST) thermal infrared radiometer flown over the Arctic Ocean against coincident Moderate Resolution Imaging Spectroradiometer (MODIS) measurements to assess the spatial variability of skin SSTs within 1-km pixels. By taking the standard deviation, σ, of the BESST measurements within individual MODIS pixels, we show that significant spatial variability of the skin temperature exists. The distribution of the surface variability measured by BESST shows a peak value of O(0.1) K, with 95% of the pixels showing σ < 0.45 K. Significantly, high-variability pixels are located at density fronts in the marginal ice zone, which are a primary source of submesoscale intermittency near the surface. SST wavenumber spectra indicate a spectral slope of −2, which is consistent with the presence of submesoscale processes at the ocean surface. Furthermore, the BESST wavenumber spectra not only match the energy distribution of MODIS SST spectra at the satellite-resolved wavelengths, they also span the spectral slope of −2 by ~3 decades, from wavelengths of 8 km to <0.08 km.

Accuracy improvement in an infrared satellite skin sea surface temperature product

Infrared skin-based sea surface temperature (SST) products offer the potential for improved accuracy over traditional bulk products since the satellite measurements are more directly related to the skin temperature. Skin and bulk SST regression algorithms derived from coincident in situ observations are directly compared to evaluate the change in accuracy. While the skin-based product shows better accuracy in some cases, the improvement is not uniform. Physical variability of the skin layer and measurement challenges impact the change in accuracy. Nonetheless, the resulting skin SST products exhibit improved accuracy over existing operational products.

Further refinements to models for the bulk-skin sea surface temperature difference

The authors set the basis for a new physical model of renewal type, and propose a parameterization for the temperature difference across the cool skin of the ocean in which the effects of thermal buoyancy, wind stress, and microscale breaking are all integrated by means of the appropriate renewal time scales. Ideally, they seek to obtain a model that will accurately apply under a wide variety of environmental conditions. The physical processes that govern the magnitude of /spl Delta/T can vary with environmental conditions. Three different possible mechanisms are shown. These mechanisms include free convection, forced convection driven by wind shear stress, and forced convection drive by microscale wave breaking. During free convection the turbulent transport of heat is buoyancy-driven. Since the cool skin is denser than the underlying water it will become gravitationally unstable and tend to sink. Evaporation generates a salinity gradient which also contributes to the gravitational instability. During free convection, mean flow and wind shear stress are absent (non-existent or very low winds). As a result, /spl Delta/T is controlled primarily by the net heat flux.