Ship present-weather reports from 1950 through 2019 are used to assess trends in the reporting of precipitation occurrence over the global oceans. Annual reported precipitation frequency shows statistically significant positive trends of up to $\sim$15\% per decade throughout most ocean areas equatorward of 45 degrees. However, latitudes poleward of 45 degrees are dominated by negative trends, some areas of which meet the 95\% confidence threshold. Nine smaller regions were subjectively selected for further investigation, revealing that the observed trends, both positive and negative, are often but not always nearly linear, with the amplitude of interannual fluctuations usually being much larger than that expected from random sampling error alone. The annual time series reveal that four comparatively dry areas are associated with the largest overall positive trends, ranging from 8.3\% to 12.8\% (relative) per decade. Trends were also computed separately for each season, revealing remarkable overall consistency in trends across seasons.
A simple yet flexible and robust algorithm is described for fully partitioning an arbitrary dataset into compact, non-overlapping groups or classes, sorted by size, based entirely on a pairwise similarity matrix and a user-specified similarity threshold. Unlike many clustering algorithms, there is no assumption that natural clusters exist in the dataset, though clusters, when present, may be preferentially assigned to one or more classes. The method also does not require data objects to be compared within any coordinate system but rather permits the user to define pairwise similarity using almost any conceivable criterion. The method therefore lends itself to certain geoscientific applications for which conventional clustering methods are unsuited, including two non-trivial and distinctly different datasets presented as examples. In addition to identifying large classes containing numerous similar dataset members, it is also well-suited for isolating rare or anomalous members of a dataset. The method is inductive, in that prototypes identified in representative subset of a larger dataset can be used to classify the remainder.
The Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June-October 2019. The purpose of the study is to examine how the atmospheric boundary layer responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model-data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10×10 km domain of a heterogeneous forest ecosystem in the Chequamegon-Nicolet National Forest in northern Wisconsin USA, centered on the existing Park Falls 447-m tower that anchors an Ameriflux/NOAA supersite (US-PFa / WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft, maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology, and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large eddy simulation and scaling experiments to better understand sub-mesoscale processes and improve formulations of sub-grid scale processes in numerical weather and climate models.
A high-resolution (1.25 m) LES simulation of the nocturnal cloud-topped marine boundary layer is used to evaluate random error as a function of continuous track length L for virtual aircraft measurements of turbulent fluxes of sensible heat, latent heat, and horizontal momentum. Results are compared with the theoretically derived formula of Lenschow and Stankov (1986). In support of these comparisons , we also evaluate and document the relevant integral length scales and correlations and show that for heights up to approximately 100 m (z/z i = 0.12), the length scales are accurately predicted by empirical expressions of the form I f = Az^b. The Lenschow and Stankov expression is found to be remarkably accurate at predicting the random error for shorter flight tracks, but our empirically determined errors decay more rapidly with L than the L^−1/2 relationship predicted from theory. Consistent with earlier findings, required track lengths to obtain useful precision increase sharply with altitude.
57 years of qualitative ship-board weather reports are used to assess apparent trends in precipitation occurrence over the global oceans. Positive trends of up to ~15% per decade, relative to the long term mean precipitation frequency at a location, are found over most tropical and temperate ocean areas, with negative trends of up to ~5% per decade being found principally at higher latitudes. While it cannot be ruled out that the observed trends are an artifact of gradual changes in shipboard weather reporting habits or procedures over time, no specific candidate for such a change has been identified that could explain the existence of robust positive and negative trends and their apparent geographic coherence.