Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples

We develop a simple Quantile Spacing (QS) method for accurate probabilistic estimation of one-dimensional entropy from equiprobable random samples, and compare it with the popular Bin-Counting (BC) method. In contrast to BC, which uses equal-width bins with varying probability mass, the QS method uses estimates of the quantiles that divide the support of the data generating probability density function (pdf) into equal-probability-mass intervals. Whereas BC requires optimal tuning of a bin-width hyper-parameter whose value varies with sample size and shape of the pdf, QS requires specification of the number of quantiles to be used. Results indicate, for the class of distributions tested, that the optimal number of quantile-spacings is a fixed fraction of the sample size (empirically determined to be ~0.25-0.35), and that this value is relatively insensitive to distributional form or sample size, providing a clear advantage over BC since hyperparameter tuning is not required. Bootstrapping is used to approximate the sampling variability distribution of the resulting entropy estimate, and is shown to accurately reflect the true uncertainty. For the four distributional forms studied (Gaussian, Log-Normal, Exponential and Bimodal Gaussian Mixture), expected estimation bias is less than 1% and uncertainty is relatively low even for very small sample sizes. We speculate that estimating quantile locations, rather than bin-probabilities, results in more efficient use of the information in the data to approximate the underlying shape of an unknown data generating pdf.Comment: 23 pages, 12 figure

Measurement Report: Long-Range Transport Patterns into the Tropical Northwest Pacific during the CAMP2Ex Aircraft Campaign: Chemical Composition, Size Distributions, and the Impact of Convection

The tropical Northwest Pacific (TNWP) is a receptor for pollution sources throughout Asia and is highly susceptible to climate change, making it imperative to understand long-range transport in this complex aerosol-meteorological environment. Measurements from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex; 24 August to 5 October 2019) and back trajectories from the National Oceanic and Atmospheric Administration Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) were used to examine transport into the TNWP from the Maritime Continent (MC), peninsular Southeast Asia (PSEA), East Asia (EA), and the West Pacific (WP). A mid-campaign monsoon shift on 20 September 2019 led to distinct transport patterns between the southwest monsoon (SWM; before 20 September) and monsoon transition (MT; after 20 September). During the SWM, long-range transport was a function of southwesterly winds and cyclones over the South China Sea. Low- (high-) altitude air generally came from MC (PSEA), implying distinct aerosol processing related to convection and perhaps wind shear. The MT saw transport from EA and WP, driven by Pacific northeasterly winds, continental anticyclones, and cyclones over the East China Sea. Composition of transported air differed by emission source and accumulated precipitation along trajectories (APT). MC air was characterized by biomass burning tracers while major components of EA air pointed to Asian outflow and secondary formation. Convective scavenging of PSEA air was evidenced by considerable vertical differences between aerosol species but not trace gases, as well as notably higher APT and smaller particles than other regions. Finally, we observed a possible wet scavenging mechanism acting on MC air aloft that was not strictly linked to precipitation. These results are important for understanding the transport and processing of air masses with further implications for modeling aerosol lifecycles and guiding international policymaking to public health and climate, particularly during the SWM and MT

Using the Airborne Snow Observatory to Assess Remotely Sensed Snowfall Products in the California Sierra Nevada

The Airborne Snow Observatory (ASO) performed two acquisitions over two mountainous basins in California on 29 January and 3 March 2017, encompassing two atmospheric river events that brought heavy snowfall to the area. These surveys produced high-resolution (50 m) maps of snow depth and snow water equivalent (SWE) that were used to estimate monthly areal snowfall accumulation. Comparison of ASO snow accumulation with point measurements showed that the ASO estimates ranged from -10 to +16% relative bias across three sites, which is likely inflated by the disagreement in areal representation of the quantities from the actual errors in these products. The aggregated SWE accumulations from ASO are then used to evaluate a suite of in situ based and remote sensing precipitation products. During the study period, Parameter-Elevation Regressions on Independent Slopes Model (PRISM) and Mountain Mapper estimates had relative bias 0.8 with ASO snow accumulation over the selected grids at the monthly scale. Finally, we leveraged the fine-scale sampling of the spatially complete ASO products to show that by moving from 100 m to 2 km spatial scales, the perceived bias errors SWE at point locations increased by an order of magnitude, displaying a nonlinear relationship. The study demonstrates that ASO acquisitions in cold months can bring a new and effective approach to spatial evaluation of precipitation products.National Aeronautics and Space Administration; NASA Energy and Water Cycle Study [NNH13ZDA001N-NEWS]; NASA Terrestrial Hydrology Programs; NASA Western Water Applications Office6 month embargo; published online: 10 September 2018This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Application of machine learning and remote sensing for gap-filling daily precipitation data of a sparsely gauged basin in East Africa

Abstract Access to spatiotemporal distribution of precipitation is needed in many hydrological applications. However, gauges often have spatiotemporal gaps. To mitigate this, we considered three main approaches: (i) using remotely sensing and reanalysis precipitation products; (ii) machine learning-based approaches; and (iii) a gap-filling software explicitly developed for filling the gaps of daily precipitation records. This study evaluated all approaches over a sparsely gauged basin in East Africa. Among the examined precipitation products, PERSIANN-CDR outperformed other satellite products in terms of root mean squared error (7.3 mm), and correlation coefficient (0.46) while having a large bias (50%) compared to the available in situ precipitation records. PERSIANN-CDR also demonstrates the highest skill in distinguishing rainy and non-rainy days. On the other hand, Random Forest outperformed all other approaches (including PERSIANN-CDR) with the least relative bias (-2%), root mean squared error (6.9 mm), and highest correlation coefficient (0.53)

Stratocumulus cloud clearings: statistics from satellites, reanalysis models, and airborne measurements

This study provides a detailed characterization of stratocumulus clearings off the US West Coast using remote sensing, reanalysis, and airborne in situ data. Ten years (2009-2018) of Geostationary Operational Environmental Satellite (GOES) imagery data are used to quantify the monthly frequency, growth rate of total area (GR(Area)), and dimensional characteristics of 306 total clearings. While there is interannual variability, the summer (winter) months experienced the most (least) clearing events, with the lowest cloud fractions being in close proximity to coastal topographical features along the central to northern coast of California, including especially just south of Cape Mendocino and Cape Blanco. From 09:00 to 18:00 (PST), the median length, width, and area of clearings increased from 680 to 1231, 193 to 443, and similar to 67 000 to similar to 250000 km(2), respectively. Machine learning was applied to identify the most influential factors governing the GR(Area) of clearings between 09:00 and 12:00 PST, which is the time frame of most rapid clearing expansion. The results from gradient-boosted regression tree (GBRT) modeling revealed that air temperature at 850 hPa (T-850), specific humidity at 950 hPa (q(950)), sea surface temperature (SST), and anomaly in mean sea level pressure (MSLPanom) were probably most impactful in enhancing GR(Area) using two scoring schemes. Clearings have distinguishing features such as an enhanced Pacific high shifted more towards northern California, offshore air that is warm and dry, stronger coastal surface winds, enhanced lower-tropospheric static stability, and increased subsidence. Although clearings are associated obviously with reduced cloud fraction where they reside, the domain-averaged cloud albedo was actually slightly higher on clearing days as compared to non-clearing days. To validate speculated processes linking environmental parameters to clearing growth rates based on satellite and reanalysis data, airborne data from three case flights were examined. Measurements were compared on both sides of the clear-cloudy border of clearings at multiple altitudes in the boundary layer and free troposphere, with results helping to support links suggested by this study's model simulations. More specifically, airborne data revealed the influence of the coastal low-level jet and extensive horizontal shear at cloud-relevant altitudes that promoted mixing between clear and cloudy air. Vertical profile data provide support for warm and dry air in the free troposphere, additionally promoting expansion of clearings. Airborne data revealed greater evidence of sea salt in clouds on clearing days, pointing to a possible role for, or simply the presence of, this aerosol type in clearing areas coincident with stronger coastal winds.Office of Naval ResearchOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Evaluating the evolution of ECMWF precipitation products using observational data for Iran:from ERA40 to ERA5

Abstract European Center for Medium-Range Weather Forecasts Reanalysis (ERA), one of the most widely used precipitation products, has evolved from ERA-40 to ERA-20CM, ERA-20C, ERA-Interim, and ERA5. Studies evaluating the performance of individual ERA products cannot adequately assess the evolution of the products. We compared the performance of all ERA precipitation products at daily, monthly, and annual data (1980–2018) using more than 2100 Iran precipitation gauges. Results indicated that ERA-40 performed worst, followed by ERA-20CM, which showed only minor improvements over ERA-40. ERA-20C considerably outperformed its predecessors, benefiting from the assimilation of observational data. Although several previous studies have reported full superiority of ERA5 over ERA-Interim, our results revealed several shortcomings in ERA5 compared with the ERA-Interim estimates. Both ERA-Interim and ERA5 performed best overall, with ERA-Interim showing better statistical and categorical skill scores, and ERA5 performing better in estimating extreme precipitations. These results suggest that the accuracy of ERA precipitation products has improved from ERA-40 to ERA-Interim, but not consistently from ERA-Interim to ERA5. This study employed a grid-grid comparison approach by first creating a gridded reference data set through the spatial aggregation of point source observations, however, the results from a point-grid approach showed no change in the overall ranking of products (despite the slight changes in the error index values). These findings are useful for model development at a global scale and for hydrological applications in Iran

Systematic review of single and combined treatments for different types of striae : A comparison of striae treatments

Acknowledgements The authors would like to express their gratitude to the staff of Rasool Akram Medical Complex Clinical Research Development Center (RCRDC) for their technical and editorial assistance.Peer reviewedPostprin