A statistical post-processor for accounting of hydrologic uncertainty in short-range ensemble streamflow prediction

International audienceIn addition to the uncertainty in future boundary conditions of precipitation and temperature (i.e. the meteorological uncertainty), parametric and structural uncertainties in the hydrologic models and uncertainty in the model initial conditions (i.e. the hydrologic uncertainties) constitute a major source of error in hydrologic prediction. As such, accurate accounting of both meteorological and hydrologic uncertainties is critical to producing reliable probabilistic hydrologic prediction. In this paper, we describe and evaluate a statistical procedure that accounts for hydrologic uncertainty in short-range (1 to 5 days ahead) ensemble streamflow prediction (ESP). Referred to as the ESP post-processor, the procedure operates on ensemble traces of model-predicted streamflow that reflect only the meteorological uncertainty and produces post-processed ensemble traces that reflect both the meteorological and hydrologic uncertainties. A combination of probability matching and regression, the procedure is simple, parsimonious and robust. For a critical evaluation of the procedure, independent validation is carried out for five basins of the Juniata River in Pennsylvania, USA, under a very stringent setting. The results indicate that the post-processor is fully capable of producing ensemble traces that are unbiased in the mean and in the probabilistic sense. Due primarily to the uncertainties in the cumulative probability distributions (CDF) of observed and simulated flows, however, the unbiasedness may be compromised to a varying degree in real world situations. It is also shown, however, that the uncertainties in the CDF's do not significantly diminish the value of post-processed ensemble traces for decision making, and that probabilistic prediction based on post-processed ensemble traces significantly improves the value of single-value prediction at all ranges of flow

On the 2d Zakharov system with L^2 Schr\"odinger data

We prove local in time well-posedness for the Zakharov system in two space dimensions with large initial data in L^2 x H^{-1/2} x H^{-3/2}. This is the space of optimal regularity in the sense that the data-to-solution map fails to be smooth at the origin for any rougher pair of spaces in the L^2-based Sobolev scale. Moreover, it is a natural space for the Cauchy problem in view of the subsonic limit equation, namely the focusing cubic nonlinear Schroedinger equation. The existence time we obtain depends only upon the corresponding norms of the initial data - a result which is false for the cubic nonlinear Schroedinger equation in dimension two - and it is optimal because Glangetas-Merle's solutions blow up at that time.Comment: 30 pages, 2 figures. Minor revision. Title has been change

A para-differential renormalization technique for nonlinear dispersive equations

For \alpha \in (1,2) we prove that the initial-value problem \partial_t u+D^\alpha\partial_x u+\partial_x(u^2/2)=0 on \mathbb{R}_x\times\mathbb{R}_t; u(0)=\phi, is globally well-posed in the space of real-valued L^2-functions. We use a frequency dependent renormalization method to control the strong low-high frequency interactions.Comment: 42 pages, no figure

Patterns and drivers of dimethylsulfide concentration in the northeast subarctic Pacific across multiple spatial and temporal scales.

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 License. The definitive version was published in Biogeosciences 16(8), (2019):1729-1754, doi:10.5194/bg-16-1729-2019.The northeast subarctic Pacific (NESAP) is a globally important source of the climate-active gas dimethylsulfide (DMS), yet the processes driving DMS variability across this region are poorly understood. Here we examine the spatial distribution of DMS at various spatial scales in contrasting oceanographic regimes of the NESAP. We present new high-spatial-resolution measurements of DMS across hydrographic frontal zones along the British Columbia continental shelf, together with key environmental variables and biological rate measurements. We combine these new data with existing observations to produce a revised summertime DMS climatology for the NESAP, yielding a broader context for our sub-mesoscale process studies. Our results demonstrate sharp DMS concentration gradients across hydrographic frontal zones and suggest the presence of two distinct DMS cycling regimes in the NESAP, corresponding to microphytoplankton-dominated waters along the continental shelf and nanoplankton-dominated waters in the cross-shelf transitional zone. DMS concentrations across the continental shelf transition (range < 1–10 nM, mean 3.9 nM) exhibited positive correlations to salinity (r=0.80), sea surface height anomaly (SSHA; r=0.51), and the relative abundance of prymnesiophyte and dinoflagellates (r=0.89). In contrast, DMS concentrations in nearshore coastal transects (range < 1–24 nM, mean 6.1 nM) showed a negative correlation with salinity (r=−0.69; r=−0.78) and SSHA (r=−0.81; r=−0.75) and a positive correlation to relative diatom abundance (r=0.88; r=0.86). These results highlight the importance of bloom-driven DMS production in continental shelf waters of this region and the role of prymnesiophytes and dinoflagellates in DMS cycling further offshore. In all areas, the rate of DMS consumption appeared to be an important control on observed concentration gradients, with higher DMS consumption rate constants associated with lower DMS concentrations. We compiled a data set of all available summertime DMS observations for the NESAP (including previously unpublished results) to examine the performance of several existing algorithms for predicting regional DMS concentrations. None of these existing algorithms was able to accurately reproduce observed DMS distributions across the NESAP, although performance was improved by the use of regionally tuned coefficients. Based on our compiled observations, we derived an average summertime distribution map for DMS concentrations and sea–air fluxes across the NESAP, estimating a mean regional flux of 0.30 Tg of DMS-derived sulfur to the atmosphere during the summer season.We dedicate this article to the memory of Ronald P. Kiene, a wonderful scientist, mentor and friend. His contributions to DMS and DMSP research have shaped our field over the past 3 decades, and he will be missed by many around the world. We also wish to thank many individuals involved in data collection and logistical aspects of the cruises presented here, including scientists from the Institute of Ocean Sciences, the captain and crew of the R/V Oceanus and the CCGS John P. Tully, and members of the Tortell, Kiene, Levine and Hatton laboratory groups. We also thank Theodore Ahlvin for GIS support and both reviewers for their insightful comments. Support for this work was provided from the US National Science Foundation (grant no. 1436344) and from the Natural Sciences and Engineering Research Council of Canada

Precipitation and temperature ensemble forecasts from single-value forecasts

International audienceA procedure is presented to construct ensemble forecasts from single-value forecasts of precipitation and temperature. This involves dividing the spatial forecast domain and total forecast period into a number of parts that are treated as separate forecast events. The spatial domain is divided into hydrologic sub-basins. The total forecast period is divided into time periods, one for each model time step. For each event archived values of forecasts and corresponding observations are used to model the joint distribution of forecasts and observations. The conditional distribution of observations for a given single-value forecast is used to represent the corresponding probability distribution of events that may occur for that forecast. This conditional forecast distribution subsequently is used to create ensemble members that vary in space and time using the "Schaake Shuffle" (Clark et al, 2004). The resulting ensemble members have the same space-time patterns as historical observations so that space-time joint relationships between events that have a significant effect on hydrological response tend to be preserved. Forecast uncertainty is space and time-scale dependent. For a given lead time to the beginning of the valid period of an event, forecast uncertainty depends on the length of the forecast valid time period and the spatial area to which the forecast applies. Although the "Schaake Shuffle" procedure, when applied to construct ensemble members from a time-series of single value forecasts, may preserve some of this scale dependency, it may not be sufficient without additional constraint. To account more fully for the time-dependent structure of forecast uncertainty, events for additional "aggregate" forecast periods are defined as accumulations of different "base" forecast periods. The generated ensemble members can be ingested by an Ensemble Streamflow Prediction system to produce ensemble forecasts of streamflow and other hydrological variables that reflect the meteorological uncertainty. The methodology is illustrated by an application to generate temperature and precipitation ensemble forecasts for the American River in California. Parameter estimation and dependent validation results are presented based on operational single-value forecasts archives of short-range River Forecast Center (RFC) forecasts and medium-range ensemble mean forecasts from the National Weather Service (NWS) Global Forecast System (GFS)

Air Pollution and Lymphocyte Phenotype Proportions in Cord Blood

Effects of air pollution on morbidity and mortality may be mediated by alterations in immune competence. In this study we examined short-term associations of air pollution exposures with lymphocyte immunophenotypes in cord blood among 1,397 deliveries in two districts of the Czech Republic. We measured fine particulate matter < 2.5 μm in diameter (PM(2.5)) and 12 polycyclic aromatic hydrocarbons (PAHs) in 24-hr samples collected by versatile air pollution samplers. Cord blood samples were analyzed using a FACSort flow cytometer to determine phenotypes of CD3(+) T-lymphocytes and their subsets CD4(+) and CD8(+), CD19(+) B-lymphocytes, and natural killer cells. The mothers were interviewed regarding sociodemographic and lifestyle factors, and medical records were abstracted for obstetric, labor and delivery characteristics. During the period 1994 to 1998, the mean daily ambient concentration of PM(2.5) was 24.8 μg/m(3) and that of PAHs was 63.5 ng/m(3). In multiple linear regression models adjusted for temperature, season, and other covariates, average PAH or PM(2.5) levels during the 14 days before birth were associated with decreases in T-lymphocyte phenotype fractions (i.e., CD3(+) CD4(+), and CD8(+)), and a clear increase in the B-lymphocyte (CD19(+)) fraction. For a 100-ng/m(3) increase in PAHs, which represented approximately two standard deviations, the percentage decrease was −3.3% [95% confidence interval (CI), −5.6 to −1.0%] for CD3(+), −3.1% (95% CI, −4.9 to −1.3%) for CD4(+), and −1.0% (95% CI, −1.8 to −0.2%) for CD8(+) cells. The corresponding increase in the CD19(+) cell proportion was 1.7% (95% CI, 0.4 to 3.0%). Associations were similar but slightly weaker for PM(2.5). Ambient air pollution may influence the relative distribution of lymphocyte immunophenotypes of the fetus