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

Bright source of spectrally uncorrelated polarization-entangled photons with nearly single-mode emission

We present results of a bright polarization-entangled photon source operating at 1552 nm via type-II collinear degenerate spontaneous parametric down-conversion in a periodically poled potassium titanyl phosphate crystal. We report a conservative inferred pair generation rate of 123,000 pairs/s/mW into collection modes. Minimization of spectral and spatial entanglement was achieved by group velocity matching the pump, signal and idler modes and through properly focusing the pump beam. By utilizing a pair of calcite beam displacers, we are able to overlap photons from adjacent down-conversion processes to obtain polarization-entanglement visibility of 94.7 +/- 1.1% with accidentals subtracted.Comment: 4 pages, 7 color figures. Revised manuscript includes the following changes: corrected pair generation rate from 44,000/s/mW pump to 123,000/s/mW pump; replaced Fig. 1b to enhance clarity; minor alterations to the title, abstract and introduction; grammatical correction

The Hydrologic Ensemble Prediction EXperiment (HEPEX)

International audienceUsers of hydrologic predictions need reliable, quantitative forecast information, including estimates of uncertainty, for lead times ranging from less than an hour during flash flooding events to more than a year for long-term water management. To meet this need, operational agencies are developing hydrological ensemble forecast techniques to account for sources of uncertainty such as future precipitation, initial hydrological conditions, and hydrological model limitations including uncertain model parameters. Research advances in areas such as hydrologic modeling, data assimilation, ensemble prediction, and forecast verification need to be incorporated into operational forecasting systems to assure that the state-of-the-art products are reaching the forecast user community. The Hydrologic Ensemble Prediction EXperiment (HEPEX) has been formed to develop and demonstrate new hydrologic forecasting technologies, and to facilitate the implementation of beneficial technologies into the operational environment

U.S. MOPEX DATA SET

A key step in applying land surface parameterization schemes is to estimate model parameters that vary spatially and are unique to each computational element. Improved methods for parameter estimation (especially for parameters important to runoff response) are needed and require data from a wide range of climate regimes throughout the world. Accordingly, the GEWEX Hydrometeorology Panel (GHP) endorsed the concept of an international Model Parameter Estimation Project (MOPEX) at its Toronto meeting, August 1996. Phase I of MOPEX was funded by NOAA in FY 1997, Phase II in FY 2000 and Phase III in FY 2003. MOPEX was adopted as projects of the IAHS/WMO Committee on GEWEX and of the WMO Commission on Hydrology (CHy) and now is a contributor to the Combine Enhanced Observing Period (CEOP) of the World Climate Research Program (WCRP). In 2004 MOPEX became a Working Group of the IAHS Prediction for Ungaged Basins (PUB) Initiative. MOPEX also is expected to contribute to the work of the Hydrologic Ensemble Prediction Experiment (HEPEX) (Franz et al, 2005). The primary goal of MOPEX is to develop techniques for the a priori estimation of the parameters used in land surface parameterization schemes of atmospheric models and in hydrologic models. A major early effort of MOPEX has been to assemble a large number of high quality historical hydrometeorological and river basin characteristics data sets for a wide range of river basins (500-10,000 km{sup 2}) throughout the world. MOPEX data sets are available via the Internet (ftp://hydrology.nws.noaa.gov). This paper documents the development of data sets for U.S. river basins. Several highly successful parameter estimation workshops have been organized by MOPEX. The first was held as part of the IAHS meeting in Birmingham, England in July, 1999. The second workshop was hosted April, 2002 in Tucson, AZ by SAHRA/University of Arizona. The third MOPEX workshop was held as part of the IAHS meeting in Sapporo, July, 2003. The fourth workshop, Paris, July,2005 was organized by the Cemagref in collaboration with the ENGREF, Meteo France, National Weather Service and the SAHRA/University of Arizona. The fifth workshop was held as part of the IAHS meeting, February, 2005, Foz do Iguacu, Brazil. The purpose of the future phases of the project is to: (1) continue collect additional international data sets; update data from the U.S. by adding recent years, including data for elevation zones in mountainous areas and refining energy forcing; (2) continue to conduct international MOPEX workshops; (3) provide leadership to develop a better scientific understanding of how to improve procedures for a priori parameter estimation, (4) make a significant hydrological contribution to CEOP and PUBS, and (5) demonstrate transferability of MOPEX results. The basic data collection strategy being used in MOPEX is to seek most readily available and highest quality data first. During the next 3 years analyses of the available MOPEX data sets by the international scientific community will be emphasized

Optimal sensor placement for measuring physical activity with a 3D accelerometer

Accelerometer-based activity monitors are popular for monitoring physical activity. In this study, we investigated optimal sensor placement for increasing the quality of studies that utilize accelerometer data to assess physical activity. We performed a two-staged study, focused on sensor location and type of mounting. Ten subjects walked at various walking speeds on a treadmill, performed a deskwork protocol, and walked on level ground, while simultaneously wearing five ProMove2 sensors with a snug fit on an elastic waist belt. We found that sensor location, type of activity, and their interaction-effect affected sensor output. The most lateral positions on the waist belt were the least sensitive for interference. The effect of mounting was explored, by making two subjects repeat the experimental protocol with sensors more loosely fitted to the elastic belt. The loose fit resulted in lower sensor output, except for the deskwork protocol, where output was higher. In order to increase the reliability and to reduce the variability of sensor output, researchers should place activity sensors on the most lateral position of a participant's waist belt. If the sensor hampers free movement, it may be positioned slightly more forward on the belt. Finally, sensors should be fitted tightly to the body

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)