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)

An efficient approach for estimating streamflow forecast skill elasticity

Seasonal streamflow prediction skill can derive from catchment initial hydrological conditions (IHCs) and from the future seasonal climate forecasts (SCFs) used to produce the hydrological forecasts. Although much effort has gone into producing state-of-the-art seasonal streamflow forecasts from improving IHCs and SCFs, these developments are expensive and time consuming and the forecasting skill is still limited in most parts of the world. Hence, sensitivity analyses are crucial to funnel the resources into useful modelling and forecasting developments. It is in this context that a sensitivity analysis technique, the variational ensemble streamflow prediction assessment (VESPA) approach, was recently introduced. VESPA can be used to quantify the expected improvements in seasonal streamflow forecast skill as a result of realistic improvements in its predictability sources (i.e., the IHCs and the SCFs) - termed ‘skill elasticity’ - and to indicate where efforts should be targeted. The VESPA approach is however computationally expensive, relying on multiple hindcasts having varying levels of skill in IHCs and SCFs. This paper presents two approximations of the approach that are computationally inexpensive alternatives. These new methods were tested against the original VESPA results using 30 years of ensemble hindcasts for 18 catchments of the contiguous United States. The results suggest that one of the methods, End Point Blending, is an effective alternative for estimating the forecast skill elasticities yielded by the VESPA approach. The results also highlight the importance of the choice of verification score for a goal-oriented sensitivity analysis

Challenges of operational river forecasting

Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human–natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1) making the most of available data, 2) making accurate predictions using models, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using human-generated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors

Spatially distributed calibration of a hydrological model with variational optimization constrained by physiographic maps for flash flood forecasting in France

This contribution presents a regionalization approach to estimate spatially distributed hydrologic parameters based on: (i) the SMASH (Spatially distributed Modelling and ASsimilation for Hydrology) hydrological modeling and assimilation platform (Jay-Allemand, 2020; Jay-Allemand et al., 2020) underlying the French national flash flood forecasting system Vigicrues Flash (Javelle et al., 2019); (ii) the variational assimilation algorithm from (Jay-Allemand et al., 2020), adapted to high dimensional inverse problems; (iii) spatial constraints added to the optimization problem, based on masks derived from physiographic maps (e.g., land cover, terrain slope); (iv) multi-site global optimization, which targets multiple independent watersheds. This method gives a regional estimation of the spatially distributed parameters over the whole modeled area. This study uses a distributed rainfall-runoff model with 4 parameters to calibrate, with a spatial resolution of 1×1 km2 and a 15 min time step. Performances of the calibrated hydrological model and the parameters robustness are evaluated on two French study areas with 20 catchments in each, in spatio-temporal extrapolation based on cross-validation experiments over a 12-year period. Several spatial regularization strategies are tested to better constrain the high dimensional optimization problem. The model parameters are calibrated based on the Nash-Sutcliffe Efficiency (NSE) computed for multiple calibration basins in the study area. Results are discussed based on the Nash-Sutcliffe Efficiency and the Kling-Gupta Efficiency criteria obtained on calibration and validation catchments for two subperiods of 6 years. Further work aims to improve the global search of prior parameter sets and to better balance the adjoint sensitivity with respect to the spatial constraints resolution and catchment characteristics. This will ensure a better consistency of simulated fluxes variabilities and enhance the applicability of the regionalization method at higher spatial scales and over larger domains.</p

Credit Where Credit is Due: Authorship of Open Ocean Data Workshop Report

Schmidt Ocean Institute, in partnership with The Ditchley Foundation, hosted Credit where credit is due: Authorship of open ocean data at Ditchley Park, in Chipping Norton, UK, on October 6-7, 2022, to identify actionable and implementable solutions to recognize and reward the dissemination of acquired data and knowledge. This report outlines the findings from that workshop

Fr. Matz. — Forschungen auf Kreta 1942.

Demargne Pierre. Fr. Matz. — Forschungen auf Kreta 1942.. In: Syria. Tome 29 fascicule 3-4, 1952. pp. 359-361

Notice sur la vie et les travaux de M. Jean Charbonneaux, membre de l'Académie

Demargne Pierre. Notice sur la vie et les travaux de M. Jean Charbonneaux, membre de l'Académie. In: Comptes rendus des séances de l'Académie des Inscriptions et Belles-Lettres, 114ᵉ année, N. 1, 1970. pp. 116-126

La couverture du monument des Néréides

Demargne Pierre. La couverture du monument des Néréides. In: Bulletin de la Société Nationale des Antiquaires de France, 1968, 1970. pp. 54-56

Discours du Président, séance publique annuelle

Demargne Pierre. Discours du Président, séance publique annuelle. In: Comptes rendus des séances de l'Académie des Inscriptions et Belles-Lettres, 124ᵉ année, N. 4, 1980. pp. 672-681