scholarly journals Precipitation and temperature ensemble forecasts from single-value forecasts

2007 ◽  
Vol 4 (2) ◽  
pp. 655-717 ◽  
Author(s):  
J. Schaake ◽  
J. Demargne ◽  
R. Hartman ◽  
M. Mullusky ◽  
E. Welles ◽  
...  
Keyword(s):  
Space Time ◽  
Streamflow Prediction ◽  
Time Step ◽  
Forecast Period ◽  
Forecast Uncertainty ◽  
Ensemble Forecasts ◽  
Space And Time ◽  
Temperature And Precipitation ◽  
Time Period ◽  
Precipitation And Temperature

Abstract. A 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).

Climate Index Weighting Schemes for NWS ESP-Based Seasonal Volume Forecasts

2004 ◽  
Vol 5 (6) ◽  
pp. 1076-1090 ◽  
Author(s):  
Kevin Werner ◽  
David Brandon ◽  
Martyn Clark ◽  
Subhrendu Gangopadhyay
Keyword(s):  
Nearest Neighbor ◽  
Southern Oscillation ◽  
Climate Index ◽  
Climate Information ◽  
Streamflow Prediction ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Weighting Schemes ◽  
Temperature And Precipitation ◽  
Spring Runoff

Abstract This study compares methods to incorporate climate information into the National Weather Service River Forecast System (NWSRFS). Three small-to-medium river subbasins following roughly along a longitude in the Colorado River basin with different El Niño–Southern Oscillation signals were chosen as test basins. Historical ensemble forecasts of the spring runoff for each basin were generated using modeled hydrologic states and historical precipitation and temperature observations using the Ensemble Streamflow Prediction (ESP) component of the NWSRFS. Two general methods for using a climate index (e.g., Niño-3.4) are presented. The first method, post-ESP, uses the climate index to weight ensemble members from ESP. Four different post-ESP weighting schemes are presented. The second method, preadjustment, uses the climate index to modify the temperature and precipitation ensembles used in ESP. Two preadjustment methods are presented. This study shows the distance-sensitive nearest-neighbor post-ESP to be superior to the other post-ESP weighting schemes. Further, for the basins studied, forecasts based on post-ESP techniques outperformed those based on preadjustment techniques.

Verification of post-processed seasonal predictions

2020 ◽  
Author(s):  
André Düsterhus
Keyword(s):  
North Atlantic ◽  
Statistical Prediction ◽  
Ensemble Prediction ◽  
Time Step ◽  
Ensemble Forecasts ◽  
Continuous Ranked Probability Score ◽  
Time Period ◽  
Model Predictions ◽  
To Come ◽  
Quadratic Distance

<p>Traditionally, verification of (ensemble) model predictions is done by comparing them to deterministic observations, e.g. with scores like the Continuous Ranked Probability Score (CRPS). While these approaches allow uncertain predictions basing on ensemble forecasts, it is open how to verify them against observations with non-parametric uncertainties.</p><p>This contribution focuses on statistically post-processed seasonal predictions of the Winter North Atlantic Oscillation (WNAO). The post-processing procedure creates in a first step for a dynamical ensemble prediction and for a statistical prediction basing on predictors two separate probability density functions (pdf). Afterwards these two distributions are combined to create a new statistical-dynamical prediction, which has been proven to be advantageous compared to the purely dynamical prediction. It will be demonstrated how this combination and with it the improvement of the prediction can be achieved before the focus will be set on the evaluation of those predictions at the hand of uncertain observations. Two new scores basing on the Earth Mover's Distance (EMD) and the Integrated Quadratic Distance (IQD) will be introduced and compared before it is shown how they can be used to effectively evaluate probabilistic predictions with uncertain observations. </p><p>Furthermore, a common approach (e.g. for correlation measures) is to compare predictions with observations over a longer time period. In this contribution a paradigm shift away from this approach towards comparing predictions for each single time step (like years) will be presented. This view give new insights into the performance of the predictions and allows to come to new understandings of the reasons for advantages or disadvantages of specific predictions. </p>

Espaço, Tempo e Modernidade

GEOgraphia ◽  
2009 ◽  
Vol 2 (3) ◽  
pp. 51
Author(s):  
Gilvan Luiz Hansen
Keyword(s):  
Modern Philosophy ◽  
Space Time ◽  
Space And Time ◽  
Time Concepts

Resumo Este artigo é uma discussão introdutória acerca da importância das concepções de espaço e tempo na modernidade. O objetivo deste texto é enfatizar os aspectos teóricos e práticos dos conceitos de espaço e tempo, mediante a apresentação de três perspectivas de interpretação desta questão na filosofia desenvolvida na modernidade. Palavras-chave: Modernidade, Espaço, Tempo, Filosofia Moderna, J. Habermas.Abstract This article is an introductory debate about the importance of space and time conceptions in modernity. The objective from this text is emphasize the theoretical and practical aspects of space and time concepts, by presentation of three interpretation perspectives of this question in the philosophy developed in modernity. Keywords: Modernity, Space, Time, Modern Philosophy, J. Habermas.

Samuel Beckett's Debt to Aristotle: Cosmology, Syllogism, Space, Time

2010 ◽  
Vol 22 (1) ◽  
pp. 181-195 ◽  
Author(s):  
Anthony Cordingley
Keyword(s):  
Trinity College ◽  
Space Time ◽  
Formal Logic ◽  
Trinity College Dublin ◽  
Space And Time ◽  
Creative Transformation ◽  
Cosmic Order

This essay argues for the presence of Aristotelian ideas of cosmic order, syllogism, space and time in Beckett's . It accounts for how such ideas impact upon the novel's 'I' as he attempts to offer a philosophical 'solution' to his predicament in an underworld divorced from the revolving heavens. Beckett's study of formal logic as a student at Trinity College, Dublin and his private study of philosophy in 1932 is examined in this light; particularly his “Philosophy Notes,” along with some possible further sources for his knowledge. The essay then reveals a creative transformation of Aristotelian ideas in which led to formal innovations, such as the continuous present of its narrative.

scholarly journals Spatial and space-time correlations in systems of subpopulations with genetic drift and migration.

Genetics ◽  
1993 ◽  
Vol 133 (3) ◽  
pp. 711-727
Author(s):  
B K Epperson
Keyword(s):  
Population Genetics ◽  
Time Correlation ◽  
Space Time ◽  
Spatial Correlations ◽  
Gene Frequencies ◽  
Migration Patterns ◽  
Migration Rates ◽  
Space And Time ◽  
Time Correlations ◽  
And Migration

Abstract The geographic distribution of genetic variation is an important theoretical and experimental component of population genetics. Previous characterizations of genetic structure of populations have used measures of spatial variance and spatial correlations. Yet a full understanding of the causes and consequences of spatial structure requires complete characterization of the underlying space-time system. This paper examines important interactions between processes and spatial structure in systems of subpopulations with migration and drift, by analyzing correlations of gene frequencies over space and time. We develop methods for studying important features of the complete set of space-time correlations of gene frequencies for the first time in population genetics. These methods also provide a new alternative for studying the purely spatial correlations and the variance, for models with general spatial dimensionalities and migration patterns. These results are obtained by employing theorems, previously unused in population genetics, for space-time autoregressive (STAR) stochastic spatial time series. We include results on systems with subpopulation interactions that have time delay lags (temporal orders) greater than one. We use the space-time correlation structure to develop novel estimators for migration rates that are based on space-time data (samples collected over space and time) rather than on purely spatial data, for real systems. We examine the space-time and spatial correlations for some specific stepping stone migration models. One focus is on the effects of anisotropic migration rates. Partial space-time correlation coefficients can be used for identifying migration patterns. Using STAR models, the spatial, space-time, and partial space-time correlations together provide a framework with an unprecedented level of detail for characterizing, predicting and contrasting space-time theoretical distributions of gene frequencies, and for identifying features such as the pattern of migration and estimating migration rates in experimental studies of genetic variation over space and time.

scholarly journals Climate Dynamics of the Spatiotemporal Changes of Vegetation NDVI in Northern China from 1982 to 2015

2021 ◽  
Vol 13 (2) ◽  
pp. 187
Author(s):  
Rui Sun ◽  
Shaohui Chen ◽  
Hongbo Su
Keyword(s):  
Solar Radiation ◽  
Vegetation Index ◽  
Global Climate ◽  
Regional Scale ◽  
Northern China ◽  
Climate Dynamics ◽  
High Coverage ◽  
Vegetation Growth ◽  
Temperature And Precipitation ◽  
Precipitation And Temperature

As an important part of a terrestrial ecosystem, vegetation plays an important role in the global carbon-water cycle and energy flow. Based on the Global Inventory Monitoring and Modeling System (GIMMS) third generation of Normalized Difference Vegetation Index (NDVI3g), meteorological station data, climate reanalysis data, and land cover data, this study analyzed the climate dynamics of the spatiotemporal variations of vegetation NDVI in northern China from 1982 to 2015. The results showed that growth season NDVI (NDVIgs) increased significantly at 0.006/10a (p < 0.01) in 1982–2015 on the regional scale. The period from 1982 to 2015 was divided into three periods: the NDVIgs increased by 0.026/10a (p < 0.01) in 1982–1990, decreased by −0.002/10a (p > 0.1) in 1990–2006, and then increased by 0.021/10a (p < 0.01) during 2006–2015. On the pixel scale, the increases in NDVIgs during 1982–2015, 1982–1990, 1990–2006, and 2006–2015 accounted for 74.64%, 85.34%, 48.14%, and 68.78% of the total area, respectively. In general, the dominant climate drivers of vegetation growth had gradually switched from solar radiation, temperature, and precipitation (1982–1990) to precipitation and temperature (1990–2015). For woodland, high coverage grassland, medium coverage grassland, low coverage grassland, the dominant climate drivers had changed from temperature and solar radiation, solar radiation and precipitation, precipitation and solar radiation, solar radiation to precipitation and solar radiation, precipitation, precipitation and temperature, temperature and precipitation. The areas controlled by precipitation increased significantly, mainly distributed in arid, sub-arid, and sub-humid areas. The dominant climate drivers for vegetation growth in the plateau climate zone or high-altitude area changed from solar radiation to temperature and precipitation, and then to temperature, while in cold temperate zone, changed from temperature to solar radiation. These results are helpful to understand the climate dynamics of vegetation growth, and have important guiding significance for vegetation protection and restoration in the context of global climate change.

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 17 (7) ◽  
pp. 2781-2796 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Data Set ◽  
Model Based ◽  
Snow Water

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

scholarly journals On the Space/Time of Information Literacy, Higher Education, and the Global Knowledge Economy

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Karen Nicholson
Keyword(s):  
Higher Education ◽  
Information Literacy ◽  
Knowledge Economy ◽  
Academic Library ◽  
Skills Training ◽  
Space Time ◽  
Global Knowledge ◽  
Spatial Turn ◽  
Space And Time ◽  
Information Work

Local sites and practices of information work become embroiled in the larger imperatives and logics of the global knowledge economy through social, technological, and spatial networks. Drawing on human geography’s central claim that space and time are dialectically produced through social practices, in this essay I use human/critical geography as a framework to situate the processes and practices—the space and time—of information literacy within the broader social, political, and economic environments of the global knowledge economy.  As skills training for the knowledge economy, information literacy lies at the intersection of the spatial and temporal spheres of higher education as the locus of human capital production. Information literacy emerges as a priority for academic librarians in the 1980s in the context of neoliberal reforms to higher education: a necessary skill in the burgeoning “information economy,” it legitimates the role of librarians as teachers. As a strategic priority, information literacy serves to demonstrate the library’s value within the university’s globalizing agenda. While there has been a renewed interest in space/time within the humanities and social sciences since the 1980s, LIS has not taken up this “spatial turn” with the same enthusiasm—or the same degree of criticality—as other social science disciplines. This article attempts to address that gap and offers new insights into the ways that the spatial and temporal registers of the global knowledge economy and the neoliberal university produce and regulate the practice of information literacy in the academic library. Pre-print first published online 12/09/2018

scholarly journals Spatio-temporal variations of land surface temperature and precipitation due to climate change in the Jhelum river basin, India

MAUSAM ◽  
2021 ◽  
Vol 71 (4) ◽  
pp. 661-674
Author(s):  
HIMAYOUN DAR ◽  
ROSHNI THENDIYATH ◽  
MOHSIN FAROOQ
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
21St Century ◽  
Temporal Variations ◽  
Average Increase ◽  
Temperature And Precipitation ◽  
Rcp 8.5 ◽  
The Mean ◽  
Spatio Temporal ◽  
Precipitation And Temperature

The present study investigated the spatio-temporal variations of precipitation and temperature for the projected period (2011-2100) in the Jhelum basin, India. The precipitation and temperature variables are projected under RCP 8.5 scenario using statistical down scaling techniques such as Artificial Neural Network (ANN) and Wavelet Artificial Neural Network (WANN) models. Firstly, the screened predictors were downscaled to predictand using ANN and WANN models for all the study stations. On the basis of the performance criteria, the WANN model is selected as an efficient model for downscaling of precipitation and temperature. The future screened predictor data pertaining to RCP 8.5 of CanESM2 model were downscaled to monthly temperature and precipitation for future periods (2011-2100) using WANN models. The investigation of the future projections revealed an average increase of 17-25% in the mean annual precipitation and 20-25% average increase in the monthly mean precipitation for all the selected stations towards the end of 21st century. The monthly mean temperature also showed an increase of 2-3 °C for all the study stations towards the end of 21st century. The mean seasonal temperature of the projected period is found to be increasing for all the four seasons in most parts of the basin.

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