169 research outputs found
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Mean radiant temperature from global-scale numerical weather prediction models
In human biometeorology, the estimation of mean radiant temperature (MRT) is generally considered challenging. This work presents a general framework to compute the MRT at the global scale for a human subject placed in an outdoor environment and irradiated by solar and thermal radiation both directly and diffusely. The proposed framework requires as input radiation fluxes computed by numerical weather prediction (NWP) models and generates as output gridded globe-wide maps of MRT. It also considers changes in the Sun’s position affecting radiation components when these are stored by NWP models as an accumulated-over-time quantity. The applicability of the framework was demonstrated using NWP reanalysis radiation data from the European Centre for Medium-Range Weather Forecasts. Mapped distributions of MRT were correspondingly computed at the global scale. Comparison against measurements from radiation monitoring stations showed a good agreement with NWP-based MRT (coefficient of determination greater than 0.88; average bias equal to 0.42 °C) suggesting its potential as a proxy for observations in application studies
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Evaluating uncertainty in estimates of soil moisture memory with a reverse ensemble approach
Soil moisture memory is a key component of seasonal predictability. However, uncertainty in current memory estimates is not clear and it is not obvious to what extent these are dependent on model uncertainties. To address this question, we perform a global sensitivity analysis of memory to key hydraulic parameters, using an uncoupled version of the H-TESSEL land surface model.
Results show significant dependency of estimates of memory and its uncertainty on these parameters, suggesting that operational seasonal forecasting models using deterministic hydraulic parameter values are likely to display a narrower range of memory than exists in reality. Explicitly incorporating hydraulic parameter uncertainty into models may then give improvements in forecast skill and reliability, as has been shown elsewhere in the literature. Our results also show significant differences with previous estimates of memory uncertainty, warning against placing too much confidence in a single quantification of uncertainty
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Cartograms for use in forecasting weather driven natural hazards
This study evaluates the potential of using cartograms to visualise, and aid interpretation of, forecasts of weather driven natural hazards in the context of global weather forecasting and early warning systems. The use of cartograms is intended to supplement traditional cartographic representations of the hazards in order to highlight the severity of an upcoming event. Cartogrammetric transformations are applied to forecasts of floods, heatwaves, windstorms and snowstorms taken from the European Centre for Medium-range Weather Forecasts (ECMWF) forecast archive. Key cartogram design principles of importance in standard weather forecast visualisation are tested in terms of the tasks needed to visualise and interpret the forecast maps. These design principles include the influence of spatial autocorrelation of the variable mapped, the minimum and maximum values of a variable, the value of the sea, the addition of geographic features and the geographic extent used. Results show that the utility of the cartograms is dependent on these design principles, but the optimal cartogram transformation is dependent on geographical features (such as coastlines) and forecast features (such as snowstorm intensity). The importance of forecaster familiarisation training is highlighted. It was found in particular that for highly spatially autocorrelated weather variables used in analysing several upcoming natural hazards such as 2m temperature anomaly, the visualisation of the distortion provides a promising addition to standard forecast visualisations for highlighting upcoming weather driven natural hazards
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Evaluation of the consistency of ECMWF ensemble forecasts
An expected benefit of ensemble forecasts is that a sequence of consecutive forecasts valid for the same time will be more consistent than an equivalent sequence of individual forecasts. Inconsistent (jumpy) forecasts can cause users to lose confidence in the forecasting system. We present a first systematic, objective evaluation of the consistency of the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble using a measure of forecast divergence that takes account of the full ensemble distribution. Focusing on forecasts of the North Atlantic Oscillation and European Blocking regimes up to two weeks ahead, we identify occasional large inconsistency between successive runs, with the largest jumps tending to occur at 7-9 days lead. However, care is needed in the interpretation of ensemble jumpiness. An apparent clear flip-flop in a single index may hide a more complex predictability issue which may be better understood by examining the ensemble evolution in phase space
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Precipitation and floodiness
There are a number of factors that lead to non-linearity between precipitation anomalies and flood hazard; this non-linearity is a pertinent issue for applications that use a precipitation forecast as a proxy for imminent flood hazard. We assessed the degree of this non-linearity for the first time using a recently developed global-scale hydrological model driven by the ERA-Interim Land precipitation reanalysis (1980–2010). We introduced new indices to assess large-scale flood hazard, or floodiness, and quantified the link between monthly precipitation, river discharge and floodiness anomalies at the global and regional scales. The results show that monthly floodiness is not well correlated with precipitation, therefore demonstrating the value of hydrometeorological systems for providing floodiness forecasts for decision-makers. A method is described for forecasting floodiness using the Global Flood Awareness System, building a climatology of regional floodiness from which to forecast floodiness anomalies out to two weeks
Statistical post-processing of heat index ensemble forecasts: is there a royal road?
We investigate the effect of statistical post-processing on the probabilistic
skill of discomfort index (DI) and indoor wet-bulb globe temperature (WBGTid)
ensemble forecasts, both calculated from the corresponding forecasts of
temperature and dew point temperature. Two different methodological approaches
to calibration are compared. In the first case, we start with joint
post-processing of the temperature and dew point forecasts and then create
calibrated samples of DI and WBGTid using samples from the obtained bivariate
predictive distributions. This approach is compared with direct post-processing
of the heat index ensemble forecasts. For this purpose, a novel ensemble model
output statistics model based on a generalized extreme value distribution is
proposed. The predictive performance of both methods is tested on the
operational temperature and dew point ensemble forecasts of the European Centre
for Medium-Range Weather Forecasts and the corresponding forecasts of DI and
WBGTid. For short lead times (up to day 6), both approaches significantly
improve the forecast skill. Among the competing post-processing methods, direct
calibration of heat indices exhibits the best predictive performance, very
closely followed by the more general approach based on joint calibration of
temperature and dew point temperature. Additionally, a machine learning
approach is tested and shows comparable performance for the case when one is
interested only in forecasting heat index warning level categories.Comment: 29 pages, 12 figure
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Imbalanced land-surface water budgets in a numerical weather prediction system
There has been a significant increase in the skill and resolution of numerical weather prediction models (NWPs) in recent decades, extending the time scales of useful weather predictions. The land-surface models (LSMs) of NWPs are often employed in hydrological applications, which raises the question of how hydrologically representative LSMs really are. In this paper, precipitation (P), evaporation (E) and runoff (R) from the European Centre for Medium-Range Weather Forecasts (ECMWF) global models were evaluated against observational products. The forecasts differ substantially from observed data for key hydrological variables. In addition, imbalanced surface water budgets, mostly caused by data assimilation, were found on both global (P-E) and basin scales (P-E-R), with the latter being more important. Modeled surface fluxes should be used with care in hydrological applications and further improvement in LSMs in terms of process descriptions, resolution and estimation of uncertainties is needed to accurately describe the land-surface water budgets
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