scholarly journals Initial state perturbations in ensemble forecasting

2008 ◽  
Vol 15 (5) ◽  
pp. 751-759 ◽  
Author(s):  
L. Magnusson ◽  
E. Källén ◽  
J. Nycander
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Empirical Orthogonal Functions ◽  
Initial State ◽  
Orthogonal Functions ◽  
Ensemble Forecasts ◽  
Long Time ◽  
Chaotic Nature ◽  
Prediction Systems ◽  
Open Question

Abstract. Due to the chaotic nature of atmospheric dynamics, numerical weather prediction systems are sensitive to errors in the initial conditions. To estimate the forecast uncertainty, forecast centres produce ensemble forecasts based on perturbed initial conditions. How to optimally perturb the initial conditions remains an open question and different methods are in use. One is the singular vector (SV) method, adapted by ECMWF, and another is the breeding vector (BV) method (previously used by NCEP). In this study we compare the two methods with a modified version of breeding vectors in a low-order dynamical system (Lorenz-63). We calculate the Empirical Orthogonal Functions (EOF) of the subspace spanned by the breeding vectors to obtain an orthogonal set of initial perturbations for the model. We will also use Normal Mode perturbations. Evaluating the results, we focus on the fastest growth of a perturbation. The results show a large improvement for the BV-EOF perturbations compared to the non-orthogonalised BV. The BV-EOF technique also shows a larger perturbation growth than the SVs of this system, except for short time-scales. The highest growth rate is found for the second BV-EOF for the long-time scale. The differences between orthogonal and non-orthogonal breeding vectors are also investigated using the ECMWF IFS-model. These results confirm the results from the Loernz-63 model regarding the dependency on orthogonalisation.

scholarly journals A Similarity-Based Implementation of the Schaake Shuffle

2016 ◽  
Vol 144 (5) ◽  
pp. 1909-1921 ◽  
Author(s):  
Roman Schefzik
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Similarity Criterion ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Prediction Horizon ◽  
Prediction Systems ◽  
Specific Implementation

Contemporary weather forecasts are typically based on ensemble prediction systems, which consist of multiple runs of numerical weather prediction models that vary with respect to the initial conditions and/or the parameterization of the atmosphere. Ensemble forecasts are frequently biased and show dispersion errors and thus need to be statistically postprocessed. However, current postprocessing approaches are often univariate and apply to a single weather quantity at a single location and for a single prediction horizon only, thereby failing to account for potentially crucial dependence structures. Nonparametric multivariate postprocessing methods based on empirical copulas, such as ensemble copula coupling or the Schaake shuffle, can address this shortcoming. A specific implementation of the Schaake shuffle, called the SimSchaake approach, is introduced. The SimSchaake method aggregates univariately postprocessed ensemble forecasts using dependence patterns from past observations. Specifically, the observations are taken from historical dates at which the ensemble forecasts resembled the current ensemble prediction with respect to a specific similarity criterion. The SimSchaake ensemble outperforms all reference ensembles in an application to ensemble forecasts for 2-m temperature from the European Centre for Medium-Range Weather Forecasts.

An Analog Method for Real-Time Forecasting of Summer Monsoon Subseasonal Variability

2007 ◽  
Vol 135 (12) ◽  
pp. 4149-4160 ◽  
Author(s):  
Prince K. Xavier ◽  
B. N. Goswami
Keyword(s):  
Real Time ◽  
Summer Monsoon ◽  
Large Scale ◽  
Initial Conditions ◽  
Empirical Orthogonal Functions ◽  
Initial Condition ◽  
Analog Method ◽  
Initial State ◽  
Orthogonal Functions ◽  
Modeling Data

Abstract A physically based empirical real-time forecasting strategy to predict the subseasonal variations of the Indian summer monsoon up to four–five pentads (20–25 days) in advance has been developed. The method is based on the event-to-event similarity in the properties of monsoon intraseasonal oscillations (ISOs). This two-tier analog method is applied to NOAA outgoing longwave radiation (OLR) pentad averaged data that have sufficiently long records of observation and are available in nearly real time. High-frequency modes in the data are eliminated by reconstructing the data using the first 10 empirical orthogonal functions (EOFs), which together explain about 75% of the total variance. In the first level of the method, the spatial analogs of initial condition pattern are identified from the modeling data. The principal components (PCs) of these spatial analogs, whose evolution history of the latest five pentads matches that of the initial condition pattern, are considered the temporal PC analogs. Predictions are generated for each PC as the average evolution of PC analogs for the given lead time. Predicted OLR values are constructed using the EOFs and predicted PCs. OLR data for 1979–99 are used as the modeling data and independent hindcasts are generated for the period 2000–05. The skill of anomaly predictions is rather high over the central and northern Indian region for lead times of four–five pentads. The phases and amplitude of intraseasonal convective spells are predicted well, especially the long midseason break of 2002 that resulted in large-scale drought conditions. Skillful predictions can be made up to five pentads when started from an active initial state, whereas the limit of useful predictions is about two–three pentads when started from break initial conditions. An important feature of this method is that unlike some other empirical methods to forecast monsoon ISOs, it uses minimal time filtering to avoid any possible endpoint effects and hence may be readily used for real-time applications. Moreover, as the modeling data grow with time as a result of the increased number of observations, the number of analogs would also increase and eventually the quality of forecasts would improve.

scholarly journals The Role of Vortex and Environment Errors in Genesis Forecasts of Hurricanes Danielle and Karl (2010)

2013 ◽  
Vol 141 (1) ◽  
pp. 232-251 ◽  
Author(s):  
Ryan D. Torn ◽  
David Cook
Keyword(s):  
Water Vapor ◽  
Vortex Structure ◽  
Wind Shear ◽  
Initial Conditions ◽  
Vertical Wind Shear ◽  
Environmental Parameters ◽  
Empirical Orthogonal Functions ◽  
Vertical Wind ◽  
Orthogonal Functions ◽  
Water Vapor Mixing Ratio

Abstract An ensemble of Weather Research and Forecasting Model (WRF) forecasts initialized from a cycling ensemble Kalman filter (EnKF) system is used to evaluate the sensitivity of Hurricanes Danielle and Karl’s (2010) genesis forecasts to vortex and environmental initial conditions via ensemble sensitivity analysis. Both the Danielle and Karl forecasts are sensitive to the 0-h circulation associated with the pregenesis system over a deep layer and to the temperature and water vapor mixing ratio within the vortex over a comparatively shallow layer. Empirical orthogonal functions (EOFs) of the 0-h ensemble kinematic and thermodynamic fields within the vortex indicate that the 0-h circulation and moisture fields covary with one another, such that a stronger vortex is associated with higher moisture through the column. Forecasts of the pregenesis system intensity are only sensitive to the leading mode of variability in the vortex fields, suggesting that only specific initial condition perturbations associated with the vortex will amplify with time. Multivariate regressions of the vortex EOFs and environmental parameters believed to impact genesis suggest that the Karl forecast is most sensitive to the vortex structure, with smaller sensitivity to the upwind integrated water vapor and 200–850-hPa vertical wind shear magnitude. By contrast, the Danielle forecast is most sensitive to the vortex structure during the first 24 h, but is more sensitive to the 200-hPa divergence and vertical wind shear magnitude at longer forecast hours.

scholarly journals Test of a Poor Man’s Ensemble Prediction System for Short-Range Probability Forecasting

2005 ◽  
Vol 133 (7) ◽  
pp. 1825-1839 ◽  
Author(s):  
A. Arribas ◽  
K. B. Robertson ◽  
K. R. Mylne
Keyword(s):  
Short Range ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Small Subset ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Sea Level Pressure ◽  
Ensemble Prediction System ◽  
To Come ◽  
Prediction Systems

Abstract Current operational ensemble prediction systems (EPSs) are designed specifically for medium-range forecasting, but there is also considerable interest in predictability in the short range, particularly for potential severe-weather developments. A possible option is to use a poor man’s ensemble prediction system (PEPS) comprising output from different numerical weather prediction (NWP) centers. By making use of a range of different models and independent analyses, a PEPS provides essentially a random sampling of both the initial condition and model evolution errors. In this paper the authors investigate the ability of a PEPS using up to 14 models from nine operational NWP centers. The ensemble forecasts are verified for a 101-day period and five variables: mean sea level pressure, 500-hPa geopotential height, temperature at 850 hPa, 2-m temperature, and 10-m wind speed. Results are compared with the operational ECMWF EPS, using the ECMWF analysis as the verifying “truth.” It is shown that, despite its smaller size, PEPS is an efficient way of producing ensemble forecasts and can provide competitive performance in the short range. The best relative performance is found to come from hybrid configurations combining output from a small subset of the ECMWF EPS with other different NWP models.

Regime-dependent statistical post-processing of ensemble forecasts

2020 ◽  
Author(s):  
Sam Allen ◽  
Christopher Ferro ◽  
Frank Kwasniok
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Systematic Errors ◽  
Ensemble Forecast ◽  
State Variables ◽  
Post Processing ◽  
Ensemble Forecasts ◽  
Nwp Model ◽  
Processing Techniques ◽  
Geographical Locations

<p>A number of realizations of one or more numerical weather prediction (NWP) models, initialised at a variety of initial conditions, compose an ensemble forecast. These forecasts exhibit systematic errors and biases that can be corrected by statistical post-processing. Post-processing yields calibrated forecasts by analysing the statistical relationship between historical forecasts and their corresponding observations. This article aims to extend post processing methodology to incorporate atmospheric circulation. The circulation, or flow, is largely responsible for the weather that we experience and it is hypothesized here that relationships between the NWP model and the atmosphere depend upon the prevailing flow. Numerous studies have focussed on the tendency of this flow to reduce to a set of recognisable arrangements, known as regimes, which recur and persist at fixed geographical locations. This dynamical phenomenon allows the circulation to be categorized into a small number of regime states. In a highly idealized model of the atmosphere, the Lorenz ‘96 system, ensemble forecasts are subjected to well-known post-processing techniques conditional on the system's underlying regime. Two different variables, one of the state variables and one related to the energy of the system, are forecasted and considerable improvements in forecast skill upon standard post-processing are seen when the distribution of the predictand varies depending on the regime. Advantages of this approach and its inherent challenges are discussed, along with potential extensions for operational forecasters.</p>

scholarly journals The potential of radar-based ensemble forecasts for flash-flood early warning in the southern Swiss Alps

2013 ◽  
Vol 17 (10) ◽  
pp. 3853-3869 ◽  
Author(s):  
K. Liechti ◽  
L. Panziera ◽  
U. Germann ◽  
M. Zappa
Keyword(s):  
Early Warning ◽  
Initial Conditions ◽  
Flash Flood ◽  
Weather Prediction ◽  
Radar Data ◽  
Ensemble Forecasting ◽  
Swiss Alps ◽  
Ensemble Forecasts ◽  
Orographic Rainfall ◽  
Forecasting System

Abstract. This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel radar-based ensemble forecasting chains for flash-flood early warning are investigated in three catchments in the southern Swiss Alps and set in relation to deterministic discharge forecasts for the same catchments. The first radar-based ensemble forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second ensemble forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialised with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 h between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing. A clear preference was found for the ensemble approach. Discharge forecasts perform better when forced by NORA and REAL-C2 rather then by deterministic weather radar data. Moreover, it was observed that using an ensemble of initial conditions at the forecast initialisation, as in REAL-C2, significantly improved the forecast skill. These forecasts also perform better then forecasts forced by ensemble rainfall forecasts (NORA) initialised form a single initial condition of the hydrological model. Thus the best results were obtained with the REAL-C2 forecasting chain. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic precipitation.

Predictability Mysteries in Cloud-Resolving Models

2006 ◽  
Vol 134 (8) ◽  
pp. 2095-2107 ◽  
Author(s):  
Cathy Hohenegger ◽  
Daniel Lüthi ◽  
Christoph Schär
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Significant Loss ◽  
Dynamical Instability ◽  
Limited Area ◽  
Error Growth ◽  
Mean Flow ◽  
Chaotic Nature ◽  
Rapid Amplification ◽  
Diabatic Forcing

Abstract The rapid amplification of small-amplitude perturbations by the chaotic nature of the atmospheric dynamics intrinsically limits the skill of deterministic weather forecasts. In this study, limited-area cloud-resolving numerical weather prediction (NWP) experiments are conducted to investigate the role of mesoscale processes in determining predictability. The focus is set on domain-internal error growth by integrating an ensemble of simulations using slightly modified initial conditions but identical lateral boundary conditions. It is found that the predictability of the three investigated cases taken from the Mesoscale Alpine Programme (MAP) differs tremendously. In terms of normalized precipitation spread, values between 0.05 (highly predictable) and 1 (virtually unpredictable) are obtained. Analysis of the derived ensemble spread demonstrates that the diabatic forcing associated with moist dynamics is the prime source of rapid error growth. However, in agreement with an earlier study it is found that the differentiation between convective and stratiform rain is unable to account for the distinctive precipitation spreads of the three cases. In particular, instability indices are demonstrated to be poor predictors of the predictability level. An alternate hypothesis is proposed and tested. It is inspired by the dynamical instability theory and states that significant loss of predictability only occurs over moist convectively unstable regions that are able to sustain propagation of energy against the mean flow. Using a linear analysis of gravity wave propagation, this hypothesis is shown to provide successful estimates of the predictability level for the three cases under consideration.

scholarly journals Hurricane Intensity Predictability

2016 ◽  
Vol 97 (10) ◽  
pp. 1847-1857 ◽  
Author(s):  
Chanh Q. Kieu ◽  
Zachary Moon
Keyword(s):  
Large Scale ◽  
Initial Conditions ◽  
Numerical Models ◽  
Absolute Intensity ◽  
Limited Range ◽  
Mature Stage ◽  
Lead Times ◽  
Initial State ◽  
Hurricane Intensity ◽  
Chaotic Nature

Abstract Weather has long been projected to possess limited predictability due to the inherent chaotic nature of the atmosphere; small changes in initial conditions could lead to an entirely different state of the atmosphere after some period of time. Given such a limited range of predictability of atmospheric flows, a natural question is, how far in advance can we predict a hurricane’s intensity? In this study, it is shown first that the predictability of a hurricane’s intensity at the 4–5-day lead times is generally determined more by the large-scale environment than by a hurricane’s initial conditions. This result suggests that future improvement in hurricane longer-range intensity forecasts by numerical models will be most realized as a result of improvement in the large-scale environment rather than in the storm’s initial state. At the mature stage of a hurricane, direct estimation of the leading Lyapunov exponent using an axisymmetric model reveals, nevertheless, the existence of a chaotic attractor in the phase space of the hurricane scales. This finding of a chaotic maximum potential intensity (MPI) attractor provides direct information about the saturation of a hurricane’s intensity errors around 8 m s−1, which prevents the absolute intensity errors at the mature stage from being reduced below this threshold. The implication of such intensity error saturation to the limited range of hurricane intensity forecasts will be also discussed.

scholarly journals Ensemble prediction using a new dataset of ECMWF initial states – OpenEnsemble 1.0

2020 ◽  
Author(s):  
Pirkka Ollinaho ◽  
Glenn D. Carver ◽  
Simon T. K. Lang ◽  
Lauri Tuppi ◽  
Madeleine Ekblom ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Complex Data ◽  
Ensemble Forecasts ◽  
Creative Commons ◽  
Initial States ◽  
Indispensable Tool ◽  
One Year

Abstract. Ensemble prediction is an indispensable tool of modern numerical weather prediction (NWP). Due to its complex data flow, global medium-range ensemble prediction has so far remained exclusively as a duty of operational weather agencies. It has been very hard for academia therefore to be able to contribute to this important branch of NWP research using realistic weather models. In order to open up the ensemble prediction research for a wider research community, we have recreated all 50+1 operational IFS ensemble initial states for OpenIFS CY43R3. The dataset (OpenEnsemble 1.0) is available for use under a Creative Commons license and is downloadable from an https-server. The dataset covers one year (December 2016 to November 2017) twice daily. Downloads in three model resolutions (TL159, TL399 and TL639) are available to cover different research needs. An open-source workflow manager, called OpenEPS, is presented here and used to launch ensemble forecast experiments from the perturbed initial conditions. The deterministic and probabilistic forecast skill of OpenIFS (cycle 40R1) using this new set of initial states is comprehensively evaluated. In addition, we present a case study of typhoon Damrey from year 2017 to illustrate the new potential of being able to run ensemble forecasts outside major global weather forecasting centres.

Genesis of Hurricane Julia (2010) within an African Easterly Wave: Sensitivity Analyses of WRF-LETKF Ensemble Forecasts

2014 ◽  
Vol 71 (9) ◽  
pp. 3180-3201 ◽  
Author(s):  
Stefan F. Cecelski ◽  
Da-Lin Zhang
Keyword(s):  
Deep Convection ◽  
Empirical Orthogonal Functions ◽  
Sensitivity Analyses ◽  
Tropical Cyclogenesis ◽  
Tropospheric Temperature ◽  
Orthogonal Functions ◽  
Ensemble Forecasts ◽  
Low Level ◽  
Modes Of Variability ◽  
Two Parameters

Abstract In this study, the predictability of tropical cyclogenesis (TCG) is explored by conducting ensemble sensitivity analyses on the TCG of Hurricane Julia (2010). Using empirical orthogonal functions (EOFs), the dominant patterns of ensemble disagreements are revealed for various meteorological parameters such as mean sea level pressure (MSLP) and upper-tropospheric temperature. Using the principal components of the EOF patterns, ensemble sensitivities are generated to elucidate which mechanisms drive the parametric ensemble differences. The dominant pattern of MSLP ensemble spread is associated with the intensity of the pre–tropical depression (pre-TD), explaining nearly half of the total variance at each respective time. Similar modes of variance are found for the low-level absolute vorticity, though the patterns explain substantially less variance. Additionally, the largest modes of variability associated with upper-level temperature anomalies closely resemble the patterns of MSLP variance, suggesting interconnectedness between the two parameters. Sensitivity analyses at both the pre-TD and TCG stages reveal that the MSLP disturbance is strongly correlated to upper-tropospheric temperature and, to a lesser degree, surface latent heat flux anomalies. Further sensitivity analyses uncover a statistically significant correlation between upper-tropospheric temperature and convective anomalies, consistent with the notion that deep convection is important for augmenting the upper-tropospheric warmth during TCG. Overall, the ensemble forecast differences for the TCG of Julia are strongly related to the processes responsible for MSLP falls and low-level cyclonic vorticity growth, including the growth of upper-tropospheric warming and persistent deep convection.

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