scholarly journals Prediction skill of tropical synoptic scale transients from ECMWF and NCEP Ensemble Prediction Systems

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
S. Taraphdar ◽  
P. Mukhopadhyay ◽  
L. Ruby Leung ◽  
Kiranmayi Landu
Keyword(s):  
Random Error ◽  
Velocity Potential ◽  
Horizontal Resolution ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Boreal Summer ◽  
Lead Times ◽  
Synoptic Scale ◽  
Planetary Scale ◽  
Prediction Systems

AbstractThe prediction skill of tropical synoptic scale transients (SSTR) such as monsoon low and depression during the boreal summer of 2007–2009 are assessed using high resolution ECMWF and NCEP TIGGE forecasts data. By analyzing 246 forecasts for lead times up to 10 days, it is found that the models have good skills in forecasting the planetary scale means but the skills of SSTR remain poor, with the latter showing no skill beyond 2 days for the global tropics and Indian region. Consistent forecast skills among precipitation, velocity potential, and vorticity provide evidence that convection is the primary process responsible for precipitation. The poor skills of SSTR can be attributed to the larger random error in the models as they fail to predict the locations and timings of SSTR. Strong correlation between the random error and synoptic precipitation suggests that the former starts to develop from regions of convection. As the NCEP model has larger biases of synoptic scale precipitation, it has a tendency to generate more random error that ultimately reduces the prediction skill of synoptic systems in that model. The larger biases in NCEP may be attributed to the model moist physics and/or coarser horizontal resolution compared to ECMWF.

scholarly journals Evaluation of Submonthly Precipitation Forecast Skill from Global Ensemble Prediction Systems

2015 ◽  
Vol 143 (7) ◽  
pp. 2871-2889 ◽  
Author(s):  
Shuhua Li ◽  
Andrew W. Robertson
Keyword(s):  
Skill Score ◽  
Forecast Skill ◽  
Prediction Skill ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Boreal Summer ◽  
Seasonal Forecasts ◽  
Maritime Continent ◽  
Prediction Systems ◽  
The Tropics

Abstract The prediction skill of precipitation at submonthly time scales during the boreal summer season is investigated based on hindcasts from three global ensemble prediction systems (EPSs). The results, analyzed for lead times up to 4 weeks, indicate encouraging correlation skill over some regions, particularly over the Maritime Continent and the equatorial Pacific and Atlantic Oceans. The hindcasts from all three models correspond to high prediction skill over the first week compared to the following three weeks. The ECMWF forecast system tends to yield higher prediction skill than the other two systems, in terms of both correlation and mean squared skill score. However, all three systems are found to exhibit large conditional biases in the tropics, highlighted using the mean squared skill score. The sources of submonthly predictability are examined in the ECMWF hindcasts over the Maritime Continent in three typical years of contrasting ENSO phase, with a focus on the combined impact of the intraseasonal MJO and interannual ENSO. Rainfall variations over Borneo in the ENSO-neutral year are found to correspond well with the dominant MJO phase. The contribution of ENSO becomes substantial in the two ENSO years, but the MJO impact can become dominant when the MJO occurs in phases 2–3 during El Niño or in phases 5–6 during the La Niña year. These results support the concept that “windows of opportunity” of high forecast skill exist as a function of ENSO and the MJO in certain locations and seasons, which may lead to subseasonal-to-seasonal forecasts of substantial societal value in the future.

scholarly journals Probabilistic Forecasting of the 500 hPa Geopotential Height over the Northern Hemisphere Using TIGGE Multi-model Ensemble Forecasts

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 253
Author(s):  
Luying Ji ◽  
Qixiang Luo ◽  
Yan Ji ◽  
Xiefei Zhi
Keyword(s):  
Northern Hemisphere ◽  
Geopotential Height ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Height Distribution ◽  
Height Field ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Geopotential Height Field

Bayesian model averaging (BMA) and ensemble model output statistics (EMOS) were used to improve the prediction skill of the 500 hPa geopotential height field over the northern hemisphere with lead times of 1–7 days based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and UK Met Office (UKMO) ensemble prediction systems. The performance of BMA and EMOS were compared with each other and with the raw ensembles and climatological forecasts from the perspective of both deterministic and probabilistic forecasting. The results show that the deterministic forecasts of the 500 hPa geopotential height distribution obtained from BMA and EMOS are more similar to the observed distribution than the raw ensembles, especially for the prediction of the western Pacific subtropical high. BMA and EMOS provide a better calibrated and sharper probability density function than the raw ensembles. They are also superior to the raw ensembles and climatological forecasts according to the Brier score and the Brier skill score. Comparisons between BMA and EMOS show that EMOS performs slightly better for lead times of 1–4 days, whereas BMA performs better for longer lead times. In general, BMA and EMOS both improve the prediction skill of the 500 hPa geopotential height field.

A Modified Multivariate Madden–Julian Oscillation Index Using Velocity Potential

2013 ◽  
Vol 141 (12) ◽  
pp. 4197-4210 ◽  
Author(s):  
Michael J. Ventrice ◽  
Matthew C. Wheeler ◽  
Harry H. Hendon ◽  
Carl J. Schreck ◽  
Chris D. Thorncroft ◽  
...  
Keyword(s):  
Zonal Wind ◽  
Velocity Potential ◽  
Longwave Radiation ◽  
Warm Pool ◽  
Boreal Summer ◽  
Madden Julian Oscillation ◽  
Systematic Analysis ◽  
Planetary Scale ◽  
Daily Data ◽  
The Indian Ocean

Abstract A new Madden–Julian oscillation (MJO) index is developed from a combined empirical orthogonal function (EOF) analysis of meridionally averaged 200-hPa velocity potential (VP200), 200-hPa zonal wind (U200), and 850-hPa zonal wind (U850). Like the Wheeler–Hendon Real-time Multivariate MJO (RMM) index, which was developed in the same way except using outgoing longwave radiation (OLR) data instead of VP200, daily data are projected onto the leading pair of EOFs to produce the two-component index. This new index is called the velocity potential MJO (VPM) indices and its properties are quantitatively compared to RMM. Compared to the RMM index, the VPM index detects larger-amplitude MJO-associated signals during boreal summer. This includes a slightly stronger and more coherent modulation of Atlantic tropical cyclones. This result is attributed to the fact that velocity potential preferentially emphasizes the planetary-scale aspects of the divergent circulation, thereby spreading the convectively driven component of the MJO’s signal across the entire globe. VP200 thus deemphasizes the convective signal of the MJO over the Indian Ocean warm pool, where the OLR variability associated with the MJO is concentrated, and enhances the signal over the relatively drier longitudes of the equatorial Pacific and Atlantic. This work provides a useful framework for systematic analysis of the strengths and weaknesses of different MJO indices.

The Vice and Virtue of Increased Horizontal Resolution in Ensemble Forecasts of Tornadic Thunderstorms in Low-CAPE, High-Shear Environments

2021 ◽  
Vol 149 (4) ◽  
pp. 921-944
Author(s):  
John R. Lawson ◽  
Corey K. Potvin ◽  
Patrick S. Skinner ◽  
Anthony E. Reinhart
Keyword(s):  
Information Gain ◽  
Horizontal Resolution ◽  
Object Identification ◽  
Ensemble Prediction ◽  
Specific Information ◽  
High Shear ◽  
Ensemble Forecasts ◽  
Forecast Lead Time ◽  
Prediction Systems ◽  
The Cost

AbstractTornadoes have Lorenzian predictability horizons O(10) min, and convection-allowing ensemble prediction systems (EPSs) often provide probabilistic guidance of such events to forecasters. Given the O(0.1)-km length scale of tornadoes and O(1)-km scale of mesocyclones, operational models running at horizontal grid spacings (Δx) of 3 km may not capture narrower mesocyclones (typical of the southeastern United States) and certainly do not resolve most tornadoes per se. In any case, it requires O(50) times more computer power to reduce Δx by a factor of 3. Herein, to determine value in such an investment, we compare two EPSs, differing only in Δx (3 vs 1 km), for four low-CAPE, high-shear cases. Verification was grouped as 1) deterministic, traditional methods using pointwise evaluation, 2) a scale-aware probabilistic metric, and 3) a novel method via object identification and information theory. Results suggest 1-km forecasts better detect storms and any associated rapid low- and midlevel rotation, but at the cost of weak–moderate reflectivity forecast skill. The nature of improvement was sensitive to the case, variable, forecast lead time, and magnitude, precluding a straightforward aggregation of results. However, the distribution of object-specific information gain over all cases consistently shows greater average benefit from the 1-km EPS. We also reiterate the importance of verification methodology appropriate for the hazard of interest.

scholarly journals African Easterly Wave Forecast Verification and Its Relation to Convective Errors within the ECMWF Ensemble Prediction System

2018 ◽  
Vol 33 (2) ◽  
pp. 461-477 ◽  
Author(s):  
Travis J. Elless ◽  
Ryan D. Torn
Keyword(s):  
Sub Saharan Africa ◽  
Ensemble Prediction ◽  
Boreal Summer ◽  
Forecast Errors ◽  
Ensemble Prediction System ◽  
Easterly Waves ◽  
Wave Forecast ◽  
Ensemble Mean ◽  
Sub Saharan ◽  
Prediction Systems

Abstract African easterly waves (AEWs) are the primary synoptic-scale weather feature found in sub-Saharan Africa during boreal summer, yet there have been few studies documenting the performance of operational ensemble prediction systems (EPSs) for these phenomena. Here, AEW forecasts in the 51-member ECMWF EPS are validated against an average of four operational analyses during two periods of enhanced AEW activity (July–September 2007–09 and 2011–13). During 2007–09, AEW position forecasts were mainly underdispersive and characterized by a slow bias, while intensity forecasts were characterized by an overintensification bias, yet the ensemble-mean errors generally matched the forecast uncertainty. Although 2011–13 position forecasts were still underdispersive with a slow bias, the ensemble-mean error is smaller than for 2007–09. In addition, the 2011–13 intensity forecasts were overdispersive and had a negligible intensity bias. Forecasts from 2007 to 2009 were characterized by higher precipitation in the AEW trough center and high correlations between divergence errors and intensity errors, suggesting the intensity bias is associated with errors in convection. By contrast, forecasts from 2011 to 2013 have smaller precipitation biases than those from 2007 to 2009 and exhibit a weaker correlation between divergence errors and intensity errors, suggesting a weaker connection between AEW forecast errors and convective errors.

Ensemble Prediction with Different Spatial Resolutions for the 2014 Sochi Winter Olympic Games: The Effects of Calibration and Multimodel Approaches

2016 ◽  
Vol 31 (6) ◽  
pp. 1833-1851 ◽  
Author(s):  
Inger-Lise Frogner ◽  
Thomas Nipen ◽  
Andrew Singleton ◽  
John Bjørnar Bremnes ◽  
Ole Vignes
Keyword(s):  
Complex Terrain ◽  
Olympic Games ◽  
Horizontal Resolution ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Medium Range ◽  
Winter Olympic Games ◽  
Prediction Systems

Abstract Three ensemble prediction systems (EPSs) with different grid spacings are compared and evaluated with respect to their ability to predict wintertime weather in complex terrain. The experiment period was two-and-a-half winter months in 2014, coinciding with the Forecast and Research in the Olympic Sochi Testbed (FROST) project, which took place during the Winter Olympic Games in Sochi, Russia. The global, synoptic-scale ensemble system used is the IFS ENS from the European Centre for Medium-Range Weather Forecasts (ECMWF), and its performance is compared with both the operational pan-European Grand Limited Area Ensemble Prediction System (GLAMEPS) at 11-km horizontal resolution and the experimental regional convection-permitting HIRLAM–ALADIN Regional Mesoscale Operational NWP in Europe (HARMONIE) EPS (HarmonEPS) at 2.5 km. Both GLAMEPS and HarmonEPS are multimodel systems, and it is seen that a large part of the skill in these systems comes from the multimodel approach, as long as all subensembles are performing reasonably. The number of members has less impact on the overall skill measurement. The relative importance of resolution and calibration is also assessed. Statistical calibration was applied and evaluated. In contrast to what is seen for the raw ensembles, the number of members, as well as the number of subensembles, is important for the calibrated ensembles. HarmonEPS shows greater potential than GLAMEPS for predicting wintertime weather, and also has an advantage after calibration.

scholarly journals Evaluation of a probabilistic hydrometeorological forecast system

2009 ◽  
Vol 13 (7) ◽  
pp. 1031-1043 ◽  
Author(s):  
S. Jaun ◽  
B. Ahrens
Keyword(s):  
Horizontal Resolution ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Limited Area ◽  
Prediction System ◽  
Distributed Hydrological Model ◽  
Probabilistic Forecasting ◽  
Ensemble Prediction System ◽  
Forecasting System ◽  
Ensemble Systems

Abstract. Medium range hydrological forecasts in mesoscale catchments are only possible with the use of hydrological models driven by meteorological forecasts, which in particular contribute quantitative precipitation forecasts (QPF). QPFs are accompanied by large uncertainties, especially for longer lead times, which are propagated within the hydrometeorological model system. To deal with this limitation of predictability, a probabilistic forecasting system is tested, which is based on a hydrological-meteorological ensemble prediction system. The meteorological component of the system is the operational limited-area ensemble prediction system COSMO-LEPS that downscales the global ECMWF ensemble to a horizontal resolution of 10 km, while the hydrological component is based on the semi-distributed hydrological model PREVAH with a spatial resolution of 500 m. Earlier studies have mostly addressed the potential benefits of hydrometeorological ensemble systems in short case studies. Here we present an analysis of hydrological ensemble hindcasts for two years (2005 and 2006). It is shown that the ensemble covers the uncertainty during different weather situations with appropriate spread. The ensemble also shows advantages over a corresponding deterministic forecast, even under consideration of an artificial spread.

scholarly journals Scientific Challenges of Convective-Scale Numerical Weather Prediction

2018 ◽  
Vol 99 (4) ◽  
pp. 699-710 ◽  
Author(s):  
Jun-Ichi Yano ◽  
Michał Z. Ziemiański ◽  
Mike Cullen ◽  
Piet Termonia ◽  
Jeanette Onvlee ◽  
...  
Keyword(s):  
Turbulent Flows ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Equation System ◽  
Bayesian Probability ◽  
Ensemble Prediction ◽  
Synoptic Scale ◽  
Probabilistic Forecasts ◽  
Convective Scale ◽  
Prediction Systems

AbstractAfter extensive efforts over the course of a decade, convective-scale weather forecasts with horizontal grid spacings of 1–5 km are now operational at national weather services around the world, accompanied by ensemble prediction systems (EPSs). However, though already operational, the capacity of forecasts for this scale is still to be fully exploited by overcoming the fundamental difficulty in prediction: the fully three-dimensional and turbulent nature of the atmosphere. The prediction of this scale is totally different from that of the synoptic scale (103 km), with slowly evolving semigeostrophic dynamics and relatively long predictability on the order of a few days.Even theoretically, very little is understood about the convective scale compared to our extensive knowledge of the synoptic-scale weather regime as a partial differential equation system, as well as in terms of the fluid mechanics, predictability, uncertainties, and stochasticity. Furthermore, there is a requirement for a drastic modification of data assimilation methodologies, physics (e.g., microphysics), and parameterizations, as well as the numerics for use at the convective scale. We need to focus on more fundamental theoretical issues—the Liouville principle and Bayesian probability for probabilistic forecasts—and more fundamental turbulence research to provide robust numerics for the full variety of turbulent flows.The present essay reviews those basic theoretical challenges as comprehensibly as possible. The breadth of the problems that we face is a challenge in itself: an attempt to reduce these into a single critical agenda should be avoided.

scholarly journals Evaluation of a probabilistic hydrometeorological forecast system

2009 ◽  
Vol 6 (2) ◽  
pp. 1843-1877 ◽  
Author(s):  
S. Jaun ◽  
B. Ahrens
Keyword(s):  
Horizontal Resolution ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Limited Area ◽  
Prediction System ◽  
Distributed Hydrological Model ◽  
Probabilistic Forecasting ◽  
Ensemble Prediction System ◽  
Forecasting System ◽  
Ensemble Systems

Abstract. Medium range hydrological forecasts in mesoscale catchments are only possible with the use of hydrological models driven by meteorological forecasts, which in particular contribute quantitative precipitation forecasts (QPF). QPFs are accompanied by large uncertainties, especially for longer lead times, which are propagated within the hydrometeorological model system. To deal with this limitation of predictability, a probabilistic forecasting system is tested, which is based on a hydrological-meteorological ensemble prediction system. The meteorological component of the system is the operational limited-area ensemble prediction system COSMO-LEPS that downscales the global ECMWF ensemble to a horizontal resolution of 10 km, while the hydrological component is based on the semi-distributed hydrological model PREVAH with a spatial resolution of 500 m. Earlier studies have mostly addressed the potential benefits of hydrometeorological ensemble systems in short case studies. Here we present an analysis of hydrological ensemble hindcasts for two years (2005 and 2006). It is shown that the ensemble covers the uncertainty during different weather situations with an appropriate spread-skill relationship. The ensemble also shows advantages over a corresponding deterministic forecast, even under consideration of an artificial spread.

scholarly journals A calibrated and consistent combination of probabilistic forecasts for the exceedance of several precipitation thresholds using neural networks.

2021 ◽  
Author(s):  
Peter Schaumann ◽  
Reinhold Hess ◽  
Martin Rempel ◽  
Ulrich Blahak ◽  
Volker Schmidt
Keyword(s):  
Neural Networks ◽  
Processing System ◽  
Skill Score ◽  
Horizontal Resolution ◽  
Grid Size ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Ensemble Prediction System ◽  
Probabilistic Forecasts ◽  
Precipitation Thresholds

<p>In this talk we present a new statistical method for the seamless combination of two different ensemble precipitation forecasts (Nowcasting and NWP) using neural networks (NNs), see [1]. The method generates probabilistic forecasts for the exceedance of a set of predetermined thresholds (from 0.1mm up to 5mm). The aim of the combination model is to produce seamless and calibrated forecasts which outperform both input forecasts for all lead times and which are consistent regarding the considered thresholds. First, the hyper-parameters of the NNs are chosen according to a certain hyper-parameter optimization algorithm (not to be confused with the training of the NNs itself) on a 3-month dataset (dataset A). Then, the resulting NNs are tested via a rolling origin validation scheme on two 3-month datasets (datasets B & C) with different input forecasts each. Datasets A & B contain forecasts of DWD's RadVOR, a radar-based nowcasting system, and Ensemble-MOS, a post-processing system of NWP ensembles made by COSMO-DE-EPS, with a horizontal resolution of 20km, which is a predecessor of ICON-D2-EPS. Ensemble-MOS forecasts were provided for up to +6h, while RadVOR forecasts were available up to +2h. For dataset C, forecasts with a grid size of 2.2km are used from STEPS-DWD, a new implementation of the Short-term Ensemble Prediction System (STEPS) by  DWD, and ICON-D2-EPS as a NWP ensemble system. Forecasts were made up to +6h. In both validation datasets (B & C), the forecasts show the well-known behavior that the nowcasting systems RadVOR & STEPS are superior for short lead times, while NWP forecasts (Ensemble-MOS & ICON-D2-EPS) outperform these systems for later lead times. Based on the comparison of several validation scores (bias, Brier skill score, reliability and reliability diagram) we can show that the combination is indeed calibrated, consistent and outperforms both input forecasts for all lead times. It should be noted that the combination works on dataset C, although the hyper-parameters were chosen based on dataset A, which contains different forecasts for a different grid size.<br><br>[1] P. Schaumann, R. Hess, M. Rempel, U. Blahak and V. Schmidt, A calibrated and consistent combination of probabilistic forecasts for the exceedance of several precipitation thresholds using neural networks. Weather and Forecasting (in print)</p>

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