scholarly journals Validation of subgrid scale ensemble precipitation forecasts based on the ECMWF’s ecPoint Rainfall project

Időjárás ◽  
2021 ◽  
Vol 125 (3) ◽  
pp. 397-418
Author(s):  
Boglárka Tóth ◽  
István Ihász
Keyword(s):  
Extreme Precipitation ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitation Forecast ◽  
Time Range ◽  
Weather Forecasts ◽  
Extreme Precipitation Events ◽  
Operational Forecasting ◽  
Processed Products ◽  
Short Time

Nowadays, state-of-the-art numerical weather prediction models successfully predict the general weather characteristics several days ahead, but forecasting extreme precipitation is quite challenging even in the short time range. In the framework of the ecPoint Project, the European Centre for Medium-Range Weather Forecasts (ECMWF) developed a new innovative probabilistic post-processing tool which produces 4-day precipitation forecast as accurate as the raw ensemble forecast at day 1. In the framework of the scientific co-operation between ECMWF and the Hungarian Meteorological Service (OMSZ), we were invited to participate in the validation of the experimental products. Quasi operational post-processed products have been available since July 1, 2018. During our work, besides using different verification technics, a new ensemble meteogram was also developed which can support operational forecasters during extreme precipitation events. As a result of our work, products of the ecPoint Project have been included in the operational forecasting activity.

Extreme Quantitative Precipitation Forecast Performance at the Weather Prediction Center from 2001 to 2011

2014 ◽  
Vol 29 (4) ◽  
pp. 894-911 ◽  
Author(s):  
Ellen M. Sukovich ◽  
F. Martin Ralph ◽  
Faye E. Barthold ◽  
David W. Reynolds ◽  
David R. Novak
Keyword(s):  
United States ◽  
Extreme Precipitation ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Forecast Performance ◽  
Extreme Precipitation Events ◽  
Quantitative Precipitation Forecast

Abstract Extreme quantitative precipitation forecast (QPF) performance is baselined and analyzed by NOAA’s Hydrometeorology Testbed (HMT) using 11 yr of 32-km gridded QPFs from NCEP’s Weather Prediction Center (WPC). The analysis uses regional extreme precipitation thresholds, quantitatively defined as the 99th and 99.9th percentile precipitation values of all wet-site days from 2001 to 2011 for each River Forecast Center (RFC) region, to evaluate QPF performance at multiple lead times. Five verification metrics are used: probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), frequency bias, and conditional mean absolute error (MAEcond). Results indicate that extreme QPFs have incrementally improved in forecast accuracy over the 11-yr period. Seasonal extreme QPFs show the highest skill during winter and the lowest skill during summer, although an increase in QPF skill is observed during September, most likely due to landfalling tropical systems. Seasonal extreme QPF skill decreases with increased lead time. Extreme QPF skill is higher over the western and northeastern RFCs and is lower over the central and southeastern RFC regions, likely due to the preponderance of convective events in the central and southeastern regions. This study extends the NOAA HMT study of regional extreme QPF performance in the western United States to include the contiguous United States and applies the regional assessment recommended therein. The method and framework applied here are readily applied to any gridded QPF dataset to define and verify extreme precipitation events.

Predictability of large-scale atmospheric flow patterns connected to extreme precipitation events in the Mediterranean

2021 ◽  
Author(s):  
Nikolaos Mastrantonas ◽  
Linus Magnusson ◽  
Florian Pappenberger ◽  
Jörg Matschullat
Keyword(s):  
Extreme Precipitation ◽  
Mediterranean Region ◽  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Flow Patterns ◽  
Atmospheric Flow ◽  
Extreme Precipitation Events ◽  
The Mediterranean ◽  
Precipitation Events

<p>The Mediterranean region frequently experiences extreme precipitation events with devastating consequences for the affected societies, economies, and environment. Being able to provide reliable and skillful predictions of such events is crucial for mitigating their adverse impacts and related risks. One important part of the risk mitigation chain is the sub-seasonal predictability of such extremes, with information provided at such timescales supporting a range of actions, as for example warn decision-makers, and preposition materials and equipment.</p><p>This work focuses on the predictability of large-scale atmospheric flow patterns connected to extreme precipitation events in the Mediterranean. Previous research has identified strong connections between localized extremes and large-scale patterns. This is promising to provide useful information at sub-seasonal timescales. For such lead times, the Numerical Weather Prediction models are more skillful in predicting large-scale patterns than localized extremes. Here, we analyze the usefulness of these connections at sub-seasonal timescales by using the ECMWF extended-range forecasts. We aim at quantifying related benefits for the different areas in the Mediterranean region and providing insights that are of interest to the operational community.</p><p>Initial results suggest that the ECMWF forecasts provide skillful information in the predictability of large-scale patterns up to about 15 days lead time.</p><p> </p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.3687c29b370068376801161/sdaolpUECMynit/12UGE&app=m&a=0&c=49e65b5908090e0787f0f7f4f8930219&ct=x&pn=gnp.elif&d=1" alt=""></p><p>Large-scale patterns over the Mediterranean based on anomalies of sea level pressure (color shades) and geopotential at 500 hPa (contours) (Figure adapted from Mastrantonas et al, 2020)</p>

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

scholarly journals Impact of the assimilation of lightning data on the precipitation forecast at different forecast ranges

2017 ◽  
Vol 14 ◽  
pp. 187-194 ◽  
Author(s):  
Stefano Federico ◽  
Marco Petracca ◽  
Giulia Panegrossi ◽  
Claudio Transerici ◽  
Stefano Dietrich
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Time Range ◽  
False Alarms ◽  
Forecast Time ◽  
The Impact

Abstract. This study investigates the impact of the assimilation of total lightning data on the precipitation forecast of a numerical weather prediction (NWP) model. The impact of the lightning data assimilation, which uses water vapour substitution, is investigated at different forecast time ranges, namely 3, 6, 12, and 24 h, to determine how long and to what extent the assimilation affects the precipitation forecast of long lasting rainfall events (> 24 h). The methodology developed in a previous study is slightly modified here, and is applied to twenty case studies occurred over Italy by a mesoscale model run at convection-permitting horizontal resolution (4 km). The performance is quantified by dichotomous statistical scores computed using a dense raingauge network over Italy. Results show the important impact of the lightning assimilation on the precipitation forecast, especially for the 3 and 6 h forecast. The probability of detection (POD), for example, increases by 10 % for the 3 h forecast using the assimilation of lightning data compared to the simulation without lightning assimilation for all precipitation thresholds considered. The Equitable Threat Score (ETS) is also improved by the lightning assimilation, especially for thresholds below 40 mm day−1. Results show that the forecast time range is very important because the performance decreases steadily and substantially with the forecast time. The POD, for example, is improved by 1–2 % for the 24 h forecast using lightning data assimilation compared to 10 % of the 3 h forecast. The impact of the false alarms on the model performance is also evidenced by this study.

scholarly journals Development of Verification Methodology for Extreme Weather Forecasts

2017 ◽  
Vol 32 (2) ◽  
pp. 479-491 ◽  
Author(s):  
Hong Guan ◽  
Yuejian Zhu
Keyword(s):  
Extreme Precipitation ◽  
Weather Forecast ◽  
Extreme Weather ◽  
Ensemble Forecast ◽  
Precipitation Forecast ◽  
Forecast System ◽  
Extreme Precipitation Events ◽  
Cold Events ◽  
Extreme Cold ◽  
Verification Methodology

Abstract In 2006, the statistical postprocessing of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) and North American Ensemble Forecast System (NAEFS) was implemented to enhance probabilistic guidance. Anomaly forecasting (ANF) is one of the NAEFS products, generated from bias-corrected ensemble forecasts and reanalysis climatology. The extreme forecast index (EFI), based on a raw ensemble forecast and model-based climatology, is another way to build an extreme weather forecast. In this work, the ANF and EFI algorithms are applied to extreme cold temperature and extreme precipitation forecasts during the winter of 2013/14. A highly correlated relationship between the ANF and EFI allows the determination of two sets of thresholds to identify extreme cold and extreme precipitation events for the two algorithms. An EFI of −0.78 (0.687) is approximately equivalent to a −2σ (0.95) ANF for the extreme cold event (extreme precipitation) forecast. The performances of the two algorithms in forecasting extreme cold events are verified against analysis for different model versions, reference climatology, and forecasts. The verification results during the winter of 2013/14 indicate that ANF forecasts more extreme cold events with a slightly higher skill than EFI. The bias-corrected forecast performs much better than the raw forecast. The current upgrade of the GEFS has a beneficial effect on the extreme cold weather forecast. Using the NCEP Climate Forecast System Reanalysis and Reforecast (CFSRR) as a climate reference gives a slightly better score than the 40-yr reanalysis. The verification methodology is also extended to an extreme precipitation case, showing a broad potential use in the future.

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.

scholarly journals A Fingerprinting Technique for Major Weather Events

2007 ◽  
Vol 46 (7) ◽  
pp. 1053-1066 ◽  
Author(s):  
Benjamin Root ◽  
Paul Knight ◽  
George Young ◽  
Steven Greybush ◽  
Richard Grumm ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Numerical Weather Prediction ◽  
Clustering Algorithm ◽  
Weather Prediction ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Middle Atlantic ◽  
Weather Events ◽  
Operational Forecasting ◽  
Multimodel Ensembles

Abstract Advances in numerical weather prediction have occurred on numerous fronts, from sophisticated physics packages in the latest mesoscale models to multimodel ensembles of medium-range predictions. Thus, the skill of numerical weather forecasts continues to increase. Statistical techniques have further increased the utility of these predictions. The availability of large atmospheric datasets and faster computers has made pattern recognition of major weather events a feasible means of statistically enhancing the value of numerical forecasts. This paper examines the utility of pattern recognition in assisting the prediction of severe and major weather in the Middle Atlantic region. An important innovation in this work is that the analog technique is applied to NWP forecast maps as a pattern-recognition tool rather than to analysis maps as a forecast tool. A technique is described that employs a new clustering algorithm to objectively identify the anomaly patterns or “fingerprints” associated with past events. The potential refinement and applicability of this method as an operational forecasting tool employed by comparing numerical weather prediction forecasts with fingerprints already identified for major weather events are also discussed.

LIMITED AREA WEATHER PREDICTION MODELS VIEWED AS DYNAMICAL SYSTEMS

1999 ◽  
Vol 09 (05) ◽  
pp. 831-842 ◽  
Author(s):  
F. CHOMÉ ◽  
C. NICOLIS
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Dynamical Behavior ◽  
Limited Area ◽  
Initial State ◽  
Weather Forecasts ◽  
Fine Mesh ◽  
Area Of Interest ◽  
Variable Mesh ◽  
High Resolution Models

Different strategies for building high-resolution models providing a more detailed description of a limited area of interest as for example, in regional weather forecasts are developed. They are subsequently compared, on the basis of the dynamical behavior generated by the corresponding models. The statistical properties of the relevant fields are analyzed, and predictability experiments are performed on statistical ensembles of close lying trajectories whose mean distance represents the uncertainty in the initial state of the system. The results show that a global, variable-mesh model performs much better than a limited area fine mesh one embedded into a coarser global model.

scholarly journals Performance of IMD multi-model ensemble and WRF (ARW) model for sub-basin wise rainfall forecast during monsoon 2012

MAUSAM ◽  
2021 ◽  
Vol 67 (2) ◽  
pp. 323-332
Author(s):  
ASHOK KUMAR DAS ◽  
SURINDER KAUR
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
India Meteorological Department ◽  
Precipitation Forecast ◽  
False Alarms ◽  
Model Ensemble ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Arw Model ◽  
Nwp Model

The Numerical Weather Prediction models, Multi-model Ensemble (MME) (27 km × 27 km) and WRF (ARW) (9 km × 9 km) operationally run by India Meteorological Department (IMD) have been utilized to estimate sub-basin wise rainfall forecast. The sub-basin wise operational Quantitative Precipitation Forecast (QPF) have been issued by 10 field offices named Flood Meteorological Offices (FMOs) of IMD located at different flood prone areas of the country. The daily sub-basin wise NWP model rainfall forecast for 122 sub basins under these 10 FMOs for the flood season 2012 have been estimated on operational basis which are used by forecasters at FMOs as a guidance for the issue of operational sub-basin QPF for flood forecasting purposes. The performance of the MME and WRF (ARW) models rainfall at the sub-basin level have been studied in detail. The performance of WRF (ARW) and MME models is compared in the heavy rainfall case over the river basins (Mahanadi etc.) falls under FMO, Bhubaneswar and it is found that WRF (ARW) model gives better result than MME. It is also found that performance of WRF (ARW) is little better than MME when compared over all the flood prone river sub basins of India. For high rainfall categories (51-100,  >100 mm), generally these leads to floods, the success rate of model rainfall forecasts are less and false alarms are more. The NWP models are able to capture the rainfall events but there is difference in magnitudes of sub basin wise rainfall estimates.

scholarly journals A multiscale approach for precipitation verification applied to the FORALPS case studies

2008 ◽  
Vol 16 ◽  
pp. 3-9 ◽  
Author(s):  
A. Lanciani ◽  
S. Mariani ◽  
M. Casaioli ◽  
C. Accadia ◽  
N. Tartaglione
Keyword(s):  
Power Spectrum ◽  
Prediction Models ◽  
Weather Prediction ◽  
Power Spectra ◽  
Multiscale Methods ◽  
Precipitation Forecast ◽  
Diagnostic Tools ◽  
Small Scale ◽  
Multiscale Approach ◽  
Skill Scores

Abstract. Multiscale methods, such as the power spectrum, are suitable diagnostic tools for studying the second order statistics of a gridded field. For instance, in the case of Numerical Weather Prediction models, a drop in the power spectrum for a given scale indicates the inability of the model to reproduce the variance of the phenomenon below the correspondent spatial scale. Hence, these statistics provide an insight into the real resolution of a gridded field and must be accurately known for interpolation and downscaling purposes. In this work, belonging to the EU INTERREG IIIB Alpine Space FORALPS project, the power spectra of the precipitation fields for two intense rain events, which occurred over the north-eastern alpine region, have been studied in detail. A drop in the power spectrum at the shortest scales (about 30 km) has been found, as well as a strong matching between the precipitation spectrum and the spectrum of the orography. Furthermore, it has also been shown how the spectra help understand the behavior of the skill scores traditionally used in Quantitative Precipitation Forecast verification, as these are sensitive to the amount of small scale detail present in the fields.

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