Verification of the European subseasonal forecasts of wind speed and temperature

2021 ◽  
Author(s):  
Naveen Goutham ◽  
Riwal Plougonven ◽  
Hiba Omrani ◽  
Sylvie Parey ◽  
Peter Tankov ◽  
...  
Keyword(s):  
Wind Speed ◽  
Value Chain ◽  
Western Europe ◽  
Grid Point ◽  
Weather Forecasts ◽  
Anomaly Correlation Coefficient ◽  
Extended Range ◽  
Medium Range ◽  
Different Seasons ◽  
Mean Wind Speed

<p>The skill of predicted wind speed at 100 m and temperature at 2 m has been assessed in extended-range forecasts and hindcasts of the European Center for Medium-Range Weather Forecasts, starting from December 2015 to November 2019. The assessment was carried out over Europe grid-point wise and also by considering several spatially averaged country-sized domains, using standard scores such as the Continuous Ranked Probability Score and Anomaly Correlation Coefficient. The (re-)forecasts showed skill over climatology in predicting weekly mean wind speed and temperature well beyond two weeks. Even at a lead time of 6 weeks, the probability of the (re-)forecasts being skillful is greater than 50%, encouraging the use of operational subseasonal forecasts in the decision making value chain. The analysis also exhibited​ significant differences in skill in the predictability of different variables, with temperature being more skillful than wind speed, and for different seasons, with winter allowing more skillful forecasts. The predictability also displayed a clear spatial pattern with forecasts for temperature having more skill for Eastern than for Western Europe, and wind speed forecasts having more skill in Northern than Southern Europe.</p>

scholarly journals Winter Subseasonal Wind Speed Forecasts for Finland from ECMWF

2021 ◽  
Vol 18 ◽  
pp. 127-134
Author(s):  
Otto Hyvärinen ◽  
Terhi K. Laurila ◽  
Olle Räty ◽  
Natalia Korhonen ◽  
Andrea Vajda ◽  
...  
Keyword(s):  
Wind Speed ◽  
Lead Time ◽  
Reference Data ◽  
Data Sets ◽  
Weather Forecasts ◽  
The Third ◽  
Probabilistic Forecasts ◽  
Medium Range ◽  
Mean Wind Speed ◽  
Gaussian Regression

Abstract. The subseasonal forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts) were used to construct weekly mean wind speed forecasts for the spatially aggregated area in Finland. Reforecasts for the winters (November, December and January) of 2016–2017 and 2017–2018 were analysed. The ERA-Interim reanalysis was used as observations and climatological forecasts. We evaluated two types of forecasts, the deterministic forecasts and the probabilistic forecasts. Non-homogeneous Gaussian regression was used to bias-adjust both types of forecasts. The forecasts proved to be skilful until the third week, but the longest skilful lead time depends on the reference data sets and the verification scores used.

scholarly journals Evaluating sub-seasonal heatwave reforecasts of the ECMWF over Europe

2021 ◽  
Author(s):  
Natalia Korhonen ◽  
Otto Hyvärinen ◽  
Matti Kämäräinen ◽  
Kirsti Jylhä
Keyword(s):  
Heat Wave ◽  
Heat Waves ◽  
Lead Times ◽  
Weather Forecasts ◽  
Wave Forecast ◽  
Extended Range ◽  
Hot Days ◽  
Medium Range ◽  
Summer Temperatures

<p>Severe heatwaves have harmful impacts on ecosystems and society. Early warning of heat waves help with decreasing their harmful impact. Previous research shows that the Extended Range Forecasts (ERF) of the European Centre for Medium-Range Weather Forecasts (ECMWF) have over Europe a somewhat higher reforecast skill for extreme hot summer temperatures than for long-term mean temperatures. Also it has been shown that the reforecast skill of the ERFs of the ECMWF was strongly increased by the most severe heat waves (the European heatwave 2003 and the Russian heatwave 2010).</p><p>Our aim is to be able to estimate the skill of a heat wave forecast at the time the forecast is given. For that we investigated the spatial and temporal reforecast skill of the ERFs of the ECMWF to forecast hot days (here defined as a day on which the 5 days running mean surface temperature is above its summer 90<sup>th</sup> percentile) in the continental Europe in summers 2000-2019. We used the ECMWF 2-meter temperature reforecasts and verified them against the ERA5 reanalysis. The skill of the hot day reforecasts was estimated by the symmetric extremal dependence index (SEDI) which considers both hit rates and false alarm rates of the hot day forecasts. Further, we investigated the skill of the heatwave reforecasts based on at which time steps of the forecast the hot days were forecasted. We found that on the mesoscale (horizontal scale of ~500 km) the ERFs of the ECMWF were most skillful in predicting the life cycle of a heat wave (lasting up to 25 days) about a week before its start and during its course. That is, on the mesoscale those reforecasts, in which hot day(s) were forecasted to occur during the first 7…11 days, were more skillful on lead times up to 25 days than the rest of the heat wave forecasts. This finding is valuable information, e.g., in the energy and health sectors while preparing for a coming heat wave.</p><p>The work presented here is part of the research project HEATCLIM (Heat and health in the changing climate) funded by the Academy of Finland.</p>

scholarly journals The wind and wave atlas of the Mediterranean Sea - the calibration phase

2005 ◽  
Vol 2 ◽  
pp. 255-257 ◽  
Author(s):  
L. Cavaleri
Keyword(s):  
Time Series ◽  
Mediterranean Sea ◽  
Extreme Values ◽  
Grid Point ◽  
Local Geometry ◽  
Weather Forecasts ◽  
Medium Range ◽  
Best Fitting ◽  
The Mediterranean ◽  
Calibration Coefficients

Abstract. Within the WW-Medatlas project, sponsored by the Italian, French and Greek Navies, an extensive atlas of the wind and wave conditions in the Mediterranean Sea has been completed. The atlas is based on the information derived from the archive of the European Centre for Medium-Range Weather Forecasts, UK, then calibrated on the base of the data available from the ERS1-2 and Topex satellites. The calibration is required because the wind, hence the wave, data are normally strongly underestimated in the enclosed seas. The calibration has been done deriving the model values at each satellite position, typically at 7 km intervals. The co-located values have then been assigned to the closest grid point. This has provided a substantial number of couples of data at each point, then used to derive, by best-fitting technique, the correction required. This turns out to vary amply throughout the basin, according to the local geometry and orography. The calibration coefficients, different for wind and waves, have been used to correct the original fields and the time series at the single points. Using the calibrated data, extensive statistics have been derived, both as fields and at each point, including extreme values.

scholarly journals Statistical Learning Methods as a Basis for Skillful Seasonal Temperature Forecasts in Europe

2019 ◽  
Vol 32 (17) ◽  
pp. 5363-5379 ◽  
Author(s):  
Matti Kämäräinen ◽  
Petteri Uotila ◽  
Alexey Yu. Karpechko ◽  
Otto Hyvärinen ◽  
Ilari Lehtonen ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Western Europe ◽  
Atlantic Multidecadal Oscillation ◽  
Ensemble Member ◽  
Seasonal Temperature ◽  
Anomaly Correlation Coefficient ◽  
Decadal Scale ◽  
Long Time ◽  
Different Seasons ◽  
Selection Operator

Abstract A statistical learning approach to produce seasonal temperature forecasts in western Europe and Scandinavia was implemented and tested. The leading principal components (PCs) of sea surface temperature (SST) and the geopotential at the 150-hPa level (GPT) were derived from reanalysis datasets and used at different lags (from one to five seasons) as predictors. Random sampling of both the fitting years and the potential predictors together with the Least Absolute Shrinkage and Selection Operator regression (LASSO) was used to create a large ensemble of statistical models. Applying the models to independent test years shows that the ensemble performs well over the target areas and that the ensemble mean is more accurate than the best individual ensemble member on average. Skillful results were especially found for summer and fall, with the anomaly correlation coefficient values ranging between 0.41 and 0.68 for these seasons. The correct simulation of decadal trends, using sufficiently long time series for fitting (70 years), and the use of lagged predictors increased the prediction skill. The decadal-scale variability of SST, most importantly the Atlantic multidecadal oscillation (AMO), and different PCs of GPT are the most important individual predictors among all predictors. Both SST and GPT bring equally much predictive power, although their importance is different in different seasons.

scholarly journals An Intercomparison between ERA-Interim Reanalysis and Observed Precipitation in Northeast China

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jie Zhou ◽  
Junhu Zhao
Keyword(s):  
Northeast China ◽  
Reanalysis Data ◽  
Observation Data ◽  
Precipitation Data ◽  
Temporal Characteristics ◽  
Weather Forecasts ◽  
Medium Range ◽  
Different Seasons ◽  
Autumn And Winter ◽  
Spatial And Temporal Characteristics

Recently, the European Center for Medium-Range Weather Forecasts (ECMWF) released a new set of reanalysis data—ERA-Interim. We make an intercomparison between ERA-Interim precipitation and observed precipitation in Northeast China. The results show that, in general, the ERA-Interim reanalysis precipitation data can describe the spatial and temporal characteristics of seasonal precipitation in Northeast China well. In terms of spatial distribution, ERA-Interim precipitation is generally consistent with the observation data in different seasons in Northeast China. There is a larger difference in the center of Northeast China than in other areas between the two kinds of data. The ERA-Interim precipitation is larger than observed precipitation in most of Northeast China. In spring, autumn, and winter, the ERA-Interim precipitation value is close to the observation one, while in summer there is a large difference in Liaoning Peninsula and Changbai Mountain between the two kinds of precipitation data. In terms of temporal characteristics, the time series of the ERA-Interim precipitation matches well with the observed precipitation in whole. In different seasons, the annual variation of the ERA-Interim precipitation is well correlated with that of the observed precipitation.

Improvement of Medium-Range Forecasts Using the Analog-Dynamical Method

2014 ◽  
Vol 142 (4) ◽  
pp. 1570-1587 ◽  
Author(s):  
Haipeng Yu ◽  
Jianping Huang ◽  
Jifan Chou
Keyword(s):  
Lead Time ◽  
Forecast Skill ◽  
Forecast Errors ◽  
Weather Forecasts ◽  
Planetary Scale ◽  
Anomaly Correlation Coefficient ◽  
Systematic Correction ◽  
Medium Range ◽  
Dynamical Method ◽  
Height Fields

Abstract This study further develops the analog-dynamical method and applies it to medium-range weather forecasts. By regarding the forecast field as a small disturbance superimposed on historical analog fields, historical analog errors can be used to estimate and correct forecast errors. This method is applied to 10-day forecasts from the Global and Regional Assimilation and Prediction System (GRAPES). Both the distribution of atmospheric circulation and the pattern of sea surface temperature (SST) are considered in choosing the analog samples from a historical dataset for 2001–10 based on NCEP Final (FNL) data. The results demonstrate that the analog-dynamical method greatly reduces forecast errors and extends the period of validity of the global 500-hPa height field by 0.8 days, which is superior to results obtained using systematic correction. The correction effect at 500 hPa is increasingly significant when the lead time increases. Although the analogs are selected using 500-hPa height fields, the forecast skill at all vertical levels is improved. The average increase of the anomaly correlation coefficient (ACC) is 0.07, and the root-mean-square error (RMSE) is decreased by 10 gpm on average at a lead time of 10 days. The magnitude of errors for most forecast fields, such as height, temperature, and kinetic energy is decreased considerably by inverse correction. The model improvement is primarily a result of improvement for planetary-scale waves, while the correction for synoptic-scale waves does not affect model forecast skill. As this method is easy to operate and transport to other sophisticated models, it could be appropriate for operational use.

The European Centre for Medium-Range Weather Forecasts (ECMWF) Program on Extended-Range Prediction

1990 ◽  
Vol 71 (9) ◽  
pp. 1317-1330 ◽  
Author(s):  
T. N. Palmer ◽  
C. Brankovic ◽  
F. Molteni ◽  
S. Tibaldi ◽  
L. Ferranti ◽  
...  
Keyword(s):  
Weather Forecasts ◽  
European Centre ◽  
Extended Range ◽  
Medium Range

scholarly journals Analysis of wind generated wave characteristics by SWAN model in Balikpapan Bay

2021 ◽  
Vol 930 (1) ◽  
pp. 012067
Author(s):  
G Napitupulu ◽  
M F Nuruddin ◽  
N A Fekranie ◽  
I Magdalena
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
Strongly Correlated ◽  
Weather Forecasts ◽  
Significant Wave ◽  
Swan Model ◽  
Bay Area ◽  
Medium Range ◽  
The Republic ◽  
Wave Energy Spectrum

Abstract The initiative to relocate the capital of the Republic of Indonesia from Jakarta to Penajam Paser Utara requires research from various sectors in the area. Since Penajam Paser Utara is located in the coastal zone of Balikpapan Bay, it requires careful preparation. This research aims to examine the characteristics of wind-generated waves in the Balikpapan Bay area from 2016 to 2018. Bathymetry data from BATNAS with a resolution of 0.00166666° and Wind data (U10 wind velocity and direction) from the European Center for Medium-Range Weather Forecasts (ECMWF) with a resolution of 0.25° × 0.25° were utilized as input data in this study during three years (2016-2018). This research used the SWAN Model to model wind-produced waves to get significant wave values in spatial and time series form and monthly and seasonal wave energy spectrum characteristics. Based on this research, it can be concluded that significant wave height values are strongly correlated with wind speed. The highest wind speed is found in the DJF (Transition I) season. Maximum Hs (wave height) is found in DJF season, while Hs tends to be high in SON (Transition II) as well as DJF (Western Season), and Hs tends to be weak in MAM (Transition I) season.

scholarly journals The representation of winds in the lower troposphere in ECMWF forecasts and reanalyses during the EUREC4A field campaign

2022 ◽  
Author(s):  
Alessandro Carlo Maria Savazzi ◽  
Louise Nuijens ◽  
Irina Sandu ◽  
Geet George ◽  
Peter Bechtold
Keyword(s):  
Wind Speed ◽  
Large Scale ◽  
Lower Troposphere ◽  
Meridional Wind ◽  
Forecast Errors ◽  
Strong Wind ◽  
Field Campaign ◽  
Weather Forecasts ◽  
Medium Range ◽  
Convective Momentum Transport

Abstract. The characterization of systematic forecast errors in lower-tropospheric winds over the ocean is a primary need for reforming models. Winds are among the drivers of convection, thus an accurate representation of winds is essential for better convective parameterizations. We focus on the temporal variability and vertical distribution of lower-tropospheric wind biases in operational medium-range weather forecasts and ERA5 reanalyses produced with the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). Thanks to several sensitivity experiments and an unprecedented wealth of measurements from the 2020 EUREC4A field campaign, we show that the wind bias varies greatly from day to day, resulting in RSME's up to 2.5 m s−1, with a mean wind speed bias up to −1 m s−1 near and above the trade-inversion in the forecasts and up to −0.5 m s−1 in reanalyses. The modeled zonal and meridional wind exhibit a too strong diurnal cycle, leading to a weak wind speed bias everywhere up to 5 km during daytime, turning into a too strong wind speed bias below 2 km at nighttime. The biases are fairly insensitive to the assimilation of sondes and likely related to remote convection and large scale pressure gradients. Convective momentum transport acts to distribute biases throughout the lowest 1.5 km, whereas at higher levels, other unresolved or dynamical tendencies play a role in setting the bias. Below 1 km, modelled friction due to unresolved physical processes appears too strong, but is (partially) compensated by dynamical tendencies, making this a challenging coupled problem.

scholarly journals Predictability of Sudden Stratospheric Warmings in the ECMWF Extended-Range Forecast System

2018 ◽  
Vol 146 (4) ◽  
pp. 1063-1075 ◽  
Author(s):  
Alexey Yu. Karpechko
Keyword(s):  
Lead Time ◽  
The Arctic ◽  
Lead Times ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Extended Range ◽  
Medium Range ◽  
Tropospheric Circulation ◽  
Mean Square Errors

The skill of the Arctic stratospheric retrospective ensemble forecasts (hindcasts) of the European Centre for Medium-Range Weather Forecasts extended-range system is analyzed with a focus on the predictability of the major sudden stratospheric warmings (SSWs) during the period 1993–2016. Thirteen SSWs took place during this period. It is found that forecasts initialized 10–15 days before the SSWs show worse skill in the stratosphere than forecasts initialized during normal conditions in terms of root-mean-square errors but not in terms of anomaly correlation. Using the spread of ensemble members to estimate forecasted SSW probability, it is shown that some SSWs can be predicted with high (>0.9) probability at lead times of 12–13 days if a difference of 3 days between actual and forecasted SSW is allowed. Focusing on SSWs with significant impacts on the tropospheric circulation, on average, the forecasted SSW probability is found to increase from nearly 0 at 1-month lead time to 0.3 at day 13 before SSW, and then rapidly increases to nearly 1 at day 7. The period between days 8 and 12 is when most of the SSWs are predicted, with a probability of 0.5–0.9, which is considerably larger than the observed SSW occurrence frequency. Therefore, this period can be thought of as an estimate of the SSW predictability limit in this system. Indications that the predictability limit for some SSWs may be longer than 2 weeks are also found; however, this result is inconclusive and more studies are needed to understand when and why such long predictability is possible.

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