Added value of dynamical downscaling of winter seasonal forecasts over North America

NASA Astrophysics Data System (ADS)

Tefera Diro, Gulilat; Sushama, Laxmi

2017-04-01

Skillful seasonal forecasts have enormous potential benefits for socio-economic sectors that are sensitive to weather and climate conditions, as the early warning routines could reduce the vulnerability of such sectors. In this study, individual ensemble members of the ECMWF global ensemble seasonal forecasts are dynamically downscaled to produce ensemble of regional seasonal forecasts over North America using the fifth generation Canadian Regional Climate Model (CRCM5). CRCM5 forecasts are initialized on November 1st of each year and are integrated for four months for the 1991-2001 period at 0.22 degree resolution to produce a one-month lead-time forecast. The initial conditions for atmospheric variables are obtained from ERA-Interim reanalysis, whereas the initial conditions for land surface are obtained from a separate ERA-interim driven CRCM5 simulation with spectral nudging applied to the interior domain. The global and regional ensemble forecasts were then verified to investigate the skill and economic benefits of dynamical downscaling. Results indicate that both the global and regional climate models produce skillful precipitation forecast over the southern Great Plains and eastern coasts of the U.S and skillful temperature forecasts over the northern U.S. and most of Canada. In comparison to ECMWF forecasts, CRCM5 forecasts improved the temperature forecast skill over most part of the domain, but the improvements for precipitation is limited to regions with complex topography, where it improves the frequency of intense daily precipitation. CRCM5 forecast also yields a better economic value compared to ECMWF precipitation forecasts, for users whose cost to loss ratio is smaller than 0.5.

Advances in air quality prediction with the use of integrated systems

NASA Astrophysics Data System (ADS)

Dragani, R.; Benedetti, A.; Engelen, R. J.; Peuch, V. H.

2017-12-01

Recent years have seen the rise of global operational atmospheric composition forecasting systems for several applications including climate monitoring, provision of boundary conditions for regional air quality forecasting, energy sector applications, to mention a few. Typically, global forecasts are provided in the medium-range up to five days ahead and are initialized with an analysis based on satellite data. In this work we present the latest advances in data assimilation using the ECMWF's 4D-Var system extended to atmospheric composition which is currently operational under the Copernicus Atmosphere Monitoring Service of the European Commission. The service is based on acquisition of all relevant data available in near-real-time, the processing of these datasets in the assimilation and the subsequent dissemination of global forecasts at ECMWF. The global forecasts are used by the CAMS regional models as boundary conditions for the European forecasts based on a multi-model ensemble. The global forecasts are also used to provide boundary conditions for other parts of the world (e.g., China) and are freely available to all interested entities. Some of the regional models also perform assimilation of satellite and ground-based observations. All products are assessed, validated and made publicly available on https://atmosphere.copernicus.eu/.

Use of wind data in global modelling

NASA Technical Reports Server (NTRS)

Pailleux, J.

1985-01-01

The European Centre for Medium Range Weather Forecasts (ECMWF) is producing operational global analyses every 6 hours and operational global forecasts every day from the 12Z analysis. How the wind data are used in the ECMWF golbal analysis is described. For each current wind observing system, its ability to provide initial conditions for the forecast model is discussed as well as its weaknesses. An assessment of the impact of each individual system on the quality of the analysis and the forecast is given each time it is possible. Sometimes the deficiencies which are pointed out are related not only to the observing system itself but also to the optimum interpolation (OI) analysis scheme; then some improvements are generally possible through ad hoc modifications of the analysis scheme and especially tunings of the structure functions. Examples are given. The future observing network over the North Atlantic is examined. Several countries, coordinated by WMO, are working to set up an 'Operational WWW System Evaluation' (OWSE), in order to evaluate the operational aspects of the deployment of new systems (ASDAR, ASAP). Most of the new systems are expected to be deployed before January 1987, and in order to make the best use of the available resources during the deployment phase, some network studies are carried out at the present time, by using simulated data for ASDAR and ASAP systems. They are summarized.

BOREAS ECMWF 6-Hour Analysis and Forecast Data

NASA Technical Reports Server (NTRS)

Viterbo, Pedro; Hall, Forrest G. (Editor); Newcommer, Jeffrey A. (Editor); Betts, Alan; Strub, Richard

2000-01-01

In cooperation with BOREAS atmospheric research efforts, the ECMWF agreed to provide BOREAS with a customized subset of its 6-hourly forecast data. This data set contains parameters from three ECMWF data products in GRIB format: Surface and Diagnostic Fields, Supplemental Fields, and Extension Data. Sample software and information are provided to assist in reading the data files. Temporally, the atmospheric parameters are available for the four main synoptic hours of 00, 06, 12, and 18 UTC from 1994 to 1996. Spatially, the data are stored in a 0.5- by 0.5-degree latitude/longitude grid. To cover the entire BOREAS study area, the grid extends from 48 to 62 degrees latitude and -92 to -114 degrees longitude. The data are stored in binary data representation known as FM 92 GRIB. Due to the complexity of the content and format of this data set, users are advised to read Sections 6, 7, 8, and 14 before using data. Based on agreements between BOREAS and ECMWF, users may legally obtain and use these data only by having a set of the BOREAS CD-ROMs that contain the data. Possession or use of these data under any other circumstance is prohibited. See Sections 11.3 and 20.4 for details.

Tropopause sharpening by data assimilation

NASA Astrophysics Data System (ADS)

Pilch Kedzierski, R.; Neef, L.; Matthes, K.

2016-08-01

Data assimilation was recently suggested to smooth out the sharp gradients that characterize the tropopause inversion layer (TIL) in systems that did not assimilate TIL-resolving observations. We investigate whether this effect is present in the ERA-Interim reanalysis and the European Centre for Medium-Range Weather Forecasts (ECMWF) operational forecast system (which assimilate high-resolution observations) by analyzing the 4D-Var increments and how the TIL is represented in their data assimilation systems. For comparison, we also diagnose the TIL from high-resolution GPS radio occultation temperature profiles from the COSMIC satellite mission, degraded to the same vertical resolution as ERA-Interim and ECMWF operational analyses. Our results show that more recent reanalysis and forecast systems improve the representation of the TIL, updating the earlier hypothesis. However, the TIL in ERA-Interim and ECMWF operational analyses is still weaker and farther away from the tropopause than GPS radio occultation observations of the same vertical resolution.

The Impact of NSCAT Data on Simulating Ocean Circulation

NASA Technical Reports Server (NTRS)

Chao, Y.; Cheng, B.; Liu, W.

1998-01-01

Wind taken from the National Aeronautics and Space Administration (NASA) scatterometer (NSCAT) is compared with the operational analysis from European Center for Medium-Rnage Forecast (ECMWF) for the entire duration (about 9 months) of the NSCAT mission.

Trends in the predictive performance of raw ensemble weather forecasts

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Scheuerer, Michael; Pappenberger, Florian; Bogner, Konrad; Haiden, Thomas

2015-04-01

Over the last two decades the paradigm in weather forecasting has shifted from being deterministic to probabilistic. Accordingly, numerical weather prediction (NWP) models have been run increasingly as ensemble forecasting systems. The goal of such ensemble forecasts is to approximate the forecast probability distribution by a finite sample of scenarios. Global ensemble forecast systems, like the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble, are prone to probabilistic biases, and are therefore not reliable. They particularly tend to be underdispersive for surface weather parameters. Hence, statistical post-processing is required in order to obtain reliable and sharp forecasts. In this study we apply statistical post-processing to ensemble forecasts of near-surface temperature, 24-hour precipitation totals, and near-surface wind speed from the global ECMWF model. Our main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the post-processed forecasts. The ECMWF ensemble is under continuous development, and hence its forecast skill improves over time. Parts of these improvements may be due to a reduction of probabilistic bias. Thus, we first hypothesize that the gain by post-processing decreases over time. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations we generate post-processed forecasts by ensemble model output statistics (EMOS) for each station and variable. Parameter estimates are obtained by minimizing the Continuous Ranked Probability Score (CRPS) over rolling training periods that consist of the n days preceding the initialization dates. Given the higher average skill in terms of CRPS of the post-processed forecasts for all three variables, we analyze the evolution of the difference in skill between raw ensemble and EMOS forecasts. The fact that the gap in skill remains almost constant over time, especially for near-surface wind speed, suggests that improvements to the atmospheric model have an effect quite different from what calibration by statistical post-processing is doing. That is, they are increasing potential skill. Thus this study indicates that (a) further model development is important even if one is just interested in point forecasts, and (b) statistical post-processing is important because it will keep adding skill in the foreseeable future.

Growth of Errors and Uncertainties in Medium Range Ensemble Forecasts of U.S. East Coast Cool Season Extratropical Cyclones

NASA Astrophysics Data System (ADS)

Zheng, Minghua

Cool-season extratropical cyclones near the U.S. East Coast often have significant impacts on the safety, health, environment and economy of this most densely populated region. Hence it is of vital importance to forecast these high-impact winter storm events as accurately as possible by numerical weather prediction (NWP), including in the medium-range. Ensemble forecasts are appealing to operational forecasters when forecasting such events because they can provide an envelope of likely solutions to serve user communities. However, it is generally accepted that ensemble outputs are not used efficiently in NWS operations mainly due to the lack of simple and quantitative tools to communicate forecast uncertainties and ensemble verification to assess model errors and biases. Ensemble sensitivity analysis (ESA), which employs a linear correlation and regression between a chosen forecast metric and the forecast state vector, can be used to analyze the forecast uncertainty development for both short- and medium-range forecasts. The application of ESA to a high-impact winter storm in December 2010 demonstrated that the sensitivity signals based on different forecast metrics are robust. In particular, the ESA based on the leading two EOF PCs can separate sensitive regions associated with cyclone amplitude and intensity uncertainties, respectively. The sensitivity signals were verified using the leave-one-out cross validation (LOOCV) method based on a multi-model ensemble from CMC, ECMWF, and NCEP. The climatology of ensemble sensitivities for the leading two EOF PCs based on 3-day and 6-day forecasts of historical cyclone cases was presented. It was found that the EOF1 pattern often represents the intensity variations while the EOF2 pattern represents the track variations along west-southwest and east-northeast direction. For PC1, the upper-level trough associated with the East Coast cyclone and its downstream ridge are important to the forecast uncertainty in cyclone strength. The initial differences in forecasting the ridge along the west coast of North America impact the EOF1 pattern most. For PC2, it was shown that the shift of the tri-polar structure is most significantly related to the cyclone track forecasts. The EOF/fuzzy clustering tool was applied to diagnose the scenarios in operational ensemble forecast of East Coast winter storms. It was shown that the clustering method could efficiently separate the forecast scenarios associated with East Coast storms based on the 90-member multi-model ensemble. A scenario-based ensemble verification method has been proposed and applied it to examine the capability of different EPSs in capturing the analysis scenarios for historical East Coast cyclone cases at lead times of 1-9 days. The results suggest that the NCEP model performs better in short-range forecasts in capturing the analysis scenario although it is under-dispersed. The ECMWF ensemble shows the best performance in the medium range. The CMC model is found to show the smallest percentage of members in the analysis group and a relatively high missing rate, suggesting that it is less reliable regarding capturing the analysis scenario when compared with the other two EPSs. A combination of NCEP and CMC models has been found to reduce the missing rate and improve the error-spread skill in medium- to extended-range forecasts. Based on the orthogonal features of the EOF patterns, the model errors for 1-6-day forecasts have been decomposed for the leading two EOF patterns. The results for error decomposition show that the NCEP model tends to better represent both EOF1 and EOF2 patterns by showing less intensity and displacement errors during 1-3 days. The ECMWF model is found to have the smallest errors in both EOF1 and EOF2 patterns during 4-6 days. We have also found that East Coast cyclones in the ECMWF forecast tend to be towards the southwest of the other two models in representing the EOF2 pattern, which is associated with the southwest-northeast shifting of the cyclone. This result suggests that ECMWF model may have a tendency to show a closer-to-shore solution in forecasting East Coast winter storms. The downstream impacts of Rossby wave packets (RWPs) on the predictability of winter storms are investigated to explore the source of ensemble uncertainties. The composited RWPA anomalies show that there are enhanced RWPs propagating across the Pacific in both large-error and large-spread cases over the verification regions. There are also indications that the errors might propagate with a speed comparable with the group velocity of RWPs. Based on the composite results as well as our observations of the operation daily RWPA, a conceptual model of errors/uncertainty development associated with RWPs has been proposed to serve as a practical tool to understand the evolution of forecast errors and uncertainties associated with the coherent RWPs originating from upstream as far as western Pacific. (Abstract shortened by ProQuest.).

Opportunities and challenges for extended-range predictions of tropical cyclone impacts on hydrological predictions

NASA Astrophysics Data System (ADS)

Tsai, Hsiao-Chung; Elsberry, Russell L.

2013-12-01

SummaryAn opportunity exists to extend support to the decision-making processes of water resource management and hydrological operations by providing extended-range tropical cyclone (TC) formation and track forecasts in the western North Pacific from the 51-member ECMWF 32-day ensemble. A new objective verification technique demonstrates that the ECMWF ensemble can predict most of the formations and tracks of the TCs during July 2009 to December 2010, even for most of the tropical depressions. Due to the relatively large number of false-alarm TCs in the ECMWF ensemble forecasts that would cause problems for support of hydrological operations, characteristics of these false alarms are discussed. Special attention is given to the ability of the ECMWF ensemble to predict periods of no-TCs in the Taiwan area, since water resource management decisions also depend on the absence of typhoon-related rainfall. A three-tier approach is proposed to provide support for hydrological operations via extended-range forecasts twice weekly on the 30-day timescale, twice-daily on the 15-day timescale, and up to four times a day with a consensus of high-resolution deterministic models.

European Wintertime Windstorms and its Links to Large-Scale Variability Modes

NASA Astrophysics Data System (ADS)

Befort, D. J.; Wild, S.; Walz, M. A.; Knight, J. R.; Lockwood, J. F.; Thornton, H. E.; Hermanson, L.; Bett, P.; Weisheimer, A.; Leckebusch, G. C.

2017-12-01

Winter storms associated with extreme wind speeds and heavy precipitation are the most costly natural hazard in several European countries. Improved understanding and seasonal forecast skill of winter storms will thus help society, policy-makers and (re-) insurance industry to be better prepared for such events. We firstly assess the ability to represent extra-tropical windstorms over the Northern Hemisphere of three seasonal forecast ensemble suites: ECMWF System3, ECMWF System4 and GloSea5. Our results show significant skill for inter-annual variability of windstorm frequency over parts of Europe in two of these forecast suites (ECMWF-S4 and GloSea5) indicating the potential use of current seasonal forecast systems. In a regression model we further derive windstorm variability using the forecasted NAO from the seasonal model suites thus estimating the suitability of the NAO as the only predictor. We find that the NAO as the main large-scale mode over Europe can explain some of the achieved skill and is therefore an important source of variability in the seasonal models. However, our results show that the regression model fails to reproduce the skill level of the directly forecast windstorm frequency over large areas of central Europe. This suggests that the seasonal models also capture other sources of variability/predictability of windstorms than the NAO. In order to investigate which other large-scale variability modes steer the interannual variability of windstorms we develop a statistical model using a Poisson GLM. We find that the Scandinavian Pattern (SCA) in fact explains a larger amount of variability for Central Europe during the 20th century than the NAO. This statistical model is able to skilfully reproduce the interannual variability of windstorm frequency especially for the British Isles and Central Europe with correlations up to 0.8.

A Comparison of Five Numerical Weather Prediction Analysis Climatologies in Southern High Latitudes.

NASA Astrophysics Data System (ADS)

Connolley, William M.; Harangozo, Stephen A.

2001-01-01

In this paper, numerical weather prediction analyses from four major centers are compared-the Australian Bureau of Meteorology (ABM), the European Centre for Medium-Range Weather Forecasts (ECMWF), the U.S. National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR), and The Met. Office (UKMO). Two of the series-ECMWF reanalysis (ERA) and NCEP-NCAR reanalysis (NNR)-are `reanalyses'; that is, the data have recently been processed through a consistent, modern analysis system. The other three-ABM, ECMWF operational (EOP), and UKMO-are archived from operational analyses.The primary focus in this paper is on the period of 1979-93, the period used for the reanalyses, and on climatology. However, ABM and NNR are also compared for the period before 1979, for which the evidence tends to favor NNR. The authors are concerned with basic variables-mean sea level pressure, height of the 500-hPa surface, and near-surface temperature-that are available from the basic analysis step, rather than more derived quantities (such as precipitation), which are available only from the forecast step.Direct comparisons against station observations, intercomparisons of the spatial pattern of the analyses, and intercomparisons of the temporal variation indicate that ERA, EOP, and UKMO are best for sea level pressure;that UKMO and EOP are best for 500-hPa height; and that none of the analyses perform well for near-surface temperature.

An application of ensemble/multi model approach for wind power production forecast.

NASA Astrophysics Data System (ADS)

Alessandrini, S.; Decimi, G.; Hagedorn, R.; Sperati, S.

2010-09-01

The wind power forecast of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast is based on a mesoscale meteorological models that provides the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. The corrected wind data are then used as input in the wind farm power curve to obtain the power forecast. These computations require historical time series of wind measured data (by an anemometer located in the wind farm or on the nacelle) and power data in order to be able to perform the statistical analysis on the past. For this purpose a Neural Network (NN) is trained on the past data and then applied in the forecast task. Considering that the anemometer measurements are not always available in a wind farm a different approach has also been adopted. A training of the NN to link directly the forecasted meteorological data and the power data has also been performed. The normalized RMSE forecast error seems to be lower in most cases by following the second approach. We have examined two wind farms, one located in Denmark on flat terrain and one located in a mountain area in the south of Italy (Sicily). In both cases we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by using two or more models (RAMS, ECMWF deterministic, LAMI, HIRLAM). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error of at least 1% compared to the singles models approach. Moreover the use of a deterministic global model, (e.g. ECMWF deterministic model) seems to reach similar level of accuracy of those of the mesocale models (LAMI and RAMS). Finally we have focused on the possibility of using the ensemble model (ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first day ahead period. In fact low spreads often correspond to low forecast error. For longer forecast horizon the correlation between RMSE and ensemble spread decrease becoming too low to be used for this purpose.

Fire danger assessment using ECMWF weather prediction system

NASA Astrophysics Data System (ADS)

Di Giuseppe, Francesca; Pappemberger, Florian; Wetterhall, Fredrik

2015-04-01

Weather plays a major role in the birth, growth and death of a wildfire wherever there is availability of combustible vegetation and suitable terrain topography. Prolonged dry periods creates favourable conditions for ignitions, wind can then increase the fire spread, while higher relative humidity, and precipitation (rain or snow) may decrease or extinguish it altogether. The European Forest Fire Information System (EFFIS), started in 2011 under the lead of the European Joint Research Centre (JRC) to monitor and forecast fire danger and fire behaviour in Europe. In 2012 a collaboration with the European Centre for Medium range Weather Forecast (ECMWF) was established to explore the potential of using state of the art weather forecast systems as driving forcing for the calculations of fire risk indices. From this collaboration in 2013 the EC-fire system was born. It implements the three most commonly used fire danger rating systems (NFDRS, FWI and MARK-5) and it is both initialised and forced by gridded atmospheric fields provided either by ECMWF re-analysis or ECMWF ensemble prediction systems. For consistency invariant fields (i.e fuel maps, vegetation cover, topogarphy) and real-time weather information are all provided on the same grid. Similarly global climatological vegetation stage conditions for each day of the year are provided by remote satellite observations. These climatological static maps substitute the traditional man judgement in an effort to create an automated procedure that can work in places where local observations are not available. The system has been in operation for the last year providing an ensemble of daily forecasts for fire indices with lead-times up to 10 days over Europe and Globally. An important part of the system is provided by its (re)-analysis dataset obtained by using the (re)-analysis forcings as drivers to calculate the fire risk indices. This is a crucial part of the whole chain since these fields are used to establish the initial conditions from which the forecast is subsequently run. The reanalysis dataset goes back to year 1980 (the starting year of ERA-Interim integrations) and is updated in quasi real time. In addition of providing the staring point for the operational forecasts it is a very useful dataset for the scope of calibration and verification of the system. Assuming reanalysis fields are good proxies for observations then, by comparison with fire events which really occurred, this dataset can be used to assess the potential predictability of fire risk indices. In this work we will introduce the EC-fire system. Then the reanalysis dataset will be used to identify regions of high fire risk predictability and where the system might be in need of further refinement.

An Assessment of ECMWF Analyses and Model Forecasts over the North Slope of Alaska Using Observations from the ARM Mixed-Phase Arctic Cloud Experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xie, Shaocheng; Klein, Stephen A.; Yio, J. John

2006-03-11

European Centre for Medium-Range Weather Forecasts (ECMWF) analysis and model forecast data are evaluated using observations collected during the Atmospheric Radiation Measurement (ARM) October 2004 Mixed-Phase Arctic Cloud Experiment (M-PACE) at its North Slope of Alaska (NSA) site. It is shown that the ECMWF analysis reasonably represents the dynamic and thermodynamic structures of the large-scale systems that affected the NSA during M-PACE. The model-analyzed near-surface horizontal winds, temperature, and relative humidity also agree well with the M-PACE surface measurements. Given the well-represented large-scale fields, the model shows overall good skill in predicting various cloud types observed during M-PACE; however, themore » physical properties of single-layer boundary layer clouds are in substantial error. At these times, the model substantially underestimates the liquid water path in these clouds, with the concomitant result that the model largely underpredicts the downwelling longwave radiation at the surface and overpredicts the outgoing longwave radiation at the top of the atmosphere. The model also overestimates the net surface shortwave radiation, mainly because of the underestimation of the surface albedo. The problem in the surface albedo is primarily associated with errors in the surface snow prediction. Principally because of the underestimation of the surface downwelling longwave radiation at the times of single-layer boundary layer clouds, the model shows a much larger energy loss (-20.9 W m-2) than the observation (-9.6 W m-2) at the surface during the M-PACE period.« less

SSM/I and ECMWF Wind Vector Comparison

NASA Technical Reports Server (NTRS)

Wentz, Frank J.; Ashcroft, Peter D.

1996-01-01

Wentz was the first to convincingly show that satellite microwave radiometers have the potential to measure the oceanic wind vector. The most compelling evidence for this conclusion was the monthly wind vector maps derived solely from a statistical analysis of Special Sensor Microwave Imager (SSM/I) observations. In a qualitative sense, these maps clearly showed the general circulation over the world's oceans. In this report we take a closer look at the SSM/I monthly wind vector maps and compare them to European Center for Medium-Range Weather Forecasts (ECMWF) wind fields. This investigation leads both to an empirical comparison of SSM/I calculated wind vectors with ECMWF wind vectors, and to an examination of possible reasons that the SSM/I calculated wind vector direction would be inherently more reliable at some locations than others.

High resolution statistical downscaling of the EUROSIP seasonal prediction. Application for southeastern Romania

NASA Astrophysics Data System (ADS)

Busuioc, Aristita; Dumitrescu, Alexandru; Dumitrache, Rodica; Iriza, Amalia

2017-04-01

Seasonal climate forecasts in Europe are currently issued at the European Centre for Medium-Range Weather Forecasts (ECMWF) in the form of multi-model ensemble predictions available within the "EUROSIP" system. Different statistical techniques to calibrate, downscale and combine the EUROSIP direct model output are used to optimize the quality of the final probabilistic forecasts. In this study, a statistical downscaling model (SDM) based on canonical correlation analysis (CCA) is used to downscale the EUROSIP seasonal forecast at a spatial resolution of 1km x 1km over the Movila farm placed in southeastern Romania. This application is achieved in the framework of the H2020 MOSES project (http://www.moses-project.eu). The combination between monthly standardized values of three climate variables (maximum/minimum temperatures-Tmax/Tmin, total precipitation-Prec) is used as predictand while combinations of various large-scale predictors are tested in terms of their availability as outputs in the seasonal EUROSIP probabilistic forecasting (sea level pressure, temperature at 850 hPa and geopotential height at 500 hPa). The predictors are taken from the ECMWF system considering 15 members of the ensemble, for which the hindcasts since 1991 until present are available. The model was calibrated over the period 1991-2014 and predictions for summers 2015 and 2016 were achieved. The calibration was made for the ensemble average as well as for each ensemble member. The model was developed for each lead time: one month anticipation for June, two months anticipation for July and three months anticipation for August. The main conclusions from these preliminary results are: best predictions (in terms of the anomaly sign) for Tmax (July-2 months anticipation, August-3 months anticipation) for both years (2015, 2016); for Tmin - good predictions only for August (3 months anticipation ) for both years; for precipitation, good predictions for July (2 months anticipation) in 2015 and August (3 months anticipation) in 2016; failed prediction for June (1-month anticipation) for all parameters. To see if the results obtained for 2015 and 2016 summers are in agreement with the general ECMWF model performance in forecast of the three predictors used in the CCA SDM calibration, the mean bias and root mean square errors (RMSE) calculated over the entire period in each grid point, for each ensemble member and ensemble average were computed. The obtained results are confirmed, showing highest ECMWF performance in forecasting of the three predictors for 3 months anticipation (August) and lowest performance for one month anticipation (June). The added value of the CCA SDM in forecasting local Tmax/Tmin and total precipitation was compared to the ECMWF performance using nearest grid point method. Comparisons were performed for the 1991-2014 period, taking into account the forecast made in May for July. An important improvement was found for the CCA SDM predictions in terms of the RMSE value (computed against observations) for Tmax/Tmin and less for precipitation. The tests are in progress for the other summer months (June, July).

Validation of the vertical profiles of three meteorological models using radiosondes from Antofagasta, Paranal and Llano de Chajnantor

NASA Astrophysics Data System (ADS)

Cortés, L.; Curé, M.

2011-11-01

This research presents an evaluation of three meteorological models, the Global Forecast System (GFS), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the mesoscale model WRF (Weather Research and Forecasting) for three sites located in north of Chile. Cerro Moreno Airport, the Paranal Observatory and Llano de Chajnantor are located at 25, 130 and 283 km from the city of Antofagasta, respectively. Results for the three sites show that the lowest correlation and the highest errors occur at the surface. ECMWF model presents the best results at these levels for the two hours analyzed. This could be due to the fact that the ECMWF model has 91 vertical levels, compared to the 64 and 27 vertical levels of GFS and WRF models, respectively. Therefore, it can represent better the processes in the Planetary Boundary Layer (PBL). In relation to the middle and upper troposphere, the three models show good agreement.

Evaluation of weather forecast systems for storm surge modeling in the Chesapeake Bay

NASA Astrophysics Data System (ADS)

Garzon, Juan L.; Ferreira, Celso M.; Padilla-Hernandez, Roberto

2018-01-01

Accurate forecast of sea-level heights in coastal areas depends, among other factors, upon a reliable coupling of a meteorological forecast system to a hydrodynamic and wave system. This study evaluates the predictive skills of the coupled circulation and wind-wave model system (ADCIRC+SWAN) for simulating storm tides in the Chesapeake Bay, forced by six different products: (1) Global Forecast System (GFS), (2) Climate Forecast System (CFS) version 2, (3) North American Mesoscale Forecast System (NAM), (4) Rapid Refresh (RAP), (5) European Center for Medium-Range Weather Forecasts (ECMWF), and (6) the Atlantic hurricane database (HURDAT2). This evaluation is based on the hindcasting of four events: Irene (2011), Sandy (2012), Joaquin (2015), and Jonas (2016). By comparing the simulated water levels to observations at 13 monitoring stations, we have found that the ADCIR+SWAN System forced by the following: (1) the HURDAT2-based system exhibited the weakest statistical skills owing to a noteworthy overprediction of the simulated wind speed; (2) the ECMWF, RAP, and NAM products captured the moment of the peak and moderately its magnitude during all storms, with a correlation coefficient ranging between 0.98 and 0.77; (3) the CFS system exhibited the worst averaged root-mean-square difference (excepting HURDAT2); (4) the GFS system (the lowest horizontal resolution product tested) resulted in a clear underprediction of the maximum water elevation. Overall, the simulations forced by NAM and ECMWF systems induced the most accurate results best accuracy to support water level forecasting in the Chesapeake Bay during both tropical and extra-tropical storms.

Potential for malaria seasonal forecasting in Africa

NASA Astrophysics Data System (ADS)

Tompkins, Adrian; Di Giuseppe, Francesca; Colon-Gonzalez, Felipe; Namanya, Didas; Friday, Agabe

2014-05-01

As monthly and seasonal dynamical prediction systems have improved their skill in the tropics over recent years, there is now the potential to use these forecasts to drive dynamical malaria modelling systems to provide early warnings in epidemic and meso-endemic regions. We outline a new pilot operational system that has been developed at ECMWF and ICTP. It uses a precipitation bias correction methodology to seamlessly join the monthly ensemble prediction system (EPS) and seasonal (system 4) forecast systems of ECMWF together. The resulting temperature and rainfall forecasts for Africa are then used to drive the recently developed ICTP malaria model known as VECTRI. The resulting coupled system of ECMWF climate forecasts and VECTRI thus produces predictions of malaria prevalence rates and transmission intensity across Africa. The forecasts are filtered to highlight the regions and months in which the system has particular value due to high year to year variability. In addition to epidemic areas, these also include meso and hyper-endemic regions which undergo considerable variability in the onset months. We demonstrate the limits of the forecast skill as a function of lead-time, showing that for many areas the dynamical system can add one to two months additional warning time to a system based on environmental monitoring. We then evaluate the past forecasts against district level case data in Uganda and show that when interventions can be discounted, the system can show significant skill at predicting interannual variability in transmission intensity up to 3 or 4 months ahead at the district scale. The prospects for a operational implementation will be briefly discussed.

Assimilation of SMOS brightness temperatures in the ECMWF EKF for the analysis of soil moisture

NASA Astrophysics Data System (ADS)

Munoz-Sabater, Joaquin

2012-07-01

Since November 2nd 2009, the European Centre for Medium-Range Weather Forecasts (ECMWF) has being monitoring, in Near Real Time (NRT), L-band brightness temperatures measured by the Soil Moisture and Ocean Salinity (SMOS) satellite mission of the European Space Agency (ESA). The main objective of the monitoring suite for SMOS data is to systematically monitor the difference between SMOS observed brightness temperatures and the corresponding model equivalent simulated by the Community Microwave Emission Model (CMEM), the so-called first guess departures. This is a crucial step, as first guess departures is the quantity used in the analysis. The ultimate goal is to investigate how the assimilation of SMOS brightness temperatures over land improves the weather forecast skill, through a more accurate initialization of the global soil moisture state. In this presentation, some significant results from the activities preparing for the assimilation of SMOS data are shown. Among these activities, an effective data thinning strategy, a practical approach to reduce noise from the observed brightness temperatures and a bias correction scheme are of special interest. Firstly, SMOS data needs to be significantly thinned as the data volume delivered for a single orbit is too large for the current operational capabilities in any Numerical Weather Prediction system. Different thinning strategies have been analysed and tested. The most suitable one is the assimilation of SMOS brightness temperatures which match the ECMWF T511 (~40 km) reduced Gaussian Grid. Secondly, SMOS observational noise is reduced significantly by averaging the data in angular bins. In addition, this methodology contributes to further thinning of the SMOS data before the analysis. Finally, a bias correction scheme based on a CDF matching is applied to the observations to ensure an unbiased dataset ready for assimilation in the ECMWF surface analysis system. The current ECMWF operational soil moisture analysis system is based on a point-wise Extended Kalman Filter (EKF). This system assimilates proxy surface observations, i.e., 2 m air temperature and relative humidity to analyse the soil moisture state. Recent developments have also made it possible to assimilate remote sensing data coming from active and passive instruments. In particular, the ECMWF EKF can also assimilate data from the Advanced Scatterometer (ASCAT) onboard METOP-A and, more recently, from SMOS brightness temperatures observations. The first preliminary assimilation results will be shown. The analysis fields will be evaluated through comparison to in-situ data from different regions.

Weather Forecasting From Woolly Art to Solid Science

NASA Astrophysics Data System (ADS)

Lynch, P.

THE PREHISTORY OF SCIENTIFIC FORECASTING Vilhelm Bjerknes Lewis Fry Richardson Richardson's Forecast THE BEGINNING OF MODERN NUMERICAL WEATHER PREDICTION John von Neumann and the Meteorology Project The ENIAC Integrations The Barotropic Model Primitive Equation Models NUMERICAL WEATHER PREDICTION TODAY ECMWF HIRLAM CONCLUSIONS REFERENCES

A study on the predictability of the transition day from the dry to the rainy season over South Korea

NASA Astrophysics Data System (ADS)

Lee, Sang-Min; Nam, Ji-Eun; Choi, Hee-Wook; Ha, Jong-Chul; Lee, Yong Hee; Kim, Yeon-Hee; Kang, Hyun-Suk; Cho, ChunHo

2016-08-01

This study was conducted to evaluate the prediction accuracies of THe Observing system Research and Predictability EXperiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data at six operational forecast centers using the root-mean square difference (RMSD) and Brier score (BS) from April to July 2012. And it was performed to test the precipitation predictability of ensemble prediction systems (EPS) on the onset of the summer rainy season, the day of withdrawal in spring drought over South Korea on 29 June 2012 with use of the ensemble mean precipitation, ensemble probability precipitation, 10-day lag ensemble forecasts (ensemble mean and probability precipitation), and effective drought index (EDI). The RMSD analysis of atmospheric variables (geopotential-height at 500 hPa, temperature at 850 hPa, sea-level pressure and specific humidity at 850 hPa) showed that the prediction accuracies of the EPS at the Meteorological Service of Canada (CMC) and China Meteorological Administration (CMA) were poor and those at the European Center for Medium-Range Weather Forecasts (ECMWF) and Korea Meteorological Administration (KMA) were good. Also, ECMWF and KMA showed better results than other EPSs for predicting precipitation in the BS distributions. It is also evaluated that the onset of the summer rainy season could be predicted using ensemble-mean precipitation from 4-day leading time at all forecast centers. In addition, the spatial distributions of predicted precipitation of the EPS at KMA and the Met Office of the United Kingdom (UKMO) were similar to those of observed precipitation; thus, the predictability showed good performance. The precipitation probability forecasts of EPS at CMA, the National Centers for Environmental Prediction (NCEP), and UKMO (ECMWF and KMA) at 1-day lead time produced over-forecasting (under-forecasting) in the reliability diagram. And all the ones at 2˜4-day lead time showed under-forecasting. Also, the precipitation on onset day of the summer rainy season could be predicted from a 4-day lead time to initial time by using the 10-day lag ensemble mean and probability forecasts. Additionally, the predictability for withdrawal day of spring drought to be ended due to precipitation on onset day of summer rainy season was evaluated using Effective Drought Index (EDI) to be calculated by ensemble mean precipitation forecasts and spreads at five EPSs.

The lunar semidiurnal air pressure tide in in-situ data and ECMWF reanalyses

NASA Astrophysics Data System (ADS)

Schindelegger, Michael; Dobslaw, Henryk

2016-04-01

A gridded empirical model of the lunar semidiurnal air pressure tide L2 is deduced through multiquadric interpolation of more than 2000 globally distributed tidal estimates from land barometers and moored buoys. The resulting climatology serves as an independent standard to validate the barometric L2 oscillations that are present in ECMWF's (European Centre for Medium-Range Weather Forecasts) global atmospheric reanalyses despite the omission of gravitational forcing mechanisms in the involved forecast routines. Inconsistencies between numerical and empirical L2 solutions are found to be small even though the reanalysis models typically underestimate equatorial peak pressures by 10-20% and produce slightly deficient tidal phases in latitudes south of 30°N. Through using a time-invariant reference surface over both land and water and assimilating marine pressure data without accounting for vertical sensor movements due to the M2 ocean tide, ECMWF-based tidal solutions are also prone to strong local artifacts. Additionally, the dependency of the lunar tidal oscillation in atmospheric analysis systems on the meteorological input data is demonstrated based on a recent ECMWF twentieth-century reanalysis (ERA-20C) which draws its all of its observational constraints from in-situ registrations of pressure and surface winds. The L2 signature prior to 1950 is particularly indicative of distinct observing system changes, such as the paucity of marine data during both World Wars or the opening of the Panama Canal in 1914 and the associated adjustment of commercial shipping routes.

Improving the Long-Term Stability of Atmospheric Surface Deformation Predictions by Mitigating the Effects of Orography Updates in Operational Weather Forecast Models

NASA Astrophysics Data System (ADS)

Dill, Robert; Bergmann-Wolf, Inga; Thomas, Maik; Dobslaw, Henryk

2016-04-01

The global numerical weather prediction model routinely operated at the European Centre for Medium-Range Weather Forecasts (ECMWF) is typically updated about two times a year to incorporate the most recent improvements in the numerical scheme, the physical model or the data assimilation procedures into the system for steadily improving daily weather forecasting quality. Even though such changes frequently affect the long-term stability of meteorological quantities, data from the ECMWF deterministic model is often preferred over alternatively available atmospheric re-analyses due to both the availability of the data in near real-time and the substantially higher spatial resolution. However, global surface pressure time-series, which are crucial for the interpretation of geodetic observables, such as Earth rotation, surface deformation, and the Earth's gravity field, are in particular affected by changes in the surface orography of the model associated with every major change in horizontal resolution happened, e.g., in February 2006, January 2010, and May 2015 in case of the ECMWF operational model. In this contribution, we present an algorithm to harmonize surface pressure time-series from the operational ECMWF model by projecting them onto a time-invariant reference topography under consideration of the time-variable atmospheric density structure. The effectiveness of the method will be assessed globally in terms of pressure anomalies. In addition, we will discuss the impact of the method on predictions of crustal deformations based on ECMWF input, which have been recently made available by GFZ Potsdam.

Does a more skilful meteorological input lead to a more skilful flood forecast at seasonal timescales?

NASA Astrophysics Data System (ADS)

Neumann, Jessica; Arnal, Louise; Magnusson, Linus; Cloke, Hannah

2017-04-01

Seasonal river flow forecasts are important for many aspects of the water sector including flood forecasting, water supply, hydropower generation and navigation. In addition to short term predictions, seasonal forecasts have the potential to realise higher benefits through more optimal and consistent decisions. Their operational use however, remains a challenge due to uncertainties posed by the initial hydrologic conditions (e.g. soil moisture, groundwater levels) and seasonal climate forcings (mainly forecasts of precipitation and temperature), leading to a decrease in skill with increasing lead times. Here we present a stakeholder-led case study for the Thames catchment (UK), currently being undertaken as part of the H2020 IMPREX project. The winter of 2013-14 was the wettest on record in the UK; driven by 12 major Atlantic depressions, the Thames catchment was subject to compound (concurrent) flooding from fluvial and groundwater sources. Focusing on the 2013-14 floods, this study aims to see whether increased skill in meteorological input translates through to more accurate forecasting of compound flood events at seasonal timescales in the Thames catchment. An earlier analysis of the ECMWF System 4 (S4) seasonal meteorological forecasts revealed that it did not skilfully forecast the extreme event of winter 2013-14. This motivated the implementation of an atmospheric experiment by the ECMWF to force the S4 to more accurately represent the low-pressure weather conditions prevailing in winter 2013-14 [1]. Here, we used both the standard and the "improved" S4 seasonal meteorological forecasts to force the EFAS (European Flood Awareness System) LISFLOOD hydrological model. Both hydrological forecasts were started on the 1st of November 2013 and run for 4 months of lead time to capture the peak of the 2013-14 flood event. Comparing the seasonal hydrological forecasts produced with both meteorological forcing data will enable us to assess how the improved meteorology translates into skill in the hydrological forecast for this extreme compound event. As primary stakeholders involved in the study, the Environment Agency and Flood Forecasting Centre are responsible for managing flood risk in the UK. For them, the detection of a potential flood signal weeks or months in advance could be of great value in terms of operational practice, decision-making and early warning. [1] Rodwell, M.J., Ferranti, L., Magnusson, L., Weisheimer, A., Rabier, F. & Richardson, D. (2015) Diagnosis of northern hemispheric regime behaviour during winter 2013/14. ECMWF Technical Memoranda 769.

An application of ensemble/multi model approach for wind power production forecasting

NASA Astrophysics Data System (ADS)

Alessandrini, S.; Pinson, P.; Hagedorn, R.; Decimi, G.; Sperati, S.

2011-02-01

The wind power forecasts of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast applied in this study is based on meteorological models that provide the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. For this purpose a training of a Neural Network (NN) to link directly the forecasted meteorological data and the power data has been performed. One wind farm has been examined located in a mountain area in the south of Italy (Sicily). First we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by the combination of models (RAMS, ECMWF deterministic, LAMI). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error (normalized by nominal power) of at least 1% compared to the singles models approach. Finally we have focused on the possibility of using the ensemble model system (EPS by ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first three days ahead period.

Initializing numerical weather prediction models with satellite-derived surface soil moisture: Data assimilation experiments with ECMWF's Integrated Forecast System and the TMI soil moisture data set

NASA Astrophysics Data System (ADS)

Drusch, M.

2007-02-01

Satellite-derived surface soil moisture data sets are readily available and have been used successfully in hydrological applications. In many operational numerical weather prediction systems the initial soil moisture conditions are analyzed from the modeled background and 2 m temperature and relative humidity. This approach has proven its efficiency to improve surface latent and sensible heat fluxes and consequently the forecast on large geographical domains. However, since soil moisture is not always related to screen level variables, model errors and uncertainties in the forcing data can accumulate in root zone soil moisture. Remotely sensed surface soil moisture is directly linked to the model's uppermost soil layer and therefore is a stronger constraint for the soil moisture analysis. For this study, three data assimilation experiments with the Integrated Forecast System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) have been performed for the 2-month period of June and July 2002: a control run based on the operational soil moisture analysis, an open loop run with freely evolving soil moisture, and an experimental run incorporating TMI (TRMM Microwave Imager) derived soil moisture over the southern United States. In this experimental run the satellite-derived soil moisture product is introduced through a nudging scheme using 6-hourly increments. Apart from the soil moisture analysis, the system setup reflects the operational forecast configuration including the atmospheric 4D-Var analysis. Soil moisture analyzed in the nudging experiment is the most accurate estimate when compared against in situ observations from the Oklahoma Mesonet. The corresponding forecast for 2 m temperature and relative humidity is almost as accurate as in the control experiment. Furthermore, it is shown that the soil moisture analysis influences local weather parameters including the planetary boundary layer height and cloud coverage.

Numerical Simulation of Intense Precipitation Events South of the Alps: Sensitivity to Initial Conditions and Horizontal Resolution

NASA Astrophysics Data System (ADS)

Cacciamani, C.; Cesari, D.; Grazzini, F.; Paccagnella, T.; Pantone, M.

In this paper we describe the results of several numerical experiments performed with the limited area model LAMBO, based on a 1989 version of the NCEP (National Center for Environmental Prediction) ETA model, operational at ARPA-SMR since 1993. The experiments have been designed to assess the impact of different horizontal resolutions and initial conditions on the quality and detail of the forecast, especially as regards the precipitation field in the case of severe flood events. For initial conditions we developed a mesoscale data assimilation scheme, based on the nudging technique. The scheme makes use of upper air and surface meteorological observations to modify ECMWF (European Centre for Medium Range Weather Forecast) operational analyses, used as first-guess fields, in order to better describe smaller scales features, mainly in the lower troposphere. Three flood cases in the Alpine and Mediterranean regions have been simulated with LAMBO, using a horizontal grid spacing of 15 and 5km and starting either from ECMWF initialised analysis or from the result of our mesoscale analysis procedure. The results show that increasing the resolution generally improves the forecast, bringing the precipitation peaks in the flooded areas close to the observed values without producing many spurious precipitation patterns. The use of mesoscale analysis produces a more realistic representation of precipitation patterns giving a further improvement to the forecast of precipitation. Furthermore, when simulations are started from mesoscale analysis, some model-simulated thermodynamic indices show greater vertical instability just in the regions where strongest precipitation occurred.

Objective Interpolation of Scatterometer Winds

NASA Technical Reports Server (NTRS)

Tang, Wenquing; Liu, W. Timothy

1996-01-01

Global wind fields are produced by successive corrections that use measurements by the European Remote Sensing Satellite (ERS-1) scatterometer. The methodology is described. The wind fields at 10-meter height provided by the European Center for Medium-Range Weather Forecasting (ECMWF) are used to initialize the interpolation process. The interpolated wind field product ERSI is evaluated in terms of its improvement over the initial guess field (ECMWF) and the bin-averaged ERS-1 wind field (ERSB). Spatial and temporal differences between ERSI, ECMWF and ERSB are presented and discussed.

Evaluating the Predictability of South-East Asian Floods Using ECMWF and GloFAS Forecasts

NASA Astrophysics Data System (ADS)

Pillosu, F. M.

2017-12-01

Between July and September 2017, the monsoon season caused widespread heavy rainfall and severe floods across countries in South-East Asia, notably in India, Nepal and Bangladesh, with deadly consequences. According to the U.N., in Bangladesh 140 people lost their lives and 700,000 homes were destroyed; in Nepal at least 143 people died, and more than 460,000 people were forced to leave their homes; in India there were 726 victims of flooding and landslides, 3 million people were affected by the monsoon floods and 2000 relief camps were established. Monsoon season happens regularly every year in South Asia, but local authorities reported the last monsoon season as the worst in several years. What made the last monsoon season particularly severe in certain regions? Are these causes clear from the forecasts? Regarding the meteorological characterization of the event, an analysis of forecasts from the European Centre for Medium-Range Weather Forecast (ECMWF) for different lead times (from seasonal to short range) will be shown to evaluate how far in advance this event was predicted and start discussion on what were the factors that led to such a severe event. To illustrate hydrological aspects, forecasts from the Global Flood Awareness System (GloFAS) will be shown. GloFAS is developed at ECMWF in co-operation with the European Commission's Joint Research Centre (JRC) and with the support of national authorities and research institutions such as the University of Reading. It will become operational at the end of 2017 as part of the Copernicus Emergency Management Service. GloFAS couples state-of-the-art weather forecasts with a hydrological model to provide a cross-border system with early flood guidance information to help humanitarian agencies and national hydro-meteorological services to strengthen and improve forecasting capacity, preparedness and mitigation of natural hazards. In this case GloFAS has shown good potential to become a useful tool for better and earlier preparedness. For instance, first tests showed that by 28th July GloFAS was able to forecast that a relatively large flood peak would probably occur between 13th and 22nd August. An actual flood peak was recorded around 16th August according to the Bangladeshi Flood Forecasting Centre.

Precipitable water and surface humidity over global oceans from special sensor microwave imager and European Center for Medium Range Weather Forecasts

NASA Technical Reports Server (NTRS)

Liu, W. T.; Tang, Wenqing; Wentz, Frank J.

1992-01-01

Global fields of precipitable water W from the special sensor microwave imager were compared with those from the European Center for Medium Range Weather Forecasts (ECMWF) model. They agree over most ocean areas; both data sets capture the two annual cycles examined and the interannual anomalies during an ENSO episode. They show significant differences in the dry air masses over the eastern tropical-subtropical oceans, particularly in the Southern Hemisphere. In these regions, comparisons with radiosonde data indicate that overestimation by the ECMWF model accounts for a large part of the differences. As a check on the W differences, surface-level specific humidity Q derived from W, using a statistical relation, was compared with Q from the ECMWF model. The differences in Q were found to be consistent with the differences in W, indirectly validating the Q-W relation. In both W and Q, SSMI was able to discern clearly the equatorial extension of the tongues of dry air in the eastern tropical ocean, while both ECMWF and climatological fields have reduced spatial gradients and weaker intensity.

Initial conditions and ENSO prediction using a coupled ocean-atmosphere model

NASA Astrophysics Data System (ADS)

Larow, T. E.; Krishnamurti, T. N.

1998-01-01

A coupled ocean-atmosphere initialization scheme using Newtonian relaxation has been developed for the Florida State University coupled ocean-atmosphere global general circulation model. The initialization scheme is used to initialize the coupled model for seasonal forecasting the boreal summers of 1987 and 1988. The atmosphere model is a modified version of the Florida State University global spectral model, resolution T-42. The ocean general circulation model consists of a slightly modified version of the Hamburg's climate group model described in Latif (1987) and Latif et al. (1993). The coupling is synchronous with information exchanged every two model hours. Using ECMWF atmospheric daily analysis and observed monthly mean SSTs, two, 1-year, time-dependent, Newtonian relaxation were performed using the coupled model prior to conducting the seasonal forecasts. The coupled initializations were conducted from 1 June 1986 to 1 June 1987 and from 1 June 1987 to 1 June 1988. Newtonian relaxation was applied to the prognostic atmospheric vorticity, divergence, temperature and dew point depression equations. In the ocean model the relaxation was applied to the surface temperature. Two, 10-member ensemble integrations were conducted to examine the impact of the coupled initialization on the seasonal forecasts. The initial conditions used for the ensembles are the ocean's final state after the initialization and the atmospheric initial conditions are ECMWF analysis. Examination of the SST root mean square error and anomaly correlations between observed and forecasted SSTs in the Niño-3 and Niño-4 regions for the 2 seasonal forecasts, show closer agreement between the initialized forecast than two, 10-member non-initialized ensemble forecasts. The main conclusion here is that a single forecast with the coupled initialization outperforms, in SST anomaly prediction, against each of the control forecasts (members of the ensemble) which do not include such an initialization, indicating possible importance for the inclusion of the atmosphere during the coupled initialization.

Ensemble superparameterization versus stochastic parameterization: A comparison of model uncertainty representation in tropical weather prediction

NASA Astrophysics Data System (ADS)

Subramanian, Aneesh C.; Palmer, Tim N.

2017-06-01

Stochastic schemes to represent model uncertainty in the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system has helped improve its probabilistic forecast skill over the past decade by both improving its reliability and reducing the ensemble mean error. The largest uncertainties in the model arise from the model physics parameterizations. In the tropics, the parameterization of moist convection presents a major challenge for the accurate prediction of weather and climate. Superparameterization is a promising alternative strategy for including the effects of moist convection through explicit turbulent fluxes calculated from a cloud-resolving model (CRM) embedded within a global climate model (GCM). In this paper, we compare the impact of initial random perturbations in embedded CRMs, within the ECMWF ensemble prediction system, with stochastically perturbed physical tendency (SPPT) scheme as a way to represent model uncertainty in medium-range tropical weather forecasts. We especially focus on forecasts of tropical convection and dynamics during MJO events in October-November 2011. These are well-studied events for MJO dynamics as they were also heavily observed during the DYNAMO field campaign. We show that a multiscale ensemble modeling approach helps improve forecasts of certain aspects of tropical convection during the MJO events, while it also tends to deteriorate certain large-scale dynamic fields with respect to stochastically perturbed physical tendencies approach that is used operationally at ECMWF.