scholarly journals Origin and Impact of Initialization Shocks in Coupled Atmosphere–Ocean Forecasts*

2015 ◽  
Vol 143 (11) ◽  
pp. 4631-4644 ◽  
Author(s):  
David P. Mulholland ◽  
Patrick Laloyaux ◽  
Keith Haines ◽  
Magdalena Alonso Balmaseda
Keyword(s):  
Data Assimilation ◽  
Coupled System ◽  
Atmospheric Temperature ◽  
Ocean Model ◽  
Forecast Skill ◽  
Weather Forecasts ◽  
Model Component ◽  
Medium Range ◽  
Series Of Experiments ◽  
Coupled Data Assimilation

Abstract Current methods for initializing coupled atmosphere–ocean forecasts often rely on the use of separate atmosphere and ocean analyses, the combination of which can leave the coupled system imbalanced at the beginning of the forecast, potentially accelerating the development of errors. Using a series of experiments with the European Centre for Medium-Range Weather Forecasts coupled system, the magnitude and extent of these so-called initialization shocks is quantified, and their impact on forecast skill measured. It is found that forecasts initialized by separate oceanic and atmospheric analyses do exhibit initialization shocks in lower atmospheric temperature, when compared to forecasts initialized using a coupled data assimilation method. These shocks result in as much as a doubling of root-mean-square error on the first day of the forecast in some regions, and in increases that are sustained for the duration of the 10-day forecasts performed here. However, the impacts of this choice of initialization on forecast skill, assessed using independent datasets, were found to be negligible, at least over the limited period studied. Larger initialization shocks are found to follow a change in either the atmosphere or ocean model component between the analysis and forecast phases: changes in the ocean component can lead to sea surface temperature shocks of more than 0.5 K in some equatorial regions during the first day of the forecast. Implications for the development of coupled forecast systems, particularly with respect to coupled data assimilation methods, are discussed.

Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill

2018 ◽  
Vol 146 (4) ◽  
pp. 1157-1180 ◽  
Author(s):  
Gregory C. Smith ◽  
Jean-Marc Bélanger ◽  
François Roy ◽  
Pierre Pellerin ◽  
Hal Ritchie ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Large Scale ◽  
Weather Forecasting ◽  
Positive Impact ◽  
Weather Prediction ◽  
Ocean Model ◽  
Forecast Skill ◽  
Medium Range ◽  
Forecasting System ◽  
The Impact

The importance of coupling between the atmosphere and the ocean for forecasting on time scales of hours to weeks has been demonstrated for a range of physical processes. Here, the authors evaluate the impact of an interactive air–sea coupling between an operational global deterministic medium-range weather forecasting system and an ice–ocean forecasting system. This system was developed in the context of an experimental forecasting system that is now running operationally at the Canadian Centre for Meteorological and Environmental Prediction. The authors show that the most significant impact is found to be associated with a decreased cyclone intensification, with a reduction in the tropical cyclone false alarm ratio. This results in a 15% decrease in standard deviation errors in geopotential height fields for 120-h forecasts in areas of active cyclone development, with commensurate benefits for wind, temperature, and humidity fields. Whereas impacts on surface fields are found locally in the vicinity of cyclone activity, large-scale improvements in the mid-to-upper troposphere are found with positive global implications for forecast skill. Moreover, coupling is found to produce fairly constant reductions in standard deviation error growth for forecast days 1–7 of about 5% over the northern extratropics in July and August and 15% over the tropics in January and February. To the authors’ knowledge, this is the first time a statistically significant positive impact of coupling has been shown in an operational global medium-range deterministic numerical weather prediction framework.

A global coupled atmosphere-ocean model

1989 ◽  
Vol 329 (1604) ◽  
pp. 263-273 ◽  
Keyword(s):  
Water Flux ◽  
Coupled Model ◽  
Ocean Model ◽  
Global Ocean ◽  
Max Planck ◽  
Weather Forecasts ◽  
Medium Range ◽  
Climate Drift ◽  
The Mean ◽  
Atmosphere Model

A low-resolution version of the European Centre for Medium Range Weather Forecasts global atmosphere model has been coupled to a global ocean model developed at the Max Planck Institut in Hamburg. The atmosphere model is driven by the sea surface temperature and the ice thickness calculated by the ocean model, which, in turn, is driven by the wind stress, the heat flux and the fresh-water flux diagnosed by the atmosphere model. Even though each model reaches stationarity when integrated on its own, the coupling of both creates problems, because the fields calculated by each model are not consistent with those the other model has to have to stay stationary, as some of the fluxes are not balanced. In the coupled experiment the combined ocean-atmosphere system drifts towards a colder state. To counteract this problem a flux correction has been applied, which balances the mean biases of each model. This method makes the climate drift of the coupled model smaller, but additional work has to be done to perfect this method.

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 Global Rainfall Data Assimilation Experimentation

2007 ◽  
pp. 447-457
Author(s):  
Peter Bauer ◽  
Philippe Lopez ◽  
Emmanuel Moreau ◽  
Frédéric Chevallier ◽  
Angela Benedetti ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Rainfall Data ◽  
Weather Forecasts ◽  
European Centre ◽  
Medium Range

scholarly journals Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill

2012 ◽  
Vol 16 (8) ◽  
pp. 2825-2838 ◽  
Author(s):  
S. Shukla ◽  
N. Voisin ◽  
D. P. Lettenmaier
Keyword(s):  
Weather Forecast ◽  
Forecast Skill ◽  
Prediction Skill ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Medium Range Weather Forecast ◽  
Weather Forecasts ◽  
Medium Range ◽  
Hydrologic Prediction ◽  
Potential Improvement

Abstract. We investigated the contribution of medium range weather forecasts with lead times of up to 14 days to seasonal hydrologic prediction skill over the conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP) based experiments were performed for the period 1980–2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980–2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 days of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts for runoff [SM] forecasts generally varies from 0 to 0.8 [0 to 0.5] as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.

scholarly journals Ship-Mounted Real-Time Surface Observational System on board Indian Vessels for Validation and Refinement of Model Forcing Fields*

2013 ◽  
Vol 30 (3) ◽  
pp. 626-637 ◽  
Author(s):  
R. Harikumar ◽  
T. M. Balakrishnan Nair ◽  
G. S. Bhat ◽  
Shailesh Nayak ◽  
Venkat Shesu Reddem ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Southern Ocean ◽  
Real Time ◽  
Weather Forecasting ◽  
Ocean Model ◽  
Weather Forecasts ◽  
European Centre ◽  
National Centre ◽  
Medium Range ◽  
Weather Stations

Abstract A network of ship-mounted real-time Automatic Weather Stations integrated with Indian geosynchronous satellites [Indian National Satellites (INSATs)] 3A and 3C, named Indian National Centre for Ocean Information Services Real-Time Automatic Weather Stations (I-RAWS), is established. The purpose of I-RAWS is to measure the surface meteorological–ocean parameters and transmit the data in real time in order to validate and refine the forcing parameters (obtained from different meteorological agencies) of the Indian Ocean Forecasting System (INDOFOS). Preliminary validation and intercomparison of analyzed products obtained from the National Centre for Medium Range Weather Forecasting and the European Centre for Medium-Range Weather Forecasts using the data collected from I-RAWS were carried out. This I-RAWS was mounted on board oceanographic research vessel Sagar Nidhi during a cruise across three oceanic regimes, namely, the tropical Indian Ocean, the extratropical Indian Ocean, and the Southern Ocean. The results obtained from such a validation and intercomparison, and its implications with special reference to the usage of atmospheric model data for forcing ocean model, are discussed in detail. It is noticed that the performance of analysis products from both atmospheric models is similar and good; however, European Centre for Medium-Range Weather Forecasts air temperature over the extratropical Indian Ocean and wind speed in the Southern Ocean are marginally better.

scholarly journals The Impact of Analysis Error on Medium-Range Weather Forecasts

2008 ◽  
Vol 136 (9) ◽  
pp. 3425-3431 ◽  
Author(s):  
Kyle L. Swanson ◽  
Paul J. Roebber
Keyword(s):  
North Pacific Ocean ◽  
Forecast Skill ◽  
Ensemble Spread ◽  
Weather Forecasts ◽  
The North ◽  
Medium Range ◽  
Difference Fields ◽  
Height Fields ◽  
Analysis Error ◽  
The Impact

Abstract All meteorological analyzed fields contain errors, the magnitude of which ultimately determines the point at which a given forecast will fail. Here, the authors explore the extent to which analysis difference fields capture certain aspects of the actual but unknowable flow-dependent analysis error. The analysis difference fields considered here are obtained by subtracting the NCEP and ECMWF reanalysis 500-hPa height fields. It is shown that the magnitude of this 500-hPa analysis difference averaged over the North Pacific Ocean has a statistically significant impact on forecast skill over the continental United States well into the medium range (5 days). Further, it is shown that the impact of this analysis difference on forecast skill is similar to that of ensemble spread well into the medium range, a measure of forecast uncertainty currently used in the operational setting. Finally, the analysis difference and ensemble spread are shown to be independent; hence, the impact of these two quantities upon forecast skill is additive.

scholarly journals On the localization in strongly coupled ensemble data assimilation using a two-scale Lorenz model

2018 ◽  
Author(s):  
Zheqi Shen ◽  
Youmin Tang ◽  
Xiaojing Li ◽  
Yanqiu Gao ◽  
Junde Li
Keyword(s):  
Data Assimilation ◽  
Spatial Scales ◽  
Coupled System ◽  
Accurate Estimation ◽  
Small Scale ◽  
Coupled Models ◽  
Cross Domain ◽  
The Cross ◽  
Coupled Data Assimilation ◽  
Lorenz 96

Abstract. In the data assimilation of coupled models, the stongly coupled data assimilation (SCDA) is much more complicated than the weakly coupled data assimilation (WCDA), since it involves the cross-domain error covariances which could be very inaccurate when the ensemble size is small. In this study, the SCDA experiments are conducted using a two-scale Lorenz '96 model, which is a coupled system composed by two Lorenz '96 models in two domains have different temporal and spatial scales. A localization strategy is specially designed for the cross-domain error covariances when the ensemble adjustment Kalman filter (EAKF) is used for the coupled data assimilation (CDA) experiments. The formulas for computing the localization factors that can deal with multiple spatial scales and provide essential information are developed to imporve the quality of analyses. The result shows that the SCDA can provides much more accurate estimation of the states than the WCDA when the localization for the cross-domain error covariances is used. Moreover, it is found that the advantage of the SCDA over the WCDA for this model is attributed to the assimilation of small scale observations into the coupled system, whereas the contribution of the assimilation of the large-scale observations to the coupled system is not obvious. This current study provides a possible strategy or idea for developing operational CDA using realistic coupled models.

scholarly journals Short-Range and Medium-Range Weather Forecasting in the Extratropics during Wintertime with and without an Interactive Ocean

2006 ◽  
Vol 134 (7) ◽  
pp. 1972-1986 ◽  
Author(s):  
Thomas Jung ◽  
Frederic Vitart
Keyword(s):  
Northern Hemisphere ◽  
Short Range ◽  
Weather Forecasting ◽  
Forecast Error ◽  
Coupled System ◽  
Atmospheric Model ◽  
Ocean Model ◽  
Significant Difference ◽  
Medium Range ◽  
The Impact

Abstract The ECMWF monthly forecasting system is used to investigate the impact that an interactive ocean has on short-range and medium-range weather predictions in the Northern Hemisphere extratropics during wintertime. On a hemispheric scale the predictive skill for mean sea level pressure (MSLP) with and without an interactive ocean is comparable. This can be explained by the relatively small impact that coupling has on MSLP forecasts. In fact, deterministic and ensemble integrations reveal that the magnitude of forecast error and the perturbation growth due to analysis uncertainties, respectively, by far outweigh MSLP differences between coupled and uncoupled integrations. Furthermore, no significant difference of the ensemble spread between the uncoupled and coupled system is found. The authors’ conclusions apply equally for a number of cases of rapidly intensifying extratropical cyclones in the North Atlantic region. Further experimentation with different atmospheric model versions, different horizontal atmospheric resolutions, and different ocean model formulation reveals the robustness of the findings. The results suggest that (for the cases, resolutions, and model complexities considered is this study) the benefit of using coupled atmosphere–ocean models to carry out 1–10-day MSLP forecasts is relatively small, at least in the Northern Hemisphere extratropics during wintertime.

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