scholarly journals Prediction of Monthly-Mean Temperature: The Roles of Atmospheric and Land Initial Conditions and Sea Surface Temperature

2010 ◽  
Vol 23 (3) ◽  
pp. 717-725 ◽  
Author(s):  
Mingyue Chen ◽  
Wanqiu Wang ◽  
Arun Kumar
Keyword(s):  
Lead Time ◽  
Initial Conditions ◽  
Lower Boundary ◽  
Atmospheric Model ◽  
Sea Surface ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Lower Boundary Condition ◽  
Asymptotic Value ◽  
Mean Temperature

Abstract Using the retrospective forecasts from the National Centers for Environmental Prediction (NCEP) coupled atmosphere–ocean Climate Forecast System (CFS) and the Atmospheric Model Intercomparison Project (AMIP) simulations from its uncoupled atmospheric component, the NCEP Global Forecast System (GFS), the relative roles of atmospheric and land initial conditions and the lower boundary condition of sea surface temperatures (SSTs) for the prediction of monthly-mean temperature are investigated. The analysis focuses on the lead-time dependence of monthly-mean prediction skill and its asymptotic value for longer lead times, which could be attributed the atmospheric response to the slowly varying SST. The results show that the observed atmospheric and land initial conditions improve the skill of monthly-mean prediction in the extratropics but have little influence in the tropics. However, the influence of initial atmospheric and land conditions in the extratropics decays rapidly. For 30-day-lead predictions, the global-mean forecast skill of monthly means is found to reach an asymptotic value that is primarily determined by the SST anomalies. The lead time at which initial conditions lose their influence varies spatially. In addition, the initial atmospheric and land conditions are found to have longer impacts in northern winter and spring than in summer and fall. The relevance of the results for constructing lagged ensemble forecasts is discussed.

scholarly journals Impact of the sea surface temperature forcing on hindcasts of Madden-Julian Oscillation events using the ECMWF model

Ocean Science ◽  
2012 ◽  
Vol 8 (6) ◽  
pp. 1071-1084 ◽  
Author(s):  
E. de Boisséson ◽  
M. A. Balmaseda ◽  
F. Vitart ◽  
K. Mogensen
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Initial Conditions ◽  
Lower Boundary ◽  
Phase Relationship ◽  
Atmospheric Model ◽  
Sea Surface ◽  
Madden Julian Oscillation ◽  
Realistic Representation ◽  
The Difference

Abstract. This paper explores the sensitivity of hindcasts of the Madden Julian Oscillation (MJO) to the use of different sea surface temperture (SST) products as lower boundary conditions in the European Centre for Medium-range Weather Forecasts (ECMWF) atmospheric model. Three sets of monthly hindcast experiments are conducted, starting from initial conditions from the ERA interim reanalysis. First, as a reference, the atmosphere is forced by the SST used to produce ERA interim. In the second and third experiments, the SST is switched to the OSTIA (Operational Sea Surface Temperature and Sea-Ice Analysis) and the AVHRR-only (Advanced Very High Resolution Radiometer) reanalyses, respectively. Tests on the temporal resolution of the SST show that monthly fields are not optimal, while weekly and daily resolutions provide similar MJO scores. When using either OSTIA or AVHRR, the propagation of the MJO is degraded and the resulting scores are lower than in the reference experiment. Further experiments show that this loss of skill cannot be attributed to either the difference in mean state or temporal variability between the SST products. Additional diagnostics show that the phase relationship between either OSTIA or AVHRR SST and the MJO convection is distorted with respect to satellite observations and the ERA interim reanalysis. This distortion is expected to impact the MJO hindcasts, leading to a relative loss of forecast skill. A realistic representation of ocean–atmosphere interactions is thus needed for MJO hindcasts, but not all SST products – though accurate for other purposes – fulfill this requirement.

scholarly journals An Analysis of the Nonstationarity in the Bias of Sea Surface Temperature Forecasts for the NCEP Climate Forecast System (CFS) Version 2

2012 ◽  
Vol 140 (9) ◽  
pp. 3003-3016 ◽  
Author(s):  
A. Kumar ◽  
M. Chen ◽  
L. Zhang ◽  
W. Wang ◽  
Y. Xue ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Long Range ◽  
Initial Conditions ◽  
Sea Surface ◽  
Climate Forecast ◽  
Prediction System ◽  
Forecast System ◽  
Climate Forecast System ◽  
Forecast Bias

Abstract For long-range predictions (e.g., seasonal), it is a common practice for retrospective forecasts (also referred to as the hindcasts) to accompany real-time predictions. The necessity for the hindcasts stems from the fact that real-time predictions need to be calibrated in an attempt to remove the influence of model biases on the predicted anomalies. A fundamental assumption behind forecast calibration is the long-term stationarity of forecast bias that is derived based on hindcasts. Hindcasts require specification of initial conditions for various components of the prediction system (e.g., ocean, atmosphere) that are generally taken from a long reanalysis. Trends and discontinuities in the reanalysis that are either real or spurious can arise due to several reasons, for example, the changing observing system. If changes in initial conditions were to persist during the forecast, there is a potential for forecast bias to depend over the period it is computed, making calibration even more of a challenging task. In this study such a case is discussed for the recently implemented seasonal prediction system at the National Centers for Environmental Prediction (NCEP), the Climate Forecast System version 2 (CFS.v2). Based on the analysis of the CFS.v2 for 1981–2009, it is demonstrated that the characteristics of the forecast bias for sea surface temperature (SST) in the equatorial Pacific had a dramatic change around 1999. Furthermore, change in the SST forecast bias, and its relationship to changes in the ocean reanalysis from which the ocean initial conditions for hindcasts are taken is described. Implications for seasonal and other long-range predictions are discussed.

scholarly journals Practical Predictability of Supercells: Exploring Ensemble Forecast Sensitivity to Initial Condition Spread

2018 ◽  
Vol 146 (8) ◽  
pp. 2361-2379 ◽  
Author(s):  
Montgomery L. Flora ◽  
Corey K. Potvin ◽  
Louis J. Wicker
Keyword(s):  
Lead Time ◽  
Weather Prediction ◽  
Object Oriented ◽  
Comprehensive Analysis ◽  
Initial Condition ◽  
Forecast System ◽  
Model Assumption ◽  
Ensemble Forecasts ◽  
Severe Thunderstorms ◽  
Perfect Model

Abstract As convection-allowing ensembles are routinely used to forecast the evolution of severe thunderstorms, developing an understanding of storm-scale predictability is critical. Using a full-physics numerical weather prediction (NWP) framework, the sensitivity of ensemble forecasts of supercells to initial condition (IC) uncertainty is investigated using a perfect model assumption. Three cases are used from the real-time NSSL Experimental Warn-on-Forecast System for Ensembles (NEWS-e) from the 2016 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. The forecast sensitivity to IC uncertainty is assessed by repeating the simulations with the initial ensemble perturbations reduced to 50% and 25% of their original magnitudes. The object-oriented analysis focuses on significant supercell features, including the mid- and low-level mesocyclone, and rainfall. For a comprehensive analysis, supercell location and amplitude predictability of the aforementioned features are evaluated separately. For all examined features and cases, forecast spread is greatly reduced by halving the IC spread. By reducing the IC spread from 50% to 25% of the original magnitude, forecast spread is still substantially reduced in two of the three cases. The practical predictability limit (PPL), or the lead time beyond which the forecast spread exceeds some prechosen threshold, is case and feature dependent. Comparing to past studies reveals that practical predictability of supercells is substantially improved by initializing once storms are well established in the ensemble analysis.

scholarly journals Global meteorological drought – Part 2: Seasonal forecasts

2014 ◽  
Vol 18 (7) ◽  
pp. 2669-2678 ◽  
Author(s):  
E. Dutra ◽  
W. Pozzi ◽  
F. Wetterhall ◽  
F. Di Giuseppe ◽  
L. Magnusson ◽  
...  
Keyword(s):  
Lead Time ◽  
Initial Conditions ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Added Value ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Global Precipitation Climatology Centre ◽  
The World

Abstract. Global seasonal forecasts of meteorological drought using the standardized precipitation index (SPI) are produced using two data sets as initial conditions: the Global Precipitation Climatology Centre (GPCC) and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (ERAI); and two seasonal forecasts of precipitation, the most recent ECMWF seasonal forecast system and climatologically based ensemble forecasts. The forecast evaluation focuses on the periods where precipitation deficits are likely to have higher drought impacts, and the results were summarized over different regions in the world. The verification of the forecasts with lead time indicated that generally for all regions the least reduction on skill was found for (i) long lead times using ERAI or GPCC for monitoring and (ii) short lead times using ECMWF or climatological seasonal forecasts. The memory effect of initial conditions was found to be 1 month of lead time for the SPI-3, 4 months for the SPI-6 and 6 (or more) months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value with skills at least equal to and often above that of climatological forecasts. Furthermore, it is very difficult to improve on the use of climatological forecasts for long lead times. Our results also support recent questions of whether seasonal forecasting of global drought onset was essentially a stochastic forecasting problem. Results are presented regionally and globally, and our results point to several regions in the world where drought onset forecasting is feasible and skilful.

Surfex : une plateforme pour simuler les flux des surfaces océaniques et continentales

2020 ◽  
pp. 082
Author(s):  
Patrick Le Moigne ◽  
Marie Minvielle
Keyword(s):  
Weather Prediction ◽  
Lower Boundary ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Atmospheric Models ◽  
Physical Processes ◽  
Surface Type ◽  
Lower Boundary Condition ◽  
Continuous Increase ◽  
The Earth

Dans les modèles atmosphériques de prévision numérique du temps et de climat, la surface constitue la condition à la limite inférieure. La grande variété des paysages présents sur l'ensemble du globe et les spécificités de chacun des types de surface rendent complexe sa description dans les modèles. Par ailleurs, l'augmentation constante de la résolution horizontale des modèles nécessite une description fine des surfaces ainsi que des processus mis en jeu lorsqu'atmosphère et surface interagissent. Cet article décrit la plateforme de modélisation Surfex, en particulier comment la grande variété des types de surface est prise en compte, quelles sont les principales paramétrisations physiques et enfin comment est réalisé le couplage à un modèle atmosphérique. Surface is the lower boundary condition of numerical weather prediction and climate atmospheric models. Its representation in models is complicated by the large diversity in landscapes over the Earth, and the specificities of each surface type. In addition, the continuous increase in model horizontal resolution requires an accurate description of surfaces as well as the processes involved when surface and atmosphere are coupled. This article describes the Surfex modelling platform, particularly how the large amount of surface types are accounted for, which are the main physical processes represented and how the coupling to an atmospheric model can be achieved.

scholarly journals Global meteorological drought – Part 2: Seasonal forecasts

2014 ◽  
Vol 11 (1) ◽  
pp. 919-944 ◽  
Author(s):  
E. Dutra ◽  
W. Pozzi ◽  
F. Wetterhall ◽  
F. Di Giuseppe ◽  
L. Magnusson ◽  
...  
Keyword(s):  
Lead Time ◽  
Initial Conditions ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Added Value ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
The World ◽  
The Standardized Precipitation Index

Abstract. Global seasonal forecasts of meteorological drought using the standardized precipitation index (SPI) are produced using two datasets as initial conditions: the Global Precipitation Climatology Center (GPCC) and the ECMWF ERA-Interim reanalysis (ERAI); and two seasonal forecasts of precipitation: the most current ECMWF seasonal forecast system and climatologically based ensemble forecasts. The forecast skill is concentrated on verification months where precipitation deficits are likely to have higher drought impacts and grouped over different regions in the world. Verification of the forecasts as a function of lead time revealed a reduced impact on skill for: (i) long lead times using different initial conditions, and (ii) short lead times using different precipitation forecasts. The memory effect of initial conditions was found to be 1 month lead time for the SPI-3, 3 to 4 months for the SPI-6 and 5 months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value, a skill similar or better than climatological forecasts. In some cases, particularly for long SPI time scales, it is very difficult to improve on the use of climatological forecasts. Our results also support recent questions whether seasonal forecasting of global drought onset was essentially a stochastic forecasting problem. Results are presented regionally and globally, and our results point to several regions in the world where drought onset forecasting is feasible and skilful.

scholarly journals Sea Surface Temperature Predictions in NCEP CFSv2 Using a Simple Ocean Initialization Scheme

2015 ◽  
Vol 143 (8) ◽  
pp. 3176-3191 ◽  
Author(s):  
Jieshun Zhu ◽  
Arun Kumar ◽  
Hui Wang ◽  
Bohua Huang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Initial Conditions ◽  
Seasonal Prediction ◽  
Sea Surface ◽  
Tropical Eastern Pacific ◽  
Multimodel Ensemble ◽  
Ensemble Forecasts ◽  
Ocean Data Assimilation ◽  
Initialization Scheme

Abstract In contrast to operational climate predictions based on sophisticated ocean data assimilation schemes at the National Centers for Environmental Predictions (NCEP), this study applied a simple ocean initialization scheme to the NCEP latest seasonal prediction model, the Climate Forecast System, version 2 (CFSv2). In the scheme, sea surface temperature (SST) was the only observed information applied to derive ocean initial states. The physical basis for the method is that, through air–sea coupling, SST is capable of reproducing some observed features of ocean evolutions by forcing the atmospheric winds. SST predictions based on the scheme are compared against hindcasts from the National (lately North American) Multimodel Ensemble (NMME) project. It was found that due to substantial biases in the tropical eastern Pacific in the ocean initial conditions produced by SST assimilation, ENSO SST predictions were not as good as those with sophisticated initialization schemes (e.g., hindcasts in the NMME project). However, in other basins, SST predictions based on a simple ocean initialization procedure were not worse (sometimes even better) than those with sophisticated initialization schemes. These comparisons indicate that it was helpful that subsurface ocean information be assimilated to improve the tropical Pacific SST predictions, while SST-based ocean assimilation was an effective way to enhance SST prediction capability in other ocean basins. By examining multimodel ensembles with the simple scheme-based hindcasts either included or excluded in NMME, it is also suggested that including the hindcast would generally benefit multimodel ensemble forecasts. In addition, possible ways to further improve ENSO SST predictions with the simple initialization scheme are also discussed.

scholarly journals Analysis of an intense bora event in the Adriatic area

2004 ◽  
Vol 4 (2) ◽  
pp. 323-337 ◽  
Author(s):  
D. Cesini ◽  
S. Morelli ◽  
F. Parmiggiani
Keyword(s):  
Satellite Data ◽  
Initial Conditions ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Sea Surface ◽  
Temperature Fields ◽  
Sea Surface Temperatures ◽  
Low Level ◽  
Surface Temperatures ◽  
The Difference

Abstract. Numerical simulations of a bora event, recently occurred in the Adriatic area, are presented. Two reference runs at different horizontal resolution (about 20km and 8km) describe the case. Initial conditions for the atmospheric model integration are obtained from ECMWF analyses. Satellite data are used for comparisons. A further run at horizontal resolution of 8km, using initial satellite sea surface temperatures, is performed to evaluate their impact on the low level wind over the Adriatic Sea. All the simulations are carried out with 50 layers in the vertical. Numerous aspects of the simulations are found to be in agreement with the understanding as well as the observational knowledge of bora distinctive characteristics. Satellite data and model results indicate that a more realistic simulation of the bora wind over the sea is achieved using the model with 8km horizontal resolution and that the low level wind in this case is sensitive, though weakly, to the difference between the used sea surface temperature fields. Simulation results also show that both wind intensity and the area around wind peaks tend to increase when relatively higher sea surface temperatures are used.

scholarly journals Scientific Verification of Deterministic River Stage Forecasts

2009 ◽  
Vol 10 (2) ◽  
pp. 507-520 ◽  
Author(s):  
Edwin Welles ◽  
Soroosh Sorooshian
Keyword(s):  
Model Calibration ◽  
Lead Time ◽  
Initial Conditions ◽  
Lead Times ◽  
Calibration Model ◽  
Forecast System ◽  
Feasible Method ◽  
River Stage ◽  
Short Lead Time

Abstract One element of a complete verification system is the ability to determine why forecasts behave as they do. This paper describes and demonstrates an operationally feasible method for conducting this type of diagnostic verification analysis. Hindcasts are generated using different configurations of the forecast system and then the skill of the generated hindcasts is compared. The hindcasts and comparisons are constructed to isolate individual elements of the forecast process. The approach is used to evaluate the role of model calibration, model initial conditions, and precipitation forecasts in generating skill for deterministic river forecasts. The authors find that calibration and initial conditions provide skill for the short lead-time forecasts, with precipitation forecasts providing the majority of the skill in forecasts of high stages at longer lead times. At all lead times, this study shows model calibration is essential, as the calibration makes forecasts reliable.

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