scholarly journals Limits, Variability, and General Behavior of Statistical Predictability of the Midlatitude Atmosphere

2008 ◽  
Vol 65 (1) ◽  
pp. 263-275 ◽  
Author(s):  
Richard Kleeman
Keyword(s):  
Initial Conditions ◽  
Storm Track ◽  
Weather Forecast ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Statistical Prediction ◽  
Ensemble Spread ◽  
Winter Storm ◽  
Global Atmospheric Model ◽  
Summer Storm

Abstract The nature of statistical predictability is analyzed in a T42 global atmospheric model that is able to adequately capture the main features of the midlatitude atmosphere. Key novel features of the present study include very large prediction ensembles and information theoretic techniques. It is found globally that predictability declines in a quasi-linear fashion with time for short-term predictions (3–25 days), while for long ranges (30–45 days) there is an exponential tail. In general, beyond 45 days the prediction and climatological ensembles have essentially converged, which means that beyond that point, atmospheric initial conditions are irrelevant to atmospheric statistical prediction. Regional predictions show considerable variation in behavior. Both of the (northern) winter storm-track regions show a close-to-quasi-linear decline in predictability toward a cutoff at around 40 days. The (southern) summer storm track shows a much more exponential and considerably slower decline with a small amount of predictability still in evidence even at 90 days. Because the winter storm tracks dominate global variance the behavior of their predictability tends to dominate the global measure, except at longer lags. Variability in predictability with respect to initial conditions is also examined, and it is found that this is related more strongly to ensemble signal rather than ensemble spread. This result may serve to explain why the relation between weather forecast skill and ensemble spread is often observed to be significantly less than perfect. Results herein suggest that the ensemble signal as well as spread variations may be a major contributor to skill variations. Finally, it is found that the sensitivity of the calculated global predictability to changes in model horizontal resolution is not large; results from a T85 resolution model are not qualitatively all that different from the T42 case.

scholarly journals The Brazilian Global Atmospheric Model (BAM): Performance for Tropical Rainfall Forecasting and Sensitivity to Convective Scheme and Horizontal Resolution

2016 ◽  
Vol 31 (5) ◽  
pp. 1547-1572 ◽  
Author(s):  
Silvio N. Figueroa ◽  
José P. Bonatti ◽  
Paulo Y. Kubota ◽  
Georg A. Grell ◽  
Hugh Morrison ◽  
...  
Keyword(s):  
South America ◽  
Weather Prediction ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Southeastern Brazil ◽  
Rainfall Forecasting ◽  
Dynamical Core ◽  
Tropical Rainfall ◽  
Global Atmospheric Model ◽  
Convective Scheme

Abstract This article describes the main features of the Brazilian Global Atmospheric Model (BAM), analyses of its performance for tropical rainfall forecasting, and its sensitivity to convective scheme and horizontal resolution. BAM is the new global atmospheric model of the Center for Weather Forecasting and Climate Research [Centro de Previsão de Tempo e Estudos Climáticos (CPTEC)], which includes a new dynamical core and state-of-the-art parameterization schemes. BAM’s dynamical core incorporates a monotonic two-time-level semi-Lagrangian scheme, which is carried out completely on the model grid for the tridimensional transport of moisture, microphysical prognostic variables, and tracers. The performance of the quantitative precipitation forecasts (QPFs) from two convective schemes, the Grell–Dévényi (GD) scheme and its modified version (GDM), and two different horizontal resolutions are evaluated against the daily TRMM Multisatellite Precipitation Analysis over different tropical regions. Three main results are 1) the QPF skill was improved substantially with GDM in comparison to GD; 2) the increase in the horizontal resolution without any ad hoc tuning improves the variance of precipitation over continents with complex orography, such as Africa and South America, whereas over oceans there are no significant differences; and 3) the systematic errors (dry or wet biases) remain virtually unchanged for 5-day forecasts. Despite improvements in the tropical precipitation forecasts, especially over southeastern Brazil, dry biases over the Amazon and La Plata remain in BAM. Improving the precipitation forecasts over these regions remains a challenge for the future development of the model to be used not only for numerical weather prediction over South America but also for global climate simulations.

scholarly journals Evaluation of the CAMS global atmospheric trace gas reanalysis 2003–2016 using aircraft campaign observations

2020 ◽  
Vol 20 (7) ◽  
pp. 4493-4521 ◽  
Author(s):  
Yuting Wang ◽  
Yong-Feng Ma ◽  
Henk Eskes ◽  
Antje Inness ◽  
Johannes Flemming ◽  
...  
Keyword(s):  
Chemical Species ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Trace Gas ◽  
Weather Forecasts ◽  
Global Atmospheric Model ◽  
Reactive Gases ◽  
Monitoring Service ◽  
Atmospheric Trace Gas ◽  
Atmospheric Concentrations

Abstract. The Copernicus Atmosphere Monitoring Service (CAMS) operated by the European Centre for Medium-Range Weather Forecasts (ECMWF) has produced a global reanalysis of aerosol and reactive gases (called CAMSRA) for the period 2003–2016. Space observations of ozone, carbon monoxide, NO2 and aerosol optical depth are assimilated by a 4D-Var method in the 60-layer ECMWF global atmospheric model, which for the reanalysis is operated at a horizontal resolution of about 80 km. As a contribution to the evaluation of the reanalysis, we compare atmospheric concentrations of different reactive species provided by the CAMS reanalysis with independent observational data gathered by airborne instrumentation during the field campaigns INTEX-A, INTEX-B, NEAQS-ITCT, ITOP, AMMA, ARCTAS, VOCALS, YAK-AEROSIB, HIPPO and KORUS-AQ. We show that the reanalysis rather successfully reproduces the observed concentrations of chemical species that are assimilated in the system, including O3 and CO with biases generally less than 20 %, but generally underestimates the concentrations of the primary hydrocarbons and secondary organic species. In some cases, large discrepancies also exist for fast-reacting radicals such as OH and HO2.

scholarly journals An Evaluation of the Software System Dependency of a Global Atmospheric Model

2013 ◽  
Vol 141 (11) ◽  
pp. 4165-4172 ◽  
Author(s):  
Song-You Hong ◽  
Myung-Seo Koo ◽  
Jihyeon Jang ◽  
Jung-Eun Esther Kim ◽  
Hoon Park ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Initial Conditions ◽  
Seasonal Prediction ◽  
Atmospheric Model ◽  
Software Systems ◽  
Processing Unit ◽  
Software System ◽  
Ensemble Spread ◽  
Rounding Errors ◽  
Central Processing

Abstract This study presents the dependency of the simulation results from a global atmospheric numerical model on machines with different hardware and software systems. The global model program (GMP) of the Global/Regional Integrated Model system (GRIMs) is tested on 10 different computer systems having different central processing unit (CPU) architectures or compilers. There exist differences in the results for different compilers, parallel libraries, and optimization levels, primarily a result of the treatment of rounding errors by the different software systems. The system dependency, which is the standard deviation of the 500-hPa geopotential height averaged over the globe, increases with time. However, its fractional tendency, which is the change of the standard deviation relative to the value itself, remains nearly zero with time. In a seasonal prediction framework, the ensemble spread due to the differences in software system is comparable to the ensemble spread due to the differences in initial conditions that is used for the traditional ensemble forecasting.

scholarly journals Vorticity-Divergence semi-Lagrangian Global Atmospheric Model SL-AV20: Dynamical Core

2016 ◽  
Author(s):  
Mikhail Tolstykh ◽  
Vladimir Shashkin ◽  
Rostislav Fadeev ◽  
Gordey Goyman
Keyword(s):  
Numerical Solutions ◽  
Climate Modeling ◽  
Region Of Interest ◽  
Weather Forecast ◽  
Seasonal Prediction ◽  
Atmospheric Model ◽  
Medium Range Weather Forecast ◽  
Variable Resolution ◽  
Dynamical Core ◽  
Global Atmospheric Model

Abstract. SL-AV (Semi-Lagranginan Absolute Vorticity) is a global atmospheric model. Its latest version SL-AV20 provides global operational medium-range weather forecast with 20 km resolution over Russia. The lower resolution configurations of SL-AV20 are being tested for seasonal prediction and climate modeling. The article presents the model dynamical core. Its main features are vorticity-divergence formulation at the unstaggered grid, high-order finite-difference approximations, semi-Lagrangian semi-implicit discretization and the reduced latitude-longitude grid with variable resolution in latitude. The accuracy of SL-AV20 numerical solutions using reduced lat-lon grid and the variable resolution in latitude is tested with two idealized testcases. The results agree well with other published model solutions. It is shown that the use of the reduced grid having up to 25 % less grid points than the regular grid does not significantly affect the accuracy. Variable resolution in latitude allows to improve the accuracy of solution in the region of interest.

scholarly journals Seasonal range test run with Global Eta Framework

2017 ◽  
Vol 14 ◽  
pp. 247-251 ◽  
Author(s):  
Dragan Latinović ◽  
Sin Chan Chou ◽  
Miodrag Rančić
Keyword(s):  
Model Performance ◽  
Seasonal Prediction ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Curvilinear Coordinates ◽  
Smooth Transition ◽  
Precipitation Pattern ◽  
Global Atmospheric Model ◽  
Coordinate Lines ◽  
Cubed Sphere

Abstract. Global Eta Framework (GEF) is a global atmospheric model developed in general curvilinear coordinates and capable of running on arbitrary rectangular quasi-uniform spherical grids, using stepwise (Eta) representation of the terrain. In this study, the model is run on a cubed-sphere grid topology, in a version with uniform Jacobians (UJ), which provides equal-area grid cells, and a smooth transition of coordinate lines across the edges of the cubed-sphere. Within a project at the Brazilian Center for Weather Forecasts and Climate Studies (CPTEC), a nonhydrostatic version of this model is under development and will be applied for seasonal prediction studies. This note describes preliminary tests with the GEF on the UJ cubed-sphere in which model performance is evaluated in seasonal simulations at a horizontal resolution of approximately 25 km, running in the hydrostatic mode. Comparison of these simulations with the ERA-Interim reanalyses shows that the 850 hPa temperature is underestimated, while precipitation pattern is mostly underestimated in tropical continental regions and overestimated in tropical oceanic regions. Nevertheless, the model is still able to well capture the main seasonal climate characteristics. These results will be used as a control run in further tests with the nonhydrostatic version of the model.

scholarly journals Assimilation of GPSRO Bending Angle Profiles into the Brazilian Global Atmospheric Model

2019 ◽  
Vol 11 (3) ◽  
pp. 256 ◽  
Author(s):  
Ivette Banos ◽  
Luiz Sapucci ◽  
Lidia Cucurull ◽  
Carlos Bastarz ◽  
Bruna Silveira
Keyword(s):  
Data Assimilation ◽  
Weather Forecast ◽  
Atmospheric Model ◽  
Meridional Wind ◽  
Point Of View ◽  
Bending Angle ◽  
Global Atmospheric Model ◽  
Angle Data ◽  
Meteorological Systems ◽  
Bending Angles

The Global Positioning System (GPS) Radio Occultation (RO) technique allows valuable information to be obtained about the state of the atmosphere through vertical profiles obtained at various processing levels. From the point of view of data assimilation, there is a consensus that less processed data are preferable because of their lowest addition of uncertainties in the process. In the GPSRO context, bending angle data are better to assimilate than refractivity or atmospheric profiles; however, these data have not been properly explored by data assimilation at the CPTEC (acronym in Portuguese for Center for Weather Forecast and Climate Studies). In this study, the benefits and possible deficiencies of the CPTEC modeling system for this data source are investigated. Three numerical experiments were conducted, assimilating bending angles and refractivity profiles in the Gridpoint Statistical Interpolation (GSI) system coupled with the Brazilian Global Atmospheric Model (BAM). The results highlighted the need for further studies to explore the representation of meteorological systems at the higher levels of the BAM model. Nevertheless, more benefits were achieved using bending angle data compared with the results obtained assimilating refractivity profiles. The highest gain was in the data usage exploring 73.4% of the potential of the RO technique when bending angles are assimilated. Additionally, gains of 3.5% and 2.5% were found in the root mean square error values in the zonal and meridional wind components and geopotencial height at 250 hPa, respectively.

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 Evaluation of the CAMS global atmospheric trace gas reanalysis 2003–2016 using aircraft campaign observations

2019 ◽  
Author(s):  
Yuting Wang ◽  
Yong-Feng Ma ◽  
Henk Eskes ◽  
Antje Inness ◽  
Johannes Flemming ◽  
...  
Keyword(s):  
Chemical Species ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Trace Gas ◽  
Weather Forecasts ◽  
Global Atmospheric Model ◽  
Reactive Gases ◽  
Monitoring Service ◽  
Atmospheric Trace Gas ◽  
Atmospheric Concentrations

Abstract. The Copernicus Atmosphere Monitoring Service (CAMS) operated by the European Centre for Medium Range Weather Forecasts (ECMWF) has produced a global reanalysis of aerosol and reactive gases (called CAMSRA) for the period 2003–2016. Space observations of ozone, carbon monoxide, NO2 and aerosol optical depth are assimilated by a 4-D Var method in the 60-layer ECMWF global atmospheric model, which for the reanalysis is operated at a horizontal resolution of about 80 km. As a contribution to the evaluation of the reanalysis, we compare atmospheric concentrations of different reactive species provided by the CAMS reanalysis with independent observational data gathered by airborne instrumentation during the field campaigns INTEX-A, INTEX-B, NEAQS-ITCT, ITOP, AMMA, ARCTAS, VOCALS, YAK-AEROSIB, HIPPO and KORUS-AQ. We show that the reanalysis reproduces rather successfully the observed concentrations of chemical species that are assimilated in the system including O3 and CO with the biases generally less than 20 %, but generally underestimate the concentrations of the primary hydrocarbons and secondary organic species. In some cases, large discrepancies also exist for fast-reacting radicals such as OH and HO2.

scholarly journals Technical note: Lowermost-stratosphere moist bias in ECMWF IFS model diagnosed from airborne GLORIA observations during winter–spring 2016

2020 ◽  
Vol 20 (23) ◽  
pp. 15379-15387
Author(s):  
Wolfgang Woiwode ◽  
Andreas Dörnbrack ◽  
Inna Polichtchouk ◽  
Sören Johansson ◽  
Ben Harvey ◽  
...  
Keyword(s):  
Water Vapor ◽  
Initial Conditions ◽  
Weather Forecast ◽  
Horizontal Resolution ◽  
Technical Note ◽  
Cold Bias ◽  
Weather Forecasts ◽  
Time Period ◽  
Forecasting System ◽  
Numerical Weather Forecast

Abstract. Numerical weather forecast systems like the ECMWF IFS (European Centre for Medium-Range Weather Forecasts – Integrated Forecasting System) are known to be affected by a moist bias in the extratropical lowermost stratosphere (LMS) which results in a systematic cold bias there. We use high-spatial-resolution water vapor measurements by the airborne infrared limb-imager GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere) during the PGS (POLSTRACC/GW-LCYCLE-II/SALSA) campaign to study the LMS moist bias in ECMWF analyses and 12 h forecasts from January to March 2016. Thereby, we exploit the two-dimensional observational capabilities of GLORIA, when compared to in situ observations, and the higher vertical and horizontal resolution, when compared to satellite observations. Using GLORIA observations taken during five flights in the polar sub-vortex region around Scandinavia and Greenland, we diagnose a systematic moist bias in the LMS exceeding +50 % (January) to +30 % (March) at potential vorticity levels from 10 PVU (∼ highest level accessed with suitable coverage) to 7 PVU. In the diagnosed time period, the moist bias decreases at the highest and driest air masses observed but clearly persists at lower levels until mid-March. Sensitivity experiments with more frequent temporal output, and lower or higher horizontal and vertical resolution, show the short-term forecasts to be practically insensitive to these parameters on timescales of < 12 h. Our results confirm that the diagnosed moist bias is already present in the initial conditions (i.e., the analysis) and thus support the hypothesis that the cold bias develops as a result of forecast initialization. The moist bias in the analysis might be explained by a model bias together with the lack of water vapor observations suitable for assimilation above the tropopause.

scholarly journals Ensemble forecasting greatly expands the prediction horizon for ocean mesoscale variability

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Prasad G. Thoppil ◽  
Sergey Frolov ◽  
Clark D. Rowley ◽  
Carolyn A. Reynolds ◽  
Gregg A. Jacobs ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Mesoscale Dynamics ◽  
Predictive Skill ◽  
Freshwater Transport ◽  
Prediction Horizon ◽  
Initial Location ◽  
Ocean Models ◽  
High Uncertainty

AbstractMesoscale eddies dominate energetics of the ocean, modify mass, heat and freshwater transport and primary production in the upper ocean. However, the forecast skill horizon for ocean mesoscales in current operational models is shorter than 10 days: eddy-resolving ocean models, with horizontal resolution finer than 10 km in mid-latitudes, represent mesoscale dynamics, but mesoscale initial conditions are hard to constrain with available observations. Here we analyze a suite of ocean model simulations at high (1/25°) and lower (1/12.5°) resolution and compare with an ensemble of lower-resolution simulations. We show that the ensemble forecast significantly extends the predictability of the ocean mesoscales to between 20 and 40 days. We find that the lack of predictive skill in data assimilative deterministic ocean models is due to high uncertainty in the initial location and forecast of mesoscale features. Ensemble simulations account for this uncertainty and filter-out unconstrained scales. We suggest that advancements in ensemble analysis and forecasting should complement the current focus on high-resolution modeling of the ocean.

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