Automatic dynamic stack management in large scientific applications: A case study using a global spectral model

Abstract. Recent advances in high-performance computers facilitate operational numerical weather prediction by global hydrostatic atmospheric models with horizontal resolutions of ∼ 10 km. Given further advances in such computers and the fact that the hydrostatic balance approximation becomes invalid for spatial scales < 10 km, the development of global nonhydrostatic models with high accuracy is urgently required. The Global 7 km mesh nonhydrostatic Model Intercomparison Project for improving TYphoon forecast (TYMIP-G7) is designed to understand and statistically quantify the advantages of high-resolution nonhydrostatic global atmospheric models to improve tropical cyclone (TC) prediction. A total of 137 sets of 5-day simulations using three next-generation nonhydrostatic global models with horizontal resolutions of 7 km and a conventional hydrostatic global model with a horizontal resolution of 20 km were run on the Earth Simulator. The three 7 km mesh nonhydrostatic models are the nonhydrostatic global spectral atmospheric Double Fourier Series Model (DFSM), the Multi-Scale Simulator for the Geoenvironment (MSSG) and the Nonhydrostatic ICosahedral Atmospheric Model (NICAM). The 20 km mesh hydrostatic model is the operational Global Spectral Model (GSM) of the Japan Meteorological Agency. Compared with the 20 km mesh GSM, the 7 km mesh models reduce systematic errors in the TC track, intensity and wind radii predictions. The benefits of the multi-model ensemble method were confirmed for the 7 km mesh nonhydrostatic global models. While the three 7 km mesh models reproduce the typical axisymmetric mean inner-core structure, including the primary and secondary circulations, the simulated TC structures and their intensities in each case are very different for each model. In addition, the simulated track is not consistently better than that of the 20 km mesh GSM. These results suggest that the development of more sophisticated initialization techniques and model physics is needed to further improve the TC prediction.

The mainstream data assimilation system in operation will still employ four-dimensional variational data assimilation(4DVAR) method in a long time of future. We develop a new 4DVAR system, i.e., YH4DVAR, using global spectral model as a constraint to impose a dynamic balance on the assimilation. The cost function of YH4DVAR consists of four terms: background, observations, digital filter, and bias correction, respectively. YH4DVAR employs the wavelet background error convariance, the multi-resolution incremental formulation, the tangent linear and adjoint models for dynamics core and physical processes, and ATOVS radiance data assimilation. Simultaneous spatial and spectral variations of horizontal and vertical covariance are achieved by dividing the control vector into several parts, each of which corresponds to a band of total spherical wavenumbers. Using NWP consisting of YH4DVAR and global spectral model, the anomaly correlation with the verifying analysis for geopotential height 8-day forecast on the 500 hPa isobaric surface at 12-month mean is above 60%. The formulation and implementation of YH4VAR are described in detail in this paper.

Does a regional climate model provide a more useful regional climatology than can be obtained from larger‐scale global analyses or a better regional forecast than can be obtained by a large‐scale seasonal prediction model? To examine these questions, U.S. regional spectral model (RSM) climate simulations forced by 1‐day global spectral model (GSM) forecasts initialized from the National Centers for Environmental Prediction (NCEP) operational analysis are compared with regional RSM simulations forced by the NASA Seasonal to Interannual Prediction Project general circulation model (GCM). The GCM was continuously forced by observed sea surface temperature variations, but since sea surface temperatures (SSTs) are so persistent, these forced GCM simulations were equivalent to seasonal GCM predictions, which are mainly based upon persistent SSTs anyhow. RSM simulations forced by these two global modes were compared at the same spatial resolution as the global models (200 km) and at higher resolution (50 km). Resolution was important for producing better geographic pictures but does not currently produce significantly more skillful regional climate simulations or forecasts of the temporal variability, which already have significant skill from the global models. However, regional climate simulations and forecasts better depict the precipitation intensity, especially over the U.S. West. Finally, re‐initialized (from the large‐scale analysis) 1‐day RSM forecasts were compared with continuous RSM simulations, and this increased the overall regional skill back to the original GSM skill, which was somewhat degraded in continuous RSM simulations.

Inaccuracies in the data at the boundaries of a limited area may be a major source of forecast errors in that area. With a global spectral shallow water model we show that the major part of these local errors propagate from the boundaries at a speed equal to the local wind speed with a maximum speed of propagation along the jet stream. Thus, taking into account the forecast length and the accuracy of the data at the boundaries in limited‐area models, one needs to adjust the extent of the buffer region where errors propagate and contaminate the forecast.

In this article, we describe an implementation of two‐way coupling between the Yin–He Global Spectral Model (YHGSM) and the Wave Model (WAM). The YHGSM atmosphere model gives the surface air water density and 10‐m wind for the WAM model, while the WAM model provides the Charnock coefficient as a response. The effects on the meteorological aptitude of the two‐way coupled system are assessed statistically. The coupled model exhibits a 75% significant positive impact on both the northern (NthH) and the southern hemisphere (SthH) for all metrics at almost all pressure levels. Typhoon track assessments of the coupled model will be implemented later. The results show that the coupling of the WAM model benefits typhoon landfall prediction. Finally, we make a comprehensive comparison of the significant wave height (SWH) between the YHGSM–WAM coupled model and the European Centre for Medium‐Range Weather Forecasts (ECMWF) model with buoy data from the Chinese Ocean Monitoring Network. The comparison shows that the YHGSM–WAM coupled model exhibits more accuracy and predictive stability of SWH forecasts against buoy observations than the ECMWF forecasts. The above results indicate that the YHGSM–WAM coupled model possesses superior performance to uncoupled models.

¶This paper deals with the systematic evolution of the North American monsoon system by analyzing the evolution of tropospheric circulation, precipitation, moisture fluxes and the adjoining sea surface temperatures from observations and the Florida State University Nested Regional Spectral Model embedded within a coupled atmosphere–ocean coupled model. The atmospheric part is a global spectral model with triangular truncation T63 and a nested regional spectral model. The global atmospheric model is coupled with the Hamburg’s Global Ocean Model. This model was integrated for six-months with 50 km resolution in the North American monsoon domain. The NCEP/NCAR reanalysis were used as the observed state for comparison. The analyzed model results shows that the above model has the capability of capturing some of the features of the North American monsoon system. Besides the evolution of the monsoon system, the out-of-phase relationship between the Mexican monsoon with south-central United States precipitation and the largest moisture supply from the Gulf of Mexico are the principal findings of this study. It is also noted that during the mature phase of the monsoon, the Gulf of California and the Pacific Ocean dominates the Gulf of Mexico in order of moisture supply but immediately after the mature phase, the Gulf of Mexico dominates.

This study explores the impact of physical initialization on the numerical weather prediction of tropical rainfall An important finding of this study is related to the point correlations of the model-based rainfall at the initial time and at the end of a one day forecast which are significantly improved with the use of physical initialization within the tropics. Physical initialization refers to the use of a number of ‘reverse’ algorithms during an assimilation phase of the model forecast. The goal is to improve the definition of the initial state by the assimilation of proposed or currently available surface and satellite-based observations during a pre-integration phase of a forecast using a global spectral model.

BL Lacertae was the target of an extensive multiwavelength monitoring campaign in the second half of 2000. In this paper, we are using leptonic and hadronic jet models to fit the observed broadband spectra and spectral variability patterns. We start out with global spectral models. Subsequently, we investigate various flaring scenarios for comparison with the observed short-term variability. For our leptonic jet model, we find that the short-term variability, in particular the optical and X-ray spectral variability, can be best represented with a flaring scenario dominated by a spectral-index change of the spectrum of ultrarelativistic electrons injected into the jet. Based on this result, a detailed model simulation, reproducing the observed optical and X-ray spectral variability and broadband SED of BL Lacertae simultaneously, is presented. Our leptonic modeling results are compared to fits using the hadronic synchrotron-proton blazar (SPB) model. That model can reproduce the observed SEDs of BL Lacertae in a scenario with muon-synchrotron dominated high-energy emission. It requires a significantly higher magnetic field than the leptonic model (~ 40 G vs. ~ 2 G in the leptonic model) and a lower Doppler factor associated with the bulk motion of the emission region (D ~ 8 vs. D ~ 18 in the leptonic model). The hadronic model predicts a significantly larger > 100 GeV flux than the leptonic models, well within the anticipated capabilities of VERITAS and MAGIC.

The impact of initial condition uncertainty (ICU) on quantitative precipitation forecasts (QPFs) is examined for a case of explosive cyclogenesis that occurred over the contiguous United States and produced widespread, substantial rainfall. The Pennsylvania State University‐National Center for Atmospheric Research (NCAR) Mesoscale Model Version 4 (MM4), a limited-area model, is run at 80-km horizontal resolution and 15 layers to produce a 25-member, 36-h forecast ensemble. Lateral boundary conditions for MM4 are provided by ensemble forecasts from a global spectral model, the NCAR Community Climate Model Version 1 (CCM1). The initial perturbations of the ensemble members possess a magnitude and spatial decomposition that closely match estimates of global analysis error, but they are not dynamically conditioned. Results for the 80-km ensemble forecast are compared to forecasts from the then operational Nested Grid Model (NGM), a single 40-km/15layer MM4 forecast, a single 80-km/29-layer MM4 forecast, and a second 25-member MM4 ensemble based on a different cumulus parameterization and slightly different unperturbed initial conditions. Large sensitivity to ICU marks ensemble QPF. Extrema in 6-h accumulations at individual grid points vary by as much as 3.000. Ensemble averaging reduces the root-mean-square error (rmse) for QPF. Nearly 90% of the improvement is obtainable using ensemble sizes as small as 8‐10. Ensemble averaging can adversely affect the bias and equitable threat scores, however, because of its smoothing nature. Probabilistic forecasts for five mutually exclusive, completely exhaustive categories are found to be skillful relative to a climatological forecast. Ensemble sizes of approximately 10 can account for 90% of improvement in categorical forecasts relative to that for the average of individual forecasts. The improvements due to short-range ensemble forecasting (SREF) techniques exceed any due to doubling the resolution, and the error growth due to ICU greatly exceeds that due to different resolutions. If the authors’ results are representative, they indicate that SREF can now provide useful QPF guidance and increase the accuracy of QPF when used with current analysis‐forecast systems.

Using a global spectral model with wave-CISK formulation we have generated an eastward de which. Resembles the observed 30-50 day mode. This has a scale of global wave number one and two years structure in the vertical. It has the structure of a composite of Kelvin and Rossby waves. This composite system moves eastwards.
 We have also studied a linear two-level analytical model to understand the nonlinear spectral model response. In the linear as well as in the nonlinear spectral model, as we Increase the moisture availability factor the speeds of the waves decrease. In the linear model this speed is found to be independent of drag for all types of waves. In the nonlinear spectral model for a given drag there is a critical value of the moisture availability factor for which the wave becomes stationary and beyond which even shows westward propagation. Thus both moisture availability and nonlinearity appear to contribute to the slow eastward speed of the equatorial 30-50 day mode.

In this chapter we provide an introduction to the topic of spherical harmonics as basis functions for a global spectral model. The spherical harmonics are made up of trigonometric functions along the zonal direction and associated Legendre functions in the meridional direction. A number of properties of these functions need to be understood for the formulation of a spectral model. This chapter describes some useful properties that will be used to illustrate the procedure for the representation of data sets over a sphere with spherical harmonics as basis functions. The calculations of Fourier and Legendre transforms and their inverse transforms are an important part of global spectral modeling, and these are covered in some detail in this chapter. Finally, this chapter addresses the formulation of two simple spectral models. One of these is a single-level barotropic model, and the other is a shallow-water model.

A global spectral model with selective high resolution region Is appropriate for the study of a sharp local weather system if it is not very sensitive to the atmospheric state outside a limited active region. If the linear dimension of the resolution required is 1 and the area of active region is A then one finds that an adequate spectral model has the following features: (a) the model pole is moved away from the geographical north and re-located In the Vicinity of the weather system under study, (b) the set of new spherical harmonics ymn as basis functions should have the parallelogrammic truncated range | m | <=n <=N, 0<= | m | <=M, where N~, 2pi a|l.
 M|N = 4f(1~f),j=AI(4" a2), where a is the radius of the earth.
 
 Since the decomposition of a vector field, in a spherical coordinate system, into the radial and horizontal parts is rotationally invariant, the form of the equations of motion do not change under the re-location of the model pole except for the Coriolis force term. Mathematics of the modelling including truncation and management of non-linear terms do not change.

Investigation of the role of moist dynamics in the atmospheric processes during the abnormally intensive stormy period of November 1–5, 1994, over the Mediterranean region is presented. Analysis focuses on the eastern Mediterranean area employing 96‐hour model predictions over the Mediterranean region with the Florida State University (FSU) global spectral model. Sensitivity simulations differ only by prescribing the initial moisture fields in the selected regions located in the Arabian Sea, Red Sea, and over large areas in the low and high latitudes. Both the tropospheric trajectories and the model tests suggest that the heavy rains over the eastern Mediterranean were associated with unusually intensive propagation of air masses from the Arabian Sea region, which took place before and during the first 24 hours of the period. Air masses from the Arabian Sea participated also in the intensive developments in Italy and southern France on November 5, 1994. According to the model simulations the convective processes over the equatorial Africa play a major role in the developments. Also, the intensification of the upper tropospheric westerly winds due to the convection over the equatorial Africa stimulates the northward extension of the Red Sea trough which consequently diminishes the moisture influx for the tropical convection and therefore seems to serve as a negative feedback.

This study seeks to find the primary influence on the seasonal cycle of the oxygen isotopic composition of precipitation (δ18Op) along the western U.S. coast. Observed long‐term mean seasonal variations ofδ18Op from 16 different stations along the west coast are presented. The most robust features in the observations are high values in the summer and a drop in δ18Opduring the winter. The Isotope‐incorporated Global Spectral Model (IsoGSM) also simulates this wintertime drop inδ18Op along the west coast of the U.S. Sensitivity experiments are performed with IsoGSM where individual oxygen isotope fractionation processes are turned off. These simulations reveal that the primary control on the seasonal variations is equilibrium oxygen isotopic fractionation during vapor condensation. There is almost no influence of the temperature dependence of equilibrium fractionation on the seasonal δ18Op cycle for both evaporation and condensation. Additional experiments (including tagging simulations) are performed to better understand why Rayleigh distillation causes the seasonal variation in δ18Op. The tagging simulations and budget calculations reveal that vertical oxygen isotope gradients and variations in condensation height cause the seasonal cycle in δ18Op. This results from seasonal changes in the polar jet, and subsequent changes to divergence and vertical velocities, which affects the uplift of moisture. These findings suggest that δ18Op in the western U.S. is a tracer of condensation height on seasonal timescales. The large influence of condensation height on δ18Op seasonality complicates interpretations of interannual climate proxy records based on isotopes in precipitation as the seasonality is likely not static.

This essay reviews the origins of key concepts used in most global models. Philosophical preconditions for validity are examined. A framework for critically evaluating existing global models is suggested. A philosophy for global modeling is outlined. Global modeling is a much deeper enterprise than its critics seem to be aware. However, because global modeling lacks an academic or institutional home, key issues that have arisen with early global models are left essentially untouched. To critique global models or global modeling meaningfully, at least three perspectives are essential: those of science, philosophy, and practical application (praxis). A firm foundation for global modeling requires that its practitioners adopt an academic paradigm (i.e., university research institutes, schools and departments, a professional association, and a professional peer-reviewed journal). Global modeling is the only methodology capable of helping humanity to self-consciously envision itself and its environment in a time frame long enough and a scale large enough to provide an effective guide in its transition from a global to an interstellar species.