ALBERT

Beschreibung: Every cloud parameterization contains structural model errors. The source of these errors is difficult to pinpoint because cloud parameterizations contain nonlinearities and feedbacks. To elucidate these model inadequacies, this paper uses a general-purpose ensemble parameter estimation technique. In principle, the technique is applicable to any parameterization that contains a number of adjustable coefficients. It optimizes or calibrates parameter values by attempting to match predicted fields to reference datasets. Rather than striving to find the single best set of parameter values, the output is instead an ensemble of parameter sets. This ensemble provides a wealth of information. In particular, it can help uncover model deficiencies and structural errors that might not otherwise be easily revealed. The calibration technique is applied to an existing single-column model (SCM) that parameterizes boundary layer clouds. The SCM is a higher-order turbulence closure model. It is closed using a multivariate probability density function (PDF) that represents subgrid-scale variability. Reference datasets are provided by large-eddy simulations (LES) of a variety of cloudy boundary layers. The calibration technique locates some model errors in the SCM. As a result, empirical modifications are suggested. These modifications are evaluated with independent datasets and found to lead to an overall improvement in the SCM’s performance.

Beschreibung: This study addresses the issue of cloud parameterization in general circulation models utilizing a twofold approach. Four versions of the Florida State University (FSU) global spectral model (GSM) were used, including four different cloud parameterization schemes in order to construct ensemble forecasts of cloud covers. Next, a superensemble approach was used to combine these model forecasts based on their past performance. It was shown that it is possible to substantially reduce the 1–5-day forecast errors of phase and amplitude of the diurnal cycle of clouds from the use of a multimodel superensemble. Further, the statistical information generated in the construction of a superensemble was used to develop a unified cloud parameterization scheme for a single model. This new cloud scheme, when implemented in the FSU GSM, carried a higher forecast accuracy compared to those of the individual cloud schemes and their ensemble mean for the diurnal cycle of cloud cover up to day 5 of the forecasts. This results in a 5–10 W m−2 improvement in the root-mean-square error to the upward longwave and shortwave flux at the top of the atmosphere, especially over deep convective regions. It is shown that while the multimodel superensemble is still the best product in forecasting the diurnal cycle of clouds, a unified cloud parameterization scheme, implemented in a single model, also provides higher forecast accuracy compared to the individual cloud models. Moreover, since this unified scheme is an integral part of the model, the forecast accuracy of the single model improves in terms of radiative fluxes and thus has greater impacts on weather and climate time scales. This new cloud scheme will be tested in real-time simulations.

Beschreibung: A discrete form of the flux-divergence operator is developed to compute advection of tracers on spherical hexagonal–pentagonal grids. An upwind-biased advection scheme based on a piecewise linear approximation for one-dimensional regular grids is extended simply for spherical hexagonal–pentagonal grids. The distribution of a tracer over the upwind side of a cell face is linearly approximated using a nodal value and a gradient at a computational node on the upwind side. A piecewise linear approximation is relaxed to a local linear approximation, and the relaxation precludes the complicated conditional branching present in remapping schemes. Results from a cosine bell advection test show that the new scheme compares favorably with other upwind-biased schemes for spherical hexagonal–pentagonal grids.

Beschreibung: A physically based empirical real-time forecasting strategy to predict the subseasonal variations of the Indian summer monsoon up to four–five pentads (20–25 days) in advance has been developed. The method is based on the event-to-event similarity in the properties of monsoon intraseasonal oscillations (ISOs). This two-tier analog method is applied to NOAA outgoing longwave radiation (OLR) pentad averaged data that have sufficiently long records of observation and are available in nearly real time. High-frequency modes in the data are eliminated by reconstructing the data using the first 10 empirical orthogonal functions (EOFs), which together explain about 75% of the total variance. In the first level of the method, the spatial analogs of initial condition pattern are identified from the modeling data. The principal components (PCs) of these spatial analogs, whose evolution history of the latest five pentads matches that of the initial condition pattern, are considered the temporal PC analogs. Predictions are generated for each PC as the average evolution of PC analogs for the given lead time. Predicted OLR values are constructed using the EOFs and predicted PCs. OLR data for 1979–99 are used as the modeling data and independent hindcasts are generated for the period 2000–05. The skill of anomaly predictions is rather high over the central and northern Indian region for lead times of four–five pentads. The phases and amplitude of intraseasonal convective spells are predicted well, especially the long midseason break of 2002 that resulted in large-scale drought conditions. Skillful predictions can be made up to five pentads when started from an active initial state, whereas the limit of useful predictions is about two–three pentads when started from break initial conditions. An important feature of this method is that unlike some other empirical methods to forecast monsoon ISOs, it uses minimal time filtering to avoid any possible endpoint effects and hence may be readily used for real-time applications. Moreover, as the modeling data grow with time as a result of the increased number of observations, the number of analogs would also increase and eventually the quality of forecasts would improve.

Beschreibung: The seasonal predictability of cold spring seasons (March–May) in Europe from hindcasts/forecasts of three operational coupled general circulation models (CGCMs) is investigated. The models used in the investigation are the Met Office Global Seasonal Forecast System (GloSea), the ECMWF System-2 (S2), and the NCEP Climate Forecast System (CFS). Using the relative operating characteristic score and the Brier skill score the long-term prediction skill for spring 2-m temperature in the lower quintile (20%) is assessed. Over much of central and eastern Europe the predictive skill is found to be high. The skill of the Met Office GloSea and ECMWF S2 models significantly surpasses that of damped persistence over much of Europe but the NCEP CFS model outperforms this reference forecast only over a small area. The higher potential predictability of cold spring seasons in eastern relative to southwestern Europe can be attributed to snow effects as areas of high skill closely correspond with the climatological snow line, and snow is shown in this paper to be linked to cold spring 2-m temperatures in eastern Europe. The ability of the models to represent snow cover during the melt season is also investigated. The Met Office GloSea and the ECMWF S2 models are able to accurately mimic the observed pattern of monthly snow-cover interannual variability, but the NCEP CFS model predicts too short a snow season. Improvements in the snow analysis and land surface parameterizations could increase the skill of seasonal forecasts for cold spring temperatures.

Beschreibung: The landfall of Hurricane Katrina (2005) near New Orleans, Louisiana, on 29 August 2005 will be remembered as one of the worst natural disasters in the history of the United States. By comparison, the extratropical transition (ET) of the system as it accelerates poleward over the following days is innocuous and the system weakens until its eventual demise off the coast of Greenland. The extent of Katrina’s perturbation of the midlatitude flow would appear to be limited given the lack of reintensification or downstream development during ET. However, the slow progression of a strong upper-tropospheric warm pool across the North Atlantic Ocean in the week following Katrina’s landfall prompts the question of whether even a nonreintensifying ET event can lead to significant modification of the midlatitude flow. Analysis of Hurricane Katrina’s outflow layer after landfall suggests that it does not itself make up the long-lived midlatitude warm pool. However, the interaction between Katrina’s anticyclonic outflow and an approaching baroclinic trough is shown to establish an anomalous southwesterly conduit or “freeway” that injects a preexisting tropospheric warm pool over the southwestern United States into the midlatitudes. This warm pool reduces predictability in medium-range forecasts over the North Atlantic and Europe while simultaneously aiding in the development of Hurricanes Maria and Nate. The origin of the warm pool is shown to be the combination of anticyclonic upper-level features generated by eastern Pacific Hurricane Hilary and the south Asian anticyclone (SAA). The hemispheric nature of the connections involved with the development of the warm pool and its injection into the extratropics has an impact on forecasting, since the predictability issue associated with ET in this case involves far more than the potential reintensification of the transitioning system itself.

Beschreibung: The sensitivity of numerical weather forecasts to small changes in initial conditions is estimated using ensemble samples of analysis and forecast errors. Ensemble sensitivity is defined here by linear regression of analysis errors onto a given forecast metric. It is shown that ensemble sensitivity is proportional to the projection of the analysis-error covariance onto the adjoint-sensitivity field. Furthermore, the ensemble-sensitivity approach proposed here involves a small calculation that is easy to implement. Ensemble- and adjoint-based sensitivity fields are compared for a representative wintertime flow pattern near the west coast of North America for a 90-member ensemble of independent initial conditions derived from an ensemble Kalman filter. The forecast metric is taken for simplicity to be the 24-h forecast of sea level pressure at a single point in western Washington State. Results show that adjoint and ensemble sensitivities are very different in terms of location, scale, and magnitude. Adjoint-sensitivity fields reveal mesoscale lower-tropospheric structures that tilt strongly upshear, whereas ensemble-sensitivity fields emphasize synoptic-scale features that tilt modestly throughout the troposphere and are associated with significant weather features at the initial time. Optimal locations for targeting can easily be determined from ensemble sensitivity, and results indicate that the primary targeting locations are located away from regions of greatest adjoint and ensemble sensitivity. It is shown that this method of targeting is similar to previous ensemble-based methods that estimate forecast-error variance reduction, but easily allows for the application of statistical confidence measures to deal with sampling error.

Beschreibung: Stonitsch and Markowski perform multiple-Doppler radar analyses of a cold front over Oklahoma and Kansas. Despite their interesting results, their explanations include a number of misconceptions about cold fronts. These misconceptions include the proper interpretation of the frontogenesis function, the role of entrainment versus differential surface sensible heat flux toward weakening the virtual potential temperature gradient across a cold front, a separation of the wind shift from the virtual potential temperature gradient, and the factors that affect the motion of the cold front.

Beschreibung: The devastating effects of Hurricane Katrina (2005) on the Gulf Coast of the United States are without compare for natural disasters in recent times in North America. With over 1800 dead and insured losses near $40 billion (U.S. dollars), Katrina ranks as the costliest and one of the deadliest Atlantic hurricanes in history. This study documents the complex life cycle of Katrina, a storm that was initiated by a tropical transition event in the Bahamas. Katrina intensified to a category-1 hurricane shortly before striking Miami, Florida; however, little weakening was observed as the system crossed the Florida peninsula. An analog climatology is used to show that this behavior is consistent with the historical record for storms crossing the southern extremity of the peninsula. Over the warm Gulf of Mexico waters, Katrina underwent two periods of rapid intensification associated with a warm core ring shed by the Loop Current. Between these spinup stages, the storm doubled in size, leading to a monotonic increase in power dissipation until Katrina reached a superintense state on 28 September. A pair of extremely destructive landfalls in Louisiana followed the weakening of the system over shelf waters. Despite its strength as a hurricane, Katrina did not reintensify following extratropical transition. The evolution of the storm’s outflow anticyclone, however, led to a perturbation of the midlatitude flow that is shown in a companion study to influence the Northern Hemisphere over a period of 2 weeks. An understanding of the varied components of Katrina’s complex evolution is necessary for further developing analysis and forecasting techniques as they apply to storms that form near the North American continent and rapidly intensify over the Gulf of Mexico. Given the observed overall increase in Atlantic hurricane activity since the mid-1990s, an enhanced appreciation for the forcings involved in such events could help to mitigate the impact of similar severe hurricanes in the future.