Abstract

In this paper, a version of the European Centre for Medium-Range Weather Forecasts (ECMWF) operational model is used to (i) diagnose the diabatic heating associated with the winter North Atlantic Oscillation (NAO) and (ii) assess the role of this heating in the dynamics of the NAO in the model. Over the North Atlantic sector, the NAO-related diabatic heating is dominated above the planetary boundary layer by the latent heat release associated with precipitation, and within the boundary layer by vertical diffusion associated with sensible heat flux from the ocean. An association between La Niña–El Niño–type conditions in the tropical Pacific and the positive/negative NAO is found in model runs using initial conditions and sea surface temperature (SST) lower boundary conditions from the period 1982–2001, but not in a companion set of model runs for the period 1962–81. Model experiments are then described in which the NAO-related diabatic heating diagnosed from the 1982–2001 control run is applied as a constant forcing in the model temperature equation using both 1982–2001 and 1962–81 model setups. To assess the local feedback from the diabatic heating, the specified forcing is first restricted to the North Atlantic sector alone. In this case, the model response (in an ensemble mean sense) is suggestive of a weak negative feedback, but exhibits more baroclinic structure and has its centers of action shifted compared to those of the NAO. On the other hand, forcing with only the tropical Pacific part of the diabatic heating leads to a robust model response in both the 1982–2001 and 1962–81 model setups. The model response projects on to the NAO with the same sign as that used to diagnose the forcing, arguing that the link between the tropical Pacific and the NAO is real in the 1982–2001 control run. The missing link in the corresponding run for 1962–81 is a result of a change in the tropical forcing between the two periods, and not the extratropical flow regime.

Abstract

Long-standing systematic model errors in both tropics and extratropics of the ECMWF model run at a horizontal resolution typical for climate models are investigated. Based on the hypothesis that the misrepresentation of unresolved scales contributes to the systematic model error, three model refinements aimed at their representation—fluctuating or deterministically—are investigated.

Increasing horizontal resolution to explicitly simulate smaller-scale features, representing subgrid-scale fluctuations by a stochastic parameterization, and improving the deterministic physics parameterizations all lead to a decrease in the systematic bias of the Northern Hemispheric circulation. These refinements reduce the overly zonal flow and improve the model’s ability to capture the frequency of blocking. However, the model refinements differ greatly in their impact in the tropics. While improving the deterministic and introducing stochastic parameterizations reduces the systematic precipitation bias and improves the characteristics of convectively coupled waves and tropical variability in general, increasing horizontal resolution has little impact.

The fact that different model refinements can lead to reductions in systematic model error is consistent with the hypothesis that unresolved scales play an important role. At the same time, this degeneracy of the response to different forcings can lead to compensating model errors. Hence, if one takes the view that stochastic parameterization should be an important element of next-generation climate models, if only to provide reliable estimates of model uncertainty, then a fundamental conclusion of this study is that stochasticity should be incorporated within the design of physical process parameterizations and improvements of the dynamical core and not added a posteriori.

Abstract

Regional frequency analysis (RFA) is widely used in the design of hydraulic structures at locations where streamflow records are not available. RFA estimates depend on the precise delineation of homogenous regions for accurate information transfer. This study proposes new physiographical variables based on river network features and tests their potential to improve the accuracy of hydrological feature estimates. Information about river network types is used both in the definition of homogenous regions and in the estimation process. Data from 105 river basins in arid and semiarid regions of the United States were used in our analysis. Artificial neural network ensemble models and canonical correlation analysis were used to produce flood quantile estimates, which were validated through tenfold cross and jackknife validations. We conducted analysis for model performance based on statistical indices, such as the Nash–Sutcliffe efficiency, root-mean-square error, relative root-mean-square error, mean absolute error, and relative mean bias. Among various combinations of variables, a model with 10 variables produced the best performance. Further, 49, 36, and 20 river networks in the 105 basins were classified as dendritic, pinnate, and trellis networks, respectively. The model with river network classification for the homogenous regions appeared to provide a superior performance compared with a model without such classification. The results indicated that including our proposed combination of variables could improve the accuracy of RFA flood estimates with the classification of the network types. This finding has considerable implications for hydraulic structure design.

Abstract

Experiments with the atmospheric component of the ECMWF Integrated Forecasting System (IFS) have been carried out to study the origin of the atmospheric circulation anomalies that led to the unusually cold European winter of 2005/06. Experiments with prescribed sea surface temperature (SST) and sea ice fields fail to reproduce the observed atmospheric circulation anomalies suggesting that the role of SST and sea ice was either not very important or the atmospheric response to SST and sea ice was not very well captured by the ECMWF model. Additional experiments are carried out in which certain regions of the atmosphere are relaxed toward analysis data thereby artificially suppressing the development of forecast error. The relaxation experiments suggest that both tropospheric circulation anomalies in the Euro–Atlantic region and the anomalously weak stratospheric polar vortex can be explained by tropical circulation anomalies. Separate relaxation experiments for the tropical stratosphere and tropical troposphere highlight the role of the easterly phase of quasi-biennial oscillation (QBO) and, most importantly, tropospheric circulation anomalies, especially over South America and the tropical Atlantic. From the results presented in this study, it is argued that the relaxation technique is a powerful diagnostic tool to understand possible remote origins of seasonal-mean anomalies.

Abstract

Experiments with the ECMWF model are carried out to study the influence that a correct representation of the lower boundary conditions, the tropical atmosphere, and the Northern Hemisphere stratosphere would have on extended-range forecast skill of the extratropical Northern Hemisphere troposphere during boreal winter. Generation of forecast errors during the course of the integration is artificially reduced by relaxing the ECMWF model toward the 40-yr ECMWF Re-Analysis (ERA-40) in certain regions. Prescribing rather than persisting sea surface temperature and sea ice fields leads to a modest forecast error reduction in the extended range, especially over the North Pacific and North America; no beneficial influence is found in the medium range. Relaxation of the tropical troposphere leads to reduced extended-range forecast errors especially over the North Pacific, North America, and the North Atlantic. It is shown that a better representation of the Madden–Julian oscillation is of secondary importance for explaining the results of the tropical relaxation experiments. The influence from the tropical stratosphere is negligible. Relaxation of the Northern Hemisphere stratosphere leads to forecast error reduction primarily in high latitudes and over Europe. However, given the strong influence from the troposphere onto the Northern Hemisphere stratosphere it is argued that stratospherically forced experiments are very difficult to interpret in terms of their implications for extended-range predictability of the tropospheric flow. The results are discussed in the context of future forecasting system development.

Abstract

Several data assimilation and forecast experiments are undertaken to determine the impact of special observations taken during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) on forecasts of the 5 June 2009 Goshen County, Wyoming, supercell. The data used in these experiments are those from the Mobile Weather Radar, 2005 X-band, Phased Array (MWR-05XP); two mobile mesonets (MM); and several mobile sounding units. Data sources are divided into “routine,” including those from operational Weather Surveillance Radar-1988 Dopplers (WSR-88Ds) and the Automated Surface Observing System (ASOS) network, and “special” observations from the VORTEX2 project.

VORTEX2 data sources are denied individually from a total of six ensemble square root filter (EnSRF) data assimilation and forecasting experiments. The EnSRF data assimilation uses 40 ensemble members on a 1-km grid nested inside a 3-km grid. Each experiment assimilates data every 5 min for 1 h, followed by a 1-h forecast. All experiments are able to reproduce the basic evolution of the supercell, though the impact of the VORTEX2 observations was mixed. The VORTEX2 sounding data decreased the mesocyclone intensity in the latter stages of the forecast, consistent with observations. The MWR-05XP data increased the forecast vorticity above approximately 1 km AGL in all experiments and had little impact on forecast vorticity below 1 km AGL. The MM data had negative impacts on the intensity of the low-level mesocyclone, by decreasing the vertical vorticity and indirectly by decreasing the buoyancy of the inflow.

Abstract

Current coupled global climate models have biases in their simulations of the tropical Pacific mean-state conditions as well as the El Niño–Southern Oscillation (ENSO) phenomenon. Specifically, in the Community Earth System Model (CESM version 1.2.2), the tropical Pacific mean state has overly weak sea surface temperature (SST) gradients in both the zonal and meridional directions, ENSO is too strong and too regular, and El Niño and La Niña events are too symmetrical. A previous study with a slab-ocean model showed that a higher elevation of the Andes can improve the tropical Pacific mean-state simulation by adjusting the atmospheric circulation and increasing the east–west and north–south SST gradients. Motivated by the link between the mean tropical Pacific climate and ENSO variations shown in previous studies, here we explored the influence of the Andes on the simulation of ENSO using the CESM 1.2.2 under full atmosphere–ocean coupling. In addition to improving the simulated tropical Pacific mean state by increasing the strength of the surface easterly and cross-equatorial southerly winds, the Higher Andes experiment decreases the amplitude of ENSO, increases the phase asymmetry, and makes ENSO events less regular, resulting in a simulated ENSO that is more consistent with observations. The weaker ENSO cycle is related to stronger damping in the Higher Andes experiment according to an analysis of the Bjerknes index. Our overall results suggest that increasing the height of the Andes reduces biases in the mean state and improves the representation of ENSO in the tropical Pacific.

Abstract

Ensemble-based probabilistic forecasts are performed for a mesoscale convective system (MCS) that occurred over Oklahoma on 8–9 May 2007, initialized from ensemble Kalman filter analyses using multinetwork radar data and different microphysics schemes. Two experiments are conducted, using either a single-moment or double-moment microphysics scheme during the 1-h-long assimilation period and in subsequent 3-h ensemble forecasts. Qualitative and quantitative verifications are performed on the ensemble forecasts, including probabilistic skill scores. The predicted dual-polarization (dual-pol) radar variables and their probabilistic forecasts are also evaluated against available dual-pol radar observations, and discussed in relation to predicted microphysical states and structures.

Evaluation of predicted reflectivity (Z) fields shows that the double-moment ensemble predicts the precipitation coverage of the leading convective line and stratiform precipitation regions of the MCS with higher probabilities throughout the forecast period compared to the single-moment ensemble. In terms of the simulated differential reflectivity (Z _DR) and specific differential phase (K _DP) fields, the double-moment ensemble compares more realistically to the observations and better distinguishes the stratiform and convective precipitation regions. The Z _DR from individual ensemble members indicates better raindrop size sorting along the leading convective line in the double-moment ensemble. Various commonly used ensemble forecast verification methods are examined for the prediction of dual-pol variables. The results demonstrate the challenges associated with verifying predicted dual-pol fields that can vary significantly in value over small distances. Several microphysics biases are noted with the help of simulated dual-pol variables, such as substantial overprediction of K _DP values in the single-moment ensemble.

Abstract

Polarimetric radar variables are simulated from members of the 2013 Center for Analysis and Prediction of Storms (CAPS) Storm-Scale Ensemble Forecasts (SSEF) with varying microphysics (MP) schemes and compared with observations. The polarimetric variables provide information on hydrometeor types and particle size distributions (PSDs), neither of which can be obtained through reflectivity (Z) alone. The polarimetric radar simulator pays close attention to how each MP scheme [including single- (SM) and double-moment (DM) schemes] treats hydrometeor types and PSDs. The recent dual-polarization upgrade to the entire WSR-88D network provides nationwide polarimetric observations, allowing for direct evaluation of the simulated polarimetric variables.

Simulations for a mesoscale convective system (MCS) and supercell cases are examined. Five different MP schemes—Thompson, DM Milbrandt and Yau (MY), DM Morrison, WRF DM 6-category (WDM6), and WRF SM 6-category (WSM6)—are used in the ensemble forecasts. Forecasts using the partially DM Thompson and fully DM MY and Morrison schemes better replicate the MCS structure and stratiform precipitation coverage, as well as supercell structure compared to WDM6 and WSM6. Forecasts using the MY and Morrison schemes better replicate observed polarimetric signatures associated with size sorting than those using the Thompson, WDM6, and WSM6 schemes, in which such signatures are either absent or occur at abnormal locations. Several biases are suggested in these schemes, including too much wet graupel in MY, Morrison, and WDM6; a small raindrop bias in WDM6 and WSM6; and the underforecast of liquid water content in regions of pure rain for all schemes.

Abstract

Doppler radar data are assimilated with an ensemble Kalman Filter (EnKF) in combination with a double-moment (DM) microphysics scheme in order to improve the analysis and forecast of microphysical states and precipitation structures within a mesoscale convective system (MCS) that passed over western Oklahoma on 8–9 May 2007. Reflectivity and radial velocity data from five operational Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars as well as four experimental Collaborative and Adaptive Sensing of the Atmosphere (CASA) X-band radars are assimilated over a 1-h period using either single-moment (SM) or DM microphysics schemes within the forecast ensemble. Three-hour deterministic forecasts are initialized from the final ensemble mean analyses using a SM or DM scheme, respectively. Polarimetric radar variables are simulated from the analyses and compared with polarimetric WSR-88D observations for verification. EnKF assimilation of radar data using a multimoment microphysics scheme for an MCS case has not previously been documented in the literature. The use of DM microphysics during data assimilation improves simulated polarimetric variables through differentiation of particle size distributions (PSDs) within the stratiform and convective regions. The DM forecast initiated from the DM analysis shows significant qualitative improvement over the assimilation and forecast using SM microphysics in terms of the location and structure of the MCS precipitation. Quantitative precipitation forecasting skills are also improved in the DM forecast. Better handling of the PSDs by the DM scheme is believed to be responsible for the improved prediction of the surface cold pool, a stronger leading convective line, and improved areal extent of stratiform precipitation.