Abstract

Using a spectral-type cumulus parameterization that includes moist downdrafts within a three-dimensional mesoscale model, various disparate closure assumptions are systematically tested within the generalized framework of dynamic control, static control, and feedback. Only one assumption at a time is changed and tested using a midlatitude environment of severe convection. A control run is presented, which shows good agreement with observations in many aspects. Results of the sensitivity tests are compared to observations in terms of sea level pressure, rainfall patterns, and domain-averaged bias errors (compared to the control run) of various properties.

The dynamic control is the part that determines the modulation of the convection by the environment. It is shown that rate of destabilization, as well as instantaneous stability, work well for the dynamic control. Integrated moisture convergence leads to underprediction of rainfall rates and subsequent degrading of the results in terms of movement and structure of the mesoscale convective system (MCS).

The feedback determines the modification of the environment by the convection, and in this study is considered together with the static control, which determines cloud properties. All feedback and static-control assumptions tested here seem very important for the prediction of sea level pressure and rainfall. The most crucial ones were downdrafts and lateral mixing.

As an interesting by-product, it is shown that a very simplistic and computationally highly efficient convective parameterization scheme leads to a very realistic simulation of the MCS, if the scheme uses a stability closure, assumes a large cloud size, parameterizes moist downdrafts, and does not assume unrealistically law lateral mixing.

Abstract

This paper continues the study of the ERICA IOP 5 storm begun in a companion paper. The latter documented the storm development, utilizing both conventional and special observations, and presented the results of a successful simulation of the storm by the Pennsylvania State University-NCAR Mesoscale Model MM4. At 24 h into the simulation, the MM4 predicted a central pressure of 984 mb, close to the observed value, whereas the Nested Grid Model (NGM) of the National Meteorological Center forecasted a depth of only 997 mb for the same hour. Here the results of experiments designed to test the sensitivity of the development to latent heating, surface energy fluxes, Gulf Stream position, and grid size are first presented. A high sensitivity to latent heating and a moderate sensitivity to the other parameters are found. A comparison with other cases in the literature reveals that the sensitivity to latent heating, and to the fluxes, was unusually large. In view of this finding, further diagnosis is made of the behavior of a number of moisture-sensitive parameters in the model, namely, the potential vorticity (PV), the stability of the storm environment to vertical and slantwise ascent, and the surface energy fluxes. The diagnosis revealed that (i) large diabatically produced PV, capable of sub-stantially impacting the storm intensity, appeared in the lower portion of the warm-frontal cloud mass, (ii) the storm environment was neutral or oven unstable to vertical ascent (near and ahead of the cold front) and to slantwise ascent (in and above the warm-frontal zone), and (iii) the movement of the storm near and parallel to the Gulf Stream allowed heated and moistened air to be continuously ingested into the storm.

In seeking clues to the cause of the superior performance of the MM4, additional experiments are carried out in which a Kuo-type convection scheme, such as employed in the NGM, replaces the Grell scheme used in the previous MM4 simulations. It is concluded that approximately 60% of the difference between the MM4 and NGM predictions can be accounted for by the utilization of the Grell scheme and a finer grid (30 km vs 85 km) and that the effects of grid size and convective parameterization are highly coupled in this case. The remaining difference is attributed to other elements of the predictions that are not further investigated. An experiment conducted on a slowly deepening ERICA storm (IOP 7) demonstrates that, in this case at least, the MM4 shows no tendency to produce excessive deepening of ocean storms.

Abstract

In this paper, we consider three disparate classes of cumulus parameterization schemes, applied to cases of severe midlatitude convective storms observed during SESAME-1979. Objective analysis of the observed data was carded out and verifying heat and moisture budgets were computed. For the three types of schemes–Arakawa-Schubert, KreitzbM-Perkey, and Kuo–the underlying closure assumptions and cloud models are tested within the generalized framework of dynamic control, static control, and feedback. Using the semiprognostic approach, single time step predictions of the heating and drying rates due to convection are obtained for the three schemes and are compared with those diagnosed from the observed budgets. The results presented should have important implications for models with a resolution of more than 1 80 km.

The vertical distributions of warming and drying are fairly well reproduced by the Arakawa-Schubert scheme, however, excessive amounts are predicted in most of the lower troposphere and insufficient drying is predicted just near the surface. This was ameliorated by incorporating moist convective-scale downdrafts into the parameterization. Although the downdraft mass flux is highly sensitive to some arbitrary parameters, the inclusion of downdrafts is shown to be crucial to predict the feedback correctly in the midlatitude environment. A test of the quasi-equilibrium assumption for these severe storm cases showed that it was valid (as had previously been demonstrated for the tropics). For the Kreitzberg-Perkey scheme, the most severe limitations were found to be a lack of dependence on large-scale destabilizing effects in the dynamic control and the assumption that clouds instantly decay and mix with their environment in the feedback. For the Kuo-type schemes, tests of its dynamic control demonstrated the need to include mesoscale moisture convergence in order to correctly predict the vertically integrated heating and drying rates, unless the resolved scale is fairly small and the moistening parameter is set to zero. Tests with the feedback-wherein the vertical distribution of heating and drying is dictated by the differences between cloud and environmental thermodynamic properties-revealed serious shortcomings. In particular, this scheme is unable to predict heating maxima for atmospheric layers exhibiting high static stability. Such stable layers are frequently noted in the midlatitude environment of severe convective storms.

Abstract

The ERICA IOP 5 storm was the third strongest cyclone observed during the three-month Experiment on Rapidly Intensifying Cyclones over the Atlantic (ERICA) and the least successfully predicted by the operational models. This paper documents the storm development with use of nearly all available observational data and presents the results of a simulation of the storm carried out by the Pennsylvania State University-NCAR mesoscale Model MM4.

The observations reveal that the storm formed in two stages: a first stage in which a weak, eastward-moving upper-level trough over the Gulf states excited the growth of two disturbances over the Gulf Stream, and a second stage in which a rapidly moving, moderately intense short-wave trough from the north-central states interacted with the more northerly of the two disturbances, producing rapid intensification. Maximum deepening rates were 11 mb (6 h)⁻¹ and 33 mb (24 h)⁻¹. At the mature stage a thermal gradient of 7°C (35 km)⁻¹ was observed near the surface by a low-flying research aircraft that traversed the occluded frontal zone.

A full-physics simulation, carried out on a movable 30-km grid embedded within a 90-km fixed grid, closely reproduced the storm development, as verified by surface ship and buoy observations, flight level and dropsonde data from research aircraft, and satellite infrared and microwave imagery. Sensitivity tests reported in a companion paper revealed that the development was highly sensitive to condensation heating and moderately sensitive to surface energy fluxes, grid size, and the location of the Gulf Stream. The companion paper also addresses the question of why in this case the MM4 outperformed the operational models of the National Meteorological Center.

Abstract

The Developmental Testbed Center (DTC) tested two convective parameterization schemes in the Hurricane Weather Research and Forecasting (HWRF) Model and compared them in terms of performance of forecasting tropical cyclones (TCs). Several TC forecasts were conducted with the scale-aware Simplified Arakawa Schubert (SAS) and Grell–Freitas (GF) convective schemes over the Atlantic basin. For this sample of over 100 cases, the storm track and intensity forecasts were superior for the GF scheme compared to SAS. A case study showed improved storm structure for GF when compared with radar observations. The GF run had increased inflow in the boundary layer, which resulted in higher angular momentum. An angular momentum budget analysis shows that the difference in the contribution of the eddy transport to the total angular momentum tendency is small between the two forecasts. The main difference is in the mean transport term, especially in the boundary layer. The temperature tendencies indicate higher contribution from the microphysics and cumulus heating above the boundary layer in the GF run. A temperature budget analysis indicated that both the temperature advection and diabatic heating were the dominant terms and they were larger near the storm center in the GF run than in the SAS run. The above results support the superior performance of the GF scheme for TC intensity forecast.

Abstract

Monthlong hindcasts of the Madden–Julian oscillation (MJO) from the atmospheric Flow-following Icosahedral Model coupled with an icosahedral-grid version of the Hybrid Coordinate Ocean Model (FIM-iHYCOM), and from the coupled Climate Forecast System, version 2 (CFSv2), are evaluated over the 12-yr period 1999–2010. Two sets of FIM-iHYCOM hindcasts are run to test the impact of using Grell–Freitas (FIM-CGF) versus simplified Arakawa–Schubert (FIM-SAS) deep convection parameterizations. Each hindcast set consists of four time-lagged ensemble members initialized weekly every 6 h from 1200 UTC Tuesday to 0600 UTC Wednesday.

The ensemble means of FIM-CGF, FIM-SAS, and CFSv2 produce skillful forecasts of a variant of the Real-time Multivariate MJO (RMM) index out to 19, 17, and 17 days, respectively; this is consistent with FIM-CGF having the lowest root-mean-square errors (RMSEs) for zonal winds at both 850 and 200 hPa. FIM-CGF and CFSv2 exhibit similar RMSEs in RMM, and their multimodel ensemble mean extends skillful RMM prediction out to 21 days. Conversely, adding FIM-SAS—with much higher RMSEs—to CFSv2 (as a multimodel ensemble) or FIM-CGF (as a multiphysics ensemble) yields either little benefit, or even a degradation, compared to the better single-model ensemble mean. This suggests that multiphysics/multimodel ensemble mean forecasts may only add value when the individual models possess similar skill and error. An atmosphere-only version of FIM-CGF loses skill after 11 days, highlighting the importance of ocean coupling. Further examination reveals some sensitivity in skill and error metrics to the choice of MJO index.

Abstract

The fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)-based regional climate model (CMM5) simulations of U.S.–Mexico summer precipitation are quite sensitive to the choice of Grell or Kain–Fritsch convective parameterization. An ensemble based on these two parameterizations provides superior performance because distinct regions exist where each scheme complementarily captures certain observed signals. For the interannual anomaly, the ensemble provides the most significant improvement over the Rockies, Great Plains, and North American monsoon region. For the climate mean, the ensemble has the greatest impact on skill over the southeast United States and North American monsoon region, where CMM5 biases associated with the individual schemes are of opposite sign. Results are very sensitive to the specific methods used to generate the ensemble. While equal weighting of individual solutions provides a more skillful result overall, considerable further improvement is achieved when the weighting of individual solutions is optimized as a function of location.

Abstract

A mesoscale atmospheric forecast model configured in a hybrid isentropic–sigma vertical coordinate and used in the NOAA Rapid Update Cycle (RUC) for operational numerical guidance is presented. The RUC model is the only quasi-isentropic forecast model running operationally in the world and is distinguished from other hybrid isentropic models by its application at fairly high horizontal resolution (10–20 km) and a generalized vertical coordinate formulation that allows model levels to remain continuous and yet be purely isentropic well into the middle and even lower troposphere.

The RUC model is fully described in its 2003 operational version, including numerics and physical parameterizations. The use of these parameterizations, including mixed-phase cloud microphysics and an ensemble-closure-based cumulus parameterization, is fully consistent with the RUC vertical coordinate without any loss of generality.

A series of experiments confirm that the RUC hybrid θ–σ coordinate reduces cross-coordinate transport over a quasi-horizontal σ coordinate. This reduction in cross-coordinate vertical transport results in less numerical vertical diffusion and thereby improves numerical accuracy for moist reversible processes.

Finally, a forecast is presented of a strong cyclogenesis case over the eastern United States in which the RUC model produced an accurate 36-h prediction, especially in a 10-km nested version. Horizontal and vertical plots from these forecasts give evidence of detailed yet coherent structures of potential vorticity, moisture, and vertical motion.

Abstract

The atmospheric hydrostatic Flow-Following Icosahedral Model (FIM), developed for medium-range weather prediction, provides a unique three-dimensional grid structure—a quasi-uniform icosahedral horizontal grid and an adaptive quasi-Lagrangian vertical coordinate. To extend the FIM framework to subseasonal time scales, an icosahedral-grid rendition of the Hybrid Coordinate Ocean Model (iHYCOM) was developed and coupled to FIM. By sharing a common horizontal mesh, air–sea fluxes between the two models are conserved locally and globally. Both models use similar adaptive hybrid vertical coordinates. Another unique aspect of the coupled model (referred to as FIM–iHYCOM) is the use of the Grell–Freitas scale-aware convective scheme in the atmosphere. A multiyear retrospective study is necessary to demonstrate the potential usefulness and allow for immediate bias correction of a subseasonal prediction model. In these two articles, results are shown based on a 16-yr period of hindcasts from FIM–iHYCOM, which has been providing real-time forecasts out to a lead time of 4 weeks for NOAA’s Subseasonal Experiment (SubX) starting July 2017. Part I provides an overview of FIM–iHYCOM and compares its systematic errors at subseasonal time scales to those of NOAA’s operational Climate Forecast System version 2 (CFSv2). Part II uses bias-corrected hindcasts to assess both deterministic and probabilistic subseasonal skill of FIM–iHYCOM. FIM–iHYCOM has smaller biases than CFSv2 for some fields (including precipitation) and comparable biases for other fields (including sea surface temperature). FIM–iHYCOM also has less drift in bias between weeks 1 and 4 than CFSv2. The unique grid structure and physics suite of FIM–iHYCOM is expected to add diversity to multimodel ensemble forecasts at subseasonal time scales in SubX.

Abstract

The authors implemented the Grell–Freitas (GF) parameterization of convection in which the cloud-base mass flux varies quadratically as a function of the convective updraft fraction in the global nonhydrostatic Model for Prediction Across Scales (MPAS). They evaluated the performance of GF using quasi-uniform meshes and a variable-resolution mesh centered over South America, the resolution of which varied between hydrostatic (50 km) and nonhydrostatic (3 km) scales. Four-day forecasts using a 50-km and a 15-km quasi-uniform mesh, initialized with GFS data for 0000 UTC 10 January 2014, reveal that MPAS overestimates precipitation in the tropics relative to the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis data. Results of 4-day forecasts using the variable-resolution mesh reveal that over the refined region of the mesh, GF performs as a precipitating shallow convective scheme, whereas over the coarse region of the mesh, GF acts as a conventional deep convective scheme. As horizontal resolution increases and subgrid-scale motions become increasingly resolved, the contribution of convective and grid-scale precipitation to the total precipitation decreases and increases, respectively. Probability density distributions of precipitation highlight a smooth transition in the partitioning between convective and grid-scale precipitation, including at gray-zone scales across the transition region between the coarsest and finest regions of the global mesh. Variable-resolution meshes spanning between hydrostatic and nonhydrostatic scales are shown to be ideal tools to evaluate the horizontal scale dependence of parameterized convective and grid-scale moist processes.