Abstract

In this study, diabatic initialization, diabatic forcing, and liquid water assimilation techniques are tested in a semi-implicit hydrostatic regional forecast model containing explicit representations of grid-scale cloud water and rainwater. Diabatic forcing, in conjunction with diabatic contributions in the initialization is found to help the forecast retain the diabatic signal found in the liquid water or heating rate data, consequently reducing the spinup time associated with grid-scale precipitation processes. Both observational Special Sensor Microwave/Imager (SSM/I) and model-generated data are used.

A physical retrieval method incorporating SSM/I radiance data is utilized to estimate the 3D distribution of precipitation in storms. In the retrieval method the relationship between precipitation distributions and upwelling microwave radiances is parameterized, based upon cloud ensemble-radiative model simulations. Regression formulae relating vertically integrated liquid and ice-phase precipitation amounts to latent beating rates are also derived from the cloud ensemble simulations. Thus, retrieved SSM/I precipitation structures can be used in conjunction with the regression formulas to infer the 3D distribution of latent heating rates. These heating rates are used directly in the forecast model to help initiate Tropical Storm Emily (21 September 1987). The 14-h forecast of Emily's development yields atmospheric precipitation water contents that compare favorably with coincident SSM/I estimates.

In additional model experiments, explicit cloud water and rainwater fields are retained through the analysis-initialization phase of the forecast cycle, (i.e., from the end of one forecast to the beginning of the next sequential forecast). This allows the continuing forecast to resume the preforecast precipitation production where it left off, provided the mixing ratio field is modified to prevent cloud evaporation. This procedure is shown to be straightforward when grid-scale cloud water and rainwater variables are explicitly computed and retained for use in prognostic calculations.

Abstract

The total (i.e., convective and stratiform) latent heat release (LHR) cycle in the eyewall region of Hurricane Opal (October 1995) has been estimated using observations from the F-10, F-11, and F-13 Defense Meteorological Satellite Program Special Sensor Microwave/Imagers (SSM/Is). This LHR cycle occurred during the hurricane’s rapid intensification and decay stages (3–5 October 1995). The satellite observations revealed that there were at least two major episodes in which a period of elevated total LHR (i.e., convective burst) occurred in the eyewall region. During these convective bursts, Opal’s minimum pressure decreased by 50 mb and the LHR generated by convective processes increased, as greater amounts of latent heating occurred at middle and upper levels. It is hypothesized that the abundant release of latent heat in Opal’s middle- and upper-tropospheric region during these convective burst episodes allowed Opal’s eyewall to become more buoyant, enhanced the generation of kinetic energy and, thereby, rapidly intensified the system. The observations also suggest that Opal’s intensity became more responsive to the convective burst episodes (i.e., shorter time lag between LHR and intensity and greater maximum wind increase) as Opal became more intense.

Analyses of SSM/I-retrieved parameters, sea surface temperature observations, and the European Centre for Medium-Range Weather Forecasts (ECMWF) data reveal that the convective rainband (CRB) cycles and sea surface and tropopause temperatures, in addition to large-scale environmental forcing, had a profound influence on Opal’s episodes of convective burst and its subsequent intensity. High sea surface (29.7°C) and low tropopause (192 K) temperatures apparently created a greater potential for Opal’s maximum intensity. Strong horizontal moisture flux convergence within Opal’s outer-core regions (i.e., outside 333-km radius from the center) appeared to help initiate and maintain Opal’s CRBs. These CRBs, in turn, propagated inward to help generate and dissipate the eyewall convective bursts. The first CRB that propagated into Opal’s eyewall region appeared to initiate the first eyewall convective burst. The second CRB propagated to within 111 km of Opal’s center and appeared to dissipate the first CRB, subjecting it to subsidence and the loss of water vapor flux. The ECMWF upper-tropospheric height and wind analyses suggest that Opal interacted with a diffluent trough that initated an outflow channel, and generated high values of upper-tropospheric eddy relative angular momentum flux convergence. The gradient wind adjustment processes associated with Opal’s outflow channel, in turn, may have helped to initiate and maintain the eyewall convective bursts. The ECMWF analyses also suggest that a dry air intrusion within the southwestern quadrant of Opal’s outer-core region, together with strong vertical wind shear, subsequently terminated Opal’s CRB cycle and caused Opal to weaken prior to landfall.

Abstract

This study seeks to evaluate the impact of several newly available sources of meteorological data on mesoscale model forecasts of the extratropical cyclone that struck Florida on 2 February 1998. Intermittent measurements of precipitation and integrated water vapor (IWV) distributions were obtained from Special Sensor Microwave/Imager (SSM/I) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations. The TMI also provided sea surface temperatures (SSTs) with structural detail of the Loop Current and Gulf Stream. Continuous lightning distributions were measured with a network of very low frequency radio receivers. Lightning data were tuned with intermittent spaceborne microwave radiometer data through a probability matching technique to continuously estimate convective rainfall rates.

A series of experiments were undertaken to evaluate the effect of those data on mesoscale model forecasts produced after assimilating processed rainfall and IWV for 6 h. Assimilating processed rainfall, IWV, and SSTs from TMI measurements in the model yielded improved forecasts of precipitation distributions and vertical motion fields. Assimilating those data also produced an improved 9-h forecast of the radar reflectivity cross section that was validated with a coincident observation from the TRMM spaceborne precipitation radar.

Sensitivity experiments showed that processed rainfall information had greater impact on the rainfall forecast than IWV and SST information. Assimilating latent heating in the correct location of the forecast model was found to be more important than an accurate determination of the rainfall intensity.

Abstract

Inadequate specification of divergence and moisture in the initial conditions of numerical models results in the well-documented “spinup” problem. Observational studies indicate that latent heat release can be a key ingredient in the intensification of extratropical cyclones. As a result, the assimilation of rain rates during the early stages of a numerical simulation results in improved forecasts of the intensity and precipitation patterns associated with extratropical cyclones. It is challenging, however, particularly over data-sparse regions, to obtain complete and reliable estimates of instantaneous rain rate. Here, a technique is described in which data from a variety of sources—passive microwave sensors, infrared sensors, and lightning flash observations—along with a classic image processing technique (digital image morphing) are combined to yield a continuous time series of rain rates, which may then be assimilated into a mesoscale model. The technique is tested on simulations of the notorious 1993 Superstorm. In this case, a fortuitous confluence of several factors—rapid cyclogenesis over an oceanic region, the occurrence of this cyclogenesis at a time inconveniently placed in between Special Sensor Microwave/Imager overpasses, intense lightning during this time, and a poor forecast in the control simulation—leads to a dramatic improvement in forecasts of precipitation patterns, sea level pressure fields, and geopotential height fields when information from all of the sources is combined to determine the rain rates. Lightning data, in particular, has a greater positive impact on the forecasts than the other data sources.

Abstract

Capitalizing on recently released reanalysis datasets and diabatic heating estimates based on Tropical Rainfall Measuring Mission (TRMM), the authors have conducted a composite analysis of vertical anomalous heating structures associated with the Madden–Julian oscillation (MJO). Because diabatic heating lies at the heart of prevailing MJO theories, the intention of this effort is to provide new insights into the fundamental physics of the MJO. However, some discrepancies in the composite vertical MJO heating profiles are noted among the datasets, particularly between three reanalyses and three TRMM estimates. A westward tilting with altitude in the vertical heating structure of the MJO is clearly evident during its eastward propagation based on three reanalysis datasets, which is particularly pronounced when the MJO migrates from the equatorial eastern Indian Ocean (EEIO) to the western Pacific (WP). In contrast, this vertical tilt in heating structure is not readily seen in the three TRMM products. Moreover, a transition from a shallow to deep heating structure associated with the MJO is clearly evident in a pressure–time plot over both the EEIO and WP in three reanalysis datasets. Although this vertical heating structure transition is detectable over the WP in two TRMM products, it is weakly defined in another dataset over the WP and in all three TRMM datasets over the EEIO.

The vertical structures of radiative heating Q_R associated with the MJO are also analyzed based on TRMM and two reanalysis datasets. A westward vertical tilt in Q_R is apparent in all these datasets: that is, the low-level Q_R is largely in phase of convection, whereas Q_R in the upper troposphere lags the maximum convection. The results also suggest a potentially important role of radiative heating for the MJO, particularly over the Indian Ocean. Caveats in heating estimates based on both the reanalysis datasets and TRMM are briefly discussed.

Abstract

This paper addresses real-time precipitation forecasts from a multianalysis–multimodel superensemble. The methodology for the construction of the superensemble forecasts follows previous recent publications on this topic. This study includes forecasts from multimodels of a number of global operational centers. A multianalysis component based on the Florida State University (FSU) global spectral model that utilizes TRMM and SSM/I datasets and a number of rain-rate algorithms is also included. The difference in the analysis arises from the use of these rain rates within physical initialization that produces distinct differences among these components in the divergence, heating, moisture, and rain-rate descriptions. A total of 11 models, of which 5 represent global operational models and 6 represent multianalysis forecasts from the FSU model initialized by different rain-rate algorithms, are included in the multianalysis–multimodel system studied here. In this paper, “multimodel” refers to different models whose forecasts are being assimilated for the construction of the superensemble. “Multianalysis” refers to different initial analysis contributing to forecasts from the same model. The term superensemble is being used here to denote the bias-corrected forecasts based on the products derived from the multimodel and the multianalysis. The training period is covered by nearly 120 forecast experiments prior to 1 January 2000 for each of the multimodels. These are all 3-day forecasts. The statistical bias of the models is determined from multiple linear regression of these forecasts against a “best” rainfall analysis field that is based on TRMM and SSM/I datasets and using the rain-rate algorithms recently developed at NASA Goddard Space Flight Center. This paper discusses the results of real-time rainfall forecasts based on this system. The main results of this study are that the multianalysis–multimodel superensemble has a much higher skill than the participating member models. The skill of this system is higher than those of the ensemble mean that assigns a weight of 1.0 to all including the poorer models and the ensemble mean of bias-removed individual models. The selective weights for the entire multianalysis–multimodel superensemble forecast system make it superior to individual models and the above mean representations. The skill of precipitation forecasts is addressed in several ways. The skill of the superensemble-based rain rates is shown to be higher than the following: (a) individual model's skills with and without physical initialization, (b) skill of the ensemble mean, and (c) skill of the ensemble mean of individually bias-removed models.

The equitable-threat scores at many thresholds of rain are also examined for the various models and noted that for days 1–3 of forecasts, the superensemble-based forecasts do have the highest skills. The training phase is a major component of the superensemble. Issues on optimizing the number of training days is addressed by examining training with days of high forecast skill versus training with low forecast skill, and training with the best available rain-rate datasets versus those from poor representations of rain. Finally the usefulness of superensemble forecasts of rain for providing possible guidance for flood events such as the one over Mozambique during February 2000 is shown.

Abstract

The Rapid Refresh (RAP), an hourly updated assimilation and model forecast system, replaced the Rapid Update Cycle (RUC) as an operational regional analysis and forecast system among the suite of models at the NOAA/National Centers for Environmental Prediction (NCEP) in 2012. The need for an effective hourly updated assimilation and modeling system for the United States for situational awareness and related decision-making has continued to increase for various applications including aviation (and transportation in general), severe weather, and energy. The RAP is distinct from the previous RUC in three primary aspects: a larger geographical domain (covering North America), use of the community-based Advanced Research version of the Weather Research and Forecasting (WRF) Model (ARW) replacing the RUC forecast model, and use of the Gridpoint Statistical Interpolation analysis system (GSI) instead of the RUC three-dimensional variational data assimilation (3DVar). As part of the RAP development, modifications have been made to the community ARW model (especially in model physics) and GSI assimilation systems, some based on previous model and assimilation design innovations developed initially with the RUC. Upper-air comparison is included for forecast verification against both rawinsondes and aircraft reports, the latter allowing hourly verification. In general, the RAP produces superior forecasts to those from the RUC, and its skill has continued to increase from 2012 up to RAP version 3 as of 2015. In addition, the RAP can improve on persistence forecasts for the 1–3-h forecast range for surface, upper-air, and ceiling forecasts.