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Manuel S. F. V. de Pondeca, Albert Barcilon, and Xiaolei Zou

Abstract

With a blocking index as the response function, the adjoint sensitivity formalism is used to assess the impact of normal modes, adjoint modes, and regional singular vectors on prediction of block onset in a two-layer model. The authors focus on three blocks excited by perturbing the model’s state vector at times preselected using the maximal perturbation that defines the direction in phase space associated with the largest possible change in the response function. The sets of normal modes, adjoint modes, and regional singular vectors (using the total energy or the L 2 norm) are computed on instantaneous basic-state flows for the preselected times and sensitivity results are presented for a time window of 3 days.

When ordered by decreasing values of the growth rates of the normal modes, the authors find that some distant normal modes and adjoint modes can produce larger changes in the response function than some of their leading counterparts. In contrast, the sets of regional singular vectors contain easily identifiable subsets of structures associated with relatively large changes in the response function. The largest changes are produced by less than the first 20 regional singular vectors. Some of these individual regional singular vectors capture the onset of the block when used as perturbations to the initial condition in a nonlinear model integration, a result of the importance for ensemble forecasting. It is found that the first five most explosive regional singular vectors of the energy (L 2) norm explain over 20% (60%) of the norm contained in the maximal perturbation at initial time.

Despite the failure of all individual normal modes to excite the block, as opposed to adjoint modes and regional singular vectors, the authors argue that, paradoxically, the normal mode concept remains a viable tool to explain the dynamics of block onset.

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Manuel S. F. V. de Pondeca, Albert Barcilon, and Xiaolei Zou

Abstract

To understand mechanisms responsible for the onset of atmospheric blocks, the authors study model blocks that form in a two-layer isentropic primitive equation model. The latter includes diabatic heating, parameterized as a Newtonian relaxation of the actual interface toward an equilibrium interface, and a zonal wavenumber-2 orography.

The study concentrates on four different blocking events. One of the blocks is present in the control run, while the remaining three are excited by appropriate perturbation of the model’s state vector at preselected times when the prevailing flows are classified as zonal. With the parameter calibration chosen in this investigation two phases in the formation of the blocks are conveniently identified: The first phase consists of the formation of cutoff or nearly cutoff cyclones in the upper layer at low latitudes, and the second phase features a rapid intensification of the upper-layer blocking ridge, accompanied by advection of high potential vorticity (PV) beneath it. While the initiation of the first phase may be perceived as far back in time as 6 days before the second phase, the latter occurs on a timescale of 1 to 2 days, giving rise to a well-defined blocking pattern.

The first phase features the Simmons–Hoskins basic baroclinic life cycle in the total PV field that acts as a conditioner of the large-scale flow for the second phase to occur. The authors hypothesize that the second phase consists of (intense) instability of normal mode form, very much as in the theory of barotropic and baroclinic instability of three-dimensional basic-state flows for the onset of blocks. From a different perspective, based on the concept of interaction between different scales of motion, both phases predominantly involve the transport of synoptic-scale potential vorticity by the planetary waves. Planetary–planetary interactions are, however, nonnegligible.

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Manuel S. F. V. De Pondeca and Xiaolei Zou

Abstract

Results from a case study of the four-dimensional variational assimilation of total zenith delay (TZD) observations from a dense global positioning system (GPS) network into the Pennsylvania State University–National Center for Atmospheric Research Fifth-Generation Mesoscale Model are reported. TZD is made up of the rescaled pressure and precipitable water at the site of the GPS receiver. Profiler-wind and radio acoustic sounding system (RASS) virtual temperature observations are also included in the assimilation experiments. Four experiments are performed. The study targets the 12-h period from 0000 to 1200 UTC 6 December 1997, characterized by the passage of a frontal system that produced intense rainfall over southern California. Forecasts prior to data assimilation underestimate the observed 6- and 12-h accumulated rainfall for most of the domain. The (sole) assimilation of TZD observations is found to have a small but beneficial impact on the short-range precipitation forecast. Measured against the control forecast, area-mean improvements of up to 33.15% and 25.08% are found in the 6- and 12-h accumulated rainfall in Los Angeles County. The inclusion of profiler-wind observations is found to have a significant impact on the model precipitation, with improvements in the 6- and 12-h accumulated precipitation as high as 88.26% and 32.53%, respectively. However, these increments are noticeably reduced when the TZD data are excluded from the assimilation experiments. Further improvements are achieved when the TZD and profiler-wind data are assimilated along with the RASS virtual temperature data. Increases of up to 93.21% and 50.58% are found in the 6- and 12-h accumulated precipitation, respectively. Because the virtual temperature also contains information on the three-dimensional moisture field, these findings point to the potential benefit that may result from the future assimilation of GPS slant-path delay data.

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Daniel P. Tyndall, John D. Horel, and Manuel S. F. V. de Pondeca

Abstract

A two-dimensional variational method is used to analyze 2-m air temperatures over a limited domain (4° latitude × 4° longitude) in order to evaluate approaches to examining the sensitivity of the temperature analysis to the specification of observation and background errors. This local surface analysis (LSA) utilizes the 1-h forecast from the Rapid Update Cycle (RUC) downscaled to a 5-km resolution terrain level for its background fields and observations obtained from the Meteorological Assimilation Data Ingest System.

The observation error variance as a function of broad network categories and the error variance and covariance of the downscaled 1-h RUC background fields are estimated using a sample of over 7 million 2-m air temperature observations in the continental United States collected during the period 8 May–7 June 2008. The ratio of observation to background error variance is found to be between 2 and 3. This ratio is likely even higher in mountainous regions where representativeness errors attributed to the observations are large.

The technique used to evaluate the sensitivity of the 2-m air temperature to the ratio of the observation and background error variance and background error length scales is illustrated over the Shenandoah Valley of Virginia for a particularly challenging case (0900 UTC 22 October 2007) when large horizontal temperature gradients were present in the mountainous regions as well as over two entire days (20 and 27 May 2009). Sets of data denial experiments in which observations are randomly and uniquely removed from each analysis are generated and evaluated. This method demonstrates the effects of overfitting the analysis to the observations.

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Matthew T. Morris, Jacob R. Carley, Edward Colón, Annette Gibbs, Manuel S. F. V. De Pondeca, and Steven Levine

Abstract

Missing observations at airports can cause delays in commercial and general aviation in the United States owing to Federal Aviation Administration (FAA) safety regulations. The Environmental Modeling Center (EMC) has provided interpolated temperature data from the National Oceanic and Atmospheric Administration’s Real-Time Mesoscale Analysis (RTMA) at airport locations throughout the United States since 2015, with these data substituting for missing temperature observations and mitigating impacts on air travel. A quality assessment of the RTMA is performed to determine if the RTMA could be used in a similar fashion for other weather observations, such as 10-m wind, ceiling, and visibility. Retrospective, data-denial experiments are used to perform the quality assessment by withholding observations from FAA-specified airports. Outliers seen in the RTMA ceiling and visibility analyses during events meeting or exceeding instrument flight rules suggest the RTMA should not be substituted for missing ceiling and visibility observations at this time. The RTMA is a suitable replacement for missing temperature observations for a subset of airports throughout most of the CONUS and Alaska, but not at all stations. Likewise, the RTMA is a suitable substitute for missing surface pressure observations at a subset of airports, with notable exceptions in regions of complex terrain. The RTMA may also be a suitable substitute for missing wind speed observations, provided the wind speed is ≤15 kt (1 kt ≈ 0.51 m s−1). Overall, these results suggest the potential for RTMA to substitute for additional missing observations while highlighting priority areas of future work for improving the RTMA.

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Manuel S. F. V. De Pondeca, Geoffrey S. Manikin, Geoff DiMego, Stanley G. Benjamin, David F. Parrish, R. James Purser, Wan-Shu Wu, John D. Horel, David T. Myrick, Ying Lin, Robert M. Aune, Dennis Keyser, Brad Colman, Greg Mann, and Jamie Vavra

Abstract

In 2006, the National Centers for Environmental Prediction (NCEP) implemented the Real-Time Mesoscale Analysis (RTMA) in collaboration with the Earth System Research Laboratory and the National Environmental, Satellite, and Data Information Service (NESDIS). In this work, a description of the RTMA applied to the 5-km resolution conterminous U.S. grid of the National Digital Forecast Database is given. Its two-dimensional variational data assimilation (2DVAR) component used to analyze near-surface observations is described in detail, and a brief discussion of the remapping of the NCEP stage II quantitative precipitation amount and NESDIS Geostationary Operational Environmental Satellite (GOES) sounder effective cloud amount to the 5-km grid is offered. Terrain-following background error covariances are used with the 2DVAR approach, which produces gridded fields of 2-m temperature, 2-m specific humidity, 2-m dewpoint, 10-m U and V wind components, and surface pressure. The estimate of the analysis uncertainty via the Lanczos method is briefly described. The strength of the 2DVAR is illustrated by (i) its ability to analyze a June 2007 cold temperature pool over the Washington, D.C., area; (ii) its fairly good analysis of a December 2008 mid-Atlantic region high-wind event that started from a very weak first guess; and (iii) its successful recovery of the finescale moisture features in a January 2010 case study over southern California. According to a cross-validation analysis for a 15-day period during November 2009, root-mean-square error improvements over the first guess range from 16% for wind speed to 45% for specific humidity.

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