Browse

You are looking at 1 - 10 of 39,979 items for :

  • Monthly Weather Review x
  • User-accessible content x
Clear All
Tobias Goecke and Ekaterina Machulskaya

Abstract

We present a detailed evaluation of the turbulence forecast product eddy dissipation parameter (EDP) used at the Deutscher Wetterdienst (DWD). It is based on the turbulence parameterization scheme TURBDIFF, which is operational within the Icosahedral Nonhydrostatic (ICON) numerical weather prediction model used operationally by DWD. For aviation purposes, the procedure provides the cubic root of the eddy dissipation rate ε 1/3 as an overall turbulence index. This quantity is a widely used measure for turbulence intensity as experienced by aircraft. The scheme includes additional sources of turbulent kinetic energy with particular relevance to aviation, which are briefly introduced. These sources describe turbulence generation by the subgrid-scale action of wake eddies, mountain waves, and convection, as well as horizontal shear as found close to fronts or the jet stream. Furthermore, we introduce a postprocessing calibration to an empirical EDR distribution, and we demonstrate the potential as well as limitations of the final EDP-based turbulence forecast by considering several case studies of typical turbulence events. Finally, we reveal the forecasting capability of this product by verifying the model results against one year of aircraft in situ EDR measurements from commercial aircraft. We find that the forecasted EDP performs favorably when compared to the Ellrod index. In particular, the turbulence signal from deep convection, which is accounted for in the EDP product, is advantageous when spatial nonlocality is allowed in the verification procedure.

Open access
Milind Sharma, Robin L. Tanamachi, Eric C. Bruning, and Kristin M. Calhoun

Abstract

We demonstrate the utility of transient polarimetric signatures (Z DR and K DP columns, a proxy for surges in a thunderstorm updraft) to explain variability in lightning flash rates in a tornadic supercell. Observational data from a WSR-88D and the Oklahoma lightning mapping array are used to map the temporal variance of polarimetric signatures and VHF sources from lightning channels. It is shown, via three-dimensional and cross-sectional analyses, that the storm was of inverted polarity resulting from anomalous electrification. Statistical analysis confirms that mean flash area in the Z DR column region was 10 times smaller than elsewhere in the storm. On an average, 5 times more flash initiations occurred within Z DR column regions, thereby supporting existing theory of an inverse relationship between flash initiation rates and lightning channel extent. Segmentation and object identification algorithms are applied to gridded radar data to calculate metrics such as height, width, and volume of Z DR and K DP columns. Variability in lightning flash rates is best explained by the fluctuations in Z DR column volume with a Spearman’s rank correlation coefficient value of 0.72. The highest flash rates occur in conjunction with the deepest Z DR columns (up to 5 km above environmental melting level) and largest volumes of Z DR columns extending up to the −20°C level (3 km above the melting level). Reduced flash rates toward the end of the analysis are indicative of weaker updrafts manifested as low Z DR column volumes at and above the −10°C level. These findings are consistent with recent studies linking lightning to the interplay between storm dynamics, kinematics, thermodynamics, and precipitation microphysics.

Open access
Stanley G. Benjamin, Eric P. James, Ming Hu, Curtis R. Alexander, Therese T. Ladwig, John M. Brown, Stephen S. Weygandt, David D. Turner, Patrick Minnis, William L. Smith Jr., and Andrew K. Heidinger

Abstract

Accurate cloud and precipitation forecasts are a fundamental component of short-range data assimilation/model prediction systems such as the NOAA 3-km High-Resolution Rapid Refresh (HRRR) or the 13-km Rapid Refresh (RAP). To reduce cloud and precipitation spin-up problems, a non-variational assimilation technique for stratiform clouds was developed within the Gridpoint Statistical Interpolation (GSI) data assimilation system. One goal of this technique is retention of observed stratiform cloudy and clear 3D volumes into the subsequent model forecast. The cloud observations used include cloud-top data from satellite brightness temperatures, surface-based ceilometer data, and surface visibility. Quality control, expansion into spatial information content, and forward operators are described for each observation type. The projection of data from these observation types into an observation-based cloud-information 3D gridded field is accomplished via identification of cloudy, clear, and cloud-unknown 3D volumes. Updating of forecast background fields is accomplished through clearing and building of cloud water and cloud ice with associated modifications to water vapor and temperature. Impact of the cloud assimilation on short-range forecasts is assessed with a set of retrospective experiments in warm and cold seasons using the RAPv5 model. Short-range (1-9h) forecast skill is improved in both seasons for cloud ceiling and visibility and for 2-m temperature in daytime and with mixed results for other measures. Two modifications were introduced and tested with success: use of prognostic subgrid-scale cloud fraction to condition cloud building (in response to a high bias) and removal of a WRF-based rebalancing.

Open access
Alex Schueth, Christopher Weiss, and Johannes M. L. Dahl

Abstract

The forward-flank convergence boundary (FFCB) in supercells has been well documented in many observational and modeling studies. It is theorized that the FFCB is a focal point for the baroclinic generation of vorticity. This vorticity is generally horizontal and streamwise in nature, which can then be tilted and converted to midlevel (3–6 km AGL) vertical vorticity. Previous modeling studies of supercells often show horizontal streamwise vorticity present behind the FFCB, with higher-resolution simulations resolving larger magnitudes of horizontal vorticity. Recently, studies have shown a particularly strong realization of this vorticity called the streamwise vorticity current (SVC). In this study, a tornadic supercell is simulated with the Bryan Cloud Model at 125-m horizontal grid spacing, and a coherent SVC is shown to be present. Simulated range–height indicator (RHI) data show the strongest horizontal vorticity is located on the periphery of a steady-state Kelvin–Helmholtz billow in the FFCB head. Additionally, a similar structure is found in two separate observed cases with the Texas Tech University Ka-band (TTUKa) mobile radar RHIs. Analyzing vorticity budgets for parcels in the vicinity of the FFCB head in the simulation, stretching of vorticity is the primary contributor to the strong streamwise vorticity, while baroclinic generation of vorticity plays a smaller role.

Open access
Peng Wang, Yishuai Jin, and Zhengyu Liu

Abstract

In this study, we investigate a diurnal predictability barrier (DPB) for weather predictions using an idealized model and observations. This DPB is referred to a maximum drop of predictability (e.g., autocorrelation) at a particular time of the day, regardless of the initial time. Previous studies demonstrated that a strong seasonal cycle of El Niño–Southern Oscillation (ENSO) growth rate is responsible for the seasonal predictability barrier of the ENSO in spring. This led us to investigate whether or not a strong diurnal cycle may generate a DPB. We study the DPB using an idealized model, the Lorenz 1963 model, with the addition of a diurnal cycle. We find that diurnal growth rate can generate a DPB in this chaotic system, regardless of the initial error. Finally, by calculating the autocorrelation function using the hourly data of surface temperature, we explore the DPB at two stations in Wisconsin and Beijing, China. A clear DPB feature is found at both stations. The dramatic drop of predictability at a specific time of the day is likely due to the diurnal variation of the system. This is a new feature that needs further study for short-term weather predictions.

Open access
Yang Liu, Laurens Bogaardt, Jisk Attema, and Wilco Hazeleger

Abstract

Operational Arctic sea ice forecasts are of crucial importance to science and to society in the Arctic region. Currently, statistical and numerical climate models are widely used to generate the Arctic sea ice forecasts at weather time scales. Numerical models require near-real-time input of relevant environmental conditions consistent with the model equations and they are computationally expensive. In this study, we propose a deep learning approach, namely convolutional long short-term memory networks (ConvLSTM), to forecast sea ice in the Barents Sea at weather to subseasonal time scales. This is an unsupervised learning approach. It makes use of historical records and it exploits the covariances between different variables, including spatial and temporal relations. With input fields from reanalysis data, we demonstrate that ConvLSTM is able to learn the variability of the Arctic sea ice and can forecast regional sea ice concentration skillfully at weekly to monthly time scales. It preserves the physical consistency between predictors and predictands, and generally outperforms forecasts with climatology, persistence, and a statistical model. Based on the known sources of predictability, sensitivity tests with different climate fields as input for learning were performed. The impact of different predictors on the quality of the forecasts are evaluated and we demonstrate that the surface energy budget components have a large impact on the predictability of sea ice at weather time scales. This method is a promising way to enhance operational Arctic sea ice forecasting in the near future.

Open access
Kieran M. R. Hunt, Andrew G. Turner, and Reinhard K. H. Schiemann

Abstract

Interactions over South Asia between tropical depressions (TDs) and extratropical storms known as western disturbances (WDs) are known to cause extreme precipitation events, including those responsible for the 2013 floods over northern India. In this study, existing databases of WD and TD tracks are used to identify potential WD–TD interactions from 1979 to 2015; these are filtered according to proximity and intensity, leaving 59 cases that form the basis of this paper. Synoptic charts, vorticity budgets, and moisture trajectory analyses are employed to identify and elucidate common interaction types among these cases. Two broad families of interaction emerge. First, a dynamical coupling of the WD and TD, whereby either the upper- and lower-level vortices superpose (a vortex merger), or the TD is intensified as it passes into the entrance region of a jet streak associated with the WD (a jet-streak excitation). Second, a moisture exchange between the WD and TD, whereby either anomalous moisture is advected from the TD to the WD, resulting in anomalous precipitation near the WD (a TD-to-WD moisture exchange), or anomalous moisture is advected from the WD to the TD (a WD-to-TD moisture exchange). Interactions are most common in the post-monsoon period as the subtropical jet, which brings WDs to the subcontinent, returns south; there is a smaller peak in May and June, driven by monsoon onset vortices. Precipitation is heaviest in dynamically coupled interactions, particularly jet-streak excitations. Criteria for automated identification of interaction types are proposed, and schematics for each type are presented to highlight key mechanisms.

Open access
Abdessamad Qaddouri, Claude Girard, Syed Zahid Husain, and Rabah Aider

Abstract

An alternate dynamical core that employs the unified equations of A. Arakawa and C. S. Konor has been developed within Environment and Climate Change Canada’s Global Environmental Multiscale (GEM) atmospheric model. As in the operational GEM dynamical core, the novel core utilizes the same fully implicit two-time-level semi-Lagrangian scheme for time discretization while the log-pressure-based terrain-following vertical coordinate has been slightly adapted. Overall, the new dynamical core implementation required only minor changes to the existing informatics code of the GEM model, and, from a computational performance perspective, the new core does not incur any significant additional cost. A broad range of tests—that include both two-dimensional idealized theoretical cases and three-dimensional deterministic forecasts covering both hydrostatic and nonhydrostatic scales—have been carried out to evaluate the performance of the new dynamical core. For all of the tested cases, when compared with the operational GEM model, the new dynamical core based on the unified equations has been found to produce statistically equivalent results. These results imply that the unified equations can be adopted for operational numerical weather prediction that would employ a single soundproof system of equations to produce reliable forecasts for all meteorological scales of interest with negligible changes for the computational overhead.

Open access
Richard B. Bagley and Craig B. Clements

Abstract

The second largest fire shelter deployment in U. S. history occurred in August 2003 during the Devil Fire, which was burning in a remote and rugged region of the San Francisco Bay Area, when relative humidity abruptly dropped in the middle of the night causing rapid fire growth. Nocturnal drying events in the higher elevations along California’s central coast are a unique phenomenon that pose a great risk to wildland firefighters. Single digit relative humidity with dew points below -25°C is not uncommon during summer nights in this region. In order to provide the fire management community with knowledge of these hazardous conditions, an event criterion was established to develop a climatology of nocturnal drying and investigate the synoptic patterns associated with these events. A lower tropospheric source region of dry air was found over the northeastern Pacific corresponding to an area of maximum low-level divergence and associated subsidence. This dry air forms above a marine inversion and advects inland overnight with the marine layer and immerses higher elevation terrain with warm and dry air. An average of 15-20 nocturnal drying events per year occur in elevations greater than 700 m in the San Francisco Bay Area and their characteristics are highly variable, making them a challenge to forecast.

Open access
Guo-Yuan Lien, Chung-Han Lin, Zih-Mao Huang, Wen-Hsin Teng, Jen-Her Chen, Ching-Chieh Lin, Hsu-Hui Ho, Jyun-Ying Huang, Jing-Shan Hong, Chia-Ping Cheng, and Ching-Yuang Huang

Abstract

The FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) Radio Occultation (RO) satellite constellation was launched on June 2019 as a successor of the FORMOSAT-3/COSMIC mission. The Central Weather Bureau (CWB) of Taiwan has received FORMOSAT-7/COSMIC-2 GNSS RO data in real time from Taiwan Analysis Center for COSMIC. With the global numerical prediction system at CWB, a parallel semi-operational experiment assimilating the FORMOSAT-7/COSMIC-2 bending angle data with all other operational observation data has been conducted to evaluate the impact of the FORMOSAT-7/COSMIC-2 data. The first seven-month results show that the quality of the early FORMOSAT-7/COSMIC-2 data has been satisfactory for assimilation. Consistent and significant positive impacts on global forecast skills have been observed since the start of the parallel experiment, with the most significant impact found in the tropical region, reflecting the low-inclination orbital design of the satellites. The impact of the FORMOSAT-7/COSMIC-2 RO data is also estimated using the Ensemble Forecast Sensitivity to Observation Impact (EFSOI) method, showing an average positive impact per observation similar to other existing GNSS RO datasets, while the total impact is impressive by virtue of its large amount. Sensitivity experiments suggest that the quality control processes built in the Gridpoint Statistical Interpolation (GSI) system for RO data work well to achieve a positive impact by the low-level FORMOSAT-7/COSMIC-2 RO data, while more effort on observation error tuning should be focused to obtain an optimal assimilation performance. This study demonstrates the usefulness of the FORMOSAT-7/COSMIC-2 RO data in global numerical weather prediction during the calibration/validation period and leads to the operational use of the data at CWB.

Open access