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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 poses a great risk to wildland firefighters. Single-digit relative humidity with dewpoints below −25°C is not uncommon during summer nights in this region. 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 to investigate the synoptic patterns associated with these events. A lower-tropospheric source region of dry air was found over the northeastern Pacific Ocean 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
Emanuel Dutra, Frederico Johannsen, and Linus Magnusson

Abstract

Subseasonal forecasts lie between medium-range and seasonal time scales with an emerging attention due to the relevance in society and by the scientific challenges involved. This study aims to (i) evaluate the development of systematic errors with lead time in the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts of surface-related variables during late spring and summer, and (ii) investigate potential relationships between the systematic errors and predictive skill. The evaluation is performed over the Northern Hemisphere midlatitudes, focusing on several regions with different climate characteristics. The results indicate five key bias patterns: (i) cold bias of daily maximum temperature (mx2t) in the April–May forecasts at all lead times in most regions; (ii) central North America with a warm bias mostly in the daily minimum temperature (mn2t); (iii) east of the Caspian Sea region with a warm and dry bias; (iv) western and Mediterranean Europe with a cold bias in mn2t mainly in April–May forecasts; and (v) continental Europe with a cold bias in the mx2t and warm bias of mn2t in the June–July forecasts. We also found substantial deviations of soil moisture and terrestrial water storage variation in most regions compared to the fifth-generation ECMWF atmospheric reanalysis (ERA5). Despite the large differences in the systematic error characteristics among the different regions, there is little relation to the skill of the subseasonal forecasts. The systematic temperature biases require further attention from model developers as diurnal cycle improvements could enhance some of the potential predictability coming from the long-memory effect of soil moisture.

Open access
James N. Marquis, Adam C. Varble, Paul Robinson, T. Connor Nelson, and Katja Friedrich

Abstract

Data from scanning radars, radiosondes, and vertical profilers deployed during three field campaigns are analyzed to study interactions between cloud-scale updrafts associated with initiating deep moist convection and the surrounding environment. Three cases are analyzed in which the radar networks permitted dual-Doppler wind retrievals in clear air preceding and during the onset of surface precipitation. These observations capture the evolution of (i) the mesoscale and boundary layer flow, and (ii) low-level updrafts associated with deep moist convection initiation (CI) events yielding sustained or short-lived precipitating storms. The elimination of convective inhibition did not distinguish between sustained and unsustained CI events, though the vertical distribution of convective available potential energy may have played a role. The clearest signal differentiating the initiation of sustained versus unsustained precipitating deep convection was the depth of the low-level horizontal wind convergence associated with the mesoscale flow feature triggering CI, a sharp surface wind shift boundary, or orographic upslope flow. The depth of the boundary layer relative to the height of the LFC failed to be a consistent indicator of CI potential. Widths of the earliest detectable low-level updrafts associated with sustained precipitating deep convection were ~3–5 km, larger than updrafts associated with surrounding boundary layer turbulence (~1–3 km wide). It is hypothesized that updrafts of this larger size are important for initiating cells to survive the destructive effects of buoyancy dilution via entrainment.

Open access
Peter N. Blossey, Christopher S. Bretherton, and Johannes Mohrmann

Abstract

The goal of this study is to challenge a large-eddy simulation model with a range of observations from a modern field campaign and to develop case studies useful to other modelers. The 2015 Cloud System Evolution in the Trades (CSET) field campaign provided a wealth of in situ and remote sensing observations of subtropical cloud transitions in the summertime northeast Pacific. Two Lagrangian case studies based on these observations are used to validate the thermodynamic, radiative, and microphysical properties of large-eddy simulations (LES) of the stratocumulus to cumulus transition. The two cases contrast a relatively fast cloud transition in a clean, initially well-mixed boundary layer versus a slower transition in an initially decoupled boundary layer with higher aerosol concentrations and stronger mean subsidence. For each case, simulations of two neighboring trajectories sample mesoscale variability and the coherence of the transition in adjacent air masses. In both cases, LES broadly reproduce satellite and aircraft observations of the transition. Simulations of the first case match observations more closely than for the second case, where simulations underestimate cloud cover early in the simulations and overestimate cloud top height later. For the first case, simulated cloud fraction and liquid water path increase if a larger cloud droplet number concentration is prescribed. In the second case, precipitation onset and inversion cloud breakup occur earlier when the LES domain is chosen to be large enough to support strong mesoscale organization.

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 in 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 the Taiwan Analysis Center for COSMIC. With the global numerical prediction system at CWB, a parallel semioperational 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
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
Matt Hawcroft, Sally Lavender, Dan Copsey, Sean Milton, José Rodríguez, Warren Tennant, Stuart Webster, and Tim Cowan

Abstract

From late January to early February 2019, a quasi-stationary monsoon depression situated over northeast Australia caused devastating floods. During the first week of February, when the event had its greatest impact in northwest Queensland, record-breaking precipitation accumulations were observed in several locations, accompanied by strong winds, substantial cold maximum temperature anomalies, and related wind chill. In spite of the extreme nature of the event, the monthly rainfall outlook for February issued by Australia’s Bureau of Meteorology on 31 January provided no indication of the event. In this study, we evaluate the dynamics of the event and assess how predictable it was across a suite of ensemble model forecasts using the Met Office numerical weather prediction (NWP) system, focusing on a 1-week lead time. In doing so, we demonstrate the skill of the NWP system in predicting the possibility of such an extreme event occurring. We further evaluate the benefits derived from running the ensemble prediction system at higher resolution than used operationally at the Met Office and with a fully coupled dynamical ocean. We show that the primary forecast errors are generated locally, with key sources of these errors including atmosphere–ocean coupling and a known bias associated with the behavior of the convection scheme around the coast. We note that a relatively low-resolution ensemble approach requires limited computing resources, yet has the capacity in this event to provide useful information to decision-makers with over a week’s notice, beyond the duration of many operational deterministic forecasts.

Open access
T. C. Johns, E. W. Blockley, and J. K. Ridley

Abstract

We present a coupled retrospective forecast (hindcast) study using the Met Office Global Coupled Model, version 2 (GC2), in which we identify and mitigate causes of initialization shock that lead to rapid error growth in sea ice forecasts. Sea ice state variables and volume budget terms as a function of forecast lead time are evaluated relative to analyses from an uncoupled Met Office ocean–sea ice analysis system [Forecast Ocean Assimilation Model, version 13 (FOAMv13)]. Two sources of initialization shock are highlighted and addressed, both of which are related to effective differences in physics between the analysis system and coupled forecast model. The primary shock to sea ice state variables arises from the use of a salinity-independent freezing temperature for seawater in GC2 as opposed to a salinity-dependent formulation in FOAMv13. A secondary effect arises from differences in the sea ice roughness and hence air–ice drag in the GC2 forecast model compared to the FOAMv13 analysis system. Generalizing from the findings of this study, we suggest that using nonnative analyses as initial conditions for coupled numerical weather prediction (NWP) studies will likely make them prone to initialization shock in some model components, to the detriment of forecast skill. To reduce the undesirable impacts of initialization shock on short-range forecast skill noted in this study we would therefore recommend the use of initial conditions (analyses) physically consistent with the native model components of the coupled forecast model, a native coupled analysis likely being the optimal initialization method.

Open access
Will Boyles and Matthias Katzfuss

Abstract

The ensemble Kalman filter (EnKF) is a popular technique for data assimilation in high-dimensional nonlinear state-space models. The EnKF represents distributions of interest by an ensemble, which is a form of dimension reduction that enables straightforward forecasting even for complicated and expensive evolution operators. However, the EnKF update step involves estimation of the forecast covariance matrix based on the (often small) ensemble, which requires regularization. Many existing regularization techniques rely on spatial localization, which may ignore long-range dependence. Instead, our proposed approach assumes a sparse Cholesky factor of the inverse covariance matrix, and the nonzero Cholesky entries are further regularized. The resulting method is highly flexible and computationally scalable. In our numerical experiments, our approach was more accurate and less sensitive to misspecification of tuning parameters than tapering-based localization.

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