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Keith D. Sherburn, Matthew J. Bunkers, and Angela J. Mose

Abstract

Straight-line winds are arguably the most challenging element considered by operational forecasters when issuing severe thunderstorm warnings. Determining the potential maximum surface wind gust prior to an observed, measured gust is very difficult. This work builds upon prior research that quantified a relationship between the observed outflow boundary speed and corresponding measured wind gusts. Whereas this prior study was limited to a 30-case dataset over eastern Colorado, the current study comprises 943 cases across the contiguous United States and encompasses all times of day, seasons, and regions while representing various convective modes and associated near-storm environments. The wind gust ratios (WGRs), or the ratio between a measured wind gust and the associated outflow boundary speed, had a nationwide median of 1.44, mean of 1.68, 25th percentile of 1.19, and 75th percentile of 1.91. WGRs varied considerably by region, season, time of day, convective mode, near-storm environment, and outflow boundary speed. WGRs tended to be higher in the plains, Intermountain West, and southern coastal regions, lower in the cool season and during the morning and overnight, and lower in linear convective modes relative to supercell and disorganized modes. Environments with stronger mean winds and low- to midlevel shear vector magnitudes tended to have lower WGRs, whereas those with steeper low-level lapse rates and other thermodynamic characteristics favorable for momentum transfer and evaporative cooling tended to have higher WGRs. As outflow boundary speed increases, WGRs—and their variability—decrease. Applying these findings may help operational meteorologists to provide more accurate severe thunderstorm warnings.

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Young-Chan Noh, Hung-Lung Huang, and Mitchell D. Goldberg

Abstract

To maximize the contribution of the Cross-track Infrared Sounder (CrIS) measurements to the global weather forecasting, we attempt to choose the CrIS channels to be assimilated in the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). From preselected 431 CrIS channels, 207 channels are newly selected using a one-dimensional variational (1D-Var) approach where the channel score index (CSI) is used as a figure of merit. Newly selected 207 channels comprise 85 temperature, 49 water vapor, and 73 surface channels, respectively. In addition, to examine how the channels are selected if the forecast error covariance is differently defined depending on the latitudinal regions (i.e., Northern and Southern Hemispheres, and tropics), the same selection process is carried out repeatedly using three regional forecast error covariances. From three regional channel sets, two-channel sets are made for the global data assimilation. One channel set is made with 134 channels overlapped between three regional channel sets. Another channel set consists of 277 channels that is the sum of 3 regional channel sets. In the global trial experiments, the global CrIS 207 channels have a significant positive forecast impact in terms of the improvement of GFS global forecasting, as compared with the forecasts with the operational 100 channels as well as the overlapped 134 and the union 277 channel sets. The improved forecast is mainly due to the additional temperature/water vapor channels of the global CrIS 207 channels that are selected optimally based on the global forecast error of operational GFS.

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Sebastian Scher, Stephen Jewson, and Gabriele Messori

Abstract

To extract the most information from an ensemble forecast, users would need to consider the possible impacts of every member in the ensemble. However, not all users have the resources to do this. Many may opt to consider only the ensemble mean and possibly some measure of spread around the mean. This provides little information about potential worst-case scenarios. We explore different methods to extract worst-case scenarios from an ensemble forecast, for a given definition of severity of impact: taking the worst member of the ensemble, calculating the mean of the N worst members, and two methods that use a statistical tool known as directional component analysis (DCA). We assess the advantages and disadvantages of the four methods in terms of whether they produce spatial worst-case scenarios that are not overly sensitive to the finite size and randomness of the ensemble or small changes in the chosen geographical domain. The methods are tested on synthetic data and on temperature forecasts from ECMWF. The mean of the N worst members is more robust than the worst member, while the DCA-based patterns are more robust than either. Furthermore, if the ensemble variability is well described by the covariance matrix, the DCA patterns have the statistical property that they are just as severe as those from the other two methods, but more likely. We conclude that the DCA approach is a tool that could be routinely applied to extract worst-case scenarios from ensemble forecasts.

Open access
Maria Pyrina, Marcel Nonnenmacher, Sebastian Wagner, and Eduardo Zorita

Abstract

Statistical climate prediction has sometimes demonstrated higher accuracy than coupled dynamical forecast systems. This study tests the applicability of springtime soil moisture (SM) over Europe and sea surface temperatures (SSTs) of three North Atlantic (NA) regions as statistical predictors of European mean summer temperature (t2m). We set up two statistical-learning (SL) frameworks, based on methods commonly applied in climate research. The SL models are trained with gridded products derived from station, reanalysis, and satellite data (ERA-20C, ERA-Land, CERA, COBE2, CRU, and ESA-CCI). The predictive potential of SM anomalies in statistical forecasting had so far remained elusive. Our statistical models trained with SM achieve high summer t2m prediction skill in terms of Pearson correlation coefficient (r), with r ≥ 0.5 over central and eastern Europe. Moreover, we find that the reanalysis and satellite SM data contain similar information that can be extracted by our methods and used in fitting the forecast models. Furthermore, the predictive potential of SSTs within different areas in the NA basin was tested. The predictive power of SSTs might increase, as in our case, when specific areas are selected. Forecasts based on extratropical SSTs achieve high prediction skill over south Europe. The combined prediction, using SM and SST predictor data, results in r ≥ 0.5 over all European regions south of 50°N and east of 5°W. This is a better skill than the one achieved by other prediction schemes based on dynamical models. Our analysis highlights specific NA midlatitude regions that are more strongly connected to summer mean European temperature.

Open access
Parthasarathi Mukhopadhyay, Peter Bechtold, Yuejian Zhu, R. Phani Murali Krishna, Siddharth Kumar, Malay Ganai, Snehlata Tirkey, Tanmoy Goswami, M. Mahakur, Medha Deshpande, V. S. Prasad, C. J. Johny, Ashim Mitra, Raghavendra Ashrit, Abhijit Sarkar, Sahadat Sarkar, Kumar Roy, Elphin Andrews, Radhika Kanase, Shilpa Malviya, S. Abhilash, Manoj Domkawale, S. D. Pawar, Ashu Mamgain, V. R. Durai, Ravi S. Nanjundiah, Ashis K. Mitra, E. N. Rajagopal, M. Mohapatra, and M. Rajeevan

Abstract

During August 2018 and 2019 the southern state of India, Kerala, received unprecedented heavy rainfall, which led to widespread flooding. We aim to characterize the convective nature of these events and the large-scale atmospheric forcing, while exploring their predictability by three state-of-the-art global prediction systems: the National Centers for Environmental Prediction (NCEP)-based India Meteorological Department (IMD) operational Global Forecast System (GFS), the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), and the Unified Model–based NCUM being run at the National Centre for Medium Range Weather Forecasting (NCMRWF). Satellite, radar, and lightning observations suggest that these rain events were dominated by cumulus congestus and shallow convection with strong zonal flow leading to orographically enhanced rainfall over the Ghats mountain range; sporadic deep convection was also present during the 2019 event. A moisture budget analyses using the fifth major global reanalysis produced by ECMWF (ERA5) and forecast output revealed significantly increased moisture convergence below 800 hPa during the main rain events compared to August climatology. The total column-integrated precipitable water tendency, however, is found to be small throughout the month of August, indicating a balance between moisture convergence and drying by precipitation. By applying a Rossby wave filter to the rainfall anomalies it is shown that the large-scale moisture convergence is associated with westward-propagating barotropic Rossby waves over Kerala, leading to increased predictability of these events, especially for 2019. Evaluation of the deterministic and ensemble rainfall predictions revealed systematic rainfall differences over the Ghats mountains and the coastline. The ensemble predictions were more skillful than the deterministic forecasts, as they were able to predict rainfall anomalies (greater than three standard deviations from climatology) beyond day 5 for August 2019 and up to day 3 for 2018.

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Charles M. Kuster, Barry R. Bowers, Jacob T. Carlin, Terry J. Schuur, Jeff W. Brogden, Robert Toomey, and Andy Dean

Abstract

Decades of research have investigated processes that contribute to downburst development, as well as identified precursor radar signatures that can accompany these events. These advancements have increased downburst predictability, but downbursts still pose a significant forecast challenge, especially in low-shear environments that produce short-lived single and multicell thunderstorms. Additional information provided by dual-polarization radar data may prove useful in anticipating downburst development. One such radar signature is the K DP core (where K DP is the specific differential phase), which can indicate processes such as melting and precipitation loading that increase negative buoyancy and can result in downburst development. Therefore, K DP cores associated with 81 different downbursts across 10 states are examined to explore if this signature could provide forecasters with a reliable and useable downburst precursor signature. The K DP core characteristics near the environmental melting layer, vertical gradients of K DP, and environmental conditions were quantified to identify any differences between downbursts of varying intensities. The analysis shows that 1) K DP cores near the environmental melting layer are a reliable signal that a downburst will develop; 2) while using K DP cores to anticipate an impending downburst’s intensity is challenging, larger K DP near the melting layer and larger vertical gradients of K DP are more commonly associated with strong downbursts than weak ones; 3) downbursts occurring in environments with less favorable conditions for downbursts are associated with higher magnitude K DP cores, and 4) K DP cores evolve relatively slowly (typically longer than 15 min), which makes them easily observable with the 5-min volumetric updates currently provided by operational radars.

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J. V. Ratnam, Masami Nonaka, and Swadhin K. Behera

Abstract

The machine learning technique, namely artificial neural networks (ANN), is used to predict the surface air temperature (SAT) anomalies over Japan in the winter months of December, January, and February for the period 1949/50–2019/20. The predictions are made for the four regions Hokkaido, North, Central, and West of Japan. The inputs to the ANN model are derived from the anomaly correlation coefficients among the SAT anomalies over the regions of Japan and the global SAT and sea surface temperature anomalies. The results are validated using anomaly correlation coefficient (ACC) skill scores with the observation. It is found that the ANN predictions over Hokkaido have higher ACC skill scores compared to the ACC scores over the other three regions. The ANN-predicted SAT anomalies are compared with that of ensemble mean of eight of the North American Multimodel Ensemble (NMME) models besides comparing them with the persistent anomalies. The ANN predictions over all the four regions have higher ACC skill scores compared to the NMME model skill scores in the common period of 1982/83–2018/19. The ANN-predicted SAT anomalies also have higher hit rate and lower false alarm rate compared to the NMME-predicted SAT anomalies. All these indicate that the ANN model is a promising tool for predicting the winter SAT anomalies over Japan.

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Branden Katona and Paul Markowski

Abstract

Storms crossing complex terrain can potentially encounter rapidly changing convective environments. However, our understanding of terrain-induced variability in convective storm environments remains limited. HRRR data are used to create climatologies of popular convective storm forecasting parameters for different wind regimes. Self-organizing maps (SOMs) are used to generate six different low-level wind regimes, characterized by different wind directions, for which popular instability and vertical wind shear parameters are averaged. The climatologies show that both instability and vertical wind shear are highly variable in regions of complex terrain, and that the spatial distributions of perturbations relative to the terrain are dependent on the low-level wind direction. Idealized simulations are used to investigate the origins of some of the perturbations seen in the SOM climatologies. The idealized simulations replicate many of the features in the SOM climatologies, which facilitates analysis of their dynamical origins. Terrain influences are greatest when winds are approximately perpendicular to the terrain. In such cases, a standing wave can develop in the lee, leading to an increase in low-level wind speed and a reduction in vertical wind shear with the valley lee of the plateau. Additionally, CAPE tends to be decreased and LCL heights are increased in the lee of the terrain where relative humidity within the boundary layer is locally decreased.

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William R. Burrows and Curtis J. Mooney

Abstract

Blizzard conditions occur regularly in the Canadian Arctic, with high impact on travel and life there. These extreme conditions are challenging to forecast for this vast domain because the observation network is sparse and remote sensing coverage is limited. To establish occurrence statistics we analyzed aviation routine weather reports (METARs) from Canadian Arctic stations between October and May 2014–18. Blizzard conditions occur most frequently in open tundra east and north of the boreal forest boundary, with the highest frequency found on the northwest side of Hudson Bay and over flat terrain in central Baffin Island. Except in sheltered locations, the reported cause of reduced visibility is blowing snow without precipitating snow in about one-half to two-thirds of METARs made by a human observer, even higher at some stations. We produce three products that forecast blizzard conditions from postprocessed NWP model output. The blizzard potential (BP), generated from expert’s rules, is intended for warning well in advance of areas where blizzard conditions may develop. A second product (BH) stems from regression equations for the probability of visibility ≤ 1 km in blowing snow and/or concurrent snow derived by Baggaley and Hanesiak. A third product (RF), generated with the random forest ensemble classification algorithm, makes a consensus YES/NO forecast for blizzard conditions. We describe the products, provide verification, and show forecasts for a significant blizzard event. Receiver operator characteristic curves and critical success index scores show RF forecasts have greater accuracy than BP and BH forecasts at all lead times.

Open access
P. Schaumann, R. Hess, M. Rempel, U. Blahak, and V. Schmidt

Abstract

The seamless combination of nowcasting and numerical weather prediction (NWP) aims to provide a functional basis for very-short-term forecasts, which are essential (e.g., for weather warnings). In this paper we propose a statistical method for precipitation using neural networks (NN) that combines nowcasting data from DWD’s radar-based RadVOR system with postprocessed forecasts of the high resolving NWP ensemble COSMO-DE-EPS. The postprocessing is performed by Ensemble-MOS of DWD. Whereas the quality of the nowcasting projections of RadVOR is excellent at the beginning, it declines rapidly after about 2 h. The postprocessed forecasts of COSMO-DE-EPS in contrast start with lower accuracy but provide meaningful information on longer forecast ranges. The combination of the two systems is performed for probabilities that the expected precipitation amounts exceed a series of predefined thresholds. The resulting probabilistic forecasts are calibrated and outperform both input systems in terms of accuracy for forecast ranges from 1 to 6 h as shown by verification. The proposed NN-model generalizes a previous statistical model based on extended logistic regression, which was restricted to only one threshold of 0.1 mm. The various layers of the NN-model are related to the conventional design elements (e.g., triangular functions and interaction terms) of the previous model for easier insight.

Open access