Browse

You are looking at 11 - 20 of 2,812 items for :

  • Weather and Forecasting x
  • Refine by Access: All Content x
Clear All
Aaron J. Hill, Christopher C. Weiss, and David C. Dowell

Abstract

Ensemble forecasts are generated with and without the assimilation of near-surface observations from a portable, mesoscale network of StickNet platforms during the Verification of the Origins of Rotation in Tornadoes Experiment–Southeast (VORTEX-SE). Four VORTEX-SE intensive observing periods are selected to evaluate the impact of StickNet observations on forecasts and predictability of deep convection within the Southeast United States. StickNet observations are assimilated with an experimental version of the High-Resolution Rapid Refresh Ensemble (HRRRE) in one experiment, and withheld in a control forecast experiment. Overall, StickNet observations are found to effectively reduce mesoscale analysis and forecast errors of temperature and dewpoint. Differences in ensemble analyses between the two parallel experiments are maximized near the StickNet array and then either propagate away with the mean low-level flow through the forecast period or remain quasi-stationary, reducing local analysis biases. Forecast errors of temperature and dewpoint exhibit periods of improvement and degradation relative to the control forecast, and error increases are largely driven on the storm scale. Convection predictability, measured through subjective evaluation and objective verification of forecast updraft helicity, is driven more by when forecasts are initialized (i.e., more data assimilation cycles with conventional observations) rather than the inclusion of StickNet observations in data assimilation. It is hypothesized that the full impact of assimilating these data is not realized in part due to poor sampling of forecast sensitive regions by the StickNet platforms, as identified through ensemble sensitivity analysis.

Open access
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.

Restricted access
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.

Restricted access
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
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.

Restricted access
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.

Restricted access
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.

Restricted access
Sean Ernst, Joe Ripberger, Makenzie J. Krocak, Hank Jenkins-Smith, and Carol Silva

Abstract

Although severe weather forecast products, such as the Storm Prediction Center (SPC) convective outlook, are much more accurate than climatology at day-to-week time scales, tornadoes and severe thunderstorms claim dozens of lives and cause billions of dollars in damage every year. While the accuracy of this outlook has been well documented, less work has been done to explore the comprehension of the product by non-expert users like the general public. This study seeks to fill this key knowledge gap by collecting data from a representative survey of U.S. adults in the lower 48 states about their use and interpretation of the SPC convective outlook. Participants in this study were asked to rank the words and colors used in the outlook from least to greatest risk, and their answers were compared through visualizations and statistical tests across multiple demographics. Results show that the US public ranks the outlook colors similarly to their ordering in the outlook but switch the positions of several of the outlook words as compared to the operational product. Logistic regression models also reveal that more numerate individuals more correctly rank the SPC outlook words and colors. These findings suggest that the words used in the convective outlook may confuse non-expert users, and that future work should continue to use input from public surveys to test potential improvements in the choice of outlook words. Using more easily understood words may help to increase the outlook’s decision support value and potentially reduce the harm caused by severe weather events.

Restricted access
Charles R. Sampson, Efren A. Serra, John A. Knaff, and Joshua H. Cossuth

Abstract

The U.S. Navy is keenly interested in analyses and predictions of waves at sea due to their effects on important tasks such as shipping, base preparedness and disaster relief. U.S. Tropical Cyclone (TC) Forecast Centers routinely disseminate wind probabilities consistent with official TC forecasts worldwide, but do not do the same for wave forecasts. These probabilities are especially important at longer leads where TC forecast accuracy diminishes. This work describes global wave probabilities consistent with both the official TC forecasts and their wind probabilities. Real-time runs for 84 TCs between May 2018 and March 2019, with probabilities generated for 12-ft and 18-ft significant wave heights are used to calculate verification statistics. This results in 347, 319, 261, 214, 155, and 112 verification cases at lead times of 1, 2, 3, 4, and 5 days where each verification case consists of a 20x20 degree latitude longitude grid around the verifying TC position. When compared with wave probabilities generated solely by a global numerical weather prediction model, the wind probability-based algorithm demonstrates improved consistency with official forecasts and provides additional benefits. Those benefits include an improved capability to discriminate between 12-ft and 18-ft significant wave events and non-events. The verification statistics also shows that the wind probability-based algorithm has a consistent high bias. How these biases can be reduced in future efforts is also discussed.

Restricted access
Yang Lyu, Xiefei Zhi, Shoupeng Zhu, Yi Fan, and Mengting Pan

Abstract

In this study, two pattern projection methods, i.e., the Stepwise Pattern Projection Method (SPPM) and the newly proposed Neighborhood Pattern Projection Method (NPPM), are investigated to improve forecast skills of daily maximum and minimum temperatures (Tmax and Tmin) over East Asia with lead times of 1–7 days. Meanwhile, the decaying averaging method (DAM) is conducted in parallel for comparison. These post-processing methods are found to effectively calibrate the temperature forecasts on the basis of the raw ECMWF output. Generally, the SPPM is slightly inferior to the DAM, while its insufficiency decreases with increasing lead times. The NPPM shows manifest superiority for all lead times, with the mean absolute errors of Tmax and Tmin decreased by ~0.7°C and ~0.9°C, respectively. Advantages of the two pattern projection methods are both mainly concentrated on the high-altitude areas such as the Tibetan Plateau, where the raw ECMWF forecasts show most conspicuous biases. In addition, aiming at further assessments of these methods on extreme event forecasts, two case experiments are carried out towards a heat wave and a cold surge, respectively. The NPPM is retained as the optimal with the highest forecast skills, which reduces most of the biases to < 2°.C for both Tmax and Tmin over all the lead days. In general, the statistical pattern projection methods are capable of effectively eliminating spatial biases in forecasts of surface air temperature. Compared with the initial SPPM, the NPPM not only produces more powerful forecast calibrations, but also provides more pragmatic calculations and greater potential economic benefits in practical applications.

Restricted access