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Jihong Moon, Jinyoung Park, Dong-Hyun Cha, and Yumin Moon

Abstract

In this study, the characteristics of simulated tropical cyclones (TCs) over the western North Pacific by a regional model (the WRF Model) are verified. We utilize 12-km horizontal grid spacing, and simulations are integrated for 5 days from model initialization. A total of 125 forecasts are divided into five clusters through the k-means clustering method. The TCs in the cluster 1 and 2 (group 1), which includes many TCs moving northward in the subtropical region, generally have larger track errors than for TCs in cluster 3 and 4 (group 2). The optimal steering vector is used to examine the difference in the track forecast skill between these two groups. The bias in the steering vector between the model and analysis data is found to be more substantial for group 1 TCs than group 2 TCs. The larger steering vector difference for group 1 TCs indicates that environmental fields tend to be poorly simulated in group 1 TC cases. Furthermore, the residual terms, including the storm-scale process, asymmetric convection distribution, or beta-related effect, are also larger for group 1 TCs than group 2 TCs. Therefore, it is probable that the large track forecast error for group 1 TCs is a result of unreasonable simulations of environmental wind fields and residual processes in the midlatitudes.

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Huijun Huang, Jinnan Yuan, Guanhuan Wen, Xueyan Bi, Ling Huang, and Mingsen Zhou

Abstract

Tropical depressions formed over the South China Sea usually produce severe flooding and wind damage when they develop into a storm and make landfall. To provide an early warning, forecasters should know when, and if, a tropical depression will develop into a tropical storm. To better understand and predict such development, we examine the dynamic and thermodynamic variables of 74 tropical depressions over the South China Sea, 52 of which developed into storms, hereafter “developing,” with the remaining being classified as “nondeveloping.” Using the National Centers for Environmental Prediction Final (NCEP FNL) data, verified with ECMWF forecast data, we examine the dynamic and thermodynamic statistics that characterize these tropical cyclones. Based on these characteristics, we propose seven criteria to determine whether a tropical depression will develop. Five had been used before, but two new criteria are also found to be useful. These two are associated with the diabatic heating rate and help to determine whether a tropical cyclone diurnal cycle exists and whether the convection system remains intact in the center: 1) presence of a regular diurnal variation of the diabatic heating rate at the center and 2) occurrence of specific peaks in the radiative-heating profile. We test all seven criteria on all tropical depression cases in 2018/19 before the system developed or decayed, showing that these criteria can help to operationally identify whether or not a tropical depression develops into a tropical storm with an average lead time of 36.6 h.

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Eva-Maria Walz, Marlon Maranan, Roderick van der Linden, Andreas H. Fink, and Peter Knippertz

Abstract

Current numerical weather prediction models show limited skill in predicting low-latitude precipitation. To aid future improvements, be it with better dynamical or statistical models, we propose a well-defined benchmark forecast. We use the arguably best available high-resolution, gauge-calibrated, gridded precipitation product, the Integrated Multisatellite Retrievals for GPM (IMERG) “final run” in a ±15-day window around the date of interest to build an empirical climatological ensemble forecast. This window size is an optimal compromise between statistical robustness and flexibility to represent seasonal changes. We refer to this benchmark as extended probabilistic climatology (EPC) and compute it on a 0.1° × 0.1° grid for 40°S–40°N and the period 2001–19. To reduce and standardize information, a mixed Bernoulli–Gamma distribution is fitted to the empirical EPC, which hardly affects predictive performance. The EPC is then compared to 1-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF) using standard verification scores. With respect to rainfall amount, ECMWF performs only slightly better than EPS over most of the low latitudes and worse over high-mountain and dry oceanic areas as well as over tropical Africa, where the lack of skill is also evident in independent station data. For rainfall occurrence, EPC is superior over most oceanic, coastal, and mountain regions, although the better potential predictive ability of ECMWF indicates that this is mostly due to calibration problems. To encourage the use of the new benchmark, we provide the data, scripts, and an interactive web tool to the scientific community.

Open access
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
Robert Conrick, Clifford F. Mass, Joseph P. Boomgard-Zagrodnik, and David Ovens

Abstract

During late summer 2020, large wildfires over the Pacific Northwest produced dense smoke that impacted the region for an extended period. During this period of poor air quality, persistent low-level cloud coverage was poorly forecast by operational numerical weather prediction models, which dissipated clouds too quickly or produced insufficient cloud coverage extent. This deficiency raises questions about the influence of wildfire smoke on low-level clouds in the marine environment of the Pacific Northwest. This paper investigates the effects of wildfire smoke on the properties of low-level clouds, including their formation, microphysical properties, and dissipation. A case study from 12 to 14 September 2020 is used as a testbed to evaluate the impact of wildfire smoke on such clouds. Observations from satellites and surface observing sites, coupled with mesoscale model simulations, are applied to understand the influence of wildfire smoke during this event. Results indicate that the presence of thick smoke over Washington led to decreased temperatures in the lower troposphere, which enhanced low-level cloud coverage, with smoke particles altering the microphysical structure of clouds to favor high concentrations of small droplets. Thermodynamic changes due to smoke are found to be the primary driver of enhanced cloud lifetime during these events, with microphysical changes to clouds as a secondary contributing factor. However, both the thermodynamic and microphysical effects are necessary to produce a realistic simulation.

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Laurel L. DeHaan, Andrew C. Martin, Rachel R. Weihs, Luca Delle Monache, and F. Martin Ralph

Abstract

Accurate forecasts of atmospheric rivers (ARs) provide advance warning of flood and landslide hazards and greatly aid effective water management. It is, therefore, critical to evaluate the skill of AR forecasts in numerical weather prediction (NWP) models. A new verification framework is proposed that leverages freely available software and metrics previously used for different applications. Specifically, AR detection and statistics are computed for the first time using the Method for Object-Based Diagnostic Evaluation (MODE). In addition, the measure of effectiveness (MoE) is introduced as a new metric for understanding AR forecast skill in terms of size and location. The MoE provides a quantitative measure of the position of an entire forecast AR relative to observation, regardless of whether the AR is making landfall. In addition, the MoE can provide qualitative information about the evolution of a forecast by lead time, with implications about the predictability of an AR. We analyze AR forecast verification and skill using 11 years of cold-season forecasts from two NWP models: one global and one regional. Four different thresholds of integrated vapor transport (IVT) are used in the verification, revealing differences in forecast skill that are based on the strength of an AR. In addition to MoE, AR forecast skill is also addressed in terms of intensity error, landfall position error, and contingency-table metrics.

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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