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Justin G. McLay and Elizabeth Satterfield

A forecast “bust” or “dropout” can be defined as an intermittent but significant loss of model forecast performance. Deterministic forecast dropouts are typically defined in terms of the 500hPa geopotential height (Φ500) anomaly correlation coefficient (ACC) in the Northern Hemisphere (NH) dropping below a predefined threshold. This study first presents a multi-model comparison of dropouts in the Navy Global Environmental Model (NAVGEM) deterministic forecast with the ensemble control members from the Environment and Climate Change Canada (ECCC) Global Ensemble Prediction System (GEPS) and the National Center for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS). Then, the relationship between dropouts and large-scale pattern variability is investigated, focusing on the temporal variability and correlation of flow indices surrounding dropout events. Finally, three severe dropout events are examined from an ensemble perspective. The main findings of this work are that: 1) Forecast dropouts exhibit some relation between models, 2) Although forecast dropouts do not have a single cause, the most severe dropouts in NAVGEM can be linked to specific behavior of the large-scale flow indices: They tend to follow periods of rapidly escalating volatility of the flow indices, and they tend to occur during intervals where the AO and Pacific North American (PNA) indices are exhibiting unusually strong interdependence, and 3) For the dropout events examined from an ensemble perspective, the NAVGEM ensemble spread does not provide a strong signal of elevated potential for very large forecast errors.

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Mark DeMaria, James L. Franklin, Rachel Zelinsky, David A. Zelinsky, Matthew J. Onderlinde, John A. Knaff, Stephanie N. Stevenson, John Kaplan, Kate D. Musgrave, Galina Chirokova, and Charles R. Sampson

Abstract

The National Hurricane Center (NHC) uses a variety of guidance models for its operational tropical cyclone track, intensity, and wind structure forecasts and as baselines for the evaluation of forecast skill. A set of the simpler models, collectively known as the NHC guidance suite, is maintained by NHC. The models comprising the guidance suite are briefly described and evaluated, with details provided for those that have not been documented previously. Decay-SHIFOR is a modified version of the Statistical Hurricane Intensity FORecast (SHIFOR) model that includes decay over land; this modification improves the SHIFOR forecasts through about 96 h. T-CLIPER, a climatology and persistence model that predicts track and intensity using a trajectory approach, has error characteristics similar to those of CLIPER track and D-SHIFOR but can be run to any forecast length. The Trajectory and Beta model (TAB), another trajectory track model, applies a grid-point spatial filter to smooth winds from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model. TAB model errors were 10%-15% lower than those of the Beta and Advection model (BAM), the model it replaced in 2017. Optimizing TAB’s vertical weights shows that the lower troposphere’s environmental flow provides a better match to observed tropical cyclone motion than does the upper troposphere’s, and that the optimal steering layer is shallower for higher-latitude and weaker tropical cyclones. The advantages and disadvantages of the D-SHIFOR, T-CLIPER and TAB models relative to their earlier counterparts are discussed.

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Briana E. Stewart, Jason M. Cordeira, and F. Martin Ralph

Abstract

Atmospheric Rivers (ARs) are long and narrow regions in the atmosphere of enhanced integrated water vapor transport (IVT) and can produce extreme precipitation and high societal impacts. Reliable and skillful forecasts of landfalling ARs in the Western US are critical to hazard preparation and aid in decision support activities, such as Forecast Informed Reservoir Operations. The purpose of this study is to compare the cool-season water year skill of the NCEP Global Ensemble Forecast System (GEFS) and ECMWF Ensemble Prediction System (EPS) forecasts of IVT along the U.S. West Coast for 2017–2020. The skill is analyzed using probability-over-threshold forecasts of IVT magnitudes ≥250 kg m−1 s−1 (P250) using contingency table skill metrics in coastal northern California and along the west coast of North America. Analysis of P250 with lead-time (dProg/dt) found the EPS provided ~1-day of additional lead-time for situational awareness over the GEFS at lead times of 6–10-days. Forecast skill analysis highlights that the EPS leads over the GEFS with success ratios 0.10 to 0.15 higher at lead times >6 days for P250 thresholds of ≥25% and ≥50%, while event-based skill analysis using the probability of detection (POD) found that both models were largely similar with minor latitudinal variations favoring higher POD for each model in different locations along the coast. The relative skill of the EPS over the GEFS is largely attributed to over-forecasting by the GEFS at longer lead times and an increase in the false alarm ratio.

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Bong-Chul Seo, Marcela Rojas, Felipe Quintero, Witold F. Krajewski, and Dong Ha Kim

Abstract

This study demonstrates an approach to expand and improve the current prediction capability of the National Water Model (NWM). The primary objective is to examine potential benefit of real-time local stage measurements in streamflow prediction, particularly for local communities that do not benefit from the improved streamflow forecasts due to the current data assimilation (DA) scheme. The proposed approach incorporates real-time local stage measurements into the NWM streamflow DA procedure by using synthetic rating curves (SRC) developed based on an established open channel flow model. For streamflow DA and its evaluation, we used six-year (2016–2021) data collected from 140 United States Geological Survey (USGS) stations, where quality-assured rating curves are consistently maintained (verification stations), and 310 stage-only stations operated by the Iowa Flood Center and the USGS in Iowa. The evaluation result from NWM’s current DA configuration based on the USGS verification stations indicated that DA improves streamflow prediction skills significantly downstream from the station locations. This improvement tends to increase as drainage scale becomes larger. The result from the new DA configuration including all stage-only sensors showed expanded domain of improved predictions, compared to those from the open-loop simulation. This reveals that the real-time low-cost stage sensors are beneficial for streamflow prediction, particularly at small basins, while their utility appears to be limited at large drainage areas because of the inherent limitations of lidar-based channel geometry used for the SRC development. The framework presented in this study can be readily applied to include numerous stage-only stream gauges nationwide in the NWM modeling and forecasting procedures.

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Brian J. Squitieri and William A. Gallus Jr.

Abstract

The degree of improvement in convective representation in NWP with horizontal grid spacings finer than 3 km remains debatable. While some research suggests sub-km horizontal grid spacing is needed to resolve details of convective structures, other studies have shown that decreasing grid spacing from 3-4 km to 1-2 km offers little additional value for forecasts of deep convection. In addition, few studies exist to show how changes in vertical grid spacing impact thunderstorm forecasts, especially when horizontal grid spacing is simultaneously decreased. The present research investigates how warm-season central U.S. simulated MCS cold pools for eleven observed cases are impacted by decreasing horizontal grid spacing from 3 to 1 km while increasing the vertical levels from 50 to 100 in WRF runs. 3 km runs with 100 levels produced the deepest and most negatively buoyant cold pools compared to all other grid spacings since updrafts were more poorly resolved, resulting in a higher flux of rearward-advected frozen hydrometeors, whose melting processes were augmented by the finer vertical grid spacing, which better resolved the melting layer. However, the more predominant signal among all eleven cases was for more expansive cold pools in 1 km runs, where the stronger and more abundant updrafts focused along the MCS leading line supported a larger volume of concentrated rearward hydrometeor advection and resultant latent cooling at lower levels.

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Brian J. Squitieri and William A. Gallus Jr.

Abstract

Several past studies have demonstrated improvement in forecasting convective precipitation by decreasing model grid spacing to the point of explicitly resolving deep convection. Real-case convective modeling studies have attempted to identify what model grid spacing feasibly provides the most optimal forecast given computational constraints. While part I of this manuscript investigated changes in MCS cold pool characteristics with varied vertical and horizontal grid spacing, part II explores changes in skill for MCS spatial placement, forward speed, and QPFs among runs with decreased horizontal and vertical grid spacing by employing the same WRF-ARW runs as in part I. QPF forecast skill significantly improved for later portions of the MCS life cycle when decreasing horizontal grid spacing from 3 to 1 km with the part-double-moment Thompson microphysics scheme. Some improvements were present in QPFs with higher precipitation amounts in the early stages of MCSs simulated with the single-moment WSM6 microphysics scheme. However, significant improvements were not common with MCS placement or QPF of the entire precipitation swath with either the Thompson or WSM6 schemes, suggesting that the benefit to MCS QPFs with decreased horizontal grid spacings is limited. Furthermore, increasing vertical resolution from 50 to 100 levels worsened WSM6 scheme QPF skill in some cases, suggesting that choices of or improvement in model physics may be equally or more positively impactful to NWP forecasts than grid spacing changes.

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Jean-François Caron and Mark Buehner

The approach of applying different amounts of horizontal localization to different ranges of background-error covariance horizontal scales as proposed by Buehner and Shlyaeva (2015) was recently implemented in the four-dimensional ensemble-variational (4DEnVar) data assimilation scheme of the global deterministic prediction system (GDPS) at Environment and Climate Change Canada operations. To maximize the benefits from this approach to reduce the sampling noise in the ensemble-derived background-error covariances, it was necessary to adopt a new weighting between the climatological and flow-dependent covariances that increases significantly the role of the latter. Thus, in December 2021 the GDPS became the first operational global deterministic medium-range weather forecasting system to rely completely on flow-dependent covariances in the troposphere and the lower stratosphere. The experiments that led to the adoption of these two related changes and their impacts on the forecasts up to 7 days for various regions of the globe during the boreal summer of 2019 and winter of 2020 are presented here. It is also illustrated that relying more on ensemble-derived covariances amplifies the positive impacts on the GDPS when the background ensemble generation strategy is improved.

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Li Jia, Fumin Ren, Chenchen Ding, and Mingyang Wang

Abstract

The Dynamical–Statistical–Analog Ensemble Forecast model for landfalling typhoon precipitation (DSAEF_LTP) was developed as a supplementary method to numerical weather prediction (NWP). A successful strategy for improving the forecasting skill of the DSAEF_LTP model is to include as many relevant variables as possible in the generalized initial value (GIV) of this model. In this study, a new variable, TC translation speed, is incorporated into the DSAEF_LTP model, producing a new version of this model named DSAEF_LTP-4. Then, the best scheme of the model for South China is obtained by applying this model to the forecast of the accumulated rainfall of thirteen landfalling tropical cyclones (LTCs) that occurred over South China during 2012–2014. In addition, the forecast performance of the best scheme is estimated by forecast experiments with eight LTCs in 2015–2016 over South China, and then compared to that of the other versions of the DSAEF_LTP model and three NWP models (i.e., ECMWF, GFS and T639). Results show further the improved performance of the DSAEF_LTP-4 model in simulating precipitation of ≥ 250 and ≥ 100 mm. However, the forecast performance of DSAEF_LTP-4 is less satisfactory than DSAEF_LTP-2. This mainly because of a large proportion of TCs with anomalous tracks and more sensitivity to the characteristics of experiment samples of DSAEF_LTP-4. Of significance is that the DSAEF_LTP model performs better than three NWP models for LTCs with typical tracks.

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Ryohei Kato, Shingo Shimizu, Tadayasu Ohigashi, Takeshi Maesaka, Ken-ichi Shimose, and Koyuru Iwanami

Meso-γ-scale (2–20 km) local heavy rain (LHR) can cause fatalities through the sudden rise of rivers and flooding of roads. To help prevent this loss of life, we developed prediction methods for these types of meteorological hazards. We assimilated ground-based cloud radar (Ka-band radar) data that can capture cloud droplets before raindrops form and attempted to predict LHR with a cloud resolving numerical weather prediction (NWP) model. High-temporal (1-min interval) three-dimensional cloud-radar data obtained through special observation were assimilated using a water vapor nudging method in the pre-rain stage of an LHR-causing cumulonimbus. While rainfall was not predicted by the NWP model without assimilation, LHR was predicted approximately 20 min after the conclusion of cloud-radar data assimilation cycling. Results suggest that NWP with cloud-radar data assimilation in the pre-rain stage has great potential for predicting LHR, and can lead to an early evacuation warning and subsequent evacuation of vulnerable populations.

Open access
Peter R. Gent
Open access