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Amit Bhardwaj, Vasubandhu Misra, Ben Kirtman, Tirusew Asefa, Carolina Maran, Kevin Morris, Ed Carter, Christopher Martinez, and Daniel Roberts

Abstract

We present here the analysis of 20 years of high-resolution experimental winter seasonal climate reforecasts for Florida (CLIFF). These winter seasonal reforecasts were dynamically downscaled by a regional atmospheric model at 10-km grid spacing from a global model run at T62 spectral resolution (~210-km grid spacing at the equator) forced with sea surface temperatures (SST) obtained from one of the global models in the North American Multimodel Ensemble (NMME). CLIFF was designed in consultation with water managers (in utilities and public water supply) in Florida targeting its five water management districts, including two smaller watersheds of two specific stakeholders in central Florida that manage the public water supply. This enterprise was undertaken in an attempt to meet the climate forecast needs of water management in Florida. CLIFF has 30 ensemble members per season generated by changes to the physics and the lateral boundary conditions of the regional atmospheric model. Both deterministic and probabilistic skill measures of the seasonal precipitation at the zero-month lead for November–December–January (NDJ) and one-month lead for December–January–February (DJF) show that CLIFF has higher seasonal prediction skill than persistence. The results of the seasonal prediction skill of land surface temperature are more sobering than precipitation, although, in many instances, it is still better than the persistence skill.

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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.

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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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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 currently high-resolution, gauge-calibrated, gridded precipitation product, the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) (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 2001–2019. In order 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 webtool to the scientific community.

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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.

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Hung Ming Cheung, Chang-Hoi Ho, Minhee Chang, Dasol Kim, Jinwon Kim, and Woosuk Choi

Abstract

Despite tremendous advancements in dynamical models for weather forecasting, statistical models continue to offer various possibilities for tropical cyclone (TC) track forecasting. Herein, a track-pattern-based approach was developed to predict a TC track for a lead time of 6–8 days over the western North Pacific (WNP), utilizing historical tracks in conjunction with dynamical forecasts. It is composed of four main steps: (1) clustering historical tracks similar to that of an operational five-day forecast in their early phase into track patterns, and calculating the daily mean environmental fields (500-hPa geopotential height and steering flow) associated with each track; (2) deriving the two environmental variables forecasted by dynamical models; (3) evaluating pattern correlation coefficients between the two environmental fields from step (1) and those from dynamical model for a lead times of 6–8 days; and (4) producing the final track forecast based on relative frequency maps obtained from the historical tracks in step (1) and the pattern correlation coefficients obtained from step (3). TCs that formed in the WNP and lasted for at least seven days, during the 9-year period 2011–2019 were selected to verify the resulting track-pattern-based forecasts. In addition to the performance comparable to dynamical models under certain conditions, the track-pattern-based model is inexpensive, and can consistently produce forecasts over large latitudinal or longitudinal ranges. Machine learning techniques can be implemented to incorporate non-linearity in the present model for improving medium-range track forecasts.

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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 mid-latitude regions that are more strongly connected to summer mean European temperature.

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Christopher J. Nowotarski, Justin Spotts, Roger Edwards, Scott Overpeck, and Gary R. Woodall

Abstract

Tropical cyclone tornadoes pose a unique challenge to warning forecasters given their often marginal environments and radar attributes. In late August 2017 Hurricane Harvey made landfall on the Texas coast and produced 52 tornadoes over a record-breaking seven consecutive days. To improve warning efforts, this case study of Harvey’s tornadoes includes an event overview as well as a comparison of near-cell environments and radar attributes between tornadic and nontornadic warned cells. Our results suggest that significant differences existed in both the near-cell environments and radar attributes, particularly rotational velocity, between tornadic cells and false alarms. For many environmental variables and radar attributes, differences were enhanced when only tornadoes associated with a tornado debris signature were considered. Our results highlight the potential of improving warning skill further and reducing false alarms by increasing rotational velocity warning thresholds, refining the use of near-storm environment information, and focusing warning efforts on cells likely to produce the most impactful tornadoes.

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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 12-14 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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Matthew C. Brown, Christopher J. Nowotarski, Andrew R. Dean, Bryan T. Smith, Richard L. Thompson, and John M. Peters

Abstract

The response of severe local storms to environmental evolution across the early evening transition (EET) remains a forecasting challenge, particularly within the context of the Southeast US storm climatology, which includes the increased presence of low-CAPE environments and tornadic non-supercell modes. To disentangle these complex environmental interactions, Southeast severe convective reports spanning 2003-2018 are temporally binned relative to local sunset. Sounding-derived data corresponding to each report are used to characterize how the near-storm environment evolves across the EET, and whether these changes influence the mode, frequency, and tornadic likelihood of their associated storms. High-shear, high-CAPE (HSHC) environments are contrasted with high-shear, low-CAPE (HSLC) environments to highlight physical processes governing storm maintenance and tornadogenesis in the absence of large instability. Lastly, statistical analysis is performed to determine which aspects of the near-storm environment most effectively discriminate between tornadic (or significantly tornadic) and nontornadic storms towards constructing new sounding-derived forecast guidance parameters for multiple modal and environmental combinations. Results indicate that HSLC environments evolve differently than HSHC environments, particularly for non-supercell (e.g., quasi-linear convective system) modes. These low-CAPE environments sustain higher values of low-level shear and storm-relative helicity (SRH) and destabilize post-sunset – potentially compensating for minimal buoyancy. Furthermore, the existence of HSLC storm environments pre-sunset increases the likelihood of non-supercellular tornadoes post-sunset. Existing forecast guidance metrics such as the significant tornado parameter (STP) remain the most skillful predictors of HSHC tornadoes. However, HSLC tornado prediction can be improved by considering variables like precipitable water, downdraft CAPE, and effective inflow base.

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