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Julia Jeworrek, Gregory West, and Roland Stull

Abstract

Physics parameterizations in the Weather Research and Forecasting (WRF) Model are systematically varied to investigate precipitation forecast performance over the complex terrain of southwest British Columbia (BC). Comparing a full year of modeling data from over 100 WRF configurations to station observations reveals sensitivities of precipitation intensity, season, location, grid resolution, and accumulation window. The choice of cumulus and microphysics parameterizations is most important. The WSM5 microphysics scheme yields competitive verification scores when compared to more sophisticated and computationally expensive parameterizations. Although the scale-aware Grell–Freitas cumulus parameterization performs better for summertime convective precipitation, the conventional Kain–Fritsch parameterization better simulates wintertime frontal precipitation, which contributes to the majority of the annual precipitation in southwest BC. Finer grid spacings have lower relative biases and a more realistic spread in precipitation intensity distribution, yet higher relative standard deviations of their errors—they produce finer spatial differences and local extrema. Finer resolutions produce the best fraction of correct-to-incorrect forecasts across all precipitation intensities, whereas the coarser 27-km domain yields the highest hit rates and equitable threat scores. Verification metrics improve greatly with longer accumulation windows—hourly precipitation values are prone to double-penalty issues, while longer accumulation windows compensate for timing errors but lose information about short-term precipitation intensities. This study provides insights regarding WRF precipitation performance in complex terrain across a wide variety of configurations, using metrics important to a range of end users.

Open access
Jonathan M. Garner, William C. Iwasko, Tyler D. Jewel, Richard L. Thompson, and Bryan T. Smith

Abstract

A dataset maintained by the Storm Prediction Center (SPC) of 6300 tornado events from 2009–2015, consisting of radar-identified convective modes and near-storm environmental information obtained from Rapid Update Cycle and Rapid Refresh model analysis grids, has been augmented with additional radar information related to the low-level mesocyclones associated with tornado longevity, path-length, and width. All EF2–EF5 tornadoes, in addition to randomly selected EF0–EF1 tornadoes, were extracted from the SPC dataset, which yielded 1268 events for inclusion in the current study. Analysis of that data revealed similar values of the effective-layer significant tornado parameter for the longest-lived (60+ min) tornadic circulations, longest-tracked (≥ 68 km) tornadoes, and widest tornadoes (≥ 1.2 km). However, the widest tornadoes occurring west of –94° longitude were associated with larger mean-layer convective available potential energy, storm-top divergence, and low-level rotational velocity. Furthermore, wide tornadoes occurred when low-level winds were out of the southeast resulting in large low-level hodograph curvature and near-surface horizontal vorticity that was more purely streamwise compared to long-lived and long-tracked events. On the other hand, tornado path-length and longevity were maximized with eastward migrating synoptic-scale cyclones associated with strong southwesterly wind profiles through much of the troposphere, fast storm motions, large values of bulk wind difference and storm-relative helicity, and lower buoyancy.

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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 Domkawle, S. D. Pawar, Ashu Mamgain, V. R. Durai, Ravi S. Nanjundiah, Ashish 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 Centre for Environmental Prediction (NCEP) based India Meteorological Department (IMD) operational Global Forecast System (GFS), the European Centre for Medium Range Weather Forecast (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 ERA5 (ECMWF Reanalyses version 5) reanalyses 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 skilful than the deterministic forecasts, as they were able to predict rainfall anomalies (>3 standard deviations from climatology) beyond day 5 for August 2019 and up to day 3 for 2018.

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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 METeorological Aerodrome Reports (METARs) from Canadian Arctic stations between October and May 2014-2018. Blizzard conditions occur most frequently in open tundra east and north of the boreal forest boundary, with 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 post-processed 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 (2005). 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
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 10km grid spacing from a global model run at T62 spectral resolution (~210km 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 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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Jeremiah O. Piersante, Russ. S. Schumacher, and Kristen L. Rasmussen

Abstract

Ensemble forecasts using the WRF Model at 20-km grid spacing with varying parameterizations are used to investigate and compare precipitation and atmospheric profile forecast biases in North and South America. By verifying a 19-member ensemble against NCEP Stage IV precipitation analyses, it is shown that the cumulus parameterization (CP), in addition to precipitation amount and season, had the largest influence on precipitation forecast skill in North America during 2016-2017. Verification of an ensemble subset against operational radiosondes in North and South America finds that forecasts in both continents feature a substantial mid-level dry bias, particularly at 700 hPa, during the warm season. Case-by-case analysis suggests that large mid-level error is associated with mesoscale convective systems (MCSs) east of the high terrain and westerly subsident flow from the Rocky and Andes Mountains in North and South America. However, error in South America is consistently greater than North America. This is likely attributed to the complex terrain and higher average altitude of the Andes relative to the Rockies, which allow for a deeper low-level jet and long-lasting MCSs, both of which 20-km simulations struggle to resolve. In the wake of data availability from the RELAMPAGO field campaign, the authors hope that this work motivates further comparison of large precipitating systems in North and South America, given their high impact in both continents.

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Akira Yamazaki, Takemasa Miyoshi, Jun Inoue, Takeshi Enomoto, and Nobumasa Komori

Abstract

An ensemble-based forecast sensitivity to observations (EFSO) diagnosis has been implemented in an atmospheric general circulation model–ensemble Kalman filter data assimilation system to estimate the impacts of specific observations from the quasi-operational global observing system on weekly short-range forecasts. It was examined whether EFSO reasonably approximates the impacts of a subset of observations from specific geographical locations for 6-hour forecasts, and how long the 6-hour observation impacts can be retained during the 7-day forecast period. The reference for these forecasts was obtained from 12 data denial experiments in each of which a subset of three radiosonde observations launched from a geographical location was excluded. The 12 locations were selected from three latitudinal bands comprising (i) four Arctic regions, (ii) four midlatitude regions in the Northern Hemisphere, and (iii) four tropical regions during the Northern Hemisphere winter of 2015/16. The estimated winter-averaged EFSO-derived observation impacts well corresponded to the 6-hour observation impacts obtained by the data denials and EFSO could reasonably estimate the observation impacts by the data denials on short-range (6-hour to 2-day) forecasts. Furthermore, during the medium-range (4-day to 7-day) forecasts, it was found that the Arctic observations tend to seed the broadest impacts and their short-range observation impacts could be projected to beneficial impacts in Arctic and midlatitude North American areas. The midlatitude area located just downstream of dynamical propagation from the Arctic toward the midlatitudes. Results obtained by repeated Arctic data-denial experiments were found to be generally common to those from the non-repeated experiments.

Open access
Samuel M. Bartlett and Jason M. Cordeira

Abstract

Atmospheric rivers (ARs) are synoptic-scale phenomena associated with long, narrow corridors of enhanced low-level water vapor transport. Landfalling ARs may produce numerous beneficial (e.g. drought amelioration and watershed recharge) and hazardous (e.g. flash flooding and heavy snow) impacts that may require the National Weather Service (NWS) to issue watches, warnings, and advisories (WWAs) for hazardous weather. Prior research on WWAs and ARs in California found that 50–70% of days with flood-related and 60–80% of days with winter weather-related WWAs occurred on days with landfalling ARs in California. The present study further investigates this relationship for landfalling ARs and WWAs during the cool seasons of 2006–2018 across the entire western U.S. and considers additional dimensions of AR intensity and duration. Across the western U.S., regional maxima of 70–90% of days with WWAs issued for any hazard type were associated with landfalling ARs. In the Pacific Northwest and Central regions, flood-related and wind-related WWAs were also more frequently associated with more intense and longer duration ARs. While a large majority of days with WWAs were associated with landfalling ARs, not all landfalling ARs were necessarily associated with WWAs (i.e., not all ARs are hazardous). For example, regional maxima of only 50–70% of AR days were associated with WWAs issued for any hazard type. However, as landfalling AR intensity and duration increased, the association with a WWA and the “hazard footprint” of WWAs increased quasi-exponentially across the western U.S.

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Regula Keller, Jan Rajczak, Jonas Bhend, Christoph Spirig, Stephan Hemri, Mark A. Liniger, and Heini Wernli

Abstract

Statistical postprocessing is applied in operational forecasting to correct systematic errors of numerical weather prediction models (NWP) and to automatically produce calibrated local forecasts for end-users. Postprocessing is particularly relevant in complex terrain, where even state-of-the-art high-resolution NWP systems cannot resolve many of the small-scale processes shaping local weather conditions. In addition, statistical postprocessing can also be used to combine forecasts from multiple NWP systems. Here we assess an ensemble model output statistics (EMOS) approach to produce seamless temperature forecasts based on a combination of short-term ensemble forecasts from a convection-permitting limited-area ensemble and a medium-range global ensemble forecasting model. We quantify the benefit of this approach compared to only postprocessing the high-resolution NWP. The multi-model EMOS approach (’Mixed EMOS’) is able to improve forecasts by 30% with respect to direct model output from the high-resolution NWP. A detailed evaluation of Mixed EMOS reveals that it outperforms either one of the single-model EMOS versions by 8-12%. Temperature forecasts at valley locations profit in particular from the model combination. All forecast variants perform worst in winter (DJF), however calibration and model combination improves forecast quality substantially. In addition to increasing skill as compared to single model postprocessing, it also enables to seamlessly combine multiple forecast sources with different time horizons (and horizontal resolutions) and thereby consolidates short-term to medium-range forecasting time horizons in one product without any user-relevant discontinuity.

Open 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 has 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 KDP core, which can indicate processes such as melting and precipitation loading that increase negative buoyancy and can result in downburst development. Therefore, KDP 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. KDP core characteristics near the environmental melting layer, vertical gradients of KDP, and environmental conditions were quantified to identify any differences between downbursts of varying intensities. The analysis shows that 1) KDP cores near the environmental melting layer are a reliable signal that a downburst will develop, 2) while using KDP cores to anticipate an impending downburst’s intensity is challenging, larger KDP near the melting layer and larger vertical gradients of KDP 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 KDP cores, and 4) KDP 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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