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Susan Rennie
,
Shaun Cooper
,
Peter Steinle
,
Gary Dietachmayer
,
Monika Krysta
,
Charmaine Franklin
,
Chris Bridge
,
Matthew Marshall
,
Yi Xiao
, and
Dean Sgarbossa

Abstract

The Australian Bureau of Meteorology recently upgraded its convection-allowing numerical weather prediction system, known as the Australian Community Climate and Earth System Simulator (ACCESS-C). ACCESS-C includes seven domains covering major population centers, nested inside the Bureau’s global NWP system. The upgrade included the introduction of data assimilation, with hourly cycling 4D-Var. With a much newer version of the Unified Model to provide the forecast, a range of storm attribute diagnostics to improve forecasting of severe weather events could be introduced. This paper details the configuration of the new version of ACCESS-C. Some verification compared with its predecessor (a downscaling system of comparable resolution) is presented. Of greater note is an exploration of the differences in the model characteristics between the new and old systems, which will affect how users interpret the outputs.

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Kai Melamed-Turkish
,
Shawn Milrad
,
John Gyakum
, and
Eyad Atallah

Abstract

This study documents the frequency and intensity of precipitation at Montreal, Canada, from 1979 to 2018 as it relates to four quadrants of a 500-hPa wave, identified by the position of troughs, ridges, and inflection points. These quadrants provide a simplified conceptualization of the contributions from the temperature and vorticity advection forcing terms in the quasigeostrophic (QG) omega equation. Precipitation is found to be significantly more intense in every season except summer in the quadrant immediately upstream of the 500-hPa ridge, where differential cyclonic vorticity advection (DCVA) and a local maximum in horizontal warm-air advection (WAA) tend to promote unambiguous QG ascent. In summer, the average precipitation is still most intense in the DCVA-WAA quadrant, but not significantly more than in the quadrant immediately downstream of the 500-hPa trough, where DCVA and a local maximum in horizontal cold-air advection (CAA) are expected to compete, resulting in ambiguous QG vertical motion. Precipitation in the DCVA-CAA quadrant is more intense in every season than in the expected differential anticyclonic vorticity advection (DAVA) quadrants, with significantly higher intensities in spring and fall. Furthermore, the DCVA quadrants exhibit significantly stronger ascent compared to the DAVA quadrants and the DCVA-WAA quadrant features significantly warmer 850-hPa equivalent potential temperatures compared to the three other quadrants in every season. Odds ratios indicate a statistically significant association between heavy precipitation episodes and the DCVA-WAA quadrant. Heavy precipitation episodes in the DCVA-CAA quadrant are associated with a negatively tilted 500-hPa geopotential height pattern in winter and fall.

Significance Statement

Operational weather forecasters apply conceptual models that connect upper-atmospheric weather patterns to vertical motion and precipitation. However, few studies have quantified this connection over a longer, continuous period of time. In this study, we examine the relationship between historical subdaily precipitation at Montreal, Canada, and a simple large-scale conceptual model that relates vertical motion to the position of upper-level troughs and ridges. We find significant evidence for heavy precipitation to occur upstream of the upper-level ridge, and for very little, or very light, precipitation to occur upstream of the upper-level trough. These results provide quantitative support to some of the conceptual methods available to operational weather forecasters in preliminary analyses that support their precipitation forecasts.

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Maxime Taillardat
,
Anne-Laure Fougères
,
Philippe Naveau
, and
Olivier Mestre
Open access
Samuel R. Harrison
,
James O. Pope
,
Robert A. Neal
,
Freya K. Garry
,
Ryosuke Kurashina
, and
Dan Suri

Abstract

Icelandic volcanic emissions have been shown historically and more recently to have an impact on public health and aviation across northern and western Europe. The severity of these impacts is governed by the prevailing weather conditions and the nature of the eruption. This study focuses on the former utilizing an existing set of 30 weather patterns produced by the Met Office. Associated daily historical classifications are used to assess which weather patterns are most likely to result in flow from Iceland into four flight information regions (FIRs) covering the British Isles and North Atlantic, which may lead to disruption to aviation during Icelandic volcanic episodes. High-risk weather patterns vary between FIRs, with a total of 14 weather patterns impacting at least one FIR. These high-risk types predominantly have a northwesterly or westerly flow from Iceland into British Isles airspace. Analysis of the historical classifications reveals a typical duration for high-risk periods of 3–5 days, when transitions between high-risk types are considered. High-risk periods lasting over a week are also possible in all four FIRs. Additionally, impacts are more likely in winter months for most FIRs. Knowledge of high-risk weather patterns for aviation can be used within existing operational probabilistic weather pattern forecasting tools. Combined probabilities for high-risk weather patterns can be derived for the medium-range (1–2 weeks ahead) and used to provide a rapid assessment as to the likelihood of flow from Iceland. This weather pattern forecasting application is illustrated using archived forecast data for the 2010 Eyjafjallajökull eruption.

Open access
Manisha Ganeshan
,
Oreste Reale
,
Erica McGrath-Spangler
, and
Niama Boukachaba

Abstract

Polar lows, and mesoscale convective cyclones bearing resemblance to tropical cyclones but originating outside of the tropics, are storms that are challenging to represent accurately in global analyses and models because of their small size, rapid growth at subsynoptic scales, occurrence in data poor oceanic regions, and difficulties in objectively validating them in analysis. Building on previous positive results obtained with respect to the representation of tropical cyclones (TCs) in a global model, a set of observing system experiments (OSEs) performed using the NASA Goddard Earth Observing System (GEOS, version 5) are investigated, focusing on three case studies—a polar low in the Sea of Okhotsk, a polar low in the Southern Ocean, and a Mediterranean Sea tropical-like cyclone that occurred during the boreal fall season of 2014. Experiments assimilating adaptively thinned cloud-cleared hyperspectral infrared radiances from the Atmospheric Infrared Sounder (AIRS) instrument on board the NASA Aqua satellite, with higher density in the vicinity of each storm and its pre-cyclogenesis environment, and lower density elsewhere, demonstrate a positive impact on the analyzed representation of each storm. The adaptive thinning experiments improve the storm intensity and structure, including vertical alignment, depth, symmetry, strength, and compactness of warm core compared to the reference experiments. The results suggest that jet-level processes associated with extremely strong horizontal velocity gradients as represented in the model analysis can be useful to locate dynamically active regions of the extratropical atmosphere where denser data coverage is likely to improve the analyzed representation of polar lows and other similar marine mesoscale convective cyclones.

Significance Statement

Extratropical maritime mesoscale convective cyclones are short-lived, elusive features that are difficult to represent accurately in global analyses. Previous work by this team demonstrated a positive impact of an adaptive thinning methodology for infrared radiances applied to the tropical cyclone (TC) analysis. The methodology allows a relatively greater volume of radiance data to be assimilated around TCs within a TC-centered moving domain in a global model, yielding an improvement in TC structure and intensity forecast. A similar approach is explored here for two polar lows and a Mediterranean Sea tropical-like cyclone, wherein infrared radiances are more densely assimilated in the vicinity of each storm and its pre-cyclogenesis environment, resulting in a positive impact on the representation of the storm. Strong jet-level horizontal velocity gradients appear to precede each storm, and could be used to automate the adaptive thinning strategy in the future.

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Sean P. F. Casey
and
Lidia Cucurull

Abstract

The impact of low data latency is assessed using observations assimilated into the NCEP Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS). Operationally, a full dataset is used to generate short-term (9-h) forecasts used as the background state for the next cycle, and a limited dataset with fewer observations is used for long-term (16-day) forecasts due to time constraints that exist in an operational setting. In this study, the sensitivity of the global weather forecast skill to the use of the full and limited datasets in both the short- and long-term forecasts (out to 10 days only) is evaluated. The results show that using the full dataset for long-term forecasts yields a slight improvement in forecast skill, while using the limited dataset for short-term forecasts yields a significant degradation. This degradation is primarily attributed to a decrease of in situ observations rather than remotely sensed observations, though no individual observation type captures the amount of degradation noted when all observations are limited. Furthermore, limiting individual types of in situ observations (aircraft, marine, rawinsonde) does not result in the level of degradation noted when limiting all in situ observations, demonstrating the importance of data redundancy in an operational observational system.

Significance Statement

Millions of observations are used in global models every day to understand the state of the atmosphere. These observations rely on quick transmission from observation source to weather centers for inclusion in operational models. For this study, we test how different groups of observations, which arrive at the model center at different times, impact the model forecast. We find that by not using the observations that take longer to arrive at the weather centers, the forecast is much worse, showing the importance of quick transmission of observations. Direct observations (those measured within the atmosphere) have a greater impact than remote observations (those viewed from afar, such as by satellites). However, no single observation type by itself causes a poor forecast by being limited, showing the importance of using different types of observations to capture the state of the atmosphere.

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John Y. N. Cho
,
James M. Kurdzo
,
Betty J. Bennett
,
Mark E. Weber
,
Joseph W. Dellicarpini
,
Andrew Loconto
, and
Hayden Frank

Abstract

The statistical relationship between supplemental adaptive intra-volume low-level scan (SAILS) usage on the Weather Surveillance Radar-1988 Doppler and National Weather Service severe storm warning performance during 2014–20 is analyzed. Results show statistically significant improvement in severe thunderstorm (SVR), flash flood (FF), and tornado (TOR) warning performance associated with SAILS-on versus SAILS-off. Within the three possible SAILS modes of one (SAILSx1), two (SAILSx2), and three (SAILSx3) additional base scans per volume, for SVR, SAILSx2 and SAILSx3 are associated with better warning performance compared to SAILSx1; for FF and TOR, SAILSx3 is associated with better warning performance relative to SAILSx1 and SAILSx2. Two severe storm cases (one that spawned a tornado, one that did not) are presented where SAILS usage helped forecasters make the correct TOR warning decision, lending real-life credence to the statistical results. Furthermore, a statistical analysis of automated volume scan evaluation and termination effects, parsed by SAILS usage and mode, yield a statistically significant association between volume scan update rate and SVR warning lead time.

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M. M. Nageswararao
,
Yuejian Zhu
, and
Vijay Tallapragada

Abstract

Indian summer monsoon rainfall (ISMR) from June to September (JJAS) contributes 80% of the total annual rainfall in India and controls the agricultural productivity and economy of the country. Extreme rainfall (ER) events are responsible for floods that cause widespread destruction of infrastructure, economic damage, and loss of life. A forecast of the ISMR and associated ER events on an extended range (beyond the conventional one-week lead time) is vital for the agronomic economy of the country. In September 2020, NOAA/NCEP implemented Global Ensemble Forecast System, version 12 (GEFSv12) for various risk management applications. It has generated consistent reanalysis and reforecast data for the period 2000–19. In the present study, the Raw-GEFSv12 with day-1–16 lead-time rainfall forecasts are calibrated using the quantile (QQ) mapping technique against Indian Monsoon Data Assimilation and Analysis (IMDAA) for further improvement. The present study evaluated the prediction skill of Raw and QQ-GEFSv12 for ISMR and ER events over India by using standard skill metrics. The results suggest that the ISMR patterns from Raw and QQ-GEFSv12 with (lead) day 1–16 are similar to IMDAA. However, Raw-GEFSv12 has a dry bias in most parts of prominent rainfall regions. The low- to medium-intensity rainfall events from Raw-GEFSv12 is remarkably higher than the IMDAA, while high- to very-high-intensity rainfall events from Raw-GEFSv12 are lower than IMDAA. The prediction skill of Raw-GEFSv12 in depicting ISMR and associated ER events decreased with lead time, while the prediction skill is almost equal for all lead times with marginal improvement after calibration.

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John L. Cintineo
,
Michael J. Pavolonis
, and
Justin M. Sieglaff

Abstract

Lightning strikes pose a hazard to human life and property, and can be difficult to forecast in a timely manner. In this study, a satellite-based machine learning model was developed to provide objective, short-term, location-specific probabilistic guidance for next-hour lightning activity. Using a convolutional neural network architecture designed for semantic segmentation, the model was trained using GOES-16 visible, shortwave infrared, and longwave infrared bands from the Advanced Baseline Imager (ABI). Next-hour GOES-16 Geostationary Lightning Mapper data were used as the truth or target data. The model, known as LightningCast, was trained over the GOES-16 ABI contiguous United States (CONUS) domain. However, the model is shown to generalize to GOES-16 full disk regions that are outside of the CONUS. LightningCast provides predictions for developing and advecting storms, regardless of solar illumination and meteorological conditions. LightningCast, which frequently provides 20 min or more of lead time to new lightning activity, learned salient features consistent with the scientific understanding of the relationships between lightning and satellite imagery interpretation. We also demonstrate that despite being trained on data from a single geostationary satellite domain (GOES-East), the model can be applied to other satellites (e.g., GOES-West) with comparable specifications and without substantial degradation in performance. LightningCast objectively transforms large volumes of satellite imagery into objective, actionable information. Potential application areas are also highlighted.

Significance Statement

The outcome of this research is a model that spatially forecasts lightning occurrence in a 0–60-min time window, using only images of clouds from the GOES-R Advanced Baseline Imager. This model has the potential to provide early alerts for developing and approaching hazardous conditions.

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Thomas Jones
,
Ravan Ahmadov
,
Eric James
,
Gabriel Pereira
,
Saulo Freitas
, and
Georg Grell

Abstract

This research begins the process of creating an ensemble-based forecast system for smoke aerosols generated from wildfires using a modified version of the National Severe Storms Laboratory (NSSL) Warn-on-Forecast System (WoFS). The existing WoFS has proven effective in generating short-term (0–3 h) probabilistic forecasts of high-impact weather events such as storm rotation, hail, severe winds, and heavy rainfall. However, it does not include any information on large smoke plumes generated from wildfires that impact air quality and the surrounding environment. The prototype WoFS-Smoke system is based on the deterministic High-Resolution Rapid Refresh-Smoke (HRRR-Smoke) model. HRRR-Smoke runs over a continental United States (CONUS) domain with a 3-km horizontal grid spacing, with hourly forecasts out to 48 h. The smoke plume injection algorithm in HRRR-Smoke is integrated into the WoFS forming WOFS-Smoke so that the advantages of the rapidly cycling, ensemble-based WoFS can be used to generate short-term (0–3 h) probabilistic forecasts of smoke. WoFS-Smoke forecasts from three wildfire cases during 2020 show that the system generates a realistic representation of wildfire smoke when compared against satellite observations. Comparison of smoke forecasts with radar data show that forecast smoke reaches higher levels than radar-detected debris, but exceptions to this are noted. The radiative effect of smoke on surface temperature forecasts is evident, which reduces forecast errors compared to experiments that do not include smoke.

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