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Adrian Rojas-Campos
,
Martin Wittenbrink
,
Pascal Nieters
,
Erik J Schaffernicht
,
Jan D Keller
, and
Gordon Pipa

Abstract

This study analyzes the potential of deep learning using probabilistic artificial neural networks (ANNs) for post-processing ensemble precipitation forecasts at four observation locations. We split the precipitation forecast problem into two tasks: estimating the probability of precipitation and predicting the hourly precipitation. We then compare the performance with classical statistical post-processing (logistical regression and GLM). ANNs show a higher performance at three of the four stations for estimating the probability of precipitation and at all stations for predicting the hourly precipitation. Further, two more general ANN models are trained using the merged data from all four stations. These general ANNs exhibit an increase in performance compared to the station-specific ANNs at most stations. However, they show a significant decay in performance at one of the stations at estimating the hourly precipitation. The general models seem capable of learning meaningful interactions in the data and generalizing these to improve the performance at other sites, which also causes the loss of local information at one station. Thus, this study indicates the potential of deep learning in weather forecasting workflows.

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Elizabeth J. McCabe
and
Jeffrey M. Freedman

Abstract

In a mid-latitude coastal region such as the New York Bight (NYB), the general thermodynamic structure and dynamics of the sea breeze circulation is poorly understood. The NYB sea breeze circulation is often amplified by and coterminous with other regional characteristics and phenomena such as complex coastal topology, a low-level jet (LLJ), and coastal upwelling. While typically considered a summertime phenomenon, the NYB sea breeze circulation occurs year-round.

This study creates a methodology to objectively identify sea breeze days and their associated LLJs from 2010 to 2020. Filtering parameters include surface-based observations of sea level pressure (SLP) gradient and diurnal tendencies, afternoon wind speed and direction tendencies, air temperature gradient, and the dewpoint depression. LLJs associated with the sea breeze circulation typically occur within 150 – 300 m MSL and are identified using a coastal New York State Mesonet (NYSM) profiler site. Along coastal Long Island, there are on average 32 sea breeze days annually, featuring winds consistently backing to the south and strengthening at or around 1800 UTC (1400 EDT). The NYB LLJ is most frequent in the summer months.

Sea breeze days are classified into two categories: Classic and Hybrid. A Classic sea breeze is driven primarily by both cross-shore pressure and temperature gradients, with light background winds; while a Hybrid sea breeze occurs in combination with other larger-scale features, such as frontal systems. Both types of sea breeze are similarly distributed with a maximum frequency during July.

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Partha S. Bhattacharjee
,
Li Zhang
,
Barry Baker
,
Li Pan
,
Raffaele Montuoro
,
Georg A. Grell
, and
Jeffery T. McQueen

Abstract

The NWS/NCEP recently implemented a new global deterministic aerosol forecast model named the Global Ensemble Forecast Systems Aerosols (GEFS-Aerosols), which is based on the Finite Volume version 3 GFS (FV3GFS). It replaced the operational NOAA Environmental Modeling System (NEMS) GFS Aerosol Component version 2 (NGACv2), which was based on a global spectral model (GSM). GEFS-Aerosols uses aerosol modules from the GOCART previously integrated in the WRF Model with Chemistry (WRF-Chem), FENGSHA dust scheme, and several other updates. In this study, we have extensively evaluated aerosol optical depth (AOD) forecasts from GEFS-Aerosols against various observations over a timespan longer than one year (2019–20). The total AOD improvement (in terms of seasonal mean) in GEFS-Aerosols is about 40% compared to NGACv2 in the fall and winter season of 2019. In terms of aerosol species, the biggest improvement came from the enhanced representation of biomass burning aerosol species as GEFS-Aerosols is able to capture more fire events in southern Africa, South America, and Asia than its predecessor. Dust AODs reproduce the seasonal variation over Africa and the Middle East. We have found that correlation of total AOD over large regions of the globe remains consistent for forecast days 3–5. However, we have found that GEFS-Aerosols generates some systematic positive biases for organic carbon AOD near biomass burning regions and sulfate AOD over prediction over East Asia. The addition of a data assimilation capability to GEFS-Aerosols in the near future is expected to address these biases and provide a positive impact to aerosol forecasts by the model.

Significance Statement

The purpose of this study is to quantify improvements associated with the newly implemented global aerosol forecast model at NWS/NCEP. The monthly and seasonal variations of AOD forecasts of various aerosol regimes are overall consistent with the observations. Our results provide a guide to downstream regional air quality models like CMAQ that will use GEFS-Aerosols to provide lateral boundary conditions.

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Aaron J. Hill
,
Russ S. Schumacher
, and
Israel L. Jirak

Abstract

Historical observations of severe weather and simulated severe weather environments (i.e., features) from the Global Ensemble Forecast System v12 (GEFSv12) Reforecast Dataset (GEFS/R) are used in conjunction to train and test random forest (RF) machine learning (ML) models to probabilistically forecast severe weather out to days 4–8. RFs are trained with ∼9 years of the GEFS/R and severe weather reports to establish statistical relationships. Feature engineering is briefly explored to examine alternative methods for gathering features around observed events, including simplifying features using spatial averaging and increasing the GEFS/R ensemble size with time lagging. Validated RF models are tested with ∼1.5 years of real-time forecast output from the operational GEFSv12 ensemble and are evaluated alongside expert human-generated outlooks from the Storm Prediction Center (SPC). Both RF-based forecasts and SPC outlooks are skillful with respect to climatology at days 4 and 5 with diminishing skill thereafter. The RF-based forecasts exhibit tendencies to slightly underforecast severe weather events, but they tend to be well-calibrated at lower probability thresholds. Spatially averaging predictors during RF training allows for prior-day thermodynamic and kinematic environments to generate skillful forecasts, while time lagging acts to expand the forecast areas, increasing resolution but decreasing overall skill. The results highlight the utility of ML-generated products to aid SPC forecast operations into the medium range.

Significance Statement

Medium-range severe weather forecasts generated from statistical models are explored here alongside operational forecasts from the Storm Prediction Center (SPC). Human forecasters at the SPC rely on traditional numerical weather prediction model output to make medium-range outlooks and statistical products that mimic operational forecasts can be used as guidance tools for forecasters. The statistical models relate simulated severe weather environments from a global weather model to historical records of severe weather and perform noticeably better than human-generated outlooks at shorter lead times (e.g., day 4 and 5) and are capable of capturing the general location of severe weather events 8 days in advance. The results highlight the value in these data-driven methods in supporting operational forecasting.

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Free access
James Taylor
,
Takumi Honda
,
Arata Amemiya
,
Shigenori Otsuka
,
Yasumitsu Maejima
, and
Takemasa Miyoshi

Abstract

A sensitivity analysis for horizontal localization scale is performed for a numerical weather prediction (NWP) system that uses a 30-second update to refresh a 500-m mesh with observations from a new-generation multi-parameter phased array weather radar (MP-PAWR). Testing is performed using three case studies of convective weather events that occurred during August/September 2019, with the aim to determine the most suitable scale for short-range forecasting of precipitating convective systems and better understand model behavior to a rapid update cycle. Results showed that while the model could provide useful skill at lead times up to 30-minutes, forecasts would consistently over-estimate rainfall and were unable to outperform nowcasts performed with a simple advection model. Using a larger localization scale e.g., 4-km, generated stronger convective and dynamical instability in the analyzes that made conditions more favorable for spurious and intense convection to develop in forecasts. It was demonstrated that lowering the localization scale reduced the size of analysis increments during early cycling, limiting the buildup of these conditions. Improved representation of the localized convection in the initial conditions was suggested as an important step to mitigating this issue in the model.

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Guo Deng
,
Jun Du
,
Yushu Zhou
,
Ling Yan
,
Jing Chen
,
Fajing Chen
,
Hongqi Li
, and
Jingzhou Wang

Abstract

Using a 3-km regional ensemble prediction system (EPS), this study tested a three-dimensional (3D) rescaling mask for initial condition (IC) perturbation. Whether the 3D mask-based EPS improves ensemble forecasts over current two-dimensional (2D) mask-based EPS has been evaluated in three aspects: ensemble mean, spread, and probability. The forecasts of wind, temperature, geopotential height, sea level pressure, and precipitation were examined for a summer month (1–28 July 2018) and a winter month (1–27 February 2019) over a region in North China. The EPS was run twice per day (initiated at 0000 and 1200 UTC) to 36 h in forecast length, providing 56 warm-season forecast cases and 54 cold-season cases for verification. The warm and cold seasons are verified separately for comparison. The study found the following: 1) The vertical profile of IC perturbation becomes closer to that of analysis uncertainty with the 3D rescaling mask. 2) Ensemble performance is significantly improved in all three aspects. The biggest improvement is in the ensemble spread, followed by the probabilistic forecast, and the least improvement is in the ensemble mean forecast. Larger improvements are seen in the warm season than in the cold season. 3) More improvement is in the shorter time range (<24 h) than in the longer range. 4) Surface and lower-level variables are improved more than upper-level ones. 5) The underlying mechanism for the improvement has been investigated. Convective instability is found to be responsible for the spread increment and, thus, overall ensemble forecast improvement. Therefore, using a 3D rescaling mask is recommended for an EPS to increase its utility especially for shorter time range and surface weather elements.

Significant Statement

A weather prediction model is a complex system that consists of nonlinear differential equations. Small errors in either its inputs or model itself will grow with time during model integration, which will contaminate a forecast. To quantify such contamination (“uncertainty”) of a forecast, the ensemble forecasting technique is used. An ensemble of forecasts is a multiple of model runs at the same time but with slightly “perturbed” inputs or model versions. These small perturbations are supposed to represent true “uncertainty” in inputs or model representation. This study proposed a technique that makes a perturbation’s vertical structure more resemble real uncertainty (intrinsic error) in input data and confirmed that it can significantly improve ensemble forecast quality especially for a shorter time range and lower-level weather elements. It is found that convective instability is responsible for the improvement.

Open access
Rory Laiho
,
Katja Friedrich
, and
Andrew C. Winters

Abstract

Warm season heavy rainfall in Minnesota can lead to flooding with serious impacts on life and infrastructure. Situated in a transition zone between humid eastern and semiarid western conditions in the United States, Minnesota experiences large spatial variability in precipitation. Previous research has often lacked spatiotemporal detail important for heavy rainfall analysis for Minnesota. This research used Stage-IV hourly precipitation data with 4-km grid spacing during May–September 2004–20 to analyze Minnesota spatial, seasonal, and event-based characteristics. Rain event frequency, accumulation, hours, and intensities were compared for all rain events (>2.5 mm) and heavy rain events (>36 mm). For all rain events, results showed the highest regional median monthly rain event frequency (>6 events) in June and the lowest (<5 events) in September. Median monthly accumulations were largest (∼75 mm) in June, followed by July and August. Monthly total rain event hours at a point peaked around 20 h in May in southeastern Minnesota. Smaller event accumulations occurred more frequently than larger accumulations, and event mean intensities were higher in summertime (June–August) than in May and September for rain events and heavy rain events. Heavy rain event region-based analyses showed monthly peaks for frequency in July–August, accumulation in July, and event hours in June–July and September. Median heavy rain event durations were shorter during June–August than in May and September. Monthly heavy rain event accumulation as a percent of all rain event accumulation was greatest in September (24%). These results establish a foundation for future research into precipitation patterns and trends.

Significance Statement

Climate analysis has indicated that Minnesota is in a region where increases in heavy rainfall are anticipated for the future. Heavy rainfall in Minnesota has led to flooding with severe adverse impacts. This study addresses a gap in information about heavy precipitation in Minnesota and provides heavy rainfall analyses useful for climate-related planning. Stage-IV hourly precipitation data for the warm season (May–September) during 2004–20 enabled the identification of rain events and heavy rain events, as well as their characteristic frequency, rainfall accumulation, duration, and intensity. The results help establish a baseline for past and future analyses of precipitation patterns and trends. They also build a foundation for future research investigating the weather patterns that lead to heavy rainfall.

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Sarah M. Purpura
,
Casey E. Davenport
,
Matthew D. Eastin
,
Katherine E. McKeown
, and
Roger R. Riggin

Abstract

The Appalachian Mountains have a considerable impact on daily weather, including severe convection, across the eastern United States. However, the impact of the Appalachians on supercells is not well understood, posing a short-term forecast challenge across the region. While case studies have been conducted, there has been no large multicase analysis of supercells interacting with complex terrain. To address this gap, we examined 62 isolated warm-season supercells that occurred within the central or southern Appalachians. Each supercell was broadly classified as “crossing” or “noncrossing” based on their maintenance of supercellular structure during interaction with significant terrain features. Rapid Update Cycle (RUC) and the Rapid Refresh (RAP) model analyses were used to identify key synoptic and mesoscale factors that distinguish between environments supportive of crossing versus noncrossing supercells. Roughly 40% of supercells were sustained crossing significant terrain. Pre-storm synoptic features common among crossing storms (relative to noncrossing storms) included a stronger polar jet, a deeper trough, a north–south-oriented cold front, a strong prefrontal low-level jet, and no wedge front leeward of the terrain. Mesoscale environmental differences were determined using near-storm model soundings collected for each supercell at three locations: upstream initiation, peak terrain, and downstream dissipation. The most significant mesoscale differences were present in the peak and downstream environments, whereby crossing storms encountered stronger low-level vertical shear, greater storm-relative helicity, and greater midlevel moisture than noncrossing storms. Such results reenforce the notion that sustained dynamical support for mesocyclones is critical to supercell maintenance when interacting with significant terrain.

Significance Statement

The ability of isolated storms with rotating updrafts to traverse complex terrain is not well understood and is a notable forecast problem in the eastern United States due to the Appalachian Mountains. This study represents the first systematic analysis of numerous warm-season supercells in the vicinity of the central and southern Appalachians. We focus on synoptic and near-storm mesoscale environmental differences between storms that maintain supercellular structure following terrain interaction (“crossing”) and those that do not (“noncrossing”). The results provide useful environmental metrics for forecasting supercell longevity in the vicinity of the Appalachian Mountains.

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I-Han Chen
,
Yi-Jui Su
,
Hsiao-Wei Lai
,
Jing-Shan Hong
,
Chih-Hsin Li
,
Pao-Liang Chang
, and
Ying-Jhang Wu

Abstract

A 16-member Convective-scale Ensemble Prediction System (CEPS) developed at the Central Weather Bureau (CWB) of Taiwan is evaluated for probability forecasts of convective precipitation. To address the issues of limited predictability of convective systems, the CEPS provides short-range forecasts using initial conditions from a rapid-updated ensemble data assimilation system. This study aims to identify the behavior of the CEPS forecasts, especially the impact of different ensemble configurations and forecast lead times. Warm-season afternoon thunderstorms (ATs) from 30 June to 4 July 2017 are selected. Since ATs usually occur between 13 and 20 LST, this study compares deterministic and probabilistic quantitative precipitation forecasts (QPFs) launched at 05,08, and 11 LST.

This study demonstrates that initial and boundary perturbations (IBP) are crucial to ensure good spread-skill consistency over the 18-h forecasts. On top of IBP, additional model perturbations have insignificant impacts on upper-air and precipitation forecasts. The deterministic QPFs launched at 11 LST outperform those launched at 05 and 08 LST, likely because the most-recent data assimilation analyses enhance the practical predictability. However, it cannot improve the probabilistic QPFs launched at 11 LST due to inadequate ensemble spreads resulting from limited error growth time. This study points out the importance of sufficient initial condition uncertainty on short-range probabilistic forecasts to exploit the benefits of rapid-update data assimilation analyses.

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