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Numerical weather prediction (NWP), when accessible, is a crucial input to short-term solar power forecasting. WRF-Solar, the first NWP model specifically designed for solar energy applications, has shown promising predictive capability. Nevertheless, few attempts have been made to investigate its performance under high aerosol loading, which attenuates incoming radiation significantly. The North China Plain is a polluted region due to industrialization, which constitutes a proper testbed for such investigation. In this paper, aerosol direct radiative effect (DRE) on three surface shortwave radiation components (i.e., global, beam, and diffuse) during five heavy pollution episodes is studied within the WRF-Solar framework. Results show that WRF-Solar overestimates instantaneous beam radiation up to 795.3 W m−2 when the aerosol DRE is not considered. Although such overestimation can be partially offset by an underestimation of the diffuse radiation of about 194.5 W m−2, the overestimation of the global radiation still reaches 160.2 W m−2. This undesirable bias can be reduced when WRF-Solar is powered by Copernicus Atmosphere Monitoring Service (CAMS) aerosol forecasts, which then translates to accuracy improvements in photovoltaic (PV) power forecasts. This work also compares the forecast performance of the CAMS-powered WRF-Solar with that of the European Centre for Medium-Range Weather Forecasts model. Under high aerosol loading conditions, the irradiance forecast accuracy generated by WRF-Solar increased by 53.2% and the PV power forecast accuracy increased by 6.8%. Significance Statement: Numerical weather prediction (NWP) is the "go-to" approach for achieving high-performance day-ahead solar power forecasting. Integrating time-varying aerosol forecasts into NWP models effectively captures aerosol direct radiation effects, thereby enhancing the accuracy of solar irradiance forecasts in heavily polluted regions. This work not only quantifies the aerosol effects on global, beam, and diffuse irradiance but also reveals the physical mechanisms of irradiance-to-power conversion by constructing a model chain. Using the North China Plain as a testbed, the performance of WRF-Solar on solar power forecasting on five severe pollution days is analyzed. This version of WRF-Solar can outperform the European Centre for Medium-Range Weather Forecasts model, confirming the need for generating high spatial–temporal NWP. [ABSTRACT FROM AUTHOR]

As several review papers have concluded, marine fog is imperfectly characterized, and quantitative visibility forecasts are difficult to produce accurately. Some unique measurements have been made offshore Newfoundland and Labrador of the climatology of occurrence and the microphysical characteristics of marine, or open-ocean, fog. Based on measurements made at an offshore installation over 21 years, the percent of time with visibilities less than 0.5 n mi or approximately 1 km (1 n mi ≈ 1.85 km) reaches 45% in July, with a low of about 5% during the winter. The occurrence of fog is mainly due to warm air advection, with the highest frequency occurring with wind directions from over the warm Gulf Stream, and with air temperatures about 2°C warmer than the sea surface temperature. There is no diurnal variation in the frequency of occurrence of fog. The microphysical properties of the fog have been documented in the summer time frame, with over 550 h of in situ measurements made offshore with fog liquid water content greater than 0.005 g m−3. The fog droplet number concentration spectra peaks near 6 μm, with a secondary peak near 25–40 μm, which typically contains most of the liquid water content. The median droplet concentration is approximately 70–100 cm−3. The microphysical spectra have been used to develop a new NWP visibility parameterization scheme, and this scheme is compared with other parameterizations currently in use. [ABSTRACT FROM AUTHOR]

Freezing rain can cause severe impacts, particularly when it persists for many hours. In this paper, we present the climatology of long-duration (6 or more hours) freezing rain events in the United States and Canada from 1979 to 2016. We identify three focus regions from this climatology and examine the archetypal thermodynamic evolution of events in each region using surface and radiosonde observations. Long-duration events occur most frequently in the northeastern United States and southeastern Canada, where freezing rain typically begins as lower-tropospheric warm-air advection develops the warm layer aloft. This warm-air advection and the latent heat of fusion released when rain freezes at the surface erode the cold layer, and freezing rain transitions to rain once the surface temperature reaches 0°C. In the southeastern United States, a larger percentage of events are of long duration than elsewhere in North America. Weak surface cold-air advection and evaporative cooling in the particularly dry onset cold layers there prevent surface temperatures from rising substantially during events. Finally, the south-central United States has a regional maximum in the occurrence of the top 1% of events by duration (18 or more hours), despite the relative rarity of freezing rain there. These events are associated with particularly warm/deep onset warm layers, with persistent low-level cold-air advection maintaining the cold layer. The thermodynamic evolutions we have identified highlight characteristics that are key to supporting persistent freezing rain in each region and may warrant particular attention from forecasters tasked with predicting these events. [ABSTRACT FROM AUTHOR]

Thunderstorms in the United States cause over 100 deaths and $10 billion (U.S. dollars) in damage per year, much of which is attributable to straight-line (nontornadic) wind. This paper describes a machine-learning system that forecasts the probability of damaging straight-line wind (≥50 kt or 25.7ms-1) for each storm cell in the continental United States, at distances up to 10 km outside the storm cell and lead times up to 90 min. Predictors are based on radar scans of the storm cell, storm motion, storm shape, and soundings of the nearstorm environment. Verification data come from weather stations and quality-controlled storm reports. The system performs very well on independent testing data. The area under the receiver operating characteristic (ROC) curve ranges from 0.88 to 0.95, the critical success index (CSI) ranges from 0.27 to 0.91, and the Brier skill score (BSS) ranges from 0.19 to 0.65 (.0 is better than climatology). For all three scores, the best value occurs for the smallest distance (inside storm cell) and/or lead time (0-15 min), while the worst value occurs for the greatest distance (5-10 km outside storm cell) and/or lead time (60-90 min). The system was deployed during the 2017 Hazardous Weather Testbed. [ABSTRACT FROM AUTHOR]

In an attempt to develop a better simulation of the climatology of monsoon precipitation in climate models, this paper investigates the impacts of different convective closures on systematic biases of an Indian monsoon precipitation climatology in a high-resolution regional climate model. For this purpose, the Weather Research Forecast (WRF) model is run at 45- and 15-km (two-way nested) resolution with three convective parameterization schemes, namely the Grell–Devenyi (GD), the Betts–Miller–Janjić (BMJ), and the Kain–Fritsch (KF), for the period 1 May–31 October 2001–07. The model is forced with the NCEP–NCAR reanalysis data as the initial and boundary conditions. The simulated June–September (JJAS) mean monsoon rainfall with the three convective schemes is compared with the observations. KF is found to have a high moist bias over the central and western coastal Indian region while GD shows the opposite. Among the three, BMJ is able to produce a reasonable mean monsoon pattern. In an attempt to get further insight into the seasonal bias and its evolution, the probability distribution function (PDF) of different rain-rate categories and their percentage contribution to the seasonal total are computed. BMJ and KF underestimate the observations for lighter rain rates and overestimate for rain-rate categories of more than 10 mm day−1. GD shows an overestimation for lighter rain and an underestimation of PDF for moderate categories. The seasonal patterns of evolution of PDF plots of three rain-rate categories are analyzed to determine whether the convective schemes show any systematic bias throughout the season or if they have problems during certain phases of the monsoon. This shows that the GD systematically overestimates the lighter rain rate and underestimates the moderate rain rate throughout the season, whereas BMJ and KF have problems in the initial stages. The heavy rain category is systematically overestimated by the KF compared to the other two. To further evaluate the proportionate contribution of each rain-rate bin to the total rain, the percentage contribution of each rain rate to the seasonal total is computed. Analyzing all the rain-rate simulations produced by the three schemes, it is found that KF has a moist bias and GD has a dry bias in the spatiotemporal distribution of the monsoon precipitation. Further, this paper investigates the causes behind the mean monsoon precipitation bias. It is shown that GD produces a model climate where the vertical velocity is less than that of the observations up to 500 hPa and the vertically integrated moist instability is also weaker. KF, on the other hand, shows a higher than the observed vertical velocity and a stronger moist instability. Along with this, the vertical profile of heating suggests a warmer middle level in the KF case and significantly reduced midlevel heating for GD. Thus, KF (GD) has produced a model atmosphere that has a stronger (weaker) convective instability to produce the observed bias in the model precipitation. BMJ is found to simulate a reasonable heating profile, along with the realistic moist instability and seasonal cycle of evaporation and condensation. Insight derived from the analysis is expected to help improve the convective parameterizations. [ABSTRACT FROM AUTHOR]

This is the second of two papers that examine the organization of the precipitation field during central U.S. cold-season cyclones involving inverted troughs (ITs). The first paper (Part I) used a climatology and composites to find synoptic-scale differences between storms with precipitation located ahead of the IT (ahead cases) and those with precipitation located behind the IT (behind cases). This paper expands the conclusions in Part I through the use of a comparative case study between two cyclones. The first cyclone, on 29 October 1996, was an ahead case that produced heavy rainfall and was associated with a potential vorticity (PV) anomaly moving across the central plains. The IT formed in the lee of the Rockies prior to 0600 UTC 29 October and moved east into the northern plains over the next 18 h. The trough itself was coincident with the limiting streamline, which separated moist air rising over the warm front from dry air subsiding behind the cyclone. The second cyclone, on 17–18 January 1996, had precipitation on both sides of the IT and was associated with heavy snow and blizzard conditions in the northern plains and significant ice accumulation in the western Great Lakes. The IT was associated with large frontogenesis over the snow area. The ascent was further enhanced by a jet streak moving across southern Canada. Dynamically, the IT resembled a warm front, with veering winds with height and a strong frontal inversion. The mechanism that appeared to control the different precipitation organization between the two systems was the orientation of the PV anomalies and the airstreams associated with their secondary circulations. This resulted in a differing orientation of the baroclinicity north and east of the cyclone. In the ahead case, the rising branches of the secondary circulations forced by the northern and southern anomalies remained separate. This allowed the baroclinicity to develop along the traditional warm front, while the IT never developed a thermal gradient as it moved east. In the both sides case, the southern stream anomaly helped to fix the northern anomaly-forced jet streak in place, so that a strong temperature gradient developed along the IT with strong frontogenesis and warm-air advection observed behind the IT. As the frontal circulation developed, the direct circulation associated with the right entrance region of a jet streak enhanced the ascent to the west of the IT. A conceptual model is proposed based upon the case studies and the results of Part I. This model can be used by forecasters to differentiate between the precipitation regimes in cyclones associated with ITs. [ABSTRACT FROM AUTHOR]

Forecasting major winter storms is a critical function for all weather services. Conventional model-derived fields from numerical weather prediction models most frequently utilized by operational forecasters, such as pressure level geopotential height, temperature fields, quantitative precipitation forecasts, and model output statistics, are often insufficient to determine whether a winter storm represents a large departure from normal, or has the potential to produce significant snowfall. This paper presents a method, using normalized departures from climatology, to assist forecasters in identifying long-duration and potentially significant winter storms. The focus of this paper is on anomalous low- and upper-level wind anomalies associated with winter storms along the U.S. east coast. Observed and forecast low-level (850 hPa) and upper-level (300 and 250 hPa) easterly wind anomalies are compared with a 30-yr (1961–90) reanalysis climatology. Anomalous easterly low-level winds are correlated with enhanced low-level forcing and frontogenesis. Strong low-level winds can also contribute to enhanced precipitation rates. Upper-level winds that are anomalously below normal, represented as easterly wind anomalies, are also correlated with systems that are cut off from the main belt of westerlies, which may result in slower movement of the system, leading to long-duration events. The proposed method of evaluating easterly wind anomalies is shown to assist in identifying potentially slow-moving storms with extended periods of enhanced precipitation. To illustrate this method, winter storms on 25–26 December 2002 and 2–4 January 2003 will be compared with past historical winter storms. The results suggest that the low- and upper-level wind anomalies in the two recent snowstorms share common characteristics with several record snowstorms over the past 52 yr. Many of these storms were associated with easterly wind anomalies that departed significantly (2 or more standard deviations) from normal. The examination of climatic anomalies from model forecasts may assist forecasters in identifying significant winter storms in the short range (2–3 days) and potentially out to ranges as long as 7 days when ensemble forecast guidance is utilized. [ABSTRACT FROM AUTHOR]

This paper is the first of two papers that examines the organization of the precipitation field in central U.S. cold-season cyclones involving inverted troughs. The first portion of the study examines the varying precipitation distribution that occurred during a 6-yr synoptic climatology of inverted trough cases. The definition of inverted trough cases has been expanded from the groundbreaking work by Keshishian et al. by 1) not requiring a closed cyclonic isobar along the frontal wave along the conventional surface front and 2) not requiring a surface thermal gradient to be present along the inverted trough. Only 8.5% of the expanded dataset produced the precipitation primarily occurring to the west of the inverted trough ("behind" cases) as seen in Keshishian et al. The largest group of cases, comprising about 40% of the cases, produced precipitation that primarily occurred between the inverted trough and the conventional warm front ("ahead" cases). A composite study compared a subset of the ahead cases with a subset of the behind cases. The ahead cases tended to be more progressive with a stronger jet stream located over the center of the parent low. Broad warm-air advection and frontogenesis in the lower troposphere were observed between the inverted trough and the surface warm front. Cold-air advection to the west of the inverted trough precluded the development of "wraparound precipitation." In contrast, the behind cases had a stronger low-latitude wave couplet with a trough upstream of the surface low and a ridge downstream. The region of warm-air advection and frontogenesis were displaced to the west of the inverted trough and surface cyclone. In addition, the entrance region of a southwest-northeast-oriented jet streak aided the development of ascent to the west of the inverted trough while precluding the development of precipitation to the north of the conventional warm front. Thus, the inverted trough tended to act like a warm front in behind cases,... [ABSTRACT FROM AUTHOR]

Forecasting significant weather events, such as floods, heat waves, arctic outbreaks, ice storms, large severe weather outbreaks, and major winter storms, is a critical function for all weather services. However, conventional pressure level geopotential and temperature fields often are insufficient to determine whether an event represents a large departure from normal. This is largely due to the variability that exists throughout the year and regionally throughout the world. What represents an unusual departure from average conditions in fall may not be as unusual in winter. What is an unusual departure from average conditions in California may be normal in New England. This paper presents a method, normalized field departures from local climatology, that gives forecasters guidance on the relative rarity of events. Thus, in this paper a method is presented to help forecasters identify potentially significant weather events. The focus of this paper is on significant winter storms. However, a record winter warmth event is shown to demonstrate the broad potential use of this method. The results suggest that many record snowstorms in the literature were associated with storms that departed significantly from normal. Using model data, it is demonstrated that models can successfully forecast events that represent a significant departure from normal. In fact, the results suggest that the models are quite successful at forecasting unusually strong weather systems in the short range (2–3 days) and show some success out to 6 days. [ABSTRACT FROM AUTHOR]

Santa Ana winds, common to Southern California from the fall through early spring, are a type of downslope windstorm originating from a direction generally ranging from 360°/0° to 100° and are usually accompanied by very low humidity. Since fuel conditions tend to be driest from late September through the middle of November, Santa Ana winds occurring during this time have the greatest potential to produce large, devastating fires upon ignition. Such catastrophic fires occurred in 1993, 2003, 2007, and 2008. Because of the destructive nature of such fires, there has been a growing desire to categorize Santa Ana wind events in much the same way that tropical cyclones have been categorized. The Santa Ana wildfire threat index (SAWTI) is a tool for categorizing Santa Ana wind events with respect to anticipated fire potential. The latest version of the index has been a result of a three-and-a-half-year collaboration effort between the USDA Forest Service, the San Diego Gas and Electric utility (SDG&E), and the University of California, Los Angeles (UCLA). The SAWTI uses several meteorological and fuel moisture variables at 3-km resolution as input to the Weather Research and Forecasting (WRF) Model to generate the index out to 6 days. In addition to the index, a 30-yr climatology of weather, fuels, and the SAWTI has been developed to help put current and future events into perspective. This paper outlines the methodology for developing the SAWTI, including a discussion on the various datasets employed and its operational implementation. [ABSTRACT FROM AUTHOR]

In this paper, the authors describe experimental forecasting tools developed from high-resolution satellite cloud composites. The satellite data were extracted from the new 5-km, hourly, global satellite database called Climatological and Historical Analysis of Clouds for Environmental Simulations (CHANCES). Analysis was focused on a region over the former Yugoslavia and Adriatic Sea during summer 1994. Cloud composite images were constructed using digital infrared data for each hour of the day. The value at each pixel in the cloud composites was the fractional coverage of cloud at that location for the season and represented its systematic variation. Composite images were also constructed for conditional probabilities of cloud 1-12 h past each hour of the day. The values at any particular pixel in the composites represented the conditional probability of cloud given an initial condition of cloudy or clear in that pixel. Data from both types of composite images were combined to produce a climatological forecasting tool. Forecast tables were constructed of values for the pixel centered over Sarajevo. These tables are similar to the conditional climatology statistics familiar to forecasters in any weather station. A more sophisticated type of conditional probability was tested in which the initial condition was dependent on the average conditions of a region of pixels surrounding the Sarajevo pixel. Results demonstrate powerful operational applications of high-resolution satellite cloud climatologies. [ABSTRACT FROM AUTHOR]

Despite the enormous potential of precipitation forecasts to save lives and property in Africa, low skill has limited their uptake. To assess the skill and improve the performance of the forecast, validation and postprocessing should continuously be carried out. Here, we evaluate the quality of reforecasts from the European Centre for Medium-Range Weather Forecasts over equatorial East Africa (EEA) against satellite and rain gauge observations for the period 2001–18. The 24-h rainfall accumulations are analyzed from short- to medium-range time scales. Additionally, 48- and 120-h rainfall accumulations were also assessed. The skill was assessed using an extended probabilistic climatology (EPC) derived from the observations. Results show that the reforecasts overestimate rainfall, especially during the rainy seasons and over high-altitude areas. However, there is potential of skill in the raw forecasts up to 14-day lead time. There is an improvement of up to 30% in the Brier score/continuous ranked probability score relative to EPC in most areas, especially the higher-altitude regions, decreasing with lead time. Aggregating the reforecasts enhances the skill further, likely due to a reduction in timing mismatches. However, for some regions of the study domain, the predictive performance is worse than EPC, mainly due to biases. Postprocessing the reforecasts using isotonic distributional regression considerably improves skill, increasing the number of grid points with positive Brier skill score (continuous ranked probability skill score) by an average of 81% (91%) for lead times 1–14 days ahead. Overall, the study highlights the potential of the reforecasts, the spatiotemporal variation in skill, and the benefit of postprocessing in EEA. [ABSTRACT FROM AUTHOR]

The National Hurricane Center (NHC) has been forecasting gale force wind radii for many years, and more recently (starting in 2004) began routine postanalysis or 'best tracking' of the maximum radial extent of gale [34 knots (kt; 1 kt = 0.514 m s−1)] force winds in compass quadrants surrounding the tropical cyclone (wind radii). At approximately the same time, a statistical wind radii forecast, based solely on climatology and persistence, was implemented so that wind radii forecasts could be evaluated for skill. If the best-track gale radii are used as ground truth (even accounting for random errors in the analyses), the skill of the NHC forecasts appears to be improving at 2- and 3-day lead times, suggesting that the guidance has also improved. In this paper several NWP models are evaluated for their skill, an equally weighted average or 'consensus' of the model forecasts is constructed, and finally the consensus skill is evaluated. The results are similar to what is found with tropical cyclone track and intensity in that the consensus skill is comparable to or better than that of the individual models. Furthermore, the consensus skill is high enough to be of potential use as forecast guidance or as a proxy for official gale force wind radii forecasts at the longer lead times. [ABSTRACT FROM AUTHOR]

Damaging wind events not associated with severe convective storms or tropical cyclones can cause significant problems with transportation, infrastructure, and public safety over the northeastern United States. These nonconvective wind events (NCWEs) are difficult to forecast in New York City and surrounding regions as revealed by the relatively poor probability of detection (POD) and false alarm ratio (FAR) in recent years. This paper investigates the climatology of NCWEs between 15 September and 15 May over 13 cool seasons from 2000/01 through 2012/13. The events are separated into three distinct synoptic patterns: pre-cold-frontal (PRF), post-cold-frontal (POF), and nor'easter/coastal storm (NEC) cases. Relationships between observed winds and some atmospheric parameters such as 900-hPa geopotential height gradient, 3-h mean sea level pressure (MSLP) tendency, low-level wind profile, and stability are also presented. Overall, PRF and NEC have the largest FAR, because several events with a low-level jet at 1-2 km above the surface have relatively strong low-level stability that limits vertical momentum mixing. The POD is lowest for the POF events, which have a strong diurnal influence given the relatively deep mixed layer. Verification is also conducted over the cool seasons from 2009/10 to 2013/14 using the Short Range Ensemble Forecast (SREF) system from the National Centers for Environmental Prediction (NCEP). Both deterministic and probabilistic verification metrics are used to evaluate the performance of the ensemble during NCWEs. Although the SREF has more forecast skill than any of the deterministic SREF control members, it is rather poorly calibrated and exhibits a significant overconfidence given its positive wind speed bias in the lower troposphere. [ABSTRACT FROM AUTHOR]

Deep-layer vertical wind shear and midtropospheric relative humidity (RH) are explored in and around environments of all intensifying North Atlantic tropical cyclones (TCs) between 1980 and 2021 using reanalysis data. Shear and RH are averaged within the standard environmental annulus of 200–800 km, along with a 100–600-km annulus, and a 0–250-km radius to represent the inner core and TC itself. Distributions of shear and RH at onset along with a time series of evolution from 48 h prior to and after onset of three different intensification rates, slight [5–10 kt (24 h)−1; 1 kt ≈ 0.51 m s−1], moderate [15–25 kt (24 h)−1], and rapid [≥30 kt (24 h)−1], are analyzed. RH is also investigated within different shear environments and in shear-relative quadrants around the storm. While low shear and high RH are found to be most favorable for rapid intensification (RI), there is still a significant probability that RI will occur within less favorable environments. RI cases decrease in 850–200-hPa shear in the 24 h leading up to RI, whereas slight intensification cases increase, which is evident in both the standard shear and a shallower layer at 48 h prior to onset. The inner-core RH for RI increases prior to onset whereas it decreases in the environments. RH analysis by shear-relative quadrants demonstrates the importance of moistening in the upshear-right quadrant before onset of RI. Results indicate the potential value of multiple annuli and shear-relative analysis for moisture and a shallower, 925–400-hPa layer for shear in RI forecasting. Significance Statement: The purpose of this study was to investigate the wind and moisture around different areas of intensifying North Atlantic tropical cyclones between 1980 and 2021 using reanalysis data. Average wind and moisture evolve differently around the onset of different intensification rates as well as in different near and far regions from the storm center. These results provide additional indicators that forecasters may consider when examining how the environment around the storm and the situation in the inner core may influence its future intensity. [ABSTRACT FROM AUTHOR]

In this paper, an objective analysis of spatial tornado risk in the United States is performed, using a somewhat different dataset than in some previous tornado climatologies. The focus is on significant tornadoes because their reporting frequency has remained fairly stable for several decades. Also, data before 1973 are excluded, since those tornadoes were rated after the fact and were often overrated. Tornado pathlength within the vicinity of a grid point is used to show tornado risk, as opposed to tornado days or the total number of reported tornadoes. The possibility that many tornadoes in the Great Plains were underrated due to the lack of damage indicators, causing a low bias in the number of significant tornadoes there, is mostly discounted through several analyses. The kernel density analysis of 1973-2011 significant tornadoes performed herein shows that the area of highest risk for tornadoes in the United States extends roughly from Oklahoma to Tennessee and northwestern Georgia, with the highest risk in the southeastern United States, from central Arkansas across most of Mississippi and northern Alabama. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) forecasts rely heavily on output from global numerical models. While considerable research has investigated the skill of various models with respect to track and intensity, few studies have considered how well global models forecast TC genesis in the North Atlantic basin. This paper analyzes TC genesis forecasts from five global models [Environment Canada's Global Environment Multiscale Model (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF) global model, the Global Forecast System (GFS), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Met Office global model (UKMET)] over several seasons in the North Atlantic basin. Identifying TCs in the model is based on a combination of methods used previously in the literature and newly defined objective criteria. All model-indicated TCs are classified as a hit, false alarm, early genesis, or late genesis event. Missed events also are considered. Results show that the models' ability to predict TC genesis varies in time and space. Conditional probabilities when a model predicts genesis and more traditional performance metrics (e.g., critical success index) are calculated. The models are ranked among each other, and results show that the best-performing model varies from year to year. A spatial analysis of each model identifies preferred regions for genesis, and a temporal analysis indicates that model performance expectedly decreases as forecast hour (lead time) increases. Consensus forecasts show that the probability of genesis noticeably increases when multiple models predict the same genesis event. Overall, this study provides a climatology of objectively identified TC genesis forecasts in global models. The resulting verification statistics can be used operationally to help refine deterministic and probabilistic TC genesis forecasts and potentially improve the models examined. [ABSTRACT FROM AUTHOR]

Forecasting year-to-year variations in East Asian summer monsoon (EASM) precipitation is one of the most challenging tasks in climate prediction because the predictors are not sufficiently well known and the forecast skill of the numerical models is poor. In this paper, a statistical forecast model for changma (the Korean portion of the EASM system) precipitation is proposed that was constructed with three physically based predictors. A forward-stepwise regression was used to select the predictors that included sea surface temperature (SST) anomalies over the North Pacific, the North Atlantic, and the tropical Pacific Ocean. Seasonal predictions with this model showed high forecasting capabilities that had a Gerrity skill score of ~0.82. The dynamical processes associated with the predictors were examined prior to their use in the prediction scheme. All predictors tended to induce an anticyclonic anomaly to the east or southeast of Japan, which was responsible for transporting a large amount of moisture to the southern Korean Peninsula. The predictor in the North Pacific formed an SST front to the east of Japan during the summertime, which maintained a lower-tropospheric baroclinicity. The North Atlantic SST anomaly induced downstream wave propagation in the upper troposphere, developing anticyclonic activity east of Japan. Forcing from the tropical Pacific SST anomaly triggered a cyclonic anomaly over the South China Sea, which was maintained by atmosphere-ocean interactions and induced an anticyclonic anomaly via northward Rossby wave propagation. Overall, the model used for forecasting changma precipitation performed well ( R = 0.85) and correctly predicted information for 16 out of 19 yr of observational data. [ABSTRACT FROM AUTHOR]

While there are a variety of modes for tropical cyclone (TC) development, there have been relatively few efforts to systematically catalog both nondeveloping and developing cases. This paper introduces an operationally derived climatology of tropical disturbances that were analyzed using the Dvorak technique at the National Hurricane Center (NHC) and the Central Pacific Hurricane Center from 2001 to 2011. Using these Dvorak intensity estimates, the likelihood of genesis is calculated as a historical baseline for TC prediction. Despite the limited period of record, the climatology of Dvorak analyses of incipient tropical systems has a spatial distribution that compares well with previous climatologies. The North Atlantic basin shows substantial regional variability in Dvorak classification frequency. In contrast, tropical disturbances in the combined eastern and central North Pacific basins (which split at 125°W into an eastern region and a central region) have a single broad frequency maximum and limited meridional extent. When applied to forecasting, several important features are discovered. Dvorak fixes are sometimes unavailable for disturbances that develop into TCs, especially at longer lead times. However, when probabilities of genesis are calculated by a Dvorak current intensity (CI) number, the likelihood stratifies well by basin and intensity. Tropical disturbances that are analyzed as being stronger (a higher Dvorak CI number) achieve genesis more often. Further, all else being equal, genesis rates are highest in the eastern Pacific, followed by the Atlantic. Out-of-sample verification of predictive skill shows comparable results to that of the NHC, with potential to inform forecasts and provide the first disturbance-centric baseline for tropical cyclogenesis potential. [ABSTRACT FROM AUTHOR]

This paper describes the climatology of tornadoes around New York City (NYC) and Long Island (LI), New York, and the structural evolution of two tornadic events that affected NYC on 8 August 2007 and 16 September 2010. Nearly half (18 of 34 events from 1950 to 2010) of NYC-LI tornadoes developed between 0500 and 1300 EDT, and August is the peak tornado month as compared to July for most of the northeast United States. A spatial composite highlights the approaching midlevel trough, moderate most unstable convective available potential energy (MUCAPE), and frontogenesis along a low-level baroclinic zone. Shortly before the early morning tornadoes on 8 August 2007, a mesoscale convective system intensified in the lee of the Appalachians in a region of low-level frontogenesis and moderate MUCAPE (~1500 J kg-1). Warm advection at low levels and evaporative cooling within an elevated mixed layer (EML) ahead of the mesoscale convective system (MCS) helped steepen the low-level lapse rates. Meanwhile, a surface mesolow along a quasi-stationary frontal zone enhanced the warm advection and low-level shear. The late afternoon event on 16 September 2010 was characterized by a quasi-linear convective system (QLCS) that also featured an EML aloft, a surface mesolow just west of NYC, low-level frontogenesis, and a southerly low-level jet ahead of an approaching midlevel trough. The QLCS intensified approaching NYC and generated meso-vortices as the QLCS bowed outward. These cases illustrate the benefit of high-density surface observations, terminal Doppler radars, and sounding profiles from commercial aircraft for nowcasting these events. [ABSTRACT FROM AUTHOR]

In 1980 the Holland tropical cyclone (TC) wind profile model was introduced. This simple model was originally intended to estimate the wind profile based on limited surface pressure information alone. For this reason and its relative simplicity, the model has been used in many practical applications. In this paper the potential of a simplified version of the Holland B parameter, which is related to the shape of the tangential wind profile, is explored as a powerful diagnostic tool for monitoring TC structure. The implementation examined is based on the limited information (maximum wind, central pressure, radius and pressure of the outer closed isobar, radii of operationally important wind radii, etc.) that is typically available in operational models and routine analyses of TC structure. This 'simplified Holland B' parameter is shown to be sensitive to TC intensity, TC size, and the rate of radial decay of the tangential winds, but relatively insensitive to the radius of maximum winds. A climatology of the simplified Holland B parameter based on historical best-track data is also developed and presented, providing the expected natural ranges of variability. The relative simplicity, predictable variability, and desirable properties of the simplified Holland B parameter make it ideal for a variety of applications. Examples of how the simplified Holland B parameter can be used for improving forecaster guidance, developing TC structure tools, diagnosing TC model output, and understanding and comparing the climatological variations of TC structure are presented. [ABSTRACT FROM AUTHOR]

This paper evaluates the quality of real-time seasonal probabilistic forecasts of the extreme 15%% tails of the climatological distribution of temperature and precipitation issued by the International Research Institute for Climate and Society (IRI) from 1998 through 2009. IRI's forecasts have been based largely on a two-tiered multimodel dynamical prediction system. Forecasts of the 15%% extremes have been consistent with the corresponding probabilistic forecasts for the standard tercile-based categories; however, nonclimatological forecasts for the extremes have been issued sparingly. Results indicate positive skill in terms of resolution and discrimination for the extremes forecasts, particularly in the tropics. Additionally, with the exception of some overconfidence for extreme above-normal precipitation and a strong cool bias for temperature, reliability analyses suggest generally good calibration. Skills for temperature are generally higher than those for precipitation, due both to correct forecasts of increased probabilities of extremely high (above the upper 15th percentile) temperatures associated with warming trends, and to better discrimination of interannual variability. However, above-normal temperature extremes were substantially underforecast, as noted also for the IRI's tercile forecasts. [ABSTRACT FROM AUTHOR]

The satellite-based Dvorak technique (DVKT) is the most widely available and readily used tool for operationally estimating the maximum wind speeds associated with tropical cyclones. The DVKT itself produces internally consistent results, is reproducible, and has shown practical accuracy given the high cost of in situ or airborne observations. For these reasons, the DVKT has been used in a reasonably uniform manner globally for approximately 20 years. Despite the nearly universal use of this technique, relatively few systematic verifications of the DVKT have been conducted. This study, which makes use of 20 yr of subjectively determined DVKT-based intensity estimates and best-track intensity estimates influenced by aircraft observations (i.e., ±2 h) in the Atlantic basin, seeks to 1) identify the factors (intensity, intensity trends, radius of outer closed isobar, storm speed, and latitude) that bias the DVKT-based intensity estimates, 2) quantify those biases as well as the general error characteristics associated with this technique, and 3) provide guidance for better use of the operational DVKT intensity estimates. Results show that the biases associated with the DVKT-based intensity estimates are a function of intensity (i.e., maximum sustained wind speed), 12-h intensity trend, latitude, and translation speed and size measured by the radius of the outer closed isobar. Root-mean-square errors (RMSE), however, are shown to be primarily a function of intensity, with the best signal-to-noise (intensity to RMSE) ratio occurring in an intensity range of 90-125 kt (46-64 m s−1). The knowledge of how these factors affect intensity estimates, which is quantified in this paper, can be used to better calibrate Dvorak intensity estimates for tropical cyclone forecast operations, postseason best-track analysis, and climatological reanalysis efforts. As a demonstration of this capability, the bias corrections developed in the Atlantic basin are also tested using a limited east Pacific basin sample, showing that biases and errors can be significantly reduced. [ABSTRACT FROM AUTHOR]

This paper analyzes long-term surface air temperature trends in a 25-yr (1982–2006) dataset of retrospective seasonal climate predictions made by the NCEP Climate Forecast System (CFS), a model that has its atmospheric greenhouse gases fixed at the 1988 concentration level. Although the CFS seasonal forecasts tend to follow the observed interannual variability very closely, there exists a noticeable time-dependent discrepancy between the CFS forecasts and observations, with a warm model bias before 1988 and a cold bias afterward except for a short-lived warm bias during 1992–94. The trend from warm to cold biases is likely caused by not including the observed increase in the anthropogenic greenhouse gases in the CFS, whereas the warm bias in 1992–94 reflects the absence of the anomalous aerosols released by the 1991 Mount Pinatubo eruption. Skill analysis of the CFS seasonal climate predictions with and without the warming trend suggests that the 1997–98 El Niño event contributes significantly to the record-breaking global warmth in 1998 whereas the record-breaking warm decade since 2000 is mainly due to the effects of the increased greenhouse gases. Implications for operational seasonal prediction will be discussed. [ABSTRACT FROM AUTHOR]

The International Research Institute for Climate and Society (IRI) has been issuing experimental seasonal tropical cyclone activity forecasts for several ocean basins since early 2003. In this paper the method used to obtain these forecasts is described and the forecast performance is evaluated. The forecasts are based on tropical cyclone–like features detected and tracked in a low-resolution climate model, namely ECHAM4.5. The simulation skill of the model using historical observed sea surface temperatures (SSTs) over several decades, as well as with SST anomalies persisted from the previous month's observations, is discussed. These simulation skills are compared with skills of purely statistically based hindcasts using as predictors recently observed SSTs. For the recent 6-yr period during which real-time forecasts have been made, the skill of the raw model output is compared with that of the subjectively modified probabilistic forecasts actually issued. Despite variations from one basin to another, the levels of hindcast skill for the dynamical and statistical forecast approaches are found, overall, to be approximately equivalent at fairly modest but statistically significant levels. The dynamical forecasts require statistical postprossessing (calibration) to be competitive with, and in some circumstances superior to, the statistical models. Skill levels decrease only slowly with increasing lead time up to 2–3 months. During the recent period of real-time forecasts, the issued forecasts have had higher probabilistic skill than the raw model output, due to the forecasters' subjective elimination of the “overconfidence” bias in the model's forecasts. Prospects for the future improvement of dynamical tropical cyclone prediction are considered. [ABSTRACT FROM AUTHOR]

A new scheme is developed to improve the seasonal prediction of summer precipitation in the East Asian and western Pacific region. The scheme is applied to the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) results. The new scheme is designed to consider both model predictions and observed spatial patterns of historical “analog years.” In this paper, the anomaly pattern correlation coefficient (ACC) between the prediction and the observation, as well as the root-mean-square error, is used to measure the prediction skill. For the prediction of summer precipitation in East Asia and the western Pacific (0°–40°N, 80°–130°E), the prediction skill for the six model ensemble hindcasts for the years of 1979–2001 was increased to 0.22 by using the new scheme from 0.12 for the original scheme. All models were initiated in May and were composed of nine member predictions, and all showed improvement when applying the new scheme. The skill levels of the predictions for the six models increased from 0.08, 0.08, 0.01, 0.14, -0.07, and 0.07 for the original scheme to 0.11, 0.14, 0.10, 0.22, 0.04, and 0.13, respectively, for the new scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of some skill measures [e.g., linear error in probability space, (LEPS), relative operating characteristics score (ROCS), Brier scores, and proportion correct rates], commonly used in the validation and verification of seasonal climate forecasts, within the context of some simple theoretical forecast models. The models considered include linear regression and linear discriminant analysis types, where the forecasts are presented in the form of above/below median probabilities and tercile probabilities. Above and below the median categories are also explored within the context of stratified climatology models, while tail categories are explored within the context of the linear regression type. The skill scores for the models are calculated in each case as functions of a parameter that expresses the strength of the relationship between the predictor and predictand. The skill scores investigated are found to exhibit different dependencies on the model parameter, implying that a given skill score value (0.1 say) can imply a range of strengths in the relationship between predictor and predictand, depending on which skill score is being considered. On the other hand, interrelationships between pairs of skill scores are found to be similar across the different types of models, provided model reliability is preserved. The two-category and three-category LEPS skill scores are found to be on approximately the same scale for the linear regression–type model, thereby enabling a direct comparison. [ABSTRACT FROM AUTHOR]

Virtually all numerical forecast models possess systematic biases. Although attempts to reduce such biases at individual stations using simple statistical corrections have met with some success, there is an acute need for bias reduction on the entire model grid. Such a method should be viable in complex terrain, for locations where gridded high-resolution analyses are not available, and where long climatological records or long-term model forecast grid archives do not exist. This paper describes a systematic bias removal scheme for forecast grids at the surface that is applicable to a wide range of regions and parameters. Using observational data and model forecasts over the Pacific Northwest, a method was developed to reduce the biases in gridded 2-m temperature, 2-m dewpoint temperature, and 12-h precipitation forecasts. The method first estimates bias at observing locations using errors from forecasts that are similar to the current forecast. These observed biases are then used to estimate bias on the model grid by pairing model grid points with stations that have similar elevation and/or land-use characteristics. Results show that this approach reduces bias substantially, particularly for periods when biases are large. Adaptations to weather regime changes are made within a short period, and the method essentially “shuts off” when model biases are small. With modest modifications, this approach can be extended to additional variables. [ABSTRACT FROM AUTHOR]

During the winter, lake-effect snowstorms that form over Lake Ontario represent a significant weather hazard for the populace around the lake. These storms, which typically are only 2 km deep, frequently can produce narrow swaths (20–50 km wide) of heavy snowfall (2–5 cm h-1 or more) that extend 50–75 km inland over populated areas. Subtle changes in the low-altitude flow direction can mean the difference between accumulations that last for 1–2 h and accumulations that last 24 h or more at a given location. Therefore, it is vital that radars surrounding the lake are able to detect the presence and strength of these shallow storms. Starting in 2002, the Canadian operational radars on the northern side of the lake at King City, Ontario, and Franktown, Ontario, began using elevation angles of as low as -0.1° and 0.0°, respectively, during the winter to more accurately estimate snowfall rates at the surface. Meanwhile, Weather Surveillance Radars-1988 Doppler in New York State on the southern and eastern sides of the lake—Buffalo (KBUF), Binghamton (KBGM), and Montague (KTYX)—all operate at 0.5° and above. KTYX is located on a plateau that overlooks the lake from the east at a height of 0.5 km. With its upward-pointing radar beams, KTYX’s detection of shallow lake-effect snowstorms is limited to the eastern quarter of the lake and surrounding terrain. The purpose of this paper is to show—through simulations—the dramatic increase in snowstorm coverage that would be possible if KTYX were able to scan downward toward the lake’s surface. Furthermore, if KBUF and KBGM were to scan as low as 0.2°, detection of at least the upper portions of lake-effect storms over Lake Ontario and all of the surrounding land area by the five radars would be complete. Overlake coverage in the lower half (0–1 km) of the typical lake-effect snowstorm would increase from about 40% to about 85%, resulting in better estimates of snowfall rates in landfalling snowbands over a much broader area. [ABSTRACT FROM AUTHOR]

A new radar-based rainfall product has been developed at Météo-France and is currently being deployed within the French operational Application Radar à la Météorologie Infra-Synoptique (ARAMIS) radar network. The rainfall product is based entirely on radar data and comprises the following successive processing steps: 1) dynamic identification of ground clutter based on the pulse-to-pulse fluctuation of the radar signal, 2) reflectivity-to-rain-rate conversion using the Marshall–Palmer Z–R relationship, 3) correction for partial beam blocking using numerical simulations of the interaction between the radar wave and the terrain, 4) correction for vertical profile of reflectivity (VPR) effects based on ratio curves and a priori climatology-based VPR candidates, 5) correction for nonsimultaneity of radar measurements by making use of a cross-correlation advection field, 6) weighted linear combination of the corrected reflectivity measurements gathered at the various elevation angles of the volume coverage pattern, and 7) production of a 5-min rain accumulation using the advection field to mitigate undersampling effects. In addition to the final Cartesian, 512 km × 512 km, 1 km2 in resolution, radar rainfall product, a map of quality indexes is automatically generated that allows for assessing empirically the accuracy of the estimation. This new product has been validated using 27 episodes observed during the autumns of 2002 and 2003 and the winter of 2005 by three S-band radars of the network. This paper is entirely devoted to the description of the methodology. [ABSTRACT FROM AUTHOR]

This paper develops a definition of a supercell reflectivity feature called the descending reflectivity core (DRC). This is a reflectivity maximum pendant from the rear side of an echo overhang above a supercell weak-echo region. Examples of supercells with and without DRCs are presented from two days during the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX), as well as one day with tornadic high-precipitation supercell storms in central Kansas. It was found that in all cases, tornado formation was preceded by the descent of a DRC. However, the sample reported herein is much too small to allow conclusions regarding the overall frequency of DRC occurrence in supercells, or the frequency with which DRCs precede tornado formation. Although further research needs to be done to establish climatological frequencies, the apparent relationship observed between DRCs and impending tornado formation in several supercells is important enough to warrant publication of preliminary findings. [ABSTRACT FROM AUTHOR]

Although skillful seasonal hurricane forecasts for the Atlantic basin are now a reality, large gaps remain in our understanding of observed variations in the distribution of activity within the hurricane season. The month of August roughly spans the first third of the climatologically most active part of the season, but activity during the month is quite variable. This paper reports on an initial investigation into forecasting year-to-year variability of August tropical cyclone (TC) activity using the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis dataset. It is shown that 55%–75% of the variance of August TC activity can be hindcast using a combination of 4–5 global predictors chosen from a 12-predictor pool with each of the predictors showing precursor associations with TC activity. The most prominent predictive signal is the equatorial July 200-mb wind off the west coast of South America. When this wind is anomalously strong from the northeast during July, Atlantic TC activity in August is almost always enhanced. Other July conditions associated with active Augusts include a weak subtropical high in the North Atlantic, an enhanced subtropical high in the northwest Pacific, and low pressure in the Bering Sea region. The most important application of the August-only forecast is that predicted net tropical cyclone (NTC) activity in August has a significant relationship with the incidence of U.S. August TC landfall events. Better understanding of August-only TC variability will allow for a more complete perspective of total seasonal variability and, as such, assist in making better seasonal forecasts. [ABSTRACT FROM AUTHOR]

This paper quantifies the impact of the Columbia Gorge on the weather and climate within and downstream of this mesoscale gap and examines the influence of synoptic-scale flow on gorge weather. Easterly winds occur more frequently and are stronger at stations such as Portland International Airport (KPDX) that are close to the western terminus of the gorge than at other lowland stations west of the Cascades. In the cool season, there is a strong correlation between east winds at KPDX and cooler temperatures in the Columbia Basin, within the gorge, and over the northern Willamette River valley. At least 56% of the annual snowfall, 70% of days with snowfall, and 90% of days with freezing rain at KPDX coincide with easterly gorge flow. Synoptic composites were created to identify the large-scale patterns leading to strong winds, snowfall, and freezing rain in the gorge. These composites showed that all gorge gap flow events are associated with a high-amplitude 500-mb ridge upstream of the Pacific Northwest, colder than normal 850-mb temperatures over the study region, and a substantial offshore sea level pressure gradient force between the interior and the northwest coast. However, the synoptic evolution varies for different kinds of gorge weather events. For example, the composite of the 500-mb field for freezing rain events features a split developing in the upstream ridge with zonal flow at midlatitudes, while for easterly gap winds accompanied by snowfall, there is an amplification of the ridge. [ABSTRACT FROM AUTHOR]

An updated statistical scheme for forecasting seasonal tropical cyclone activity in the Atlantic basin by 1 December of the previous year is presented. Previous research by Gray and colleagues at Colorado State University showed that a statistical forecast issued on 1 December of the previous year could explain up to about 50% of the jackknife hindcast variance for the 1950–90 time period. Predictors utilized in the original forecast scheme included a forward extrapolation of the quasi-biennial oscillation (QBO) and two measures of West African rainfall. This forecast has been issued since 1991 but has shown little skill because of the as yet unexplained failure of the West African rainfall predictors during the 1990s. The updated scheme presented in this paper does not utilize West African rainfall predictors. It employs the new NCEP–NCAR reanalysis data and involves predictors that span the globe. Much experimentation has led to the choosing of conditions associated with the El Niño-Southern Oscillation (ENSO), the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the Pacific–North American pattern (PNA), and the QBO. This new statistical scheme shows similar cross-validated (jackknifed) hindcast skill to the original scheme (explaining up to about 50% of the variance) but is developed over a decade longer time period (1950–2001). In addition, since all predictors are taken directly from the NCEP–NCAR reanalysis, one does not need to consider rainfall collection issues that have caused major problems with the West African rainfall data since 1995. Hypothetical physical linkages between the predictors and the following year's hurricane activity are also presented. Based on the net tropical cyclone (NTC) activity prediction and a weighted average of North Atlantic sea surface temperatures, forecasts of U.S. hurricane landfall probability that showed considerable hindcast skill over the 52-yr period of 1950–2001 can also be issued. [ABSTRACT FROM AUTHOR]

This paper demonstrates the proper use of geostationary satellite imagery in wildland fire detection. The roles of both the visible and the 3.9- m channels are emphasized. Case studies from June 2002 are presented to illustrate techniques that can be utilized in both the detection and short-range forecasting processes. The examples demonstrate that, when utilized correctly, the sensitivity of the shortwave infrared channel to subpixel heat sources can often result in detections that match the timelines of human observations. Finally, a derived satellite product that increases the detection rate of wildland fires from space is described. [ABSTRACT FROM AUTHOR]

Tropical cyclone track forecasting has improved recently to the point at which extending the official forecasts of both track and intensity to 5 days is being considered at the National Hurricane Center and the Joint Typhoon Warning Center. Current verification procedures at both of these operational centers utilize a suite of control models, derived from the "climatology" and "persistence" techniques, that make forecasts out to 3 days. To evaluate and verify 5-day forecasts, the current suite of control forecasts needs to be redeveloped to extend the forecasts from 72 to 120 h. This paper describes the development of 5-day tropical cyclone intensity forecast models derived from climatology and persistence for the Atlantic, the eastern North Pacific, and the western North Pacific Oceans. Results using independent input data show that these new models possess similar error and bias characteristics when compared with their predecessors in the North Atlantic and eastern North Pacific but that the west Pacific model shows a statistically significant improvement when compared with its forerunner. Errors associated with these tropical cyclone intensity forecast models are also shown to level off beyond 3 days in all of the basins studied. [ABSTRACT FROM AUTHOR]

Persistent midwinter cold air pools produce multiday periods of cold, dreary weather in basins and valleys. Persistent stable stratification leads to the buildup of pollutants and moisture in the pool. Because the pool sometimes has temperatures below freezing while the air above is warmer, freezing precipitation often occurs, with consequent effects on transportation and safety. Forecasting the buildup and breakdown of these cold pools is difficult because the interacting physical mechanisms leading to their formation, maintenance, and destruction have received little study. In this paper, persistent wintertime cold pools in the Columbia River basin of eastern Washington are studied. First a succinct meteorological definition of a cold pool is provided and then a 10-yr database is used to develop a cold pool climatology. This is followed by a detailed examination of two cold pool episodes that were accompanied by fog and stratus using remote and in situ temperature and wind sounding data. The two episodes illustrate many of the physical mechanisms that affect cold pool evolution. In one case, the cold pool was formed by warm air advection above the basin and was destroyed by downslope winds that descended into the southern edge of the basin and progressively displaced the cold air in the basin. In the second case, the cold pool began with a basin temperature inversion on a clear night and strengthened when warm air was advected above the basin by a westerly flow that descended from the Cascade Mountains. The cold pool was nearly destroyed one afternoon by cold air advection aloft and by the growth of a convective boundary layer (CBL) following the partial breakup of the basin stratus. The cold pool restrengthened, however, with nighttime cooling and was destroyed the next afternoon by a growing CBL. [ABSTRACT FROM AUTHOR]

Tropical cyclones are extreme events with enormous and devastating consequences to life, property, and our economies. As a result, large-scale efforts have been devoted to improving tropical cyclone forecasts with lead times ranging from a few days to months. More recently, subseasonal forecasts (e.g., 2–6-week lead time) of tropical cyclones have received greater attention. Here, we study whether bias-corrected, subseasonal tropical cyclone reforecasts of the GEFS and ECMWF models are skillful in the Atlantic basin. We focus on the peak hurricane season, July–November, using the reforecast years 2000–19. Model reforecasts of accumulated cyclone energy (ACE) are produced, and validated, for lead times of 1–2 and 3–4 weeks. Week-1–2 forecasts are substantially more skillful than a 31-day moving-window climatology, while week-3–4 forecasts still exhibit positive skill throughout much of the hurricane season. Furthermore, the skill of the combination of the two models is found to be an improvement with respect to either individual model. In addition to the GEFS and ECMWF model reforecasts, we develop a statistical modeling framework that solely relies on daily sea surface temperatures. The reforecasts of ACE from this statistical model are capable of producing better skill than the GEFS or ECMWF model, individually, and it can be leveraged to further enhance the model combination reforecast skill for the 3–4-week lead time. [ABSTRACT FROM AUTHOR]

The occurrence and properties of hail smaller than severe thresholds (diameter, 25 mm) are poorly understood. Prior climatological hail studies have predominantly focused on large or severe hail (diameter at least 25 mm or 1 in.). Through use of data from the Meteorological Phenomena Identification Near the Ground project, Storm Data, and the Community Collaborative Rain, Hail and Snow Network the occurrence and characteristics of both severe and sub-severe hail are explored. Spatial distributions of days with the different classes of hail are developed on an annual and seasonal basis for the period 2013-20. Annually, there are several hail-day maxima that do not follow the maxima of severe hail: the peak is broadly centered over Oklahoma (about 28 days yr21). A secondary maximum exists over the Colorado Front Range (about 26 days yr21), a third extends across northern Indiana from the southern tip of Lake Michigan (about 24 days yr21 with hail), and a fourth area is centered over the corners of southwest North Carolina, northwest South Carolina, and the northeast tip of Georgia. Each of these maxima in hail days are driven by sub-severe hail. While similar patterns of severe hail have been previously documented, this is the first clear documentation of sub-severe hail patterns since the early 1990s. Analysis of the hail size distribution suggests that to capture the overall hail risk, each of the datasets provide a complimentary data source. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, several analyses were conducted that were aimed at improving sustained wind speed and gust forecasts for tropical cyclones (TCs) affecting coastal regions. An objective wind speed forecast analysis of recent TCs affecting the mid-Atlantic region was first conducted to set a benchmark for improvement. Forecasts from the National Digital Forecast Database were compared to observations and surface wind analyses in the region. The analysis suggests a general overprediction of sustained wind speeds, especially for areas affected by the strongest winds. Currently, National Weather Service Weather Forecast Offices use a software tool known as the Tropical Cyclone Forecast/Advisory (TCM) wind tool (TCMWindTool) to develop their wind forecast grids. The tool assumes linear decay in the sustained wind speeds when interpolating the National Hurricane Center 12-24-hourly TCM product to hourly grids. An analysis of postlandfall wind decay for recent TCs was conducted to evaluate this assumption. Results indicate that large errors in the forecasted wind speeds can emerge, especially for stronger storms. Finally, an analysis of gust factors for recent TCs affecting the region was conducted. Gust factors associated with weak sustained wind speeds are shown to be highly variable but average around 1.5. The gust factors decrease to values around 1.2 for wind speeds above 40 knots (kt; 1 kt = 0.51 m s−1) and are in general insensitive to the wind direction, suggesting local rather than upstream surface roughness largely dictates the gust factor at a given location. Forecasters are encouraged to increase land reduction factors used in the TCMWindTool and to modify gust factors to account for factors including the sustained wind speed and local surface roughness. [ABSTRACT FROM AUTHOR]

To examine the utility of smartphone pressure observations (SPOs), a climatology of mesoscale pressure features was developed to evaluate whether SPOs could better resolve mesoscale phenomena than existing surface pressure networks (MADIS). A comparison between MADIS and smartphone pressure analyses was performed by tracking and characterizing bandpass-filtered, mesoscale pressure features. Over the year 2018, nearly 3000 pressure features were tracked across the central and eastern United States. Pressure features identified by smartphone observations lasted, on average, 25 min longer, traveled 25 km farther, and exhibited larger amplitudes than features observed by MADIS. An examination of smartphone pressure features tracks by season and location found that almost all pressure features propagated eastward. With over 87% of observed pressure features associated with convection, the climatology of surface pressure features largely reflects the geographic and seasonal variation of mesoscale convection. Phase relationships between pressure features and other surface variables were consistent with those expected for mesohighs and wake lows. These results suggest that SPOs could enhance convective analyses and forecasts compared to existing surface networks like MADIS by better resolving mesoscale structures and features, such as wake lows and mesohighs. Significance Statement: While smartphone pressure networks provide unprecedented observation coverage and density, it was unclear whether they can add value to existing surface pressure networks. This study addresses this question by developing a yearlong record of mesoscale pressure features over the eastern and central United States. Analysis of this record revealed that smartphone analyses better resolved mesoscale pressure features, especially across the central United States where existing surface pressure networks are sparser. Nearly all observed pressure features were observed near precipitation, with five in six associated with convection. Relationships between mesoscale pressure features and other surface state variables were consistent with those expected for mesohighs and wake lows. [ABSTRACT FROM AUTHOR]

Discusses the verification concepts in the book "Forecast Verification: A Practitioner's Guide in Atmospheric Science". Verification of meteorological and climate forecasts; Statistics related to meteorology and climatology; Introduction of the relative operating characteristic into meteorology.

Nocturnal tornadoes are challenging to forecast and even more challenging to communicate. Numerous studies have evaluated the forecasting challenges, but fewer have investigated when and where these events pose the greatest communication challenges. This study seeks to evaluate variation in confidence among U.S. residents in receiving and responding to tornado warnings by hour of day. Survey experiment data come from the Severe Weather and Society Survey, an annual survey of U.S. adults. Results indicate that respondents are less confident about receiving warnings overnight, specifically in the early morning hours [from 12:00 AM to 4:00 AM local time (0000–0400 LT)]. We then use the survey results to inform an analysis of hourly tornado climatology data. We evaluate where nocturnal tornadoes are most likely to occur during the time frame when residents are least confident in their ability to receive tornado warnings. Results show that the Southeast experiences the highest number of nocturnal tornadoes during the time period of lowest confidence, as well as the largest proportion of tornadoes in that time frame. Finally, we estimate and assess two multiple linear regression models to identify individual characteristics that may influence a respondent's confidence in receiving a tornado between 12:00 AM and 4:00 AM. These results indicate that age, race, weather awareness, weather sources, and the proportion of nocturnal tornadoes in the local area relate to warning reception confidence. The results of this study should help inform policymakers and practitioners about the populations at greatest risk for challenges associated with nocturnal tornadoes. Discussion focuses on developing more effective communication strategies, particularly for diverse and vulnerable populations. Significance Statement: We aimed to understand what time of the day members of the public were least confident that they would receive and respond to a tornado warning. Our results demonstrate that members of the public are not only less confident that they would receive warnings overnight, but they are least confident between midnight and 4:00 AM local time (0000–0400 LT). We then used a climatology of tornado reports to see where tornadoes occur during this time frame. Most of the tornadoes that occur between midnight and 4:00 AM occur in the Southeast, which is troubling because this area also has large numbers of people living in poverty or in less robust structures, like mobile homes. We also show that individual characteristics like age and the number of weather information sources someone accesses impact confidence in one's ability to receive warnings during this time frame. These results should help inform forecasters and emergency managers about the communities that need more time to respond to overnight tornado events. [ABSTRACT FROM AUTHOR]

Conventional data and mesoscale model simulations are used to analyze two cyclones that developed east of the Rocky Mountains in June and November 1998. Both cyclones formed when a Pacific cold front overtook a lee trough/dryline east of the Rockies. In one case the leading edge of the Pacific cold front was on the surface, as depicted in the classic Norwegian model of a cyclone. In the other case, which is referred to as a cold front aloft (CFA) cyclone, the leading edge of the Pacific cold front was aloft and in advance of the lee trough. The lifting and severe weather associated with the Pacific cold front was along the leading edge of this front in both the Norwegian-type and CFA-type cyclones. To obtain an estimate of how often CFA cyclones, with a coincident CFA rainband, occurred in the central United States during the period 15 September 1994 through 15 September 1995, 70 cyclones that maintained closed surface low pressure centers for at least 24 h, and produced precipitation in the United States east of the Rockies, were analyzed. Analysis of these cyclones revealed that 46% were CFA type, 23% were Norwegian type, and the remaining 31% could not be readily classified into either type. Lee troughs were common features east of the Rocky Mountains during this period. They were present in 62% of the 70 cyclones analyzed. The 1000-500-hPa thickness field is suggested as a useful tool in locating the leading edge of a Pacific cold front, and in determining whether a cyclone is a Norwegian type or CFA type. The issue of how the frontal structures in CFA-type cyclones should be analyzed on surface weather charts is discussed, and some suggestions offered. [ABSTRACT FROM AUTHOR]

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 U.S. storm climatology, which includes the increased presence of low-CAPE environments and tornadic nonsupercell modes. To disentangle these complex environmental interactions, Southeast severe convective reports spanning 2003–18 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. Last, statistical analysis is performed to determine which aspects of the near-storm environment most effectively discriminate between tornadic (or significantly tornadic) and nontornadic storms toward 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 nonsupercell (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 postsunset—potentially compensating for minimal buoyancy. Furthermore, the existence of HSLC storm environments presunset increases the likelihood of nonsupercellular tornadoes postsunset. 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. SIGNIFICANCE STATEMENT: The environments in which storms occur change near and after sunset, making it difficult to anticipate how these storms will respond and whether they can produce tornadoes. Southeast U.S. tornadoes can occur even with limited instability, which only adds to this challenge. To this end, we examine the different pathways that Southeast storm environments can evolve into the evening and consider how the frequency and characteristics of their tornadoes change for each pathway. We found that the amount of instability present before sunset influences how storm environments change afterward, and, therefore, how those storms produce tornadoes. Last, we identify what variables best predict tornadoes for each pathway, and these are used to construct new Southeast tornado forecasting parameters. [ABSTRACT FROM AUTHOR]

The Multi-Radar Multi-Sensor (MRMS) system generates an operational suite of derived products in the National Weather Service useful for real-time monitoring of severe convective weather. One such product generated by MRMS is the maximum estimated size of hail (MESH) that estimates hail size based on the radar reflectivity properties of a storm above the environmental 0°C level. The MRMS MESH product is commonly used across the National Weather Service (NWS), including the Storm Prediction Center (SPC), to diagnose the expected hail size in thunderstorms. Previous work has explored the relationship between the MRMS MESH product and severe hail (≥25.4mm or 1 in.) reported at the ground. This work provides an hourly climatology of severe MRMS MESH across the contiguous United States from 2012 to 2019, including an analysis of how the MESH climatology differs from the severe hail reports climatology. Results suggest that the MESH can provide beneficial hail risk information in areas where population density is low. Evidence also shows that the MESH can provide potentially beneficial information about severe hail occurrence during the night in locations that are climatologically favored for upscale convective growth and elevated convection. These findings have important implications for the use of MESH as a verification dataset for SPC probabilistic hail forecasts as well as severe weather watch decisions in areas of higher hail risk but low population density. [ABSTRACT FROM AUTHOR]

This work investigates how the relationship between tropical cyclone (TC) tornadoes and ambient (i.e., synoptic-scale) deep-tropospheric (i.e., 850-200-hPa) vertical wind shear (VWS) varies between coastal and inland environments. Observed U.S. TC tornado track data are used to study tornado frequency and location, while dropsonde and radiosonde data are used to analyze convective-scale environments. To study the variability in the TC tornado--VWS relationship, these data are categorized by both 1) their distance from the coast and 2) reanalysis-derived VWS magnitude. The analysis shows that TCs produce coastal tornadoes regardless of VWS magnitude primarily in their downshear sector, with tornadoesmost frequently occurring in strongly sheared cases. Inland tornadoes, including the most damaging cases, primarily occur in strongly sheared TCs within the outer radii of the downshear-right quadrant. Consistent with these patterns, dropsondes and coastal radiosondes show that the downshear-right quadrant of strongly sheared TCs has the most favorable combination of enhanced lower-tropospheric near-surface speed shear and veering, and reduced lower-tropospheric thermodynamic stability for tornadic supercells. Despite the weaker intensity farther inland, these kinematic conditions are even more favorable in inland environments within the downshear-right quadrant of strongly sheared TCs, due to the strengthened veering of the ambient winds and the lack of changes in the TC outer tangential wind field strength. The constructive superposition of the ambient and TC winds may be particularly important to inland tornado occurrence. Together, these results will allow forecasters to anticipate how the frequency and location of tornadoes and, more broadly, convection may change as TCs move inland. [ABSTRACT FROM AUTHOR]

Collecting hail reports to build a climatology is challenging in a sparsely populated country such as Finland. To expand an existing database, a new approach involving daily verification of a radar- and numerical weather prediction–based hail detection algorithm was trialed during late May–August for the 10-yr period, 2008–17. If the algorithm suggested a high likelihood of hail from each identified convective cell in specified locations, then an email survey was sent to people and businesses in these locations. Telephone calls were also used occasionally. Starting from 2010, the experiment was expanded to include trained storm spotters performing the surveys (project called TATSI). All the received hail reports were documented (severe or ≥2 cm, and nonsevere, excluding graupel), giving a more complete depiction of hail occurrence in Finland. In combination with reports from the general public, news, and social media, our hail survey resulted in a 292% increase in recorded severe hail days and a 414% increase in observed severe hail cases compared to a climatological study (1930–2006). More than 2200 email surveys were sent, and responses to these surveys accounted for 53% of Finland's severe hail cases during 2008–17. Most of the 2200 emails were sent into rural locations with low population density. These additional hail reports allowed problems with the initial radar-based hail detection algorithm to be identified, leading to the introduction of a new hail index in 2009 with improved detection and nowcasting of severe hail. This study shows a way to collect hail reports in a sparsely populated country to mitigate underreporting and population biases. [ABSTRACT FROM AUTHOR]