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Interannual fluctuations in average summertime temperatures across the western United States are captured by a leading empirical orthogonal function that explains over 50% of the total observed variance. In this paper, we explain the origins of this pattern of interannual temperature variability by examining soil moisture–temperature coupling that acts across seasons in observations and climate models. We find that a characteristic pattern of coupled temperature–soil moisture climate variability accounts for 34% of the total observed variance in summertime temperature across the region. This pattern is reproduced in state-of-the-art global climate models, where experiments that eliminate soil moisture variability reduce summertime average temperature variance by a factor of 3 on average. We use an idealized model of the coupled atmospheric boundary layer and underlying land surface to demonstrate that feedbacks between soil moisture, boundary layer relative humidity, and precipitation can explain the observed relations between springtime soil moisture and summertime temperature. Our results suggest that antecedent soil moisture conditions and subsequent land–atmosphere interactions play an important role in interannual summertime temperature variability in the western United States; soil moisture variations cause distal temperature anomalies and impart predictability at time scales longer than one season. Our results indicate that 40% of the observed warming trend across the western United States since 1981 has been driven by wintertime precipitation trends in the U.S. southwest. Significance Statement: Year-to-year fluctuations in summertime temperatures have a large impact on drought, wildfire, and extreme heat across the Western United States. We find strong evidence that soil moisture deficits in the preceding spring may be the primary driver of higher-than-average summer temperatures in this region. Our results suggest that memory in the water cycle may lead to greater predictability in the climate system from season to season and that trends in southwest precipitation have exerted a considerable influence on observed warming over the past 40 years. [ABSTRACT FROM AUTHOR]

Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitation-type diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h−1, as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs. Significance Statement: Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h−1. Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models. [ABSTRACT FROM AUTHOR]

Harmful algae and cyanobacteria blooms are increasing in frequency and intensity in freshwater systems due to anthropogenic impacts such as nutrient loading in watersheds and engineered alterations of natural waterways. There are multiple physical factors that affect the conditions in a freshwater system that contribute to optimal habitats for harmful algae and toxin-producing cyanobacteria. A growing body of research shows that climate change stressors also are impacting water-body conditions that favor harmful algae and cyanobacteria species over other phytoplankton. The overgrowth of these organisms, or a "bloom," increases the opportunity for exposure to toxins by humans, companion animals, livestock, and wildlife. As waters warm and precipitation patterns change over time, exposure to these blooms is projected to increase. Hence, it is important that states and tribes develop monitoring and reporting strategies as well as align governmental policies to protect their citizens and ecosystems within their jurisdiction. Currently, the policies and approaches taken to monitor and report on harmful algae and cyanobacteria blooms vary widely among states, and it is undetermined if any tribes have specific policies on harmful algae blooms. This paper synthesizes research on algal blooms in inland freshwater systems of the United States. This review examines how climate change contributes to trends in bloom frequency or severity and outlines approaches that states and tribes may use to monitor, report, and respond to harmful algae and cyanobacteria blooms. Significance Statement: Inland bodies of freshwater supply drinking water for humans and animals, water for irrigating crops, habitats for aquatic species, places of cultural significance for Indigenous peoples, and other important functions. Many of these bodies of water have been polluted with runoff from industry, including agriculture, and already support harmful algal blooms during warm conditions. Hot extremes associated with climate change are expected to increase the occurrence and duration of harmful algal blooms, and in some places, initiate blooms where none have been recorded previously. These toxic blooms are harmful to people, companion animals, livestock, and wildlife. It is important to review the interconnections among biological, climate, and water systems to monitor blooms and alert the public about their toxin production. [ABSTRACT FROM AUTHOR]

Predicting drought severity is essential for drought management and early warning systems. Although numerous physical model-based and data-driven methods have been put forward for drought prediction, their abilities are largely constrained by data requirements and modeling complexity. There remains a challenging task to efficiently predict categorial drought, especially for the U.S. Drought Monitor (USDM). Aiming at this issue, multiple Markov chains for USDM-based categorial drought prediction are successfully proposed and evaluated in this paper. In particular, this study concentrated on how the Markov order, step size, and training set length affected prediction accuracy (PA). According to experiments from 2000 to 2021, it was found that the 1-step and first-order Markov models had the best accuracy in predicting droughts up to 4 weeks ahead. The PA steadily dropped with increasing step size, and the average accuracy at monthly scale was 88%. In terms of seasonal variability, summer (July-August) had the lowest PA while winter had the highest (January-February). In comparison with the western region, the PA in the eastern United States is 25% higher. Moreover, the length of the training set had an obvious impact on the PA of the model. The PA in 1-step prediction was 87% and 78% under 20- and 5-yr training sets, respectively. The results of the study showed that Markov models predicted categorical drought with high accuracy in the short term and showed different performances on time and space scales. Proper use of Markov models would help disaster managers and policy makers to put mitigation policies and measures into practice. [ABSTRACT FROM AUTHOR]

This study uses Global Historical Climate Network (GHCN) data in each season to identify the days with the most extreme precipitation ("EP days") in the mid-Atlantic and northeast United States between 1979 and 2019. These days are sorted according to the fraction of extreme precipitation attributed to tropical cyclone (TC), atmospheric river (AR), and extreme integrated vapor transport (IVT) influences. In winter and spring, there have been increases in seasonal precipitation from the most extreme days, associated with a combination of frequency and intensity changes. These increasing trends come primarily from atmospheric rivers. In summer and fall, there have also been large increases in precipitation on extreme days, in this case due entirely to increased event frequency. These changes come from a combination of AR, TC, and extreme IVT influences. Synoptic characteristics of AR-related EP days in winter and spring have changed significantly. In winter, there has been an amplification of the Atlantic ridge and a deepening of the upstream trough over the upper Great Plains, as well as enhanced AR detection and IVT on these days. The composite low has shifted north and intensified. In spring, the trough has weakened and 1000–500-hPa thickness has increased broadly to the south. These changes are related to changes in the large-scale flow. In winter and spring, the North Atlantic subtropical high (NASH) has strengthened and shifted west, leading to increased southwesterly IVT over the mid-Atlantic and Northeast United States. In summer, southerly IVT along the east coast has increased, and 1000–500-hPa climatological thickness has increased broadly in all seasons. Significance Statement: This paper studies the days with the most extreme precipitation over the mid-Atlantic and Northeast United States in each season and finds that there have been large changes in the frequency, intensity, and characteristics of some of these days. Extreme days associated with atmospheric rivers (ARs) and tropical cyclones (TCs) have become more frequent. Precipitation on AR-related days has also become more intense in some areas. On winter extreme days associated with ARs, the pattern has become more amplified and moisture fluxes have become stronger; these changes are likely associated with strengthened high pressure over the Atlantic. Throughout all seasons, there is evidence of warming on extreme days. This is important because it adds to our understanding of how total and AR-related extreme precipitation is changing in a warming climate. [ABSTRACT FROM AUTHOR]

Turbulence and winds below 328 m were measured on 5 successive nights in a program to study tracer transport in the nocturnal boundary layer at a site with moderately complex terrain and mixed land use. The instruments included sonic anemometers and CO2/H2O analyzers at four levels on a 328 m tall tower, a minisodar/RASS system, a midrange sodar, a ceilometer, and an array of 61 m towers. Preliminary simulations indicated satisfactory perfluorocarbon mixing to 68 m but insufficient transport to the 328 m level on both weakly stable and stable nights, possibly due to insufficient turbulence kinetic energy and/or small vertical mixing lengths, or the presence of meso-β fronts, e.g., sea-breeze fronts, that could transport trace chemicals efficiently to 328 m. To examine the problem further, time–height distributions of turbulence kinetic energy (TKE), mixing length, Richardson number, potential temperature, and winds were derived from the observations of mean winds and temperature and the TKE budget equation, interpolated to fit the observations, under the flux/gradient and z-less scaling assumptions, and displayed with aerosol profiles. The results indicated higher and more variable levels of TKE and mixing lengths above a typical turbulence maximum at 30–50 m. Oscillations with periods of ∼2 h were common and occasional meso-β fronts and shear zones between 75 and 150 m were seen, which increased TKE aloft and in some cases led to a poorly defined boundary layer top. Significance Statement: The atmosphere's boundary layer is the interface between the free atmosphere and natural and human activity near Earth's surface. The daytime boundary layer has been studied extensively and, because of vigorous sun-driven mixing, is well understood and readily parameterized in forecast and global climate models. In contrast, the nocturnal boundary layer is less well understood or predictable because turbulence is weak and tends to decouple it from the surface and the free atmosphere above. This paper focuses on the least-studied upper part of the nocturnal boundary layer over the southeastern United States where topography and land–sea contrast affect winds, turbulence, and chemical transport. [ABSTRACT FROM AUTHOR]

Microburst wind shear has caused or contributed to a significant number of aviation accidents. Since 1943, wind shear accidents have been responsible for more than 1,400 fatalities worldwide, including over 400 deaths in the United States between 1973 and 1985. In this paper, we describe one of the more successful and societally impactful research-to-operations (R2O) programs in atmospheric science history. The remarkable R2O journey included the discovery of microburst wind shear in the late 1970s and early 1980s, the scientific efforts to understand this phenomenon and its impact on aircraft operations, the development of a wind shear training program for pilots, and the rapid development, testing, and implementation of wind shear detection systems that successfully saved lives and property. The article includes a chronological description of the wind shear research and development program, key milestones toward implementation, and the research-to-operations best practices employed for successful technology transfer. [ABSTRACT FROM AUTHOR]

In this paper, a new set of daily gridded fields and area averages of temperature and precipitation is introduced that covers the contiguous United States (CONUS) from 1951 to present. With daily updates and a grid resolution of approximately 0.0417° (nominally 5 km), the product, named nClimGrid-Daily, is designed to be used particularly in climate monitoring and other applications that rely on placing event-specific meteorological patterns into a long-term historical context. The gridded fields were generated by interpolating morning and midnight observations from the Global Historical Climatology Network–Daily dataset using thin-plate smoothing splines. Additional processing steps limit the adverse effects of spatial and temporal variations in station density, observation time, and other factors on the quality and homogeneity of the fields. The resulting gridded data provide smoothed representations of the point observations, although the accuracy of estimates for individual grid points and days can be sensitive to local spatial variability and the ability of the available observations and interpolation technique to capture that variability. The nClimGrid-Daily dataset is therefore recommended for applications that require the aggregation of estimates in space and/or time, such as climate monitoring analyses at regional to national scales. Significance Statement: Many applications that use historical weather observations require data on a high-resolution grid that are updated daily. Here, a new dataset of daily temperature and precipitation for 1951–present is introduced that was created by interpolating irregularly spaced observations to a regular grid with a spacing of 0.0417° across the contiguous United States. Compared to other such datasets, this product is particularly suitable for monitoring climate and drought on a daily basis because it was processed so as to limit artificial variations in space and time that may result from changes in the types and distribution of observations used. [ABSTRACT FROM AUTHOR]

Portions of the northeastern United States (NE) have experienced drought every year since 2016. The U.S. Drought Monitor (USDM) has played an important role in drought characterization and management by providing weekly drought maps across the entire United States, including the NE. Unfortunately, the USDM lacks consistency between input variables leading to difficulties in defining boundaries between drought categories. This paper evaluates the National Water Model's (NWM) ability to model streamflow and soil moisture, two important hydrological products that are frequently incorporated in drought indices. Using a 26-yr NWM retrospective simulation, comparisons were conducted between NWM output and observations of streamflow and soil moisture, as well as between drought categories derived from the NWM and observations and the USDM. Results indicate that NWM provides moderate predictions of streamflow at NE stations when comparing to historical observations, that NWM streamflow estimators are generally upwardly biased, and performance is worse at lower streamflow magnitudes. The NWM's ability to predict soil moisture is worse than streamflow, with again a positive bias at most sites and strong variations in anomaly correlation across sites. When predicting drought categories, NWM streamflow is as strong a predictor of USDM drought categories as observed streamflow. Extending the NWM streamflow series using a maintenance of variance technique and only past records provides slight improvements over drought categories derived from the entire 26-yr retrospective simulation. Output from the NWM appears to have some skill in characterizing drought in the NE and provides a spatial resolution to improve the designation of drought boundaries. [ABSTRACT FROM AUTHOR]

Conventional statistical postprocessing techniques offer limited ability to improve the skills of probabilistic guidance for heavy precipitation. This paper introduces two artificial neural network (ANN)-based, geographically aware, and computationally efficient postprocessing schemes, namely, the ANN-multiclass (ANN-Mclass) and the ANN–censored, shifted gamma distribution (ANN-CSGD). Both schemes are implemented to postprocess Global Ensemble Forecast System (GEFS) forecasts to produce probabilistic quantitative precipitation forecasts (PQPFs) over the contiguous United States (CONUS) using a short (60 days), rolling training window. The performances of these schemes are assessed through a set of hindcast experiments, wherein postprocessed 24-h PQPFs from the two ANN schemes were compared against those produced using the benchmark quantile mapping algorithm for lead times ranging from 1 to 8 days. Outcomes of the hindcast experiments show that ANN schemes overall outperform the benchmark as well as the raw forecast over the CONUS in predicting probability of precipitation over a range of thresholds. The relative performance varies among geographic regions, with the two ANN schemes broadly improving upon quantile mapping over the central, south, and southeast, and slightly underperforming along the Pacific coast where skills of raw forecasts are the highest. Between the two schemes, the hybrid ANN-CSGD outperforms at higher rainfall thresholds (i.e., >50 mm day−1), though the outperformance comes at a slight expense of sharpness and spatial specificity. Collectively, these results confirm the ability of the ANN algorithms to produce skillful PQPFs with a limited training window and point to the prowess of the hybrid scheme for calibrating PQPFs for rare-to-extreme rainfall events. [ABSTRACT FROM AUTHOR]

This paper utilizes statistical and statistical–dynamical methodologies to select, from the full observational record, a minimal subset of dates that would provide representative sampling of local precipitation distributions across the contiguous United States (CONUS). The CONUS region is characterized by a great diversity of precipitation-producing systems, mechanisms, and large-scale meteorological patterns (LSMPs), which can provide favorable environment for local precipitation extremes. This diversity is unlikely to be adequately captured in methodologies that rely on grossly reducing the dimensionality of the data—by representing it in terms of a few patterns evolving in time—and thus requires data thinning techniques based on high-dimensional dynamical or statistical data modeling. We have built a novel high-dimensional empirical model of temperature and precipitation capable of producing statistically accurate surrogate realizations of the observed 1979–99 (training period) evolution of these fields. This model also provides skillful hindcasts of precipitation over the 2000–20 (validation) period. We devised a subsampling strategy based on the relative entropy of the empirical model's precipitation (ensemble) forecasts over CONUS and demonstrated that it generates a set of dates that captures a majority of high-impact precipitation events, while substantially reducing a heavy-precipitation bias inherent in an alternative methodology based on the direct identification of large precipitation events in the Global Ensemble Forecast System (GEFS), version 12 reforecasts. The impacts of data thinning on the accuracy of precipitation statistical postprocessing, as well as on the calibration and validation of the Hydrologic Ensemble Forecast Service (HEFS) reforecasts are yet to be established. Significance Statement: High-impact weather events are usually associated with extreme precipitation, which is notoriously difficult to predict even using highly resolved state-of-the-art numerical weather prediction models based on first physical principles. The same is true for statistical models that use past data to anticipate the future behavior likely to stem from an observed initial state. Here we use both types of models to identify the occurrences of the states, over the historical climate record, which are likely to lead to extreme precipitation events. We show that the overall statistics of precipitation over the contiguous United States is encapsulated in a greatly reduced set of such states, which could substantially alleviate the computational burden associated with testing of hydrological forecast models used for decision support. [ABSTRACT FROM AUTHOR]

During the summer of 2018, the upward-pointing Wyoming Cloud Lidar (WCL) was deployed on board the University of Wyoming King Air (UWKA) research aircraft for the Biomass Burning Flux Measurements of Trace Gases and Aerosols (BB-FLUX) field campaign. This paper describes the generation of calibrated attenuated backscatter coefficients and aerosol extinction coefficients from the WCL measurements. The retrieved aerosol extinction coefficients at the flight level strongly correlate (correlation coefficient, rr > 0.8) with in situ aerosol concentration and carbon monoxide (CO) concentration, providing a first-order estimate for converting WCL extinction coefficients into vertically resolved CO and aerosol concentration within wildfire smoke plumes. The integrated CO column concentrations from the WCL data in nonextinguished profiles also correlate (rr = 0.7) with column measurements by the University of Colorado Airborne Solar Occultation Flux instrument, indicating the validity of WCL-derived extinction coefficients. During BB-FLUX, the UWKA sampled smoke plumes from more than 20 wildfires during 35 flights over the western United States. Seventy percent of flight time was spent below 3 km above ground level (AGL) altitude, although the UWKA ascended up to 6 km AGL to sample the top of some deep smoke plumes. The upward-pointing WCL observed a nearly equal amount of thin and dense smoke below 2 km and above 5 km due to the flight purpose of targeted fresh fire smoke. Between 2 and 5 km, where most of the wildfire smoke resided, the WCL observed slightly more thin smoke than dense smoke due to smoke spreading. Extinction coefficients in dense smoke were 2–10 times stronger, and dense smoke tended to have larger depolarization ratio, associated with irregular aerosol particles. [ABSTRACT FROM AUTHOR]

The NOAA Science Advisory Board appointed a task force to prepare a white paper on the use of observing system simulation experiments (OSSEs). Considering the importance and timeliness of this topic and based on this white paper, here we briefly review the use of OSSEs in the United States, discuss their values and limitations, and develop five recommendations for moving forward: national coordination of relevant research efforts, acceleration of OSSE development for Earth system models, consideration of the potential impact on OSSEs of deficiencies in the current data assimilation and prediction system, innovative and new applications of OSSEs, and extension of OSSEs to societal impacts. OSSEs can be complemented by calculations of forecast sensitivity to observations, which simultaneously evaluate the impact of different observation types in a forecast model system. [ABSTRACT FROM AUTHOR]

One of the most prolific tornado outbreaks ever documented occurred on 26–27 April 2011 and comprised three successive episodes of tornadic convection that culminated with the development of numerous long-track, violent tornadoes over the southeastern United States during the afternoon of 27 April. This notorious afternoon supercell outbreak was preceded by two quasi-linear convective systems (hereinafter QLCS1 and QLCS2), the first of which was an anomalously severe nocturnal system that rapidly grew upscale during the previous evening. Here in Part II, we use a series of RUC 1-h forecasts and output from convection-permitting WRF-ARW simulations configured both with and without latent heat release to investigate how environmental modifications and upscale feedbacks produced by the two QLCSs contributed to the evolution and exceptional severity of this multiepisode outbreak. QLCS1 was primarily responsible for amplifying the large-scale flow pattern, inducing two upper-level jet streaks, and promoting secondary surface cyclogenesis downstream from the primary baroclinic system. Upper-level divergence markedly increased after QLCS1 developed, which yielded strong isallobaric forcing that rapidly strengthened the low-level jet (LLJ) and vertical wind shear over the warm sector and contributed to the system's upscale growth and notable severity. Moreover, QLCS2 modified the mesoscale environment prior to the supercell outbreak by promoting the downstream formation of a pronounced upper-level jet streak, altering the midlevel jet structure, and furthering the development of a highly ageostrophic LLJ over the Southeast. Collectively, the flow modifications produced by both QLCSs contributed to the notably favorable shear profiles present during the afternoon supercell outbreak. Significance Statement: The tornado outbreak that impacted the United States on 26–27 April 2011 was part of an extended outbreak that produced 343 tornadoes and numerous fatalities. This paper is Part II of a study that describes the meteorological factors supporting such a prolific event. Herein we investigate the convectively forced environmental modifications that occurred during a 36-h period encompassing three successive convective episodes. The first two episodes collectively altered the upper-level flow pattern and markedly enhanced low-level winds throughout the warm sector. These modifications served as upscale feedbacks that contributed to the first episode's exceptional severity and to the remarkable vertical shear profiles that supported numerous long-track and violent tornadoes during the final episode on the afternoon of 27 April. [ABSTRACT FROM AUTHOR]

One of the most prolific tornado outbreaks ever documented occurred on 26–27 April 2011 and comprised three successive episodes of tornadic convection that primarily impacted the southeastern United States, including two quasi-linear convective systems (hereinafter QLCS1 and QLCS2) that preceded the notorious outbreak of long-track, violent tornadoes spawned by numerous supercells on the afternoon of 27 April. The ∼36-h period encompassing these three episodes was part of a longer multiday outbreak that occurred ahead of a slowly moving upper-level trough over the Rocky Mountains. Here in Part I, we detail how the environment evolved to support this extended outbreak, with particular attention given to the three successive systems that each exhibited a different morphology and severity. The amplifying upper-level trough and attendant jet streak resulted from a Rossby wave–breaking event that yielded a complex tropopause structure and supported three prominent shortwave troughs that sequentially moved into the south-central United States. QLCS1 formed ahead of the second shortwave and was accompanied by rapid flow modifications, including considerable low-level jet (LLJ) intensification. The third shortwave moved into the lee of the Rockies early on 27 April to yield destabilization behind QLCS1 and support the formation of QLCS2, which was followed by further LLJ intensification and helped to establish favorable deep-layer shear profiles over the warm sector. The afternoon supercell outbreak commenced following the movement of this shortwave into the Mississippi Valley, which was attended by a deep tropopause fold, cold front aloft, and dryline that promoted two prominent bands of tornadic supercells over the Southeast. Significance Statement: The tornado outbreak that impacted the United States on 26–27 April 2011 was part of an extended outbreak that produced 343 tornadoes and numerous fatalities. This paper is Part I of a study that describes the meteorological factors supporting such a prolific event. We focus on a 36-h period encompassing three convective episodes that impacted the Southeast and culminated in the widespread formation of long-track, violent tornadoes. Flow enhancements accompanying the first two episodes and the movement of a cold front aloft into the Southeast were found to contribute to the evolution of the final and most devastating episode. Part II describes how the first two episodes directly modified the environment to enhance the severity of this outbreak. [ABSTRACT FROM AUTHOR]

One of the most prolific tornado outbreaks ever documented occurred on 26–27 April 2011 and comprised three successive episodes of tornadic convection that culminated with the development of numerous long-track, violent tornadoes over the southeastern United States during the afternoon of 27 April. This notorious afternoon supercell outbreak was preceded by two quasi-linear convective systems (hereinafter QLCS1 and QLCS2), the first of which was an anomalously severe nocturnal system that rapidly grew upscale during the previous evening. Here in Part II, we use a series of RUC 1-h forecasts and output from convection-permitting WRF-ARW simulations configured both with and without latent heat release to investigate how environmental modifications and upscale feedbacks produced by the two QLCSs contributed to the evolution and exceptional severity of this multiepisode outbreak. QLCS1 was primarily responsible for amplifying the large-scale flow pattern, inducing two upper-level jet streaks, and promoting secondary surface cyclogenesis downstream from the primary baroclinic system. Upper-level divergence markedly increased after QLCS1 developed, which yielded strong isallobaric forcing that rapidly strengthened the low-level jet (LLJ) and vertical wind shear over the warm sector and contributed to the system's upscale growth and no table severity. Moreover, QLCS2 modified the mesoscale environment prior to the supercell outbreak by promoting the downstream formation of a pronounced upper-level jet streak, altering the midlevel jet structure, and furthering the development of a highly ageostrophic LLJ over the Southeast. Collectively, the flow modifications produced by both QLCSs contributed to the notably favorable shear profiles present during the afternoon supercell outbreak. Significance Statement: The tornado outbreak that impacted the United States on 26–27 April 2011 was part of an extended outbreak that produced 343 tornadoes and numerous fatalities. This paper is Part II of a study that describes the meteorological factors supporting such a prolific event. Herein we investigate the convectively forced environmental modifications that occurred during a 36-h period encompassing three successive convective episodes. The first two episodes collectively altered the upper-level flow pattern and markedly enhanced low-level winds throughout the warm sector. These modifications served as upscale feedbacks that contributed to the first episode's exceptional severity and to the remarkable vertical shear profiles that supported numerous long-track and violent tornadoes during the final episode on the afternoon of 27 April. [ABSTRACT FROM AUTHOR]

One of the most prolific tornado outbreaks ever documented occurred on 26–27 April 2011 and comprised three successive episodes of tornadic convection that primarily impacted the southeastern United States, including two quasi-linear convective systems (hereinafter QLCS1 and QLCS2) that preceded the notorious outbreak of long-track, violent tornadoes spawned by numerous supercells on the afternoon of 27 April. The ∼36-h period encompassing these three episodes was part of a longer multiday outbreak that occurred ahead of a slowly moving upper-level trough over the Rocky Mountains. Here in Part I, we detail how the environment evolved to support this extended outbreak, with particular attention given to the three successive systems that each exhibited a different morphology and severity. The amplifying upper-level trough and attendant jet streak resulted from a Rossby wave–breaking event that yielded a complex tropopause structure and supported three prominent shortwave troughs that sequentially moved into the south-central United States. QLCS1 formed ahead of the second shortwave and was accompanied by rapid flow modifications, including considerable low-level jet (LLJ) intensification. The third shortwave moved into the lee of the Rockies early on 27 April to yield destabilization behind QLCS1 and support the formation of QLCS2, which was followed by further LLJ intensification and helped to establish favorable deep-layer shear profiles over the warm sector. The afternoon supercell outbreak commenced following the movement of this shortwave into the Mississippi Valley, which was attended by a deep tropopause fold, cold front aloft, and dryline that promoted two prominent bands of tornadic supercells over the Southeast. Significance Statement: The tornado outbreak that impacted the United States on 26–27 April 2011 was part of an extended outbreak that produced 343 tornadoes and numerous fatalities. This paper is Part I of a study that describes the meteorological factors supporting such a prolific event. We focus on a 36-h period encompassing three convective episodes that impacted the Southeast and culminated in the widespread formation of long-track, violent tornadoes. Flow enhancements accompanying the first two episodes and the movement of a cold front aloft into the Southeast were found to contribute to the evolution of the final and most devastating episode. Part II describes how the first two episodes directly modified the environment to enhance the severity of this outbreak. [ABSTRACT FROM AUTHOR]

Hurricanes are the costliest type of natural disaster in the United States. Every year, these natural phenomena destroy billions of dollars in physical capital, displace thousands, and greatly disrupt local economies. While this damage will never be eliminated, the number of fatalities and the cost of preparing and evacuating can be reduced through improved forecasts. This paper seeks to establish the public's willingness to pay for further improvement of hurricane forecasts by integrating atmospheric modeling and a double-bounded dichotomous choice method in a large-scale contingent valuation experiment. Using an interactive survey, we focus on areas affected by hurricanes in 2018 to elicit residents' willingness to pay for improvements along storm track, wind speed, and precipitation forecasts. Our results indicate improvements in wind speed forecast are valued the most, followed by storm track and precipitation, and that maintaining the current annual rate of error reduction for another decade is worth between $90.25 and $121.86 per person in vulnerable areas. Our study focuses on areas recently hit by hurricanes in the United States, but the implications of our results can be extended to areas vulnerable to tropical cyclones globally. In a world where the intensity of hurricanes is expected to increase and research funds are limited, these results can inform relevant agencies regarding the effectiveness of different private and public adaptive actions, as well as the value of publicly funded hurricane research programs. [ABSTRACT FROM AUTHOR]

In any discussion of forecast evaluation, it is tempting to fall back on statements reflecting unverified assumptions: "this tornado warning had lower skill because the underlying meteorology reflected a complicated or atypical scenario," or "that forecast performed worse than we would have expected given the straightforward setup." These statements of what is and is not a reasonable expectation for warning skill are particularly relevant as the meteorological community's focus has begun to emphasize non-classic storm environments (e.g., tornadoes spawned by quasi-linear convective systems). In this paper, we build a proof-of-concept methodology to quantify the effect of the near-storm environment on tornado warning skill, and we then test these methods on a 15-yr dataset composed of tens of thousands of tornado events and warnings over the contiguous United States. Our findings include that significant tornadoes rated (E)F2+ have a higher probability of detection (POD) than expected based on their near-storm environments, that nocturnal tornadoes have both worse POD and false alarm ratio (FAR) than even their marginal near-storm environments would suggest, and that tornadoes occurring during the summer months also show worse POD and FAR than their environment-based expectation. Quantifying these shifts in performance in an environmental skill score framework allows us to target the situations in which the greatest improvements may be possible, in terms of forecaster training and/or conceptual models. This work also highlights the essential question that should always be asked in the context of forecast verification: what, exactly, is the baseline standard to which we are comparing forecast performance? [ABSTRACT FROM AUTHOR]

Although they share many common qualities in design and operation, mesonetworks across the United States were established independently and organically over the last several decades. In numerous instances, the unique ways each network matured and developed new protocols has led to important lessons learned. These experiences have been shared in informal ways among various network operators over the years to promote reliable operation. As existing networks begin to introduce new sensors and technologies, and as new networks come online, there is a common need for guidance on best practices. This paper aims to formally provide recommendations to improve and harmonize the various aspects of operating a "mesonet," including siting, sensors, maintenance, quality assurance, and data processing. [ABSTRACT FROM AUTHOR]

Extraordinary precipitation events have impacted the United States recently, including Hurricanes Harvey (2017) and Florence (2018), with 3-day precipitation totals larger than any others reported in the United States during the past 70 years. The rainfall category (R-CAT) scaling method is used here to document extreme precipitation events and test for trends nationally. The R-CAT scale uses thresholds of 3-day precipitation total in 100-mm increments (starting with 200 mm) that do not vary temporally or geographically, allowing for simple, intuitive comparisons of extremes over space and time. The paper that introduced the scale only required levels 1-4 to represent historical extremes, finding that R-CATs 3-4 strike the conterminous United States about as frequently as EF 4-5 tornadoes or category 3-5 hurricanes. Remarkably, Florence and Harvey require extending the scale to R-CAT 7 and 9, respectively. Trend analyses of annual maximum 3-day totals (1950-2019) here identify significant increases in the eastern United States, along with declines in Northern California and Oregon. Consistent with these results, R-CAT storms have been more frequent in the eastern, and less frequent in western, United States during the past decade compared to 1950-2008. Tropical storms dominate R-CAT events along the southeastern coast and East Coast with surprising contributions from atmospheric rivers, while atmospheric rivers completely dominate along the West Coast. [ABSTRACT FROM AUTHOR]

Differences between forecasts and observations at eight atmospheric river observatories (AROs) in the western United States during winter 2015/16 are analyzed. NOAA's operational RAP and HRRR 3-h forecasts of wind, integrated water vapor (IWV), integrated water vapor flux (IWV flux), and precipitation from the grid points nearest the AROs were paired with ARO observations presented in the NOAA/Physical Sciences Division's water vapor flux tool (WVFT). The focus of this paper is to characterize and quantify the differences in the WVFT observations and forecasts. We used traditional forecast evaluation methods since they were compatible with the design of the tool: a near-real-time visual depiction of hourly observed and forecasted variables at a single location. Forecast root-mean-squared errors (RMSEs) and unbiased RMSEs, standard deviations of the observed and forecasted variables, and frequency bias scores (FBS) for all of the fields, plus equitable threat scores for precipitation, are presented. Both models forecasted IWV at all AROs and the winds that drive orographic precipitation at most AROs within a reasonable range of the observations as indicated by comparisons of the standard deviations and RMSEs of the forecasts with the standard deviations of the observations and FBS. These results indicated that forecasted advection of moisture to the stations was adequate for generating precipitation. At most stations and most hourly precipitation rates, the HRRR underpredicted precipitation. At several AROs the RAP precipitation forecasts more closely matched the observations at smaller (<1.27 mm h-1) precipitation rates, but underpredicted precipitation rates > 2 mm h-1. [ABSTRACT FROM AUTHOR]

This paper investigates the ability of the Weather Research and Forecasting (WRF) Model in simulating multiple small-scale precipitation bands (multibands) within the extratropical cyclone comma head using four winter storm cases from 2014 to 2017. Using the model output, some physical processes are explored to investigate band prediction. A 40-member WRF ensemble was constructed down to 2-km grid spacing over the Northeast United States using different physics, stochastic physics perturbations, different initial/boundary conditions from the first five perturbed members of the Global Forecast System (GFS) Ensemble Reforecast (GEFSR), and a stochastic kinetic energy backscatter scheme (SKEBS). It was found that 2-km grid spacing is adequate to resolve most snowbands. A feature-based verification is applied to hourly WRF reflectivity fields from each ensemble member and the WSR-88D radar reflectivity at 2-km height above sea level. The Method for Object-Based Diagnostic Evaluation (MODE) tool is used for identifying multibands, which are defined as two or more bands that are 5–20 km in width and that also exhibit a >2:1 aspect ratio. The WRF underpredicts the number of multibands and has a slight eastward position bias. There is no significant difference in frontogenetical forcing, vertical stability, moisture, and vertical shear between the banded versus nonbanded members. Underpredicted band members tend to have slightly stronger frontogenesis than observed, which may be consolidating the bands, but overall there is no clear linkage in ambient condition errors and band errors, thus leaving the source for the band underprediction motivation for future work. [ABSTRACT FROM AUTHOR]

The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a new variable-resolution global model with the ability to represent convective-scale features that serves as a prototype of the Next Generation Global Prediction System (NGGPS). The goal of this prediction system is to maintain the skill in large-scale features while simultaneously improving the prediction skill of convectively driven mesoscale phenomena. This paper demonstrates the new capability of this model in convective-scale prediction relative to the current operational Global Forecast System (GFS). This model uses the stretched-grid functionality of the Finite-Volume Cubed-Sphere Dynamical Core (FV3) to refine the global 13-km uniform-resolution model down to 4-km convection-permitting resolution over the contiguous United States (CONUS), and implements the GFDL single-moment 6-category cloud microphysics to improve the representation of moist processes. Statistics gathered from two years of simulations by the GFS and select configurations of the FV3-based model are carefully examined. The variable-resolution FV3-based model is shown to possess global forecast skill comparable with that of the operational GFS while quantitatively improving skill and better representing the diurnal cycle within the high-resolution area compared to the uniform mesh simulations. Forecasts of the occurrence of extreme precipitation rates over the southern Great Plains are also shown to improve with the variable-resolution model. Case studies are provided of a squall line and a hurricane to demonstrate the effectiveness of the variable-resolution model to simulate convective-scale phenomena. [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]

Forecasting the timing and magnitude of snowmelt and runoff is critical to managing mountain water resources. Warming temperatures are increasing the rain–snow transition elevation and are limiting the forecasting skill of statistical models relating historical snow water equivalent to streamflow. While physically based methods are available, they require accurate estimations of the spatial and temporal distribution of meteorological variables in complex terrain. Across many mountainous areas, measurements of precipitation and other meteorological variables are limited to a few reference stations and are not adequate to resolve the complex interactions between topography and atmospheric flow. In this paper, we evaluate the ability of the Weather Research and Forecasting (WRF) Model to approximate the inputs required for a physics-based snow model, iSnobal, instead of using meteorological measurements, for the Boise River Basin (BRB) in Idaho, United States. An iSnobal simulation using station data from 40 locations in and around the BRB resulted in an average root-mean-square error (RMSE) of 4.5 mm compared with 12 SNOTEL measurements. Applying WRF forcings alone was associated with an RMSE of 10.5 mm, while including a simple bias correction to the WRF outputs of temperature and precipitation reduced the RMSE to 6.5 mm. The results highlight the utility of using WRF outputs as input to snowmelt models, as all required input variables are spatiotemporally complete. This will have important benefits in areas with sparse measurement networks and will aid snowmelt and runoff forecasting in mountainous basins. [ABSTRACT FROM AUTHOR]

Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months. [ABSTRACT FROM AUTHOR]

The prediction of tropical cyclone rapid intensification is one of the most pressing unsolved problems in hurricane forecasting. The signatures of gravity waves launched by strong convective updrafts are often clearly seen in airglow and carbon dioxide thermal emission spectra under favorable atmospheric conditions. By continuously monitoring the Atlantic hurricane belt from the main development region to the vulnerable sections of the continental United States at high cadence, it will be possible to investigate the utility of storm-induced gravity wave observations for the diagnosis of impending storm intensification. Such a capability would also enable significant improvements in our ability to characterize the 3D transient behavior of upper-atmospheric gravity waves and point the way to future observing strategies that could mitigate the risk to human life caused by severe storms. This paper describes a new mission concept involving a midinfrared imager hosted aboard a geostationary satellite positioned at approximately 80°W longitude. The sensor's 3-km pixel size ensures that the gravity wave horizontal structure is adequately resolved, while a 30-s refresh rate enables improved definition of the dynamic intensification process. In this way the transient development of gravity wave perturbations caused by both convective and cyclonic storms may be discerned in near–real time. [ABSTRACT FROM AUTHOR]

Traditionally, the daily average air temperature at a weather station is computed by taking the mean of two values, the maximum temperature (Tmax) and the minimum temperature (Tmin), over a 24-h period. These values form the basis for numerous studies of long-term climatologies (e.g., 30-yr normals) and recent temperature trends and changes. However, many first-order weather stations-such as those at airports-also record hourly temperature data. Using an average of the 24 hourly temperature readings to compute daily average temperature has been shown to provide a more precise and representative estimate of a given day's temperature. This study assesses the spatial variability of the differences in these two methods of daily temperature averaging [i.e., (Tmax 1 Tmin)/2; average of 24 hourly temperature values] for 215 first-order weather stations across the conterminous United States (CONUS) over the 30-yr period 1981-2010. A statistically significant difference is shown between the two methods, as well as consistent overestimation of temperature by the traditional method [(Tmax 1 Tmin)/2], particularly in southern and coastal portions of the CONUS. The explanation for the long-term difference between the two methods is the underlying assumption for the twice-daily method that the diurnal curve of temperature is symmetrical. Moreover, this paper demonstrates a spatially coherent pattern in the difference compared to the most recent part of the temperature record (2001-15). The spatial and temporal differences shown have implications for assessments of the physical factors influencing the diurnal temperature curve, as well as the exact magnitude of contemporary climate change. [ABSTRACT FROM AUTHOR]

Phase analyses of the annual cycle of monthly temperature time series that date back to the eighteenth century show trending behavior that has been difficult to interpret. Negative trends in the estimated phase have been identified with precession of Earth's axis of rotation, but the implied later onset of seasons is at odds with recent satellite measurements and with the phenological record. Positive trends in the phase and the occurrence of trends of both signs in temperature time series from geographically nearby locations have remained mysterious. This paper shows that there is a mathematical equivalence between trends in phases and seasonally differing warming trends, in particular more intense warming in winters than in summers. Using temperature time series from 16 Northern Hemispheric locations reaching back to the eighteenth century and a statistical model that can estimate the seasonal warming trends, the authors reject the hypothesis that the timing of the seasons in these locations is jointly driven by precession. [ABSTRACT FROM AUTHOR]

Storm-scale and mesocyclone-scale processes occurring contemporaneously with a tornado in the Goshen County, Wyoming, supercell observed on 5 June 2009 during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) are examined using ensemble analyses produced by assimilating mobile radar and in situ observations into a high-resolution convection-resolving model. This paper focuses on understanding the evolution of the vertical structure of the storm, the outflow buoyancy, and processes affecting the vertical vorticity and circulation within the mesocyclone that correspond to changes in observed tornado intensity. Tornadogenesis occurs when the low-level mesocyclone is least negatively buoyant relative to the environment, possesses its largest circulation, and is collocated with the largest azimuthally averaged convergence during the analysis period. The average buoyancy, circulation, and convergence within the near-surface mesocyclone (on spatial scales resolved by the model) all decrease as the tornado intensifies and matures. The tornado and its parent low-level mesocyclone both dissipate surrounded by a weakening rear-flank downdraft. The decreasing buoyancy of parcels within the low-level mesocyclone may partly be responsible for the weakening of the updraft surrounding the tornado and decoupling of the mid- and low-level circulation. Although the supply of horizontal vorticity generated in the forward flank of the storm increases throughout the life cycle of the tornado, it is presumably less easily tilted and stretched on the mesocyclone-scale during tornado maturity owing to the disruption of the low-level updraft/downdraft structure. Changes in radar-measured tornado intensity lag those of ensemble Kalman filter (EnKF) mesocyclone vorticity and circulation. [ABSTRACT FROM AUTHOR]

This paper explores the link between the anomalous warming hole in the southeastern United States and a major land-use/land-cover (LULC) change in the region. Land surface and satellite observations were analyzed to estimate the net radiative forcing due to LULC change. Albedo and latent energy were specifically addressed for the dominant LULC change of agriculture to forests. It was assumed that in the energy-limited environment of the region, the partition of changes in available energy due to albedo will mostly impact the sensible heat. The results show that in the southeastern United States, for the period of 1920 to 1992, the changes in sensible (as a result of albedo) and latent energies are in direct competition with each other. In the spring and early summer months, the croplands are in peak production and the latent energy associated with their evapotranspiration (ET) is comparable to that of the forests so the decrease in radiation due to albedo dominates the signal. However, during the late summer and fall months, most major crops have matured, thus reducing their transpiration rate while forests (particularly evergreens) maintain their foliage and with their deep roots are able to continue to transpire as long as atmospheric conditions are favorable. This later influence of latent energy appears to more than offset the increased radiative forcing from the spring and early summer. Overall, a mean annual net radiative forcing resulting from a LULC change from cropland to forests was estimated to be −1.06 W m−2 and thus a probable contribution to the 'warming hole' over the Southeast during the majority of the twentieth century. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite series R (GOES-R) will greatly expand the ability to observe the earth from geostationary orbit compared to the current-generation GOES, with more than 3 times as many spectral bands and a 75% reduction in footprint size. These enhanced capabilities are beneficial to rainfall rate estimation since they provide sensitivity to cloud-top properties such as phase and particle size that cannot be achieved using the limited channel selection of current GOES. The GOES-R rainfall rate algorithm, which is an infrared-based algorithm calibrated in real time against passive microwave rain rates, has been previously described in an algorithm theoretical basis document (ATBD); this paper describes modifications since the release of the ATBD, including a correction for evaporation of precipitation in dry regions and improved calibration updates. These improvements have been evaluated using a simplified version applicable to current-generation GOES to take advantage of the high-resolution ground validation data routinely available over the conterminous United States. Correcting for subcloud evaporation using relative humidity from a numerical model reduced false alarm rainfall by half and reduced the overall error by 35% for hourly accumulations validated against the National Centers for Environmental Prediction stage IV radar-gauge field; however, the number of missed events did increase slightly. Reducing the size of the calibration regions and increasing the training data requirements improved the consistency of the retrieved rates in time and space and reduced the overall error by an additional 4%. [ABSTRACT FROM AUTHOR]

In late October 2012, Hurricane Sandy tracked along the eastern U.S. coastline and made landfall over New Jersey after turning sharply northwest and becoming posttropical while interacting with a complex upper-level low pressure system that had brought cold air into the Appalachian region. The cold air, intensified by the extreme low pressure tracking just north of the region, combined with deep moisture and topographically enhanced ascent to produce an unusual and high-impact early season northwest flow snow (NWFS) that has no analog in recent history. This paper investigates the importance of the synoptic-scale pattern, forcing mechanisms, moisture characteristics (content, depth, and likely sources), and low-level winds, as well as the evolution of some of these features compared to more typical NWFS events in the southern Appalachian Mountains. Several other aspects of the Sandy snowfall event are investigated, including low-level stability and mountain wave formation as manifested in vertical profiles and radar observations. The importance to operational forecasters of recognizing and understanding these factors and differences from more common NWFS events is also discussed. [ABSTRACT FROM AUTHOR]

Recently, two analog-based postprocessing methods were demonstrated to reduce the systematic and random errors from Weather Research and Forecasting (WRF) Model predictions of 10-m wind speed over the central United States. To test robustness and generality, and to gain a deeper understanding of postprocessing forecasts with analogs, this paper expands upon that work by applying both analog methods to surface stations evenly distributed across the conterminous United States over a 1-yr period. The Global Forecast System (GFS), North American Mesoscale Forecast System (NAM), and Rapid Update Cycle (RUC) forecasts for screen-height wind, temperature, and humidity are postprocessed with the two analog-based methods and with two time series-based methods-a running mean bias correction and an algorithm inspired by the Kalman filter. Forecasts are evaluated according to a range of metrics, including random and systematic error components; correlation; and by conditioning the error distributions on lead time, location, error magnitude, and day-to-day error variability. Results show that the analog methods are generally more effective than time series-based methods at reducing the random error component, leading to an overall reduction in root-mean-square error. Details among the methods differ and are elucidated upon in this study. The relative levels of random and systematic error in the raw forecasts determine, to a large extent, the effectiveness of each postprocessing method in reducing forecast errors. When the errors are dominated by random errors (e.g., where thunderstorms are common), the analog-based methods far outperform the time series-based methods. When the errors are strictly systematic (i.e., a bias), the analog methods lose their advantage over the time series methods. It is shown that slowly evolving systematic errors rarely dominate, so reducing the random error component is most effective at reducing the error magnitude. The results are shown to be valid for all seasons. The analog methods show similar performance to the operational model output statistics (MOS) while showing greater reduction of random errors at certain lead times. [ABSTRACT FROM AUTHOR]

Climatologies have been developed to highlight variability of the frequency and intensity of hail in the United States. However, the intraseasonal variability of hail, including why one week might be active while the following inactive despite both having similar climatological probabilities, has not yet been explored. This paper presents relationships between spring-season (April-June) hail days and the leading mode of atmospheric intraseasonal variability, the Madden-Julian oscillation (MJO). It extends recent work on intraseasonal tornado variability to smaller spatial scales. In April, May, and June, statistically significant variability in hail days was found for different Real-time Multivariate MJO (RMM) phases of the MJO. For April, the strongest correlations between hail-day anomalies and anomalies of the product of convective available potential energy (CAPE) and 0-6-km vertical wind shear were found in RMM phase 5, with above-normal likelihood of a hail day found in the south-central United States. For May, the strongest correlations were found in RMM phase 3, with below-normal likelihood of a hail day located over the north-central United States. For June, the strongest correlations were found in phase 8, with above-normal likelihood of hail in west Texas and below-normal likelihood of hail over much of the middle of the United States. In all phases, 300-hPa height anomalies in the United States formed part of a global wave train similar to MJO patterns in both modeling and observational studies. [ABSTRACT FROM AUTHOR]

This paper presents the development of a single executable four-dimensional variational data assimilation (4D-Var) system based on the Weather Research and Forecasting (WRF) Model through coupling the variational data assimilation algorithm (WRF-VAR) with the newly developed WRF tangent linear and adjoint model (WRFPLUS). Compared to the predecessor Multiple Program Multiple Data version, the new WRF 4D-Var system achieves major improvements in that all processing cores are able to participate in the computation and all information exchanges between WRF-VAR and WRFPLUS are moved directly from disk to memory. The single executable 4D-Var system demonstrates desirable acceleration and scalability in terms of the computational performance, as demonstrated through a series of benchmarking data assimilation experiments carried out over a continental U.S. domain. To take into account the nonlinear processes with the linearized minimization algorithm and to further decrease the computational cost of the 4D-Var minimization, a multi-incremental minimization that uses multiple horizontal resolutions for the inner loop has been developed. The method calculates the innovations with a high-resolution grid and minimizes the cost function with a lower-resolution grid. The details regarding the transition between the high-resolution outer loop and the low-resolution inner loop are introduced. Performance of the multi-incremental configuration is found to be comparable to that with the full-resolution 4D-Var in terms of 24-h forecast accuracy in the week-long analysis and forecast experiment over the continental U.S. domain. Moreover, the capability of the newly developed multi-incremental 4D-Var system is further demonstrated in the convection-permitting analysis and forecast experiment for Hurricane Sandy (2012), which was hardly computationally feasible with the predecessor WRF 4D-Var system. [ABSTRACT FROM AUTHOR]

Land use and land cover change (LULCC) significantly influences the climate system. Hence, to prepare the nation for future climate change and variability, a sustained assessment of LULCC and its climatic impacts needs to be undertaken. To address this objective, not only do we need to determine contemporary trends in land use and land cover that affect, or are affected by, weather and climate but also identify sectors and regions that are most affected by weather and climate variability. Moreover, it is critical that we recognize land cover and regions that are most vulnerable to climate change and how end-use practices are adapting to climate change. This paper identifies a series of steps that need to be undertaken to address these key items. In addition, national-scale institutional capabilities are identified and discussed. Included in the discussions are challenges and opportunities for collaboration among these institutions for a sustained assessment. [ABSTRACT FROM AUTHOR]

During the summer, strong surface heating combines with the terrain and land-water contrasts of the northwest United States to create a complex array of diurnal circulations. Though observational and modeling studies have described some of these circulations, advances in high-resolution numerical modeling allow for a more comprehensive and three-dimensional examination. To simulate typical summer conditions over the Pacific Northwest, 3-hourly Global Forecast System (GFS) model output for July and August 2009-11 was used to initialize and provide boundary conditions for a high-resolution Weather Research and Forecasting (WRF) Model run. To ensure the realism of the simulation, it was compared to observations from a collection of days representing typical summer conditions. Generally, it was found that the simulated diurnal wind, relative humidity, and temperature were close to the observations. It is shown that regional diurnal circulations over the Pacific Northwest occur on a number of interacting scales, ranging from upslope/downslope winds on local terrain features to larger-scale circulations such as between the Pacific Ocean and the western Oregon and Washington interiors. Such multiscale diurnal circulations occur concurrently, with the interactions producing complex structures, several of which are described in this paper. Wind speeds in the Strait of Juan de Fuca and downstream of the major Cascade Mountain gaps reach maxima between 2100 and 2400 local daylight time (LDT), while most other areas have peak winds earlier in the day. Localized nocturnal low-level wind maxima are described, including one over the northern Willamette Valley and another over the high plateau of eastern Oregon. [ABSTRACT FROM AUTHOR]

A reanalysis of the Atlantic basin tropical storm and hurricane database ('best track') for the period from 1931 to 1943 has been completed as part of the Atlantic Hurricane Database Reanalysis Project. This reassessment of the main archive for tropical cyclones of the North Atlantic Ocean, Caribbean Sea, and Gulf of Mexico was necessary to correct systematic biases and random errors in the data as well as to search for previously unrecognized systems. Methodology for the reanalysis process for revising the track and intensity of tropical cyclone data is largely unchanged from that of the preceding couple of decades and has been detailed in a previous paper on the reanalysis. Accurate Environmental Forecasting's numerical weather prediction-based wind field model was utilized here to help determine which states were impacted by various hurricane force winds in several U.S. landfalling major hurricanes during this era. The 1931-43 dataset now includes 23 new tropical cyclones, excludes five systems previously considered tropical storms, makes generally large alterations in the intensity estimates of most tropical cyclones (at various times both toward stronger and weaker intensities), and typically adjusts existing tracks with minor corrections. Average errors in intensity and track values are estimated for both open ocean conditions as well as for landfalling systems. Finally, highlights are given for changes to the more significant hurricanes to impact the United States, Central America, and the Caribbean for this time period. [ABSTRACT FROM AUTHOR]