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The article assesses the usefulness of synthetic vortex insertion prior to the extratropical transition of hurricanes. Significant improvements in the wind and precipitation patterns within the storm at landfall were observed in the atmospheric model with vortex insertion employed in the initial conditions. The numerical hindcasts employing the technique resolved a particular forecast dilemma involving interaction of Hurricane Michael with a preexisting area of low pressure to its north. Operational forecast models at the time had incorrectly developed the northern low at the expense of the hurricane, while the opposite occurred in reality and in the numerical simulations run in hindcast mode.

The article offers news briefs related to climatology. A cold tongue which is a narrow band of cool sea surface temperature (SST) is one of the salient features in the equatorial Pacific Ocean. Two of the factors that contributed to the weather-forecasting errors increase in time are the amplification uncertainties of nonlinearities in the initial state of the atmosphere and the model deficiencies leading the model to predict the wrong rate of change for even the most certain initial conditions. INSETS: DUDE, WE'RE, LIKE, PROTESTING;PLEASED TO ZAP YOU.

Artificial intelligence (AI) can be used to improve performance across a wide range of Earth system prediction tasks. As with any application of AI, it is important for AI to be developed in an ethical and responsible manner to minimize bias and other effects. In this work, we extend our previous work demonstrating how AI can go wrong with weather and climate applications by presenting a categorization of bias for AI in the Earth sciences. This categorization can assist AI developers to identify potential biases that can affect their model throughout the AI development life cycle. We highlight examples from a variety of Earth system prediction tasks of each category of bias. SIGNIFICANCE STATEMENT: As artificial intelligence (AI) grows in popularity, its methods are being applied to a wide range of Earth system prediction tasks. Although AI can facilitate more accurate prediction at many tasks, it is not without potential pitfalls, especially if the developers are not as familiar with its potential drawbacks. In this paper, we provide a classification system for the types of bias that one is likely to see in applying AI to Earth sciences. Our classification system will assist current and future AI developers to recognize where their AI system or data are biased so they can take steps to alleviate this bias. [ABSTRACT FROM AUTHOR]

Numerical weather prediction models operate on grid spacings of a few kilometers, where deep convection begins to become resolvable. Around this scale, the emergence of coherent structures in the planetary boundary layer, often hypothesized to be caused by cold pools, forces the transition from shallow to deep convection. Yet, the kilometer-scale range is typically not resolved by standard surface operational measurement networks. The measurement campaign Field Experiment on Submesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) aimed at addressing this gap by observing atmospheric variability at the hectometer-to-kilometer scale, with a particular emphasis on cold pools, wind gusts, and coherent patterns in the planetary boundary layer during summer. A unique feature was the distribution of 150 self-developed and low-cost instruments. More specifically, FESSTVaL included dense networks of 80 autonomous cold pool loggers, 19 weather stations, and 83 soil sensor systems, all installed in a rural region of 15-km radius in eastern Germany, as well as self-developed weather stations handed out to citizens. Boundary layer and upper-air observations were provided by eight Doppler lidars and four microwave radiometers distributed at three supersites; water vapor and temperature were also measured by advanced lidar systems and an infrared spectrometer; and rain was observed by a X-band radar. An uncrewed aircraft, multicopters, and a small radiometer network carried out additional measurements during a 4-week period. In this paper, we present FESSTVaL's measurement strategy and show first observational results including unprecedented highly resolved spatiotemporal cold-pool structures, both in the horizontal as well as in the vertical dimension, associated with overpassing convective systems. [ABSTRACT FROM AUTHOR]

A unique, high-resolution, hydroclimate reanalysis, 40-plus-year (October 1979–September 2021), 4 km (named as CONUS404), has been created using the Weather Research and Forecasting Model by dynamically downscaling of the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate dataset (ERA5) over the conterminous United States. The paper describes the approach for generating the dataset, provides an initial evaluation, including biases, and indicates how interested users can access the data. The motivation for creating this National Center for Atmospheric Research (NCAR)–U.S. Geological Survey (USGS) collaborative dataset is to provide research and end-user communities with a high-resolution, self-consistent, long-term, continental-scale hydroclimate dataset appropriate for forcing hydrological models and conducting hydroclimate scientific analyses over the conterminous United States. The data are archived and accessible on the USGS Black Pearl tape system and on the NCAR supercomputer Campaign storage system. [ABSTRACT FROM AUTHOR]

Cities around the world are experiencing the effects of climate change via increasing extreme heat worsened by urbanization. Within cities, there are disparities in extreme heat exposure that are apparent in various surface and remotely sensed observations, as well as in the health impacts. There are, however, large data gaps in our ability to quantify the heat experienced by people in their daily lives across urban areas. In this paper, we use hyperlocal observations to measure heat around Miami–Dade County, Florida. Temperature and humidity measurements were collected at sites throughout the county between 2018 and 2021 with low-cost sensors. By comparing these hyperlocal observations with a National Weather Service (NWS) site at the Miami International Airport (MIA), we show that maximum temperatures are on average 6°F (3.3°C) higher and maximum heat index values are 11°F (6.1°C) higher at sites in the county than at MIA. These measurements show that many sites frequently record a heat index above the local threshold value for heat advisory. This is in contrast with the fact that few forecast advisories are issued, and there are correspondingly few exceedances of the threshold at MIA. We use these results to motivate a discussion about the issues of this particular threshold for Miami–Dade County. We highlight the need for data that are closer to residents' lived experience to assess the impacts of heat and help inform local and regional decision-making, particularly where heat exposure may be underappreciated as a potential public health hazard. [ABSTRACT FROM AUTHOR]

The U.S.–Taiwan Partnership for International Research and Education (PIRE) "Building Extreme Weather Resiliency through Improved Weather and Climate Prediction and Emergency Response Strategies" was an NSF-funded grant between universities and institutions in the United States and Taiwan that intended to understand 1) weather forecast uncertainty during extreme precipitation events and 2) how emergency managers use such information to make decisions. In this reflective paper, graduate students from the project's working groups, including climate, ensemble, microphysics, and decision science, share their experiences of being involved in this ambitious program. A notable strength of this PIRE was its opportunities for international collaboration and related cultural experiences; however, despite direct student involvement in PIRE, student experiences varied considerably (e.g., research experiences, cultural exposure). Recommendations for improvement are informed predominantly by U.S.-based graduate student experiences and are discussed with the intention of bolstering future interdisciplinary research for students and investigators. To this end, projects of this scale and scope could benefit from more frequent communication among leadership and research groups, as well as explicitly outlining and prioritizing interactions between groups to focus and strengthen collaboration toward the completion of interdisciplinary research goals. [ABSTRACT FROM AUTHOR]

The presence of persistent heavy fog in northern India during winter creates hazardous situations for transportation systems and disrupts the lives of about 400 million people. The meteorological factors responsible for its genesis and predictability are not yet completely understood in this region. Given its high potential for socioeconomic impact, there is a pressing need for extensive research that understands the inherently complex nature of the phenomena through field observations and modeling exercises. WiFEX is a first-of-its-kind multi-institutional initiative dealing with intensive ground-based measurement campaigns for developing a suitable fog forecasting capability under the aegis of the smart cities mission of India. Measuring campaigns were conducted during the 2015–20 winters at the Indira Gandhi International Airport, New Delhi, covering more than 90 dense fog events. The field experiments involved extensive suites of in situ instruments and gathered simultaneous observations of micrometeorological conditions, radiative fluxes, turbulence, droplet/aerosol microphysics, aerosol optical properties, fog water chemistry, and vertical thermodynamical structure to describe the environmental stability in which fog develops. An operational modeling framework, the WRF Model, was set up to provide fog predictions during the measurement campaign. These field observations helped to interpret the strengths and deficiencies in the numerical modeling framework. Four scientific objectives were pursued: (i) the life cycle of optically thin and thick fog, (ii) microphysical properties in the polluted boundary layer, (iii) fog water chemistry, gas–aerosol partitioning during the fog life cycle, and (iv) numerical prediction of fog. This paper presents an overview of WiFEX and a synthesis of selected observational and modeling analyses/findings related to the abovementioned scientific topics. [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]

The Copernicus Atmosphere Monitoring Service (CAMS), part of the European Union's Earth observation program Copernicus, entered operations in July 2015. Implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) as a truly European effort with over 23,500 direct data users and well over 200 million end users worldwide as of March 2022, CAMS delivers numerous global and regional information products about air quality, inventory-based emissions and observation-based surface fluxes of greenhouse gases and from biomass burning, solar energy, ozone and UV radiation, and climate forcings. Access to CAMS products is open and free of charge via the Atmosphere Data Store. The CAMS global atmospheric composition analyses, forecasts, and reanalyses build on ECMWF's Integrated Forecasting System (IFS) and exploit over 90 different satellite data streams. The global products are complemented by coherent higher-resolution regional air quality products over Europe derived from multisystem analyses and forecasts. CAMS information products also include policy support such as quantitative impact assessment of short- and long-term pollutant-emission mitigation scenarios, source apportionment information, and annual European air quality assessment reports. Relevant CAMS products are cited and used for instance in IPCC Assessment Reports. Providing dedicated support for users operating smartphone applications, websites, or TV bulletins in Europe and worldwide is also integral to the service. This paper presents key achievements of the CAMS initial phase (2014–21) and outlines some of its new components for the second phase (2021–28), e.g., the new Copernicus anthropogenic CO2 emissions Monitoring and Verification Support capacity that will monitor global anthropogenic emissions of key greenhouse gases. [ABSTRACT FROM AUTHOR]

In the last years, ensemble methods have been widely popular in atmospheric, climate, and ocean dynamics investigations and forecasts as convenient methods to obtain statistical information on these systems. In many cases, ensembles have been used as an approximation to the probability distribution that has acquired more and more a central role, as the importance of a single trajectory, or member, was recognized as less informative. This paper shows that using results from the dynamical systems and more recent results from the machine learning and AI communities, we can arrive at a direct estimation of the probability distribution evolution and also at the formulation of predictor systems based on a nonlinear formulation. The paper introduces the theory and demonstrates its application to two examples. The first is a one-dimensional system based on the Niño-3 index; the second is a multidimensional case based on time series of monthly mean SST in the Pacific. We show that we can construct the probability distribution and set up a system to forecast its evolution and derive various quantities from it. The objective of the paper is not strict realism, but the introduction of these methods and the demonstration that they can be used also in the complex, multidimensional environment typical of atmosphere and ocean applications. [ABSTRACT FROM AUTHOR]

In a companion paper, the Multiscale Gravity Wave Model (MS-GWaM) has been introduced and its application to a global model as a transient subgrid-scale parameterization has been described. This paper focuses on the examination of intermittency of gravity waves (GWs) modeled by MS-GWaM. To introduce the variability and intermittency in wave sources, convective GW sources are formulated, using diabatic heating diagnosed by the convection parameterization, and they are coupled to MS-GWaM in addition to a flow-independent source in the extratropics accounting for GWs due neither to convection nor to orography. The probability density function (PDF) and Gini index for GW pseudomomentum fluxes are assessed to investigate the intermittency. Both are similar to those from observations in the lower stratosphere. The intermittency of GWs over tropical convection is quite high and is found not to change much in the vertical direction. In the extratropics, where nonconvective GWs dominate, the intermittency is lower than that in the tropics in the stratosphere and comparable to that in the mesosphere, exhibiting a gradual increase with altitude. The PDFs in these latitudes seem to be close to the lognormal distributions. Effects of transient GW–mean-flow interactions on the simulated GW intermittency are assessed by performing additional simulations using the steady-state assumption in the GW parameterization. The intermittency of parameterized GWs over tropical convection is found to be overestimated by the assumption, whereas in the extratropics it is largely underrepresented. Explanation and discussion of these effects are included. [ABSTRACT FROM AUTHOR]

The Arctic is warming at more than twice the rate of the global average. This warming is influenced by clouds, which modulate the solar and terrestrial radiative fluxes and, thus, determine the surface energy budget. However, the interactions among clouds, aerosols, and radiative fluxes in the Arctic are still poorly understood. To address these uncertainties, the Ny-Ålesund Aerosol Cloud Experiment (NASCENT) study was conducted from September 2019 to August 2020 in Ny-Ålesund, Svalbard. The campaign's primary goal was to elucidate the life cycle of aerosols in the Arctic and to determine how they modulate cloud properties throughout the year. In situ and remote sensing observations were taken on the ground at sea level, at a mountaintop station, and with a tethered balloon system. An overview of the meteorological and the main aerosol seasonality encountered during the NASCENT year is introduced, followed by a presentation of first scientific highlights. In particular, we present new findings on aerosol physicochemical and molecular properties. Further, the role of cloud droplet activation and ice crystal nucleation in the formation and persistence of mixed-phase clouds, and the occurrence of secondary ice processes, are discussed and compared to the representation of cloud processes within the regional Weather Research and Forecasting Model. The paper concludes with research questions that are to be addressed in upcoming NASCENT publications. [ABSTRACT FROM AUTHOR]

Two spaceborne radars currently in orbit enable the sampling of snowfall near the surface and throughout the atmospheric column, namely, CloudSat's Cloud Profiling Radar (CPR) and the Global Precipitation Measurement mission's Dual-Frequency Precipitation Radar (GPM-DPR). In this paper, a direct comparison of the CPR's 2C-SNOW-PROFILE (2CSP), the operational GPM-DPR algorithm (2ADPR) and a neural network (NN) retrieval applied to the GPM-DPR data is performed using coincident observations between both radars. Examination of over 3500 profiles within moderate to strong precipitation (Ka band ≥ 18 dBZ) show that the NN retrieval provides the closest retrieval of liquid equivalent precipitation rate R immediately above the melting level to the R retrieved just below the melting layer, agreeing within 5%. Meanwhile, 2CSP retrieves a maximum value of R at −15°C, decreases by 35% just above the melting layer, and is about 50% smaller than the GPM-DPR retrieved R below the melting layer. CPR-measured reflectivity shows median reduction of 2–3 dB from −15° to −2.5°C, likely the reason for the 2CSP retrieval reduction of R. Two case studies from NASA field campaigns [i.e., Olympic Mountains Experiment (OLYMPEX) and Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS)] provide analogs to the type of precipitating systems found in the comparison between retrieval products. For the snowfall events that GPM-DPR can observe, this work suggests that the 2CSP retrieval is likely underestimating the unattenuated reflectivity, resulting in a potential negative, or low, bias in R. Future work should investigate how frequently the underestimated reflectivity profiles occur within the CPR record and quantify its potential effects on global snowfall accumulation estimation. [ABSTRACT FROM AUTHOR]

The southwest vortex (SWV) is a critical weather system in China, but our knowledge of this system remains incomplete. Here, we investigate the cloud properties in the SWV. First, we search for the SWVs with time steps and center locations that are consistent between the SWV yearbook and ERA-Interim reanalysis data. Second, we supplement these SWVs' life spans and movement paths. Third, we relocate the Fengyun (FY) satellite FY-4A cloud retrievals in the 10° × 10° region centered on each SWV and analyze the cloud occurrence frequency (COF), cloud-top height (CTH), and cloud optical thickness (COT). A distribution mode of cloud types is summarized from the COFs, with water clouds, supercooled clouds, mixed clouds, ice clouds, cirrus clouds, and overlap clouds occurring sequentially from west to east. The CTH probability density (PD) distribution features a significant north–south difference. In addition, the COT PD distributions exhibit a common trend: with increasing COT, the PD increases rapidly and then slowly before peaking, whereupon the PD decreases abruptly. From spring to summer, the region with the highest convective COF shifts from the northeast to the northwest, and an east–west gradient of the convective COF appears in autumn and winter. Furthermore, we investigate the cloud properties during SWV-related heavy rainfall. Heavy rain occurs mainly in the west of the SWV, and convective clouds are mainly in the northwest, partly in the southwest and near the SWV center. The average CTH in heavy rainfall is generally higher than 6 km, and the average COT is greater than 20. Significance Statement: The southwest vortex (SWV) is an important weather system in China. However, we do not yet comprehensively know this weather system. The cloud properties can indicate the structures of weather systems and are key parameters in numerical weather prediction (NWP) models. Thus, investigating cloud properties is necessary and meaningful to understand the SWV and accurately predict SWV-related precipitation in NWP models. In this paper, a typical distribution mode of six cloud types in the SWV is summarized from the cloud occurrence frequency, and the distribution features of convective clouds, cloud-top height, and cloud optical thickness in the SWV are analyzed. Furthermore, the cloud properties in SWV-related heavy rain are also studied. [ABSTRACT FROM AUTHOR]

There is great interest in improving our understanding of the respective roles of the ocean and atmosphere in variability and change in weather and climate. Due to the sparsity of sustained observing sites in the open ocean, information about the air–sea exchanges of heat, freshwater, and momentum is often drawn from models. In this paper observations from three long-term surface moorings deployed in the trade wind regions of the Pacific and Atlantic Oceans are used to compare observed means and low-passed air–sea fluxes from the moorings with coincident records from three atmospheric reanalyses (ERA5, NCEP-2, and MERRA-2) and from CMIP6 coupled models. To set the stage for the comparison, the methodologies of maintaining the long-term surface moorings, known as ocean reference stations (ORS), and assessing the accuracies of their air–sea fluxes are described first. Biases in the reanalyses' means and low-passed wind stresses and net air–sea heat fluxes are significantly larger than the observational uncertainties and in some case show variability in time. These reanalyses and most CMIP6 models fail to provide as much heat into the ocean as observed. In the discussion and conclusions section, long-term observing sites in the open ocean are seen as essential, independent benchmarks not only to document the coupling between the atmosphere and ocean but also to promote collaborative efforts to assess and improve the ability of models to simulate air–sea fluxes. [ABSTRACT FROM AUTHOR]

There are a couple of reasons to stimulate a discussion about the definition of clouds. One reason is that the American Meteorological Society (AMS) and the World Meteorological Organization (WMO) define clouds differently, and that in two aspects. AMS defines clouds as visible objects, and WMO as perceivable objects. Furthermore, AMS includes all minute particles independent of their nature and composition whereas WMO considers only such minute particles that consist of water, ice, or a mixture of both. Additionally, the so-called invisible or subvisible clouds are perceivable objects in the definition of WMO but not of AMS. Clouds can be observed by humans and be detected by active and passive sensors from ground and space. The detection limits of instruments span more than two orders of magnitude in visible optical thickness ranging from about 0.001 from satellite lidar (in case of horizontal and vertical averaging or 0.07 for single lidar shots) to about 0.3 to 0.45 for multispectral satellite imaging spectrometers. Human observers see clouds when their optical thickness is larger than about 0.03 at day and 0.05 at night. This paper gives a brief overview of cloud detection from ground and space and of the occurrence, characteristics, and impacts of subvisible clouds. Pros and cons of these definitions are discussed, followed by a proposal for a more precise definition. This definition is, like those of AMS and WMO, a one-parametric definition and does not cover all aspects of clouds. [ABSTRACT FROM AUTHOR]

Aircraft flying through areas of high ice water content (HIWC) can experience engine damage and/or failure. HIWC is typically associated with convection and the microphysical properties of tropical oceanic and coastal convection are well documented as a result of several field campaigns in the past decade. HIWC appears to be less common in extratropical convection, but instances of HIWC-related aircraft issues have been recorded in extratropical weather, even during winter. The present study documents the microphysical properties of HIWC between −25° and 0°C and the meteorological and thermodynamic conditions around that HIWC from five flights from the In-Cloud Icing and Large-Drop Experiment (ICICLE) in the midwestern United States in February 2019. All five cases contained elevated convection above a strong low-level temperature inversion. Values for top-of-inversion mixing ratios and total column precipitable water were about 5 g kg−1 and 20 mm, respectively, according to soundings near each case. A maximum ice water content of 2.1 g m−3 was observed over a length scale of about 500 m, and ice particle size distributions had mean volume equivalent diameters around 1000 μm. Supercooled drizzle droplets were also observed in the vicinity of the HIWC, raising questions about the possible role of secondary ice production via the freezing and shattering of supercooled large droplets in HIWC formation. The generalizability of these results is limited by the small number of cases, but they provide some of the first in situ observations of extratropical winter HIWC and highlight the need for continued research on these conditions. Significance Statement: High ice water content (HIWC) conditions can cause engine damage, stall, and failure in aircraft and can cause air data probes to report erroneous values. Most research on HIWC has focused on tropical convection. This paper serves to draw more attention to the aviation hazard posed by extratropical winter HIWC and the need for additional research into these environments. The cases examined also contained supercooled precipitation-sized liquid droplets. Combined with other recent research, these observations may help to motivate laboratory experiments investigating the role of secondary ice production via droplet freezing and shattering in the formation of HIWC. [ABSTRACT FROM AUTHOR]

Near-real-time satellite-derived temperature and moisture soundings provide information about the changing atmospheric vertical thermodynamic structure occurring between successive routine National Weather Service (NWS) radiosonde launches. In particular, polar-orbiting satellite soundings become critical to the computation of stability indices over the central United States in the midafternoon, when there are no operational NWS radiosonde launches. Accurate measurements of surface temperature and dewpoint temperature are key in the calculation of severe weather indices, including surface-based convective available potential energy (SBCAPE). This paper addresses a shortcoming of current operational infrared-based satellite soundings, which underestimate the surface parcel temperature and dewpoint when CAPE is nonzero. This leads to a systematic underestimate of SBCAPE. This paper demonstrates a merging of satellite-derived vertical profiles with surface observations to address this deficiency for near-real-time applications. The National Oceanic and Atmospheric Administration (NOAA) Center for Environmental Prediction (NCEP) Meteorological Assimilation Data Ingest System (MADIS) hourly surface observation data are blended with satellite soundings derived using the NOAA Unique Combined Atmospheric Processing System (NUCAPS) to create a greatly improved SBCAPE calculation. This study is not intended to validate NUCAPS or the combined NUCAPS + MADIS product, but to demonstrate the benefits of combining observational weather satellite profile data and surface observations. Two case studies, 18 June 2017 and 3 July 2017, are used in this study to illustrate the success of the combined NUCAPS + MADIS SBCAPE compared to the NUCAPS-only SBCAPE estimate. In addition, a 6-month period, April–September 2018, was analyzed to provide a comprehensive analysis of the impact of using surface observations in satellite SBCAPE calculations. To address the need for reduced data latency, a near-real-time merged satellite and surface observation product is demonstrated using NUCAPS products from the Community Satellite Processing Package (CSPP) applied to direct broadcast data received at the University of Wisconsin–Madison, Hampton University in Virginia, and the Naval Research Laboratory in Monterey, California. Through this study, it is found that the combination of the MADIS surface observation data and the NUCAPS satellite profile data improves the SBCAPE estimate relative to comparisons with the Storm Prediction Center (SPC) mesoscale analysis and the NAM analysis compared to the NUCAPS-only SBCAPE estimate. An assessment of the 6-month period between April and September 2018 determined the dry bias in NUCAPS at the surface is the primary cause of the underestimation of the NUCAPS-only SBCAPE estimate. [ABSTRACT FROM AUTHOR]

This paper describes how a combination of high-resolution numerical modeling, a robust homogenization algorithm, and local pressure observations have been used to understand and reconcile time series of daily, seasonal, and annual peak wind gusts recorded at observing sites in the Cook Strait region of New Zealand. The homogenization algorithm consists of corrections for the relocation of masts, changes in instrumentations, data acquisition and signal processing, surrounding surface roughness, and measurement heights. In addition, a statistical method, penalized maximal F test (PMFT), was used to assess the homogeneity of the wind speed time series and detect and eliminate all remaining, undocumented, artificial (i.e., nonclimatic) breakpoints. A three-dimensional time-dependent computational fluid dynamics (CFD) simulation was carried out using the Gerris model to characterize the turbulence environment at the mast sites and to estimate topographic speedup effects. Trends in magnitudes and frequencies of the homogenized seasonal and annual peak wind gusts are evaluated and presented for the Cook Strait region. The pressure gradients between pairs of stations were used to study the correlation between the gust wind speeds and the pressure field. A high-resolution convection-resolving numerical weather prediction model [the New Zealand Convective-Scale Model (NZCSM)] was employed to aid the interpretation of results and analyze wind trends. The trend in gust speeds is also shown to be consistent with larger-scale NCEP–NCAR reanalysis pressure trends. The homogenization algorithm showed promising results in eliminating the artificial breakpoints and trends. Overall, strong correlations were found between the homogenized gust speeds, the pressure field across the region, and NZCSM predictions. [ABSTRACT FROM AUTHOR]

A frequency–wavenumber power () spectrum was constructed using satellite-derived outgoing longwave radiation (OLR) and brightness temperature for the tropical latitudes. Since the two datasets overlap for over 34 years with nonintersecting sources in error and compare relatively well with each other, it is possible to diagnose trends in the tropical wave activity from the two datasets with confidence. The results suggest a weakening trend in characterized by high interannual variability for wave activity occurring in the low-frequency part of the spectrum and a steady increase in with relatively low interannual variability for wave activity occurring in the high-frequency part of the spectrum. The results show the parts of the spectrum representing the Madden–Julian oscillation and equatorial Rossby wave losing and other parts of the spectrum representing Kelvin waves, mixed Rossby–gravity waves, and tropical disturbance–like wave activity gaining . Similar results were obtained when trends in variance corresponding to the first principal component were produced using spectrally filtered OLR data representative of atmospheric equatorial waves. Spatial trends in the active phase of wave events and the mean duration of events are also shown for the different wave types. Linear trends in for the entire spectrum and regional means in the spectrum corresponding to the abovementioned five wave types with confidence intervals are also presented in the paper. Finally, we demonstrate that El Niño–Southern Oscillation variability does not appear to control the overall spatial patterns and trends observed in the spectrum. [ABSTRACT FROM AUTHOR]

Data storage and data processing generate significant cost for weather and climate modeling centers. The volume of data that needs to be stored and data that are disseminated to end users increases with increasing model resolution and the use of larger forecast ensembles. If precision of data is reduced, cost can be reduced accordingly. In this paper, three new methods to allow a reduction in precision with minimal loss of information are suggested and tested. Two of these methods rely on the similarities between ensemble members in ensemble forecasts. Therefore, precision will be high at the beginning of forecasts when ensemble members are more similar, to provide sufficient distinction, and decrease with increasing ensemble spread. To keep precision high for predictable situations and low elsewhere appears to be a useful approach to optimize data storage in weather forecasts. All methods are tested with data of operational weather forecasts of the European Centre for Medium-Range Weather Forecasts. [ABSTRACT FROM AUTHOR]

The design and application of bridge deicing systems require an understanding of heat-transfer mechanisms of the bridge. One of these systems is geothermal foundations that support structural loads and utilize heat exchange with the surrounding soil. To design such a bridge deicing system and ensure ice-free surfaces during winter, accurate prediction of the temperature of bridge decks is vital. Heat-transfer mechanisms of the bridge deck include many factors such as convection between air and deck, solar radiation, and longwave radiation. Despite considerable research in this area, traffic vehicle effects on the heat balance of the bridge have not been fully investigated. In this paper, a two-dimensional finite-element analysis that focuses on natural factors is proposed and validated using measured data. After the validation, the model was extended to include the vehicle effects for conditions of both light and heavy traffic, considering tire frictional heat and vehicle-induced convection and radiation. The results show that the vehicular traffic increased the bridge surface temperature by up to 2°C with light traffic and by up to 4°C with heavy traffic, thus providing an advantage for deicing. Traffic effects can cool the bridge surface temperature down by up to 2°C, mainly during the summertime. Therefore, the traffic effects can be optionally considered during the design of bridge deicing systems. [ABSTRACT FROM AUTHOR]

Prediction of weather is a main goal of atmospheric science. Its importance to society is growing continuously due to factors such as vulnerability to natural disasters, the move to renewable energy sources, and the risks of climate change. But prediction is also a major scientific challenge due to the inherently limited predictability of a chaotic atmosphere, and has led to a revolution in forecasting methods as we have moved to probabilistic prediction. These changes provide the motivation for Waves to Weather (W2W), a major national research program in Germany with three main university partners in Munich, Mainz, and Karlsruhe. We are currently in the second 4-yr phase of our planned duration of 12 years and employ 36 doctoral and postdoctoral scientists. In the context of this large program, we address what we have identified to be the most important and challenging scientific questions in predictability of weather, namely, upscale error growth, errors associated with cloud processes, and probabilistic prediction of high-impact weather. This paper presents some key results of the first phase of W2W and discusses how they have influenced our understanding of predictability. The key role of interdisciplinary research linking atmospheric scientists with experts in visualization, statistics, numerical analysis, and inverse methods will be highlighted. To ensure a lasting impact on research in our field in Germany and internationally, we have instituted innovative programs for training and support of early-career scientists, and to support education, equal opportunities, and outreach, which are also described here. [ABSTRACT FROM AUTHOR]

Recently, strong sound wave was proposed to enhance precipitation. The theoretical basis of this proposal has not been effectively studied either experimentally or theoretically. On the basis of the microscopic parameters of atmospheric cloud physics, this paper solved the complex nonlinear differential equation to show the movement characteristics of cloud droplets under the action of sound waves. The motion process of an individual cloud droplet in a cloud layer in the acoustic field is discussed as well as the relative motion between two cloud droplets. The effects of different particle sizes and sound field characteristics on particle motion and collision are studied to analyze the dynamic effects of thunderlevel sound waves on cloud droplets. The amplitude of velocity variation has positive correlation with sound pressure level (SPL) and negative correlation with the frequency of the surrounding sound field. Under the action of low-frequency sound waves with sufficient intensity, individual cloud droplets could be forced to oscillate significantly. A droplet smaller than 40 mm can be easily driven by sound waves of 50 Hz and 123.4 dB. The calculation of the collision process of two droplets reveals that the disorder of motion for polydisperse droplets is intensified, resulting in the broadening of the collision time range and spatial range. When the acoustic frequency is less than 100 Hz (at 123.4 dB) or the SPL is greater than 117.4 dB (at 50 Hz), the sound wave can affect the collision of cloud droplets significantly. This study provides a theoretical perspective of the acoustic effect on the microphysics of atmospheric clouds. [ABSTRACT FROM AUTHOR]

The En4DVar method is designed to combine the flow-dependent statistical covariance information of EnKF into the traditional 4DVar method. However, the En4DVar method is still hampered by its strong dependence on the adjoint model of the underlying forecast model and by its complexity, maintenance requirements, and the high cost of computer implementation and simulation. The primary goal of this paper is to propose an alternative approach to overcome the main difficulty of the En4DVar method caused by the use of adjoint models. The proposed approach, the nonlinear least squares En4DVar (NLS-En4DVar) method, begins with rewriting the standard En4DVar formulation into a nonlinear least squares problem, which is followed by solving the resulting NLS problem by aGauss-Newton iterative method. To reduce the computational and implementation complexity of the proposedNLS-En4DVar method, a few variants of the new method are proposed; thesemodifications make the model cheaper and easier to use than the full NLS-En4DVar method at the expense of reduced accuracy. Furthermore, an improved iterative method based on the comprehensive analysis on the above NLSi-En4DVar family of methods is also proposed. These proposed NLSi-En4DVar methods provide more flexible choices of the computational capabilities for the broader and more realistic data assimilation problems arising from various applications. The pros and cons of the proposed NLSi-En4DVar family of methods are further examined in the paper and their relationships and performance are also evaluated by several sets of numerical experiments based on the Lorenz-96 model and the Advanced Research WRF (ARW) Model, respectively. [ABSTRACT FROM AUTHOR]

Weak-temperature-gradient influences from the tropics and quasigeostrophic influences from the extratropics plausibly constrain the subtropical-mean static stability in terrestrial atmospheres. Because mean descent acting on this static stability is a leading-order term in the thermodynamic balance, a state-invariant static stability would impose constraints on the Hadley cells, which this paper explores in simulations of varying planetary rotation rate. If downdraftaveraged effective heating (the sum of diabatic heating and eddy heat flux convergence) too is invariant, so must be vertical velocity--an ''omega governor.'' In that case, the Hadley circulation overturning strength and downdraft width must scale identically--the cell can strengthen only by widening or weaken only by narrowing. Semiempirical scalings demonstrate that subtropical eddy heat flux convergence weakens with rotation rate (scales positively) while diabatic heating strengthens (scales negatively), compensating one another if they are of similar magnitude. Simulations in two idealized, dry GCMs with a wide range of planetary rotation rates exhibit nearly unchanging downdraft-averaged static stability, effective heating, and vertical velocity, as well as nearly identical scalings of the Hadley cell downdraft width and strength. In one, eddy stresses set this scaling directly (the Rossby number remains small); in the other, eddy stress and bulk Rossby number changes compensate to yield the same,~;Ω-1/3 scaling. The consistency of this power law for cell width and strength variations may indicate a common driver, and we speculate that Ekman pumping could be the mechanism responsible for this behavior. Diabatic heating in an idealized aquaplanet GCM is an order of magnitude larger than in dry GCMs and reanalyses, and while the subtropical static stability is insensitive to rotation rate, the effective heating and vertical velocity are not. [ABSTRACT FROM AUTHOR]

The NASA Goddard Space Flight Center's (GSFC's) W-band (94 GHz) Cloud Radar System (CRS) has been comprehensively updated to modern solid-state and digital technology. This W-band (94 GHz) radar flies in nadir-pointing mode on the NASA ER-2 high-altitude aircraft, providing polarimetric reflectivity and Doppler measurements of clouds and precipitation. This paper describes the design and signal processing of the upgraded CRS. It includes details on the hardware upgrades [solid-state power amplifier (SSPA) transmitter, antenna, and digital receiver] including a new reflectarray antenna and solid-state transmitter. It also includes algorithms, including internal loop-back calibration, external calibration using a direct relationship between volume reflectivity and the range-integrated backscatter of the ocean, and a modified staggered–pulse repetition frequency (PRF) Doppler algorithm that is highly resistant to unfolding errors. Data samples obtained by upgraded CRS through recent NASA airborne science missions are provided. [ABSTRACT FROM AUTHOR]

Global positioning system (GPS) radio occultation (RO) observations, first made of Earth's atmosphere in 1995, have contributed in new ways to the understanding of the thermal structure and variability of the tropical upper troposphere–lower stratosphere (UTLS), an important component of the climate system. The UTLS plays an essential role in the global radiative balance, the exchange of water vapor, ozone, and other chemical constituents between the troposphere and stratosphere, and the transfer of energy from the troposphere to the stratosphere. With their high accuracy, precision, vertical resolution, and global coverage, RO observations are uniquely suited for studying the UTLS and a broad range of equatorial waves, including gravity waves, Kelvin waves, Rossby and mixed Rossby–gravity waves, and thermal tides. Because RO measurements are nearly unaffected by clouds, they also resolve the upper-level thermal structure of deep convection and tropical cyclones as well as volcanic clouds. Their low biases and stability from mission to mission make RO observations powerful tools for studying climate variability and trends, including the annual cycle and intraseasonal-to-interannual atmospheric modes of variability such as the quasi-biennial oscillation (QBO), Madden–Julian oscillation (MJO), and El Niño–Southern Oscillation (ENSO). These properties also make them useful for evaluating climate models and detection of small trends in the UTLS temperature, key indicators of climate change. This paper reviews the contributions of RO observations to the understanding of the three-dimensional structure of tropical UTLS phenomena and their variability over time scales ranging from hours to decades and longer. [ABSTRACT FROM AUTHOR]

The term "tornado outbreak" appeared in the meteorological literature in the 1950s and was used to highlight severe weather events with multiple tornadoes. The exact meaning of "tornado outbreak," however, evolved over the years. Depending on the availability of scientific data, technological advancements, and the intended purpose of these definitions, authors offered a diverse set of approaches to shape the perception and applications of the term "tornado outbreak." This paper reviews over 200 peer-reviewed publications--by decade--to outline the evolving nature of the "tornado outbreak" definition and to examine the changes in the "tornado outbreak" definition or its perception. A final discussion highlights the importance, limitations, and potential future evolution of what defines a "tornado outbreak.". [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]

The sampling error caused by the uneven distribution of radio occultation (RO) profiles in both space and time domains is an important error source of RO climatologies. In this paper, the sampling error RO temperature climatologies is investigated using the 4-yr (2007-10) data from the Constellation Observing System for Meteorology, Ionosphere, and Climate mission. The error is divided into three parts, including local time component (LTC), temporal component (TC), and spatial component (SC). The characteristics of the three components are investigated. Results show the following: 1) The LTC part of the total sampling error is characterized by a pattern of periodic positive and negative deviations, with a full cycle of about four months. The most significant LTC values are found in the area around 608N/S and the polar regions. 2) The TC part is mainly associated with the extent of day-to-day temperature variability and the daily number of RO profiles observed in each month. The most pronounced TC part is shown in high-latitude areas in wintertime, where the day-to-day temperature variability is high. 3) The SC part shows distinct features in different altitude ranges. It is characterized by a systemic error in the lower troposphere (2-8 km) but exhibits a seasonal trend at the altitude range from 8 to 40 km. 4) The total sampling error is dominated by the TC and SC parts in the troposphere and lower stratosphere, whereas in the upper stratosphere it is dominated by the LTC part. [ABSTRACT FROM AUTHOR]

Continuous monitoring of the earth radiation budget (ERB) is critical to the understanding of Earth's climate and its variability with time. The Clouds and the Earth's Radiant Energy System (CERES) instrument is able to provide a long record of ERB for such scientific studies. This manuscript, which is the first of a two-part paper, describes the new CERES algorithm for improving the clear/cloudy scene classification without the use of coincident cloud imager data. This new CERES algorithm is based on a subset of the modern artificial intelligence (AI) paradigm called machine learning (ML) algorithms. This paper describes the development and application of the ML algorithm known as random forests (RF), which is used to classify CERES broadband footprint measurements into clear and cloudy scenes. Results from the RF analysis carried using the CERES Single Scanner Footprint (SSF) data for January and July are presented in the manuscript. The daytime RF misclassification rate (MCR) shows relatively large values (>30%) for snow, sea ice, and bright desert surface types, while lower values (<10%) for the forest surface type. MCR values observed for the nighttime data in general show relatively larger values for most of the surface types compared to the daytime MCR values. The modified MCR values show lower values (<4%) for most surface types after thin cloud data are excluded from the analysis. Sensitivity analysis shows that the number of input variables and decision trees used in the RF analysis has a substantial influence on determining the classification error. [ABSTRACT FROM AUTHOR]

In this paper, the reliability of the wind speed, temperature, humidity, pressure, and precipitation values of three surface meteorological forcing products [China Meteorological Administration Land Data Assimilation System, version 2 (CLDAS-2); China Meteorological Forcing Dataset (CMFD); and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2)] in the Tibetan Plateau (TP) region was investigated from 2008 to 2014. Compared with the China Meteorological Administration (CMA) observations, CLDAS-2 exhibited the highest correlation coefficient for wind speed, CMFD displayed the best coefficients for temperature and specific humidity, and MERRA-2 best reflected pressure variations. Based on the biases, CLDAS-2 exhibited the best overall performance for temperature, specific humidity, and pressure, while CMFD displayed the best performance for wind speed. The high overall accuracy and false alarm ratio of precipitation based on MERRA-2 both stem from its continuous overestimation of the precipitation frequency. Both CLDAS-2 and CMFD overestimated the nonprecipitation frequency in comparisons with CMA observations, and a significant positive bias exists in MERRA-2 based on the analysis of daily precipitation. The results obtained from the comparisons with field observations over the TP and CMA observations are similar, except for the temperature and humidity biases of CLDAS-2. The meteorological effects on the coupled land-blowing snow modeling discussed in this paper suggest that the occurrence of blowing snow and snowdrift sublimation are projected to be reduced by CLDAS-2 due to the underestimation of wind speed, continual lack of snowfall events, and the positive biases in low temperatures and humidity, while simulations of blowing processes by MERRA-2 are likely to be much more severe than they actually are. These results may contribute to identifying deficiencies associated with the development of land surface models coupled with a blowing snow model. [ABSTRACT FROM AUTHOR]

This paper reports the findings of a comprehensive field investigation on flow through a mountain gap subject to a range of stably stratified environmental conditions. This study was embedded within the Perdigão field campaign, which was conducted in a region of parallel double-ridge topography with ridge-normal wind climatology. One of the ridges has a well-defined gap (col) at the top, and an array of in situ and remote sensors, including a novel triple Doppler lidar system, was deployed around it. The experimental design was mostly guided by previous numerical and theoretical studies conducted with an idealized configuration where a flow (with characteristic velocity U0 and buoyancy frequency N) approaches normal to a mountain of height h with a gap at its crest, for which the governing parameters are the dimensionless mountain height G = Nh/U0 and various gap aspect ratios. Modified forms of G were proposed to account for real-world atmospheric variability, and the results are discussed in terms of a gap-averaged value Gc. The nature of gap flow was highly dependent on Gc, wherein a nearly neutral flow regime (Gc < 1), a transitional mountain wave regime [Gc ~ O(1)], and a gap-jetting regime [Gc > O(1)] were identified. The measurements were in broad agreement with previous numerical and theoretical studies on a single ridge with a gap or double-ridge topography, although details vary. This is the first-ever detailed field study reported on microscale [O(100) m] gap flows, and it provides useful data and insights for future theoretical and numerical studies. [ABSTRACT FROM AUTHOR]

The outgoing longwave radiation (OLR) emitted to space is a fundamental component of the Earth's energy budget. There are numerous, entangled physical processes that contribute to OLR and that are responsible for driving, and responding to, climate change. Spectrally resolved observations can disentangle these processes, but technical limitations have precluded accurate space-based spectral measurements covering the far infrared (FIR) from 100 to 667 cm−1 (wavelengths between 15 and 100 µm). The Earth's FIR spectrum is thus essentially unmeasured even though at least half of the OLR arises from this spectral range. The region is strongly influenced by upper-tropospheric–lower-stratospheric water vapor, temperature lapse rate, ice cloud distribution, and microphysics, all critical parameters in the climate system that are highly variable and still poorly observed and understood. To cover this uncharted territory in Earth observations, the Far-Infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission has recently been selected as ESA's ninth Earth Explorer mission for launch in 2026. The primary goal of FORUM is to measure, with high absolute accuracy, the FIR component of the spectrally resolved OLR for the first time with high spectral resolution and radiometric accuracy. The mission will provide a benchmark dataset of global observations which will significantly enhance our understanding of key forcing and feedback processes of the Earth's atmosphere to enable more stringent evaluation of climate models. This paper describes the motivation for the mission, highlighting the scientific advances that are expected from the new measurements. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (AR) are narrow bands of strong horizontal transport of water vapor in the midlatitudes that can cause extreme precipitation, which contributes to beneficial water supply and sometimes flooding. The precipitation productivity of an AR is affected by microphysical processes, including the influence of aerosols. Earlier case studies have shown that some ARs over the North Pacific Ocean contain dust from Africa and Asia that can strongly influence precipitation by acting as ice nuclei. This paper explores how commonly dust and ARs occur together, or in close proximity. A ''dust score'' is introduced to characterize the dustiness of the environment associated with ARs by using satellite-based observations. This method is applied to days on which one or more ARs made landfall along the west coast of the United States between 2001 and 2018. The dust score is used to describe the seasonality and year-to-year variability of dustyenvironment ARs. Dusty ARs occur primarily in the early spring (March-April), and dust is preferentially found within the warm sector of AR-associated extratropical cyclones and is also enhanced in the cold sector. Year-to-year variability in dust score is dependent more on year-to-year variability in dust than on the frequency ofARdays. This year-to-year variability is also strongly related to correlations between the frequency of ARs and the dustiness of the northeastern Pacific, motivating additional investigation into potential dynamical association between dust and ARs. [ABSTRACT FROM AUTHOR]

Models are attractive tools to deepen the understanding of atmospheric and climate processes. In practice, such investigations often involve numerical experiments that switch on or off individual factors (such as latent heating, nonlinear coupling, or some climate forcing). However, as in general many factors can be considered, the analysis of these experiments is far from straightforward. In particular, as pointed out in an influential study on factor separation by Stein and Alpert, the analysis will often require the consideration of nonlinear interaction terms. In the current paper an alternative factor separation methodology is proposed and analyzed. Unlike the classical method, sequential factor separation (SFS) does not involve the derivation of the interaction terms but, rather, provides some uncertainty measure that addresses the quality of the separation. The main advantage of the proposed methodology is that in the case of n factors it merely requires 2 n simulations (rather than 2 n for the classical analysis). The paper provides an outline of the methodology, a detailed mathematical analysis, and a theoretical intercomparison against the classical methodology. In addition, an example and an intercomparison using regional climate model experiments with n = 3 factors are presented. The results relate to the Mediterranean amplification and demonstrate that-at least in the particular example considered-the two methodologies yield almost identical results and that the SFS is rather insensitive with respect to design choices. [ABSTRACT FROM AUTHOR]

This paper describes a forecasting configuration of the Geophysical Fluid Dynamics Laboratory (GFDL) High-resolution Atmospheric Model (HiRAM). HiRAM represents an early attempt in unifying, within a global modeling framework, the capabilities of GFDL's low-resolution climate models for Intergovernmental Panel on Climate Change (IPCC) type climate change assessments and high-resolution limited-area models for hurricane predictions. In this study, the potential of HiRAM as a forecasting tool is investigated by applying the model to the near-term and intraseasonal hindcasting of tropical cyclones (TCs) in the Atlantic basin from 2006 to 2009. Results demonstrate that HiRAM provides skillful near-term forecasts of TC track and intensity relative to their respective benchmarks from t == 48 h through t == 144 h. At the intraseasonal time scale, a simple HiRAM ensemble provides skillful forecasts of 21-day Atlantic basin TC activity at a 2-day lead time. It should be noted that the methodology used to produce these hindcasts is applicable in a real-time forecasting scenario. While the initial experimental results appear promising, the HiRAM forecasting system requires various improvements in order to be useful in an operational setting. These modifications are currently under development and include a data assimilation system for forecast initialization, increased horizontal resolution to better resolve the vortex structure, 3D ocean model coupling, and wave model coupling. An overview of these ongoing developments is provided, and the specifics of each will be described in subsequent papers. [ABSTRACT FROM AUTHOR]

We present in this paper the results of the Russian Academy of Sciences North Atlantic Atmospheric Downscaling (RAS-NAAD) project, which provides a 40-yr 3D hindcast of the North Atlantic (10°–80°N) atmosphere at 14-km spatial resolution with 50 levels in the vertical direction (up to 50 hPa), performed with a regional setting of the WRF-ARW 3.8.1 model for the period 1979–2018 and forced by ERA-Interim as a lateral boundary condition. The dataset provides a variety of surface and free-atmosphere parameters at sigma model levels and meets many demands of meteorologists, climate scientists, and oceanographers working in both research and operational domains. Three-dimensional model output at 3-hourly time resolution is freely available to the users. Our evaluation demonstrates a realistic representation of most characteristics in both datasets and also identifies biases mostly in the ice-covered regions. High-resolution and nonhydrostatic model settings in NAAD resolve mesoscale dynamics first of all in the subpolar latitudes. NAAD also provides a new view of the North Atlantic extratropical cyclone activity with a much larger number of cyclones as compared with most reanalyses. It also effectively captures highly localized mechanisms of atmospheric moisture transports. Applications of NAAD to ocean circulation and wave modeling are demonstrated. [ABSTRACT FROM AUTHOR]

Banner clouds are clouds that appear to be attached to the leeward face of a steep mountain. This paper investigates the role of wind speed and wind shear for the formation of banner clouds. Large-eddy simulations are performed to simulate the flow of dry air past an idealized pyramid-shaped mountain. The potential for cloud formation is diagnosed through the Lagrangian vertical parcel displacement, which in the case of a banner cloud shows a plume of large values in the lee of the mountain. In addition, vortical structures are visualized through streamlines and their curvature. A series of sensitivity experiments indicates that both the flow and the banner cloud occurrence are largely independent of the ambient wind speed U. On the other hand, the shear of the ambient wind has a profound impact on the location of the stagnation point on the windward face as well as on the flow geometry in the lee of the mountain. The relevant measure for shear is H/Hs, where H denotes the height of the mountain and Hs = U/Uz is the scale height of the shear (with Uz denoting the scale of the shear). The simulations are also used to compute the line-of-sight velocity component seen by a hypothetical Doppler wind lidar positioned in the lee of the mountain; the analysis suggests that such sensitivities can potentially be detected using modern wind lidar technology. [ABSTRACT FROM AUTHOR]

Using a two-year dataset (2016–17) from 17 one-minute rain gauges located in the moist forest region of Ghana, the performance of Integrated Multisatellite Retrievals for GPM, version 6b (IMERG), is evaluated based on a subdaily time scale, down to the level of the underlying passive microwave (PMW) and infrared (IR) sources. Additionally, the spaceborne cloud product Cloud Property Dataset Using SEVIRI, edition 2 (CLAAS-2), available every 15 min, is used to link IMERG rainfall to cloud-top properties. Several important issues are identified: 1) IMERG's proneness to low-intensity false alarms, accounting for more than a fifth of total rainfall; 2) IMERG's overestimation of the rainfall amount from frequently occurring weak convective events, while that of relatively rare but strong mesoscale convective systems is underestimated, resulting in an error compensation; and 3) a decrease of skill during the little dry season in July and August, known to feature enhanced low-level cloudiness and warm rain. These findings are related to 1) a general oversensitivity for clouds with low ice and liquid water path and a particular oversensitivity for low cloud optical thickness, a problem which is slightly reduced for direct PMW overpasses; 2) a pronounced negative bias for high rain intensities, strongest when IR data are included; and 3) a large fraction of missed events linked with rainfall out of warm clouds, which are inherently misinterpreted by IMERG and its sources. This paper emphasizes the potential of validating spaceborne rainfall products with high-resolution rain gauges on a subdaily time scale, particularly for the understudied West African region. [ABSTRACT FROM AUTHOR]

A new framework is proposed for the bulk parameterization of rain microphysics: the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS). It is designed to facilitate direct constraint by observations using Bayesian inference. BOSS combines existing process-level microphysical knowledge with flexible process rate formulations and parameters constrained by observations within a Bayesian framework. Using a raindrop size distribution (DSD) normalization method that relates DSD moments to one another via generalized power series, generalized multivariate power expressions are derived for the microphysical process rates as functions of a set of prognostic DSD moments. The scheme is flexible and can utilize any number and combination of prognostic moments and any number of terms in the process rate formulations. This means that both uncertainty in parameter values and structural uncertainty associated with the process rate formulations can be investigated systematically, which is not possible using traditional schemes. In this paper, BOSS is compared to two- and three-moment versions of a traditional bulk rain microphysics scheme (denoted as MORR). It is shown that some process formulations in MORR are analytically equivalent to the generalized power expressions in BOSS using one or two terms, while others are not. BOSS is able to replicate the behavior of MORR in idealized one-dimensional rainshaft tests, but with a much more flexible and systematic design. Part II of this study describes the application of BOSS to derive rain microphysical process rates and posterior parameter distributions in Bayesian experiments using Markov chain Monte Carlo sampling constrained by synthetic observations. [ABSTRACT FROM AUTHOR]

This paper reports preliminary yet encouraging findings on the use of causal discovery methods to understand the interaction between atmospheric planetary- and synoptic-scale disturbances in the Northern Hemisphere. Specifically, constraint-based structure learning of probabilistic graphical models is applied to the spherical harmonics decomposition of the daily 500-hPa geopotential height field in boreal winter for the period 1948–2015. Active causal pathways among different spherical harmonics components are identified and documented in the form of a temporal probabilistic graphical model. Since, by definition, the structure learning algorithm used here only robustly identifies linear causal effects, we report only causal pathways between two groups of disturbances with sufficiently large differences in temporal and/or spatial scales, that is, planetary-scale (mainly zonal wavenumbers 1–3) and synoptic-scale disturbances (mainly zonal wavenumbers 6–8). Daily reconstruction of geopotential heights using only interacting scales suggest that the modulation of synoptic-scale disturbances by planetary-scale disturbances is best characterized by the flow of information from a zonal wavenumber-1 disturbance to a synoptic-scale circumglobal wave train whose amplitude peaks at the North Pacific and North Atlantic storm-track region. The feedback of synoptic-scale to planetary-scale disturbances manifests itself as a zonal wavenumber-2 structure driven by synoptic-eddy momentum fluxes. This wavenumber-2 structure locally enhances the East Asian trough and western Europe ridge of the wavenumber-1 planetary-scale disturbance that actively modulates the activity of synoptic-scale disturbances. The winter-mean amplitude of the actively interacting disturbances are characterized by pronounced fluctuations across interannual to decadal time scales. [ABSTRACT FROM AUTHOR]

In February 2017, a wildfire occurred in the Port Hills on the southern boundary of Christchurch city in New Zealand. It was one of the country's most severe fires of the last decade in terms of the scale of evacuation, infrastructure damage, and property loss. On the third day of the fire, fire behavior was unexpectedly active, and two rapid downhill fire-spread events took place, creating a dangerous situation for firefighters. The aim of this paper is to explore the atmospheric processes that influenced the fire behavior at a range of meteorological scales, from the synoptic to meso- and microscales. Meteorological and fire data analyzed include observed data together with model simulations of weather conditions at different scales: 1) the Weather Research and Forecasting (WRF) numerical weather prediction model, which provided the regional context of the fire; and 2) the California Meteorological (CALMET) diagnostic model, which was used to undertake a higher-resolution investigation of atmospheric processes near the fire. Results indicate that the fire was not strongly seasonally influenced. Instead, it appears the fire conditions were the effect of a specific combination of synoptic weather conditions and local meteorological conditions. The first rapid downhill fire-spread event was the result of airflow interaction with the intricate terrain of the Port Hills under stable nocturnal conditions. The second downhill fire-spread event bears similarities to vorticity-driven lateral spread, because the downhill component of the spread happened on a broad fire flank perpendicular to the surface wind direction and characteristic pyrocumulus convection occurred. [ABSTRACT FROM AUTHOR]

To mark the publication of the special collection in honor of Robert (Bob) A. Duce in the Journal of the Atmospheric Sciences, we have summarized his most important contributions to the subject of biogeochemical coupling between the atmosphere and ocean. Here we have divided these contributions into four themes--deposition from the atmosphere and its effects on the oceans, volatile elements emitted from the oceans, sea surface biology and aerosol formation, and marine aerosols and clouds. It is our intent that this summary along with the papers in this special collection provide an overview of the enormous contributions that Bob Duce has made to the subject during his distinguished scientific career. [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]

This paper presents a study on long-term surface solar radiation (SSR) changes over China under clear- and all-sky conditions and analyzes the causes of the "dimming" and "brightening." To eliminate the nonclimatic signals in the historical records, the daily SSR dataset was first homogenized using quantile-matching (QM) adjustment. The results reveal rapid dimming before 2000 not only under all-sky conditions, but also under clear-sky conditions, at a decline rate of −9.7 ± 0.4 W m−2 decade−1 (1958–99). This is slightly stronger than that under all-sky conditions at −7.4 ± 0.4 W m−2 decade−1, since the clear-sky dimming stopped 15 years later. A rapid "wettening" of about 40-Pa surface water vapor pressure (SWVP) from 1985 to 2000 was found over China. It contributed 2.2% to the SSR decline under clear-sky conditions during the whole dimming period (1958–99). Therefore, water vapor cannot be the main cause of the long-term dimming in China. After a stable decade (1999–2008), an intensive brightening appeared under the clear-sky conditions at a rate of 10.6 ± 2.0 W m−2 decade−1, whereas a much weaker brightening (−0.8 ± 3.1 W m−2 decade−1) has been observed under all-sky conditions between 2008 and 2016. The remarkable divergence between clear- and all-sky trends in recent decades indicates that the clouds played two opposite roles in the SSR changes during the past 30 years, by compensating for the declining SSR under the cloud-free conditions in 1985–99 and by counteracting the increasing SSR under cloud-free conditions in 2008–16. Aerosols remain as the main cause of dimming and brightening over China in the last 60 years, although the clouds counteract the effects of aerosols after 2000. [ABSTRACT FROM AUTHOR]