Search

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.

The interferometric radar altimeter (IRA) is an innovative remote sensing sensor that enables the observation of mesoscale and submesoscale (meso–submesoscale) ocean dynamic phenomena. The charge-neutral atmosphere introduces path delay and bending in signal propagation. In this study, three types of sea surface height (SSH) errors caused by charge-neutral atmosphere propagation path for IRA were identified: differential delay error (DDE), path delay error (PDE), and path bending error (PBE). Among them, DDE exhibits a proportionality to the negative zenith neutral delay (ZND) and demonstrates a significant increase with the incident angle; PDE is solely reliant on the ZND; PBE is like DDE in trend and magnitude resembling a ramp. Intriguingly, PBE exhibits insensitivity to variations in the charge-neutral atmosphere, behaving more like a systematic error. Theoretically, PBE leads to an increase in the SSH error of about 1.2 cm at far range for SWOT. The ZND spectrum fitted from the Jason-3 zenith delay correction data is additionally utilized to simulate the spatial distribution of ZND anomaly within the SWOT observation swaths. Then, the impact of PDE anomaly (PDEA), PBE, and DDE anomaly (DDEA) on the observation performance of SWOT is also considered in conjunction with SSH data provided by HYCOM. The findings indicate that both PDEA and PBE significantly reduce IRA's performance in oceanic phenomena, while the impact of DDEA can be disregarded. The PBE can distort the sea surface trend and increases the mean sea level within the range, requiring further attention. Significance Statement: This paper focuses on how the signal path affects the accuracy of measuring sea surface height (SSH) using the interferometric radar altimeter (IRA) in the charge-neutral atmosphere. The present paper defines three types of SSH errors caused by propagation path to IRA, namely, differential delay error (DDE), path delay error (PDE), and path bending error (PBE). Three types of SSH errors will make a significant impact on the altimetry performance of IRA. Among them, PDE spatial anomaly and PBE will reduce the expected observation performance of the meso–submesoscale ocean phenomena in IRA's swath, while PBE has not received enough attention in previous studies. Therefore, the research will provide a theoretical basis for IRA to correct the above SSH errors. [ABSTRACT FROM AUTHOR]

The global perspective presented here is built on earlier papers discussing the dynamics of the upper branch of the Hadley cell in the two solsticial seasons. The role of the tropics is made explicit in a conceptual model that is presented and evaluated. The fluctuation of deep tropical convection in longitude and time is seen as crucial. The filamentary outflows from such convective events move westward and across the equator deep into the winter hemisphere. The horizontal tilt of the cross-equatorial flow implies a significant upper-tropospheric flux of westerly momentum from the winter tropics to the summer hemisphere. These properties are related to the cross-equatorial propagation of wave activity triggered by deep tropical convection in the summer hemisphere. The filaments carry with them near-equatorial values of absolute vorticity and potential vorticity. After turning anticyclonically, some filaments move eastward and poleward to the equatorial edge of the winter subtropical jet. There is strong evidence they can interact with the eddies on this jet and enhance their poleward westerly momentum flux. In the global perspective, tropical and extratropical systems and the interaction between them are all important for the dynamics of the upper branch of the Hadley cell. significance statement: The Hadley cell is the large-scale overturning in the atmosphere with air in the upper troposphere moving from the equatorial region to near 30° in the winter hemisphere. In the standard view it is midlatitude weather systems that are responsible for removing angular momentum from this upper branch of the Hadley cell. Here it is proposed that tropical systems and their interaction with the midlatitude systems are also important. Insight into the role of the tropics in the dynamics of the Hadley cell can be obtained by considering it as the sum of many events of active deep convection occurring in different longitudes and at different times. [ABSTRACT FROM AUTHOR]

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

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]

Regional weather networks—also referred to as mesonets—are imperative for filling in the spatial and temporal data gaps between nationally supported weather stations. The North Carolina Environment and Climate Observing Network (ECONet) fills this regional role; it is a mesoscale network of 44 (as of 2023) automated stations collecting 12 environmental variables every minute across North Carolina. Measured variables include air temperature, precipitation, relative humidity, barometric pressure, wind speed, wind direction, total solar radiation, photosynthetically active solar radiation, soil temperature, soil moisture, leaf wetness index, and black globe temperature. All data undergo quality control procedures and are made freely available to the public via data portals hosted by the State Climate Office of North Carolina at North Carolina State University. This paper provides a technical overview of ECONet, including a description of the siting criteria, station maintenance procedures, data quality control procedures, and data availability. We also summarize unique aspects of ECONet data collection as well as innovative research and applications that rely on ECONet data. ECONet data are used by many sectors including, but not limited to, emergency management, natural resources management, public health, agriculture, forestry, science education, outdoor recreation, and research. ECONet data and data-powered applications offer valuable insights to local, regional, and federal partners, yet opportunities to expand ECONet research and applications remain. Significance Statement: This paper explains the ongoing and emerging impacts of a statewide weather station network called the North Carolina Environment and Climate Observing Network (ECONet). ECONet consists of 44 (as of 2023) automated stations located across the state. Each station collects 12 environmental variables every minute. ECONet data and data-powered applications offer valuable insights to local, regional, and federal partners. There are many opportunities to expand ECONet-based research and applications. [ABSTRACT FROM AUTHOR]

The climate system responds to changes in the amount of atmospheric greenhouse gases or aerosols through rapid processes, triggered within hours and days, and through slower processes, where the full response may only be seen after centuries. In this paper, we aim to elucidate the mechanisms operating on time scales of hours to years to better understand the response of key climate quantities such as energy fluxes, temperature, and precipitation after a sudden increase in either carbon dioxide (CO2), black carbon (BC), or sulfate (SO4) aerosols. The results are based on idealized simulations from six global climate models. We find that the effect of changing ocean temperatures kicks in after a couple of months. Rapid precipitation reductions start occurring instantly and are established after just a few days. For BC, they constitute most of the equilibrium response. For CO2 and SO4, the magnitude of the precipitation response gradually increases as surface warming/cooling evolves, and for CO2, the sign of the response changes from negative to positive after 2 years. Rapid cloud adjustments are typically established within the first 24 h, and while the magnitude of cloud feedbacks for CO2 and SO4 increases over time, the geographical pattern of the equilibrium cloud change is present already after the first year. While there are model differences, our work underscores the overall similarity of the major time-varying processes and responses simulated by current global models and hence the robustness of key features of simulated responses to historical and future anthropogenic forcing. Significance Statement: How does the climate system respond to a change in the amount of atmospheric greenhouse gases or aerosols? Some processes are rapid, responding within hours and days. Others are slow, and the full response to a forcing of the climate may only be seen after centuries. In this paper, we use six global climate models to investigate the time scales of climate responses to carbon dioxide, black carbon, and sulfate, focusing on key climate quantities, such as temperature, precipitation, and clouds. While there are ample model differences, our work underscores the overall similarity of the major time-varying processes and responses simulated by current global models and hence the robustness of key features of simulated responses to historical and future anthropogenic forcing. [ABSTRACT FROM AUTHOR]

An unprecedented heat wave occurred over the Pacific Northwest and southwest Canada on 25–30 June 2021, resulting in all-time temperature records that greatly exceeded previous record maximum temperatures. The impacts were substantial, including several hundred deaths, thousands of hospitalizations, a major wildfire in Lytton, British Columbia, Canada, and severe damage to regional vegetation. Several factors came together to produce this extreme event: a record-breaking midtropospheric ridge over British Columbia in the optimal location, record-breaking midtropospheric temperatures, strong subsidence in the lower atmosphere, low-level easterly flow that produced downslope warming on regional terrain and the removal of cooler marine air, an approaching low-level trough that enhanced downslope flow, the occurrence at a time of maximum insolation, and drier-than-normal soil moisture. It is shown that all-time-record temperatures have not become more frequent and that annual high temperatures only increased at the rate of baseline global warming. Although anthropogenic warming may have contributed as much as 1°C to the event, there is little evidence of further amplification from increasing greenhouse gases. Weather forecasts were excellent for this event, with highly accurate predictions of the extreme temperatures. Significance Statement: This paper describes the atmospheric evolution that produced an extreme heat wave over the Pacific Northwest during June 2021 and puts this event into historical perspective. [ABSTRACT FROM AUTHOR]

In this paper, we revisit the problem of wind–wave interaction with emphasis on strong winds. For these events, it is assumed that nonlinearity is so large that the slope of the wind waves has reached a limiting steepness. Recent observations suggest that the drag decreases with wind in the strong wind speed regime. In this paper, we try to explain this. In the first step, we introduce a model for surface gravity waves and calculate explicitly the background roughness length from the original approach of Janssen. It is found that for young, steep wind sea, the background roughness length almost vanishes, giving a reduced drag. In addition, it is shown that for steep waves, the slowing down of the wind by waves is a nonlinear process; hence, the growth rate of the waves by wind depends in a nonlinear fashion on the wave spectrum. For strong winds, it is found that, as waves are typically steep, this nonlinear effect gives a further reduction of the wind input. As a consequence, in these extreme circumstances, the drag coefficient decreases with wind. [ABSTRACT FROM AUTHOR]

Estimation of uncertainties (random error statistics) of radio occultation (RO) observations is important for their effective assimilation in numerical weather prediction (NWP) models. Average uncertainties can be estimated for large samples of RO observations and these statistics may be used for specifying the observation errors in NWP data assimilation. However, the uncertainties of individual RO observations vary, and so using average uncertainty estimates will overestimate the uncertainties of some observations and underestimate those of others, reducing their overall effectiveness in the assimilation. Several parameters associated with RO observations or their atmospheric environments have been proposed to estimate individual RO errors. These include the standard deviation of bending angle (BA) departures from either climatology in the upper stratosphere and lower mesosphere (STDV) or the sample mean between 40 and 60 km (STD4060), the local spectral width (LSW), and the magnitude of the horizontal gradient of refractivity (|∇HN|). In this paper we show how the uncertainties of two RO datasets, COSMIC-2 and Spire BA, as well as their combination, vary with these parameters. We find that the uncertainties are highly correlated with STDV and STD4060 in the stratosphere, and with LSW and |∇HN| in the lower troposphere. These results suggest a hybrid error model for individual BA observations that uses an average statistical model of RO errors modified by STDV or STD4060 above 30 km, and LSW or |∇HN| below 8 km. Significance Statement: These results contribute to the understanding of the sources of uncertainties in radio occultation observations. They could be used to improve the effectiveness of these observations in their assimilation into numerical weather prediction and reanalysis models by improving the estimation of their observational errors. [ABSTRACT FROM AUTHOR]

This study examines extratropical cyclone tracks, central pressure, and maximum intensification rates from a widely used automated cyclone tracking scheme and compares them with the manual tracking of five well-known North Atlantic cyclones whose histories are available in the refereed literature. The automated tracking scheme is applied to sea level pressure data from four different reanalyses of varying levels of sophistication to test the sensitivity of the results to input data resolution and quality. Further, we test the tracking scheme using lower-tropospheric vorticity obtained from the most recent reanalysis (ERA5) for four of these cyclone events. Substantial discrepancies in cyclone position, intensity, and maximum intensification rates exist between the manual tracking and the automated tracking and are not eliminated by using higher-resolution reanalyses or by "turning off" the spatial smoothing feature of the automated tracking scheme (needed to reduce spurious cyclone detections). The results point to a particular problem in detecting weaker and earlier stage cyclones and confirm findings from studies that have examined a broad range of cyclone tracking schemes for a range of reanalyses. Notably, this early cyclone stage often involves a smaller-scale secondary cyclogenesis or cyclone wave, which are detected by the automated scheme only after subsequent growth in the ensuing 6–12 h. It is known that these early stages are critical for a comprehensive understanding of rapid intensification events. A discussion of possible future solutions to this problem is presented. Significance Statement: Because of the availability of large modern datasets portraying sea level pressure across the globe, meteorologists have turned to automated detection and tracking of midlatitude cyclones. Detection and tracking are of interest since these storm systems play an important role in weather and climate and potential changes in their location, frequency, and intensity are of considerable societal interest given climate change. This paper compares the results obtained from one commonly used automated tracking method with tracks obtained by human analysts. We find substantial discrepancies in cyclone position, intensity, and intensification rates and that these differences are not eliminated by using improved analyses. A discussion of possible future solutions is presented. [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]

This paper describes the convection parameterization in the Navy Earth System Prediction Capability (ESPC) system developed at the Naval Research Laboratory, with a focus on the scheme configuration in the v2.0 system. The parameterization is an update of a modification of the Kain–Fritsch convection scheme by Ridout et al. based on an assumed quasi-balance of updraft parcel buoyancy at the cloud-base level. Scheme updates include the treatment of updraft/environment mixing and additional updraft model features, including a parameterized reduction in net detrainment in cases of significant near-cloud upward motion, and a modified cloud-top condition. The scheme includes two convection modes: a turbulence-triggered and a dynamically triggered mode. Hindcast sensitivity with Navy ESPC to features of the scheme is investigated with 45-day integrations from 1 November 2011 for a portion of the Dynamics of the Madden–Julian Oscillation (DYNAMO) research program observational period that overlaps with the occurrence of two episodes of the MJO. The modified updraft mixing is critical in the hindcasts for consistent MJO eastward propagation, whereas the additional updraft updates significantly improve the representation of small-scale rainfall variability, while helping to inhibit development of excessive low-level easterly flow. The added turbulence-triggered convection mode helps to improve the representation of the separation of periods of enhanced MJO convection. The relative occurrence frequency of convective cloud-top height and column water vapor in the equatorial Indo-Pacific is investigated in the hindcasts, showing significant similarities with satellite retrieval results. Significance Statement: This study describes the scheme used to represent the effects of convective clouds such as cumulus and cumulonimbus (thunderstorm clouds) in computerized 45-day global forecasts of the Earth system in a forecast model developed at the Naval Research Laboratory, focusing on the version currently undergoing testing for use by the U.S. Navy. Some of the development history and physical basis for the scheme are presented, and results from test simulations are included. The test results investigate potential forecast sensitivity to various features of the scheme and illustrate that the scheme can successfully represent certain effects of convective clouds on large-scale storm systems in the tropics that have global-scale impacts on extended-range (several weeks) prediction of the Earth's atmosphere/ocean 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]

A review of many studies published since the late 1920s reveals that the main driving mechanisms responsible for the early-twentieth-century Arctic warming (ETCAW) are not fully recognized. The main obstacle seems to be our limited knowledge about the climate of this period and some forcings. A deeper knowledge based on greater spatial and temporal resolution data is needed. The article provides new (or improved) knowledge about surface air temperature (SAT) conditions (including their extreme states) in the Arctic during the ETCAW. Daily and subdaily data have been used (mean daily air temperature, maximum and minimum daily temperature, and diurnal temperature range). These were taken from 10 individual years (selected from the period 1934–50) for six meteorological stations representing parts of five Arctic climatic regions. Standard SAT characteristics were analyzed (monthly, seasonal, and yearly means), as were rarely investigated aspects of SAT characteristics (e.g., number of characteristic days, day-to-day temperature variability, and the onset, end, and duration of thermal seasons). The results were compared with analogical calculations done for data taken from the contemporary Arctic warming (CAW) period (2007–16). The Arctic experienced warming between the ETCAW and the CAW. The magnitude of warming was greatest in the Pacific (2.7°C) and Canadian Arctic (1.9°C) regions. A shortening of winter and lengthening of summer were noted. Furthermore, the climate was also a little more continental (except the Russian Arctic) and less stable (greater day-to-day variability and diurnal temperature range) during the ETCAW than during the CAW. Significance Statement: It is well established that human activity (particularly increased greenhouse gas emissions) is the primary driving mechanism of the recent dramatic warming in the Arctic. However, the causes of a similar warming here in the first half of the twentieth century remain uncertain. The limited knowledge about the climate of that period—which mainly results from the low resolution of data—is a significant obstacle to a definitive determination of the forcing mechanisms. Therefore, the main aim of our paper is to improve our understanding of specific aspects of weather and climate (including extremes) using long-term series of daily and subdaily data that have rarely been applied for this purpose. This new, more comprehensive knowledge about the historical Arctic climate should allow the scientific community (particularly climate modelers) to better validate both climate models and reanalysis products and, consequently, to more precisely identify the causes of the early-twentieth-century Arctic warming. [ABSTRACT FROM AUTHOR]

It is typically interpreted that more moisture in the atmosphere leads to more intense rains. This notion may be supported, for example, by taking a scatterplot between rain and column precipitable water. The present paper suggests, however, that the main consequence of intense rains with more moisture in the atmosphere is that there is a higher chance of occurrence rather than an increase in the expected magnitude. This tendency equally applies to any rains above 1 mm (6 h)−1 to a lesser extent. The result is derived from an analysis of 33 local rain gauge station data and a shared sounding over Friuli Venezia Giulia, northeast Italy. Significance Statement: Moisture is the source of clouds. Clouds, in turn, are source of rain. So we may expect that more moisture in the atmosphere causes more intense rains. We may further speculate that with more moisture in the atmosphere as a consequence of the global warning, we must face more catastrophic rain events and floods. However, this paper, by analyzing data over Friuli Venezia Giulia, northeast Italy, suggests otherwise: more moisture indeed increases frequencies of intense rains, but not their magnitudes as much. [ABSTRACT FROM AUTHOR]

Turbulence is ever produced in the low-viscosity/large-scale fluid flows by velocity shears and, in unstable stratification, by buoyancy forces. It is commonly believed that both mechanisms produce the same type of chaotic motions, namely, the eddies breaking down into smaller ones and producing direct cascade of turbulent kinetic energy and other properties from large to small scales toward viscous dissipation. The conventional theory based on this vision yields a plausible picture of vertical mixing and has remained in use since the middle of the twentieth century in spite of increasing evidence of the fallacy of almost all other predictions. This paper reveals that in fact buoyancy produces chaotic vertical plumes, merging into larger ones and producing an inverse cascade toward their conversion into the self-organized regular motions. Herein, the velocity shears produce usual eddies spreading in all directions and making the direct cascade. This new paradigm is demonstrated and proved empirically; so, the paper launches a comprehensive revision of the theory of unstably stratified turbulence and its numerous geophysical or astrophysical applications. [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]

Reconstructing the history of polar temperature from ice core water isotope (δ18O) calibration has remained a challenge in paleoclimate research, because of our incomplete understanding of various temperature--δ18O relationships. This paper resolves this classical problem in a new framework called the unified slope equations (USE), which illustrates the general relations among spatial and temporal δ18O--surface temperature slopes. The USE is applied to the Antarctica temperature change during the last deglaciation in model simulations and observations. It is shown that the comparable Antarcticamean spatial slope with deglacial temporal slope in δ18O--surface temperature reconstruction is caused, accidentally, by the compensation responses between the δ18O--inversion layer temperature relation and the inversion layer temperature itself. Furthermore, in light of the USE, we propose that the present seasonal slope of δ18O--inversion layer temperature is an optimal paleothermometer that is more accurate and robust than the spatial slope. This optimal slope suggests the possibility of reconstructing past Antarctic temperature changes using present and future instrumental observations. [ABSTRACT FROM AUTHOR]

The role of the Gulf of California (GoC) in the North American monsoon (NAM) is investigated using a global climate model with 50-km horizontal atmospheric resolution and prescribed SSTs. Specifically, two 135-yr simulations are compared to quantify the influence of the GoC on the NAM: in the first simulation a realistic representation of the GoC is incorporated, while in the second simulation the GoC is replaced with land surface. The results suggest that the GoC has a significant impact on circulation, with cooler surface air temperatures and lower surface friction allowing for south-southeasterly surface flow along the entire length of the GoC, in turn increasing low-level moisture fluxes into the NAM region. Cooler air over the GoC also leads to lower heights at 700–500 hPa, with a corresponding cyclonic moisture flux anomaly, further increasing moisture fluxes into the NAM region. Correspondingly, precipitation is substantially higher over the NAM region and even east of the Continental Divide in areas such as New Mexico and the U.S. Great Plains. July/August precipitation with a realistic GoC is generally 25%–50% greater in northwestern Mexico than the land-filled case, with precipitation 50% greater in much of the southwestern United States. Due to enhanced surface evaporation, areas with increased precipitation also tend to have lower surface temperatures, higher sea level pressure, and lower mid- to upper-tropospheric heights, thus altering the large-scale circulation. These results highlight the importance of the GoC in the NAM and demonstrate the necessity of resolving the GoC in climate simulations. Significance Statement: This paper seeks to improve our understanding of the North American monsoon, an important moisture source in the relatively arid region of northwest Mexico and the Southwest United States. Specifically, we investigate the role of the Gulf of California: a long, narrow sea surrounded by elevated topography within the North American monsoon region, oriented roughly southeast–northwest. The results suggest the Gulf of California is an important component of the North American monsoon system. We find that the Gulf of California acts to enhance moisture transports into the North American monsoon, leading to substantially higher precipitation over a large region. Therefore, the Gulf of California should be carefully considered in climate simulations and future projections, especially given it is often not well resolved. [ABSTRACT FROM AUTHOR]

A companion paper by Lund et al. employed a compressible model to describe the evolution of mountain waves arising due to increasing flow with time over the southern Andes, their breaking, secondary gravity waves and acoustic waves arising from these dynamics, and their local responses. This paper describes the mountain wave, secondary gravity wave, and acoustic wave vertical fluxes of horizontal momentum, and the local and large-scale three-dimensional responses to gravity breaking and wave–mean-flow interactions accompanying this event. Mountain wave and secondary gravity wave momentum fluxes and deposition vary strongly in space and time due to variable large-scale winds and spatially localized mountain wave and secondary gravity wave responses. Mountain wave instabilities accompanying breaking induce strong, local, largely zonal forcing. Secondary gravity waves arising from mountain wave breaking also interact strongly with large-scale winds at altitudes above ~80 km. Together, these mountain wave and secondary gravity wave interactions reveal systematic gravity wave–mean-flow interactions having implications for both mean and tidal forcing and feedbacks. Acoustic waves likewise achieve large momentum fluxes, but typically imply significant responses only at much higher altitudes. [ABSTRACT FROM AUTHOR]

Robert's rising thermal bubble (RRTB) is a benchmark case used to assess atmospheric models. In this paper, RRTB is further studied both using an analytical and a numerical approach, improving to a greater extent the qualitative description found in the literature. The theoretical framework used is that of buoyant thermals and scaling theory that together are able to predict part of the expected behavior of the bubble as it rises and, therefore, can be used to further validate the simulations. For the numerical experiments, we simulate both a two-dimensional and three-dimensional RRTB using a variety of convection schemes under the Boussinesq approximation and with a higher resolution. While the results are in agreement with those presented by previous authors on the same benchmark and also with the theoretical framework established, we add the quantitative measures to validate the underlying physics of the numerical model. Our results also show that, due to its completely chaotic nature when confined in a 2D plane, RRTB becomes a very challenging candidate to be used as a benchmark if only compared in a qualitative way, and when the 3D bubble is simulated, the shape changes significantly. [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]

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]

Severe weather probabilities are derived from the Warn-on-Forecast System (WoFS) run by NOAA's National Severe Storms Laboratory (NSSL) during spring 2018 using the random forest (RF) machine learning algorithm. Recent work has shown this method generates skillful and reliable forecasts when applied to convection-allowing model ensembles for the "Day 1" time range (i.e., 12–36-h lead times), but it has been tested in only one other study for lead times relevant to WoFS (e.g., 0–6 h). Thus, in this paper, various sets of WoFS predictors, which include both environment and storm-based fields, are input into a RF algorithm and trained using the occurrence of severe weather reports within 39 km of a point to produce severe weather probabilities at 0–3-h lead times. We analyze the skill and reliability of these forecasts, sensitivity to different sets of predictors, and avenues for further improvements. The RF algorithm produced very skillful and reliable severe weather probabilities and significantly outperformed baseline probabilities calculated by finding the best performing updraft helicity (UH) threshold and smoothing parameter. Experiments where different sets of predictors were used to derive RF probabilities revealed 1) storm attribute fields contributed significantly more skill than environmental fields, 2) 2–5 km AGL UH and maximum updraft speed were the best performing storm attribute fields, 3) the most skillful ensemble summary metric was a smoothed mean, and 4) the most skillful forecasts were obtained when smoothed UH from individual ensemble members were used as predictors. [ABSTRACT FROM AUTHOR]

An interesting aspect of the El Ni˜no-Southern Oscillation (ENSO) phenomenon is the asymmetry between its two phases. This paper evaluates the simulations of this property of ENSO by the CoupledModel Intercomparison Project phase 6 (CMIP6) models. Both the surface and subsurface signals of ENSO are examined for this purpose. The results show that the models still underestimate ENSO asymmetry as shown in the SST field, but do a better job in the subsurface. A much weaker negative feedback from the net surface heat flux during La Ni˜na in the models is identified as a factor causing the degradation of the ENSO asymmetry at the surface. The simulated asymmetry in the subsurface is still weaker than the observations owing to a weaker dynamic coupling between the atmosphere and ocean. Consistent with the finding of a weaker dynamic coupling strength, the precipitation response to the SST changes is also found to be weaker in the models. The results underscore that a more objective assessment of the simulation of ENSO by climate models may have to involve the examination of the subsurface signals. Future improvements in simulating ENSO will likely require a better simulation of the surface heat flux feedback from the atmosphere as well as the dynamical coupling strength between the atmosphere and ocean. [ABSTRACT FROM AUTHOR]

Along-track wavenumber spectral densities of sea surface height (SSH) are estimated from Jason-2 altimetry data as a function of spatial location and calendar month to understand the seasonality of meso- and submesoscale balanced dynamics across the global ocean. Regions with significant mode-1 and mode-2 baroclinic tides are rejected, restricting the analysis to the extratropics. Where balanced motion dominates, the SSH spectral density is averaged over all pass segments in a region for each calendar month and is fit to a four-parameter model consisting of a flat plateau at low wavenumbers, a transition at wavenumber k0 to a red power law spectrum k−s, and a white spectrum at high wavenumbers that models the altimeter noise. The monthly time series of the model parameters are compared to the evolution of the mixed layer. The annual mode of the spectral slope s reaches a minimum after the mixed layer deepens, and the annual mode of the bandpassed kinetic energy in the ranges [2k0, 4k0] and [k0, 2k0] peak ∼2 and ∼4 months, respectively, after the maximum of the annual mode of the mixed layer depth. This analysis is consistent with an energization of the submesoscale by a winter mixed layer instability followed by an inverse cascade of kinetic energy to the mesoscale, in agreement with prior modeling studies and in situ measurements. These results are compared to prior modeling, in situ, and satellite investigations of specific regions and are broadly consistent with them within measurement uncertainties. Significance Statement: This paper uses satellite observations to understand the source of ocean dynamics at the 1–100-km scales at which vertical motion becomes important and which are thus relevant for biology and for the exchange of heat and carbon with the atmosphere. The observations are consistent with a seasonal variation of dynamics at these scales, predicted by a specific theory of upper-ocean turbulence and confirmed by modeling studies and regional observations. We update prior satellite-based studies by excluding regions with competing effects, by our treatment of the noise, and by our characterization of the seasonality. This work provides a template for analyzing data from the upcoming Surface Water and Ocean Topography (SWOT) satellite. [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]

We evaluate two stochastic subcolumn generators used in GCMs to emulate subgrid cloud variability enabling comparisons with satellite observations and simulations of certain physical processes. Our evaluation necessitated the creation of a reference observational dataset that resolves horizontal and vertical cloud variability. The dataset combines two CloudSat cloud products that resolve two-dimensional cloud optical depth variability of liquid, ice, and mixed-phase clouds when blended at ∼200 m vertical and ∼2 km horizontal scales. Upon segmenting the dataset to individual "scenes," mean profiles of the cloud fields are passed as input to generators that produce scene-level cloud subgrid variability. The assessment of generator performance at the scale of individual scenes and in a mean sense is largely based on inferred joint histograms that partition cloud fraction within predetermined combinations of cloud-top pressure–cloud optical thickness ranges. Our main finding is that both generators tend to underestimate optically thin clouds, while one of them also tends to overestimate some cloud types of moderate and high optical thickness. Associated radiative flux errors are also calculated by applying a simple transformation to the cloud fraction histogram errors, and are found to approach values almost as high as 3 W m−2 for the cloud radiative effect in the shortwave part of the spectrum. Significance Statement: The purpose of the paper is to assess the realism of relatively simple ways of producing fine-scale cloud variability in global models from coarsely resolved cloud properties. The assessment is achieved via comparisons to observed cloud fields where the fine-scale variability is known in both the horizontal and vertical directions. Our results show that while the generators have considerable skill, they still suffer from consistent deficiencies that need to be addressed with further development guided by appropriate observations. [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]

The image-based visibility detection methods have been one of the active research issues in surface meteorological observation. The visual feature extraction is the basis of these methods, and its effectiveness has become a key factor in accurately estimating visibility. In this study, we compare and analyze the effectiveness of various visual features in visibility detection from three aspects, namely, visibility sensitivity, environmental variables robustness, and object depth sensitivity in multiscene, including three traditional visual features such as local binary patterns (LBP), histograms of oriented gradients (HOG), and contrast as well as three deep learned features extracted from the Neural Image Assessment (NIMA) and VGG-16 networks. Then the support vector regression (SVR) models, which are used to map visual features to visibility, are also trained, respectively based on the region of interest (ROI) and the whole image of each scene. The experiment results show that compared to traditional visual features, deep learned features exhibit better performance in both feature analysis and model training. In particular, NIMA, with lower dimensionality, achieves the best fitting effect and therefore is found to show good application prospects in visibility detection. Significance Statement: The visual feature extraction is a basic step for image-based visibility detection and significantly affects the detection performance. In this paper, we compare six candidate visual features, including traditional and deep learned features, from visibility sensitivity, environmental variables robustness, and object depth sensitivity in multiscene. Then the SVR models are also trained to construct the mapping relations between different kinds of features and the visibility of each scene. The experiment results show that the deep learned features exhibit better performance in both feature analysis and model training, especially NIMA achieves the best fitting performance with fewer feature dimensions. [ABSTRACT FROM AUTHOR]

This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial–temporal data with statistical data by extracting features with deep learning encoder–decoder architectures and predicting with gradient-boosted trees. We evaluate our models in the North Atlantic and eastern Pacific basins in 2016–19 for 24-h lead-time track and intensity forecasts and show they achieve comparable mean absolute error and skill to current operational forecast models while computing in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model could improve upon the National Hurricane Center's official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that utilizing machine learning techniques to combine different data sources can lead to new opportunities in tropical cyclone forecasting. Significance Statement: Machine learning techniques have not been fully explored for improving tropical cyclone movement and intensity changes. This work shows how advanced machine learning techniques combined with routinely available information can be used to improve 24-h tropical cyclone forecasts efficiently. The successes demonstrated for 24-h forecasts provide a recipe for improving predictions for longer lead times, further reducing forecast uncertainties and benefiting society. [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]

Projections of future West African monsoon (WAM) precipitation change in response to increasing greenhouse gases are uncertain, and an improved understanding of the drivers of WAM precipitation change is needed to help aid model development and better inform adaptation policies in the region. This paper addresses one of those drivers, the direct radiative effect of increased CO2 (i.e., the impact of increased CO2 in the absence of SST warming and changes in plant physiology). An atmosphere-only model is used to examine both the equilibrium response and the evolution of the change over the days following the instantaneous CO2 increase. In response to the direct radiative effect, WAM precipitation increases due to a weakening of the shallow meridional circulation over North Africa, advecting less dry air into the convective column associated with the monsoon. Changes in the shallow circulation are associated with atmospheric and surface warming patterns over North Africa. A large-scale atmospheric warming pattern, whereby North Africa warms more than the monsoon region, leads to a northward shift in the Saharan heat low. In response to increased precipitation in the Sahel, local soil moisture feedbacks play a key role in determining the low-level circulation change and the location of the intertropical discontinuity. The large-scale warming patterns over North Africa result from differing levels of constraint applied by convective quasi-equilibrium. While this constraint acts strongly in the equatorial WAM region, preventing the region from warming in response to the direct radiative effect, North Africa is not strongly constrained and is therefore able to warm. [ABSTRACT FROM AUTHOR]

Recent rapid melting of the summer Greenland ice sheet (GrIS) and its impact on Earth's climate has attracted much attention. In this paper, we establish a connection between the melting of GrIS and the variability of summer sea surface temperature (SST) anomalies over the North Atlantic on interannual to interdecadal time scales through changes in subseasonal Greenland blocking (GB). It is found that the latitude and width of GB are important for the spatial patterns of the GrIS melting. The melting of the GrIS on interdecadal time scales is most prominent for the positive Atlantic multidecadal oscillation phase (AMO+) because the high-latitude GB and its large width, long lifetime, and slow decay are favored. However, the North Atlantic mid-high latitude warm–cold–warm (cold–warm–cold) tripole, referred to as the NAT+ (NAT−) pattern, on interannual time scales tends to strengthen (weaken) the role of AMO+ in the GrIS melting, especially on the northern or northeastern periphery of Greenland, by promoting (inhibiting) high-latitude GB and increasing (decreasing) its width. It is further revealed that AMO+ (NAT+) favors the persistence and width of GB mainly through producing weak summer zonal winds and a small summer meridional potential vorticity gradient (PVy) in the North Atlantic mid-high latitudes at 55°–70°N (55°–65°N) compared to the role of negative AMO (NAT−). The event frequency and zonal width of GB events and their poleward shift are favored by the combination of NAT+ with AMO+. In contrast, the combination of NAT− and AMO+ tends to suppress reduced summer zonal winds and PVy, thus inhibiting the event frequency of GB events and their poleward shift and zonal width. Significance Statement: Rapid melting of the summer Greenland ice sheet (GrIS) has been observed to frequently occur, especially after the year 2000, leading to a rise in sea level and other effects on Earth's climate. The physical cause of the rapid melting of the GrIS is an important area of research. We establish a connection between the summer melting of the GrIS and different sea surface temperature (SST) modes in the North Atlantic via changes in Greenland blocking. Although the positive Atlantic multidecadal oscillation (AMO+) phase favors the overall melting of GrIS, the phase of the North Atlantic tripole (NAT) SST pattern modulates the strength and location of the GrIS melting. The positive NAT phase (NAT+) with a warm–cold–warm tripole structure in the North Atlantic mid-high latitudes and AMO+ combine to result in a strong warm SST anomaly in the high latitudes of the North Atlantic north of 60°N, which promotes the melting of GrIS on the western, northern, and northeastern peripheries of Greenland via high-latitude Greenland blocking with an increased zonal width. The combination of the negative NAT phase (NAT−) with a cold–warm–cold tripole structure and AMO+ tends to suppress this effect. Thus, our results provide a new understanding of why the melting of GrIS shows a strong variability in strength and region. [ABSTRACT FROM AUTHOR]

The "bag breakup" fragmentation is the dominant mechanism for spume droplet production in high winds, which substantially affects the ocean–atmosphere exchange processes. The amount of droplets ejected from the surface, as well as their typical sizes, is prescribed by the surface wind velocity and fetch. The corresponding empirical correlations were obtained only for the limited parameters of the laboratory environment. The applicability range can be extended through the construction of a theoretical model that describes the initiation of the bag-breakup fragmentation, estimates the fragmenting liquid volume prescribing the droplet sizes, and determines the dependence on the wind parameters. This paper presents such a model. First, we conducted linear stability analysis of small-scale disturbances at the water surface under a high wind; this showed that the small-scale ripples (about 1 cm) propagating against the wind in the surface wind drift following the reference frame grew fast due to the Kelvin–Helmholtz instability, when the wind friction velocity u * exceeded the threshold of about 1 m s−1. Given the weak dispersion, the nonlinear stage of evolution was addressed using the Riemann simple wave equation modified to describe the increasing disturbances. The analytical solution for the equation suggested the scaling of the volume of liquid undergoing the bag-breakup fragmentation and its dependence on u * in agreement with the laboratory data. Using the scaling, we calculated the statistics of the bag-breakup fragmentation based on the lognormal size distribution of the fragmenting objects. Significance Statement: The "bag breakup" fragmentation is the dominant mechanism for generating spray in hurricane winds. The parameters of spray droplets substantially affect the exchange processes between the ocean and the atmosphere and, thereby, the development of sea storms. The rapid process of spray generation can only be studied in laboratory environments using sophisticated experimental techniques. To apply the laboratory data to field conditions, we need a theoretical model that describes the threshold for fragmentation initiation, the fragmenting liquid volume, which scales the size and number of spray droplets, their dependence on wind parameters, etc. In the present work, we suggest a simple analytical model of the bag-breakup initiation, verify it in the laboratory experiment, and suggest the statistical description of the fragmentation events. [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]