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The Madden–Julian oscillation (MJO) identified by Madden and Julian in the early 1970s has been well recognized as the most prominent intraseasonal signal in the tropics. Its discovery and its relationship with other weather phenomena such as tropical cyclones (TCs) are among the most significant advancements in modern meteorology with broad and far-reaching impacts. The original study by Madden and Julian used radiosonde data on Canton Island, and their spectral analysis revealed the signal of a 40–50-day oscillation. It has come to our attention that an earlier study by Xie et al. published in a Chinese journal documented an oscillatory signal of a 45-day period using radiosonde data from several stations between 70° and 125°E in the tropics. The 40–50-day signal found by Xie et al. is strikingly evident without any filtering. Xie et al. identified that occurrences of TCs are correlated with the 40–50-day variation of low-level westerlies at these stations. The original figures in Xie et al.'s article were hand drawn. Their results are verified using data from a longer period of 1958–70. The 40–50-day oscillation in the monsoon westerlies and its relationship with the occurrence of TCs are confirmed and further expanded upon. This study serves the purpose of bringing recognition to the community of the identification of a 40–50-day signal published in Chinese in 1963 and the discovery of the correlation between MJO phases and TC genesis three decades earlier than studies on this subject published outside China. [ABSTRACT FROM AUTHOR]

This paper analyzes the relationships between the circulation regimes of the 500-hPa height (z500) and 250-hPa zonal winds (u250) in the Pacific–North America region during boreal winter, and the 45-day Northern Hemisphere oscillation in z500. The regimes were calculated using a k-means clustering applied to the leading 12 principal components of the combined z500–u250 anomaly fields. We divided the oscillation into eight arbitrary phases. The oscillation phase z500 composite maps are spatially well correlated with regime z500 composites: phases 1–2 are best correlated with the Arctic Low, phases 3–5 are best correlated with the Pacific Trough, phase 6 is best correlated with the Arctic High, and phases 7–8 are best correlated with the Alaskan Ridge. We found that these correlations are generally consistent with the regimes that tend to occur during the individual oscillation phases: the Arctic Low occurs above significance in phases 1–2, the Pacific Trough occurs above significance in phase 3, and Alaskan Ridge occurs above significance in phases 7–8. Therefore, the oscillation has a preferred order with respect to the regimes. The regime transitions indicate a pattern that moves through the Pacific Wavetrain, a regime that appears for k = 5 as a mean state. Transitions out of this regime into different regimes are preferred in different phases of the oscillation. These results imply a possible enhancement to regime prediction using the low-frequency oscillations in combination with regimes. Significance Statement: Subseasonal prediction, weather forecasting in the 2–4-week range, is important for many parts of society, e.g., water managers, emergency response units, and farmers. However, current prediction skill in this time range is low. This paper performs an initial analysis of a possible method to increase weather statistic prediction skill beyond 10 days in the winter for the Pacific–North America region. This is done by combining two ways of looking at large, long-lasting patterns of pressure systems in the atmosphere, which are associated with various weather statistics like precipitation extremes and storminess. The results indicate this method holds potential skill for enhancing subseasonal prediction. Further investigation might yield forecasting improvements in this important time range. [ABSTRACT FROM AUTHOR]

Visible satellite imagery is widely used by operational weather forecast centers for tropical and extratropical cyclone analysis and marine forecasting. The absence of visible imagery at night can significantly degrade forecast capabilities, such as determining tropical cyclone center locations or tracking warm-topped convective clusters. This paper documents ProxyVis imagery, an infrared-based proxy for daytime visible imagery developed to address the lack of visible satellite imagery at night and the limitations of existing nighttime visible options. ProxyVis was trained on the VIIRS day/night band imagery at times close to the full moon using VIIRS IR channels with closely matching GOES-16/17/18, Himawari-8/9, and Meteosat-9/10/11 channels. The final operational product applies the ProxyVis algorithms to geostationary satellite data and combines daytime visible and nighttime ProxyVis data to create full-disk animated GeoProxyVis imagery. The simple versions of the ProxyVis algorithm enable its generation from earlier GOES and Meteosat satellite imagery. ProxyVis offers significant improvement over existing operational products for tracking nighttime oceanic low-level clouds. Further, it is qualitatively similar to visible imagery for a wide range of backgrounds and synoptic conditions and phenomena, enabling forecasters to use it without special training. ProxyVis was first introduced to National Hurricane Center (NHC) operations in 2018 and was found to be extremely useful by forecasters becoming part of their standard operational satellite product suite in 2019. Currently, ProxyVis implemented for GOES-16/18, Himawari-9, and Meteosat-9/10/11 is being used in operational settings and evaluated for transition to operations at multiple NWS offices and the Joint Typhoon Warning Center. Significance Statement: This paper describes ProxyVis imagery, a new method for combining infrared channels to qualitatively mimic daytime visible imagery at nighttime. ProxyVis demonstrates that a simple linear regression can combine just a few commonly available infrared channels to develop a nighttime proxy for visible imagery that significantly improves a forecaster's ability to track low-level oceanic clouds and circulation features at night, works for all current geostationary satellites, and is useful across a wide range of backgrounds and meteorological scenarios. Animated ProxyVis geostationary imagery has been operational at the National Hurricane Center since 2019 and is also currently being transitioned to operations at other NWS offices and the Joint Typhoon Warning Center. [ABSTRACT FROM AUTHOR]

Because of its high spatial and temporal variability, rainfall remains one of the most challenging meteorological variables to measure accurately. Obtaining high-quality rainfall products is essential for flood monitoring, disaster warning, and weather forecasting systems, but this is not always possible on the basis of current rainfall observation networks. Innovative alternatives draw inspiration from "citizen science" and "crowdsourcing," allowing for opportunistic sensing of rainfall from existing measurements at a low cost, which has become a popular topic and is beginning to play an important role in developing rainfall observation systems. This paper reviews the current state of new rainfall observation approaches and explores their opportunities to complement more traditional ways of rainfall data collection in a hydrological context. Furthermore, the challenges of each new approach are discussed. Although these new options show great potential in enhancing the current rainfall network, they still face problems in terms of their accuracy, real-time accessibility, and limited applicability when individually employed. In contrast, the fusion of new measurements with traditional observation networks is feasible and will be effective for regional rainfall monitoring. This study also serves as an important reference in developing monitoring techniques for other environmental factors. Significance Statement: New rainfall observation techniques provide a meaningful supplement to current rainfall networks in terms of spatiotemporal resolution and accuracy. In this paper, we present a comprehensive overview of the innovations in rainfall observation and their popularity in different regions around the world. Then, the application value and future opportunities that new techniques bring to hydrological research are analyzed. It is anticipated that this paper will be of value to researchers with an interest in improving the quality of rainfall data, thus paving the way to accelerate these studies, as well as the application and implementation of their findings, to the next stage. Furthermore, we expect to prompt a rethink on utilizing and exploiting these new rainfall products to enhance our understanding and optimization of current rainfall sensing systems. [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]

Accurate, timely, and accessible meteorological and soil moisture measurements are essential for a number of applications including weather forecasting, agricultural decision-making, and flood and drought prediction. Such data are becoming increasingly available globally, but the large number of networks and various data reporting formats often make utilization of such data difficult. The TexMesonet is a "network of networks" developed within the state of Texas to collect, process, and make public data collected from more than 1700 monitoring stations throughout the state. This paper describes the TexMesonet, with special attention paid to monitoring sites installed and managed by the Texas Water Development Board. It also provides a case study exemplifying how these data may be used and gives recommendations for future data applications. [ABSTRACT FROM AUTHOR]

This paper examines the trends in hot summer days for the Pacific Northwest in observations and a regional climate model ensemble. Hot days are identified by the temperature threshold for several percentile values computed over 10-yr intervals (85th, 90th, and 95th percentiles and absolute maximum) to differentiate heat events of different intensities and are compared to the median temperature (50th percentile). For the stations analyzed, the observed rate of warming during hot days is not statistically different from the warming rate of median days since the 1950s. However, for projections to 2100, hot days show a statistically significant increase in the warming rate of the hottest days compared to the warming rate for median days. Depending on location, the 95th-percentile daily maximum temperature shows a warming rate of up to 0.2°C decade−1 above the median warming rate. The divergence in the trends of median and extreme temperature shows substantial regional variation depending on local terrain and coastlines. The warming trend during hot days is related to the unique circulation patterns during heat events, which respond to different feedbacks and amplifying effects in the land–atmosphere system from those that prevail during typical days. The regional climate model simulations are taken from an ensemble of the Weather Research and Forecasting (WRF) Model simulations forced by 12 global climate model simulations from phase 5 of the Climate Model Intercomparison Project (CMIP5) using the RCP8.5 emissions scenario and 12-km grid spacing. Significance Statement: The heatwave of June 2021 had substantial societal and ecological impacts, illustrating vulnerability to record-shattering events. This paper addresses whether climate change can cause heat events in the Pacific Northwest to warm at a more rapid rate than typical days. Based on observations, in the recent past, the hottest days warmed at about the same rate as typical days in the recent past. However, results from a high-resolution climate model show a higher rate of warming during the warmest days relative to median days under the most aggressive future emissions scenario. This effect could cause heatwaves to become more intense relative to typical days by 2100, making adaptation to extreme events more difficult. [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 downscaling of an ensemble of 12 general circulation models (GCMs) using the Weather Research and Forecasting (WRF) Model at 12-km grid spacing over the period 1970–2099, examining the mesoscale impacts of global warming as well as the uncertainties in its mesoscale expression. The RCP8.5 emissions scenario was used to drive both global and regional climate models. The regional climate modeling system reduced bias and improved realism for a historical period, in contrast to substantial errors for the GCM simulations driven by lack of resolution. The regional climate ensemble indicated several mesoscale responses to global warming that were not apparent in the global model simulations, such as enhanced continental interior warming during both winter and summer as well as increasing winter precipitation trends over the windward slopes of regional terrain, with declining trends to the lee of major barriers. During summer there is general drying, except to the east of the Cascades. The 1 April snowpack declines are large over the lower-to-middle slopes of regional terrain, with small snowpack increases over the lower elevations of the interior. Snow-albedo feedbacks are very different between GCM and RCM projections, with the GCMs producing large, unphysical areas of snowpack loss and enhanced warming. Daily average winds change little under global warming, but maximum easterly winds decline modestly, driven by a preferential sea level pressure decline over the continental interior. Although temperatures warm continuously over the domain after approximately 2010, with slight acceleration over time, occurrences of temperature extremes increase rapidly during the second half of the twenty-first century. Significance Statement: This paper provides a unique high-resolution view of projected climate change over the Pacific Northwest and does so using an ensemble of regional climate models, affording a look at the uncertainties in local impacts of global warming. The paper examines regional meteorological processes influenced by global warming and provides guidance for adaptation and preparation. [ABSTRACT FROM AUTHOR]

This paper describes the downscaling of an ensemble of 12 general circulation models (GCMs) using the Weather Research and Forecasting (WRF) Model at 12-km grid spacing over the period 1970–2099, examining the mesoscale impacts of global warming as well as the uncertainties in its mesoscale expression. The RCP8.5 emissions scenario was used to drive both global and regional climate models. The regional climate modeling system reduced bias and improved realism for a historical period, in contrast to substantial errors for the GCM simulations driven by lack of resolution. The regional climate ensemble indicated several mesoscale responses to global warming that were not apparent in the global model simulations, such as enhanced continental interior warming during both winter and summer as well as increasing winter precipitation trends over the windward slopes of regional terrain, with declining trends to the lee of major barriers. During summer there is general drying, except to the east of the Cascades. The 1 April snowpack declines are large over the lower-to-middle slopes of regional terrain, with small snowpack increases over the lower elevations of the interior. Snow-albedo feedbacks are very different between GCM and RCM projections, with the GCMs producing large, unphysical areas of snowpack loss and enhanced warming. Daily average winds change little under global warming, but maximum easterly winds decline modestly, driven by a preferential sea level pressure decline over the continental interior. Although temperatures warm continuously over the domain after approximately 2010, with slight acceleration over time, occurrences of temperature extremes increase rapidly during the second half of the twenty-first century. Significance Statement: This paper provides a unique high-resolution view of projected climate change over the Pacific Northwest and does so using an ensemble of regional climate models, affording a look at the uncertainties in local impacts of global warming. The paper examines regional meteorological processes influenced by global warming and provides guidance for adaptation and preparation. [ABSTRACT FROM AUTHOR]

In the postprocessing of ensemble forecasts of weather variables, it is standard practice to first calibrate the forecasts in a univariate setting, before reconstructing multivariate ensembles that have a correct covariability in space, time, and across variables, via so-called “reordering” methods. Within this framework though, postprocessors cannot fully extract the skill of the raw forecast that may exist at larger scales. A multi-temporal-scale modulation mechanism for precipitation is here presented, which aims at improving the forecasts over different accumulation periods, and which can be coupled with any univariate calibration and multivariate reordering techniques. The idea, originally known under the term “canonical events,” has been implemented for more than a decade in the Meteorological Ensemble Forecast Processor (MEFP), a component of the U.S. National Weather Service’s (NWS) Hydrologic Ensemble Forecast Service (HEFS), although users were left with material in the gray literature. This paper proposes a formal description of the mechanism and studies its intrinsic connection with the multivariate reordering process. The verification of modulated and unmodulated forecasts, when coupled with two popular methods for reordering, the Schaake shuffle and ensemble copula coupling (ECC), is performed on 11 Californian basins, on both precipitation and streamflow. Results demonstrate the clear benefit of the multi-temporal-scale modulation, in particular on multiday total streamflow. However, the relative gain depends on the method used for reordering, with more benefits expected when this latter method is not able to reconstruct an adequate temporal structure on the calibrated precipitation forecasts. [ABSTRACT FROM AUTHOR]

This paper investigates the surface-layer processes associated with the morning transition from nighttime downslope winds to daytime upslope winds over a semi-isolated massif. It provides an insight into the characteristics of the transition and its connection with the processes controlling the erosion of the temperature inversion at the foot of the slope. First, a criterion for the identification of days prone to the development of purely thermally driven slope winds is proposed and adopted to select five representative case studies. Then, the mechanisms leading to different patterns of erosion of the nocturnal temperature inversion at the foot of the slope are analyzed. Three main patterns of erosion are identified: the first is connected to the growth of the convective boundary layer at the surface, the second is connected to the descent of the inversion top, and the third is a combination of the previous two. The first pattern is linked to the initiation of the morning transition through surface heating, and the second pattern is connected to the top-down dilution mechanism and so to mixing with the above air. The discriminating factor in the determination of the erosion pattern is identified in the partitioning of turbulent sensible heat flux at the surface. Significance Statement: The purpose of this study is to improve our understanding of the thermally driven slope circulations with a focus on the unsteady processes associated with the morning transition and the erosion patterns of the nocturnal temperature inversion, so far in the literature less investigated and understood than the evening transition. Understanding this diurnal process will advance our abilities to model it and to improve the accuracy of weather forecasting in complex terrain. [ABSTRACT FROM AUTHOR]

The Excessive Rainfall Outlook (ERO), issued operationally from the Weather Prediction Center (WPC), serves the Weather and Water Enterprise and decision-makers with probabilistic guidance and messaging context out to 3 days for excessive rainfall leading to impacts, including flash flooding. Eighty-three percent of all flood-related damages and 39% of all flood-related fatalities reported in Storm Data from 2010 to 2020 occurred in or near an ERO High Risk. Given that a High Risk is issued on only 4% of days, the presence of such risk can serve as an important step in raising situational awareness of a greater likelihood of a damaging and deadly flash flood day. This paper details the operational construction of the ERO at WPC and discusses the role of the ERO in the National Weather Service collaborative forecast process for heavy rainfall. Case studies where the ERO achieved a High Risk are presented for the deadly Montecito, California, flash flood and debris flow (2018) and Hurricane Harvey (2017). More broadly, challenges are highlighted which could be overcome by research to further improve ERO utility. [ABSTRACT FROM AUTHOR]

To mitigate the impacts associated with adverse weather conditions, meteorological services issue weather warnings to the general public. These warnings rely heavily on forecasts issued by underlying prediction systems. When deciding which prediction system(s) to utilize when constructing warnings, it is important to compare systems in their ability to forecast the occurrence and severity of high-impact weather events. However, evaluating forecasts for particular outcomes is known to be a challenging task. This is exacerbated further by the fact that high-impact weather often manifests as a result of several confounding features, a realization that has led to considerable research on so-called compound weather events. Both univariate and multivariate methods are therefore required to evaluate forecasts for high-impact weather. In this paper, we discuss weighted verification tools, which allow particular outcomes to be emphasized during forecast evaluation. We review and compare different approaches to construct weighted scoring rules, both in a univariate and multivariate setting, and we leverage existing results on weighted scores to introduce conditional probability integral transform (PIT) histograms, allowing forecast calibration to be assessed conditionally on particular outcomes having occurred. To illustrate the practical benefit afforded by these weighted verification tools, they are employed in a case study to evaluate probabilistic forecasts for extreme heat events issued by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss). [ABSTRACT FROM AUTHOR]

The Global Energy and Water Cycle Exchanges (GEWEX) project was created more than 30 years ago within the framework of the World Climate Research Programme (WCRP). The aim of this initiative was to address major gaps in our understanding of Earth's energy and water cycles given a lack of information about the basic fluxes and associated reservoirs of these cycles. GEWEX sought to acquire and set standards for climatological data on variables essential for quantifying water and energy fluxes and for closing budgets at the regional and global scales. In so doing, GEWEX activities led to a greatly improved understanding of processes and our ability to predict them. Such understanding was viewed then, as it remains today, essential for advancing weather and climate prediction from global to regional scales. GEWEX has also demonstrated over time the importance of a wider engagement of different communities and the necessity of international collaboration for making progress on understanding and on the monitoring of the changes in the energy and water cycles under ever increasing human pressures. This paper reflects on the first 30 years of evolution and progress that has occurred within GEWEX. This evolution is presented in terms of three main phases of activity. Progress toward the main goals of GEWEX is highlighted by calling out a few achievements from each phase. A vision of the path forward for the coming decade, including the goals of GEWEX for the future, are also described. [ABSTRACT FROM AUTHOR]

Producing an accurate and calibrated probabilistic forecast has high social and economic value. Systematic errors or biases in the ensemble weather forecast can be corrected by postprocessing models whose development is an urgent challenge. Traditionally, the bias correction is done by employing linear regression models that estimate the conditional probability distribution of the forecast. Although this model framework works well, it is restricted to a prespecified model form that often relies on a limited set of predictors only. Most machine learning (ML) methods can tackle these problems with a point prediction, but only a few of them can be applied effectively in a probabilistic manner. The tree-based ML techniques, namely, natural gradient boosting (NGB), quantile random forests (QRF), and distributional regression forests (DRF), are used to adjust hourly 2-m temperature ensemble prediction at lead times of 1–10 days. The ensemble model output statistics (EMOS) and its boosting version are used as benchmark models. The model forecast is based on the European Centre for Medium-Range Weather Forecasts (ECMWF) for the Czech Republic domain. Two training periods 2015–18 and 2018 only were used to learn the models, and their prediction skill was evaluated in 2019. The results show that the QRF and NGB methods provide the best performance for 1–2-day forecasts, while the EMOS method outperforms other methods for 8–10-day forecasts. Key components to improving short-term forecasting are additional atmospheric/surface state predictors and the 4-yr training sample size. Significance Statement: Machine learning methods have great potential and are beginning to be widely applied in meteorology in recent years. A new technique called natural gradient boosting (NGB) has been released and used in this paper to refine the probabilistic forecast of surface temperature. It was found that the NGB has better prediction skills than the traditional ensemble model output statistics in forecasting 1 and 2 days in advance. The NGB has similar prediction skills with lower computational demands compared to other advanced machine learning methods such as the quantile random forests. We showed a path to employ the NGB method in this task, which can be followed for refining other and more challenging meteorological variables such as wind speed or precipitation. [ABSTRACT FROM AUTHOR]

The USAF Weather (AFW) supports a number of military and U.S. government agencies by providing authoritative weather analysis and forecast products for any location globally, including soil moisture analyses. The long history of supporting soil moisture products and partnering with other U.S. government agencies led to the partnering between the U.S. Air Force (USAF) and NASA Goddard Space Flight Center, resulting in a merger of those organizations' modeling systems, collaborative development of the Land Information System (LIS), and operational fielding of the system within the USAF 557th Weather Wing [557 WW; formerly, Headquarters Air Force Weather Agency (HQ AFWA)]. In 2009, the USAF implemented the NASA LIS and later made it the primary software system to generate global soil hydrology and energy budget products. The implementation of LIS delivered a significant upgrade over the existing Land Data Assimilation System (LDAS) the USAF operated, the Agriculture Meteorology (AGRMET) system. Implementation enabled the rapid integration of new LDAS technology into USAF operations, and led to a long-term NASA–USAF partnership resulting in continued development, integration, and implementation of new LIS capabilities. This paper documents both the history of the USAF Weather organization capabilities enabling the generation of soil moisture and other land surface analysis products, and describes the USAF–NASA partnership leading to the development of the merged LIS-AGRMET system. The article also presents a successful example of a mutually beneficial partnership that has enabled cutting-edge land analysis capabilities at the USAF, while transitioning NASA software and satellite data into USAF operations. [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]

The analysis and forecast of precipitation characteristics is a key task for national meteorological services for providing high-quality weather forecasts and warnings. Beside the precipitation amount, the precipitation type is essential to describe and evaluate the recent, ongoing, and future weather situations. This paper introduces a new surface-based hybrid hydrometeor classification algorithm. The presented method combines polarimetric radar observations at radar beam height from the C-band dual-polarization weather radar network of the German Weather Service [Deutscher Wetterdienst (DWD)] with corrected thermodynamical profiles of numerical weather prediction (NWP) model output and extrapolates the hydrometeor classes at radar beam height to a height of 2 m above ground level (AGL). The implemented technique parameterizes the microphysical processes in the lower troposphere based on the appropriate thermodynamical profile that the hydrometeors have to pass along their way from the radar beam height to the surface. Due to errors in NWP output, the NWP vertical profiles of temperature and humidity are adjusted by using several types of surface stations with high spatial and temporal resolution. Verification results show considerable improvements in the hydrometeor classification near the ground compared to the radar sweep height. After an additional positive in-house evaluation, the presented method is integrated into DWD's operational environment. The topic of this paper is to describe the processing steps for the computation of the near-surface precipitation type. In addition, example cases and a verification study complement the explanations. Significance Statement: For a national weather service and its customers, the separation of precipitation types into solid and liquid is a key issue for improving weather forecasts and warnings. We combined measurements from weather radars and weather stations together with output of numerical weather prediction models and developed a method of estimating the precipitation type at a height of 2 m. Previous studies of the hydrometeor classification field concentrate on warm season weather conditions using "radar observations only" or apply a hybrid combination of radar observations with model information. The uniqueness of our work is the additional introduction of surface observations with high temporal and spatial resolution for a better estimation of the thermodynamical profiles to improve the "all season alignment." [ABSTRACT FROM AUTHOR]

Improving the quality of weather forecasts and the reliability of climate research requires increasing the reliability of measurements. This paper presents results for optical sensors attached to radiosondes. These sensors can measure cloud-top height (CTH) with high accuracy and determine the presence of precipitation particles in clouds and the height of the boundary between the tropospheric and stratospheric air masses. These research findings are especially important in the Arctic, where the reliability of cloud data is poor, especially during polar nights. With the help of a visible range optical sensor, during the daytime, it is possible to measure CTH with an accuracy of 50 m. Using data from an IR sensor it is possible to measure CTH both day and night. The paper also discusses the possibility of using optical sensors in an observational network. The results from this study could be useful for both weather forecasting and climate research. Significance Statement: Remote satellite methods allow us to get information quickly from all over Earth. One of the important variables is the presence of clouds and the cloud-top height because they determines the flow of solar energy to Earth's surface. We suggest equipping a standard radiosonde with an optical sensor to detect cloud cover and measure cloud-top height. In addition, the optical sensor allows us to measure the height of the boundary between the troposphere and the stratosphere. [ABSTRACT FROM AUTHOR]

The paper describes the observational and modeling efforts performed under the Bolzano Tracer Experiment (BTEX). BTEX focused on the basin surrounding the city of Bolzano, at the junction of three tributary valleys on the southern side of the Alps, to characterize the ground-level impact of pollutants emitted by a waste incinerator close to the city, and atmospheric factors controlling dispersion processes in the whole basin, under different winter weather situations. As part of the experiment, two controlled releases of a passive gas tracer (sulfur hexafluoride, SF6) were performed through the stack of the incinerator on 14 February 2017 at two different times, starting respectively at 0700 and 1245 LST, representative of distinct phases of the daily cycle. Samples of ambient air were collected at target sites, and later analyzed using a mass spectrometer, allowing a detectability limit down to 30 ppt. Meanwhile, meteorological conditions were continuously monitored by means of a high-resolution, nonconventional network of ground-based instruments, including 15 weather stations, one temperature profiler, one sodar, and one Doppler wind lidar. Data from the above measurements represent one of the rare examples of integrated datasets available to the community for the characterization of dispersion processes in a typical mountainous environment. In particular, they offered a reference benchmark for testing and calibrating a series of combined numerical modeling suites for weather prediction and pollutant dispersion simulation in such a complex terrain, as shown in the paper. [ABSTRACT FROM AUTHOR]

In this research, we develop a three-way coupled prediction system to advance the realization of air-sea interaction processes. This study considers the sea-state-dependent momentum flux and nonlinear interactions between waves, winds, and ocean currents using the U.S. National Centers for Environmental Prediction's operational Hurricane Weather Research and Forecasting (HWRF)-Hybrid Coordinate Ocean Model (HYCOM) coupled modeling system. Wave feedback is performed through the air-sea interaction module (ASIM) added to WAVEWATCH III (WW3), which employs the wave boundary layer to parameterize unresolved high-frequency tail spectra by using the mean flux profile constructed from the conservation of total momentum and wave energy. The atmospheric momentum flux is updated using the sea-state-dependent Charnock coefficient, wave-induced stress, and ocean surface currents before being passed to HYCOM. Wave coupling in HYCOM includes Coriolis-Stokes forcing to simulate wave-current interactions and to enhance mixing to account for Langmuir turbulence. The fully coupled system is tested for Hurricane Laura (2020). This paper examines the forecast skills of the individual component models by comparing simulations with observations. Without skill degradation of HYCOMandWW3, the three-way coupling method improves the track and intensity forecast skills by 5% each over those of HWRF-HYCOM coupling, and 27% and 17% over those of uncoupling, respectively. Importantly, this fully coupled system outperforms rapid intensification by reducing the intensification magnitude and matching the occurrence and duration. Overall, the forecast performance evaluated in the study establishes a baseline for the nextgeneration hurricane prediction system. SIGNIFICANCE STATEMENT: This study is the documentation of the numerical advancement of tropical cyclone (TC) forecasting and the demonstration of the improvement of the TC intensity forecast. A key asset is the importance of wave coupling and inclusion of the nonlinear interactions in the air-sea interaction zone, and is to advance the current U.S. NCEP operational coupled hurricane modeling system. By assessing simulations for Hurricane Laura (2020), we demonstrate skill improvement of the storm structure, and intensity forecasts, especially for rapid intensification (RI) by correcting the timing and the magnitude of RI simulated by uncoupling and two-way coupling. [ABSTRACT FROM AUTHOR]

We compare two seemingly different methods of estimating random error statistics (uncertainties) of observations, the three-cornered hat (3CH) method and Desroziers method, and show several examples of estimated uncertainties of COSMIC-2 (C2) radio occultation (RO) observations. The two methods yield similar results, attesting to the validity of both. The small differences provide insight into the sensitivity of the methods to the assumptions and computational details. These estimates of RO error statistics differ considerably from several RO error models used by operational weather forecast centers, suggesting that the impact of RO observations on forecasts can be improved by adjusting the RO error models to agree more closely with the RO error statistics. Both methods show RO uncertainty estimates that vary with latitude. In the troposphere, uncertainties are higher in the tropics than in the subtropics and middle latitudes. In the upper stratosphere–lower mesosphere, we find the reverse, with tropical uncertainties slightly less than in the subtropics and higher latitudes. The uncertainty estimates from the two techniques also show similar variations between a 31-day period during Northern Hemisphere tropical cyclone season (16 August–15 September 2020) and a month near the vernal equinox (April 2021). Finally, we find a relationship between the vertical variation of the C2 estimated uncertainties and atmospheric variability, as measured by the standard deviation of the C2 sample. The convergence of the error estimates and the standard deviations above 40 km indicates a lessening impact of assimilating RO above this level. Significance Statement: Uncertainties of observations are of general interest and their knowledge is important for assimilation in numerical weather prediction models. This paper compares two methods of estimating these uncertainties and shows that they give nearly identical results under certain conditions. The estimation of the COSMIC-2 bending angle uncertainties and how they compare to the assumed bending angle error models in several operational weather centers suggests that there is an opportunity for obtaining improved impact of RO observations in numerical model forecasts. Finally, the relationship between the COSMIC-2 bending angle errors and atmospheric variability provides insight into the sources of RO observational uncertainties. [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]

Artificial intelligence (AI) and machine learning (ML) have become important tools for environmental scientists and engineers, both in research and in applications. Although these methods have become quite popular in recent years, they are not new. The use of AI methods began in the 1950s and environmental scientists were adopting them by the 1980s. Although an "AI winter" temporarily slowed the growth, a more recent resurgence has brought it back with gusto. This paper tells the story of the evolution of AI in the field through the lens of the AMS Committee on Artificial Intelligence Applications to Environmental Science. The environmental sciences possess a host of problems amenable to advancement by intelligent techniques. We review a few of the early applications along with the ML methods of the time and how their progression has impacted these sciences. While AI methods have changed from expert systems in the 1980s to neural networks and other data-driven methods, and more recently deep learning, the environmental problems tackled have remained similar. We discuss the types of applications that have shown some of the biggest advances due to AI usage and how they have evolved over the past decades, including topics in weather forecasting, probabilistic prediction, climate estimation, optimization problems, image processing, and improving forecasting models. We finish with a look at where AI as employed in environmental science appears to be headed and some thoughts on how it might be best blended with physical/dynamical modeling approaches to further advance our science. [ABSTRACT FROM AUTHOR]

This paper presents an introduction to the Southern Hemisphere High Altitude Experiment on Particle Nucleation and Growth (SALTENA). This field campaign took place between December 2017 and June 2018 (wet to dry season) at Chacaltaya (CHC), a GAW (Global Atmosphere Watch) station located at 5,240 m MSL in the Bolivian Andes. Concurrent measurements were conducted at two additional sites in El Alto (4,000 m MSL) and La Paz (3,600 m MSL). The overall goal of the campaign was to identify the sources, understand the formation mechanisms and transport, and characterize the properties of aerosol at these stations. State-of-the-art instruments were brought to the station complementing the ongoing permanent GAW measurements, to allow a comprehensive description of the chemical species of anthropogenic and biogenic origin impacting the station and contributing to new particle formation. In this overview we first provide an assessment of the complex meteorology, airmass origin, and boundary layer–free troposphere interactions during the campaign using a 6-month high-resolution Weather Research and Forecasting (WRF) simulation coupled with Flexible Particle dispersion model (FLEXPART). We then show some of the research highlights from the campaign, including (i) chemical transformation processes of anthropogenic pollution while the air masses are transported to the CHC station from the metropolitan area of La Paz–El Alto, (ii) volcanic emissions as an important source of atmospheric sulfur compounds in the region, (iii) the characterization of the compounds involved in new particle formation, and (iv) the identification of long-range-transported compounds from the Pacific or the Amazon basin. We conclude the article with a presentation of future research foci. The SALTENA dataset highlights the importance of comprehensive observations in strategic high-altitude locations, especially the undersampled Southern Hemisphere. [ABSTRACT FROM AUTHOR]

We report on redesigning the undergraduate course in synoptic meteorology and weather forecasting at Wageningen University (the Netherlands) to meet the current-day requirements for operational forecasters. Weather strongly affects human activities through its impact on transportation, energy demand planning, and personal safety, especially in the case of weather extremes. Numerical weather prediction (NWP) models have developed rapidly in recent decades, with reasonably high scores, even on the regional scale. The amount of available NWP model output has sharply increased. Hence, the role and value of the operational weather forecaster has evolved into the role of information selector, data quality manager, storyteller, and product developer for specific customers. To support this evolution, we need new academic training methods and tools at the bachelor's level. Here, we present a renewed education strategy for our weather forecasting class, called Atmospheric Practical, including redefined learning outcomes, student activities, and assessments. In addition to teaching the interpretation of weather maps, we underline the need for twenty-first-century skills like dealing with open data, data handling, and data analysis. These skills are taught using Jupyter Python Notebooks as the leading analysis tool. Moreover, we introduce assignments about communication skills and forecast product development as we aim to benefit from the internationalization of the classroom. Finally, we share the teaching material presented in this paper for the benefit of the community. [ABSTRACT FROM AUTHOR]

Keywords: History EN History 837 839 3 12/12/22 20221101 NES 221101 We would like to thank Dave Schultz and Sean Potter for their interest in and comments on our paper "The myth of Cleveland Abbe: A review of a manufactured history of the creation of the National Weather Service" ([7]; [8]; [6]). 8 Schultz, D. M., 2022: Comments on "The myth of Cleveland Abbe: A review of a manufactured history of the creation of the National Weather Service." And, as part of this historiography, we show how those with a vested interest in raising Abbe's profile - including Abbe himself - did so at the expense of the others, an effort led by the newly created American Meteorological Society and the Weather Bureau. [Extracted from the article]

Satellite retrievals strive to exploit the information contained in thousands of channels provided by hyperspectral sensors and show promise in providing a gain in computational efficiency over current radiance assimilation methods by transferring computationally expensive radiative transfer calculations to retrieval providers. This paper describes the implementation of a new approach based on the transformation proposed in 2008 by Migliorini et al., which reduces the impact of the a priori information in the retrievals and generates transformed retrievals (TRs) whose assimilation does not require knowledge of the hyperspectral instruments characteristics. Significantly, the results confirm both the viability of Migliorini's approach and the possibility of assimilating data from different hyperspectral satellite sensors regardless of the instrument characteristics. The Weather Research and Forecasting (WRF) Model's Data Assimilation (WRFDA) 3-h cycling system was tested over the central North Pacific Ocean, and the results show that the assimilation of TRs has a greater impact in the characterization of the water vapor distribution than on the temperature field. These results are consistent with the knowledge that temperature field is well constrained by the initial and boundary conditions of the Global Forecast System (GFS), whereas the water vapor distribution is less well constrained in the GFS. While some preliminary results on the comparison between the assimilation with and without TRs in the forecasting system are presented in this paper, additional work remains to explore the impact of the new assimilation approach on forecasts and will be provided in a follow-up publication. [ABSTRACT FROM AUTHOR]

Climate predictions, from three weeks to a decade into the future, can provide invaluable information for climate-sensitive socioeconomic sectors, such as renewable energy, agriculture, or insurance. However, communicating and interpreting these predictions is not straightforward. Barriers hindering user uptake include a terminology gap between climate scientists and users, the difficulties of dealing with probabilistic outcomes for decision-making, and the lower skill of climate predictions compared to the skill of weather forecasts. This paper presents a gaming approach to break communication and understanding barriers through the application of the Weather Roulette conceptual framework. In the game, the player can choose between two forecast options, one that uses ECMWF seasonal predictions against one using climatology-derived probabilities. For each forecast option, the bet is spread proportionally to the predicted probabilities, either in a single year game or a game for the whole period of 33 past years. This paper provides skill maps of forecast quality metrics commonly used by the climate prediction community (e.g., ignorance skill score and ranked probability skill score), which in the game are linked to metrics easily understood by the business sector (e.g., interest rate and return on investment). In a simplified context, we illustrate how in skillful regions the economic benefits of using ECMWF predictions arise in the long term and are higher than using climatology. This paper provides an example of how to convey the usefulness of climate predictions and transfer the knowledge from climate science to potential users. If applied, this approach could provide the basis for a better integration of knowledge about climate anomalies into operational and managerial processes. [ABSTRACT FROM AUTHOR]

Realistic climate and weather prediction models are necessary to produce confidence in projections of future climate over many decades and predictions for days to seasons. These models must be physically justified and validated for multiple weather and climate processes. A key opportunity to accelerate model improvement is greater incorporation of process-oriented diagnostics (PODs) into standard packages that can be applied during the model development process, allowing the application of diagnostics to be repeatable across multiple model versions and used as a benchmark for model improvement. A POD characterizes a specific physical process or emergent behavior that is related to the ability to simulate an observed phenomenon. This paper describes the outcomes of activities by the Model Diagnostics Task Force (MDTF) under the NOAA Climate Program Office (CPO) Modeling, Analysis, Predictions and Projections (MAPP) program to promote development of PODs and their application to climate and weather prediction models. MDTF and modeling center perspectives on the need for expanded process-oriented diagnosis of models are presented. Multiple PODs developed by the MDTF are summarized, and an open-source software framework developed by the MDTF to aid application of PODs to centers' model development is presented in the context of other relevant community activities. The paper closes by discussing paths forward for the MDTF effort and for community process-oriented diagnosis. [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]

Doppler spectra measured by vertically pointing radars are inherently linked to raindrop size distributions (DSDs). But accurate estimation of DSDs remains challenging because raindrop spectra are broadened by atmospheric turbulence and shifted by vertical air motions. This paper presents a novel method to estimate vertical air motions in which there is no need to assume a model for DSD at each range gate. The theory of the new method is that the spectral difference between the adjacent range gates is contributed by vertical air motions and the variability of DSDs. The contribution of the change of DSDs is estimated by looking up the prepared tables [lookup tables (LUTs)] of raindrop velocity difference and shape function difference. Then the vertical air motions can be estimated by minimizing the cost function of the two spectra between the adjacent range gates. The retrieval algorithm is applied to three cases including a stratiform case and two convective cases observed by a C-band vertically pointing radar in Longmen, Guangdong Province, China, in June 2016. Before that, the spectrum broadening effect is removed by the traditional deconvolution method with a wind profiler. The vertical profiles of precipitation parameters are also retrieved to investigate the microphysical process. The precipitation parameters retrieved near the surface are compared with the ground data collected by a two-dimensional video disdrometer (2DVD), and the results show good agreement. Significance Statement: Estimating vertical air motions and raindrop size distributions are of great help to improve precipitation forecasts and weather modification. This study developed a new method to estimate the vertical air motions using Doppler spectra measured by a vertically pointing radar. The advantage of the new method is that there is no need to assume models for raindrop size distributions. We applied the new method to a stratiform case and two convective cases, and the vertical profiles of precipitation parameters are retrieved. The results show that both the dynamic and microphysical processes in convective cases have larger variability than those in the stratiform case. In the future, the characteristics of more types of precipitation clouds will be investigated. [ABSTRACT FROM AUTHOR]

As Earth's largest collection of freshwater, the Laurentian Great Lakes have enormous ecological and socioeconomic value. Their basin has become a regional hotspot of climatic and limnological change, potentially threatening its vital natural resources. Consequentially, there is a need to assess the current state of climate models regarding their performance across the Great Lakes region and develop the next generation of high-resolution regional climate models to address complex limnological processes and lake–atmosphere interactions. In response to this need, the current paper focuses on the generation and analysis of a 20-member ensemble of 3-km National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) simulations for the 2014/15 cold season. The study aims to identify the model's strengths and weaknesses; optimal configuration for the region; and the impacts of different physics parameterizations, coupling to a 1D lake model, time-variant lake-surface temperatures, and spectral nudging. Several key biases are identified in the cold-season simulations for the Great Lakes region, including an atmospheric cold bias that is amplified by coupling to a 1D lake model but diminished by applying the Community Atmosphere Model radiation scheme and Morrison microphysics scheme; an excess precipitation bias; anomalously early initiation of fall lake turnover and subsequent cold lake bias; excessive and overly persistent lake ice cover; and insufficient evaporation over Lakes Superior and Huron. The research team is currently addressing these key limitations by coupling NU-WRF to a 3D lake model in support of the next generation of regional climate models for the critical Great Lakes Basin. Significance Statement: Climate change poses a serious threat to the vital natural resources of the Laurentian Great Lakes region. Complex lake–atmosphere interactions and limnological processes are a challenge for regional climate models. To address the threat of climate change, there is a clear need to further evaluate and develop modeling tools for the Great Lakes Basin. Here, we evaluate the regional performance of the National Aeronautics and Space Administration's regional climate model at high spatial resolution in support of ongoing efforts to develop the next generation modeling tool for the Great Lakes region. [ABSTRACT FROM AUTHOR]

Agriculture is a highly weather-dependent activity, and climatic conditions impact both directly crop growth and indirectly diseases and pest developments causing yield losses. Weather forecasts are now a major component of various decision-support systems that assist farmers to optimize the positioning of crop protection treatments. However, properly accounting for weather uncertainty in these systems still remains a challenge. In this paper, three global and regional ensemble prediction systems (EPSs), covering different spatiotemporal scales, are coupled to a temperature-driven developmental model for grapevine moths in order to provide probabilistic forecasts of treatment dates. It is first shown that a parametric postprocessing of the EPSs significantly improves the prediction of treatment dates. Anticipating the need for phytosanitary treatments also requires seamless weather forecasts from the next hour to subseasonal time scales. An approach is presented to design seamless ensemble forecasts from the combination of the three EPSs used. The proposed method is able to leverage the increased performance of high-resolution EPS at short ranges, while ensuring a smooth transition toward larger-scale EPSs for longer ranges. The added value of this seamless integration on agronomic predictions is, however, difficult to assess with the current experimental setup. Additional simulations over a larger number of locations and years may be required. [ABSTRACT FROM AUTHOR]

After successful launch in November 2018 and successful commissioning of Metop-C, all three satellites of the EUMETSAT Polar System (EPS) are in orbit together and operational. EPS is part of the Initial Joint Polar System (IJPS) with the United States (NOAA) and provides the service in the midmorning orbit. The Metop satellites carry a mission payload of sounding and imaging instruments, which allow provision of support to operational meteorology and climate monitoring, which are the main mission objectives for EPS. Applications include numerical weather prediction, atmospheric composition monitoring, and marine meteorology. Climate monitoring is supported through the generation of long time series through the program duration of 20+ years. The payload was developed and contributed by partners, including NOAA, CNES, and ESA. EUMETSAT and ESA developed the space segment in cooperation. The system has proven its value since the first satellite Metop-A, with enhanced products at high reliability for atmospheric sounding, delivered a very strong positive impact on NWP and results beyond expectations for atmospheric composition and chemistry applications. Having multiple satellites in orbit—now three—has enabled enhanced and additional products with increased impact, like atmospheric motion vector products at latitudes not accessible to geostationary observations or increased probability of radio occultations and hence atmospheric soundings with the Global Navigation Satellite System (GNSS) Radio-Occultation Atmospheric Sounder (GRAS) instruments. The paper gives an overview of the system and the embarked payload and discusses the benefits of generated products for applications and services. The conclusions point to the follow-on system, currently under development and assuring continuity for another 20+ years. [ABSTRACT FROM AUTHOR]

Horizontal boundary layer roll vortices are a series of large-scale turbulent eddies that prevail in a hurricane's boundary layer. In this paper, a one-way nested sub-kilometer-scale large-eddy simulation (LES) based on the Weather Research and Forecasting (WRF) Model was used to examine the impact of roll vortices on the evolution of Hurricane Harvey around its landfall from 0000 UTC 25 August to 1800 UTC 27 August 2017. The simulation results imply that the turbulence in the LES can be attributed mainly to roll vortices. With the representation of roll vortices, the LES provided a better simulation of hurricane wind vertical structure and precipitation. In contrast, the mesoscale simulation with the YSU PBL scheme overestimated the precipitation for the hurricane over the ocean. Further analysis indicates that the roll vortices introduced a positive vertical flux and thinner inflow layer, whereas a negative flux maintained the maximum tangential wind at around 400 m above ground. During hurricane landfall, the weak negative flux maintained the higher wind in the LES. The overestimated low-level vertical flux in the mesoscale simulation with the YSU scheme led to overestimated hurricane intensity over the ocean and accelerated the decay of the hurricane during landfall. Rainfall analysis reveals that the roll vortices led to a weak updraft and insufficient water vapor supply in the LES. For the simulation with the YSU scheme, the strong updraft combined with surplus water vapor eventually led to unrealistic heavy rainfall for the hurricane over the ocean. [ABSTRACT FROM AUTHOR]

This study examines how varying wind profile coverages in the tropical cyclone (TC) core, near environment, and broader synoptic environment affects the structure and evolution of a simulated Atlantic Ocean hurricane through data assimilation. Three sets of observing system simulation experiments are examined in this paper. The first experiment establishes a benchmark for the case study specific to the forecast system used by assimilating idealized profiles throughout the parent domain. The second presents how TC analyses and forecasts respond to varying the coverage of swaths produced by polar-orbiting satellites of idealized wind profiles. The final experiment assesses the role of TC inner-core observations by systematically removing them radially from the center. All observations are simulated from a high-resolution regional "nature run" of a hurricane and the tropical atmosphere, assimilating with an ensemble square root Kalman filter and using the Hurricane Weather and Research Forecast regional model. Results compare observation impact with the analyses, domainwide and TC-centric error statistics, and TC structural differences among the experiments. The study concludes that the most accurate TC representation is a result of the assimilation of collocated and uniform thermodynamic and kinematics observations. Intensity forecasts are improved with increased inner-core wind observations, even if the observations are only available once daily. Domainwide root-mean-square errors are significantly reduced when the TC is observed during a period of structural change, such as rapid intensification. The experiments suggest the importance of wind observations and the role of inner-core surveillance when analyzing and forecasting realistic TC structure. [ABSTRACT FROM AUTHOR]

Microphysical processes within mixed-phase convective clouds can have cascading impacts on cloud properties and resultant precipitation. This paper investigates the role of microphysics in the lake-effect storm (LES) observed during intensive observing period 4 of the Ontario Winter Lake-effect Systems field campaign. Amicrophysical ensemble is composed of 24 simulations that differ in the microphysics scheme used (e.g., Weather Research and Forecasting Model microphysics options or a choice of two bulk adaptive habit models) along with changes in the representation of aerosol and potential ice nuclei concentrations, ice nucleation parameterizations, rain and ice fall speeds, spectral indices, ice habit assumptions, and the number of moments used for modeling ice-phase hydrometeors in each adaptive habit model. Each of these changes to microphysics resulted in varied precipitation types at the surface; 15 members forecast a mixture of snow, ice, and graupel, 7 members forecast only snow and ice, and the remaining 2 members forecast a combination of snow, ice, graupel, and rain. Observations from an optical disdrometer positioned to the south of the LES core indicate that 92% of the observed particles were snow and ice, 5% were graupel, and 3% were rain and drizzle. Analysis of observations spanning more than a point location, such as polarimetric radar observations and aircraft measurements of liquid water content, provides insight into cloud composition and processes leading to the differences at the surface. Ensemble spread is controlled by hydrometeor type differences spurred by processes or parameters (e.g., ice fall speed) that affect graupel mass. [ABSTRACT FROM AUTHOR]

While Hurricane Harvey will best be remembered for record rainfall that led to widespread flooding in southeastern Texas and western Louisiana, the storm also produced some of the most extreme wind speeds ever to be captured by an adaptive mesonet at landfall. This paper describes the unique tools and the strategy used by the Digital Hurricane Consortium (DHC), an ad hoc group of atmospheric scientists and wind engineers, to intercept and collect high-resolution measurements of Harvey's inner core and eyewall as it passed over Aransas Bay into mainland Texas. The DHC successfully deployed more than 25 observational assets, leading to an unprecedented view of the boundary layer and winds aloft in the eyewall of a major hurricane at landfall. Analysis of anemometric measurements and mobile radar data during heavy convection shows the kinematic structure of the hurricane at landfall and the suspected influence of circulations aloft on surface winds and extreme surface gusts. Evidence of mesoscale vortices in the interior of the eyewall is also presented. Finally, the paper reports on an atmospheric sounding in the inner eyewall that produced an exceptionally large and potentially record value of precipitable water content for observed soundings in the continental United States. [ABSTRACT FROM AUTHOR]

An important step in an ensemble Kalman filter (EnKF) algorithm is the integration of an ensemble of short-range forecasts with a numerical weather prediction (NWP) model. A multiphysics approach is used in the Canadian global EnKF system. This paper explores whether the many integrations with different versions of the model physics can be used to obtain more accurate and more reliable probability distributions for the model parameters. Some model parameters have a continuous range of possible values. Other parameters are categorical and act as switches between different parameterizations. In an evolutionary algorithm, the member configurations that contribute most to the quality of the ensemble are duplicated, while adding a small perturbation, at the expense of configurations that perform poorly. The evolutionary algorithm is being used in the migration of the EnKF to a new version of the Canadian NWP model with upgraded physics. The quality of configurations is measured with both a deterministic and an ensemble score, using the observations assimilated in the EnKF system. When using the ensemble score in the evaluation, the algorithm is shown to be able to converge to non-Gaussian distributions. However, for several model parameters, there is not enough information to arrive at improved distributions. The optimized system features slight reductions in biases for radiance measurements that are sensitive to humidity. Modest improvements are also seen in medium-range ensemble forecasts. [ABSTRACT FROM AUTHOR]

Current statistical postprocessing methods for probabilistic weather forecasting are not capable of using full spatial patterns from the numerical weather prediction (NWP) model. In this paper, we incorporate spatial wind speed information by using convolutional neural networks (CNNs) and obtain probabilistic wind speed forecasts in the Netherlands for 48 h ahead, based on KNMI's deterministic HARMONIE-AROME NWP model. The probabilistic forecasts from the CNNs are shown to have higher Brier skill scores for medium to higher wind speeds, as well as a better continuous ranked probability score (CRPS) and logarithmic score, than the forecasts from fully connected neural networks and quantile regression forests. As a secondary result, we have compared the CNNs using three different density estimation methods [quantized softmax (QS), kernel mixture networks, and fitting a truncated normal distribution], and found the probabilistic forecasts based on the QS method to be best. [ABSTRACT FROM AUTHOR]

Record-breaking precipitation due to a back-building convective system occurred in Kyushu Island, Japan, on 5 July 2017. In this paper, a quasi-stationary back-building convective system is reproduced using a regional weather prediction model initialized with a single representative sounding in which the land–sea distribution around the observed convective system is crudely simplified. The simulated convective system and heavy precipitation are reasonably similar to observations. Horizontal resolution finer than 1 km is found to be necessary for reproducing the convective system. The area of heavy precipitation tends to shift downstream with finer horizontal resolution. The surface temperature contrasts at the northern and southern coastlines cause sea breezes and a stationary convergence line between them continuously triggers cumulus clouds. The horizontal convergence near the surface is further enhanced by preceding cumulus clouds that cause the latent heating aloft and generate a mesoscale surface pressure depression. Vertical shear of the environmental wind is also found to be important for organizing the convective system but veering of its wind direction and a cold pool are not essential. [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]

The New York State Mesonet (NYSM) is a network of 126 standard environmental monitoring stations deployed statewide with an average spacing of 27 km. The primary goal of theNYSMis to provide high-quality weather data at high spatial and temporal scales to improve atmospheric monitoring and prediction, especially for extreme weather events. As compared with other statewide networks, the NYSM faced considerable deployment obstacles with New York's complex terrain, forests, and very rural and urban areas; its wide range of weather extremes; and its harsh winter conditions. To overcome these challenges, the NYSM adopted a number of innovations unique among statewide monitoring systems, including 1) strict adherence to international siting standards and metadata documentation; 2) a hardened system design to facilitate continued operations during extreme, high-impact weather; 3) a station design optimized to monitor winter weather conditions; and 4) a camera installed at every site to aid situational awareness. The network was completed in spring of 2018 and provides data and products to a variety of sectors including weather monitoring and forecasting, emergency management, agriculture, transportation, utilities, and education. This paper focuses on the standard network of the NYSM and reviews the network siting, site configuration, sensors, site communications and power, network operations and maintenance, data quality control, and dissemination. A few example analyses are shown that highlight the benefits of the NYSM. [ABSTRACT FROM AUTHOR]

Accurate simulation of planetary boundary layer height (PBLH) is key to greenhouse gas emission estimation, air quality prediction, and weather forecasting. This paper describes an extensive performance assessment of several Weather Research and Forecasting (WRF) Model configurations in which novel observations from ceilometers, surface stations, and a flux tower were used to study their ability to reproduce the PBLH and the impact that the urban heat island (UHI) has on the modeled PBLHs in the greaterWashington, D.C., area. In addition, CO2 measurements at two urban towers were compared with tracer transport simulations. The ensemble of models used four PBL parameterizations, two sources of initial and boundary conditions, and one configuration including the building energy parameterization urban canopy model. Results have shown low biases over the whole domain and period for wind speed, wind direction, and temperature, with no drastic differences between meteorological drivers. We find that PBLH errors are mostly positively correlated with sensible heat flux errors and that modeled positive UHI intensities are associated with deeper modeled PBLs over the urban areas. In addition, we find that modeled PBLHs are typically biased low during nighttime for most of the configurations with the exception of those using theMYNN parameterization, and these biases directly translate to tracer biases. Overall, the configurations using the MYNN scheme performed the best, reproducing the PBLH and CO2 molar fractions reasonably well during all hours and thus opening the door to future nighttime inverse modeling. [ABSTRACT FROM AUTHOR]

Extracting explicit severe weather forecast guidance from convection-allowing ensembles (CAEs) is challenging since CAEs cannot directly simulate individual severe weather hazards. Currently, CAE-based severe weather probabilities must be inferred from one or more storm-related variables, which may require extensive calibration and/or contain limited information. Machine learning (ML) offers a way to obtain severe weather forecast probabilities from CAEs by relating CAE forecast variables to observed severe weather reports. This paper develops and verifies a random forest (RF)-based ML method for creating day 1 (1200–1200 UTC) severe weather hazard probabilities and categorical outlooks based on 0000 UTC Storm-Scale Ensemble of Opportunity (SSEO) forecast data and observed Storm Prediction Center (SPC) storm reports. RF forecast probabilities are compared against severe weather forecasts from calibrated SSEO 2–5-km updraft helicity (UH) forecasts and SPC convective outlooks issued at 0600 UTC. Continuous RF probabilities routinely have the highest Brier skill scores (BSSs), regardless of whether the forecasts are evaluated over the full domain or regional/seasonal subsets. Even when RF probabilities are truncated at the probability levels issued by the SPC, the RF forecasts often have BSSs better than or comparable to corresponding UH and SPC forecasts. Relative to the UH and SPC forecasts, the RF approach performs best for severe wind and hail prediction during the spring and summer (i.e., March–August). Overall, it is concluded that the RF method presented here provides skillful, reliable CAE-derived severe weather probabilities that may be useful to severe weather forecasters and decision-makers. [ABSTRACT FROM AUTHOR]

Launched in 2006, the Formosa Satellite Mission 3-Constellation Observing System for Meteorology, Ionosphere and Climate (FORMOSAT-3/COSMIC) was the first constellation of microsatellites carrying global positioning system (GPS) radio occultation (RO) receivers. Radio occultation is an active remote sensing technique that provides valuable information on the vertical variations of electron density in the ionosphere, and temperature, pressure, and water vapor in the stratosphere and troposphere. COSMIC has demonstrated the great value of RO data in ionosphere, climate, and meteorological research and operational weather forecasting. However, there are still challenges using RO data, particularly in the moist lower troposphere and upper stratosphere. A COSMIC follow-on constellation, COSMIC-2, was launched into equatorial orbit in 2019. With increased signal-to-noise ratio (SNR) from improved receivers and digital beam steering antennas, COSMIC-2 will produce at least 5,000 high-quality RO profiles daily in the tropics and subtropics. In this paper, we summarize 1) recent (since 2011 when the last review was published) contributions of COSMIC and other RO observations to weather, climate, and space weather science; 2) the remaining challenges in RO applications; and 3) potential contributions to research and operations of COSMIC-2. [ABSTRACT FROM AUTHOR]

This paper describes the development of convolutional neural networks (CNN), a type of deep-learning method, to predict next-hour tornado occurrence. Predictors are a storm-centered radar image and a proximity sounding from the Rapid Refresh model. Radar images come from the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) and Gridded NEXRAD WSR-88D Radar dataset (GridRad), both of which are multiradar composites. We train separate CNNs on MYRORSS and GridRad data, present an experiment to optimize the CNN settings, and evaluate the chosen CNNs on independent testing data. Both models achieve an area under the receiver-operating-characteristic curve (AUC) well above 0.9, which is considered to be excellent performance. The GridRad model achieves a critical success index (CSI) of 0.31, and the MYRORSS model achieves a CSI of 0.17. The difference is due primarily to event frequency (percentage of storms that are tornadic in the next hour), which is 3.52% for GridRad but only 0.24% for MYRORSS. The best CNN predictions (true positives and negatives) occur for strongly rotating tornadic supercells and weak nontornadic cells in mesoscale convective systems, respectively. The worst predictions (false positives and negatives) occur for strongly rotating nontornadic supercells and tornadic cells in quasilinear convective systems, respectively. The performance of our CNNs is comparable to an operational machine-learning system for severe weather prediction, which suggests that they would be useful for real-time forecasting. [ABSTRACT FROM AUTHOR]