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Global Navigation Satellite System Reflectometry (GNSS-R)-based wind retrieval techniques use the global positioning system (GPS) signals scattered from the ocean surface in the forward direction, and can potentially work in all weather conditions. An overview of recent progress made in the Cyclone Global Navigation Satellite System (CYGNSS) level-2 surface wind products is given. To this end, four publicly released CYGNSS surface wind products—Science Data Record (SDR) v2.1, SDR v3.0, Climate Data Record (CDR) v1.1, and science wind speed product NOAA v1.1—are validated quantitatively against high-quality data from tropical buoy arrays. The latest released CYGNSS wind products (e.g., CDR v1.1, SDR v3.0, NOAA v1.1), as compared with these tropical buoy data, significantly outperform the SDR v2.1. Moreover, the uncertainty among these products is found to be less than 2 m s−1 root-mean-squared difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s−1. The quality of the CYGNSS wind is further assessed under different precipitation conditions in low winds, and in large-scale convective regions. Results show that the presence of rain appears to cause a slightly positive wind speed bias in all CYGNSS data. Nonetheless, the outcomes are encouraging for the recently released CYGNSS wind products in general, and for CYGNSS data in regions with precipitating deep convection. The overall comparison indicates a significant improvement in wind speed quality and sample size when going from the older version to any of the newer datasets.

In this study, we investigate the tendencies of gamma parameters for particle size distributions (PSDs) containing snowflake aggregates in orographic, convective, and stratiform clouds, above snowstorms and above rainstorms, in temperatures ranging from 0° to −45°C. We find a strong relationship between μ and Λ but no dependence on temperature. Higher μ are observed during the experiments sampling winter snowstorms, and lower μ are observed during experiments sampling frozen clouds above convective and orographic storms. We find that a gamma function with a μ of −1.25 provides the best average representation of PSD shape and the most accurate representation of PSD moments related to mass and reflectivity. We also provide a lookup table of maximum particle size boundaries that can be used to parameterize incomplete gamma functions with negative μ values. Significance Statement In many weather models and satellite retrieval algorithms, frozen clouds and precipitation are governed by the same assumptions even though they develop through different growth processes. This paper provides recommendations for snowflake aggregate size distributions that reflect natural conditions, and these recommended assumptions are demonstrated to improve estimates of mass and radar reflectivity. We studied a variety of storms, such as thunderstorms, snow storms, and winter rainstorms, and we found that our model for snowflake aggregates was nearly identical in all observed conditions.

Atmospheric deep moist convection has emerged as one of the most challenging topics for numerical weather prediction, due to its chaotic process of development and multiscale physical interactions. This study examines the dynamics and predictability of a weakly organized linear convective system using convection permitting EnKF analysis and forecasts with assimilating all-sky satellite radiances from a water vapor sensitive band of the Advanced Baseline Imager on GOES-16. The case chosen occurred over the Gulf of Mexico on 11 June 2017 during the NASA Convective Processes Experiment (CPEX) field campaign. Analysis of the water vapor and dynamic ensemble covariance structures revealed that meso-α-scale (2000–200 km) and meso-β-scale (200–20 km) initial features helped to constrain the general location of convection with a few hours of lead time, contributing to enhancing convective activity, but meso-γ-scale (20–2 km) or even-smaller-scale features with less than 30-min lead time were identified to be essential for capturing individual convective storms. The impacts of meso-α-scale initial features on the prediction of particular individual convective cells were found to be classified into two regimes; in a relatively dry regime, the meso-α-scale environment needs to be moist enough to support the development of the convection of interest, but in a relatively wet regime, a drier meso-α-scale environment is preferable to suppress the surrounding convective activity. This study highlights the importance of high-resolution initialization of moisture fields for the prediction of a quasi-linear tropical convective system, as well as demonstrating the accuracy that may be necessary to predict convection exactly when and where it occurs.

Forecast observing system simulation experiments (OSSEs) are conducted to assess the potential impact of geostationary microwave (GeoMW) sounder observations on numerical weather prediction forecasts. A regional OSSE is conducted using a tropical cyclone (TC) case that is very similar to Hurricane Harvey (2017), as hurricanes are among the most devastating of weather-related natural disasters, and hurricane intensity continues to pose a significant challenge for numerical weather prediction. A global OSSE is conducted to assess the potential impact of a single GeoMW sounder centered over the continental United States versus two sounders positioned at the current locations of the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellites (GOES) East and West. It is found that assimilation of GeoMW soundings result in better characterization of the TC environment, especially before and during intensification, which leads to significant improvements in forecasts of TC track and intensity. TC vertical structure (warm core thermal perturbation and horizontal wind distribution) is also substantially improved, as are the surface wind and precipitation extremes. In the global OSSE, assimilation of GeoMW soundings leads to slight improvement globally and significant improvement regionally, with regional impact equal to or greater than nearly all other observation types. Significance Statement This work seeks to determine the impact of a new geostationary microwave (GeoMW) sounder on tropical cyclone forecasts in particular, and on weather forecasts in general. It does so by assimilating simulated GeoMW sounder data into two different forecast models: one global and one regional. The data have a small positive impact globally, and a significant positive impact over the region viewed by the GeoMW instrument. In particular, assimilation of GeoMW data has a significant and positive impact on forecasts of tropical cyclone track, strength, and structure.

Vertical velocities and microphysical processes within deep convection are intricately linked, having wide-ranging impacts on water and mass vertical transport, severe weather, extreme precipitation, and the global circulation. The goal of this research is to investigate the functional form of the relationship between vertical velocity (w) and microphysical processes that convert water vapor into condensed water (M) in deep convection. We examine an ensemble of high-resolution simulations spanning a range of tropical and midlatitude environments, a variety of convective organizational modes, and different model platforms and microphysics schemes. The results demonstrate that the relationship between w and M is robustly linear, with the slope of the linear fit being primarily a function of temperature and secondarily a function of supersaturation. The R2 of the linear fit is generally above 0.6 except near the freezing and homogeneous freezing levels. The linear fit is examined both as a function of local in-cloud temperature and environmental temperature. The results for in-cloud temperature are more consistent across the simulation suite, although environmental temperatures are more useful when considering potential observational applications. The linear relationship between w and M is substituted into the condensate tendency equation and rearranged to form a diagnostic equation for w. The performance of the diagnostic equation is tested in several simulations, and it is found to diagnose the storm-scale updraft speeds to within 1 m s−1 throughout the upper half of the clouds. Potential applications of the linear relationship between w and M and the diagnostic w equation are discussed.

Much of the errors of atmospheric motion vectors (AMV) may be a consequence of algorithms not incorporating dynamical information. A physics-informed, artificial intelligence algorithm was developed that corrects errors of moisture tracking AMV (from the movement of water vapor) using numerical weather prediction (NWP) fields. The University of Arizona (UA) algorithm uses a variational method as a first step (fsUA); the second step then filters the first-stage AMVs using a random forest model that learns the error correction from NWP fields. The UA algorithm is compared with a traditional image feature tracking algorithm (JPL) using a global nature run as the “ground truth.” Experiments use global all-sky humidity fields at 500 and 850 hPa for 1–3 January 2006 and 1–3 July 2006. UA outputs AMVs with root-mean-square vector differences (RMSVDs) of 2 m s−1 for the tropics and ∼2–3 m s−1 for midlatitudes and the poles, whereas JPL outputs much higher RMSVDs of ∼3 m s−1 for the tropics and ∼3–9 m s−1 for the midlatitudes and poles. Although the algorithm fsUA produces approximately the same global RMSVDs as the JPL algorithm, fsUA has a higher resolution since it outputs an AMV per pixel, whereas the JPL algorithm uses a target box that effectively smooths the vectors. Furthermore, UA’s RMSVDs are lower than the intrinsic error (calculated from the differences between two reanalysis datasets). Even for error-prone regions with low moisture gradients and where winds are oriented along moisture isolines, UA’s absolute speed difference with “truth” stays within ∼3 m s−1.

Surface wind plays a crucial role in many local/regional weather and climate processes, especially through the exchanges of energy, mass, and momentum across Earth’s surface. However, there is a lack of consistent observations with continuous coverage over the global tropical ocean. To fill this gap, the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016, consisting of a constellation of eight small spacecrafts that remotely sense near-surface wind speed over the tropical and subtropical oceans with relatively high sampling rates both temporally and spatially. This current study uses data obtained from the Tropical Moored Buoy Arrays to quantitatively characterize and validate the CYGNSS derived winds over the tropical Indian, Pacific, and Atlantic Oceans. The validation results show that the uncertainty in CYGNSS wind speed, as compared with these tropical buoy data, is less than 2 m s−1 root-mean-square difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s−1. The quality of the CYGNSS wind is further assessed under different precipitation conditions, and in convective cold-pool events, identified using buoy rain and temperature data. Results show that CYGNSS winds compare fairly well with buoy observations in the presence of rain, though at low wind speeds the presence of rain appears to cause a slight positive wind speed bias in the CYGNSS data. The comparison indicates the potential utility of the CYGNSS surface wind product, which in turn may help to unravel the complexities of air–sea interaction in regions that are relatively undersampled by other observing platforms.

Latent and sensible heat fluxes over the oceans are believed to play an important role in the genesis and evolution of marine-based extratropical cyclones (ETCs) and affect rapid cyclogenesis. Observations of ocean surface heat fluxes are limited from existing in situ and remote sensing platforms, which may not offer sufficient spatial and temporal resolution. In addition, substantial precipitation frequently veils the ocean surface around ETCs, limiting the capacity of spaceborne instruments to observe the surface processes within maturing ETCs. Although designed as a tropics-focused mission, the Cyclone Global Navigation Satellite System (CYGNSS) can observe ocean surface wind speed and heat fluxes within a notable quantity of low-latitude extratropical fronts and cyclones. These observations can assist in understanding how surface processes may play a role in cyclogenesis and evolution. This paper illustrates CYGNSS’s capability to observe extratropical cyclones manifesting in various ocean basins throughout the globe and shows that the observations provide a robust sample of ETCs winds and surface fluxes, as compared with a reanalysis dataset.

Wind-tracking algorithms produce atmospheric motion vectors (AMVs) by tracking clouds or water vapor across spatial–temporal fields. Thorough error characterization of wind-tracking algorithms is critical in properly assimilating AMVs into weather forecast models and climate reanalysis datasets. Uncertainty modeling should yield estimates of two key quantities of interest: bias, the systematic difference between a measurement and the true value, and standard error, a measure of variability of the measurement. The current process of specification of the errors in inverse modeling is often cursory and commonly consists of a mixture of model fidelity, expert knowledge, and need for expediency. The method presented in this paper supplements existing approaches to error specification by providing an error characterization module that is purely data-driven. Our proposed error characterization method combines the flexibility of machine learning (random forest) with the robust error estimates of unsupervised parametric clustering (using a Gaussian mixture model). Traditional techniques for uncertainty modeling through machine learning have focused on characterizing bias but often struggle when estimating standard error. In contrast, model-based approaches such as k-means or Gaussian mixture modeling can provide reasonable estimates of both bias and standard error, but they are often limited in complexity due to reliance on linear or Gaussian assumptions. In this paper, a methodology is developed and applied to characterize error in tracked wind using a high-resolution global model simulation, and it is shown to provide accurate and useful error features of the tracked wind.

This study applies an idealized modeling framework, alongside a Bayesian Markov chain Monte Carlo (MCMC) algorithm, to explore which combinations of upstream environmental conditions and cloud microphysical parameter values can produce a particular precipitation distribution over an idealized two-dimensional, bell-shaped mountain. Simulations focus on orographic precipitation produced when an atmospheric river interacts with topography. MCMC-based analysis reveals that different combinations of parameter values produce a similar precipitation distribution, with the most influential parameters being relative humidity (RH), horizontal wind speed (U), surface potential temperature (θsfc), and the snow fall speed coefficient (As). RH, U, and As exhibit interdependence: changes in one or more of these factors can be mitigated by compensating changes in the other(s) to produce similar orographic precipitation rates. The results also indicate that the parameter sensitivities and relationships can vary for spatial subregions and given different environmental conditions. In particular, high θsfc values are more likely to produce the target precipitation rate and spatial distribution, and thus the ensemble of simulations shows a preference for liquid precipitation at the surface. The results presented here highlight the complexity of orographic precipitation controls, and have implications for flood and water management, observational efforts, and climate change.

We develop a methodology for identification of candidate observables that best constrain the parameterization of physical processes in numerical models. This methodology consists of three steps: (i) identifying processes that significantly impact model results, (ii) identifying observables that best constrain the influential processes, and (iii) investigating the sensitivity of the model results to the measurement error and vertical resolution of the constraining observables. This new methodology is applied to the Jet Propulsion Laboratory stochastic multiplume Eddy-Diffusivity/Mass-Flux (JPL-EDMF) model for two case studies representing nonprecipitating marine stratocumulus and marine shallow convection. The uncertainty of physical processes is characterized with uncertainty of model parameters. We find that the most uncertain processes in the JPL-EDMF model are related to the representation of lateral entrainment for convective plumes and parameterization of mixing length scale for the eddy-diffusivity part of the model. The results show a strong interaction between these uncertain processes. Measurements of the water vapor profile for shallow convection and of the cloud fraction profile for the stratocumulus case are among those measurements that best constrain the uncertain JPL-EDMF processes. The interdependence of the required vertical resolution and error characteristics of the observational system are shown. If the observations are associated with larger error, their vertical resolution has to be finer and vice versa. We suggest that the methodology and results presented here provide an objective basis for defining requirements for future observing systems such as future satellite missions to observe clouds and the planetary boundary layer.

Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.

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

A technology revolution in Earth observation sensor design is occurring. This revolution in part is associated with the emergence of CubeSat platforms that have forced a de facto standardization on the volume and power into which sensors have to fit. The extent that small sensors can indeed provide similar or replacement capabilities compared to larger and more expensive counterparts has barely been demonstrated and any loss of capability of smaller systems weighed against the gains in costs and new potential capabilities offered by implementing them with a more distributed observing strategy also has not yet been embraced. This paper provides four examples of observations made with prototype miniaturized observing systems, including from CubeSats, that offer a glimpse of this emerging sensor revolution and a hint at future observing system design.

Predictions of the Earth system, such as weather forecasts and climate projections, require models informed by observations at many levels. Some methods for integrating models and observations are very systematic and comprehensive (e.g., data assimilation), and some are single purpose and customized (e.g., for model validation). We review current methods and best practices for integrating models and observations. We highlight how future developments can enable advanced heterogeneous observation networks and models to improve predictions of the Earth system (including atmosphere, land surface, oceans, cryosphere, and chemistry) across scales from weather to climate. As the community pushes to develop the next generation of models and data systems, there is a need to take a more holistic, integrated, and coordinated approach to models, observations, and their uncertainties to maximize the benefit for Earth system prediction and impacts on society.

It has long been known that model physics uncertainty can contribute as much or more to errors in forecasting and data assimilation as errors in initial conditions. Many studies have attempted to include the effects of model physics uncertainty in data assimilation by introducing static perturbations to model parameters. In such studies, parameter values are modified at the beginning of a simulation and remain unchanged throughout the duration of the forecast. Uncertainty is spanned by generating an ensemble of forecasts, each member having a different set of parameter values. Other studies have implemented dynamic perturbations to parameters, introducing methods that modify parameter values online in a stochastic fashion. We present here the results of a study that investigates the sensitivity of convective cloud structures to static and stochastic cloud microphysical parameter perturbations. Static parameter values are drawn from a database produced by a Markov chain Monte Carlo algorithm, while stochastic perturbations are applied via a stochastically perturbed parameterization (SPP) scheme. Both static parameter perturbations and SPP are applied to multiple microphysical parameters within a Lagrangian column model, used in several prior published studies. The 1D column microphysics model is forced with prescribed time-varying profiles of temperature, humidity and vertical velocity in such a way as to emulate the environment inside of a convective storm. This modeling framework allows for investigation of the effect of changes in model physics parameters on the model output in isolation from any feedback to the cloud-scale dynamics. The results are evaluated in terms of changes to the ensemble mean and variance of time evolving profiles of hydrometeor mass quantities, the microphysics processes within the model as well as in terms of the simulated column integral microphysics-sensitive satellite-based observables. The outcomes of our experiments indicate a high degree of sensitivity of the to the way in which the SPP scheme is implemented. In particular, the distributions from which parameters are drawn, as well as the decorrelation time scale, have a large effect on the simulation outcomes. We discuss the results of SPP, compare with our static perturbation experiments, and note the implications for convective scale data assimilation.

Surface latent and sensible heat fluxes are important for extratropical cyclone evolution and intensification. Because extratropical cyclone genesis often occurs at low-latitude, CYGNSS surface latent and sensible heat flux retrievals are composited to provide a mean picture of their spatial distribution in low-latitude oceanic extratropical cyclones. CYGNSS heat fluxes are not affected by heavy precipitation and offer observations of storms with frequent revisit times. Consistent with prior results obtained for cyclones in the Gulf Stream region, the fluxes are strongest in the wake of the cold fronts, and weakest to negative in the warm sector in advance of the cold fronts. As cyclone strength increases, or mean precipitable water decreases, the maximum in surface heat fluxes increases while the minimum decreases. This impacts the changes in fluxes during cyclone intensification: the post-cold frontal surface heat flux maximum increases due to the increase in near surface winds. During cyclone dissipation, the fluxes in this sector decrease, due to the decrease in winds and in temperature and humidity contrast. The warm sector minimum decreases throughout the entire cyclone lifetime and is mostly driven by sea-air temperature and humidity contrast changes. However, during cyclone dissipation, the surface heat fluxes increase along the cold front in a narrow band to the east, independently from changes in the cyclone characteristics. This suggests that, during cyclone dissipation, energy transfers from the ocean to the atmosphere are linked to frontal in addition to synoptic-scale processes.

The recent successful space-borne demonstration of a miniaturized CubeSat precipitation radar is highlighted. The low cost of such a radar, together with the availability of small satellite, platforms to carry it, now make it feasible to consider employing a more distributed approach to observe important atmospheric processes that relate to precipitation. An approach to quantify the transport of water and air by deep convection is described based on a clustering of small radar satellites providing measurements seconds apart. This strategy now adds time as a new dimension for observing such processes. A mission concept, referred to as D-train, comprised of a train of three satellites 30, 90, and 120 s apart is described, and the expected performance of it for providing measures of convective transport is examined based on a large ensemble of simulations of convection with an advanced cloud-resolving model.

A Bayesian Markov chain Monte Carlo (MCMC) algorithm is utilized to compare the skill of an A-Train-like observing system with a cloud, convection, and precipitation (CCP) observing system like that contemplated for the 2020s by the 2017 National Academy of Sciences Decadal Survey. The main objective is to demonstrate a framework for observational trade space studies. This initial work focuses on weakly precipitating warm shallow cumulus constructed from in situ data. Radiative computations are based on Mie theory with spherical assumptions. Simulated measurements in the CCP configuration consist of W- and Ka-band radar reflectivity and path-integrated attenuation, 31 and 94 GHz brightness temperatures (Tb), and visible and near-infrared reflectances. The collection of measurements in the CloudSat configuration is identical, but includes a single 94 GHz radar frequency, and the uncertainty in the 94 GHz microwave brightness temperature is increased to mimic the CloudSat Tb product. The experiments demonstrate that it remains a challenge to diagnose cloud properties in the presence of light rain because of the tendency of microwave remote sensing to respond to the higher moments of the hydrometeor populations. Rain properties are significantly better constrained than cloud properties, even in the optimal CCP configuration. The addition of Ka-band measurements places substantial constraints on the precipitation rain effective radius and rain rates. The Tb offers important information regarding the column-integrated condensate mass, the measurement accuracy of which appears more likely to affect the retrievals of clouds with low liquid water path. The constraints provided by reflectances are largely restricted to regions near the cloud top, particularly in the raining cases.

This study examines the error characteristics of atmospheric motion vectors (AMVs) obtained by tracking the movement of water vapor features. A high-resolution numerical simulation of a dynamic weather event is used as a baseline, and AMVs tracked from retrieved water vapor fields are compared with the “true” winds produced by the model. The sensitivity of AMV uncertainty to time interval, AMV tracking window size, water vapor content, horizontal gradient, and wind structure is examined. AMVs are derived from the model water vapor field at a specific height and also from water vapor fields vertically blurred using smoothing functions consistent with high-spectral-resolution infrared (IR) and high-frequency microwave (MW) water vapor sounders. Uncertainties in water vapor AMVs are state dependent and are largest for regions with small water vapor content and small water vapor spatial gradient and in places where the flow runs parallel to contours of constant water vapor content. Smoothing of water vapor consistent with IR and MW retrievals does not increase AMV uncertainty; however, the yield of AMVs from IR sounders is much lower than from MW sounders because of the inability of IR sounders to retrieve water vapor below clouds. The yield and error are similar for AMVs in the lower and upper troposphere, even though the water vapor content in the upper troposphere is much smaller. The results have implications for the design of new observing systems, as well as the specification of errors when AMVs are ingested in data assimilation systems.

The precise influence of climate change on extratropical cyclone genesis and evolution is an important (but as yet unsolved) problem, given their physical and economic impact on a large portion of the planet’s population. However, extratropical cyclones are also affected by the competing influences of forcing mechanisms at a wide range of spatial scales, complicating the problem. While the advent of idealized numerical modeling has allowed great strides in addressing these complications and achieving some qualitative consensus in the literature, there is still some quantitative disagreement about response magnitude and where local maxima and minima in the response may be located. Thus, the advantages inherent in the variety of idealized numerical modeling methods used to address this problem are also a drawback, as it can be difficult to draw one-to-one comparisons across experiments. Although the effects of particular model architecture choices such as microphysical and cumulus schemes are well-documented, others are less understood. In this study, we examine the role of Coriolis approximations by comparing a new set of ETC sensitivity experiments using a linear β-plane approximation to an existing set of extratropical sensitivity experiments using a constant f-plane approximation. ETCs within the new β-plane experiment are found to generally decrease in strength with temperature, as measured by both minimum sea level pressure and maximum eddy kinetic energy (EKE). A small increase in EKE is observed at the warmest temperatures, likely due to diabatic influences disrupting flow within the warm conveyor belt. While seemingly contradictory to the previous f-plane results, the two experiments are instead found to be qualitatively similar upon further inspection, with an offset of approximately 8 K. This offset is primarily due to the Coriolis approximations, although the initial stability profile (affected by the Coriolis approximation) has a marginal influence.

The NASA Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) project included goals related to aerosol particle life cycle in convective regimes. Using the University of Wisconsin High Spectral Resolution Lidar system at Huntsville, Alabama, USA, and the NASA DC-8 research aircraft, we investigate the altitude dependence of aerosol, water vapor and Altocumulus (Ac) properties in the free troposphere from a canonical 12 August 2013 convective storm case as a segue to a presentation of a mission-wide analysis. It stands to reason that any moisture detrainment from convection must have an associated aerosol layer. Modes of covariability between aerosol, water vapor and Ac are examined relative to the boundary layer entrainment zone, 0 ∘C level, and anvil, a region known to contain Ac clouds and a complex aerosol layering structure (Reid et al., 2017). Multiple aerosol layers in regions warmer than 0 ∘C were observed within the planetary boundary layer entrainment zone. At 0 ∘C there is a proclivity for aerosol and water vapor detrainment from storms, in association with melting level Ac shelves. Finally, at temperatures colder than 0 ∘C, weak aerosol layers were identified above Cumulus congestus tops (∼0 and ∼-20 ∘C). Stronger aerosol signals return in association with anvil outflow. In situ data suggest that detraining particles undergo aqueous-phase or heterogeneous chemical or microphysical transformations, while at the same time larger particles are being scavenged at higher altitudes leading to enhanced nucleation. We conclude by discussing hypotheses regarding links to aerosol emissions and potential indirect effects on Ac clouds.

Microphysical (MP) schemes contain parameters whose values can impact the amount and location of forecasted precipitation, and sensitivity is typically explored by perturbing one parameter at a time while holding the rest constant. Although much can be learned from these “one-at-a-time” studies, the results are limited as these methods do not allow for nonlinear interactions of multiple perturbed parameters. This study applies the Morris one-at-a-time (MOAT) method, a robust statistical tool allowing for simultaneous perturbation of numerous parameters, to explore orographic precipitation sensitivity to changes in microphysical and environmental parameters within an environment characteristic of an atmospheric river. Results show parameters associated with snow fall speed coefficient As and density ρs, ice-cloud water collection efficiency (ECI), rain accretion (WRA), relative humidity, zonal wind speed, and surface potential temperature cause the largest influence on simulated precipitation. MP and environmental parameter perturbations can cause precipitation changes of similar magnitude, but results vary by location on the mountain. Different environments are also tested, with As being the most influential MP parameter regardless of environment. Fewer MP parameters influence precipitation in a faster-wind-speed environment, possibly due to the stronger dynamical forcing upwind and different wave dynamics downwind, compared to a slower-wind-speed environment. Finally, perturbing MP parameters within a single scheme can result in precipitation variations of similar magnitude compared to using entirely different microphysics schemes. MOAT results presented in this study have implications for Bayesian parameter estimation methods and stochastic parameterization within ensemble forecasting.

Monte Carlo and Morris screening techniques are used to examine the relative sensitivity of deep convective simulations to changes in initial conditions (IC) versus changes to microphysical parameters (MP). IC are perturbed using a set of empirical orthogonal function–principal component pairs obtained from a database of tropical soundings, while MP are perturbed consistent with their range of realistic values. Monte Carlo experiments provide a broad overview of parameter–output response, while Morris screening techniques identify the most significant influences on specific model output variables. Changes to MP produce a similar order-of-magnitude response in convective hydrologic cycle, dynamics, and latent heating as changes to IC. Changes in IC appear to have a larger effect on radiative fluxes than perturbations to MP. The distribution of low-level latent heating reveals that changes in MP have a larger influence on cold pool properties than changes to the environment. The dominant effects are produced by a subset of cloud MP and thermodynamic structure functions, indicating perturbation of a subset of the control factors may be used to produce most of the variability in a short-term convective-scale ensemble forecast. The most influential MP are the autoconversion threshold, the rain particle size distribution intercept, and the ice particle fall speed parameters. The most influential EOFs are those that correspond to variability in lower- to midtropospheric temperature and water vapor, as well as zonal low-level shear. The results have implications for both the understanding of what influences convective development and the design of ensemble-based prediction and data assimilation systems.

Wind-tracking algorithms produce Atmospheric Motion Vectors (AMVs) by tracking clouds or water vapor across spatial-temporal fields. Thorough error characterization (also known as uncertainty quantification) of wind-tracking algorithms is critical in properly assimilating AMVs into weather forecast models and climate reanalysis datasets. Uncertainty quantification should yield estimates of two key quantities of interest: bias, the systematic difference between a measurement and the true value, and standard error, a measure of variability of the measurement. The current process of specification of the errors input into inverse modelling is often cursory and commonly consists of a mixture of model fidelity, expert knowledge, and need for expediency. The methods presented in this paper supplement existing approaches to error specification by providing an error-characterization module that is purely data-driven and requires few tuning parameters. This paper proposes an error-characterization method that combines the flexibility of machine learning (random forest) with the robust error estimates of unsupervised parametric clustering (using a Gaussian Mixture Model). Traditional techniques for uncertainty quantification through machine learning have focused on characterizing bias, but often struggle when estimating standard error. In contrast, model-based approaches such as k-means or Gaussian mixture modelling can provide reasonable estimates of both bias and standard error, but they are often limited in complexity due to reliance on linear or Gaussian assumptions. In this paper, a methodology is developed and applied to characterize error in tracked-wind using a high-resolution global model simulation, and it is shown to adequately capture the error features of the tracked wind.

Where clouds occur, their water content is always positive definite, and may be near zero. In addition, it is common for errors in remote-sensing observations of clouds and rainfall to be represented as a fraction of the measurement. Furthermore, there is nonlinearity in the relationships among cloud environment, cloud microphysical processes, and the amount and distribution of cloud and precipitation. For these reasons, data assimilation algorithms that rely on linearity and assumptions of Gaussian probability distributions may have difficulty in assimilating observations in cloudy regions, as well as producing an analysis that realistically represents the actual distribution of clouds and precipitation. A recently developed ensemble filter algorithm, the Gamma, Inverse-Gamma, and Gaussian Ensemble Kalman Filter (GIGG-EnKF), allows for fractional observation errors and positive-definite quantities. As such, it has promise for producing more effective and accurate data assimilation and retrievals of clouds and precipitation. This study evaluates the effectiveness of the GIGG-EnKF by using it to estimate the values of warm rain cloud microphysical parameters in a cloud model using observations of precipitation. GIGG-EnKF estimates of cloud parameters and rainfall are compared with a Bayesian reference solution returned by a Markov chain Monte Carlo algorithm, and with a perturbed-observations Ensemble transform Kalman filter (ETKF). The ETKF produces an ensemble with a substantial number of negative (non-physical) precipitation rates and parameter values. In contrast, the GIGG-EnKF precipitation analysis is positive definite, and is able to adapt to changes in the observation value (and observation uncertainty). Nonlinearity in the parameter–precipitation relationship is handled by implementing an iterative outer loop regression from the observation space to state space. The positive-definite constraint and natural accommodation of fractional error, along with the iterative outer loop, produces an accurate reproduction of the Bayesian analysis of the precipitation rate and cloud microphysical parameter values.

This work demonstrates the use of Sobol’s sensitivity analysis framework to examine multivariate input–output relationships in dynamical systems. The methodology allows simultaneous exploration of the effect of changes in multiple inputs, and accommodates nonlinear interaction effects among parameters in a computationally affordable way. The concept is illustrated via computation of the sensitivities of atmospheric general circulation model (AGCM)-simulated tropical cyclones to changes in model initial conditions. Specifically, Sobol’s variance-based sensitivity analysis is used to examine the response of cyclone intensity, cloud radiative forcing, cloud content, and precipitation rate to changes in initial conditions in an idealized AGCM-simulated tropical cyclone (TC). Control factors of interest include the following: initial vortex size and intensity, environmental sea surface temperature, vertical lapse rate, and midlevel relative humidity. The sensitivity analysis demonstrates systematic increases in TC intensity with increasing sea surface temperature and atmospheric temperature lapse rates, consistent with many previous studies. However, there are nonlinear interactions among control factors that affect the response of the precipitation rate, cloud content, and radiative forcing. In addition, sensitivities to control factors differ significantly when the model is run at different resolution, and coarse-resolution simulations are unable to produce a realistic TC. The results demonstrate the effectiveness of a quantitative sensitivity analysis framework for the exploration of dynamic system responses to perturbations, and have implications for the generation of ensembles.

This study explores the sensitivity of clouds and precipitation to microphysical parameter perturbations using idealized simulations of moist, nearly neutral flow over a bell-shaped mountain. Numerous parameters are perturbed within the Morrison microphysics scheme. The parameters that most affect cloud and precipitation characteristics are the snow fall speed coefficient As, snow particle density ρs, rain accretion (WRA), and ice–cloud water collection efficiency (ECI). Surface precipitation rates are affected by As and ρs through changes to the precipitation efficiency caused by direct and indirect impacts on snow fall speed, respectively. WRA and ECI both affect the amount of cloud water removed, but the precipitation sensitivity differs. Large WRA results in increased precipitation efficiency and cloud water removal below the freezing level, indirectly decreasing cloud condensation rates; the net result is little precipitation sensitivity. Large ECI removes cloud water above the freezing level but with little influence on overall condensation rates. Two environmental experiments are performed to test the robustness of the results: 1) reduction of the wind speed profile by 30% (LowU) and 2) decreasing the surface potential temperature to induce a freezing level below the mountain top (LowFL). Parameter perturbations within LowU result in similar mechanisms acting on precipitation, but a much weaker sensitivity compared to the control. The LowFL case shows ρs is no longer a dominant parameter and As now induces changes to cloud condensation, since more of the cloud depth is present above the freezing level. In general, perturbations to microphysical parameters affect the location of peak precipitation, while the total amount of precipitation is more sensitive to environmental parameter perturbations.

In Naud et al., a compositing method was utilized with CloudSat– CALIPSO observations to obtain mean transects of cloud vertical distribution and surface precipitation across cold fronts, and to examine their sensitivity to the large-scale properties of the parent extratropical cyclone. This reply demonstrates the value of compositing for evaluating numerical models, and presents additional results that address the issue of the sensitivity of the initial results to the frontal detection methodology and the potential misclassification of occlusions as cold fronts. Here a sensitivity study of the cold front composite transects of cloud cover to the input datasets or the method utilized to locate the cold fronts demonstrates that these composite transects are robust and only marginally sensitive to cold front location methods. The same conclusion is reached for the robustness of the contrast between Northern and Southern Hemisphere cloud transects. While occlusions cannot directly be flagged within the database at this point, comparisons of transects obtained for subsets of cyclones of different age indicate that the misclassification of occluded fronts as cold fronts does not explain the predominance of cloud and precipitation on the warm side of the cold fronts. The strong signal on the warm side might be better explained by a predominance of forward sloping cold fronts, or the presence of the warm conveyor belt.

The dynamics and precipitation in extratropical cyclones (ETCs) are known to be sensitive to changes in the cyclone environment, with increases in bulk water vapor and baroclinicity both leading to increases in storm strength and precipitation. Studies that demonstrate this sensitivity have commonly varied either the cyclone moisture or baroclinicity, but seldom both. In a changing climate, in which the near-surface equator to pole temperature gradient may weaken while the bulk water vapor content of the atmosphere increases, it is important to understand the relative response of ETC strength and precipitation to changes in both factors simultaneously. In this study, idealized simulations of ETC development are conducted in a moist environment using a model with a full suite of moist physics parameterizations. The bulk temperature (and water vapor content) and baroclinicity are systematically varied one at a time, then simultaneously, and the effect of these variations on the storm strength and precipitation is assessed. ETC intensity exhibits the well-documented response to changes in baroclinicity, with stronger ETCs forming in higher baroclinicity environments. However, increasing water vapor content produces non-monotonic changes in storm strength, in which storm intensity first increases with increasing environmental water vapor, then decreases above a threshold value. Examination of the storm geographic extent indicates cyclone size also decreases above a threshold value of bulk environmental temperature (and water vapor). Decrease in storm size is concomitant with an increase in the convective fraction of precipitation and a shift in the vertical distribution of latent heating. The results indicate the existence of at least two regimes for ETC development, each of which exhibit significantly different distributions of PV due to differences in timing and location of convective heating.

The Cyclone Global Navigation Satellite System (CYGNSS) mission, launched in December 2016, is designed to estimate surface wind speeds over the global tropical oceans. Nevertheless, its orbit allows the constellation to view regions up to 40° latitude. As such, it is possible that CYGNSS will provide observations of a number of low-latitude extratropical cyclones and their associated fronts. In this study, one year of simulated CYGNSS specular point locations is combined with a database of objectively identified fronts and cyclones to assess the potential efficacy of CYGNSS for observing extratropical systems. It is found that, with the exception of regions poleward of warm fronts, the subset of locations in the simulated CYGNSS dataset nearly exactly matches the distribution of wind speeds and surface fluxes across frontal zones and near cyclone centers in the reanalysis database.

Three-dimensional large eddy simulations (LES) are used to analyze a springtime Arctic mixed-phase stratocumulus observed on 26 April 2008 during the Indirect and Semi-Direct Aerosol Campaign (ISDAC). Two subgrid-scale turbulence parameterizations are compared. The first scheme is a 1.5-order turbulent kinetic energy (1.5-TKE) parameterization that has been previously applied to boundary layer cloud simulations. The second scheme, Cloud Layers Unified By Binormals (CLUBB), provides higher-order turbulent closure with scale-awareness. The simulations, in comparisons with observations, show that both schemes produce the liquid profiles within measurement variability, but underpredict ice water mass and overpredict ice number concentration. The simulation using CLUBB underpredicted liquid water path more than the simulation using the 1.5-TKE scheme, so the turbulent length scale and horizontal grid box size were increased to increase liquid water path and reduce dissipative energy. The LES simulations show this stratocumulus cloud to maintain a closed cellular structure, similar to observations. The updraft and downdraft cores self-organize into a larger meso-γ scale convective pattern with the 1.5-TKE scheme, but the cores remain more isotropic with the CLUBB scheme. Additionally, the cores are often composed of liquid and ice instead of exclusively containing one or the other. These results provide insight into traditionally unresolved and unmeasurable aspects of an Arctic mixed-phase cloud. From analysis, this cloud's updraft and downdraft cores appear smaller than other closed-cell stratocumulus such as midlatitude stratocumulus and Arctic autumnal mixed-phase stratocumulus due to the weaker downdrafts and lower precipitation rates.

As part of the Southeast United States based Studies of Emissions & Atmospheric Composition, Clouds & Climate Coupling by Regional Surveys (SEAC4RS), and collinear with part of the Southeast Atmosphere Study (SAS), the University of Wisconsin High Spectral Resolution Lidar (UW-HSRL) system was deployed to the University of Alabama from June 19th through November 4th, 2013. With a collocated Aerosol Robotic NETwork (AERONET) sun photometer, a nearby Chemical Speciation Network (PM2.5) measurement station, and near daily ozonesonde releases for the August-September SEAC4RS campaign, the site allowed the region's first comprehensive diurnal monitoring of aerosol particle vertical structure. A 532 nm lidar ratio of 55 sr provided good closure between aerosol backscatter and AERONET Aerosol Optical Thickness (AOT). A principle component analysis was performed to identify key modes of variability in aerosol backscatter. “Fair weather” days exhibited classic planetary boundary layer (PBL) structure of a mixed layer accounting for ~50% of AOT and an entrainment zone providing another 25%. An additional 5-15% of variance is gained from the lower free troposphere from either convective detrainment or frequent intrusions of Western United States biomass burning smoke. Generally aerosol particles were contained below the 0o C level, a common level of stability in convective regimes. However, occasional strong injections of smoke to the upper troposphere were also observed, accounting for the remaining 10-15% variability in AOT. Examples of these common modes of variability in frontal and convective regimes are presented, demonstrating why AOT often has only a weak relationship to surface PM2.5 concentration.

Retrievals of liquid cloud properties from remote sensing observations by necessity assume sufficient information is contained in the measurements, and in the prior knowledge of the cloudy state, to uniquely determine a solution. Bayesian algorithms produce a retrieval that consists of the joint probability distribution function (PDF) of cloud properties given the measurements and prior knowledge. The Bayesian posterior PDF provides the maximum likelihood estimate, the information content in specific measurements, the effect of observation and forward model uncertainties, and quantitative error estimates. It also provides a test of whether, and in which contexts, a set of observations is able to provide a unique solution. In this work, a Bayesian Markov chain Monte Carlo (MCMC) algorithm is used to sample the joint posterior PDF for retrieved cloud properties in shallow liquid clouds over the remote Southern Ocean. Combined active and passive observations from spaceborne W-band cloud radar and visible and near-infrared reflectance are used to retrieve the parameters of a gamma particle size distribution (PSD) for cloud droplets and drizzle. Combined active and passive measurements are able to distinguish between clouds with and without precipitation; however, unique retrieval of PSD properties requires specification of a scene-appropriate prior estimate. While much of the uncertainty in an unconstrained retrieval can be mitigated by use of information from 94-GHz passive brightness temperature measurements, simply increasing measurement accuracy does not render a unique solution. The results demonstrate the robustness of a Bayesian retrieval methodology and highlight the importance of an appropriately scene-consistent prior constraint in underdetermined remote sensing retrievals.

The NASA Cyclone Global Navigation Satellite System (CYGNSS) was launched in late 2016. It will make available frequent ocean surface wind speed observations throughout the life cycle of tropical storms and hurricanes. In this study, the impact of CYGNSS ocean surface winds on numerical simulations of a hurricane case is assessed with a research version of the Hurricane Weather Research and Forecasting Model and a Gridpoint Statistical Interpolation analysis system in a regional observing system simulation experiment framework. Two different methods for reducing the CYGNSS data volume were tested: one in which the winds were thinned and one in which the winds were superobbed.The results suggest that assimilation of the CYGNSS winds has great potential to improve hurricane track and intensity simulations through improved representations of the surface wind fields, hurricane inner-core structures, and surface fluxes. The assimilation of the superobbed CYGNSS data seems to be more effective in improving hurricane track forecasts than thinning the data.

In this study, an ice-phase microphysics forward model has been developed for the Weather Research and Forecasting (WRF) Model three-dimensional variational data assimilation (WRF 3D-Var) system. Radar forward operators for reflectivity and the polarimetric variable, specific differential phase ( KDP), have been built into the ice-phase WRF 3D-Var package to allow modifications in liquid (cloud water and rain) and solid water (cloud ice and snow) fields through data assimilation. Experiments have been conducted to assimilate reflectivity and radial velocity observations collected by the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Hytop, Alabama, for a mesoscale convective system (MCS) on 15 March 2008. Numerical results have been examined to assess the impact of the WSR-88D data using the ice-phase WRF 3D-Var radar data assimilation package. The main goals are to first demonstrate radar data assimilation with an ice-phase microphysics forward model and second to improve understanding on how to enhance the utilization of radar data in numerical weather prediction. Results showed that the assimilation of reflectivity and radial velocity data using the ice-phase system provided significant improvement especially in the mid- to upper troposphere. The improved initial conditions led to apparent improvement in the short-term precipitation forecast of the MCS. An additional experiment has been conducted to explore the assimilation of KDP data collected by the Advanced Radar for Meteorological and Operational Research (ARMOR). Results showed that KDP data have been successfully assimilated using the ice-phase 3D-Var package. A positive impact of the KDP data has been found on rainwater in the lower troposphere and snow in the mid- to upper troposphere.

In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth's atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle‐based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next‐generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process‐level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle‐based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods., Key Points Microphysics is an important component of weather and climate models, but its representation in current models is highly uncertainTwo critical challenges are identified: representing cloud and precipitation particle populations and knowledge gaps in cloud physicsA possible blueprint for addressing these challenges is proposed to accelerate progress in improving microphysics schemes

The CYGNSS constellation was successfully launched on 15 Dec 2016 and has been operating continuously in science data-taking mode since March 2017. Updates will be presented on the mission status, calibration and validation activities for its science data products, and recent scientific applications of the measurements. Those applications include the use of ocean wind measurements to estimate air-sea latent heat flux, the assimilation of wind measurements made near tropical cyclones into hurricane numerical prediction models, the retrieval of soil moisture from the scattering measurements made over land, and the imaging of inland flooding using overland measurements.

The NASA Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) project included goals related to aerosol particle life cycle in convective regimes. Using the University of Wisconsin High Spectral Resolution Lidar system at Huntsville, Alabama, USA, and the NASA DC-8 research aircraft, we investigate the altitude dependence of aerosol, water vapor and Altocumulus (Ac) properties in the free troposphere from a canonical 12 August 2013 convective storm case as a segue to a presentation of a mission-wide analysis. It stands to reason that any moisture detrainment from convection must have an associated aerosol layer. Modes of covariability between aerosol, water vapor and Ac are examined relative to the boundary layer entrainment zone, 0 ∘C level, and anvil, a region known to contain Ac clouds and a complex aerosol layering structure (Reid et al., 2017). Multiple aerosol layers in regions warmer than 0 ∘C were observed within the planetary boundary layer entrainment zone. At 0 ∘C there is a proclivity for aerosol and water vapor detrainment from storms, in association with melting level Ac shelves. Finally, at temperatures colder than 0 ∘C, weak aerosol layers were identified above Cumulus congestus tops (∼0 and ∼-20 ∘C). Stronger aerosol signals return in association with anvil outflow. In situ data suggest that detraining particles undergo aqueous-phase or heterogeneous chemical or microphysical transformations, while at the same time larger particles are being scavenged at higher altitudes leading to enhanced nucleation. We conclude by discussing hypotheses regarding links to aerosol emissions and potential indirect effects on Ac clouds.

Clouds are both produced by and interact with the mesoscale and synoptic-scale structure of extratropical cyclones (ETCs) in ways that are still not well understood. Cloud-scale radiative and latent heating modifies the thermal environment, leading to a response in the dynamics that can in turn feed back on cloud distribution and microphysical properties. Key to the structure of ETCs is the warm conveyor belt (WCB); the poleward-ascending airstream that produces the bulk of the clouds and precipitation. This paper examines a long-lived WCB that persisted over the western North Atlantic Ocean in nearly the same location for several days. During this time, the storm was sampled multiple times by NASA’s A-Train satellite constellation, and a clear transition from stratiform to convective clouds was observed. Examination of coincident temperature and water vapor data reveals destabilization of the thermodynamic profile after the cyclone reached maturity. CloudSat radar reflectivity from two sequential overpasses of the warm front depicts a change from stratiform to convective cloud structure, and high-frequency microwave data reveal an increase in the amount of ice hydrometeors. The presence of convection may serve to strengthen the warm frontal trough while slowing the movement of the primary low pressure center. The stratiform–convective transition cannot be detected from passive measurements of cloud-top pressure. The results demonstrate the effectiveness of multivariate satellite observations for examining the outcome of dynamic processes in ETCs, and highlight the need for more rapid temporal profiling in future remote sensing observing systems.