Passive ground-based remote sensing of radiation fog

Accurate boundary layer temperature and humidity profiles are crucial for successful forecasting of fog, and accurate retrievals of liquid water path are important for understanding the climatological significance of fog. Passive ground-based remote sensing systems such as microwave radiometers (MWRs) and infrared spectrometers like the Atmospheric Emitted Radiance Interferometer (AERI), which measures spectrally resolved infrared radiation (3.3 to 19.2 µm), can retrieve both thermodynamic profiles and liquid water path. Both instruments are capable of long-term unattended operation and have the potential to support operational forecasting. Here we compare physical retrievals of boundary layer thermodynamic profiles and liquid water path during 12 cases of thin (LWP<40 g m−2) supercooled radiation fog from an MWR and an AERI collocated in central Greenland. We compare both sets of retrievals to in-situ measurements from radiosondes and surface-based temperature and humidity sensors. The retrievals based on AERI observations accurately capture shallow surface-based temperature inversions (0–10 m a.g.l.) with lapse rates of up to −1.2 ∘C m−1, whereas the strength of the surface-based temperature inversions retrieved from MWR observations alone are uncorrelated with in-situ measurements, highlighting the importance of constraining MWR thermodynamic profile retrievals with accurate surface meteorological data. The retrievals based on AERI observations detect fog onset (defined by a threshold in liquid water path) earlier than those based on MWR observations by 25 to 185 min. We propose that, due to the high sensitivity of the AERI instrument to near-surface temperature and small changes in liquid water path, the AERI (or an equivalent infrared spectrometer) could be a useful instrument for improving fog monitoring and nowcasting, particularly for cases of thin radiation fog under otherwise clear skies, which can have important radiative impacts at the surface

Improving the Prediction of Nocturnal Convection through the Assimilation of Novel Datasets: Observation Impacts and Error Treatment

Numerical weather prediction (NWP) models often fail to correctly forecast both the initiation and evolution of nocturnal convection. To improve our understanding of such events, researchers collected a unique dataset of thermodynamic and kinematic remote sensing profilers as part of the Plains Elevated Convection at Night (PECAN) experiment. This dissertation evaluates the impacts made to forecasts of nocturnal convection when assimilating this network that includes atmospheric emitted radiance interferometers (AERIs), Doppler lidars, radar wind profilers (RWPs), high-frequency rawinsondes, and mobile surface observations. Using an advanced, ensemble-based data assimilation system, we first evaluate the impacts of these datasets for a single nocturnal convection initiation (CI) event. Compared to operational forecasts, assimilating the PECAN dataset improves the timing, location, and orientation of the CI forecast. We show that AERIs, RWPs, and rawinsondes produce the largest benefits by enhancing the moisture advection into the region of CI. The impacts of assimilating these datasets also remains large throughout the growth of the CI event into a mesoscale convective system (MCS). Assimilating Doppler lidar and surface data only slightly improves the CI forecasts by enhancing the convergence along an outflow boundary that partially forces the CI. While this case study suggests positive results from assimilating high-frequency profiling data, one single event cannot fully represent the wide diversity of mechanisms and environments that can lead to nocturnal convection. To address additional types of nocturnal CI, we next expand our work into a systematic evaluation of the impact of assimilating a collocated network of high-frequency, ground-based thermodynamic and kinematic profilers collected during PECAN. For 13 nocturnal CI events, we find small but consistent improvements when assimilating thermodynamic observations collected by AERIs. Through midlevel cooling and moistening, assimilating the AERIs increases the skill for both nocturnal CI and precipitation forecasts. Assimilating composite kinematic datasets collected by Doppler lidars and RWPs instead results in slight degradations to the forecast quality, including decreases in skill and traditional contingency metrics. The impacts from assimilating thermodynamic and kinematic profilers often counteract each other, such that we find little impact on the detection of CI when both are assimilated. However, assimilating both datasets improves various properties of the CI events that are successfully detected (timing, distance, shape, etc.). We hypothesize that a lack of flow-dependent methods to assign observation errors likely contributes to the forecast degradations for some cases. This theory motivates our final study which evaluates the impact of using novel methods for assigning observation errors when assimilating ground-based thermodynamic profilers. We find that a static error inflation method results in forecast degradations compared to a reference experiment where no remote sensing data are assimilated. These issues are partially resolved when adaptively inflating the observation errors or when using a method that computes the full observation error based on observation-space diagnostics. Flow-dependent extensions of each method are shown to further improve forecasts compared to their static counterpart by increasing observation errors for problematic retrievals. Assuming that the observation errors are correctly diagnosed, the results from this dissertation suggest that assimilating a network of ground-based thermodynamic profilers can greatly improve forecasts of nocturnal convection

Remote-sensing and radiosonde datasets collected in the San Luis Valley during the LAPSE-RATE campaign

In July 2018, the International Society for Atmospheric Research using Remotely piloted Aircraft (ISARRA) hosted a flight week to showcase the role remotely piloted aircraft systems (RPASs) can have in filling the atmospheric data gap. This campaign was called Lower Atmospheric Process Studies at Elevation &ndash; a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). In support of this campaign, ground-based remote and in situ systems were also deployed for the campaign. The University of Oklahoma deployed the Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS), the University of Colorado deployed two Doppler wind lidars, and the National Severe Storms Laboratory deployed a mobile mesonet with the ability to launch radiosondes. This paper focuses on the data products from these instruments that result in profiles of the atmospheric state. The data are publicly available in the Zenodo LAPSE-RATE community portal (https://zenodo.org/communities/lapse-rate/, 19 January 2021). The profile data discussed are available at https://doi.org/10.5281/zenodo.3780623 (Bell and Klein,&nbsp;2020), https://doi.org/10.5281/zenodo.3780593 (Bell et&nbsp;al.,&nbsp;2020b), https://doi.org/10.5281/zenodo.3727224 (Bell et&nbsp;al.,&nbsp;2020a), https://doi.org/10.5281/zenodo.3738175 (Waugh,&nbsp;2020b), https://doi.org/10.5281/zenodo.3720444 (Waugh,&nbsp;2020a), and https://doi.org/10.5281/zenodo.3698228 (Lundquist et&nbsp;al.,&nbsp;2020). &nbsp;</p

Examining Novel Profiling Systems and Their Synergy for Advancing Boundary-Layer Research

In recent years, increased attention has turned to studying the atmospheric boundary layer (ABL) as new observing systems have been developed. Traditionally, the ABL has been drastically under-sampled by conventional observing systems (e.g., radiosonde and meteorological towers). Filling this so-called ``ABL data gap" using new, high-resolution observing systems has the potential to assist with the development of next-generation parameterization schemes for scales ranging from large-eddy simulation to climate scales, improve forecaster situational awareness during high-impact weather, and provide detailed information for assimilation into numerical weather prediction. Specifically, commercial availability of ground-based remote sensors and the recent widespread availability of small uncrewed aerial systems (UAS) has opened up a world of opportunity to observe and study the complex processes that occur in the ABL which have previously not been routinely observed. However, it is important to evaluate the utility of each system by directly comparing them with one another in a variety of environments. In the following studies, thermodynamic and kinematic data from a suite of remote sensors contained in the Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS) and state-of-the-art weather-sensing UAS (WxUAS) are compared to one another. CLAMPS contains an Atmospheric Emitted Radiance Interferometer (AERI) and a microwave radiometer (MWR) for thermodynamic profiling and a scanning Doppler wind lidar (DL) for kinematic profiling. The WxUAS used is the CopterSonde, which has been developed specifically to provide accurate kinematic and thermodynamic measurements. Data from two campaigns, one which took place in the San Luis Valley in Colorado and the other at the Kessler Atmospheric and Ecological Field Station in central Oklahoma, are used for the comparison. From these intercomparisons, multiple instrument deficiencies are examined. Compared to both the DL and high-resolution radiosondes, the CopterSonde tended to underestimate the wind speed using an empirically derived function that relates the tilt of the UAS to the wind speed. Utilizing the DL retrieved wind profiles, a new function is proposed and validated. Additionally, issues are identified with thermodynamic retrievals performed in locations where appropriate prior information is unavailable. A modified thermodynamic retrieval, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) algorithm, is used to combine multiple observation types to attempt to improve the retrievals. Additionally, data collected from the Verification of the Origins of Rotation in Tornadoes Experiment Southeast (VORTEX-SE) are used to examine sensitivities in TROPoe. Throughout the analyses, synergies are present between the remote sensing and UAS. These synergies are discussed in the context of next generation profiling networks to fill the ABL data gap and suggestions are made for how a next generation network could function with remote-sensing and WxUAS

CO2 Profiling in the Lower Troposphere using a High Spectral Resolution Infrared Radiometer

The rapid increase of CO2 concentration in the atmosphere due to the anthropogenic activities since the beginning of the industrial revolution in 1750 makes CO2 the most important anthropogenic atmospheric trace gas. Improvements in space-based and ground-based instrumentation during the last decades provide a high potential to observe atmospheric CO2 spatial and temporal variability in unprecedented details. The interaction of atmospheric CO2 with the terrestrial ecosystem such as plant photosynthesis and soil respiration can produce a considerable diurnal change in the CO2 concentration near the surface. The measurement of this diurnal evolution would provide a valuable tool to study the land-vegetation interaction with the atmospheric CO2. Such a tool would also be useful to help evaluate the output of numerical models which predict the CO2 flux near the surface. However, despite all improvements in the measurement capability, capturing this diurnal change in the boundary layer still remains a challenge. One possibility to improve the observation of the diurnal CO2 cycle is to use the Fourier Transform InfraRed (FTIR) spectrometer. One example of ground-based FTIR spectrometer is Atmospheric Emitted Radiance Interferometer (AERI). The AERI was installed in 2011, at Jülich ObservatorY for Cloud Evolution (JOYCE), in Germany. It measures the downwelling atmospheric radiation in the mid-infrared region from 520 cm−1 (19 μm) to 3020 cm−1 (3.3 μm). High temporal (less than 30 s) and spectral (better than 1 cm−1) resolution as well as continuous measurements of the AERI give the opportunity to retrieve the atmospheric thermodynamic profiles and cloud properties. In addition, the AERI also observes the emission from several trace gas absorption bands, such as the 15 μm CO2 band. These bands can be used to provide useful information about the atmospheric concentration of these trace gases. In the present work, the ability to retrieve profiles of CO2 over the diurnal cycle from AERI-observed radiances is investigated. For this purpose, an algorithm, called AERIoe is utilized and modified to retrieve the CO2 profile in the boundary layer. Prior to applying the AERIoe to real AERI measurements, simulated radiances are used to evaluate the potential of retrieving atmospheric CO2 concentration from AERI radiance observations. A line-by-line radiative transfer model (LBLRTM) using numerical model profiles as input are utilized to compute downwelling radiances, which are convolved with the instrument function and random noise added in order to simulate an AERI observation. In the first step, a constant atmospheric mixing ratio is considered for the atmospheric CO2 profile. AERIoe results show about 2 ppm overestimation in retrieving the constant CO2 mixing ratio. In order to improve the results, reduced noise, which can be interpreted as using temporally averaged AERI radiances, is added to the simulated radiances. These results show considerable improvement compared to the previous results where by ∼ 70% of the retrieved values are within the expected uncertainty. However, a constant atmospheric profile can not provide any information related to the diurnal change of CO2 concentration near the surface, meaning that a profile which can represent the diurnal CO2 variation needs to be retrieved. Due to the low numbers of degrees of freedom for signal in retrieving the CO2 concentration, the CO2 profile is parametrized using an exponential function. This exponential function gives the opportunity to calculate the CO2 profile by retrieving 2 shape parameters, rather than retrieving whole profile. In order to evaluate the modified AERIoe, simulated radiances with the reduced noise for one month are provided to the algorithm. The AERIoe is then run while temperature and humidity profiles are considered as known profiles. The CO2 concentrations in different levels are captured quite accurately by the algorithm where the root mean square values between true and retrieved CO2 concentrations are 6.8, 5.4, 4.0 and 1.9 ppm at the surface, 90 m, 200 m and 1 km respectively. The retrieved profiles improved the root mean square between true and prior profiles by ∼ 50%. The algorithm is then used to retrieve the temperature, humidity and CO2 profiles simultaneously. These results show a significant reduction in the CO2 degrees of freedom which causes poor retrieval results. Consequently, a second method is used wherein a principal component noise filter is applied to reduce the random error in the AERI observations. High temporal resolution simulated radiances are used to test the new method. The results of the AERIoe run with the noise-filtered radiances demonstrate considerable improvement in retrieving the CO2 concentration near the surface. The AERIoe is then applied to real AERI observations from two clear sky days at Ju ̈lich to retrieve profiles of CO2. The tower in-situ measurements at Jülich are utilized to compare with the retrieved CO2 concentration near the surface. It is shown that the AERI radiances have the potential to capture the diurnal variation of the CO2 concentration near the surface. The retrieved values for the surface CO2 concentration show between 5 to 10 ppm difference with the tower measurements during these two days, while the uncertainties in the retrieved values are between 4 to 7 ppm. The AERI radiances are also used to estimate the height where the CO2 concentration deviates from its background value. The diurnal change of the derived heights for one of these two days are in good agreement with the expected diurnal change of the boundary layer for a sunny and clear sky day

Progress toward characterization of the atmospheric boundary layer over northern Alabama using observations by a vertically pointing, S-band profiling radar during VORTEX-Southeast

During spring 2016 and spring 2017, a vertically pointing, S-band FMCW radar (UMass FMCW) was deployed in northern Alabama under the auspices of the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX) – Southeast. In total, ~14 weeks’ worth of data were collected, in conditions ranging from quiescent clear skies to severe thunderstorms. The principal objective of these deployments was to characterize the boundary layer evolution near the VORTEX-Southeast domain. In this paper, we describe intermediate results in service of this objective. Specifically, we describe updates to the UMass FMCW system, document its deployments for VORTEX-Southeast, and apply three automated algorithms: (1) an dealiasing algorithm to the Doppler velocities, (2) a fuzzy logic scatterer classification scheme to separate precipitation from non-precipitation observations, (3) a bright band / melting layer identification algorithm for stratiform precipitation, and (4) an extended Kalman filter-based convective boundary layer depth (mixing height) measurement algorithm for non-precipitation observations. Results from the latter two applications are qualitatively verified against retrieved soundings from a collocated thermodynamic profiling system.Peer ReviewedPostprint (author's final draft

Disentangling the relative contribution of land-atmosphere coupling toward the evolution of extreme events

The Great Plains region is characterized by highly variable and often extreme precipitation. Devastating multi-year droughts are common in the region, and can often be followed by years of above normal precipitation. Drought onset can be difficult to predict, especially in cases known as flash drought when both onset and intensification are rapid. These flash drought events can often lead into longer term drought such as was observed across much of the central United States during 2012. \par On the other side of the precipitation distribution lies excessive rainfall, and the impacts of resulting flooding can be just as detrimental to agriculture and society in the region. While landlocked states in the Great Plains aren't typically associated with tropical cyclones, recent studies have begun to draw attention to the idea that these storms may reintensify over land in the presence of anomalously wet soils. This is colloquially known as the Brown Ocean Effect. It has been hypothesized that above normal precipitation during spring of 2015 provided sufficiently wet soils to aid in the reintensification of Tropical Storm Bill as it propagated inland over the Great Plains and Midwest. \par Though the flash drought/drought of 2012 and Tropical Storm Bill during 2015 seem unrelated except for shared geography, both extremes may have been influenced by land-atmosphere feedbacks. It is well known that drought and heavy precipitation are often tied to larger scale atmospheric forcing, however, numerous studies have demonstrated that land-atmosphere feedbacks may also amplify extremes in the region. It remains a challenge to disentangle the influence of the land surface from that of the overall atmospheric pattern. However, the key to increased predictability for events such as flash drought may rely on answering the question "to what extent does the land surface really influence hydroclimate extremes?" This study presents new approaches to better estimate the relative contributions of land-atmosphere feedbacks toward precipitation extremes in the Great Plains, with a primary focus on the flash drought/drought of 2012 and the overland maintenance/reintensification of Tropical Storm Bill during 2015

Synoptic and Local Influences on a Summertime, Long-Lived, Mixed-Phase Cloud Event Over Summit, Greenland

Long-lived, Arctic mixed-phase clouds play a crucial role in modulating the surface energy balance over the Greenland Ice Sheet. However, due to temporally and spatially inconsistent observations, little is known about the mechanisms that cause their longevity. A persistent, single-layer, mixed-phase cloud was observed from 20-24 July 2012 at the “Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit” (ICECAPS) cloud-atmosphere observatory in Summit Station, Greenland. The hypothesis in this study is motivated by \citet{morrison2012resilience}; this study investigates the hypothesis that local processes promote a cloud’s persistent state, while synoptic-scale processes influence the thermodynamic structure of the lower troposphere. This hypothesis is examined on the 20-24 July 2012 ICECAPS cloud event using the Weather Research and Forecasting model with polar modifications (Polar WRF) in a series of controlled experiments. First, the role of the synoptic-scale processes is examined by fixing the boundary conditions to isolate the influence of the large-scale flow. Westerly winds over western Greenland and easterly winds over eastern Greenland, driven by a surface cyclone off southeastern Greenland, causes flow to converge atop the ice sheet, converse of the usual state due to the katabatic winds. This deeper vertical motion leads to the formation of ice rather that liquid water, leading to cloud dissipation. In the wake of the surface cyclone and moisture boundary, colder, drier air advects over Summit resulting in a very different thermodynamic profile in the boundary layer inhibiting the cloud from reforming. Second, the role of local-scale processes is examined. An experimental simulation investigating the sensitivity of the cloud to its microphysics shows the cloud liquid water mixing ratio (cloud liquid water content) is sensitive to the ice mixing ratio. For lower ice mixing ratios, the cloud liquid water content is higher as a result of a less effective Wegner-Bergeron-Findeisen process. Another experiment looking at the sensitivity of the simulated cloud to the choice of planetary boundary layer scheme reveals that deeper mixing by larger eddies in the boundary layer is important for cloud maintenance. Finally, the role of local processes is examined by modifying the cloud radiative forcings. In all simulations, the cloud forms at the surface as a result of strong surface radiative cooling under a surface-based inversion. Once the cloud forms, the radiative regime changes as there is now emission from the liquid water resulting in cloud-top longwave radiative cooling. This drives buoyancy-driven updrafts that elevate the cloud and result in two feedbacks: one, condensation of moist air near the surface maintaining the cloud liquid water and cloud-top longwave radiative cooling and two, a well-mixed layer that couples the cloud with the surface which maintains the cloud through moisture and energy contributions from the surface fluxes. The surface fluxes are also greater in the presence of the cloud as a result of the increased downwelling longwave flux at the surface from the cloud. As the strength of the cloud-top longwave radiative cooling is determined strongly by the cloud liquid water content, there exists a minimum amount of liquid water to drive strong enough cloud-top cooling and induced buoyancy-driven updrafts needed to one, maintain the cloud and two, elevate it from the surface. There is also a point where increasing the liquid water does not strengthen the above described processes. In addition, shortwave radiation does not significantly impact the cloud maintenance. However, there is some impact on the liquid content of the cloud; this can affect the amount of cloud-top longwave radiative cooling and its induced processes

Connecting Land–Atmosphere Interactions to Surface Heterogeneity in CHEESEHEAD19

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models

Observations of fog‐aerosol interactions over central Greenland

Supercooled fogs can have an important radiative impact at the surface of the Greenland Ice Sheet, but they are difficult to detect and our understanding of the factors that control their lifetime and radiative properties is limited by a lack of observations. This study demonstrates that spectrally resolved measurements of downwelling longwave radiation can be used to generate retrievals of fog microphysical properties (phase and particle effective radius) when the fog visible optical depth is greater than ∼0.25. For 12 cases of fog under otherwise clear skies between June and September 2019 at Summit Station in central Greenland, nine cases were mixed-phase. The mean ice particle (optically-equivalent sphere) effective radius was 24.0 ± 7.8 µm, and the mean liquid droplet effective radius was 14.0 ± 2.7 µm. These results, combined with measurements of aerosol particle number concentrations, provide evidence supporting the hypotheses that (a) low surface aerosol particle number concentrations can limit fog liquid water path, (b) fog can act to increase near-surface aerosol particle number concentrations through enhanced mixing, and (c) multiple fog events in quiescent periods gradually deplete near-surface aerosol particle number concentrations