ALBERT

Description: Delivering reliable nowcasts (short-range forecasts) of severe rainfall and the resulting flash floods is important in densely populated urban areas. The conventional method is advection-based extrapolation of radar echoes. However, during rapidly evolving convective rainfall this so-called Lagrangian persistence (LP) approach is limited to deterministic and very short-range nowcasts. To address these limitations in the one-hour time range, a novel extension of LP, called Lagrangian INtegro-Difference equation model withAutoregression (LINDA), is proposed. The model consists of five components: 1) identification of rain cells, 2) advection, 3) autoregressive process describing growth and decay of the cells, 4) convolution describing loss of predictability at small scales and 5) stochastic perturbations to simulate forecast uncertainty. Advection is separated from the other components that are applied in the Lagrangian coordinates. The reliability of LINDA is evaluated using the NEXRAD WSR-88D radar that covers the Dallas-Fort Worth metropolitan area, as well as the NEXRAD mosaic covering the continental United States. This is done with two different configurations: LINDA-D for deterministic and LINDA-P for probabilistic nowcasts. The validation dataset consists of 11 rainfall events during 2018-2020. For predicting moderate to heavy rainfall (5-20 mm/h), LINDA outperforms the previously proposed LP-based approaches. The most significant improvement is seen for the ETS and POD statistics with the 5 mm/h threshold. For 30-minute nowcasts, they show 15% and 16% increase, respectively, to the second-best method and 48% and 34% increase compared to LP. For the 5 mm/h threshold, the increase in the ROC skill score of 30-minute nowcasts from the second-best method is 10%.

Description: We propose a methodology for despiking ocean surface wave time series based on a Bayesian approach to data-driven learning known as Gaussian Process (GP) regression. We show that GP regression can be used for both robust detection of erroneous measurements and interpolation over missing values, while also obtaining a measure of the uncertainty associated with these operations. In comparison with a recent dynamical phase space-based despiking method, our data-driven approach is here shown to lead to improved wave signal correlation and spectral tail consistency, although at a significant increase in computational cost. Our results suggest that GP regression is thus especially suited for offline quality control requiring robust noise detection and replacement, where the subsequent analysis of the despiked data is sensitive to the accidental removal of extreme or rare events such as abnormal or rogue waves. We assess our methodology on measurements from an array of four co-located 5-Hz laser altimeters during a much-studied storm event the North Sea covering a wide range of sea states.

Description: Developed at the National Oceanic and Atmospheric Administration (NOAA) and the Joint Center for Satellite Data Assimilation (JCSDA), the Community Global Observing System Simulation Experiment (OSSE) Package (CGOP) provides a vehicle to quantitatively evaluate the impacts of emerging environmental observing systems or emerging in-situ or remote sensing instruments on NOAA numerical weather prediction (NWP) forecast skill. The typical first step for the OSSE is to simulate observations from the so-called “nature run”. Therefore, the observation spatial, temporal, and view geometry are needed to extract the atmospheric and surface variables from the nature run, which are then input to the observation forward operator (e.g., radiative transfer models) to simulate the new observations. This is a challenge for newly proposed systems for which instruments are not yet built or platforms are not yet deployed. To address this need, this study introduces an orbit simulator to compute these parameters based on the specific hosting platform and onboard instrument characteristics, which has been recently developed by the NOAA Center for Satellite Applications and Research (STAR) and added to the GCOP framework. In addition to simulating existing polar-orbiting and geostationary orbits, it is also applicable to emerging near space platforms (e.g., stratospheric balloons), cube satellite constellations, and Tundra orbits. The observation geometry simulator includes not only passive microwave and infrared sounders but also Global Navigation Satellite System/Radio Occultation (GNSS/RO) instruments. For passive atmospheric sounders, it calculates the geometric parameters of proposed instruments on different platforms, such as time varying location (latitude and longitude), scan geometry (satellite zenith and azimuth angles), and Ground Instantaneous Field of View (GIFOV) parameters for either cross-track or conical scanning mechanisms. For RO observations, it determines the geometry of the transmitters and receivers either on satellites or stratospheric balloons and computes their slant paths. The simulator has been successfully applied for recent OSSE studies (e.g., evaluating the impacts of future geostationary hyperspectral infrared sounders and RO observations from stratospheric balloons).

Description: Coincident Geostationary Lightning Mapper (GLM) and National Lightning Detection Network (NLDN) observations are used to build a generator of realistic lightning optical signal in the perspective to simulate Lightning Imager (LI) signal from European NLDN-like observations. Characteristics of GLM and NLDN flashes are used to train different machine learning (ML) models, that predict simulated pseudo-GLM flash extent, flash duration, and event number per flash (targets) from several NLDN flash characteristics. Comparing statistics of observed GLM targets and simulated pseudo-GLM targets, the most suitable ML-based target generators are identified. The simulated targets are then further processed to obtain pseudo-GLM events and flashes. In the perspective of lightning data assimilation, Flash Extent Density (FED) is derived from both observed and simulated GLM data. The best generators simulate accumulated hourly FED sums with a bias of 2% to the observation, while cumulated absolute differences remain of about 22 %. A visual comparison reveals that hourly simulated FED features local maxima at the similar geolocations as the FED derived from GLM observations. However, the simulated FED often exceeds the observed FED in regions of convective cores and high flash rates. The accumulated hourly area with FED〉0 flashes per 5 km×5 km pixel simulated by some pseudo-GLM generators differs by only 7% to 8% from the observed values. The recommended generator uses a linear Support Vector Regressor (linSVR) to create pseudo-GLM FED. It provides the best balance between target simulation, hourly FED sum, and hourly electrified area.

Description: We report on the installation and first results of one compact oceanographic radar in the region of Nice for a long-term observation of the coastal surface currents in the North-West Mediterranean Sea. We describe the specific processing and calibration techniques that were developed at the laboratory to produce high-quality radial surface current maps. In particular, we propose an original self-calibration technique of the antenna patterns, which is based on the sole analysis of the database and does not require any shipborne transponder or other external transmitters. The relevance of the self-calibration technique and the accuracy of inverted surface currents have been assessed with the launch of 40 drifters that remained under the radar coverage for about 10 days.

Description: Sea salt aerosol(s) (SSA) play a significant role in the atmosphere through aerosol direct and indirect effects, and in atmospheric chemistry as a source of tropospheric bromine. In-situ measurements of coarse-mode SSA particles are limited because of their low concentration and relatively large sizes (dry radius, rd 〉 0.5 μm). With this in mind, a new, low-cost, easily usable method for sampling coarse-mode SSA particles in the marine boundary layer was developed. A SSA particle sampler that uses an impaction method was designed and built using 3D printing and Arduino microcontrollers and sensors. It exposes polycarbonate slides to ambient airflow remotely on a kite-based platform to capture coarse-mode SSA particles. Because it is a smaller version of the Giant Nucleus Impactor (GNI), designed for use on aircraft, it is named the miniature-Giant Nucleus Impactor, or “mini-GNI”. After sample collection, the same optical microscope methodology utilized by the GNI was used to analyze the wetted salt particles that impacted onto the slides. In this proof-of-concept study, multiple mini-GNIs were attached serially to a kite string, allowing for sampling at multiple altitudes simultaneously. The robustness of the results from this new instrument and methodology for sampling at ambient RH (~ 75 %) the SSA particle size distribution with rd 〉 3.3 μ m are compared with a similar study. We find that the SSA particle number concentration decreases weakly with altitude and shows no correlation to instantaneous U10 wind speed along the windward coastline of O‘ ahu in the Hawaiian Islands.

Description: Detection of in-flight icing hazard is a priority of the aviation safety community. The ‘Radar Icing Algorithm’ (RadIA) has been developed to indicate the presence, phase, and relative size of supercooled drops. This paper provides an evaluation of RadIA via comparison to in-situ microphysical measurements collected with a research aircraft during the 2017 'Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment' (SNOWIE) field campaign.RadIA uses Level 2 dual-polarization radar moments from operational National Weather Service WSR-88D radar and a numerical weather prediction model temperature profile as inputs. Moment membership functions are defined based on the results of previous studies, and fuzzy logic is used to combine the output of these functions to create a 0 to 1 interest for detecting small-drop, large-drop and mixed phase icing.Data from the 2D-S particle probe on board the University of Wyoming King Air aircraft were categorized as either liquid or solid phase water with a shape classification algorithm and binned by size. RadIA interest values from 17 cases were matched to statistical measures of the solid/liquid particle size distributions (such as maximum particle diameter) and values of LWC from research aircraft flights. Receiver Operating Characteristic Area Under the Curve (AUC) values for RadIA algorithms were 0.75 for large-drop, 0.73 for small-drop, and 0.83 for mixed-phase cases. RadIA is proven to be a valuable new capability for detecting the presence of in-flight icing hazards from ground-based precipitation radar.

Description: In the atmospheric boundary layer, phenomena exist with challenging properties such as spatial heterogeneity, particularly during stable weak wind situations. Studying spatially heterogeneous features requires spatially distributed measurements on fine spatial and temporal scales. Fiber-Optic Distributed Sensing (FODS) can provide spatially distributed measurements, simultaneously offering a spatial resolution on the order of decimeters and a temporal resolution on the order of seconds. While FODS has already been deployed to study various variables, FODS wind direction sensing has only been demonstrated in idealized wind tunnel experiments. We present the first distributed observations of FODS wind directions from field data. The wind direction sensing is accomplished by using pairs of actively heated fiber optic cables with cone-shaped microstructures attached to them. Here we present three different methods of calculating wind directions from the FODS measurements, two based on using combined wind speed and direction information and one deriving wind direction independently from FODS wind speed. For each approach, the effective temporal and spatial resolution is quantified using spectral coherence. With each method of calculating wind directions, temporal resolutions on the order of tens of seconds can be achieved. The accuracy of FODS wind directions was evaluated against a sonic anemometer, showing deviations of less than 15° most of the time. The applicability of FODS for wind direction measurements in different environmental conditions is tested by analysing the dependence of FODS wind direction accuracy and observable scales on environmental factors. Finally, we demonstrate the potential of this technique by presenting a period that displays spatial and temporal structures in the wind direction.

Description: Radio occultation (RO) measurements have little direct sensitivity to clouds, but recent studies have shown that they may have an indirect sensitivity to thin, high clouds that are difficult to detect using conventional passive space-based cloud sensors. We implement two RO-based cloud detection (ROCD) algorithms for atmospheric layers in the middle and upper troposphere. The first algorithm is based on the methodology of a previous study, which explored signatures caused by upper tropospheric clouds in RO profiles according to retrieved relative humidity, temperature lapse rate, and gradients in log-refractivity (ROCD-P), and the second is based on inferred relative humidity alone (ROCD-M). In both, atmospheric layers are independently predicted as cloudy or clear based on observational data, including high performance RO retrievals. In a demonstration, we use data from 10 days spanning seven months in 2020 of FORMOSAT-7/COSMIC-2. We use the forecasts of NOAA GFS to aid in the retrieval of relative humidity. The prediction is validated with a cloud truth dataset created from the imagery of the GOES-16 Advanced Baseline Imager (ABI) satellite and the GFS three-dimensional analysis of cloud state conditions. Given these two algorithms for the presence or absence of clouds, confusion matrices and receiver operating characteristic (ROC) curves are used to analyze how well these algorithms perform. The ROCD-M algorithm has a balanced accuracy, which defines the quality of the classification test that considers both the sensitivity and specificity, greater than 70% for all altitudes between 6 and 10.25 km.

Description: Four scatterometers, namely: METOP-A, METOP-B, ERS-2 and OCEANSAT-2 were re-calibrated against combined National Data Buoy Center (NDBC) data and aircraft Stepped Frequency Microwave Radiometer (SFMR) data from hurricanes. As a result, continuous calibration relations over the wind speed range 0 to 45 ms-1 were developed. The calibration process uses match-up criteria of 50 km and 30 min for the buoy data. However, due to the strong spatio-temporal wind speed gradients in hurricanes, a method which considers both scatterometer and SFMR data in a storm-centred translating frame of reference is adopted. The results show that although the scatterometer radar cross-section is degraded at high wind speeds, it is still possible to recover wind speed data using the re-calibration process. Data validation between the scatterometers shows that the calibration relations produce consistent results across all scatterometers and reduce the bias and root mean square error compared to previous calibrations. In addition, the results extend the useful range of scatterometer measurements to as high as 45 ms-1.