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Noureddine Semane, Richard Anthes, Jeremiah Sjoberg, Sean Healy, and Benjamin Ruston

Abstract

We compare two seemingly different methods of estimating random error statistics (uncertainties) of observations, the three-cornered hat (3CH) method and Desroziers method, and show several examples of estimated uncertainties of COSMIC-2 (C2) radio occultation (RO) observations. The two methods yield similar results, attesting to the validity of both. The small differences provide insight into the sensitivity of the methods to the assumptions and computational details. These estimates of RO error statistics differ considerably from several RO error models used by operational weather forecast centers, suggesting that the impact of RO observations on forecasts can be improved by adjusting the RO error models to agree more closely with the RO error statistics. Both methods show RO uncertainty estimates that vary with latitude. In the troposphere, uncertainties are higher in the tropics than in the subtropics and middle latitudes. In the upper stratosphere–lower mesosphere, we find the reverse, with tropical uncertainties slightly less than in the subtropics and higher latitudes. The uncertainty estimates from the two techniques also show similar variations between a 31-day period during Northern Hemisphere tropical cyclone season (16 August–15 September 2020) and a month near the vernal equinox (April 2021). Finally, we find a relationship between the vertical variation of the C2 estimated uncertainties and atmospheric variability, as measured by the standard deviation of the C2 sample. The convergence of the error estimates and the standard deviations above 40 km indicates a lessening impact of assimilating RO above this level.

Significance Statement

Uncertainties of observations are of general interest and their knowledge is important for assimilation in numerical weather prediction models. This paper compares two methods of estimating these uncertainties and shows that they give nearly identical results under certain conditions. The estimation of the COSMIC-2 bending angle uncertainties and how they compare to the assumed bending angle error models in several operational weather centers suggests that there is an opportunity for obtaining improved impact of RO observations in numerical model forecasts. Finally, the relationship between the COSMIC-2 bending angle errors and atmospheric variability provides insight into the sources of RO observational uncertainties.

Open access
Guillaume Dodet, Saleh Abdalla, Matias Alday, Mickaël Accensi, Jean Bidlot, and Fabrice Ardhuin

Abstract

Ocean wave measurements are of major importance for a number of applications including climate studies, ship routing, marine engineering, safety at sea, and coastal risk management. Depending on the scales and regions of interest, a variety of data sources may be considered (e.g., in situ data, Voluntary Observing Ship observations, altimeter records, numerical wave models), each one with its own characteristics in terms of sampling frequency, spatial coverage, accuracy, and cost. To combine multiple source of wave information (e.g., for data assimilation scheme in numerical weather prediction models), the error characteristics of each measurement system need to be defined. In this study, we use the triple collocation technique to estimate the random error variance of significant wave heights from a comprehensive collection of collocated in situ, altimeter, and model data. The in situ dataset is a selection of 122 platforms provided by the Copernicus Marine Service In Situ Thematic Center. The altimeter dataset is the ESA Sea State CCI version1 L2P product. The model dataset is the WW3-LOPS hindcast forced with bias-corrected ERA5 winds and an adjusted T475 parameterization of wave generation and dissipation. Compared to previous similar analyses, the extensive (∼250 000 entries) triple collocation dataset generated for this study provides some new insights on the error variability associated to differences in in situ platforms, satellite missions, sea state conditions, and seasonal variability.

Restricted access
Boyan Hu, Jinfeng Ding, Gang Liu, and Jianping Tang

Abstract

This study analyzes the spatial and temporal distribution characteristics of the in situ aircraft observations in the middle and higher troposphere in 2019. These aircraft observations are mainly distributed in China, and relatively evenly recorded between 0000 and 1500 UTC in time and 6 and 10 km in height. Based on the 3395 stronger clear-air turbulence (CAT) events and 4038 weaker CAT events selected from the observations in the study region (15°–55°N, 70°–140°E), the performances of 24 CAT diagnostics calculated from the ERA5 data are evaluated. Results show that the diagnostics connected with vertical wind shear (i.e., version 1 of the North Carolina State University index, negative Richardson number, variant 3 and variant 1 of Ellrod’s turbulence index) have the best performances. However, the performances vary greatly from season to season, and overall performances are the best in winter and worst in summer. The annual and seasonal best thresholds for these diagnostics are also listed in this study.

Restricted access
Zhijin Qiu, Tong Hu, Bo Wang, Jing Zou, and Zhiqian Li

Abstract

The evaporation duct is an abnormal refractive phenomenon with wide distribution and frequency occurrence at the boundary between the atmosphere and the ocean, which directly affects electromagnetic wave propagation. In recent years, the use of meteorological and hydrological data to predict the evaporation duct height has become an emerging and promising approach. There are some evaporation duct models that have been proposed based on the Monin–Obukhov similarity theory. However, each model adopts different stability functions and roughness length parameterization methods, so the prediction accuracies are different under different environmental conditions. To improve the prediction accuracy of the evaporation duct under different environmental conditions, a model selection optimization method (MSOM) of the evaporation duct model is proposed based on sensitivity analysis. According to the sensitivity of each model to input parameters analyzed by the sensor observation accuracy, curve graph, and Sobol sensitivity, the model input parameters are divided into several intervals. Then the optimization model is selected in different intervals. The model was established using numerical simulation data from local areas in the South China Sea, and its accuracy was verified by the observational data from the offshore observation platform located in the South China Sea. The results show that the MSOM can effectively improve the prediction accuracy of the evaporation duct height. Under unstable conditions, the maximum relative error is reduced by 7.1%, and under stable conditions, the relative error is reduced by 10.7%.

Significance Statement

The evaporation duct height has a significant effect on marine radar or wireless apparatus applications. To obtain the evaporation duct height, there are some evaporation duct models that have been proposed. However, different evaporation duct models are applicable to different meteorological and hydrological environments. A single model cannot achieve accurate evaporation duct height predictions in all environments. We propose a model selection optimization method of the evaporation duct model based on sensitivity analysis. This method can dynamically select the optimal model according to different meteorological and hydrological environment, and improve the prediction accuracy of the evaporation duct height. Under unstable conditions, the maximum relative error is reduced by 7.1%, and under stable conditions, the relative error is reduced by 10.7%.

Open access
Beth Reid and Tom Swanson

Abstract

Loon LLC collected 794 000 h of corona current observations between 15 and ∼20 km above sea level with time resolution between 1 and 30 min. We are publicly releasing this dataset to enable the research community’s understanding of electrical activity in the stratosphere. We validate the reliability of these measurements by aligning our flight data with both nearby Geostationary Lightning Mapper (GLM) events and the Convective Diagnostic Oceanic (CDO) indicator. Corona current observations that exceeded the sensor maximum of 10 μA were associated with high GLM optical flux accumulations along the flight trajectory. Using the CDO indicator as a persistence forecast for future electrical activity was effective at predicting corona current events, and so we highly recommend this data source for real-time stratospheric navigation for vehicles sensitive to the harsh electrical environment of the stratosphere.

Significance Statement

Loon LLC operated a fleet of balloons in the stratosphere, between 15 and 20 km above sea level. The balloons were instrumented with a sensor that measured the current flowing through a wire dangling from the flight vehicle. The observed currents were caused by the motion of nearby charged particles that are often associated with thunderstorms and lightning activity. In this paper we show that Loon’s sensor registered current at the same time lightning was recorded near the balloon by other instruments like the Geostationary Lightning Mapper satellite. This is the first dataset of its kind and size, reaching 794 000 flight hours. We are publicly releasing these data in hopes of aiding scientific discovery by researchers and to help future stratospheric vehicle operators better understand and plan for the electrical environment.

Open access
C. O. Collins III and R. E. Jensen

Abstract

We identify and characterize an error in the National Data Buoy Center (NDBC) wave records due to the sustained tilt of a buoy under high winds. We use a standard, operational 3-m aluminum discus buoy from NDBC with two wave systems, one gimballed, and the other strapped down but uncorrected. By comparing the two, we find that the most extreme significant wave heights are systematically overestimated. The overestimation is shown to be confined to a region around the peak frequency in the spectra: 0.05–0.15 Hz. Wave direction and directional spread are unaffected. A bias due to tilt error can be observed starting at winds of 10 m s−1 or wave heights of 4 m. The bias increases as a function of wind speed and wave height, i.e., the bias is +10% when winds are 20 m s−1. Very high waves and winds are relatively rare, so while the tilt error does not affect overall statistics and basic analyses it could potentially affect analysis sensitive to the extremes. A correction is derived for significant wave height, which is a quadratic function of wind speed. The correction is shown to reduce wave heights in uncorrected records, but is found inadequate for general use. There is evidence of tilt error at other NDBC stations, but the full extent of prevalence in the record is not known at this time.

Open access
Terhi Mäkinen, Jenna Ritvanen, Seppo Pulkkinen, Nadja Weisshaupt, and Jarmo Koistinen

Abstract

ABSTRACT: The latest established generation of weather radars provides polarimetric measurements of a wide variety of meteorological and non-meteorological targets. While the classification of different precipitation types based on polarimetric data has been studied extensively, non-meteorological targets have garnered relatively less attention beyond an effort to detect them for removal from meteorological products. In this paper we present a supervised learning classification system developed in the Finnish Meteorological Institute (FMI) that uses Bayesian inference with empirical probability density distributions to assign individual range gate samples into 7 meteorological and 12 non-meteorological classes, belonging to five top level categories of hydrometeors, terrain, zoogenic, anthropogenic, and immaterial. We demonstrate how the accuracy of the class probability estimates provided by a basic Naive Bayes classifier can be further improved by introducing synthetic channels created through limited neighborhood filtering, by properly managing partial moment nonresponse, and by considering spatial correlation of class membership of adjacent range gates. The choice of Bayesian classification provides well-substantiated quality estimates for all meteorological products, a feature that is being increasingly requested by users of weather radar products. The availability of comprehensive, fine-grained classification of non-meteorological targets also enables a large array of emerging applications, utilizing non-precipitation echo types and demonstrating the need to move from a single, universal quality metric of radar observations to one that depends on the application, the measured target type, and on the specificity of the customers’ requirements.

Restricted access
Theodore M. McHardy, James R. Campbell, David A. Peterson, Simone Lolli, Anne Garnier, Arunas P. Kuciauskas, Melinda L. Surratt, Jared W. Marquis, Steven D. Miller, Erica K. Dolinar, and Xiquan Dong

Abstract

This study develops a new thin cirrus detection algorithm applicable to over-land scenes. The methodology builds from a previously developed over-water algorithm (McHardy et al. 2021), which makes use of the Geostationary Operational Environmental Satellite 16 (GOES-16) Advanced Baseline Imager (ABI) channel 4 radiance (1.378 μm “cirrus” band). Calibration of this algorithm is based on coincident Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud profiles. Emphasis is placed on rejection of false detections that are more common in over-land scenes. Clear sky false alarm rates over land are examined as a function of precipitable water vapor (PWV), showing that nearly all pixels having a PWV of < 0.4 cm produce false alarms. Enforcing an above-cloud PWV minimum threshold of ~1 cm ensures that most low/mid-level clouds are not misclassified as cirrus by the algorithm. Pixel-filtering based on the total column PWV and the PWV for a layer between the top of the atmosphere (TOA) and a pre-determined altitude H removes significant land-surface and low/mid-level cloud false alarms from the overall sample while preserving over 80% of valid cirrus pixels. Additionally, the use of an aggressive PWV layer threshold preferentially removes non-cirrus pixels such that the remaining sample is comprised of nearly 70% cirrus pixels, at the cost of a much-reduced overall sample size. This study shows that lower-tropospheric clouds are a much more significant source of uncertainty in cirrus detection than the land surface.

Restricted access
Jie Zhou, Hang Gao, Xuesong Wang, and Jianbing Li

Abstract

The hydrogen balloon is widely used for wind sensing by tracking it with optical theodolites. The traditional theodolite observation (single- and double-theodolite) methods assume that the balloon is a perfect tracer of the background wind and it rises with a constant speed during the whole observation period, but these assumptions may not hold well in complex wind circumstances. In this paper, an accurate wind field retrieval method based on multi-theodolite measurement is proposed. The Extended Kalman Filter algorithm is used to filter the angle data observed by the theodolites in order to accurately estimate the trajectory of the balloon, and the motion equation is used to correct the velocity difference between the background wind and the balloon. As a result, not only the horizontal velocity but also the vertical velocity can be accurately retrieved by this method. Numerical simulation and field experiments show that the multi-theodolite observation method excels the traditional single theodolite method, and the velocity errors can be reduced by even more than 40% in comparison with the single theodolite method for complex wind cases.

Restricted access
Dudley B. Chelton, Roger M. Samelson, and J. Thomas Farrar

Abstract

The Ka-band Radar Interferometer on the Surface Water and Ocean Topography (SWOT) satellite will revolutionize satellite altimetry by measuring sea surface height (SSH) with unprecedented accuracy and resolution across two 50-km swaths separated by a 20-km gap. The original plan to provide an SSH product with a footprint diameter of 1 km has changed to providing two SSH data products with footprint diameters of 0.5 km and 2 km. The swathaveraged standard deviations and wavenumber spectra of the uncorrelated measurement errors for these footprints are derived from the SWOT science requirements that are expressed in terms of the wavenumber spectrum of SSH after smoothing with a filter cutoff wavelength of 15 km. The availability of 2-dimensional fields of SSH within the measurement swaths will provide the first spaceborne estimates of instantaneous surface velocity and vorticity through the geostrophic equations. The swath-averaged standard deviations of the noise in estimates of velocity and vorticity derived by propagation of the uncorrelated SSH measurement noise through the finite difference approximations of the derivatives are shown to be too large for the SWOT data products to be used directly in most applications, even with the footprint diameter of 2 km. It is shown from wavenumber spectra and maps constructed from simulated SWOT data that additional smoothing will be required for most applications of SWOT estimates of velocity and vorticity. Equations are presented for the swath-averaged standard deviations and wavenumber spectra of residual noise in SSH and geostrophically computed velocity and vorticity after isotropic 2-dimensional smoothing for any user-defined filter cutoff wavelength of the smoothing.

Open access