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Christopher S. Ruf

Abstract

Statistical properties of the brightness temperature (T B) measured by a low-earth-orbiting radiometer operating at 1.4 GHz are considered as a means of calibrating and validating the sensor. Mapping of ocean salinity by such an instrument requires that its calibration be extremely stable over time. Whether certain statistical properties of the measurements are stationary (time invariant) enough to be of value as benchmarks to which the calibration can be referenced is considered. The global minimum, maximum, and average T B are considered, together with a vicarious cold T B statistic that makes use of a sharp lower bound on naturally occurring values for T B. Examination of simulated global distributions of the T B measurements suggests several things about the stationarity (or lack thereof) of the statistics in question. Global minima can vary widely due to instrument noise and are not a reliable calibration reference. Global maxima are strongly influenced by a number of environmental factors as well as by instrument noise and are even less stationary than the minima. Global averages are largely insensitive to instrument noise and, in most cases, to environmental conditions as well. The global average T B varies at only the 0.1-K rms level except in cases of anomalously high winds, when it can increase considerably more. The vicarious cold T B is similarly insensitive to instrument effects and most environmental factors. It is not significantly affected by high wind conditions. The stability of the vicarious cold T B is, however, found to be sensitive at the several tenths of a kelvin level to variations in the background cold space brightness. The global average is much less sensitive to this parameter, so using the two approaches together should be mutually beneficial.

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David Mayers and Christopher Ruf

Abstract

The maximum sustained wind speed Vm of a tropical cyclone (TC) observed by a sensor varies with its spatial resolution. If unaccounted for, the difference between the “true” and observed Vm results in an error in estimation of Vm. The magnitude of the error is found to depend on the radius of maximum wind speed Rm and Vm itself. Quantitative relationships are established between Vm estimation errors and the TC characteristics. A correction algorithm is constructed as a scale factor to estimate the true Vm from coarsely resolved wind speed measurements observed by satellites. Without the correction, estimates of Vm made directly from the observations have root-mean-square differences of 1.77, 3.41, and 6.11 m s−1 given observations with a spatial resolution of 25, 40, and 70 km, respectively. When the proposed scale factors are applied to the observations, the errors are reduced to 0.69, 1.23, and 2.12 m s−1. A demonstration of the application of the correction algorithm throughout the life cycle of Hurricane Sergio in 2018 is also presented. It illustrates the value of having the scale factor depend on Rm and Vm, as opposed to using a fixed value, independent of TC characteristics.

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David Mayers and Christopher Ruf

Abstract

A new method is described for determining the center location of a tropical cyclone (TC) using wind speed measurements by the NASA Cyclone Global Navigation Satellite System (CYGNSS). CYGNSS measurements made during TC overpasses are used to constrain a parametric wind speed model in which storm center location is varied. The “MTrack” storm center location is selected to minimize the residual difference between model and measurement. Results of the MTrack center fix are compared to the National Hurricane Center (NHC) Best Track, the Automated Rotational Center Hurricane Eye Retrieval (ARCHER), and aircraft reconnaissance fixes for category 1–category 3 TCs during the 2017 and 2018 hurricane seasons. MTrack produces storm center locations at intermediate times between NHC fixes with a factor of 5.6 overall reduction in sensitivity to uncertainties in the NHC fixes between which it interpolates. The MTrack uncertainty is found to be larger in the cross-track direction than the along-track direction, although this behavior and the absolute accuracy of position estimates require further investigation.

Open access
David Mayers and Christopher Ruf

Abstract

MTrack is an automated algorithm which determines the center location (latitude and longitude) of a tropical cyclone from a scalar wind field derived from satellite observations. Accurate storm centers are useful for operational forecasting of tropical cyclones and for their reanalysis (e.g. research on storm surge modeling). Currently, storm center fixes have significantly larger errors for weak, disorganized storms. The MTrack algorithm presented here improves storm centers in some of those cases. It is also automated and, therefore, less subjective than manual fixes made by forecasters. The MTrack algorithm, which was originally designed to work with CYGNSS wind speed measurements, is applied to SMAP winds for the first time. The average difference between MTrack and Best Track storm center locations is 21, 36 and 46 km for major hurricanes, category 1-2 hurricanes, and tropical storms, respectively. MTrack is shown to operate successfully when a storm is only partially sampled by the observing satellite and when the eye of the storm is not resolved.

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Mary Morris and Christopher S. Ruf

Abstract

Low-frequency passive microwave observations allow for oceanic remote sensing of surface wind speed and rain rate from spaceborne and airborne platforms. For most instruments, the modeling of contributions of rain absorption and reemission in a particular field of view is simplified by the observing geometry. However, the simplifying assumptions that can be applied in most applications are not always valid for the scenes that the airborne Hurricane Imaging Radiometer (HIRAD) regularly observes. Collocated Stepped Frequency Microwave Radiometer (SFMR) and HIRAD observations of Hurricane Earl (2010) indicate that retrieval algorithms based on the usual simplified model, referred to here as the decoupled-pixel model (DPM), are not able to resolve two neighboring rainbands at the edge of HIRAD’s swath. The DPM does not allow for the possibility that a single column of atmosphere can affect the observations at multiple cross-track positions. This motivates the development of a coupled-pixel model (CPM) that is developed and tested in this paper. Simulated observations as well as HIRAD’s observations of Hurricane Earl (2010) are used to test the CPM algorithm. Key to the performance of the CPM algorithm is its ability to deconvolve the cross-track scene, as well as unscramble the signatures of surface wind speed and rain rate in HIRAD’s observations. While the CPM approach was developed specifically for HIRAD, other sensors could employ this method in similar complicated observing scenarios.

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Mary Morris and Christopher S. Ruf

Abstract

The Cyclone Global Navigation Satellite System (CYGNSS) constellation is designed to provide observations of surface wind speed in and near the inner core of tropical cyclones with high temporal resolution throughout the storm’s life cycle. A method is developed for estimating tropical cyclone integrated kinetic energy (IKE) using CYGNSS observations. IKE is calculated for each geographically based quadrant out to an estimate of the 34-kt (1 kt = 0.51 m s−1) wind radius. The CYGNSS-IKE estimator is tested and its performance is characterized using simulated CYGNSS observations with realistic measurement errors. CYGNSS-IKE performance improves for stronger, more organized storms and with increasing number of observations over the extent of the 34-kt radius. Known sampling information can be used for quality control. While CYGNSS-IKE is calculated for individual geographic quadrants, using a total-IKE—a sum over all quadrants—improves performance. CYGNSS-IKE should be of interest to operational and research meteorologists, insurance companies, and others interested in the destructive potential of tropical cyclones developing in data-sparse regions, which will now be covered by CYGNSS. The CYGNSS-IKE product will be available for the 2017 Atlantic Ocean hurricane season.

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Mary Morris and Christopher S. Ruf

Abstract

The Cyclone Global Navigation Satellite System (CYGNSS) consists of a constellation of eight microsatellites that provide observations of surface wind speed in all precipitating conditions. A method for estimating tropical cyclone (TC) metrics—maximum surface wind speed V MAX, radius of maximum surface wind speed R MAX, and wind radii (R 64, R 50, and R 34)—from CYGNSS observations is developed and tested using simulated CYGNSS observations with realistic measurement errors. Using two inputs, 1) CYGNSS observations and 2) the storm center location, estimates of TC metrics are possible through the use of a parametric wind model algorithm that effectively interpolates between the available observations as a constraint on the assumed wind speed distribution. This methodology has a promising performance as evaluated from the simulations presented. In particular, after quality-control filters based on sampling properties are applied to the population of test cases, the standard deviation of retrieval error for V MAX is 4.3 m s−1 (where 1 m s−1 = 1.94 kt), for R MAX is 17.4 km, for R 64 is 16.8 km, for R 50 is 21.6 km, and for R 34 is 41.3 km (where 1 km = 0.54 n mi). These TC data products will be available for the 2017 Atlantic Ocean hurricane season using on-orbit CYGNSS observations, but near-real-time operations are the subject of future work. Future work will also include calibration and validation of the algorithm once real CYGNSS data are available.

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Shannon T. Brown and Christopher S. Ruf

Abstract

A physically based method is developed to estimate the microphysical structure of the melting layer in stratiform rain using airborne observations by a dual-frequency radar and a 10.7-GHz radiometer. The method employs a nonlinear optimal estimation approach to find two parameters of the gamma drop size distribution (DSD) at each radar range gate from the Ku/Ka-band reflectivities. The DSD profile is used to determine the atmospheric absorption/extinction profile, which enables the surface contribution to the measured brightness temperature to be estimated. The surface wind speed is estimated from the surface emissivity by inverting the forward model, which relates the two. Retrievals in stratiform precipitation require a model to describe the thermodynamic and electromagnetic properties of melting hydrometeors. The melting layer can contribute a majority of the total atmospheric absorption, making it a key component for accurate retrievals in stratiform rain. Several melting layer models were evaluated based on their fit to the dual-frequency reflectivity measurements in the melting layer. A candidate model is selected and tuned to match the radar measurements. The melting layer model is then incorporated into the full forward model for the brightness temperature observed by the radiometer. The surface wind speed assumed in the forward model is forced by the radiometer observations. If the actual surface wind speed is known, this approach provides a powerful constraint on the possible melting layer model. A case study is presented from an airborne campaign over areas of precipitation off the coast of Vancouver Island, British Columbia, Canada. The estimated wind speeds are found to be uncorrelated with the reflectivity and their average value is within 1 m s−1 of that retrieved in a clear area adjacent to the rain.

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Christopher S. Ruf and Calvin T. Swift

Abstract

A tropospheric water vapor density profiling system is presented. The hardware consists of an autocorrelation radiometer (CORRAD) operating over a frequency range from 20.5 to 23.5 GHz. The CORRAD directly measures the autocorrelation of downwelling thermal emission from the atmosphere. The 3 GHz predetection bandwidth of each measurement provides for extremely rapid decorrelation of the noise inherent in all radiometer measurements. This, in turn, allows for high temporal resolution of the water vapor dynamics. Fourier transformation of the raw data produces a brightness temperature spectrum with 100 MHz resolution across the frequency range. Inversion of the radiative transfer integral equation to solve for the water vapor distribution is constrained by the 31 equivalent frequency channels. Previous microwave profilers of the troposphere, with 2 to 5 frequency channels, were much less constrained and the inversion process was accordingly more sensitive to measurement noise. Water vapor profiles estimated by the inversion are in good agreement with coincident radiosonde measurements made by the National Weather Service.

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Maria Paola Clarizia and Christopher S. Ruf

Abstract

Spaceborne Global Navigation Satellite System reflectometry observations of the ocean surface are found to respond to components of roughness forced by local winds and to a longer wave swell that is only partially correlated with the local wind. This dual sensitivity is largest at low wind speeds. If left uncorrected, the error in wind speeds retrieved from the observations is strongly correlated with the significant wave height (SWH) of the ocean. A Bayesian wind speed estimator is developed to correct for the long-wave sensitivity at low wind speeds. The approach requires a characterization of the joint probability of occurrence of wind speed and SWH, which is derived from archival reanalysis sea-state records. The Bayesian estimator is applied to spaceborne data collected by the Technology Demonstration Satellite-1 (TechDemoSat-1) and is found to provide significant improvement in wind speed retrieval at low winds, relative to a conventional retrieval that does not account for SWH. At higher wind speeds, the wind speed and SWH are more highly correlated and there is much less need for the correction.

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