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Corey G. Amiot, Sayak K. Biswas, Timothy J. Lang, and David I. Duncan

Abstract

Recent upgrades, calibration, and scan-angle bias reductions to the Advanced Microwave Precipitation Radiometer (AMPR) have yielded physically realistic brightness temperatures (T b) from the Olympic Mountains Experiment and Radar Definition Experiment (OLYMPEX/RADEX) dataset. Measured mixed-polarization T b were converted to horizontally and vertically polarized T b via dual-polarization deconvolution, and linear regression equations were developed to retrieve integrated cloud liquid water (CLW), water vapor (WV), and 10-m wind speed (WS) using simulated AMPR T b and modeled atmospheric profiles. These equations were tested using AMPR T b collected during four OLYMPEX/RADEX cases; the resulting geophysical values were compared with independent retrieval (1DVAR) results from the same dataset, while WV and WS were also compared with in situ data. Geophysical calculations using simulated T b yielded relatively low retrieval and crosstalk errors when compared with modeled profiles; average CLW, WV, and WS root-mean-square deviations (RMSD) were 0.11 mm, 1.28 mm, and 1.11 m s−1, respectively, with median absolute deviations (MedAD) of 2.26 × 10−2 mm, 0.22 mm, and 0.55 m s−1, respectively. When applied to OLYMPEX/RADEX data, the new retrieval equations compared well with 1DVAR; CLW, WV, and WS RMSD were 9.95 × 10−2 mm, 2.00 mm, and 2.35 m s−1, respectively, and MedAD were 2.88 × 10−2 mm, 1.14 mm, and 1.82 m s−1, respectively. WV MedAD between the new equations and dropsondes were 2.10 and 1.80 mm at the time and location of minimum dropsonde altitude, respectively, while WS MedAD were 1.15 and 1.53 m s−1, respectively, further indicating the utility of these equations.

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Karly J. Reimel and Matthew Kumjian

Abstract

Accurate estimation of specific differential phase (K DP) is necessary for rain rate estimation, attenuation correction, and hydrometeor classification algorithms. There are numerous published methods to process polarimetric radar observations of propagation differential phase shift (ΦDP) and estimate K DP, but the corresponding K DP estimate uncertainty is unquantified. This study provides guidance on how commonly used K DP estimation algorithms perform in various environments. We create numerous synthetic (“true”) K DP profiles, integrate over them to obtain “smoothed” ΦDP, and then add noise typical of S-band operational weather radar measurements. Each algorithm is applied to our noisy ΦDP profiles and compared to the true K DP profile such that the errors and uncertainty are quantified. The synthetic K DP profiles are Gaussian in shape, which allows systematic variations in their magnitude and width to determine how each algorithm performs in smooth, slowly changing K DP profiles, as well as steep profiles. Results demonstrate that algorithm performance is dependent on the ΦDP field received. These results are further supported by an error analysis of each algorithm for two more complicated synthetic K DP profiles. Some K DP algorithms allow users to change various tuning parameters; a subset of these tuning parameters is tested to provide guidance on how changing these parameters impacts algorithm performance. We then provide evidence that our known-truth framework provides insight into algorithm performance in observed data through two case studies.

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Brian E. Sheppard, Merhala Thurai, Peter Rodriguez, Patrick C. Kennedy, and David R. Hudak

Abstract

The Precipitation Occurrence Sensor System (POSS) is a small X-band Doppler radar that measures the Doppler velocity spectra from precipitation falling in a small volume near the sensor. The sensor records a 2D frequency of occurrence matrix of the velocity and power at the mode of each spectrum measured over 1 min. The centroid of the distribution of these modes, along with other spectral parameters, defines a data vector input to a multiple discriminant analysis (MDA) for classification of the precipitation type. This requires the a priori determination of a training set for different types, particle size distributions (PSDs), and wind speed conditions. A software model combines POSS system parameters, a particle scattering cross section, and terminal velocity models, to simulate the real-time Doppler signal measured by the system for different PSDs and wind speeds. This is processed in the same manner as the system hardware to produce bootstrap samples of the modal centroid distributions for the MDA training set. MDA results are compared to images from the Multi-Angle Snowflake Camera (MASC) at the MASCRAD site near Easton, Colorado, and to the CSU–CHILL X-band radar observations from Greeley, Colorado. In the four case studies presented, POSS successfully identified precipitation transitions through a range of types (rain, graupel, rimed dendrites, aggregates, unrimed dendrites). Also two separate events of hail were reported and confirmed by the images.

Open access
Philippe Keckhut, Alain Hauchecorne, Mustapha Meftah, Sergey Khaykin, Chantal Claud, and Pierre Simoneau

Abstract

While meteorological numerical models extend upward to the mesopause, mesospheric observations are required for leading simulations and numerical weather forecasts and climate projections. This work reviews some of the challenges about temperature observation requirements and the limiting factors of the actual measurements associated with atmospheric tides. A new strategy is described here using a limb-scattering technique that is based on previous experiments in space. Such observations can be used with cube satellites. Technical issues are the large dynamic range (4 orders of magnitude) required for the measurements, the accuracy of the limb pointing, and the level of stray light. The technique described here will expect accuracy of 1–2 K with a vertical resolution of 1–2 km. A constellation of 100 platforms could provide temperature observations with space (100 km) and time (3 h) resolutions recommended by the World Meteorological Organization, and tidal issues could be resolved with a minimum of 3–5 platforms with specific orbit maintained to avoid drifts.

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Arnaud Le Boyer, Matthew H. Alford, Nicole Couto, Michael Goldin, Sean Lastuka, Sara Goheen, San Nguyen, Andrew J. Lucas, and Tyler D. Hennon

Abstract

The Epsilometer (“epsi”) is a small (7 cm diameter × 30 cm long), low-power (0.15 W), and extremely modular microstructure package measuring thermal and kinetic energy dissipation rates, χ and ε. Both the shear probes and FP07 temperature sensors are fabricated in house following techniques developed by Michael Gregg at the Applied Physics Laboratory/University of Washington (APL/UW). Sampling eight channels (two shear, two temperature, three-axis accelerometer, and a spare for future sensors) at 24 bit precision and 325 Hz, the system can be deployed in standalone mode (battery power and recording to microSD cards) for deployment on autonomous vehicles, wave powered profilers, or it can be used with dropping body termed the “epsi-fish” for profiling from boats, autonomous surface craft or ships with electric fishing reels or other simple winches. The epsi-fish can also be used in real-time mode with the Scripps “fast CTD” winch for fully streaming, altimeter-equipped, line-powered, rapid-repeating, near-bottom shipboard profiles to 2200 m. Because this winch has a 25 ft (~7.6 m) boom deployable outboard from the ship, contamination by ship wake is reduced one to two orders of magnitude in the upper 10–15 m. The noise floor of ε profiles from the epsi-fish is ~10−10 W kg−1. This paper describes the fabrication, electronics, and characteristics of the system, and documents its performance compared to its predecessor, the APL/UW Modular Microstructure Profiler (MMP).

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Sang-Moo Lee and Byung-Ju Sohn

Abstract

The widely used Fast Microwave Ocean Surface Emissivity Model (FASTEM) does not include the interaction between small-scale and large-scale roughness, which seems to induce errors in the ocean surface emissivity estimation. In this study, we attempt to develop a new model that might be included in the FASTEM-like model. In the developed model, the large-scale roughness is expressed as a function of the local incidence angle (LIA) within the context of Fresnel reflection theory, incorporating the interactions between the small-scale and large-scale roughness into the fast ocean surface emissivity model, as done in the two-scale approach. With the new expression of the large-scale roughness, we also provide a more physically based form of the equation for the fast ocean surface emissivity calculation that includes the small-scale scattering over a geometrically rough surface. In addition, an algorithm for estimating two-scale roughness from the measured or modeled polarized emissivities in conjunction with the proposed fast ocean surface emissivity equation is provided. The results demonstrate that the interactions between two-scale roughness should be considered in order to estimate accurate two-scale roughness influences on the ocean surface emissivity.

Open access
Nicolas Kolodziejczyk, Mathieu Hamon, Jacqueline Boutin, Jean-Luc Vergely, Gilles Reverdin, Alexandre Supply, and Nicolas Reul

Abstract

Ten years of L-band radiometric measurements have proven the capability of satellite sea surface salinity (SSS) to resolve large-scale-to-mesoscale SSS features in tropical to subtropical ocean. In mid-to-high latitudes, L-band measurements still suffer from large-scale and time-varying errors. Here, a simple method is proposed to mitigate the large-scale and time-varying errors. First, an optimal interpolation using a large correlation scale (~500 km) is used to map independently Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) level-3 (L3) data. The mapping is compared with the equivalent mapping of in situ observations to estimate the large-scale and seasonal biases. A second mapping is performed on adjusted SSS at the scale of SMOS/SMAP spatial resolution (~45 km). This procedure merges both products and increases the signal-to-noise ratio of the absolute SSS estimates, reducing the root-mean-square difference of in situ satellite products by about 26%–32% from mid- to high latitudes, respectively, in comparison with the existing SMOS and SMAP L3 products. However, in the Arctic Ocean, some issues on satellite retrieved SSS related to, for example, radio frequency interferences, land–sea contamination, and ice–sea contamination remain challenging to reduce given the low sensitivity of L-band radiometric measurements to SSS in cold water. Using the International Thermodynamic Equation Of Seawater—2010 (TEOS-10), the resulting level-4 SSS satellite product is combined with satellite-microwave SST products to estimate sea surface density, spiciness, and haline contraction and thermal expansion coefficients. For the first time, we illustrate how useful these satellite-derived parameters are to fully characterize the surface ocean water masses at large mesoscale.

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Shuang-Xi Guo, Xian-Rong Cen, Ling Qu, Yuan-Zheng Lu, Peng-Qi Huang, and Sheng-Qi Zhou

Abstract

Flow speed past the measuring probe is definitely needed for the estimation of the turbulent kinetic energy dissipation rates ε and temperature dissipation rates χ based on the Taylor frozen hypothesis. This speed is usually measured with current instruments. Occasional failed work of these instruments may lead to unsuccessful speed measurement. For example, low concentration of suspended particles in water could make the observed speed invalid when using acoustic measuring instruments. In this study, we propose an alternative approach for quantifying the flow speeds by only using the microstructure shear or temperature data, according to the spectral theories of the inertial and dissipation subranges. A dataset of the microstructure profiler, vertical microstructure profiler (VMP), collected in the South China Sea (SCS) during 2017, is used to describe this approach, and the inferred speeds are compared with the actual passing-probe speeds, i.e., the falling speeds of the VMP. Probability density functions (PDFs) of the speed ratios, i.e., the ratios of the speeds respectively inferred from the inertial and dissipation subranges of the shear and temperature spectra to the actual speeds, follow the lognormal distribution, with corresponding mean values of 1.32, 1.03, 1.56, and 1.43, respectively. This result indicates that the present approach for quantifying the flow speeds is valid, and the speeds inferred from the dissipation subrange of shear spectrum agree much better with the actual ones than those from the inertial subrange of shear spectrum and the inertial and dissipation subranges of temperature spectrum. The present approach may be complementary and useful in the evaluation of turbulent mixing when the directly observed speeds are unavailable.

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Ricardo Reinoso-Rondinel and Marc Schleiss

Abstract

Conventionally, Micro Rain Radars (MRRs) have been used as a tool to calibrate reflectivity from weather radars, estimate the relation between rainfall rate and reflectivity, and study microphysical processes in precipitation. However, limited attention has been given to the reliability of the retrieved drop size distributions (DSDs) from MRRs. This study sheds more light on this aspect by examining the sensitivity of retrieved DSDs to the assumptions made to map Doppler spectra into size distributions, and investigates the capability of an MRR to assess polarimetric observations from operational weather radars. For that, an MRR was installed near the Cabauw observatory in the Netherlands, between the International Research Center for Telecommunications and Radar (IRCTR) Drizzle Radar (IDRA) X-band radar and the Herwijnen operational C-band radar. The measurements of the MRR from November 2018 to February 2019 were used to retrieve DSDs and simulate horizontal reflectivity Z e, differential reflectivity Z DR, and specific differential phase K DP in rain. Attention is given to the impact of aliased spectra and right-hand-side truncation on the simulation of polarimetric variables. From a quantitative assessment, the correlations of Z e and Z DR between the MRR and Herwijnen radar were 0.93 and 0.70, respectively, while those between the MRR and IDRA were 0.91 and 0.69. However, Z e and Z DR from the Herwijnen radar showed slight biases of 1.07 and 0.25 dB. For IDRA, the corresponding biases were 2.67 and −0.93 dB. Our results show that MRR measurements are advantageous to inspect the calibration of scanning radars and validate polarimetric estimates in rain, provided that the DSDs are correctly retrieved and controlled for quality assurance.

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Zhen Shen, Kefei Zhang, Qimin He, Moufeng Wan, Longjiang Li, and Suqin Wu

Abstract

The sampling error caused by the uneven distribution of radio occultation (RO) profiles in both space and time domains is an important error source of RO climatologies. In this paper, the sampling error RO temperature climatologies is investigated using the 4-yr (2007–10) data from the Constellation Observing System for Meteorology, Ionosphere, and Climate mission. The error is divided into three parts, including local time component (LTC), temporal component (TC), and spatial component (SC). The characteristics of the three components are investigated. Results show the following: 1) The LTC part of the total sampling error is characterized by a pattern of periodic positive and negative deviations, with a full cycle of about four months. The most significant LTC values are found in the area around 60°N/S and the polar regions. 2) The TC part is mainly associated with the extent of day-to-day temperature variability and the daily number of RO profiles observed in each month. The most pronounced TC part is shown in high-latitude areas in wintertime, where the day-to-day temperature variability is high. 3) The SC part shows distinct features in different altitude ranges. It is characterized by a systemic error in the lower troposphere (2–8 km) but exhibits a seasonal trend at the altitude range from 8 to 40 km. 4) The total sampling error is dominated by the TC and SC parts in the troposphere and lower stratosphere, whereas in the upper stratosphere it is dominated by the LTC part.

Open access