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H. Wang, R. J. Barthelmie, A. Clifton, and S. C. Pryor

Abstract

Defining optimal scanning geometries for scanning lidars for wind energy applications remains an active field of research. This paper evaluates uncertainties associated with arc scan geometries and presents recommendations regarding optimal configurations in the atmospheric boundary layer. The analysis is based on arc scan data from a Doppler wind lidar with one elevation angle and seven azimuth angles spanning 30° and focuses on an estimation of 10-min mean wind speed and direction. When flow is horizontally uniform, this approach can provide accurate wind measurements required for wind resource assessments in part because of its high resampling rate. Retrieved wind velocities at a single range gate exhibit good correlation to data from a sonic anemometer on a nearby meteorological tower, and vertical profiles of horizontal wind speed, though derived from range gates located on a conical surface, match those measured by mast-mounted cup anemometers. Uncertainties in the retrieved wind velocity are related to high turbulent wind fluctuation and an inhomogeneous horizontal wind field. The radial velocity variance is found to be a robust measure of the uncertainty of the retrieved wind speed because of its relationship to turbulence properties. It is further shown that the standard error of wind speed estimates can be minimized by increasing the azimuthal range beyond 30° and using five to seven azimuth angles.

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R. Cifelli, V. Chandrasekar, S. Lim, P. C. Kennedy, Y. Wang, and S. A. Rutledge

Abstract

The efficacy of dual-polarization radar for quantitative precipitation estimation (QPE) has been demonstrated in a number of previous studies. Specifically, rainfall retrievals using combinations of reflectivity (Z h), differential reflectivity (Z dr), and specific differential phase (K dp) have advantages over traditional ZR methods because more information about the drop size distribution (DSD) and hydrometeor type are available. In addition, dual-polarization-based rain-rate estimators can better account for the presence of ice in the sampling volume.

An important issue in dual-polarization rainfall estimation is determining which method to employ for a given set of polarimetric observables. For example, under what circumstances does differential phase information provide superior rain estimates relative to methods using reflectivity and differential reflectivity? At Colorado State University (CSU), an optimization algorithm has been developed and used for a number of years to estimate rainfall based on thresholds of Z h, Z dr, and K dp. Although the algorithm has demonstrated robust performance in both tropical and midlatitude environments, results have shown that the retrieval is sensitive to the selection of the fixed thresholds.

In this study, a new rainfall algorithm is developed using hydrometeor identification (HID) to guide the choice of the particular rainfall estimation algorithm. A separate HID algorithm has been developed primarily to guide the rainfall application with the hydrometeor classes, namely, all rain, mixed precipitation, and all ice.

Both the data collected from the S-band Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar and a network of rain gauges are used to evaluate the performance of the new algorithm in mixed rain and hail in Colorado. The evaluation is also performed using an algorithm similar to the one developed for the Joint Polarization Experiment (JPOLE). Results show that the new CSU HID-based algorithm provides good performance for the Colorado case studies presented here.

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Jinbo Wang, Lee-Lueng Fu, Hector S. Torres, Shuiming Chen, Bo Qiu, and Dimitris Menemenlis

Abstract

The Surface Water and Ocean Topography (SWOT) mission aims to measure the sea surface height (SSH) at a high spatial resolution using a Ka-band radar interferometer (KaRIn). The primary oceanographic objective is to characterize the ocean eddies at a spatial resolution of 15 km for 68% of the ocean surface. This resolution is derived from the ratio between the wavenumber spectrum of the conventional altimeter (projected to submesoscale) and the SWOT SSH errors. While the 15-km threshold is useful as a global approximation of the spatial scales resolved by SWOT (SWOT scale), it can be misleading for regional studies. Here we revisit the problem using a high-resolution (~2-km horizontal grid spacing) tide-resolving global ocean simulation and map the SWOT scale as a function of location and season. The results show that the SWOT scale increases, in general, from about 15 km at low latitudes to ~30–45 km at mid- and high latitudes but with a large geographical dependence. A SWOT scale smaller than 30 km is expected in the high-latitude energetic regions. The SWOT scale varies seasonally as a result of the seasonality in both the noise and ocean signals. The seasonality also has a geographical dependence. Both eddies and internal gravity waves/tides contribute significantly to the SWOT scale variation. Our analysis provides model predictions for interpreting the anticipated observations from SWOT and guidance for the development of analysis methodologies.

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Tian-You Yu, Yadong Wang, Alan Shapiro, Mark B. Yeary, Dusan S. Zrnić, and Richard J. Doviak

Abstract

Distinct tornado spectral signatures (TSSs), which are similar to white noise spectra or have bimodal features, have been observed in both simulations and real data from Doppler radars. The shape of the tornado spectrum depends on several parameters such as the range of the tornado, wind field within the storm, and the reflectivity structure. In this work, one of the higher-order spectra (HOS), termed bispectrum, is implemented to characterize TSS, in which the Doppler spectrum is considered a 1D pattern. Bispectrum has been successfully applied to pattern recognition in other fields owing to the fact that bispectrum can retain the shape information of the signal. Another parameter, termed spectral flatness, is proposed to quantify the spectrum variations. It is shown in simulation that both parameters can characterize TSS and provide information in addition to the three spectral moments. The performance of the two parameters and the spectrum width for characterizing TSS are statistically analyzed and compared for various conditions. The potential of the three parameters for improving tornado detection is further demonstrated by tornadic time series data collected by a research Weather Surveillance Radar-1988 Doppler, KOUN, operated by the National Severe Storms Laboratory.

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David A. Marks, David B. Wolff, David S. Silberstein, Ali Tokay, Jason L. Pippitt, and Jianxin Wang

Abstract

Since the Tropical Rainfall Measuring Mission (TRMM) satellite launch in November 1997, the TRMM Satellite Validation Office (TSVO) at NASA Goddard Space Flight Center (GSFC) has been performing quality control and estimating rainfall from the KPOL S-band radar at Kwajalein, Republic of the Marshall Islands. Over this period, KPOL has incurred many episodes of calibration and antenna pointing angle uncertainty. To address these issues, the TSVO has applied the relative calibration adjustment (RCA) technique to eight years of KPOL radar data to produce Ground Validation (GV) version 7 products. This application has significantly improved stability in KPOL reflectivity distributions needed for probability matching method (PMM) rain-rate estimation and for comparisons to the TRMM precipitation radar (PR). In years with significant calibration and angle corrections, the statistical improvement in PMM distributions is dramatic. The intent of this paper is to show improved stability in corrected KPOL reflectivity distributions by using the PR as a stable reference. Intermonth fluctuations in mean reflectivity differences between the PR and corrected KPOL are on the order of ±1–2 dB, and interyear mean reflectivity differences fluctuate by approximately ±1 dB. This represents a marked improvement in stability with confidence comparable to the established calibration and uncertainty boundaries of the PR. The practical application of the RCA method has salvaged eight years of radar data that would have otherwise been unusable and has made possible a high-quality database of tropical ocean–based reflectivity measurements and precipitation estimates for the research community.

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Gregory S. Poulos, Junhong Wang, Dean K. Lauritsen, and Harold L. Cole

Abstract

The dropwindsonde (or dropsonde) is a frequently utilized tool in geophysical research and its use over ocean and flat terrain is a reliable and well-established practice. Its use in complex terrain, however, is complicated by signal acquisition challenges that can be directly related to the ground target location, local relief, and line of sight to flight tracks relevant to the observation sought. This note describes a straightforward technique to calculate the theoretical altitude above ground to which a ground-targeted dropsonde will provide data for a given airborne platform. It is found that this height H Cq can be calculated from expected airborne platform horizontal velocity U ag, mean dropwindsonde vertical velocity Ws, the relevant barrier maximum HB, and the horizontal distance from the target area to the barrier maximum DB. Here, H Cq is found to be weakly dependent on release altitude through Ws. An example from the Terrain-induced Rotor Experiment (T-REX) is used to show that for modern aircraft platforms and dropwindsondes signal loss can occur 1–2 km above ground if mitigation is not pursued. Practical mitigation techniques are described for those complex terrain cases where signal propagation problems would create a significant negative scientific impact.

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D. Wang, C. Prigent, L. Kilic, S. Fox, C. Harlow, C. Jimenez, F. Aires, C. Grassotti, and F. Karbou

Abstract

The Tool to Estimate Land Surface Emissivity from Microwave to Submillimeter Waves (TELSEM2) is linked to a climatology of monthly emissivity estimates and provides a parameterization of the surface emissivity up to 700 GHz, in the framework of the preparation for the Ice Cloud Imager (ICI) on board the Meteorological Operational Satellite Second Generation (MetOp-SG). It is an updated version of the Tool to Estimate Land Surface Emissivities at Microwave Frequencies (TELSEM; ). This study presents the parameterization of continental snow and ice and sea ice emissivities in TELSEM2. It relies upon satellite-derived emissivities up to 200 GHz, and it is anchored to the Special Sensor Microwave Imager (SSM/I) TELSEM monthly climatology dataset (19–85 GHz). Emissivities from Météo-France and the National Oceanic and Atmospheric Administration (NOAA) at frequencies up to 190 GHz were used, calculated from the Special Sensor Microwave Imager/Sounder (SSMIS) and the Advanced Microwave Sounding Unit-B (AMSU-B) observations. TELSEM2 has been evaluated up to 325 GHz with the observations of the International Submillimeter Airborne Radiometer (ISMAR) and the Microwave Airborne Radiometer Scanning System (MARSS), which were operated on board the Facility for Airborne Atmospheric Measurements (FAAM) aircraft during the Cold-Air Outbreak and Submillimeter Ice Cloud Study (COSMICS) campaign over Greenland. Above continental snow and ice, TELSEM2 is very consistent with the aircraft estimates in spatially homogeneous regions, especially at 89 and 157 GHz. Over sea ice, the aircraft estimates are very variable spatially and temporally, and the comparisons with the TELSEM2 were not conclusive. TELSEM2 will be distributed in the new version of the RTTOV radiative transfer community code, to be available in 2017.

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P. Racette, R. F. Adler, J. R. Wang, A. J. Gasiewski, D. M. Jakson, and D. S. Zacharias

Abstract

A six-channel airborne total-power Millimeter-wave Imaging Radiometer (MIR) was recently built to provide measurements of atmospheric water vapor, clouds, and precipitation. The instrument is a cross-track scanner that has a 3-dB beamwidth of 3.5° and an angular swath of 100°. It measures radiation at the frequencies of 89, 150, 183.3 ± 1, 183.3 ± 3, 183.3 ± 7, and 220 GHz. The inclusion of the 220-GHz receiver makes this instrument unique; no other instrument has made atmospheric radiation measurements using this combination of frequencies. The temperature sensitivities ΔT, based on the actual flight data with a 6.8-ms integration time, are found to be 0.44, 0.44, 1.31, 1.30. 1.02, and 1.07 K. The instrument has two external calibration loads maintained at the temperatures of 330 and 250 K (the ambient temperature at an aircraft altitude of 20 km). These calibration load temperatures are monitored precisely so that the radiometric measurements of the instrument could be made to better than 1 K of accuracy in the brightness temperature range of 240–300 K. Measurements made with a calibration target emmersed in liquid nitrogen indicate a measurement accuracy of 2–4 K for brightness temperatures below 100 K. The instrument has flown successfully aboard the National Aeronautics and Space Administration (NASA) ER-2 aircraft for more than 130 h. This paper is an overview of the system design, calibration, and measurement capabilities.

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D. N. Whiteman, B. Demoz, G. Schwemmer, B. Gentry, P. Di Girolamo, D. Sabatino, J. Comer, I. Veselovskii, K. Evans, R-F. Lin, Z. Wang, A. Behrendt, V. Wulfmeyer, E. Browell, R. Ferrare, S. Ismail, and J. Wang

Abstract

The NASA GSFC Scanning Raman Lidar (SRL) participated in the International H2O Project (IHOP) that occurred in May and June 2002 in the midwestern part of the United States. The SRL system configuration and methods of data analysis were described in Part I of this paper. In this second part, comparisons of SRL water vapor measurements and those of Lidar Atmospheric Sensing Experiment (LASE) airborne water vapor lidar and chilled-mirror radiosonde are performed. Two case studies are then presented: one for daytime and one for nighttime. The daytime case study is of a convectively driven boundary layer event and is used to characterize the daytime SRL water vapor random error characteristics. The nighttime case study is of a thunderstorm-generated cirrus cloud case that is studied in its meteorological context. Upper-tropospheric humidification due to precipitation from the cirrus cloud is quantified as is the cirrus cloud optical depth, extinction-to-backscatter ratio, ice water content, cirrus particle size, and both particle and volume depolarization ratios. A stability and back-trajectory analysis is performed to study the origin of wave activity in one of the cloud layers. These unprecedented cirrus cloud measurements are being used in a cirrus cloud modeling study.

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Christian D. Kummerow, David L. Randel, Mark Kulie, Nai-Yu Wang, Ralph Ferraro, S. Joseph Munchak, and Veljko Petkovic

Abstract

The Goddard profiling algorithm has evolved from a pseudoparametric algorithm used in the current TRMM operational product (GPROF 2010) to a fully parametric approach used operationally in the GPM era (GPROF 2014). The fully parametric approach uses a Bayesian inversion for all surface types. The algorithm thus abandons rainfall screening procedures and instead uses the full brightness temperature vector to obtain the most likely precipitation state. This paper offers a complete description of the GPROF 2010 and GPROF 2014 algorithms and assesses the sensitivity of the algorithm to assumptions related to channel uncertainty as well as ancillary data. Uncertainties in precipitation are generally less than 1%–2% for realistic assumptions in channel uncertainties. Consistency among different radiometers is extremely good over oceans. Consistency over land is also good if the diurnal cycle is accounted for by sampling GMI product only at the time of day that different sensors operate. While accounting for only a modest amount of the total precipitation, snow-covered surfaces exhibit differences of up to 25% between sensors traceable to the availability of high-frequency (166 and 183 GHz) channels. In general, comparisons against early versions of GPM’s Ku-band radar precipitation estimates are fairly consistent but absolute differences will be more carefully evaluated once GPROF 2014 is upgraded to use the full GPM-combined radar–radiometer product for its a priori database. The combined algorithm represents a physically constructed database that is consistent with both the GPM radars and the GMI observations, and thus it is the ideal basis for a Bayesian approach that can be extended to an arbitrary passive microwave sensor.

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