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Jared W. Marquis, Mayra I. Oyola, James R. Campbell, Benjamin C. Ruston, Carmen Córdoba-Jabonero, Emilio Cuevas, Jasper R. Lewis, Travis D. Toth, and Jianglong Zhang

Abstract

Numerical weather prediction systems depend on Hyperspectral Infrared Sounder (HIS) data, yet the impacts of dust-contaminated HIS radiances on weather forecasts has not been quantified. To determine the impact of dust aerosol on HIS radiance assimilation, we use a modified radiance assimilation system employing a one-dimensional variational assimilation system (1DVAR) developed under the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Numerical Weather Prediction–Satellite Application Facility (NWP-SAF) project, which uses the Radiative Transfer for TOVS (RTTOV). Dust aerosol impacts on analyzed temperature and moisture fields are quantified using synthetic HIS observations from rawinsonde, Micropulse Lidar Network (MPLNET), and Aerosol Robotic Network (AERONET). Specifically, a unit dust aerosol optical depth (AOD) contamination at 550 nm can introduce larger than 2.4 and 8.6 K peak biases in analyzed temperature and dewpoint, respectively, over our test domain. We hypothesize that aerosol observations, or even possibly forecasts from aerosol predication models, may be used operationally to mitigate dust induced temperature and moisture analysis biases through forward radiative transfer modeling.

Open access
Michiko Otsuka, Hiromu Seko, Masahiro Hayashi, and Ko Koizumi

Abstract

Himawari-8 optimal cloud analysis (OCA), which employs all 16 channels of the Advanced Himawari Imager, provides cloud properties such as cloud phase, top pressure, optical thickness, effective radius, and water path. By using OCA, the water vapor distribution can be inferred with high spatiotemporal resolution and with a wide coverage, including over the ocean, which can be useful for improving initial states for prediction of the torrential rainfalls that occur frequently in Japan. OCA products were first evaluated by comparing them with different kinds of datasets (surface, sonde, and ceilometer observations) and with model outputs, to determine their data characteristics. Overall, OCA data were consistent with observations of water clouds with moderate optical thicknesses at low to midlevels. Next, pseudorelative humidity data were derived from the OCA products, and utilized in assimilation experiments of a few heavy rainfall cases, conducted with the Japan Meteorological Agency’s nonhydrostatic model–based Variational Data Assimilation System. Assimilation of OCA pseudorelative humidities caused there to be significant differences in the initial conditions of water vapor fields compared to the control, especially where OCA clouds were detected, and their influence lasted relatively long in terms of forecast hours. Impacts of assimilation on other variables, such as wind speed, were also seen. When the OCA data successfully represented low-level inflows from over the ocean, they positively impacted precipitation forecasts at extended forecast times.

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Shihe Ren, Xueming Zhu, Marie Drevillon, Hui Wang, Yunfei Zhang, Ziqing Zu, and Ang Li

Abstract

A frontal detection algorithm is developed with the capability of detecting significant frontal segments of sea surface temperature (SST) in the high-resolution South China Sea Operational Forecasting System (SCSOFS). To effectively obtain frontal information, a gradient-based Canny edge detection algorithm is improved with postprocessing designed for high-resolution numerical models, aiming at extracting primary ocean fronts while ensuring the balance of frontal continuity and positioning accuracy. Metrics of frontal probability and strength are used to measure the robustness of the results in terms of mean state and seasonal variability of frontal activities in the South China Sea (SCS). Most fronts are found in the nearshore and form a strip shape extending from the Taiwan Strait to the coast of Vietnam. The SCSOFS is found to reproduce strong seasonal signals dominating the variability of the frontal strength and occurrence probability in the SCS. We implement the algorithm on the daily averaged SST derived from two other SST analyses for intercomparison in the SCS.

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Mohammad Kazemi, Babak Khorsandi, and Laurent Mydlarski

Abstract

The relatively large sampling volume of acoustic Doppler velocimeters (ADVs) is expected to influence their measurement of turbulence. To study this effect, a series of experiments using different sampling volume sizes was conducted in an axisymmetric turbulent jet. The results show that the mean velocities are not significantly affected by the size of the sampling volume. On the other hand, reducing the sampling volume size results in an increase in the variances of the u and υ velocities, while its effect on the variance of the w velocity is negligible. Application of a noise-reduction method to the data renders the velocity variances nearly independent of sampling volume size, suggesting that the difference was mainly due to Doppler noise. The principal conclusion of this work is, therefore, that—as long as the characteristic length of sampling volume is much smaller than the integral length scale of flow—increasing the sampling volume size (i.e., increasing spatial averaging over highly correlated scatterers) can reduce Doppler noise and result in more accurate measurements of the velocity variances. Application of noise-reduction methods to the data is found to be especially important when the sampling volume size is reduced to capture smaller scales, or for near-boundary measurements. Furthermore, noise due to mean velocity shear, even at the largest velocity gradient along the jet radial profile, is found to be negligible in the present work.

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Andrew C. Kren and Richard A. Anthes

Abstract

This study estimates the random error variances and standard deviations (STDs) for four datasets: Global Hawk (GH) dropsondes (DROP), the High-Altitude Monolithic Microwave Integrated Circuit Sounding Radiometer (HAMSR) aboard the GH, the fifth European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), and the Hurricane Weather Research and Forecasting (HWRF) Model, using the three-cornered hat (3CH) method. These estimates are made during the 2016 Sensing Hazards with Operational Unmanned Technology (SHOUT) season in the environment of four tropical cyclones from August to October. For temperature and specific and relative humidity, the ERA5, HWRF, and DROP datasets all have similar magnitudes of errors, with ERA5 having the smallest. The error STDs of temperature and specific humidity are less than 0.8 K and 1.0 g kg−1 over most of the troposphere, while relative humidity error STDs increase from less than 5% near the surface to between 10% and 20% in the upper troposphere. The HAMSR bias-corrected data have larger errors, with estimated error STDs of temperature and specific humidity in the lower troposphere between 1.5 and 2.0 K and between 1.5 and 2.5 g kg−1. HAMSR’s relative humidity error STD increases from approximately 10% in the lower troposphere to 30% in the upper troposphere. The 3CH method error estimates are generally consistent with prior independent estimates of errors and uncertainties for the HAMSR and dropsonde datasets, although they are somewhat larger, likely due to the inclusion of representativeness errors (differences associated with different spatial and temporal scales represented by the data).

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Lisa Milani, Mark S. Kulie, Daniele Casella, Pierre E. Kirstetter, Giulia Panegrossi, Veljko Petkovic, Sarah E. Ringerud, Jean-François Rysman, Paolo Sanò, Nai-Yu Wang, Yalei You, and Gail Skofronick-Jackson

Abstract

This study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the U.S. lower Great Lakes region. GPM Microwave Imager (GMI) high-frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard Profiling (GPROF) QPE retrievals produce inconsistent results when compared with the Multi-Radar Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not accord with MRMS observations. Ad hoc precipitation-rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-m temperature, total precipitable water, and background surface type) used to constrain the GPROF a priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using a snow-cover a priori database in the locations originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a priori databases to improve intense lake-effect snow detection and retrieval performance.

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Wenjun Tang, Kun Yang, Jun Qin, Jun Li, and Jiangang Ye

Abstract

Surface solar radiation (SSR) over the ocean is essential for studies of ocean–atmosphere interactions and marine ecology, and satellite remote sensing is a major way to obtain the SSR over ocean. A new high-resolution (10 km; 3 h) SSR product has recently been developed, mainly based on the newly released cloud product of the International Satellite Cloud Climatology Project H series (ISCCP-HXG), and is available for the period from July 1983 to December 2018. In this study, we compared this SSR product with in situ observations from 70 buoy sites in the Global Tropical Moored Buoy Array (GTMBA) and also compared it with another well-known satellite-derived SSR product from the Clouds and the Earth’s Radiant Energy System (CERES; edition 4.1), which has a spatial resolution of approximately 100 km. The results show that the ISCCP-HXG SSR product is generally more accurate than the CERES SSR product for both ocean and land surfaces. We also found that the accuracy of both satellite-derived SSR products (ISCCP-HXG and CRERS) was higher over ocean than over land and that the accuracy of ISCCP-HXG SSR improves greatly when the spatial resolution of the product is coarsened to ≥ 30 km.

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Shelby Metoyer, Mohammad Barzegar, Darek Bogucki, Brian K. Haus, and Mingming Shao

Abstract

Short-range infrared (IR) observations of ocean surface reveal complicated spatially varying and evolving structures. Here we present an approach to use spatially correlated time series IR images, over a time scale of one-tenth of a second, of the water surface to derive underlying surface velocity and turbulence fields. The approach here was tested in a laboratory using grid-generated turbulence and a heater assembly. The technique was compared with in situ measurements to validate our IR-derived remote measurements. The IR-measured turbulent kinetic energy (TKE) dissipation rates were consistent with in situ–measured dissipation using a vertical microstructure profiler (VMP). We used measurements of the gradient of the velocity field to calculate TKE dissipation rates at the surface. Based on theoretical and experimental considerations, we have proposed two models of IR TKE dissipation rate retrievals and designed an approach for oceanic field IR applications.

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Will McCarty, David Carvalho, Isaac Moradi, and Nikki C. Privé

Abstract

A set of observing system simulation experiments (OSSEs) was performed to investigate the utility of a constellation of passive infrared spectrometers, strategically designed with the aim of deriving the three-dimensional retrievals of the horizontal wind via atmospheric motion vectors (AMVs) from instruments with the spectral resolution of an infrared sounder. The instrument and constellation designs were performed in the context of the Midwave Infrared Sounding of Temperature and humidity in a Constellation for Winds (MISTiC Winds). The Global Modeling and Assimilation Office OSSE system, which includes a full suite of operational meteorological observations, served as the control. To illustrate the potential impact of this observing strategy, two experiments were performed by adding the new simulated observations to the control. First, perfect (error free) simulated AMVs and radiances were assimilated. Second, the data were made imperfect by adding realistic modeled errors to the AMVs and radiances that were assimilated. The experimentation showed beneficial impacts on both the mass and wind fields, as based on analysis verification, forecast verification, and the assessment of the observations using the forecast sensitivity to observation impact (FSOI) metric. In all variables and metrics, the impacts of the imperfect observations were smaller than those of the perfect observations, although much of the positive benefit was retained. The FSOI metric illustrated two key points. First, the largest impacts were seen in the middle troposphere AMVs, which is a targeted capability of the constellation strategy. Second, the addition of modeled errors showed that the assimilation system was unable to fully exploit the 4.3-μm carbon dioxide absorption radiances.

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Dusan Zrnić and David Schvartzman

Abstract

We review cubic phase codes for mitigating ambiguities in range and velocity before introducing two specific codes. The two have periodicities of 5 and 7 samples for both the transmitted and the modulation code sequences. The short periods are suitable for generating codes of arbitrary length starting with about 15. We abbreviate the two codes with L5 and L7 and describe generation of the codes starting with kernels (i.e., minimum length sequences that repeat to generate the codes of desired lengths). The L5 modulation code produces 5 spectral replicas of the coded signal and the L7 produces 7. We apply the L7 code to a sinusoid and reveal spectra of the modulated signals from several ambiguous range intervals. Through simulation, we show application to weatherlike signals and construct examples whereby two weather signals and ground clutter are overlaid. Using theory, we define the operating region of the codes in the signal parameter space. The region covers a wide range of overlaid returned powers and spectrum widths; it is obtained from simulations involving the L codes and the SZ(8/64) code. The technique is effective in distinguishing the returns from two trip regions separated by no more than L − 2 ambiguous range intervals and reconstructing the corresponding spectral moments. The L5 and L7 codes protect from trip returns up to the fifth and seventh, making them suitable for short-wavelength (3 and 5 cm) radars as their PRTs must be relatively short to accommodate the expected spread of velocities in storms.

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