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Kevin J. Nelson, Feiqin Xie, Chi O. Ao, and Mayra I. Oyola-Merced

Abstract

The planetary boundary layer (PBL) height (PBLH) is a key physical parameter of the PBL affected by numerous physical processes within the boundary layer. Specifically, the PBLH over land exhibits large spatial and temporal variation across different geographical regions. In this study, the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) radio occultation (RO) profiles and high-resolution radiosonde profiles from 2007 to 2013 were analyzed to estimate the diurnal cycle of the PBLH over the Southern Great Plains (SGP) in the United States. Large variations in PBLH derived from radiosonde temperature, moisture, and refractivity are observed on seasonal scales. COSMIC RO is capable of observing diurnal and seasonal variations in the terrestrial PBLH over the SGP region. Annual mean diurnal amplitude of approximately 250 m in the terrestrial PBLH was observed, with maxima occurring at around 1500 local solar time (LST) in both the collocated radiosondes and COSMIC RO profiles. Seasonal changes in the PBLH diurnal cycles ranging from approximately 100 to 400 m were also observed. Such PBL diurnal and seasonal changes can be further incorporated into PBL parameterizations to help improve weather and climate model prediction.

Significance Statement

The atmospheric planetary boundary layer (PBL) and its height (PBLH) control many atmospheric processes that affect our everyday lives. Observations of the PBL are usually limited to radiosondes at limited time intervals. GNSS radio occultation (RO) provide high-vertical-resolution atmospheric observations that are ideal for PBL study. This study demonstrates that the GNSS RO is capable of capturing the diurnal and seasonal variations of the PBLH over the southern Great Plains (SGP) well as compared to the collocated radiosonde observations.

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Stephen S. Leroy, Chi O. Ao, Olga P. Verkhoglyadova, and Mayra I. Oyola

Abstract

Bayesian interpolation has previously been proposed as a strategy to construct maps of radio occultation (RO) data, but that proposition did not consider the diurnal dimension of RO data. In this work, the basis functions of Bayesian interpolation are extended into the domain of the diurnal cycle, thus enabling monthly mapping of radio occultation data in synoptic time and analysis of the atmospheric tides. The basis functions are spherical harmonics multiplied by sinusoids in the diurnal cycle up to arbitrary spherical harmonic degree and diurnal cycle harmonic. Bayesian interpolation requires a regularizer to impose smoothness on the fits it produces, thereby preventing the overfitting of data. In this work, a formulation for the regularizer is proposed and the most probable values of the parameters of the regularizer determined. Special care is required when obvious gaps in the sampling of the diurnal cycle are known to occur in order to prevent the false detection of statistically significant high-degree harmonics of the diurnal cycle in the atmosphere. Finally, this work probes the ability of Bayesian interpolation to generate a valid uncertainty analysis of the fit. The postfit residuals of Bayesian interpolation are dominated not by measurement noise but by unresolved variability in the atmosphere, which is statistically nonuniform across the globe, thus violating the central assumption of Bayesian interpolation. The problem is ameliorated by constructing maps of RO data using Bayesian interpolation that partially resolve the temporal variability of the atmosphere, constructing maps for approximately every 3 days of RO data.

Full access
Arunas P. Kuciauskas, Peng Xian, Edward J. Hyer, Mayra I. Oyola, and James R. Campbell

Abstract

During the spring and summer months, the greater Caribbean region typically experiences pulses of moderate to heavy episodes of airborne African dust concentrations that originate over the Sahara Desert and propagate westward across the tropical North Atlantic basin. These dust episodes are often contained within the Saharan air layer (SAL), an elevated air mass (between 850–500 hPa) marked by very dry and warm conditions within the lowest levels. During its westward transport, the SAL’s distinct environmental characteristics can persist well into the Gulf of Mexico and southern United States. As a result, the Caribbean population is susceptible to airborne dust levels that often exceed healthy respiratory limits. One of the major responsibilities within the National Weather Service in San Juan, Puerto Rico (NWS-PR), is preparing the public within their area of responsibility (AOR) for such events. The Naval Research Laboratory Marine Meteorology Division (NRL-MMD) is sponsored by the National Oceanic and Atmospheric Administration (NOAA) to support the NWS-PR by providing them with an invaluable “one stop shop” web-based resource (hereafter SAL-WEB) that is designed to monitor these African dust events. SAL-WEB consists of near-real-time output generated from ground-based instruments, satellite-derived imagery, and dust model forecasts, covering the extent of dust from North Africa, westward across the Atlantic basin, and extending into Mexico. The products within SAL-WEB would serve to augment the Advanced Weather Interactive Processing System (AWIPS-II) infrastructure currently in operation at the NWS-PR. The goal of this article is to introduce readers to SAL-WEB, along with current and future research underway to provide improvements in African dust prediction capabilities.

Open access
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
James R. Campbell, David A. Peterson, Jared W. Marquis, Gilberto J. Fochesatto, Mark A. Vaughan, Sebastian A. Stewart, Jason L. Tackett, Simone Lolli, Jasper R. Lewis, Mayra I. Oyola, and Ellsworth J. Welton
Open access