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Micheal Hicks
,
Ricardo Sakai
, and
Everette Joseph

Abstract

A new automatic mixing layer height detection method for lidar observations of aerosol backscatter profiles is presented and evaluated for robustness. The new detection method incorporates the strengths of Steyn et al.’s error function–ideal profile (ERF) method and Davis et al.’s wavelet covariance transform (WCT) method. These two methods are critical components of the new method, and their robustness is also evaluated and then contrasted to the new method. The new method is applied to aerosol backscatter observations in two ways: 1) by looking for the most realistic mixing height throughout the entire profile and 2) by searching for mixing height below significant elevated obscurations (e.g., clouds or aerosol layers). The first approach is referred to as the hybrid method and the second as the hybrid-lowest method. Coincident radiosounding observations of mixing heights are used to independently reference the lidar-based estimates.

There were 4030 cases examined over a 5-yr period for mixing heights. The efficacy of the lidar-based methods was determined based on diurnal, seasonal, stability, and sky obscuration conditions. Of these conditions, the hybrid method performed best for unstable and cloudy situations. It determined mixing heights reliably (less than ±0.30-km bias) for close to 70% of those cases. The hybrid-lowest method performed best in stable and clear-sky conditions; it determined mixing heights reliably for over 70% of those cases. The WCT method performed the best overall.

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Shadya Sanders
,
Terri Adams
, and
Everette Joseph

Abstract

This paper uses the “Super Outbreak” of 2011 as a case study to examine the potential gaps between the dissemination of severe weather warnings and the public’s behavioral response to this information. This study focuses on a single tornado track that passed through Tuscaloosa, Alabama. The tornado caused massive damage and destruction and led to a total of 62 fatalities. The threat of severe storms was known days in advance, and forecasts were disseminated to the public. Questions were raised about the forecasts, warning lead times, and the perception of the warnings among residents. This paper examines the potential gaps that exist between the dissemination of tornadic warning information and citizen response. The analysis of data collected through a mixed-method approach suggests that, regardless of weather forecast accuracy, a significant chasm exists between the dissemination of warnings and the personalizing of risks, which results in limited use of protective measures in the face of severe weather threats.

Open access

Education

What Does It Take to Get into Graduate School? A Survey of Atmospheric Science Programs

John W. Nielsen-Gammon
,
Lourdes B. Avilés
, and
Everette Joseph

Abstract

The AMS's Board on Higher Education undertook a survey of atmospheric science graduate programs in the United States and Canada during the fall and winter of 2007–08. The survey involved admission data for the three previous years and was performed with assistance from AMS headquarters and in cooperation with the University Corporation for Atmospheric Research (UCAR). Usable responses were received from 29 programs, including most major atmospheric science programs.

The responding schools receive between 6 and 140 applications per year, and typical incoming class sizes range from 1 to 24. About 69% of applicants and 76% of enrollees are domestic students. At the majority of schools, all incoming students receive full financial support.

The average graduate program looks for undergraduate grade point averages of at least 3.3 to 3.5, higher for nonscience majors. Grade point averages in math and science courses, typically 3.5 or better, are particularly important. The typical midclass GRE of entering graduate students was a combined verbal and quantitative score of 1,300. Larger schools tend to place particular emphasis on math/ science grades and letters of recommendation, while smaller schools typically value a broader range of application characteristics.

Students considering graduate school should make a special effort to cultivate potential letter writers, working on research projects if possible. They should also become informed about the particular requirements and values of the programs to which they are applying by visiting them if possible or by contacting professors with active research programs in the student's area of interest.

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John W. Nielsen-Gammon
,
Lourdes B. Avilés
, and
Everette Joseph

Abstract

No Abstract available.

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Gregory S. Jenkins
,
Andre Kamga
,
Adamou Garba
,
Arona Diedhiou
,
Vernon Morris
, and
Everette Joseph
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Hsiao-Chun Lin
,
Juanzhen Sun
,
Tammy M. Weckwerth
,
Everette Joseph
, and
Junkyung Kay

Abstract

The New York State Mesonet (NYSM) has provided continuous in situ and remote sensing observations near the surface and within the lower troposphere since 2017. The dense observing network can capture the evolution of mesoscale motions with high temporal and spatial resolution. The objective of this study was to investigate whether the assimilation of NYSM observations into numerical weather prediction models could be beneficial for improving model analysis and short-term weather prediction. The study was conducted using a convective event that occurred in New York on 21 June 2021. A line of severe thunderstorms developed, decayed, and then reintensified as it propagated eastward across the state. Several data assimilation (DA) experiments were conducted to investigate the impact of NYSM data using the operational DA system Gridpoint Statistical Interpolation with rapid update cycles. The assimilated datasets included National Centers for Environmental Prediction Automated Data Processing global upper-air and surface observations, NYSM surface observations, Doppler lidar wind retrievals, and microwave radiometer (MWR) thermodynamic retrievals at NYSM profiler sites. In comparison with the control experiment that assimilated only conventional data, the timing and location of the convection reintensification was significantly improved by assimilating NYSM data, especially the Doppler lidar wind data. Our analysis indicated that the improvement could be attributed to improved simulation of the Mohawk–Hudson Convergence. We also found that the MWR DA resulted in degraded forecasts, likely due to large errors in the MWR temperature retrievals. Overall, this case study suggested the positive impact of assimilating NYSM surface and profiler data on forecasting summertime severe weather.

Open access
Xin-Zhong Liang
,
Hyun I. Choi
,
Kenneth E. Kunkel
,
Yongjiu Dai
,
Everette Joseph
,
Julian X. L. Wang
, and
Praveen Kumar

Abstract

This paper utilizes the best available quality data from multiple sources to develop consistent surface boundary conditions (SBCs) for mesoscale regional climate model (RCM) applications. The primary SBCs include 1) fields of soil characteristic (bedrock depth, and sand and clay fraction profiles), which for the first time have been consistently introduced to define 3D soil properties; 2) fields of vegetation characteristic fields (land-cover category, and static fractional vegetation cover and varying leaf-plus-stem-area indices) to represent spatial and temporal variations of vegetation with improved data coherence and physical realism; and 3) daily sea surface temperature variations based on the most appropriate data currently available or other value-added alternatives. For each field, multiple data sources are compared to quantify uncertainties for selecting the best one or merged to create a consistent and complete spatial and temporal coverage. The SBCs so developed can be readily incorporated into any RCM suitable for U.S. climate and hydrology modeling studies, while the data processing and validation procedures can be more generally applied to construct SBCs for any specific domain over the globe.

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Nicholas R. Nalli
,
Christopher D. Barnet
,
Tony Reale
,
Quanhua Liu
,
Vernon R. Morris
,
J. Ryan Spackman
,
Everette Joseph
,
Changyi Tan
,
Bomin Sun
,
Frank Tilley
,
L. Ruby Leung
, and
Daniel Wolfe

Abstract

This paper examines the performance of satellite sounder atmospheric vertical moisture profiles under tropospheric conditions encompassing moisture contrasts driven by convection and advection transport mechanisms, specifically Atlantic Ocean Saharan air layers (SALs), tropical Hadley cells, and Pacific Ocean atmospheric rivers (ARs). Operational satellite sounder moisture profile retrievals from the Suomi National Polar-Orbiting Partnership (SNPP) NOAA Unique Combined Atmospheric Processing System (NUCAPS) are empirically assessed using collocated dedicated radiosonde observations (raobs) obtained from ocean-based intensive field campaigns. The raobs from these campaigns provide uniquely independent correlative truth data not assimilated into numerical weather prediction (NWP) models for satellite sounder validation over oceans. Although ocean cases are often considered “easy” by the satellite remote sensing community, these hydrometeorological phenomena present challenges to passive sounders, including vertical gradient discontinuities (e.g., strong inversions), as well as persistent uniform clouds, aerosols, and precipitation. It is found that the operational satellite sounder 100-layer moisture profile NUCAPS product performs close to global uncertainty requirements in the SAL/Hadley cell environment, with biases relative to raob within 10% up to 350 hPa. In the more difficult AR environment, bias relative to raob is found to be within 20% up to 400 hPa. In both environments, the sounder moisture retrievals are comparable to NWP model outputs, and cross-sectional analyses show the capability of the satellite sounder for detecting and resolving these tropospheric moisture features, thereby demonstrating a near-real-time forecast utility over these otherwise raob-sparse regions.

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Hua Xie
,
Nicholas R. Nalli
,
Shanna Sampson
,
Walter W. Wolf
,
Jun Li
,
Timothy J. Schmit
,
Christopher D. Barnet
,
Everette Joseph
,
Vernon R. Morris
, and
Fanglin Yang

Abstract

An ocean-based prelaunch evaluation of the Geostationary Operational Environmental Satellite (GOES)-R series Advanced Baseline Imager (ABI) legacy atmospheric profile (LAP) products is conducted using proxy data based upon the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation satellite. SEVIRI-based LAP temperature and moisture profile retrievals are validated against in situ correlative data obtained over the open ocean from multiple years of the National Oceanic and Atmospheric Administration (NOAA) Aerosols and Ocean Science Expeditions (AEROSE). The NOAA AEROSE data include dedicated radiosonde observations (RAOBs) launched from the NOAA ship Ronald H. Brown over the tropical Atlantic: a region optimally situated within the full-disk scanning range of SEVIRI and one of great meteorological importance as the main development area of Atlantic hurricanes. The most recent versions of the GOES-R Algorithm Working Group team algorithms (e.g., cloud mask, aerosol detection products, and LAP) implemented within the algorithms integration team framework (the NOAA operational system that will host these operational product algorithms) are used in the analyses. Forecasts from the National Centers for Environmental Prediction Global Forecasting System (NCEP GFS) are used for the LAP regression and direct comparisons. The GOES-R LAP retrievals are found to agree reasonably with the AEROSE RAOB observations, and overall retrievals improve both temperature and moisture against computer model NCEP GFS outputs. The validation results are then interpreted within the context of a difficult meteorological regime (e.g., Saharan air layers and dust) coupled with the difficulty of using a narrowband imager for the purpose of atmospheric sounding.

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Xin-Zhong Liang
,
Min Xu
,
Xing Yuan
,
Tiejun Ling
,
Hyun I. Choi
,
Feng Zhang
,
Ligang Chen
,
Shuyan Liu
,
Shenjian Su
,
Fengxue Qiao
,
Yuxiang He
,
Julian X. L. Wang
,
Kenneth E. Kunkel
,
Wei Gao
,
Everette Joseph
,
Vernon Morris
,
Tsann-Wang Yu
,
Jimy Dudhia
, and
John Michalakes

The CWRF is developed as a climate extension of the Weather Research and Forecasting model (WRF) by incorporating numerous improvements in the representation of physical processes and integration of external (top, surface, lateral) forcings that are crucial to climate scales, including interactions between land, atmosphere, and ocean; convection and microphysics; and cloud, aerosol, and radiation; and system consistency throughout all process modules. This extension inherits all WRF functionalities for numerical weather prediction while enhancing the capability for climate modeling. As such, CWRF can be applied seamlessly to weather forecast and climate prediction. The CWRF is built with a comprehensive ensemble of alternative parameterization schemes for each of the key physical processes, including surface (land, ocean), planetary boundary layer, cumulus (deep, shallow), microphysics, cloud, aerosol, and radiation, and their interactions. This facilitates the use of an optimized physics ensemble approach to improve weather or climate prediction along with a reliable uncertainty estimate. The CWRF also emphasizes the societal service capability to provide impactrelevant information by coupling with detailed models of terrestrial hydrology, coastal ocean, crop growth, air quality, and a recently expanded interactive water quality and ecosystem model.

This study provides a general CWRF description and basic skill evaluation based on a continuous integration for the period 1979– 2009 as compared with that of WRF, using a 30-km grid spacing over a domain that includes the contiguous United States plus southern Canada and northern Mexico. In addition to advantages of greater application capability, CWRF improves performance in radiation and terrestrial hydrology over WRF and other regional models. Precipitation simulation, however, remains a challenge for all of the tested models.

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