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Callyn Bloch, Robert O. Knuteson, Antonia Gambacorta, Nicholas R. Nalli, Jessica Gartzke, and Lihang Zhou

Abstract

Near-real-time satellite-derived temperature and moisture soundings provide information about the changing atmospheric vertical thermodynamic structure occurring between successive routine National Weather Service (NWS) radiosonde launches. In particular, polar-orbiting satellite soundings become critical to the computation of stability indices over the central United States in the midafternoon, when there are no operational NWS radiosonde launches. Accurate measurements of surface temperature and dewpoint temperature are key in the calculation of severe weather indices, including surface-based convective available potential energy (SBCAPE). This paper addresses a shortcoming of current operational infrared-based satellite soundings, which underestimate the surface parcel temperature and dewpoint when CAPE is nonzero. This leads to a systematic underestimate of SBCAPE. This paper demonstrates a merging of satellite-derived vertical profiles with surface observations to address this deficiency for near-real-time applications. The National Oceanic and Atmospheric Administration (NOAA) Center for Environmental Prediction (NCEP) Meteorological Assimilation Data Ingest System (MADIS) hourly surface observation data are blended with satellite soundings derived using the NOAA Unique Combined Atmospheric Processing System (NUCAPS) to create a greatly improved SBCAPE calculation. This study is not intended to validate NUCAPS or the combined NUCAPS + MADIS product, but to demonstrate the benefits of combining observational weather satellite profile data and surface observations. Two case studies, 18 June 2017 and 3 July 2017, are used in this study to illustrate the success of the combined NUCAPS + MADIS SBCAPE compared to the NUCAPS-only SBCAPE estimate. In addition, a 6-month period, April–September 2018, was analyzed to provide a comprehensive analysis of the impact of using surface observations in satellite SBCAPE calculations. To address the need for reduced data latency, a near-real-time merged satellite and surface observation product is demonstrated using NUCAPS products from the Community Satellite Processing Package (CSPP) applied to direct broadcast data received at the University of Wisconsin–Madison, Hampton University in Virginia, and the Naval Research Laboratory in Monterey, California. Through this study, it is found that the combination of the MADIS surface observation data and the NUCAPS satellite profile data improves the SBCAPE estimate relative to comparisons with the Storm Prediction Center (SPC) mesoscale analysis and the NAM analysis compared to the NUCAPS-only SBCAPE estimate. An assessment of the 6-month period between April and September 2018 determined the dry bias in NUCAPS at the surface is the primary cause of the underestimation of the NUCAPS-only SBCAPE estimate.

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Jacola A. Roman, Robert O. Knuteson, Steven A. Ackerman, David C. Tobin, and Henry E. Revercomb

Abstract

Precipitable water vapor (PWV) observations from the National Center of Atmospheric Research (NCAR) SuomiNet networks of ground-based global positioning system (GPS) receivers and the National Oceanic and Atmospheric Administration (NOAA) Profiler Network (NPN) are used in the regional assessment of global climate models. Study regions in the U.S. Great Plains and Midwest highlight the differences among global climate model output from the Fourth Assessment Report (AR4) Special Report on Emissions Scenarios (SRES) A2 scenario in their seasonal representation of column water vapor and the vertical distribution of moisture. In particular, the Community Climate System model, version 3 (CCSM3) is shown to exhibit a dry bias of over 30% in the summertime water vapor column, while the Goddard Institute for Space Studies Model E20 (GISS E20) agrees well with PWV observations. A detailed assessment of vertical profiles of temperature, relative humidity, and specific humidity confirm that only GISS E20 was able to represent the summertime specific humidity profile in the atmospheric boundary layer (<3%) and thus the correct total column water vapor. All models show good agreement in the winter season for the region. Regional trends using station-elevation-corrected GPS PWV data from two complimentary networks are found to be consistent with null trends predicted in the AR4 A2 scenario model output for the period 2000–09. The time to detect (TTD) a 0.05 mm yr−1 PWV trend, as predicted in the A2 scenario for the period 2000–2100, is shown to be 25–30 yr with 95% confidence in the Oklahoma–Kansas region.

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Suzanne W. Seemann, Eva E. Borbas, Robert O. Knuteson, Gordon R. Stephenson, and Hung-Lung Huang

Abstract

A global database of infrared (IR) land surface emissivity is introduced to support more accurate retrievals of atmospheric properties such as temperature and moisture profiles from multispectral satellite radiance measurements. Emissivity is derived using input from the Moderate Resolution Imaging Spectroradiometer (MODIS) operational land surface emissivity product (MOD11). The baseline fit method, based on a conceptual model developed from laboratory measurements of surface emissivity, is applied to fill in the spectral gaps between the six emissivity wavelengths available in MOD11. The six available MOD11 wavelengths span only three spectral regions (3.8–4, 8.6, and 11–12 μm), while the retrievals of atmospheric temperature and moisture from satellite IR sounder radiances require surface emissivity at higher spectral resolution. Emissivity in the database presented here is available globally at 10 wavelengths (3.6, 4.3, 5.0, 5.8, 7.6, 8.3, 9.3, 10.8, 12.1, and 14.3 μm) with 0.05° spatial resolution. The wavelengths in the database were chosen as hinge points to capture as much of the shape of the higher-resolution emissivity spectra as possible between 3.6 and 14.3 μm. The surface emissivity from this database is applied to the IR regression retrieval of atmospheric moisture profiles using radiances from MODIS, and improvement is shown over retrievals made with the typical assumption of constant emissivity.

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Robert E. Holz, Steve Ackerman, Paolo Antonelli, Fred Nagle, Robert O. Knuteson, Matthew McGill, Dennis L. Hlavka, and William D. Hart

Abstract

An improvement to high-spectral-resolution infrared cloud-top altitude retrievals is compared to existing retrieval methods and cloud lidar measurements. The new method, CO2 sorting, determines optimal channel pairs to which the CO2 slicing retrieval will be applied. The new retrieval is applied to aircraft Scanning High-Resolution Interferometer Sounder (S-HIS) measurements. The results are compared to existing passive retrieval methods and coincident Cloud Physics Lidar (CPL) measurements. It is demonstrated that when CO2 sorting is used to select channel pairs for CO2 slicing there is an improvement in the retrieved cloud heights when compared to the CPL for the optically thin clouds (total optical depths less than 1.0). For geometrically thick but tenuous clouds, the infrared retrieved cloud tops underestimated the cloud height, when compared to those of the CPL, by greater than 2.5 km. For these cases the cloud heights retrieved by the S-HIS correlated closely with the level at which the CPL-integrated cloud optical depth was approximately 1.0.

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William L. Smith, Wayne F. Feltz, Robert O. Knuteson, Henry E. Revercomb, Harold M. Woolf, and H. Ben Howell

Abstract

The surface-based Atmospheric Emitted Radiance Interferometer (AERI) is an important measurement component of the Department of Energy Atmospheric Radiation Measurement Program. The method used to retrieve temperature and moisture profiles of the plantetary boundary layer from the AERI’s downwelling spectral radiance observations is described.

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Wayne F. Feltz, William L. Smith, Robert O. Knuteson, Henry E. Revercomb, Harold M. Woolf, and H. Ben Howell

Abstract

The Atmospheric Emitted Radiance Interferometer (AERI) is a well-calibrated ground-based instrument that measures high-resolution atmospheric emitted radiances from the atmosphere. The spectral resolution of the instrument is better than one wavenumber between 3 and 18 μm within the infrared spectrum. The AERI instrument detects vertical and temporal changes of temperature and water vapor in the planetary boundary layer. Excellent agreement between radiosonde and AERI retrievals for a 6-month sample of coincident profiles is presented in this paper. In addition, a statistical seasonal analysis of retrieval and radiosonde differences is discussed. High temporal and moderate vertical resolution in the lowest 3 km of the atmosphere allows meteorologically important mesoscale features to be detected. AERI participation in the Department of Energy Atmospheric Radiation Measurement program at the Southern Great Plains Cloud and Radiation Testbed (SGP CART) has allowed development of a robust operational atmospheric temperature and water vapor retrieval algorithm in a dynamic meteorological environment near Lamont, Oklahoma. Operating in a continuous mode, AERI temperature and water vapor retrievals obtained through inversion of the infrared radiative transfer equation provide profiles of atmospheric state every 10 min to 3 km in clear sky or below cloud base. Boundary layer evolution, cold or warm frontal passages, drylines, and thunderstorm outflow boundaries are all recorded, offering important meteorological information. With important vertical thermodynamic information between radiosonde locations and launch times, AERI retrievals provide data for planetary boundary layer research, mesoscale model initialization, verification, and nowcasting. This paper discusses retrieval performance at the SGP CART site, as well as interesting meteorological case studies captured by AERI profiles. The AERI system represents an important new capability for operational weather- and airport-monitoring applications.

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Hung-Lung Huang, William L. Smith, Jun Li, Paolo Antonelli, Xiangqian Wu, Robert O. Knuteson, Bormin Huang, and Brian J. Osborne

Abstract

This paper describes the theory and application of the minimum local emissivity variance (MLEV) technique for simultaneous retrieval of cloud pressure level and effective spectral emissivity from high-spectral-resolution radiances, for the case of single-layer clouds. This technique, which has become feasible only with the recent development of high-spectral-resolution satellite and airborne instruments, is shown to provide reliable cloud spectral emissivity and pressure level under a wide range of atmospheric conditions. The MLEV algorithm uses a physical approach in which the local variances of spectral cloud emissivity are calculated for a number of assumed or first-guess cloud pressure levels. The optimal solution for the single-layer cloud emissivity spectrum is that having the “minimum local emissivity variance” among the retrieved emissivity spectra associated with different first-guess cloud pressure levels. This is due to the fact that the absorption, reflection, and scattering processes of clouds exhibit relatively limited localized spectral emissivity structure in the infrared 10–15-μm longwave region. In this simulation study it is shown that the MLEV cloud pressure root-mean-square errors for a single level with effective cloud emissivity greater than 0.1 are ∼30, ∼10, and ∼50 hPa, for high (200– 300 hPa), middle (500 hPa), and low (850 hPa) clouds, respectively. The associated cloud emissivity root-mean-square errors in the 900 cm−1 spectral channel are less than 0.05, 0.04, and 0.25 for high, middle, and low clouds, respectively.

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Steven A. Ackerman, Ed W. Eloranta, Chris J. Grund, Robert O. Knuteson, Henry E. Revercomb, William L. Smith, and Donald P. Wylie

During the period of 26 October 1989 through 6 December 1989 a unique complement of measurements was made at the University of Wisconsin—Madison to study the radiative properties of cirrus clouds. Simultaneous observations were obtained from a scanning lidar, two interferometers, a high spectral resolution lidar, geostationary and polar orbiting satellites, radiosonde launches, and a whole-sky imager. This paper describes the experiment, the instruments deployed, and, as an example, the data collected during one day of the experiment.

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Madison J. Post, Christopher W. Fairall, Jack B. Snider, Yong Han, Allen B. White, Warner L. Ecklund, Klaus M. Weickmann, Patricia K. Quinn, Daniel I. Cooper, Steven M. Sekelsky, Robert E. McIntosh, Peter Minnett, and Robert O. Knuteson

Twelve national research organizations joined forces on a 30-day, 6800 n mi survey of the Central and Tropical Western Pacific on NOAA's Research Vessel Discoverer. The Combined Sensor Program (CSP), which began in American Samoa on 14 March 1996, visited Manus Island, Papua New Guinea, and ended in Hawaii on 13 April, used a unique combination of in situ, satellite, and remote sensors to better understand relationships between atmospheric and oceanic variables that affect radiative balance in this climatically important region. Besides continuously measuring both shortwave and longwave radiative fluxes, CSP instruments also measured most other factors affecting the radiative balance, including profiles of clouds (lidar and radar), aerosols (in situ and lidar), moisture (balloons, lidar, and radiometers), and sea surface temperature (thermometers and Fourier Transform Infrared Radiometers). Surface fluxes of heat, momentum, and moisture were also measured continuously. The Department of Energy's Atmospheric Radiation Measurement Program used the mission to validate similar measurements made at their CART site on Manus Island and to investigate the effect (if any) of large nearby landmasses on the island-based measurements.

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Bruce A. Wielicki, D. F. Young, M. G. Mlynczak, K. J. Thome, S. Leroy, J. Corliss, J. G. Anderson, C. O. Ao, R. Bantges, F. Best, K. Bowman, H. Brindley, J. J. Butler, W. Collins, J. A. Dykema, D. R. Doelling, D. R. Feldman, N. Fox, X. Huang, R. Holz, Y. Huang, Z. Jin, D. Jennings, D. G. Johnson, K. Jucks, S. Kato, D. B. Kirk-Davidoff, R. Knuteson, G. Kopp, D. P. Kratz, X. Liu, C. Lukashin, A. J. Mannucci, N. Phojanamongkolkij, P. Pilewskie, V. Ramaswamy, H. Revercomb, J. Rice, Y. Roberts, C. M. Roithmayr, F. Rose, S. Sandford, E. L. Shirley, Sr. W. L. Smith, B. Soden, P. W. Speth, W. Sun, P. C. Taylor, D. Tobin, and X. Xiong

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREO's inherently high absolute accuracy will be verified and traceable on orbit to Système Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earth's thermal infrared spectrum (5–50 μm), the spectrum of solar radiation reflected by the Earth and its atmosphere (320–2300 nm), and radio occultation refractivity from which accurate temperature profiles are derived. The mission has the ability to provide new spectral fingerprints of climate change, as well as to provide the first orbiting radiometer with accuracy sufficient to serve as the reference transfer standard for other space sensors, in essence serving as a “NIST [National Institute of Standards and Technology] in orbit.” CLARREO will greatly improve the accuracy and relevance of a wide range of space-borne instruments for decadal climate change. Finally, CLARREO has developed new metrics and methods for determining the accuracy requirements of climate observations for a wide range of climate variables and uncertainty sources. These methods should be useful for improving our understanding of observing requirements for most climate change observations.

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