Abstract

A principal component noise filter has been applied to ground-based high-spectral-resolution infrared radiance observations collected by the Atmospheric Emitted Radiance Interferometers (AERIs) deployed by the Atmospheric Radiation Measurement (ARM) program. The technique decomposes the radiance observations into their principal components, selects the ones that describe the most variance in the data, and reconstructs the data from these components. An empirical function developed for chemical analysis is utilized to determine the number of principal components to be used in the reconstruction of the data. Statistical analysis of the noise-filtered minus original radiance data, as well as side-by-side analysis of data from two AERI systems utilizing different temporal sampling, demonstrates the ability of the noise filter using this empirical function to retain most of the atmospheric signal above the AERI noise level in the filtered data. The noise filter is applied to data collected at ARM’s tropical, midlatitude, and Arctic sites, demonstrating that the random variability in the data is reduced by 5% to over 450%, depending on the spectral element and location of the instrument. A seasonal analysis of the number of principal components required by the noise filter for each site shows a strong seasonal dependence in the atmospheric variability at the Arctic and midlatitude sites but not at the tropical site.

Abstract

A technique for estimating cloud radiative properties (i.e., spectral emissivity and reflectivity) in the infrared is developed based on observations at a spectral resolution of approximately 0.5 cm⁻¹. The algorithm makes use of spectral radiance observations and theoretical calculations of the infrared spectra for clear and cloudy conditions along with lidar-determined cloud-base and cloud-top pressure. An advantage of the high spectral resolution observations is that the absorption effects of atmospheric gases are minimized by analyzing between gaseous absorption lines. The technique is applicable to both ground-based and aircraft-based platforms and derives the effective particle size and associated cloud water content required to satisfy, theoretically, the observed cloud infrared spectra. The algorithm is tested using theoretical simulations and applied to observations made with the University of Wisconsin's ground-based and NASA ER-2 aircraft High-Resolution Infrared Spectrometer instruments.

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.

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.

Abstract

The Marine-Atmospheric Emitted Radiance Interferometer (M-AERI) is described, and some examples of the environmental variables that can be derived from its measurements and the types of research that these can support are briefly presented. The M-AERI is a robust, accurate, self-calibrating, seagoing Fourier-transform interferometric infrared spectroradiometer that is deployed on marine platforms to measure the emission spectra from the sea surface and marine atmosphere. The instrument works continuously under computer control and functions well under a very wide range of environmental conditions with a high rate of data return. Spectral measurements are made in the range of ∼3 to ∼18 μm wavelength and are calibrated using two internal, National Institute of Standards and Technology–traceable blackbody cavities. The environmental variables derived from the spectra include the surface skin temperature of the ocean, surface emissivity, near-surface air temperature, and profiles of temperature and humidity through the lower troposphere. These measurements are sufficiently accurate both to validate satellite-derived surface temperature fields and to study the physics of the skin layer.

Abstract

The characteristics of the ER-2 aircraft and ground-based High Resolution Interferometer Sounder (HIS) instruments deployed during FIRE II are described. A few example spectra are given to illustrate the HIS cloud and molecular atmosphere remote sensing capabilities.

Abstract

This paper parts analysis of cloud observations by the High-Resolution Interferometer Sounder made from the NASA ER-2 aircraft during FIRE II. Clear and cloudy sky radiance spectra are presented in terms of differences between observations and radiative transfer model simulations.

Doubling/adding radiative transfer model simulations demonstrate that the magnitude of the brightness temperature differences (ΔBT) is a function of the cloud particle size distribution and the cloud ice water path. For effective radii greater than approximately 30 µm (size parameter of 18) there is little spectral variation in the brightness temperature (BT). An analysis of brightness temperature differences indicates that cirrus clouds over the FIRE II central site possessed a small-particle mode. The cases analyzed had similar appearances in a plot of ΔBT between 11 and 12 µm (BT₁₁ – BT₁₂) versus the observed ΔBT between 8 and 11 µm (BT₈ – BT₁₁), suggesting similarity in the microphysical properties of nongray cirrus. Brightness temperature differences between cirrus cloud over the central site and the Gulf of Mexico are presented to illustrate differences in the cirrus microphysical properties at the two different locations.

Cloud effective emissivities and effective radiative temperature were derived for observations over the FIRE central site using complementary lidar and radiosonde data. Small variations in these effective properties were seen on 5 December and 22 November. Although they had similar effective temperatures, the emissivities were very different. Very few clouds were observed to have an emissivity near unity.

Abstract

During FIRE II, cirrus clouds were observed in the wavelength range 3–19, µm with two High Resolution Interferometer Sounders as described in the Part I companion paper. One, known as AC-HIS, was mounted on the NASA ER-2 aircraft in order to look down on the clouds; these results are described in the Part II companion paper. The other, GB-HIS, also known as the Atmospheric Emitted Radiance Interferometer (AERI), was ground based. The AERI observations have been simulated, assuming scattering from spherical ice particles, using a single-layer doubling model for the cloud, for two atmospheric windows at 700–1250 and 2650–3000 cm⁻¹. The second of these windows is affected by scattered sunlight, which has been included in the calculations. The sensitivity of the cloud signal to quantities such as the ice water path (IWP) and effective radius (r _eff) have been determined. Using the cloud model, best fits have been derived for IWP and r _eff, for both windows individually and together. Possible errors in these derivations have been investigated.

Abstract

The Department of Energy Atmospheric Radiation Measurement Program (ARM) has funded the development and installation of five ground-based atmospheric emitted radiance interferometer (AERI) systems at the Southern Great Plains (SGP) site. The purpose of this paper is to provide an overview of the AERI instrument, improvement of the AERI temperature and moisture retrieval technique, new profiling utility, and validation of high-temporal-resolution AERI-derived stability indices important for convective nowcasting. AERI systems have been built at the University of Wisconsin—Madison, Madison, Wisconsin, and deployed in the Oklahoma–Kansas area collocated with National Oceanic and Atmospheric Administration 404-MHz wind profilers at Lamont, Vici, Purcell, and Morris, Oklahoma, and Hillsboro, Kansas. The AERI systems produce absolutely calibrated atmospheric infrared emitted radiances at one-wavenumber resolution from 3 to 20 μm at less than 10-min temporal resolution. The instruments are robust, are automated in the field, and are monitored via the Internet in near–real time. The infrared radiances measured by the AERI systems contain meteorological information about the vertical structure of temperature and water vapor in the planetary boundary layer (PBL; 0–3 km). A mature temperature and water vapor retrieval algorithm has been developed over a 10-yr period that provides vertical profiles at less than 10-min temporal resolution to 3 km in the PBL. A statistical retrieval is combined with the hourly Geostationary Operational Environmental Satellite (GOES) sounder water vapor or Rapid Update Cycle, version 2, numerical weather prediction (NWP) model profiles to provide a nominal hybrid first guess of temperature and moisture to the AERI physical retrieval algorithm. The hourly satellite or NWP data provide a best estimate of the atmospheric state in the upper PBL; the AERI radiances provide the mesoscale temperature and moisture profile correction in the PBL to the large-scale GOES and NWP model profiles at high temporal resolution. The retrieval product has been named AERIplus because the first guess used for the mathematical physical inversion uses an optimal combination of statistical climatological, satellite, and numerical model data to provide a best estimate of the atmospheric state. The AERI physical retrieval algorithm adjusts the boundary layer temperature and moisture structure provided by the hybrid first guess to fit the observed AERI downwelling radiance measurement. This provides a calculated AERI temperature and moisture profile using AERI-observed radiances “plus” the best-known atmospheric state above the boundary layer using NWP or satellite data. AERIplus retrieval accuracy for temperature has been determined to be better than 1 K, and water vapor retrieval accuracy is approximately 5% in absolute water vapor when compared with well-calibrated radiosondes from the surface to an altitude of 3 km. Because AERI can monitor the thermodynamics where the atmosphere usually changes most rapidly, atmospheric stability tendency information is readily available from the system. High-temporal-resolution retrieval of convective available potential energy, convective inhibition, and PBL equivalent potential temperature θ _e are provided in near–real time from all five AERI systems at the ARM SGP site, offering a unique look at the atmospheric state. This new source of meteorological data has shown excellent skill in detecting rapid synoptic and mesoscale meteorological changes within clear atmospheric conditions. This method has utility in nowcasting temperature inversion strength and destabilization caused by θ _e advection. This high-temporal-resolution monitoring of rapid atmospheric destabilization is especially important for nowcasting severe convection.

The Atmospheric Emitted Radiance Interferometer (AERI) was used to measure the infrared radiative properties and the temperature of the Gulf of Mexico during a 5-day oceanographic cruise in January 1995. The ocean skin temperature was measured with an accuracy believed to be better than 0.1 °C. The surface reflectivity/emissivity was determined as a function of view angle and sea state. The radiative properties are in good theoretical consistency with in situ measurements of ocean bulk temperature and the meteorological observations made from the oceanographic vessel. The AERI and in situ measurements provide a strong basis for accurate global specifications of sea surface temperature and ocean heat flux from satellites and ships.