Eva Borbas

Development of a Global Infrared Land Surface Emissivity Database for Application to Clear Sky Sounding Retrievals from Multispectral Satellite Radiance Measurements

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 reso...

Dual-Regression Retrieval Algorithm for Real-Time Processing of Satellite Ultraspectral Radiances

AbstractA fast physically based dual-regression (DR) method is developed to produce, in real time, accurate profile and surface- and cloud-property retrievals from satellite ultraspectral radiances observed for both clear- and cloudy-sky conditions. The DR relies on using empirical orthogonal function (EOF) regression “clear trained” and “cloud trained” retrievals of surface skin temperature, surface-emissivity EOF coefficients, carbon dioxide concentration, cloud-top altitude, effective cloud optical depth, and atmospheric temperature, moisture, and ozone profiles above the cloud and below thin or broken cloud. The cloud-trained retrieval is obtained using cloud-height-classified statistical datasets. The result is a retrieval with an accuracy that is much higher than that associated with the retrieval produced by the unclassified regression method currently used in the International Moderate Resolution Imaging Spectroradiometer/Atmospheric Infrared Sounder (MODIS/AIRS) Processing Package (IMAPP) retriev...

An objective methodology for infrared land surface emissivity evaluation

[1] Land surface emissivity (LSE) in the infrared (IR) window region (8–12 mm) governs the thermal emissions from the Earth’s surface. Many LSE databases, retrieved from various satellite instruments, are available for studying climate, Earth‐atmosphere interaction, weather, and the environment. The precision (standard deviation) and accuracy (bias) of these databases remain unclear. In this study, we introduce an objective and efficient method for quantitatively evaluating the LSE precision using satellite radiance observations. The LSE brightness temperature (Tb) deviations, defined as the standard deviations of Tb differences between satellite observations and radiative transfer calculations, can be estimated by minimizing the impacts from land surface temperature (LST) and atmospheric profiles. This is followed by the estimation of LSE precision. This method does not need the true LSE measurements. It only needs ancillary information such as atmospheric profiles and LST, both of which do not require high accuracy and thus can be obtained from a numerical weather prediction forecast or analysis. The method is applied to six different monthly LSE databases from August 2006 and 2007, and the results are presented. The error sources affecting the method are identified and the sensitivity to these errors is studied.

International MODIS and AIRS processing package: AIRS products and applications

The high-spectral-resolution AIRS (Atmospheric InfraRed Sounder) instrument onboard the NASA (National Aeronautics and Space Administration) Earth Observing System (EOS)-Aqua satellite represents the most advanced sounding system in space and provides unprecedented wealth of highly accurate radiance measurements. This paper describes a standalone and fast single field-of-view (FOV) algorithm to retrieve atmospheric sounding profiles (temperature, humidity, ozone) and surface parameters (surface skin temperature, surface emissivity) from AIRS Level 1B (L1B) clear only infrared radiance measurements. The retrieval algorithm is part of the International MODIS (Moderate Resolution Imaging Spectroradiometer)/AIRS Processing Package (IMAPP) software package, which provides international users with the capability of receiving and processing direct broadcast data in real-time. The IMAPP AIRS retrieval algorithm is based on principal component regression to obtain fast and accurate estimates of the atmospheric state at single FOV. This algorithm is designed specifically for real-time direct broadcast applications where sounding products can be processed efficiently at highest possible spatial resolution. Simulated radiance data is trained on a global set of profiles, representative of a wide variety of atmospheric scenes, which makes the algorithm globally applicable. The results presented and discussed in this paper demonstrate that the IMAPP AIRS retrieval product is rigorously evaluated by various product sources such as numerical weather prediction model analysis fields, retrieved parameters from the operational AIRS L2 product and data from other instruments.

Derivation of precipitable water from GPS data: an application to meteorology

Abstract It has been demonstrated in a number of previous publications that it is possible to derive very accurate and almost time continuous precipitable water vapour (PWV) values from the zenith tropospheric delays after processing the measurements of a Global Positioning System (GPS) network. Since February 1996 the Penc GPS station is a permanent site in Hungary, integrated into the network data processing of two GPS data centres. Our purpose was to investigate the quality of PWV values derived from the estimates provided by these two networks. In this paper comparisons and collocation statistics between the two types of GPS-derived PWV data and both meteorological measurements, and numerical prediction model analyses are presented.

Deriving Atmospheric Temperature of the Tropopause Region-Upper Troposphere by Combining Information from GPS Radio Occultation Refractivity and High-Spectral-Resolution Infrared Radiance Measurements

Global positioning system radio occultation (GPS/RO) measurements from the Challenging Minisatellite Payload (CHAMP) and Satelite de Aplicaciones Cientificas-C (SAC-C) satellites are used to improve tropospheric profile retrievals derived from the Aqua platform high-spectral-resolution Atmospheric Infrared Sounder (AIRS) and broadband Advanced Microwave Sounding Unit (AMSU) measurements under clear-sky conditions. This paper compares temperature retrievals from combined AIRS, AMSU, and CHAMP/SAC-C measurements using different techniques: 1) a principal component statistical regression using coefficients established between real (and in a few cases calculated) measurements and radiosonde atmospheric profiles; and 2) a Bayesian estimation method applied to AIRS plus AMSU temperature retrievals and GPS/RO temperature profiles. The Bayesian estimation method was also applied to GPS/RO data and the AIRS Science Team operational level-2 (version 4.0) temperature products for comparison. In this study, including GPS/RO data in the tropopause region produces the largest improvement in AIRS– AMSU temperature retrievals—about 0.5 K between 100 and 300 hPa. GPS/RO data are found to provide valuable upper-tropospheric information that improves the profile retrievals from AIRS and AMSU.

The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation

Under the National Aeronautics and Space Administration’s (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) Land Surface Temperature and Emissivity project, a new global land surface emissivity dataset has been produced by the University of Wisconsin–Madison Space Science and Engineering Center and NASA’s Jet Propulsion Laboratory (JPL). This new dataset termed the Combined ASTER MODIS Emissivity over Land (CAMEL), is created by the merging of the UW–Madison MODIS baseline-fit emissivity dataset (UWIREMIS) and JPL’s ASTER Global Emissivity Dataset v4 (GEDv4). CAMEL consists of a monthly, 0.05° resolution emissivity for 13 hinge points within the 3.6–14.3 µm region and is extended to 417 infrared spectral channels using a principal component regression approach. An uncertainty product is provided for the 13 hinge point emissivities by combining temporal, spatial, and algorithm variability as part of a total uncertainty estimate. Part 1 of this paper series describes the methodology for creating the CAMEL emissivity product and the corresponding high spectral resolution algorithm. This paper, Part 2 of the series, details the methodology of the CAMEL uncertainty calculation and provides an assessment of the CAMEL emissivity product through comparisons with (1) ground site lab measurements; (2) a long-term Infrared Atmospheric Sounding Interferometer (IASI) emissivity dataset derived from 8 years of data; and (3) forward-modeled IASI brightness temperatures using the Radiative Transfer for TOVS (RTTOV) radiative transfer model. Global monthly results are shown for different seasons and International Geosphere-Biosphere Programme land classifications, and case study examples are shown for locations with different land surface types.

Improvements to Terra MODIS L1B, L2, and L3 science products through using crosstalk corrected L1B radiances.

Observations in the Terra MODIS PVLWIR bands 27 – 30 are known to be influenced by electronic crosstalk from those bands as senders and into those same bands as receivers. The magnitude of this crosstalk affecting L1B radiances has been steadily increasing throughout the mission lifetime, and has resulted in several detectors within these bands to be unusable for making L2 and L3 science products. In recent years, the crosstalk contamination has been recognized as compromising the climate quality status of several MODIS L2 and L3 science products that depend on the PVLWIR bands. In response, the MODIS Characterization Support Team (MCST) has undertaken an effort to generate a crosstalk correction algorithm in the operational L1B radiance algorithm. The correction algorithm has been tested and established and crosstalk corrected L1B radiances have been tested in several Terra MODIS L2 science product algorithms, including MOD35 (Cloud Mask), MOD06 (Cloud Fraction, Cloud Particle Phase, Cloud Top Properties), and MOD07 (Water Vapor Profiles). Comparisons of Terra MODIS to Aqua MODIS and Terra MODIS to MetOp-A IASI show that long-term trends in Collection 6 L1B radiances and the associated L2 and L3 science products are greatly improved by the crosstalk correction. The crosstalk correction is slated for implementation into Collect 6.1 of MODIS processing.

Reprocessing of HIRS Satellite Measurements from 1980 to 2015: Development toward a Consistent Decadal Cloud Record

AbstractThis paper presents the cloud-parameter data records derived from High Resolution Infrared Radiation Sounder (HIRS) measurements from 1980 through 2015 on the NOAA and MetOp polar-orbiting platforms. Over this time period, the HIRS sensor has been flown on 16 satellites from TIROS-N through NOAA-19 and MetOp-A and MetOp-B, forming a 35-yr cloud data record. Intercalibration of the Infrared Advanced Sounding Interferometer (IASI) and HIRS on MetOp-A has created confidence in the onboard calibration of this HIRS as a reference for others. A recent effort to improve the understanding of IR-channel response functions of earlier HIRS sensor radiance measurements using simultaneous nadir overpasses has produced a more consistent sensor-to-sensor calibration record. Incorporation of a cloud mask from the higher-spatial-resolution Advanced Very High Resolution Radiometer (AVHRR) improves the subpixel cloud detection within the HIRS measurements. Cloud-top pressure and effective emissivity (ef, or cloud em...

An Approach for Improving Cirrus Cloud-Top Pressure/Height Estimation by Merging High-Spatial-Resolution Infrared-Window Imager Data with High-Spectral-Resolution Sounder Data

AbstractThe next-generation Visible and Infrared Imaging Radiometer Suite (VIIRS) offers infrared (IR)-window measurements with a horizontal spatial resolution of at least 1 km, but it lacks IR spectral bands that are sensitive to absorption by carbon dioxide (CO2) or water vapor (H2O). The CO2 and H2O absorption bands have high sensitivity for the inference of cloud-top pressure (CTP), especially for semitransparent ice clouds. To account for the lack of vertical resolution, the “merging gradient” (MG) approach is introduced, wherein the high spatial resolution of an imager is combined with the high vertical resolution of a sounder for improved CTP retrievals. The Cross-Track Infrared Sounder (CrIS) is on the same payload as VIIRS. In this paper Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) data are used as proxies for VIIRS and CrIS, respectively, although the approach can be applied to any imager–sounder pair. The MG method establishes a regression relati...