International MODIS and AIRS processing package: AIRS products and applications

Weisz, Elisabeth; Huang, Hung‐Lung; Li, Jun; Borbás, Éva; Baggett, Kevin; Thapliyal, P. K.; Guan, Li

doi:10.1117/1.2766867

Cited by 29 publications

(36 citation statements)

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“…To show the effect of the high vertical resolution GPS/RO data, the statistics were calculated for 101 pressure levels instead of 1 or 2 km layers as is more common (Divakarla et al 2006;Susskind et al 2006;Tobin et al 2006;Weisz et al 2007;Wu et al 2005). Using 101 levels revealed details that are suppressed when using just layers.…”

Section: Retrieval Methodsmentioning

confidence: 99%

“…GPS/RO refractivity profiles with 200-m vertical resolution be- tween only 8-and 26-km height were used. The IMAPP algorithm (Weisz et al 2007) includes a brightness temperature (6 categories) and a scanning angle classification (with 11 categories); these classifications were not included for the real data coefficient determination as the collocated training dataset was too small (650 profiles). Temperature retrievals with and without GPS/RO refractivity data were compared to 164 (PREPQC) radiosonde profiles.…”

Section: A Pc Statistical Regression Methodsmentioning

confidence: 99%

“…The PC statistical regression is a least squares regression that uses principal components (eigenvectors) for predictors (see Goldberg et al 2003). The single field of view (FOV) University of Wisconsin-Madison International Moderate Resolution Imaging Spectroradiometer (MODIS)/ AIRS Processing Package (IMAPP) (Huang et al 2004;Weisz et al 2003Weisz et al , 2007 was modified to accommodate the addition of AMSU and GPS/RO data (referred to as the AIRS-AMSU-GPS/RO PC statistical regression method). In the statistical regression method, a linear relationship is assumed between the atmospheric state vector [X (nl, ns), deviation from the mean value] and the measurements [Y (ns, nd), deviation from the mean value]: X ϭ CY T , where C (nl, nd) is the matrix of the regression coefficients, nl stands for the number of levels, ns is the number of samples in the training dataset, and nd is the dimension of the measurements (the number of independent pieces of information, which is the sum of the number of AIRS and AMSU channels plus the number of GPS levels in our study).…”

Section: A Pc Statistical Regression Methodsmentioning

confidence: 99%

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

Borbás

Menzel

Weisz

et al. 2008

Journal of Applied Meteorology and Climatology

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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.

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Section: Retrieval Methodsmentioning

confidence: 99%

Section: A Pc Statistical Regression Methodsmentioning

confidence: 99%

Section: A Pc Statistical Regression Methodsmentioning

confidence: 99%

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

Borbás

Menzel

Weisz

et al. 2008

Journal of Applied Meteorology and Climatology

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“…Various algorithms have been developed to invert the sounding observations to obtain atmospheric profiles. Linear-regression-based algorithms have been developed by Smith and Woolf [1] for Nimbus-6 HIRS, Goldberg et al [2] and Weisz et al [3] for AIRS, and Seemann et al [4] for MODIS. Physical retrieval algorithms have been developed by Smith and Woolf [5], Smith [6], Hayden [7], Ma et al [8], Li et al [9], Susskind et al [10], etc., for various satellite sounders.…”

mentioning

confidence: 99%

A New Technique for Temperature and Humidity Profile Retrieval From Infrared-Sounder Observations Using the Adaptive Neuro-Fuzzy Inference System

Ajil

Thapliyal

Shukla

et al. 2010

IEEE Trans. Geosci. Remote Sensing

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The accuracy of the atmospheric profiles of temperature and humidity, retrieved from infrared-sounder observations using physical retrieval algorithms, depends directly on the quality of the first-guess profiles. In the past, forecasts from the numericalweather-prediction models were extensively used as the first guess. During the past few years, the first guess for physical retrieval is being estimated using regression techniques from sounder observations. In this study, a new nonlinear technique has been described to improve the first guess using simulated infrared brightness temperatures for GOES-12 Sounder channels. The present technique uses fuzzy logic and data clustering to establish a relationship between simulated sounder observations and atmospheric profiles. This relationship is further strengthened using the Adaptive Neuro-Fuzzy Inference System (ANFIS) by fine-tuning the existing fuzzy-rule base. The results of ANFIS retrieval have been compared with those from the nonlinear (polynomial) regression retrieval. It has been found that ANFIS is more robust and reduces root mean squared error by 20% in humidity profile retrieval compared with the nonlinear regression technique. In addition, it has been shown that the ANFIS technique has an added advantage of its global application without any need for classification of the training data that is required in the regression techniques.Index Terms-Atmospheric profiles, fuzzy inference system (FIS), fuzzy neural networks, infrared sounder.

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“…This is a research product from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) Hyperspectral Infrared Sounder Retrieval (CHISR) at University of Wisconsin-Madison [19,20]. The CHISR has the capability to retrieve atmospheric temperature and moisture profiles simultaneously from advanced infrared radiances in clear-sky and some cloudy conditions, in which the first guess comes from a statistical eigenvector regression method based on a global training dataset [21,22]. The global training dataset consists of more than 15,000 atmospheric profiles and corresponding to simulate AIRS radiances from the Stand-alone Radiative Transfer Algorithm (SARTA) [23] forward model calculations.…”

Section: Full-spatial-resolution Airs Sounding Systemmentioning

confidence: 99%

Optimal Use of Space-Borne Advanced Infrared and Microwave Soundings for Regional Numerical Weather Prediction

et al. 2016

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Satellite observations can either be assimilated as radiances or as retrieved physical parameters to reduce error in the initial conditions used by the Numerical Weather Prediction (NWP) model. Assimilation of radiances requires a radiative transfer model to convert atmospheric state in model space to that in radiance space, thus requiring a lot of computational resources especially for hyperspectral instruments with thousands of channels. On the other hand, assimilating the retrieved physical parameters is computationally more efficient as they are already in thermodynamic states, which can be compared with NWP model outputs through the objective analysis scheme. A microwave (MW) sounder and an infrared (IR) sounder have their respective observational limitation due to the characteristics of adopted spectra. The MW sounder observes at much larger field-of-view (FOV) compared to an IR sounder. On the other hand, MW has the capability to reveal the atmospheric sounding when the clouds are presented, but IR observations are highly sensitive to clouds, The advanced IR sounder is able to reduce uncertainties in the retrieved atmospheric temperature and moisture profiles due to its higher spectral-resolution than the MW sounder which has much broader spectra bands. This study tries to quantify the optimal use of soundings retrieved from the microwave sounder AMSU and infrared sounder AIRS onboard the AQUA satellite in the regional Weather and Research Forecasting (WRF) model through three-dimensional variational (3D-var) data assimilation scheme. Four experiments are conducted by assimilating soundings from: (1) clear AIRS single field-of-view (SFOV); (2) retrieved from using clear AMSU and AIRS observations at AMSU field-of-view (SUP); (3) all SFOV soundings within AMSU FOVs must be clear; and (4) SUP soundings which must have all clear SFOV soundings within the AMSU FOV. A baseline experiment assimilating only conventional data is generated for comparison. Various atmospheric state variables at different pressure levels are used to assess the impact from assimilating these different data by comparing them with European Centre for Medium Range Weather Forecast (ECMWF) reanalysis data. Results indicate assimilation of SUP soundings improve the mid and upper troposphere, whereas assimilation of SFOV soundings has positive impact on the lower troposphere. Two additional assimilation experiments are carried out to determine the combination of SUP and SFOV soundings that will provide the best performance throughout the troposphere. The results indicate that optimal combination is to assimilate clear-sky matched IR retrievals with non-matched MW soundings.

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International MODIS and AIRS processing package: AIRS products and applications

Cited by 29 publications

References 1 publication

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

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

A New Technique for Temperature and Humidity Profile Retrieval From Infrared-Sounder Observations Using the Adaptive Neuro-Fuzzy Inference System

Optimal Use of Space-Borne Advanced Infrared and Microwave Soundings for Regional Numerical Weather Prediction

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