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Xia L. Ma, Timothy J. Schmit, and William L. Smith

Abstract

A nonlinear physical retrieval algorithm is developed and applied to the GOES-8/9 sounder radiance observations. The algorithm utilizes Newtonian iteration in which the maximum probability solution for temperature and water vapor profiles is achieved through the inverse solution of the nonlinear radiative transfer equation. The nonlinear physical retrieval algorithm has been tested for one year. It has also been implemented operationally by the National Oceanic and Atmospheric Administration National Environmental Satellite, Data and Information Service during February 1997. Results show that the GOES retrievals of temperature and moisture obtained with the nonlinear algorithm more closely agree with collocated radiosondes than the National Centers for Environmental Prediction (NCEP) forecast temperature and moisture profile used as the initial profile for the solution. The root-mean-square error of the total water vapor from the solution first guess, which is the NCEP 12-h forecast (referred to as the “background”), is reduced approximately 20% over the conventional data-rich North American region with the largest changes being achieved in areas of sparse radiosonde data coverage.

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Xia-Lin Ma, William L. Smith, and Harold M. Woolf

Abstract

A physical model is presented for calculating the total ozone amount from HIRS radiance measurements from TIROS-N/NOAA satellites. Simulations and retrievals indicate that the total ozone amount can be retrieved with an accuracy better than 5%. Comparisons are presented of analyses of real data physical ozone retrievals from HIRS observations with analyses of regression ozone retrievals from HIRS observations obtained operationally by NOAA/NESDIS, and with analyses of ozone measurements from the Nimbus-G satellite by the Total Ozone Mapping Spectrometer(TOMS). Comparisons of these analyses show that small scale ozone features not resolved by operational regression retrievals are resolved quite well by the higher spatial resolution HIRS retrievals using the physical algorithm.

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L. Xia, F. Zhao, Y. Ma, Z. W. Sun, X. Y. Shen, and K. B. Mao

Abstract

Cirrus clouds play an important role in the global radiation budget balance. However, the existing MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) cirrus cloud test algorithms struggle to provide accurate cirrus cloud information for the Tibetan Plateau region. In this study, the 1.38-μm cirrus cloud test was improved by adding 11-μm brightness temperature and a multiday average land surface temperature test. An algorithm sensitivity analysis indicated that the proposed algorithm lowered the threshold of the existing 1.38-μm algorithm to 0.005 in the winter and did not produce any observable misclassifications. Compared to the existing 1.38-μm cirrus test algorithm, the accuracy validation indicated that the improved algorithm detected 31.7% more cirrus clouds than the existing VIIRS 1.38-μm cirrus test and yielded 14% fewer misclassifications than the MODIS 1.38-μm cirrus test.

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Youlong Xia, Mrinal K. Sen, Charles S. Jackson, and Paul L. Stoffa

Abstract

This study evaluates the ability of Bayesian stochastic inversion (BSI) and multicriteria (MC) methods to search for the optimal parameter sets of the Chameleon Surface Model (CHASM) using prescribed forcing to simulate observed sensible and latent heat fluxes from seven measurement sites representative of six biomes including temperate coniferous forests, tropical forests, temperate and tropical grasslands, temperate crops, and semiarid grasslands. Calibration results with the BSI and MC show that estimated optimal values are very similar for the important parameters that are specific to the CHASM model. The model simulations based on estimated optimal parameter sets perform much better than the default parameter sets. Cross-validations for two tropical forest sites show that the calibrated parameters for one site can be transferred to another site within the same biome. The uncertainties of optimal parameters are obtained through BSI, which estimates a multidimensional posterior probability density function (PPD). Marginal PPD analyses show that nonoptimal choices of stomatal resistance would contribute most to model simulation errors at all sites, followed by ground and vegetation roughness length at six of seven sites. The impact of initial root-zone soil moisture and nonmosaic approach on estimation of optimal parameters and their uncertainties is discussed.

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Claudia Schmid, Robert L. Molinari, Reyna Sabina, Yeun-Ho Daneshzadeh, Xiangdong Xia, Elizabeth Forteza, and Huiqin Yang

Abstract

Argo is an internationally coordinated program directed at deploying and maintaining an array of 3000 temperature and salinity profiling floats on a global 3° latitude × 3° longitude grid. Argo floats are deployed from research vessels, merchant ships, and aircraft. After launch they sink to a prescribed pressure level (typically 1000–2000 dbar), where most floats remain for 10 days. The floats then return to the surface, collecting temperature and salinity profiles. At the surface they transmit the data to a satellite and sink again to repeat the cycle. As of 10 August 2006 there are 2489 floats reporting data. The International Argo Data Management Team oversees the development and implementation of the data management protocols of Argo. Two types of data systems are active—real time and delayed mode. The real-time system receives the transmissions from the Argo floats, extracts the data, checks their quality, and makes them available to the users. The objective of the real-time system is to provide Argo profiles to the operational and research community within 24 h of their measurement. This requirement makes it necessary to control the quality of the data automatically. The delayed-mode quality control is directed at a more detailed look at the profiles using statistical methods and scientific review of the data. In this paper, the real-time data processing and quality-control methodology is described in detail. Results of the application of these procedures to Argo profiles are described.

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William L. Smith, Xia Lin Ma, Steven A. Ackerman, H. E. Revercomb, and R. O. Knuteson

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.

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Y. Xia, A. J. Pitman, H. V. Gupta, M. Leplastrier, A. Henderson-Sellers, and L. A. Bastidas

Abstract

The multicriteria methodology, which provides a means to estimate optimal ranges for land surface model parameter values via calibration, is evaluated. Following calibration, differences between schemes resulting from effective parameter values can be isolated from differences resulting from scheme structure or scheme parameterizations. The method is applied to the Project for the Intercomparison of Land Surface Parameterization Schemes (PILPS) phase-2a data from the Cabauw site in the Netherlands using the Chameleon Surface Model (CHASM) as the surrogate for a range of land surface schemes. Simulations are performed calibrating six modes of CHASM, representing a range of land surface complexity, against observed net radiation and latent and sensible heat fluxes. The six modes range from a simple bucket model to a complex mosaic-type structure with separate energy balances for each mosaic tile and explicit treatment of transpiration, canopy interception, and bare-ground evaporation. Results demonstrate that the performance of CHASM depends on the complexity of the representation of the surface energy balance. If the multicriteria method is used with two observed variables, the performance of the model improves little with incremental increases in complexity until the most complex version of the model is reached. If the multicriteria method is used with three observed variables, the most complex mode is shown to calibrate more accurately and more precisely than the simple modes. In all cases, every calibrated mode performs better than simulations using the default PILPS phase-2a parameters. The performance of the most complex mode of CHASM suggests that more complex representations of the surface energy balance generally improve the calibrated performance of land surface schemes. However, all modes, when calibrated, retain a residual error that most likely is due to parameterization errors included in the scheme. Most error is contained in the simulation of the latent heat flux, which suggests that, to improve CHASM further, the representation of the surface hydrological processes should be developed. Thus, the multicriteria method provides a means to assess the performance of a single model or group of land surface models and provides guidance as to the directions scheme development should take.

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