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Jean-Luc Moncet, Gennady Uymin, Pan Liang, and Alan E. Lipton

Abstract

The optimal spectral sampling (OSS) method provides a fast and accurate way to model radiometric observations and their Jacobians (required for inversion problems) as a linear combination of monochromatic quantities. The method is flexible and versatile with respect to the treatment of variable constituents, and the method’s fidelity to reference line-by-line (LBL) calculations is tunable. The focus of this paper is on the modeling of radiances from hyperspectral infrared sounders in both clear and cloudy (scattering) atmospheres for application to retrieval and data assimilation. In earlier articles, the authors presented an approach that performed spectral sampling for each channel sequentially. This approach is particularly robust in terms of preserving fidelity to LBL models and yields ratios of monochromatic calculations per channel of approximately 1:1 for such hyperspectral sensors as the Infrared Atmospheric Sounding Interferometer (IASI) or the Atmospheric Infrared Sounder (AIRS) (when tuned for nominal 0.05-K accuracy). This paper describes the generalization of the OSS concept to minimize the total number of monochromatic points required to model a set of channels across individual spectral bands or across the entire domain of the measurements. Its application to principal components of radiance measurements is addressed. It is found that the optimal solution produced by the OSS method offers computational advantages over existing models based on principal components, but, more importantly, it has superior error characteristics.

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Thomas Nehrkorn, Ross N. Hoffman, Jean-François Louis, Ronald G. Isaacs, and Jean-Luc Moncet

Abstract

The potential improvements of analyses and forecasts from the use of satellite-observed rainfall and water vapor measurements from the Defense Meteorological Satellite Program Special Sensor Microwave (SSM) T-1 and T-2 instruments are investigated in a series of observing system simulation experiments using the Air Force Phillips Laboratory (formerly Air Force Geophysics Laboratory) data assimilation system. Simulated SSM radiances are used directly in a radiance retrieval step following the conventional optimum interpolation analysis. Simulated rainfall rates in the tropics are used in a moist initialization procedure to improve the initial specification of divergence, moisture, and temperature.

Results show improved analyses and forecasts of relative humidity and winds compared to the control experiment in the tropics and the Southern Hemisphere. Forecast improvements are generally restricted to the first 1–3 days of the forecast.

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Jean-Luc Moncet, Gennady Uymin, Alan E. Lipton, and Hilary E. Snell

Abstract

This paper describes a rapid and accurate technique for the numerical modeling of band transmittances and radiances in media with nonhomogeneous thermodynamic properties (i.e., temperature and pressure), containing a mixture of absorbing gases with variable concentrations. The optimal spectral sampling (OSS) method has been designed specifically for the modeling of radiances measured by sounding radiometers in the infrared and has been extended to the microwave; it is applicable also through the visible and ultraviolet spectrum. OSS is particularly well suited for remote sensing applications and for the assimilation of satellite observations in numerical weather prediction models. The novel OSS approach is an extension of the exponential sum fitting of transmittances technique in that channel-average radiative transfer is obtained from a weighted sum of monochromatic calculations. The fact that OSS is fundamentally a monochromatic method provides the ability to accurately treat surface reflectance and spectral variations of the Planck function and surface emissivity within the channel passband, given that the proper training is applied. In addition, the method is readily coupled to multiple scattering calculations, an important factor for treating cloudy radiances. The OSS method is directly applicable to nonpositive instrument line shapes such as unapodized or weakly apodized interferometric measurements. Among the advantages of the OSS method is that its numerical accuracy, with respect to a reference line-by-line model, is selectable, allowing the model to provide whatever balance of accuracy and computational speed is optimal for a particular application. Generally only a few monochromatic points are required to model channel radiances with a brightness temperature accuracy of 0.05 K, and computation of Jacobians in a monochromatic radiative transfer scheme is straightforward. These efficiencies yield execution speeds that compare favorably to those achieved with other existing, less accurate parameterizations.

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Paolo Antonelli, Tiziana Cherubini, Steven Businger, Siebren de Haan, Paolo Scaccia, and Jean-Luc Moncet

Abstract

Satellite retrievals strive to exploit the information contained in thousands of channels provided by hyperspectral sensors and show promise in providing a gain in computational efficiency over current radiance assimilation methods by transferring computationally expensive radiative transfer calculations to retrieval providers. This paper describes the implementation of a new approach based on the transformation proposed in 2008 by Migliorini et al., which reduces the impact of the a priori information in the retrievals and generates transformed retrievals (TRs) whose assimilation does not require knowledge of the hyperspectral instruments characteristics. Significantly, the results confirm both the viability of Migliorini’s approach and the possibility of assimilating data from different hyperspectral satellite sensors regardless of the instrument characteristics. The Weather Research and Forecasting (WRF) Model’s Data Assimilation (WRFDA) 3-h cycling system was tested over the central North Pacific Ocean, and the results show that the assimilation of TRs has a greater impact in the characterization of the water vapor distribution than on the temperature field. These results are consistent with the knowledge that temperature field is well constrained by the initial and boundary conditions of the Global Forecast System (GFS), whereas the water vapor distribution is less well constrained in the GFS. While some preliminary results on the comparison between the assimilation with and without TRs in the forecasting system are presented in this paper, additional work remains to explore the impact of the new assimilation approach on forecasts and will be provided in a follow-up publication.

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Paolo Antonelli, Henry E. Revercomb, Graziano Giuliani, Tiziana Cherubini, Steven Businger, Ryan Lyman, Stephen Tjemkes, Rolf Stuhlmann, and Jean-Luc Moncet

Abstract

The Space Science Engineering Center, in collaboration with the Mauna Kea Weather Center at the University of Hawai’i at Mānoa, has developed a regional retrieval processor for high-spectral-resolution infrared data. The core of the processor makes use of an inversion system, referred to as Mirto, which combines, in a Bayesian way, the a priori knowledge of the atmospheric state, based on available numerical weather prediction forecasts, with the physical information embedded in satellite observations. Forecast temperature and water vapor mixing ratio fields over the central North Pacific Ocean are adjusted to produce synthetic radiances closer and closer to the Suomi NPP Cross-track Infrared Sounder (CrIS) observations taken in clear-sky conditions. The paucity of synoptic observations over this area and the highly homogeneous background represented by the ocean provide a good framework for the implementation of this hyperspectral data inversion system. Nearly real-time (less than 60 min from overpass time) Internet publication of retrieved atmospheric profiles is made possible by the availability of a direct broadcast system that provides data from the Suomi NPP platform (CrIS and VIIRS). The main goal for the implemented system is to provide the forecasting community with products suitable for nowcasting applications and for optimal data assimilation. The implemented processor has been running routinely since August 2013. Validation based on the comparisons of retrievals with rawinsonde data from Hilo, Hawaii, and Lihue, Hawaii, and GPS-derived total precipitable water from four stations, performed over a time period of more than 1 year, shows a statistically significant improvement on the background atmospheric state used as a priori information.

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Robert P. d’Entremont, Richard Lynch, Gennadi Uymin, Jean-Luc Moncet, Ryan B. Aschbrenner, Mark Conner, and Gary B. Gustafson

Abstract

The Cloud Depiction and Forecast System version 2 (CDFS II) is the operational global cloud analysis and forecasting model of the 557th Weather Wing, formerly the U.S. Air Force Weather Agency. The CDFS II cloud-detection algorithms are threshold-based tests that compare satellite-observed multispectral reflectance and brightness temperature signatures with those expected for the clear atmosphere. User-prescribed quantitative differences between sensor observations and the expected clear-scene radiances denote cloudy pixels. These radiances historically have been modeled at 24-km resolution from a running 10-day statistical analysis of cloud-free pixels that requires the entire global cloud analysis to be executed twice in real time: once in operational cloud detection mode and a second time in a cloud-clearing mode that is designed explicitly for generating clear-scene statistics. Having to run the cloud analysis twice means the availability of fewer compute cycles for other operational models and requires costly interactive maintenance of distinct cloud-detection and cloud-clearing threshold sets. Additionally, this technique breaks down whenever a region is persistently cloudy. These problems are eliminated by means of the optimal spectral sampling (OSS) radiative transfer model of Moncet et al., optimized for execution in the CDFS run-time environment. OSS is particularly well suited for real-time remote sensing applications because of its user-tunable computational speed and numerical accuracy, with respect to a reference line-by-line model. The use of OSS has cut cloud model processing times in half, eliminated the influence of cloudy pixel artifacts in the statistical time series prescription of cloud-cleared radiances, and improved cloud-mask quality.

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