Fast Radiative Transfer Model Research Articles

Radiative transfer simulations for the MADRAS imager of Megha-Tropiques

This paper reports the radiative transfer simulations for the passive microwave radiometer onboard the proposed Indian climate research satellite Megha-Tropiques due to be launched in 2011. These simulations have been performed by employing an in-house polarized radiative transfer code for raining systems ranging from depression and tropical cyclones to the Indian monsoon. For the sake of validation and completeness, simulations have also been done for the Tropical Rainfall Measuring Mission (TRMM)’s Microwave Imager (TMI) of the highly successful TRMM mission of NASA and JAXA. The paper is essentially divided into two parts: (a) Radiometer response with specific focus on high frequency channels in both the radiometers is discussed in detail with a parametric study of the effect of four hydrometeors (cloud liquid water, cloud ice, precipitating water and precipitating ice) on the brightness temperatures. The results are compared with TMI measurements wherever possible. (b) Development of a neural network-based fast radiative transfer model is elucidated here. The goal is to speed up the computational time involved in the simulation of brightness temperatures, necessitated by the need for quick and online retrieval strategies. The neural network model uses hydrometeor profiles as inputs and simulates spectral microwave brightness temperature at multiple frequencies as output. A huge database is generated by executing the in-house radiative transfer code for seven different cyclones occurred in North Indian Ocean region during the period 2001–2006. A part of the dataset is used to train the network while the remainder is used for testing purposes. For the purpose of testing, a typical scene from the southwest monsoon rain is also considered. The results obtained are very encouraging and show that the neural network is able to mimic the underlying physics of the radiative transfer simulations with a correlation coefficient of over 99%.

Inter-channel correlation of transmittances in a high-resolution satellite sounder

Recent infrared sounders such as the Atmospheric Infrared Sounder and the Infrared Atmospheric Sounding Interferometer (IASI) have thousands of channels. Since the channels have fewer degrees of freedom than the number of channels, the channels are not independent, and thus correlations are expected among them. On examination of the transmittances of IASI channels, we found that many combinations of channels have high statistical correlations. Furthermore, the transmittances of 387 of the 616 IASI channels are statistically predictable from only 41 channels. An empirical estimation of transmittances by using a fast radiative transfer model greatly improved the computational efficiency of an atmospheric sounder.

Assimilation of Kalpana very high resolution radiometer water vapor channel radiances into a mesoscale model

The very high resolution radiometer (VHRR) channel 3 (at 6.2 μm) on the Indian Geostationary satellite Kalpana is sensitive to mid‐upper tropospheric water vapor. This paper describes the assimilation of Kalpana VHRR channel 3 clear‐sky radiances (hereafter water vapor (WV) radiances) in the Weather Research and Forecasting (WRF) three‐dimensional variational assimilation (3D‐Var) system. The Kalpana WV radiances (in terms of blackbody equivalent brightness temperatures) are used in combination with a fast radiative transfer model and 3D‐Var assimilation system in the WRF model. Extensive preassimilation monitoring of the WV radiances has been carried out, showing a diurnal bias of approximately 1 K in the Kalpana WV radiances compared to radiances simulated from the WRF model first‐guess fields. The control (without Kalpana WV radiances) as well as experimental (which assimilated bias corrected Kalpana WV radiances) runs were made for 24 h starting at 0000 UTC during July 2008. The assimilation experiments for July 2008 (22 cases) demonstrated a positive impact of the assimilated Kalpana WV radiances on both the analysis state as well as subsequently short‐term (6–24 h) forecasts. At 0 h (analysis), the agreement of mid‐upper tropospheric moisture sensitive channels (channels 11 and 12) in NOAA/HIRS, which is an independent observation, is improved (improvement is 19% for channel 11 and 17% for channel 12) with the assimilation of Kalpana WV radiances compared to control experiment. In the radiance assimilation experiment, at 6 h (24 h), the root mean square differences between model equivalent Kalpana WV radiances and Kalpana observed WV radiances showed an improvement of 20% (1.7%) relative to the control experiment. Compared with National Center for Environmental Prediction analysis, assimilation of Kalpana WV radiances shows positive impact on the mid‐upper tropospheric moisture and a neutral impact on the temperature and wind forecasts. Compared with atmospheric infrared sounder retrieved and radiosonde observed thermodynamic profiles, Kalpana radiances show positive impact on the mid‐upper tropospheric moisture and temperature and a mixed (negative/positive) impact on the lower and upper tropospheric moisture and temperature forecasts. The comparison of model predicted rainfall with TRMM measurements indicates that Kalpana radiances impacted the rainfall positively.

A principal component based version of the RTTOV fast radiative transfer model

A new version of the RTTOV fast radiative transfer model has been developed that exploits principal component analysis. The model is based on the computation of a database of line-by-line spectra for a large training set of diverse atmospheric situations. The principal component scores obtained from the eigenvectors of the covariance matrix of the simulated radiances are used as input data in a linear regression scheme where they are expressed as a linear combination of profile-dependent predictors. The predictors consist of a selected number of polychromatic radiances computed using the standard RTTOV fast transmittance model. The linear regression scheme can then be used to simulate principal component scores and consequently reconstruct radiances for any input atmospheric profile. The principal component based model compares favourably to the conventional RTTOV model both in terms of speed and accuracy: the dimensionality reduction inherent in the use of principal component analysis makes the principal component based RTTOV much more computationally efficient, and because of the highly linear relationship between the principal component scores and the independent variables used in the regression scheme, the principal component based RTTOV can reproduce the underlying line-by-line radiances to a much higher degree of accuracy. The availability of a principal component based fast radiative transfer will enable ECMWF to exploit the noise reduction capability of principal component analysis and investigate the direct assimilation of IASI principal component scores in the spectral regions affected by high instrument noise. Copyright © 2010 Royal Meteorological Society

An improved fast radiative transfer model for special sensor microwave imager/sounder upper atmosphere sounding channels

Special sensor microwave imager/sounder (SSMIS) on board the U. S. Defense Meteorology Satellite Program satellites includes six upper atmosphere sounding (UAS) channels for probing air temperature in the upper stratosphere and mesosphere. Three of the UAS channels 19–21 are sensitive to the Doppler frequency shift due to Earth's rotation. The sensitivity to the frequency shift in large degree depends on the O2 Zeeman splitting effect, which is a function of the Earth's magnetic field strength and the angle between the Earth's magnetic field and propagation direction of the electromagnetic wave. Since the brightness temperatures can change up to 2 K as a result of the Doppler shift, the fast radiative transfer model developed earlier for the SSMIS UAS channels has recently been improved to take the Doppler shift into account. In the fast model, an averaged transmittance within the channel frequency passbands is parameterized and trained with a line‐by‐line radiative transfer model that accurately computes the monochromatic transmittances at fine frequency steps within each passband. The model is evaluated by comparing it with the line‐by‐line model in an independent experiment. The root mean square differences between the two models are 0.21, 0.39, 0.34, and 0.19 K for channels 19–22, respectively. Using the model, the sensitivities of the radiances to the Doppler shift are analyzed through simulations. A theoretical explanation is given for the dependence of the sensitivities on the Zeeman splitting effect. Results from the analysis are then compared to the observations and a good agreement is achieved.

On water vapor Jacobian in fast radiative transfer model

The Community Radiative Transfer Model (CRTM) is a powerful numerical software used for satellite data assimilation and remote sensing applications. Its accuracies in simulating satellite radiances and their gradients relative to water vapor (or Jacobians) are improved through this study when the CRTM includes additional gaseous absorbers. Three water vapor transmittance regression methods (labeled with A–C, respectively) are discussed that differ primarily in vertical coordinates and the application of constraints to smooth vertical structures of the regression coefficients. Method A computes optical depth profiles at fixed pressure levels, whereas method B computes the profiles at fixed levels of the integrated absorber amount. Method C is a derived version of method B with an addition that a polynomial function is applied to the regression coefficients to improve the water vapor Jacobians. The intercomparison focuses on the modeling of 22 sounding channels routinely used at numerical weather prediction (NWP) centers: 9 Atmospheric Infrared Radiance Sounder channels, 4 High‐Resolution Infrared Sounder channels, 4 Advanced Microwave Sounding Unit‐A channels, and 5 Microwave Humidity Sounder channels. An ensemble of 48 diverse atmosphere profiles at the University of Maryland at Baltimore County was used to test the results. The results were compiled for methods A and C for water vapor line absorption only while keeping the other components the same under the CRTM framework. The comparison quantities include the water vapor Jacobians, temperature Jacobians, and the forward top‐of‐the‐atmosphere brightness temperature (BT). In the infrared, the forward models mean errors are very small (less than 0.03 K) compared to the line‐by‐line model. Temperature and water vapor Jacobian goodness‐of‐fit measure values are very small and sufficient for NWP application, except for some dry atmospheric profiles. For the cold and dry atmospheric profiles, method C can significantly improve the water vapor Jacobian profile and remove the unphysical kinks (oscillations) that appear in method A. The improved water vapor Jacobian profile results in the improved temperature Jacobian. For the microwave channels, the forward BTs show very small biases less than 0.1 K for all the channels, and the overall water vapor Jacobian using method A is better than those using method C, especially for warm and wet atmospheric profiles.

Modeling of wave-induced irradiance fluctuations at near-surface depths in the ocean: a comparison with measurements

We develop a computationally fast radiative transfer model for simulating the fluctuations of the underwater downwelling irradiance E(d) at near-surface depths, which occur due to focusing of sunlight by wind-driven surface waves. The model is based on the hybrid matrix operator-Monte Carlo method, which was specifically designed for simulating radiative transfer in a coupled atmosphere-surface-ocean system involving a dynamic ocean surface. In the current version of the model, we use a simplified description of surface waves, which accounts for surface slope statistics, but not surface wave elevation, as a direct source of underwater light fluctuations. We compare the model results with measurements made in the Santa Barbara Channel. The model-simulated and measured time series of E(d)(t) show remarkable similarity. Major features of the probability distribution of instantaneous irradiance, the frequency content of irradiance fluctuations, and the statistical properties of light flashes produced by wave focusing are also generally consistent between the model simulations and measurements for a few near-surface depths and light wavelengths examined. Despite the simplification in the representation of surface waves, this model provides a reasonable first-order approximation to modeling the wave focusing effects at near-surface depths, which require high temporal and spatial resolution (of the order of 1 ms and 1 mm, respectively) to be adequately resolved.

Global distributions of nitric acid from IASI/MetOP measurements

Abstract. This paper presents the first global distributions of HNO3 total columns acquired by the Infrared Atmospheric Sounding Interferometer (IASI) instrument, launched onboard the MetOp platform in October 2006. IASI is an infrared nadir-looking Fourier transform spectrometer providing atmospheric radiance spectra at 0.5 cm−1 spectral resolution, from which temperature and infrared absorbing gas concentration profiles are retrieved with global Earth coverage twice a day. A first analysis of the IASI measurements in terms of information content demonstrates the possibility of retrieving a total column for HNO3 at all latitudes with a maximal sensitivity in the middle stratosphere. The retrievals are performed from IASI spectra in the atmospheric window using a fast radiative transfer model and inversion software (FORLI) relying on the Optimal Estimation Method. The operational processing of HNO3 total columns is achieved since March 2008. We show that FORLI-HNO3 performs well at all latitudes (RMS of the spectral residuals around 2.3×10−6 W/m2 sr m−1) and provides HNO3 total columns with on average statistical errors of about 12%, reaching the threshold value of 32% at the equatorial belt. The global distributions of the retrieved total columns for one year (from March 2008 to February 2009) are presented and discussed with emphasis given to seasonal and interhemispheric variations. Local seasonal variations at 6 specific locations are also described and discussed in comparison with MLS volume mixing ratios at 46.5 hPa. The seasonal cycle observed in Polar regions is highlighted, with maxima observed in fall-winter and minima during spring-summer. The denitrification inside the Antarctic polar vortex during winter is clearly revealed with unprecedented horizontal resolution: HNO3 columns decreasing down to about 1×1016 molecules cm−2 are observed, which is consistent with the lower values of temperature observed between 50 and 15 hPa (~20–25 km) and the resulting formation and sedimentation of polar stratospheric clouds. During the same period, the collar region of high quantities of HNO3 at the vortex edge is also observed around 65–60° S latitude. Preliminary correlations between IASI derived HNO3 and O3 columns inside the polar vortex are presented and discussed.

Technical Note: An assessment of the accuracy of the RTTOV fast radiative transfer model using IASI data

Abstract. IASI measurements of spectral radiances made between the 1st April 2008 and the 15th April 2008 are compared with simulations performed using the RTTOV fast radiative transfer model utilizing regression coefficients based on different line-by-line models. The comparisons are performed within the framework of the European Centre for Medium-Range Weather Forecasts Integrated Forecast System using fields of temperature, water vapour and ozone obtained from short-range forecasts. Simulations are performed to assess the accuracy of the RTTOV computations and investigate relative differences between the line-by-line models and the quality of the spectroscopic databases on which the RTTOV coefficients are based.

A fast radiative transfer model for the assimilation of water vapor radiances from the Kalpana very high resolution radiometer

A fast radiative transfer model (RTM) to compute emitted infrared radiances for a very high resolution radiometer (VHRR), onboard the operational Indian geostationary satellite Kalpana has been developed and verified. This work is a step towards the assimilation of Kalpana water vapor (WV) radiances into numerical weather prediction models. The fast RTM uses a regression‐based approach to parameterize channel‐specific convolved level to space transmittances. A comparison between the fast RTM and the line‐by‐line RTM demonstrated that the fast RTM can simulate line‐by‐line radiances for the Kalpana WV channel to an accuracy better than the instrument noise, while offering more rapid radiance calculations. A comparison of clear sky radiances of the Kalpana WV channel with the ECMWF model first guess radiances is also presented, aiming to demonstrate the fast RTM performance with the real observations. In order to assimilate the radiances from Kalpana, a simple scheme for bias correction has been suggested.

Implementation of the Community Radiative Transfer Model in Advanced Clear‐Sky Processor for Oceans and validation against nighttime AVHRR radiances

The fast Community Radiative Transfer Model (CRTM) has been integrated into National Environmental Satellite Data and Information Service's newly developed Advanced Clear‐Sky Processor for Oceans (ACSPO). CRTM is used in conjunction with the National Centers for Environmental Prediction (NCEP) Global Forecast System atmospheric profiles and Reynolds weekly version 2 sea surface temperatures (SST) to simulate clear‐sky brightness temperatures (BT). Model BTs are used to improve the ACSPO clear‐sky mask, monitor quality of advanced very high resolution radiometer (AVHRR) BTs, and explore physical SST retrievals. This paper documents CRTM implementation in ACSPO version 1 and evaluates nighttime “model minus observation” (M‐O) BT biases in three bands (3.7, 11, and 12 μm) of four AVHRR/3 instruments onboard NOAA‐16, NOAA‐17, NOAA‐18, and MetOp‐A. With careful treatment of input atmospheric and SST data, the agreement is generally good, showing only weak dependencies of M‐O biases on view zenith angle, column water vapor, and wind speed. The agreement improves if Reynolds weekly SST is used instead of NCEP SST. Including surface reflection also reduces the M‐O bias. After all optimizations, the M‐O biases are within several tenths of a Kelvin. Consistency between different platforms is ∼0.1K, except for NOAA‐16 channel 3B, which is biased low compared to other platforms by ∼0.4K. Our future plans include extending the analyses to daytime data and exploring physical SST retrievals. A web‐based tool is being established to continuously monitor the M‐O biases and physical SSTs. The validation methodology employed in this paper will be used to quantitatively measure the effect of each improvement on the M‐O bias and physical SST.

Sea surface temperature from a geostationary satellite by optimal estimation

Optimal estimation (OE) is applied as a technique for retrieving sea surface temperature (SST) from thermal imagery obtained by the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) on Meteosat 9. OE requires simulation of observations as part of the retrieval process, and this is done here using numerical weather prediction fields and a fast radiative transfer model. Bias correction of the simulated brightness temperatures (BTs) is found to be a necessary step before retrieval, and is achieved by filtered averaging of simulations minus observations over a time period of 20 days and spatial scale of 2.5° in latitude and longitude. Throughout this study, BT observations are clear-sky averages over cells of size 0.5° in latitude and longitude. Results for the OE SST are compared to results using a traditional non-linear retrieval algorithm (“NLSST”), both validated against a set of 30108 night-time matches with drifting buoy observations. For the OE SST the mean difference with respect to drifter SSTs is − 0.01 K and the standard deviation is 0.47 K, compared to − 0.38 K and 0.70 K respectively for the NLSST algorithm. Perhaps more importantly, systematic biases in NLSST with respect to geographical location, atmospheric water vapour and satellite zenith angle are greatly reduced for the OE SST. However, the OE SST is calculated to have a lower sensitivity of retrieved SST to true SST variations than the NLSST. This feature would be a disadvantage for observing SST fronts and diurnal variability, and raises questions as to how best to exploit OE techniques at SEVIRI's full spatial resolution.

Parameterization of single scattering properties of mid‐latitude cirrus clouds for fast radiative transfer models using particle mixtures

A new parameterization of single scattering properties has been developed for mid latitude cirrus clouds for application to weather prediction and global circulation models. Ice clouds are treated as a mixture of ice crystals with various habits. The bulk optical properties of ice crystals are parameterized as functions of the effective dimension (De) of measured particle size distributions that are representative of mid latitude cirrus clouds.Deis itself parameterized as a function of temperature and ice water content. The paper describes the results obtained with a stand‐alone version of the radiation routine of the COSMO model and preliminary tests with the full forecast model in various meteorological situations characterized by extended high cirrus clouds over Europe.

Retrieval of Atmospheric Integrated Water Vapor and Cloud Liquid Water Content Over the Ocean From Satellite Data Using the 1-D-Var Ice Cloud Microphysics Data Assimilation System (IMDAS)

Reliable prediction of precipitation by numerical weather prediction (NWP) models depends on the appropriate representation of cloud microphysical processes and accurate initial conditions of observations of atmospheric variables. Therefore, to retrieve reasonable cloud distributions, a 1-D variational Ice Cloud Microphysics Data Assimilation System (IMDAS) is developed to improve the predictability of NWP models. The general framework of IMDAS includes the Lin ice cloud microphysics scheme as a model operator, a four-stream fast microwave radiative transfer model in the atmosphere as an observation operator, and a global minimization method that is known as the shuffled complex evolution. IMDAS assimilates the satellite microwave radiometer data set of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and retrieves integrated water vapor and integrated cloud liquid water content. This new method successfully introduces heterogeneity into the initial state of the atmosphere, and the modeled microwave brightness temperatures agree well with the observations of the Wakasa Bay Experiment 2003 in Japan. It has significantly improved the performance of the cloud microphysics scheme by the intrusion of heterogeneity into the external global reanalysis data, which resultantly improved atmospheric initial conditions.

Interpretation of AIRS Data in Thin Cirrus Atmospheres Based on a Fast Radiative Transfer Model

Abstract A thin cirrus cloud thermal infrared radiative transfer model has been developed for application to cloudy satellite data assimilation. This radiation model was constructed by combining the Optical Path Transmittance (OPTRAN) model, developed for the speedy calculation of transmittances in clear atmospheres, and a thin cirrus cloud parameterization using a number of observed ice crystal size and shape distributions. Numerical simulations show that cirrus cloudy radiances in the 800–1130-cm−1 thermal infrared window are sufficiently sensitive to variations in cirrus optical depth and ice crystal size as well as in ice crystal shape if appropriate habit distribution models are selected a priori for analysis. The parameterization model has been applied to the Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite to interpret clear and thin cirrus spectra observed in the thermal infrared window. Five clear and 29 thin cirrus cases at nighttime over and near the Atmospheric Radiation Measurement program (ARM) tropical western Pacific (TWP) Manus Island and Nauru Island sites have been chosen for this study. A χ2-minimization program was employed to infer the cirrus optical depth and ice crystal size and shape from the observed AIRS spectra. Independent validation shows that the AIRS-inferred cloud parameters are consistent with those determined from collocated ground-based millimeter-wave cloud radar measurements. The coupled thin cirrus radiative transfer parameterization and OPTRAN, if combined with a reliable thin cirrus detection scheme, can be effectively used to enhance the AIRS data volume for data assimilation in numerical weather prediction models.

A Fast Cloud Overlap Parameterization for Microwave Radiance Assimilation

Abstract The assimilation of cloud- and rain-affected radiances in numerical weather prediction systems requires fast and accurate radiative transfer models. One of the largest sources of modeling errors originates from the assumptions regarding the vertical and horizontal subgrid-scale variability of model clouds and precipitation. In this work, cloud overlap assumptions are examined in the context of microwave radiative transfer and used to develop an accurate reference model. A fast cloud overlap algorithm is presented that allows for the accurate simulation of microwave radiances with a small number of radiative transfer calculations. In particular, the errors for a typical two-column approach currently used operationally are found to be relatively large for many cases of cloudy fields containing precipitation, even those with an overall cloud fraction of unity; these errors are largely eliminated by using the new approach presented here, at the cost of a slight increase in computation time. Radiative transfer cloud overlap errors are also evident in simulations when compared to actual satellite observations, in that the biases are somewhat reduced when applying a more accurate treatment of cloud overlap.

Advances in Radiative Transfer Modeling in Support of Satellite Data Assimilation

Abstract Development of fast and accurate radiative transfer models for clear atmospheric conditions has enabled direct assimilation of clear-sky radiances from satellites in numerical weather prediction models. In this article, fast radiative transfer schemes and their components critical for satellite data assimilation are summarized and discussed for their potential applications in operational global data assimilation systems. The major impediments to the fast radiative transfer schemes are highlighted and a call is made for broader community efforts to develop advanced radiative transfer components that can better handle the scattering from atmospheric constituents (e.g., aerosols, clouds, and precipitation) and surface materials (e.g., snow, sea ice, deserts).

Jacobian mapping between vertical coordinate systems in data assimilation

AbstractRadiances measured by remote‐sensing instruments are now the largest component of the atmospheric observation network. The assimilation of radiances from nadir sounders involves fast radiative transfer (RT) models which project profiles provided by forecast models onto the observation space for direct comparison with the measurements. One of the features typically characterizing fast RT models is the use of a fixed vertical coordinate. If the vertical coordinate of the RT model is not identical to that used by the forecast model, an interpolation of forecast profiles to the RT model coordinate is necessary. In variational data assimilation, the mapping of the Jacobians (derivatives of the RT model output with respect to its inputs) from the RT model coordinate to the forecast model coordinate is also required. This mapping of Jacobians is accomplished through the adjoint of the forecast profile interpolator. As shown, the nearest‐neighbour log‐linear interpolator commonly used operationally can lead to incorrect mapping of Jacobians and, consequently, to incorrect assimilation. This incorrect mapping occurs as a result of leaving out intermediate levels in the interpolation. This problem has been previously masked in part through the smoothing effect of forecast‐error vertical correlations on the analysis increments. To solve this problem, two simple versions of an interpolator relying on piecewise log‐linear weighted averaging over the layers are investigated. Both markedly improve Jacobian mappings in the assimilation of observations, with one being slightly favoured over the other. This interpolator is being incorporated into the RTTOV model used by several operational weather forecasting centres. Copyright © 2007 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.

A fast radiative transfer model for SSMIS upper atmosphere sounding channels

Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorology Satellite Program (DMSP) F‐16 satellite probes the atmospheric temperature from surface to 100 km. SSMIS channels 19–22 are significantly affected by Zeeman splitting, which is dependent on the Earth’s magnetic field. Thus, in satellite data assimilation or retrieval systems, SSMIS brightness temperatures and their Jacobians (or gradient with respect to temperature) must be computed with a fast radiative transfer (RT) scheme that takes into account the Zeeman‐splitting effect. In this study, an averaged transmittance within the channel frequency passbands is parameterized and predicted with atmospheric temperature, geomagnetic field strength, and the angle between the geomagnetic field vector and the electromagnetic wave propagation direction. The coefficients of predictors are trained with a line‐by‐line (LBL) radiative transfer model that accurately computes the monochromatic transmittances at fine frequency steps within each passband. The new radiative transfer scheme is compared to the results from the line‐by‐line model for the dependent and independent data sets. It is shown that the differences between the two models are well below the instrument noise levels but the new scheme is much faster. It is also shown that the SSMIS measurements agree well with the simulations that are based on the atmospheric profiles from the sounding of the atmosphere using broadband emission radiometry (SABER) on the Thermosphere‐lonosphere‐Mesosphere Energetics and Dynamics satellite and the COSPAR international reference atmosphere (CIRA) model.

Physically Retrieving Cloud and Thermodynamic Parameters from Ultraspectral IR Measurements

Abstract A physical inversion scheme has been developed dealing with cloudy as well as cloud-free radiance observed with ultraspectral infrared sounders to simultaneously retrieve surface, atmospheric thermodynamic, and cloud microphysical parameters. A fast radiative transfer model, which applies to the clouded atmosphere, is used for atmospheric profile and cloud parameter retrieval. A one-dimensional (1D) variational multivariable inversion solution is used to improve an iterative background state defined by an eigenvector-regression retrieval. The solution is iterated in order to account for nonlinearity in the 1D variational solution. It is shown that relatively accurate temperature and moisture retrievals can be achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud-top level are obtained. For both optically thin and thick cloud situations, the cloud-top height can be retrieved with relatively high accuracy (i.e., error <1 km). National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed Interferometer (NAST-I) retrievals from the The Observing-System Research and Predictability Experiment (THORPEX) Atlantic Regional Campaign are compared with coincident observations obtained from dropsondes and the nadir-pointing cloud physics lidar (CPL). This work was motivated by the need to obtain solutions for atmospheric soundings from infrared radiances observed for every individual field of view, regardless of cloud cover, from future ultraspectral geostationary satellite sounding instruments, such as the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS). However, this retrieval approach can also be applied to the ultraspectral sounding instruments to fly on polar satellites, such as the Infrared Atmospheric Sounding Interferometer (IASI) on the European MetOp satellite, the Cross-track Infrared Sounder (CrIS) on the NPOESS Preparatory Project, and the follow-on NPOESS series of satellites.