Advanced Infrared Sounder Research Articles

Geostationary Hyperspectral Infrared Sounder Channel Selection for Capturing Fast-Changing Atmospheric Information

Various methodologies have been developed for selecting a subset of channels from a hyperspectral infrared (IR) sounder for assimilation. The information entropy iterative method was considered optimal for channel selection. However, this method only considers the decrease in uncertainty in the atmospheric state caused by measurements at a single time, without considering the dynamic effect of measurements over a period of time; therefore, it might not be optimal for hyperspectral IR sounders onboard geosynchronous satellites that mainly aim to observe rapidly changing weather events. An alternative channel selection method is developed by adding an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> index, which reflects the Jacobian variance over time; the adjusted algorithm is ideal for the Geosynchronous Interferometric Infrared Sounder (GIIRS), which is the first high-spectral-resolution advanced IR sounder onboard a geostationary weather satellite. Comparisons between the conventional algorithm (information entropy iterative method) and the adjusted algorithm show that the channels selected from GIIRS by the adjusted algorithm will have larger brightness temperature diurnal variations and better information content than the conventional algorithm, based on the same background error covariance matrix, the observational error covariance matrix, and the channel blacklist. The adjusted algorithm is able to select the channels for monitoring atmospheric temporal variation while retaining the information content from the conventional method. The 1-D variational (1Dvar) retrieval experiment also verifies the superiority of this adjusted algorithm; it indicates that using the channel selected by the adjusted algorithm could enhance the water vapor profile retrieval accuracy, especially for the lower and middle troposphere atmosphere.

Optimal Configuration of a Far‐Infrared Radiometer to Study the Arctic Winter Atmosphere

AbstractSeveral far‐infrared (FIR) satellite missions are planned for the next decade, with a special interest for the Arctic region. A theoretical study is performed to help with the design of an FIR radiometer, whose configuration in terms of channels number and frequencies is optimized based on information content analysis. The problem is cast in a context of vertical column experiments (1D) to determine the optimal configuration of a FIR radiometer to study the Arctic polar night. If only observations of the FIR radiometer were assimilated, the results show that for humidity, 90% of the total information content is obtained with four bands, whereas for temperature, 10 bands are needed. When the FIR measurements are assimilated on top of those from the advanced infrared sounder (AIRS), the former bring in additional information between the surface and 850 hPa and from 550 to 250 hPa for humidity. Moreover, between 400 and 200 hPa, the FIR radiometer is better than AIRS at reducing the analysis error variance for humidity. This indicates the potential of FIR observations for improving water vapor analysis in the Arctic.

Development of a Methodology to Generate In-Orbit Electrooptical Module Temperature-Based Calibration Coefficients for INSAT-3D/3DR Infrared Imager Channels

It has been observed that the lab-based calibration coefficients of a satellite instrument differ in the actual in-orbit operation due to different environmental conditions. The lab-based coefficients are measured when all the components of the satellite instrument are in thermal equilibrium, while during in-orbit operations, there may be significant variations in temperature between various components as well as the diurnal temperature gradients of subsystems, especially in the geostationary platform due to midnight sunray intrusion. This article proposes a new technique to measure the in-orbit calibration coefficients using the collocated matchup data at different electrooptical module temperatures of the INSAT-3D/3DR imager counts and hyperspectral sounder radiances such as Advanced InfraRed Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) that are considered as the reference instruments. The radiances generated by new calibration coefficients show less bias and standard deviations for all infrared (IR) channels of INSAT-3D and INSAT-3DR when compared with the convolved radiances generated from the AIRS and IASI radiances using the procedure established by the Global Space-based Intercalibration System (GSICS).

Adaptive bias correction of advanced infrared sounding radiance assimilation in a regional model and its impact on typhoon forecast

Hyperspectral infrared remote sensing can provide the information about temperature and humidity of the atmosphere at high vertical resolution and high accuracy. To assimilate its radiances directly, we must correct biases between the observed radiances and the simulated ones from the model first guess, caused by systematic error of radiances and by the radiative transfer model and assimilating system. The method used for bias correction was developed for global models, and its adaptation to regional models raises further questions. This study is based on coupling the mesoscale numerical model weather research and forecast and gridpoint statistical interpolation assimilation system, using adaptive variational bias correction (VarBC) to the scan angle and air-mass factor, and investigates the characteristics of bias correction coefficients for the regional model. It was found that advanced infrared sounder (AIRS) channels located in the 15-μm CO2 absorption band had large scan bias, and that its nadir bias had a time dependence, which is probably due to the bias from the radiative transfer model. By contrast, other channels had small scan bias and weak time dependence. In air-mass bias correction, predictors of zenith and temperature lapse rate had huge oscillations due to variations in data coverage from the regional models. The effect of this scheme on correction in a regional model was verified via the histogram analysis of innovation. The verification showed that correction on most of the channels got satisfactory results except for several land surface channels. The corrected histogram satisfied the requirement of an unbiased normal distribution. In a typhoon forecast experiment, the influence of radiance bias correction on forecast result was tested. It showed that, compared with parameters from the global model, regional radiance correction parameters study improved the prediction of the typhoon 72-h forecast.

Profiling water vapor mixing ratios in Finland by means of a Raman lidar, a satellite and a model

Abstract. We present tropospheric water vapor profiles measured with a Raman lidar during three field campaigns held in Finland. Co-located radio soundings are available throughout the period for the calibration of the lidar signals. We investigate the possibility of calibrating the lidar water vapor profiles in the absence of co-existing on-site soundings using water vapor profiles from the combined Advanced InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU) satellite product; the Aire Limitée Adaptation dynamique Développement INternational and High Resolution Limited Area Model (ALADIN/HIRLAM) numerical weather prediction (NWP) system, and the nearest radio sounding station located 100 km away from the lidar site (only for the permanent location of the lidar). The uncertainties of the calibration factor derived from the soundings, the satellite and the model data are &lt; 2.8, 7.4 and 3.9 %, respectively. We also include water vapor mixing ratio intercomparisons between the radio soundings and the various instruments/model for the period of the campaigns. A good agreement is observed for all comparisons with relative errors that do not exceed 50 % up to 8 km altitude in most cases. A 4-year seasonal analysis of vertical water vapor is also presented for the Kuopio site in Finland. During winter months, the air in Kuopio is dry (1.15±0.40 g kg−1); during summer it is wet (5.54±1.02 g kg−1); and at other times, the air is in an intermediate state. These are averaged values over the lowest 2 km in the atmosphere. Above that height a quick decrease in water vapor mixing ratios is observed, except during summer months where favorable atmospheric conditions enable higher mixing ratio values at higher altitudes. Lastly, the seasonal change in disagreement between the lidar and the model has been studied. The analysis showed that, on average, the model underestimates water vapor mixing ratios at high altitudes during spring and summer.

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

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.

Multi-instrument gravity-wave measurements over Tierra del Fuego and the Drake Passage – Part 1: Potential energies and vertical wavelengths from AIRS, COSMIC, HIRDLS, MLS-Aura, SAAMER, SABER and radiosondes

Abstract. Gravity waves in the terrestrial atmosphere are a vital geophysical process, acting to transport energy and momentum on a wide range of scales and to couple the various atmospheric layers. Despite the importance of these waves, the many studies to date have often exhibited very dissimilar results, and it remains unclear whether these differences are primarily instrumental or methodological. Here, we address this problem by comparing observations made by a diverse range of the most widely used gravity-wave-resolving instruments in a common geographic region around the southern Andes and Drake Passage, an area known to exhibit strong wave activity. Specifically, we use data from three limb-sounding radiometers (Microwave Limb Sounder, MLS-Aura; HIgh Resolution Dynamics Limb Sounder, HIRDLS; Sounding of the Atmosphere using Broadband Emission Radiometry, SABER), the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) GPS-RO constellation, a ground-based meteor radar, the Advanced Infrared Sounder (AIRS) infrared nadir sounder and radiosondes to examine the gravity wave potential energy (GWPE) and vertical wavelengths (λz) of individual gravity-wave packets from the lower troposphere to the edge of the lower thermosphere ( ∼ 100 km). Our results show important similarities and differences. Limb sounder measurements show high intercorrelation, typically &gt; 0.80 between any instrument pair. Meteor radar observations agree in form with the limb sounders, despite vast technical differences. AIRS and radiosonde observations tend to be uncorrelated or anticorrelated with the other data sets, suggesting very different behaviour of the wave field in the different spectral regimes accessed by each instrument. Evidence of wave dissipation is seen, and varies strongly with season. Observed GWPE for individual wave packets exhibits a log-normal distribution, with short-timescale intermittency dominating over a well-repeated monthly-median seasonal cycle. GWPE and λz exhibit strong correlations with the stratospheric winds, but not with local surface winds. Our results provide guidance for interpretation and intercomparison of such data sets in their full context.

Comparisons of Two Cloud-Detection Schemes for Infrared Radiance Observations

AbstractThe cloud-detection procedure developed by McNally and Watts (MW03) was added to the Weather Research and Forecasting Data Assimilation System. To provide some guidelines for setting up cloud-detection schemes, this study compares the MW03 scheme to the Multivariate and Minimum Residual (MMR) scheme for both simulated and real Advanced Infrared Sounder (AIRS) radiances. Results show that there is a high level of consistency between the results from simulated and real AIRS data. As expected, both cloud-detection schemes perform well in finding the cloud-contaminated channels based on the channels' peak levels. The clouddetection results from MW03 are sensitive to the prescribed brightness temperature innovation threshold and brightness temperature gradient threshold. When increasing the brightness temperature innovation threshold for MW03 to roughly eight times the default threshold, the two cloud-detection schemes produce consistent data rejection distributions overall for high channels. MMR gener...

A validation of the multivariate and minimum residual method for cloud retrieval using radiance from multiple satellites

The Multivariate and Minimum Residual (MMR) cloud detection and retrieval algorithm, previously developed and tested on simulated observations and Advanced Infrared Sounder radiance, was explored and validated using various radiances from multiple sensors. For validation, the cloud retrievals were compared to independent cloud products from CloudSat, MODIS (Moderate Resolution Imaging Spectroradiometer), and GOES (Geostationary Operational Environmental Satellites). We found good spatial agreement within a single instrument, although the cloud fraction on each pixel was estimated independently. The retrieved cloud properties showed good agreement using radiances from multiple satellites, especially for the vertically integrated cloud mask. The accuracy of the MMR scheme in detecting mid-level clouds was found to be higher than for higher and lower clouds. The accuracy in retrieving cloud top pressures and cloud profiles increased with more channels from observations. For observations with fewer channels, the MMR solution was an “overly smoothed” estimation of the true vertical profile, starting from a uniform clear guess. Additionally, the retrieval algorithm showed some meaningful skill in simulating the cloudy radiance as a linear observation operator, discriminating between numerical weather prediction (NWP) error and cloud effects. The retrieval scheme was also found to be robust when different radiative transfer models were used. The potential application of the MMR algorithm in NWP with multiple radiances is also discussed.

Comparisons of Two Cloud-Detection Schemes for Infrared Radiance Observations

The cloud-detection procedure developed by McNally and Watts(MW03) was added to the Weather Research and Forecasting Data Assimilation System. To provide some guidelines for setting up cloud-detection schemes, this study compares the MW03 scheme to the Multivariate and Minimum Residual(MMR) scheme for both simulated and real Advanced Infrared Sounder(AIRS) radiances. Results show that there is a high level of consistency between the results from simulated and real AIRS data. As expected, both cloud-detection schemes perform well in finding the cloud-contaminated channels based on the channels' peak levels. The clouddetection results from MW03 are sensitive to the prescribed brightness temperature innovation threshold and brightness temperature gradient threshold. When increasing the brightness temperature innovation threshold for MW03 to roughly eight times the default threshold, the two cloud-detection schemes produce consistent data rejection distributions overall for high channels. MMR generally retains more data for long-wave channels. For both cloud-detection schemes, there is a high level of consistency between the cloud-free pixels and the visible/near-IR(Vis/NIR) cloud mask.

Validation of Humidity Profiles Obtained from Saphir, On-Board Megha-Tropiques

The Megha-Tropiques, an Indo French mission, with four on-board payloads was launched in October 2011 to improve our understanding on hydrological cycle and radiation budget. The SAPHIR (Sondeur Atmospherique du Profil d'Humidite Intertropicale par Radiometrie), a sounder for profiling humidity, is a key payload and expected to play a major role in fulfilling the mission objectives. The present article focuses on the evaluation of SAPHIR-derived humidity profiles against a variety of reference datasets, like measurements from GPS radiosondes and groundbased microwave radiometer, reanalysis datasets and satellite retrievals. The data collected during July- November 2012 were employed to validate humidity profiles. A variety of colocations (matching the sampling volumes of radiosonde and SAPHIR) were employed for the validation against radiosondes, launched from Gadanki. The bias (SAPHIR-derived RH - reference RH) and the rms error are found to be small for near-nadir measurements than those far away from the nadir. Further, the bias shows a clear height dependence with positive (negative) bias dominating in the lowest (uppermost) layers. The RH bias and rms errors are small (within 15%) in the middle layers, altitudes at which the sensitivity of SAPHIR channels is high. The comparisons with ECMWF interim reanalysis (ERA) and advanced infrared sounder (AIRS) data, used to extend the evaluation of SAPHIR data to the entire tropics, reveal strikingly similar spatial and vertical structure in RH bias. The vertical structure of RH bias is somewhat similar to that obtained with the radiosonde. Large biases are seen in regions adjacent to South America and Africa in the latitude band of 20°-30°S and large negative bias is seen along the Intertropical Convergence Zone. Possible reasons for such large biases in those regions are discussed.

A Coupled Data Assimilation Framework utilizing multifrequency passive microwave remote sensing in retrieval of land surface variables and integrated atmospheric variables: development and application over the Tibetan Plateau

Retrieval of land surface variables and atmospheric variables over land from passive microwave remote-sensing data sets has been a challenge for many years. A lot of progress has been made in these quests such as using cloud-resolving models and data assimilation. Data assimilation allows the integration of observations (including observation errors) into imperfect models, thereby yielding more improved model forecasts. In this work, a coupled data assimilation framework (CDAF) is proposed and applied to predict the evolution of land surface and atmospheric conditions. CDAF comprises a coupling of two data assimilation schemes, namely a land data assimilation scheme (LDAS) and an ice microphysics data assimilation scheme (IMDAS). This system has been developed and evaluated using data for the Tibetan Plateau. In this framework, both low-frequency and high-frequency passive microwave brightness temperatures (T Bs) are assimilated. Low-frequency T Bs are assimilated in the LDAS subsystem and used to obtain land surface conditions, which are subsequently used as improved initial conditions together with high-frequency T Bs and assimilated in the IMDAS subsystem to obtain atmospheric conditions. The retrieved land surface variables and integrated atmospheric variables are demonstrated to show good agreement with observed land and atmosphere conditions such as those derived from point measurements of temperature and soil moisture (using the Soil Moisture and Temperature Measurement System (SMTMS)), sonde, Advanced Infrared Sounder (AIRS) and Global Precipitation Climatology Project (GPCP) products. The distribution of integrated cloud liquid water and cloud ice is shown to follow the observed cloud distribution over the study area. It is shown that by using IMDAS with modifications to account for precipitation and a good description of land surface emission, it is possible to obtain precipitation information of high fidelity over the land surface. Retrieved integrated water vapour using IMDAS shows correspondence with ‘corrected’ AIRS total precipitable water product. It is also shown that the relative humidity profile obtained from IMDAS agrees with the corresponding sonde profile. From the simulations, it is clear that by using the CDAF, there is marked improvement in the forecast conditions compared with the non-assimilation scenario for all of the variables considered. Comparisons with observed land surface conditions and inferences of atmosphere state from the Geostationally Operational Environmental Satellite Series 9 (GOES-9) InfraRed Channel 1 (IR1) brightness temperatures and the GCPC's cumulative daily precipitation indicate that the CDAF is able to generate reliable forecasts that agree with observation-derived products.

Warning Information in a Preconvection Environment from the Geostationary Advanced Infrared Sounding System—A Simulation Study Using the IHOP Case

Abstract In this paper, a convective initiation event from the International H2O Project (IHOP) field experiment is used to demonstrate the potential utility of a future geostationary advanced infrared (IR) sounder for severe storm nowcasting applications. An advanced IR sounder would provide detailed stability information (e.g., lifted index and other parameters) with high temporal resolution useful for determining favorable locations for convective initiation. Atmospheric data from a high-resolution Weather Research and Forecasting model simulation was used to generate simulated Hyperspectral Environmental Suite (HES) and Advanced Baseline Imager (ABI) stability products. Comparison of these products shows that the ABI [or the current Geostationary Operational Environmental Satellite (GOES) Sounder] provides limited stability information before the storm development as a result of the limited spectral IR information for temperature and moisture profiling. The high spatial and temporal geostationary advanced IR sounder, however, can provide critical information about the destabilization much earlier than the current GOES Sounder or ABI.

Detection of Sulfur Dioxide in AIRS Data With the Wavelet Packet Subspace

A generalized method for trace-gas detection in hyperspectral data using the wavelet packet transform is being developed. This new method decomposes the input signal using a wavelet packet transform. A best basis that is optimized for the target signature is selected for pattern matching. The wavelet packet transform, an extension of the wavelet transform, fully decomposes a signal into a library of wavelet packet bases. The application of the wavelet packet transform to hyperspectral data for the detection of trace gases takes advantage of the ability of the wavelet transform to locate spectral features in both scale and location. By analyzing the wavelet packet tree of a specific target gas, the nodes of the tree that represent an orthogonal best basis are selected. The best basis represents the significant spectral features of that gas. This is then used to identify pixels in the scene using existing matching algorithms such as spectral angle. Using data from NASA's Advanced Infrared Sounder, this method is used to detect sulfur dioxide. Initial results demonstrate a promising wavelet-packet-subspace technique for trace-gas-detection applications.

Development of a neural network algorithm for the retrieval of TPW from NOAA16 AMSU measurements

A neural‐network‐based algorithm for the retrieval of Total Precipitable Water (TPW) using Advanced Microwave Sounding Unit (AMSU) data available in real time from NOAA16 satellite has been developed. The retrieval method benefits from reliable surface observations, which includes the skin temperature and ocean surface wind speed and direction. The algorithm uses the simulated brightness temperatures at four frequencies, 23.4 Ghz, 31.4 Ghz, 50.3 Ghz, and 89.0 Ghz, of AMSU‐A as input and TPW derived from Radiosonde Observations (RAOB) profiles as output. The pairs of input and output are restricted to the homogeneous emitting areas, e.g. over Bay of Bengal and Arabian Sea. The performance of the algorithm is assessed using independent RAOB measurements. The bias and rms differences are found to be 0.21 mm and 2.03 mm respectively over the range of 15 and 75 mm. Further, extensive comparisons are made between the TPW obtained from the neural network algorithm and those obtained using other satellite instruments like the Tropical Rainfall Measuring Mission Microwave Imager, Advanced Infrared Sounder, and Moderate Resolution Imaging Spectroradiometer, in which the results are found to be in close agreement.

Comparing the Vertical Structures of Weighting Functions and Adjoint Sensitivity of Radiance and Verifying Mesoscale Forecasts Using AIRS Radiance Observations

Abstract An adjoint sensitivity analysis is conducted using the adjoint of the hyperspectral radiative transfer model (RTM) that simulates the radiance spectrum from the Advanced Infrared Sounder (AIRS). It is shown, both theoretically and numerically, that the height of the maximum sensitivity of radiance in a channel could be higher or lower than the height of the maximum weighting function of that channel. It is shown that the discrepancy between the two heights is determined by the vertical structures of the atmospheric thermodynamic state. The sensitivity finds the level at which changes in temperature and/or moisture will have the largest influence on the simulated brightness temperature (BT), and the maximum weighting function (WF) height indicates the level where the model atmosphere contributes most significantly to the emission at the top of the atmosphere. Based on the above findings, an adjoint method for forecast verification using AIRS radiances is presented. In this method, model forecasts are first mapped into radiance space by an RTM so that they can be compared directly with the observed radiance values. The adjoint sensitivity analysis results are then used to connect the deviations of the model forecasts from observed radiances to the changes of temperature and moisture variables in model space. This adjoint sensitivity based model verification provides useful information on forecast model performances based on indirect observations from satellites.

Identifying Cloud-Uncontaminated AIRS Spectra from Cloudy FOV Based on Cloud-Top Pressure and Weighting Functions

Abstract An effort is made to increase the number of Advanced Infrared Sounder (AIRS) cloud-uncontaminated infrared data for regional mesoscale data assimilation and short-term quantitative precipitation forecast (QPF) applications. The cloud-top pressure from Moderate Resolution Imaging Spectroradiometer (MODIS) is utilized in combination with weighting functions (WFs) to develop a channel-based cloudy-data-removal algorithm. This algorithm identifies “clear channels” for which the brightness temperature (BT) values are not cloud contaminated. A channel-dependent cutoff pressure (COP) level is first determined based on the structure of the WF of each channel. It is usually below the maximum WF level. If the cloud top (as identified by a MODIS cloud mask) is above (below) the COP level of a channel, this channel is then deemed cloudy (clear) and removed (retained). Using this algorithm, a sizable increase of cloud-uncontaminated AIRS data can be obtained. There are more usable domain points for those channels with higher COP levels. A case study is conducted. It is shown that instead of having less than 20% AIRS clear-sky observations, the algorithm finds 80% (58%) of the AIRS pixels on which there are channels whose COP levels are at or above 300 hPa (500 hPa) and the BT data in these channels at these pixels are cloud uncontaminated. Such a significant increase of the usable AIRS cloud-uncontaminated data points is especially useful for regional mesoscale data assimilation and short-term QPF applications.

The capability of 4D‐Var systems to assimilate cloud‐affected satellite infrared radiances

AbstractFour‐dimensional variational (4D‐Var) assimilation schemes assume the linearity of their forward model in the vicinity of prior information and usually do not properly handle variables that have finer temporal and spatial scales in the real world than in the forward model. Hence cloud‐affected satellite infrared radiances are discarded from numerical weather‐prediction 4D‐Var systems, despite the critical need of observations within the cloudy regions. This paper suggests the reappraisal of that choice, subject to achieving improvements in the numerical simulation of cloudiness.A new observation operator, that computes cloud‐affected infrared radiances from 4D‐Var control variables, namely atmospheric temperature, humidity, ozone, surface temperature and surface pressure, is presented. The vertical distributions of cloud cover and of cloud condensate are diagnosed in the operator itself. The goal of this paper is to assess the feasibility of using it to assimilate cloud‐affected infrared radiances, such as those from the narrow‐band Advanced Infrared Sounder on‐board the Aqua platform or those from the broad‐band Meteosat Visible and Infrared Imager. It is shown that there is a potential benefit in assimilating directly in 4D‐Var some of the upper‐tropospheric channels at 4.5, 6.3 and 14.3 µm in the presence of clouds, for instance the 6.3 µm channel on board all the geostationary satellites. The approach is illustrated with one‐dimensional variational retrievals collocated with radiosonde observations. © Royal Meteorological Society, 2004.

A cloud detection algorithm for high‐spectral‐resolution infrared sounders

AbstractA method for detecting cloud contamination in radiances measured by high‐spectral‐resolution infrared sounders is presented. It seeks to identify clear channels within a measured spectrum, rather than the locations of completely clear spectra. Applied to simulated cloudy spectra, the scheme is able to detect clear channels with residual cloud contamination better than (i.e. less than) 0.2 K in many channels, and is thus considered sufficiently stringent for numerical weather‐prediction applications. The scheme has been applied to spectra measured by the Advanced InfraRed Sounder and, whilst a quantitative validation is more difficult with real data (without true clear radiances), it is found to perform well compared with coincident imagery. Copyright © 2003 Royal Meteorological Society

Parametrization of the effect of surface reflection on spectral infrared radiance measurements. Application to IASI

The recent launch of the Advanced Infrared Sounder (AIRS) on board EOS-Aqua and the scheduled launch of the Infrared Atmospheric Sounder Interferometer (IASI) on board the Meteorological Operational Satellite (METOP) in 2005 open interesting perspectives for remote sensing applications. Owing to their enhanced spectral resolution and sensitivity, this new generation of high-resolution infrared vertical sounders is first aimed at improving the vertical resolution of temperature and water vapor profile retrievals needed by the weather forecasting community. Another important possible use of these instruments, in the context of the study of global warming, is to permit the retrieval of the concentrations of greenhouse gases like CO 2, N 2 O, CH 4 , etc. In order to reach these two main objectives, improvement in the modeling of the radiative transfer is therefore necessary. One of the points which still needs some improvements is the contribution of the downward radiation reflected by the surface back to the satellite which is often improperly accounted for in radiative transfer calculation to save computer time. In this article, we show how it is possible to simplify the problem through the computation of a spectrally dependent “effective” emissivity for which a simple parametrization is proposed, while preserving the accuracy of the results.