High Resolution Infrared Sounder Research Articles

The Moon at thermal infrared wavelengths: a benchmark for asteroid thermal models

Thermal-infrared measurements of asteroids, satellites, and distant minor bodies are crucial for deriving the objects’ sizes, albedos, and in some cases, also the thermophysical properties of the surface material. Depending on the available measurements and auxiliary data, such as visual light curves, spin and shape information, or direct size measurements from occultations or high-resolution imaging techniques, a range of simple to complex thermal models are applied to achieve specific science goals. However, testing these models is often a difficult process and the uncertainties of the derived parameters are not easy to estimate. Here, we make an attempt to verify a widely accepted thermophysical model (TPM) against unique thermal infrared (IR), full-disk, and well-calibrated measurements of the Moon. The data were obtained by the High-resolution InfraRed Sounder (HIRS) instruments on board a fleet of Earth weather satellites that serendipitously scan the surface of the Moon. We found 22 Moon intrusions, taken in 19 channels between 3.75 μm and 15.0 μm, and over a wide phase angle range from −73.1° (waxing Moon) to +73.8° (waning Moon). These measurements include the entire Moon in a single pixel, seen almost simultaneously in all bands. The HIRS filters are narrow and outside the wavelength regime of the Christiansen feature. The similarity between these Moon data and typical asteroid spectral-IR energy distributions allows us to benchmark the TPM concepts and to point out problematic aspects. The TPM predictions match the HIRS measurements within 5% (10% at the shortest wavelengths below 5 μm) when using the Moon’s known properties (size, shape, spin, albedo, thermal inertia, roughness) in combination with a newly established wavelength-dependent hemispherical emissivity. In the 5–7.5 μm and in the 9.5–11 μm ranges, the global emissivity model deviates considerably from the known lunar sample spectra. Our findings will influence radiometric studies of near-Earth and main-belt asteroids in cases where only short-wavelength data (from e.g., NEOWISE, the warm Spitzer mission, or ground-basedM-band measurements) are available. The new, full-disk IR Moon model will also be used for the calibration of IR instrumentation on interplanetary missions (e.g., for Hayabusa-2) and weather satellites.

Evaluating the simulated radiative forcings, aerosol properties, and stratospheric warmings from the 1963 Mt Agung, 1982 El Chichón, and 1991 Mt Pinatubo volcanic aerosol clouds

Abstract. Accurately quantifying volcanic impacts on climate is a key requirement for robust attribution of anthropogenic climate change. Here we use the Unified Model – United Kingdom Chemistry and Aerosol (UM-UKCA) composition–climate model to simulate the global dispersion of the volcanic aerosol clouds from the three largest eruptions of the 20th century: 1963 Mt Agung, 1982 El Chichón, and 1991 Mt Pinatubo. The model has interactive stratospheric chemistry and aerosol microphysics, with coupled aerosol–radiation interactions for realistic composition–dynamics feedbacks. Our simulations align with the design of the Interactive Stratospheric Aerosol Model Intercomparison (ISA-MIP) “Historical Eruption SO2 Emissions Assessment”. For each eruption, we perform three-member ensemble model experiments for upper, mid-point, and lower estimates of SO2 emission, each re-initialised from a control run to approximately match the observed transition in the phase of the quasi-biennial oscillation (QBO) in the 6 months after the eruptions. With this experimental design, we assess how each eruption's emitted SO2 translates into a tropical reservoir of volcanic aerosol and analyse the subsequent dispersion to mid-latitudes. We compare the simulations to the volcanic forcing datasets (e.g. Space-based Stratospheric Aerosol Climatology (GloSSAC); Sato et al., 1993, and Ammann et al., 2003) that are used in historical integrations for the two most recent Coupled Model Intercomparison Project (CMIP) assessments. For Pinatubo and El Chichón, we assess the vertical extent of the simulated volcanic clouds by comparing modelled extinction to the Stratospheric Aerosol and Gas Experiment (SAGE-II) v7.0 satellite measurements and to 1964–1965 Northern Hemisphere ground-based lidar measurements for Agung. As an independent test for the simulated volcanic forcing after Pinatubo, we also compare simulated shortwave (SW) and longwave (LW) top-of-the-atmosphere radiative forcings to the flux anomalies measured by the Earth Radiation Budget Experiment (ERBE) satellite instrument. For the Pinatubo simulations, an injection of 10 to 14 Tg SO2 gives the best match to the High Resolution Infrared Sounder (HIRS) satellite-derived global stratospheric sulfur burden, with good agreement also with SAGE-II mid-visible and near-infra-red extinction measurements. This 10–14 Tg range of emission also generates a heating of the tropical stratosphere that is consistent with the temperature anomaly present in the ERA-Interim reanalysis. For El Chichón, the simulations with 5 and 7 Tg SO2 emission give best agreement with the observations. However, these simulations predict a much deeper volcanic cloud than represented in the GloSSAC dataset, which is largely based on an interpolation between Stratospheric Aerosol Measurements (SAM-II) satellite and aircraft measurements. In contrast, these simulations show much better agreement during the SAGE-II period after October 1984. For 1963 Agung, the 9 Tg simulation compares best to the forcing datasets with the model capturing the lidar-observed signature of the altitude of peak extinction descending from 20 km in 1964 to 16 km in 1965. Overall, our results indicate that the downward adjustment to SO2 emission found to be required by several interactive modelling studies when simulating Pinatubo is also needed when simulating the Agung and El Chichón aerosol clouds. This strengthens the hypothesis that interactive stratospheric aerosol models may be missing an important removal or re-distribution process (e.g. effects of co-emitted ash) which changes how the tropical reservoir of volcanic aerosol evolves in the initial months after an eruption. Our model comparisons also identify potentially important inhomogeneities in the CMIP6 dataset for all three eruption periods that are hard to reconcile with variations predicted in the interactive stratospheric aerosol simulations. We also highlight large differences between the CMIP5 and CMIP6 volcanic aerosol datasets for the Agung and El Chichón periods. Future research should aim to reduce this uncertainty by reconciling the datasets with additional stratospheric aerosol observations.

Ten Years of Satellite Infrared Radiance Monitoring With the Met Office NWP Model

Satellite sounder infrared radiances are among the most important contributions to the global observing system and have been assimilated into global numerical weather prediction (NWP) analyses for many years. They are also used as fundamental climate data records for climate monitoring. Prior to assimilation or producing climate records, the radiances should have all residual instrument biases removed. One way of estimating the mean biases is to continuously monitor the measured radiances against the NWP model equivalent radiances. This article is an extension of one published in 2012 which documented these biases for three years but now the time span of the monitoring has extended to beyond ten years, allowing the long-term stability of the instruments to be assessed. Data from high-resolution infrared sounder (HIRS), Advanced Along Track Scanning Radiometer (AATSR), and Spinning Enhanced Visible and Infrared Imager (SEVIRI), radiometers; atmospheric infrared sounder (AIRS), a spectrometer; and infrared atmospheric sounding interferometer (IASI), an interferometer, were included. Changes in mean biases and standard deviations were used to investigate the temporal stability of the bias and radiometric noise of the instruments over ten years. A double difference technique was employed to remove the effect of changes or deficiencies in the NWP system and radiative transfer (RT) model, which can contribute to the biases. The IASI and AIRS radiances were stable but with a different bias between the two instruments due to different versions of the RT model used. The SEVIRI radiometers were stable in most channels with the exception of the 13.4 μm channel. The HIRS instruments were subject to sudden changes in bias and increases in standard deviation compared with NWP simulations during the past decade.

Impact of lateral boundary and initial conditions in the prediction of Bay of Bengal cyclones using WRF model and its 3D-VAR data assimilation system

This study examines the impact of lateral boundary conditions (LBCs) and initial conditions (ICs) in the prediction of land-falling tropical cyclones (TCs) using Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-VAR) data assimilation system for the Bay of Bengal (BoB) region. The LBCs are derived from the National Centre for Environmental Prediction (NCEP) Final (FNL) analysis and GFS forecasted datasets at different horizontal and temporal resolutions. The assimilated datasets in the model comprises of NCEP PREBUFR (PILOT, SOUND, SYNOP, METAR, SONDE, BUOY, SHIP and QuikSCAT) and satellite radiances from Advanced Microwave Sounding Unit (AMSU-A, AMSU-B), High Resolution Infra-Red Sounder (HIRS), Microwave Humidity Sounder (MHS) at different assimilation cycles. The impact of the LBCs with varying spatial and temporal resolutions are investigated in detail and the results indicate that the forecasted track during the first 48 h are not affected, however the subsequent forecasted track is significantly influenced by the 3 hourly GFS LBCs. The initial intensity, positional errors, and storm structure show improvements with the assimilation of NCEP PREBUFR and radiance observations at different assimilation cycles. Simulations carried out with improved initial conditions indicate that the forecast (in terms of track, intensity, and trends in the intensification and dissipation) of the cyclonic storm Sidr improved significantly. In addition, five severe land-falling BoB cyclones (Phailin, Lehar, Helen, Madi, and Hudhud) during 2013–2014 were studied using 3 hourly GFS LBCs and with assimilation of NCEP PREBUFR and satellite radiance observations. The results clearly signify the ability of the WRF modeling system in forecasting the track, intensity, and landfall of the storms. The forecasted mean track errors during 2013–2014 cyclones are 65 km, 58 km, 99 km, and 103 km from day-1 to day-4 respectively, while the initial positional errors was about 41 km. The mean landfall time and positional errors are about 3 h and 15 km respectively. The forecasted intensity of the storm in terms of maximum surface wind (MSW) is also reasonably well predicted, and these errors are 4 ms-1, 10 ms-1, 6ms-1, and 8 ms-1 from day-1 to day-4 respectively.

Impact of radiance data assimilation on the prediction performance of cyclonic storm SIDR using WRF-3DVAR modelling system

This study attempts to investigate the impact of assimilation of satellite radiances and its role to improve the model initial condition and forecast of Bay of Bengal cyclone ‘Sidr’ by using the weather research and forecasting model and its three-dimensional variational data assimilation system. Results signify that the assimilation of high-resolution satellite radiances of advanced microwave sounding unit B (AMSU-B) data at peak channels has led to significant improvement in the initial fields of the storm structure. It improved the initial condition of moisture profile more significantly than the temperature profile, when radiances are assimilated into the model. In addition, the assimilation of AMSU-B showed a more positive impact on the prediction of the track and intensity of the storm than the assimilation of radiances of advanced microwave sounding unit A (AMSU-A) and high-resolution infra-red sounder (HIRS). The assimilation exercise with all observations (NCEP PREBUFR, AMSU-A, AMSU-B, HIRS, microwave humidity sounder, and atmospheric infra-red sounder) indicate that the track errors are reduced by about 46, 62, 90, and 86%, respectively, at 24, 48, 72, and 96 h forecasts compared to the experiment considering without data assimilation. The landfall, intensity, and structure of storm are well captured when all observations are assimilated into the model. Overall, it is concluded that assimilation of radiances is beneficial for the analysis and forecast of the storm. The results suggest that assimilating of both NCEP PREBUFR and radiance observations into the mesoscale model improves the initial condition and forecast of the storm.

Impact of the assimilation of INSAT‐3D radiances on short‐range weather forecasts

The present study concentrates on the assimilation of clear‐sky radiances from the recently launched INSAT‐3D satellite. The imager and sounder are two primary meteorological instruments aboard the INSAT‐3D satellite. Pre‐assimilation monitoring of the radiances has been carried out from April to July 2014. A double‐difference technique using the High Resolution Infrared Sounder (HIRS) on MetOp‐A is employed to remove the effect of deficiencies in the model‐analysed profiles which can contribute to the biases. Observed radiances of both instruments appear to be colder (∼1.5–2 K) with respect to radiative transfer model (RTM) simulated radiances, except water vapour channel radiances, which are warmer (0.5–1 K). The standard deviations of observed minus RTM‐simulated radiances are between 0.5 and 1.5 K. Prior to data assimilation, these biases are corrected using a variational bias correction scheme. The largest impacts on the analyses, when assimilating imager radiances, are found in the mid‐ and upper‐tropospheric moisture, while assimilation of sounder radiances impacted both moisture and temperature throughout the troposphere. The more accurate analyses with the INSAT‐3D radiance assimilation lead to improved moisture, wind, temperature and precipitation forecasts compared to the control case in which only conventional observations were assimilated. The results demonstrate the ability of temperature‐ and water vapour‐sensitive radiances to improve not only the temperature and moisture fields, but also the wind fields, enhancing their importance, particularly over tropical regions where wind observations are more important.

Satellite data assimilation of upper-level sounding channels in HWRF with two different model tops

The Advanced Microwave Sounding Unit-A (AMSU-A) onboard the NOAA satellites NOAA-18 and NOAA-19 and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) MetOp-A, the hyperspectral Atmospheric Infrared Sounder (AIRS) onboard Aqua, the High resolution InfraRed Sounder (HIRS) onboard NOAA-19 and MetOp-A, and the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) satellite provide upper-level sounding channels in tropical cyclone environments. Assimilation of these upper-level sounding channels data in the Hurricane Weather Research and Forecasting (HWRF) system with two different model tops is investigated for the tropical storms Debby and Beryl and hurricanes Sandy and Isaac that occurred in 2012. It is shown that the HWRF system with a higher model top allows more upper-level microwave and infrared sounding channels data to be assimilated into HWRF due to a more accurate upper-level background profile. The track and intensity forecasts produced by the HWRF data assimilation and forecast system with a higher model top are more accurate than those with a lower model top.

A comparison of the performance of Kalpana and HIRS water vapor radiances in the WRF 3D-Var assimilation system for mesoscale weather predictions

[1] An impact study is presented to compare the performance of the Weather Research and Forecasting (WRF) model in response to assimilation of water vapor (WV) channel radiances from Indian geostationary satellite Kalpana and from High-Resolution Infrared Sounder (HIRS) HIRS-3/4 instruments on board NOAA satellites. A three-dimensional variational data assimilation (3D-Var) system is used for assimilation of radiances from above mentioned instruments, and the impacts of these radiances were assessed on 24 h forecasts starting on each day at 0000 UTC for the period 10–20 July 2008. It was found that the Kalpana radiances provide significant improvement over the HIRS radiances. Compared to HIRS, the assimilation of the Kalpana WV radiances improved the mid–upper tropospheric moisture (∼6%) and temperature (∼1.5%) analysis when verified against National Centers for Environmental Prediction (NCEP) analysis. Simulated microwave radiances using model fields assimilated with Kalpana radiances match significantly closer to Advanced Microwave Sounding Unit-B (AMSU-B) radiances, compared to those assimilated with HIRS radiances. Impact on mid–upper tropospheric moisture, temperature and lower and upper tropospheric wind forecast was positive. The reduction in the root mean square errors by the Kalpana radiance assimilation reached up to 1.5% for the temperature forecast against NCEP analysis, and about 2% for the moisture forecast. The improved mid to upper tropospheric humidity and temperature with the use of Kalpana radiances led to better rain forecasts.

Diagnosis and impacts of non-Gaussianity of innovations in data assimilation

Most of the atmospheric and oceanic data assimilation (DA) schemes rely on the Best Linear Unbiased Estimator (BLUE), which is sub-optimal if errors of assimilated data are non-Gaussian, thus calling for a full Bayesian data assimilation. This paper contributes to the study of the non-Gaussianity of errors in the observational space. Possible sources of non-Gaussianity range from the inherent statistical skewness and positiveness of some physical observables (e.g. moisture, chemical species), the nonlinearity, both of the data assimilation models and of the observation operators among others. Deviations from Gaussianity can be justified from a priori hypotheses or inferred from statistical diagnostics of innovations (observation minus background), leading to consistency relationships between the error statistics. From samples of observations and backgrounds as well as their specified error variances, we evaluate some measures of the innovation non-Gaussianity, such as the skewness, kurtosis and negentropy. Under the assumption of additive errors and by relating statistical moments from both data errors and innovations, we identify potential sources of the innovation non-Gaussianity. These sources range from: (1) univariate error non-Gaussianity, (2), nonlinear correlations between errors, (3) spatio-temporal variability of error variances (heteroscedasticity) and (4) multiplicative noise. Observational and background errors are often assumed independent. This leads to variance-dependent bounds for the skewness and the kurtosis of errors. From innovation statistics, we assess the potential DA impact of some scenarios of non-Gaussian errors. This impact is measured through the mean square difference between the BLUE and the Minimum Variance Unbiased Estimator (MVUE), obtained with univariate observations and background estimates. In order to accomplish this, we compute maximum entropy probability density functions (pdfs) of the errors, constrained by the first four order moments. These pdfs are then used to compute the Bayesian posterior pdf and the MVUE. The referred impact is studied for a large range of statistical moments, being higher for skewed innovations and growing in average with the skewness of data errors, specially if the skewnesses have the same sign. An application has been performed to the quality-accepted ECMWF innovations of brightness temperatures of a set of High Resolution Infrared Sounder (HIRS) channels. In this context, the MVUE has led in some extreme cases to a potential reduction of 20%–60% of the posterior error variance as compared to the BLUE, specially for extreme values of the innovations.

MODIS Global Cloud-Top Pressure and Amount Estimation: Algorithm Description and Results

Abstract The Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Earth Observing System (EOS) Terra and Aqua platforms provides unique measurements for deriving global and regional cloud properties. MODIS has spectral coverage combined with spatial resolution in key atmospheric bands, which is not available on previous imagers and sounders. This increased spectral coverage/spatial resolution, along with improved onboard calibration, enhances the capability for global cloud property retrievals. MODIS operational cloud products are derived globally at spatial resolutions of 5 km (referred to as level-2 products) and are aggregated to a 1° equal-angle grid (referred to as level-3 product), available for daily, 8-day, and monthly time periods. The MODIS cloud algorithm produces cloud-top pressures that are found to be within 50 hPa of lidar determinations in single-layer cloud situations. In multilayer clouds, where the upper-layer cloud is semitransparent, the MODIS cloud pressure is representative of the radiative mean between the two cloud layers. In atmospheres prone to temperature inversions, the MODIS cloud algorithm places the cloud above the inversion and hence is as much as 200 hPa off its true location. The wealth of new information available from the MODIS operational cloud products offers the promise of improved cloud climatologies. This paper 1) describes the cloud-top pressure and amount algorithm that has evolved through collection 5 as experience has been gained with in-flight data from NASA Terra and Aqua platforms; 2) compares the MODIS cloud-top pressures, converted to cloud-top heights, with similar measurements from airborne and space-based lidars; and 3) introduces global maps of MODIS and High Resolution Infrared Sounder (HIRS) cloud-top products.

The assimilation of AIRS radiance data at ECMWF

AbstractThe development of an assimilation system for radiance data from the Atmospheric InfraRed Sounder (AIRS) is described, in particular the identification of cloud contamination, bias correction and the characterization of errors in the measured radiances and radiative‐transfer model. The results of assimilation experiments are presented. These show that a conservative use of AIRS radiance data (in a system already extensively observed with other satellite data) results in a small, but consistent, improvement in the quality of analyses and forecasts. Larger impacts of AIRS are found in hypothetical experiments that test the use of radiances from only a single sounding instrument. In these, the use of AIRS is found to outperform the use of data either from a single Advanced Microwave Sounding Unit‐A (AMSU‐A) or from a single High‐resolution InfraRed Sounder (HIRS). In this hypothetical context the relative forecast performance of each sensor is found to correlate with the size and vertical scale of increments caused by the assimilation of the radiances. Copyright © 2006 Royal Meteorological Society.

Enhancement of ERBS data by using data from sounders onboard NPP/NPOESS and METOP satellites

The operational climate monitoring and diagnostic community will continue to favor in using outgoing longwave radiation (OLR) estimates that have long and continuous time series such as those estimated from the High Resolution Infrared Sounder (HIRS). OLR from HIRS data could be generated since these data exist since 1979 and will continue during NPOESS era with Cross-Track Infrared Sounder (CrIS) and HIRS/4 on NPOESS and METOP satellites, respectively. OLR estimated from Earth Radiation Budget Suite (ERBS)/Clouds and the Earth’s Radiant Energy System (CERES) can be used to calibrate the HIRS technique that will be used to generate continuous time series of OLR. This will ensure to have a consistent climate data involving different data sources. OLR generated from CrIS on the afternoon orbit could provide a backup in the event of ERBS’s failure. There will be three evenly spaced sounders (two CrIS and one HIRS/4) that could be used to retrieve OLR allowing therefore a better definition of the diurnal variability of OLR. This paper will present results from GOES sounder data to estimate OLR using an approach similar to that used for HIRS. The instantaneous OLR estimates are compared with collocated OLR products from the CERES instruments onboard TRMM and Terra spacecraft to establish the instantaneous accuracy of the multi-spectral estimation techniques for homogeneous scenes. The hourly GOES sounder OLR estimates are used to study the diurnal cycle of OLR. The time-averaged data are used to estimate the accuracy of the ERBE/CERES and HIRS diurnal cycle models as applied to daily- and monthly-averaged products at different spatial resolutions. The study suggests that the technique can be implemented for NPOESS using the CrIS and HIRS/4 for both augmenting the temporal sampling of OLR estimates and reducing the risk of OLR EDR generation.

Evaluation of moisture in the Hadley Centre climate model using simulations of HIRS water‐vapour channel radiances

AbstractIt is important to establish that climate models can accurately simulate the observed present‐day fluctuations of water vapour. In particular, water‐vapour and cloud‐radiative feedbacks are intrinsically linked to processes governing relative‐humidity distribution and variability. To explore these issues, clear‐sky radiances, sensitive to upper‐tropospheric relative humidity, are simulated within the Hadley Centre atmospheric climate model, version HadAM3, allowing direct comparison with High Resolution Infrared Sounder (HIRS) observations. The model is forced by observed sea surface temperatures and sea‐ice fields over the period 1979–98. Evaluation of the simulated distribution and variability of water vapour is undertaken utilizing the HIRS 6.7 μm brightness temperature (T6.7) and satellite measurements of column‐integrated water vapour and clear‐sky outgoing long‐wave radiation (OLR). Modifications are made to the clear‐sky OLR and T6.7 HadAM3 diagnostics to reduce sampling inconsistencies with the observed products. Simulated T6.7 over subtropical dry zones are higher than T6.7 from observations, particularly in the southern hemisphere, and is symptomatic of an overactive circulation. The observed spatial signature of the T6.7 interannual variability is dominated by El Niño and is captured well by HadAM3. Interannual variability of the tropical ocean mean T6.7 is consistent between HadAM3 and the HIRS observations, suggesting that the small simulated decadal changes in relative humidity are realistic. © Crown copyright, 2003. Royal Meteorological Society

Formulation and validation of simulated data for the atmospheric infrared sounder (AIRS)

Models for synthesizing radiance measurements by the Atmospheric Infrared Sounder (AIRS) are described. Synthetic radiances have been generated for developing and testing data processing algorithms. The radiances are calculated from geophysical states derived from weather forecasts and climatology using the AIRS rapid transmission algorithm. The data contain horizontal variability at the spatial resolution of AIRS from the surface and cloud fields. This is needed to test retrieval algorithms under partially cloudy conditions. The surface variability is added using vegetation and International Geosphere Biosphere Programme surface type maps, while cloud variability is added randomly. The radiances are spectrally averaged to create High Resolution Infrared Sounder (HIRS) data, and this is compared with actual HIRS2 data on the NOAA 14 satellite. The simulated data under-represent high-altitude equatorial cirrus clouds and have too much local variability. They agree in the mean to within 1-4 K, and global standard deviation agrees to better than 2 K. Simulated data have been a valuable tool for developing retrieval algorithms and studying error characteristics and will continue to be so after launch.

Applications of satellite remote sensing in numerical weather and climate prediction

The year 2000 marks the 40 th anniversary of the launch of the first weather satellite. The images of cloud systems from the early satellites enabled forecasters to locate and monitor the movements of storms. Today's satellites provide a wealth of quantitative information about the constantly changing state of the Earth's atmosphere, ocean, and land surface. Significant strides are being made by operational centers around the world to effectively use these remotely-sensed observations in forecast models. The satellite measurements are used to initialize, provide boundary conditions for, and verify predictions of models. As an example of the state of the art, this paper reviews how satellite observations are used in the numerical weather and climate prediction models of the U.S. National Weather Service. The National Weather Service, National Centers for Environmental Prediction (NCEP), Environmental Modeling Center (EMC) develops regional and global weather prediction models, coupled ocean-atmosphere models for seasonal to interannual climate predictions, and a coastal ocean forecast model. A three dimensional variational data assimilation system is used to specify the initial conditions for the forecast models. Data from the following satellite instruments are currently used in one or more of these models: High Resolution Infrared Sounder (HIRS), Microwave Sounding Unit (MSU), Advanced Microwave Sounding Unit-A (AMSU-A), Geostationary Operational Environmental Satellites (GOES) sounder, GOES, METEOSAT, and Geostationary Meteorology Satellite (GMS) imagers, Advanced Very High Resolution Radiometer (AVHRR), Special Sensor Microwave/Imager (SSM/I), ESA Remote-sensing Satellite-2 (ERS-2) scatterometer, Solar Backscatter Ultraviolet Spectrometer/2 (SBUV/2), and Oceanic Topography Experiment (TOPEX) and ERS-2 altimeters.

Adjoint sensitivity of the forecast to TOVS observations

AbstractThe adjoint sensitivity to observations which is based on the adjoint operator of the variational assimilation process is used here on ten cases of the Fronts and Atlantic Storm‐Track EXperiment (FASTEX), conducted in January and February 1997. It is used as a diagnostic tool allowing one to indicate which TIROS‐N (Television Infrared Observation Satellite) Operational Vertical Sounder (TOVS) channels have an influence on the forecast of mid‐latitude lows. In order to study the effects of the observations on the modification of the forecast from the guess, a particular cost function has been chosen: the energy of the difference between the forecast derived from the guess and the one resulting from the analysis. The first part of the paper deals with the sensitivity to the assimilated TOVS observations for the intensive observation period 17 of FASTEX. Then, the influence of TOVS data is compared with one of the other conventional datasets assimilated at the same time. After that, these results are generalized in an overview of the ten studied cases. This study highlights that the Microwave Sounder Unit and clear sky or partly cloudy High‐resolution Infra‐Red Sounder have the larger influence of the TOVS observations on the modification of the forecast. However, the other conventional data have a larger absolute contribution than the TOVS ones. Copyright © 2002 Royal Meteorological Society.

Signatures of Annual and Seasonal Variations of CO2and Other Greenhouse Gases from Comparisons between NOAA TOVS Observations and Radiation Model Simulations

Abstract Since 1979, sensors on board the National Oceanic and Atmospheric Administration (NOAA) series of polar meteorological satellites have provided continuous measurements of the earth's surface and atmosphere. One of these sensors, the Television Infrared Observational Satellite (TIROS-N) Operational Vertical Sounder (TOVS), observes earth-emitted radiation in the infrared—with the High-Resolution Infrared Sounder (HIRS)—and in the microwave—with the Microwave Sounding Unit (MSU)—portions of the spectrum. The NOAA and National Aeronautics and Space Administration (NASA) Pathfinder program was designed to make these data more readily accessible to the community in the form of processed geophysical variables (temperature, water vapor, cloud characteristics, and so on) through the “interpretation” of the infrared and microwave radiances. All presently developed interpretation algorithms more or less directly rely on the comparison between a set of observed and a set of simulated radiances. For that rea...

Radiance and Jacobian intercomparison of radiative transfer models applied to HIRS and AMSU channels

The goals of this study are the evaluation of current fast radiative transfer models (RTMs) and line‐by‐line (LBL) models. The intercomparison focuses on the modeling of 11 representative sounding channels routinely used at numerical weather prediction centers: 7 HIRS (High‐resolution Infrared Sounder) and 4 AMSU (advanced microwave sounding unit) channels. Interest in this topic was evident by the participation of 24 scientists from 16 institutions. An ensemble of 42 diverse atmospheres was used and results compiled for 19 infrared models and 10 microwave models, including several LBL RTMs. For the first time, not only radiances but also Jacobians (of temperature, water vapor, and ozone) were compared to various LBL models for many channels. In the infrared, LBL models typically agree to within 0.05–0.15 K (standard deviation) in terms of top‐of‐the‐atmosphere brightness temperature (BT). Individual differences up to 0.5 K still exist, systematic in some channels, and linked to the type of atmosphere in others. The best fast models emulate LBL BTs to within 0.25 K, but no model achieves this desirable level of success for all channels. The ozone modeling is particularly challenging. In the microwave, fast models generally do quite well against the LBL model to which they were tuned. However, significant differences were noted among LBL models. Extending the intercomparison to the Jacobians proved very useful in detecting subtle or more obvious modeling errors. In addition, total and single gas optical depths were calculated, which provided additional insight on the nature of differences.

Calibration of Meteosat water vapor channel observations with independent satellite observations

The objective of this study is to identify potential problems of and increase confidence in the operational calibration of the water vapor channel of the Meteosat Visible and Infrared Radiation Imager (MVIRI). First, the operational vicarious calibration method of the MVIRI water vapor channel is analyzed. This is done through a comparison of the operational calibration coefficient with a calibration coefficient derived from collocated satellite observations taken by water‐vapor‐sensitive channel 12 of the High Resolution Infrared Sounder (HIRS) on NOAA 14. The analysis suggests a relatively large difference between the two calibration coefficients, the operational calibration coefficient being 10–15% larger than the one from the intersatellite calibration. Second, no such difference is found to exist between the MVIRI operational calibration coefficient and an intersatellite calibration coefficient derived from collocated GOES 8 water‐vapor‐sensitive channel 3 observations. Third, it is shown that the operational calibration coefficient for the MVIRI water vapor channel on Meteosat 7 is consistent with its blackbody calibration. At the present stage of the research no definite conclusions can be made. The results support the previously documented accuracy of 5% for the operational calibration coefficient of the MVIRI water vapor channel.

Global Soundings of the Atmosphere from ATOVS Measurements: The Algorithm and Validation

The International Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) Processing Package (IAPP) has been developed to retrieve the atmospheric temperature profile, moisture profile, atmospheric total ozone, and other parameters in both clear and cloudy atmospheres from the ATOVS measurements. The algorithm that retrieves these parameters contains four steps: 1) cloud detection and removal, 2) bias adjustment for ATOVS measurements, 3) regression retrieval processes, and 4) a nonlinear iterative physical retrieval. Nine (3 × 3) adjacent High-Resolution Infrared Sounder (HIRS)/3 spot observations, together with Advanced Microwave Sounding Unit-A observations remapped to the HIRS/3 resolution, are used to retrieve the temperature profile, moisture profile, surface skin temperature, total atmospheric ozone and microwave surface emissivity, and so on. ATOVS profile retrieval results are evaluated by root-mean-square differences with respect to radiosonde observation profiles. The accuracy of the retrieval is about 2.0 K for the temperature at 1-km vertical resolution and 3.0–6.0 K for the dewpoint temperature at 2-km vertical resolution in this study. The IAPP is now available to users worldwide for processing the real-time ATOVS data.