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We present the microwave line-by-line model AMSUTRAN, which has been developed at the Met-Office (UK) for over 20 years as part of the EUMETSAT-funded Numerical Weather Prediction Satellite Application Facility (NWP SAF). It produces profiles of layer-to-space transmittances that are representative of the swath of a satellite channel, which are used to train coefficients for the fast radiative transfer model RTTOV (Radiative Transfer for TOVS). At its core are absorption routines based on the Millimeter-wave Propagation Model (MPM), with subsequent modifications to its structure and spectroscopy that have been implemented over time. The most significant of these are: adoption of the Curtis-Godson method of determining the most representative quantities for an atmospheric layer, the complete replacement of all oxygen line parameters, replacement of the air-broadened half-width parameters of the 22.235 and 183.31 GHz water vapour lines, the addition of 35 ozone lines from the HITRAN database, and modifications to the dry continua. The impact of each change is shown in terms of the change in Top Of Atmosphere (TOA) brightness temperature simulated for the 22 channels of the Advanced Technology Microwave Sounder (ATMS) satellite instrument. The biggest effect by far is seen in the method of determining layer quantities, with sensitivities up to many degrees kelvin. To date, developments have focused on the 0–200 GHz range as this is the spectral limit of the current microwave radiometers in-orbit, however, a new generation of instruments heralded by the forthcoming Ice Cloud Imager (ICI) planned for launch in 2022, raises the performance requirements of AMSUTRAN to sub-millimetre frequencies.

To better understand the impacts of climate change, environmental monitoring capabilities must be enhanced by deploying additional and more accurate satellite- and ground-based (including in situ) sensors. In addition, reanalysis of observations collected decades ago but long forgotten can unlock precious information about the recent past. Historical, in situ observations mainly cover densely inhabited areas and frequently traveled routes. In contrast, large selections of early meteorological satellite data, waiting to be exploited today, provide information about remote areas unavailable from any other source. When initially collected, these satellite data posed great challenges to transmission and archiving facilities. As a result, data access was limited to the main teams of scientific investigators associated with the instruments. As archive media have aged, so have the mission scientists and other pioneers of satellite meteorology, who sometimes retired in possession of unique and unpublished information. This paper presents examples of recently recovered satellite data records, including satellite imagery, early infrared hyperspectral soundings, and early microwave humidity soundings. Their value for climate applications today can be realized using methods and techniques that were not yet available when the data were first collected, including efficient and accurate observation simulators and data assimilation into reanalyses. Modern technical infrastructure allows serving entire mission datasets online, enabling easy access and exploration by a broad range of users, including new and old generations of climate scientists.

We present a comparison of a reference and a fast radiative transfer model using numerical weather prediction profiles for the Zeeman-affected high-altitude Special Sensor Microwave Imager/Sounder channels 19–22. We find that the models agree well for channels 21 and 22 compared to the channels' system noise temperatures (1.9 and 1.3 K, respectively) and the expected profile errors at the affected altitudes (estimated to be around 5 K). For channel 22 there is a 0.5 K average difference between the models, with a standard deviation of 0.24 K for the full set of atmospheric profiles. Concerning the same channel, there is 1.2 K on average between the fast model and the sensor measurement, with 1.4 K standard deviation. For channel 21 there is a 0.9 K average difference between the models, with a standard deviation of 0.56 K. Regarding the same channel, there is 1.3 K on average between the fast model and the sensor measurement, with 2.4 K standard deviation. We consider the relatively small model differences as a validation of the fast Zeeman effect scheme for these channels. Both channels 19 and 20 have smaller average differences between the models (at below 0.2 K) and smaller standard deviations (at below 0.4 K) when both models use a two-dimensional magnetic field profile. However, when the reference model is switched to using a full three-dimensional magnetic field profile, the standard deviation to the fast model is increased to almost 2 K due to viewing geometry dependencies, causing up to ±7 K differences near the equator. The average differences between the two models remain small despite changing magnetic field configurations. We are unable to compare channels 19 and 20 to sensor measurements due to limited altitude range of the numerical weather prediction profiles. We recommended that numerical weather prediction software using the fast model takes the available fast Zeeman scheme into account for data assimilation of the affected sensor channels to better constrain the upper atmospheric temperatures.

This paper gives an update of the RTTOV (Radiative Transfer for TOVS) fast radiative transfer model, which is widely used in the satellite retrieval and data assimilation communities. RTTOV is a fast radiative transfer model for simulating top-of-atmosphere radiances from passive visible, infrared and microwave downward-viewing satellite radiometers. In addition to the forward model, it also optionally computes the tangent linear, adjoint and Jacobian matrix providing changes in radiances for profile variable perturbations assuming a linear relationship about a given atmospheric state. This makes it a useful tool for developing physical retrievals from satellite radiances, for direct radiance assimilation in NWP models, for simulating future instruments, and for training or teaching with a graphical user interface. An overview of the RTTOV model is given, highlighting the updates and increased capability of the latest versions, and it gives some examples of its current performance when compared with more accurate line-by-line radiative transfer models and a few selected observations. The improvement over the original version of the model released in 1999 is demonstrated.

To document the performance of current line-by-line radiative transfer models, a study was performed to compare the model simulations with real observations and also inter-compare the simulations themselves. Two broadband mid-IR observed spectra with high spectral resolution were analyzed. The observations were done in nadir mode, and at the same time the atmospheric state was carefully monitored. The first dataset consisted of radiance observations using the HIS interferometer during the CAMEX-1 campaign off the east coast of the USA. The second dataset consisted of observations from the ARIES interferometer collected during the Ascension Island campaign over the tropical Atlantic. These two cases are very different with the Ascension Island case being much warmer and more humid than the CAMEX case. In total 13 different research groups participated with seven different line-by-line models. The results of this study indicated that in many spectral regions the models are capable of reproducing the observations to within the observed noise. In some spectral regions relatively large differences between the simulations and observations exist.

The Advanced TIROS Operational Vertical Sounder (ATOVS) was launched on the NOAA-15 satellite in May 1998. This provided a very significant improvement in the information available from meteorological polar-orbiting satellites compared with the previous TIROS Operational Vertical Sounder system, particularly for humidity and vertical resolution of temperature in cloudy areas. In preparation for assimilation of the observations into a three-dimensional analysis of atmospheric temperature and humidity, the observations have been compared with calculated top-of-atmosphere brightness temperatures computed from numerical weather prediction model profiles of temperature and humidity. Differences between observed and modelled brightness temperature are small. In some parts of the tropics and northern hemisphere the standard deviation of these differences for the tropospheric Advanced Microwave Sounding Unit sounding channels is only marginally higher than the radiometric noise of the observations. Early in 1999 a series of observation-system experiments were completed in which ATOVS observations were assimilated using a one-dimensional variational analysis. No use of the new humidity information could be made because of interference problems experienced by the microwave humidity sounder on ATOVS. Nonetheless, these experiments showed that the assimilation of the new temperature information provided by the radiance observations reduces forecast errors by as much as 20% in the southern hemisphere and 5% in the northern hemisphere. Further improvements have been found by assimilating more data over land. The major impact arises from the microwave channels. Whilst forward-model errors may be slightly lower for the microwave channels than the infrared channels the primary reason is the provision of sounding information in active weather systems, which are usually cloudy.

An intercomparison of radiation codes used in retrieving upper tropospheric humidity (UTH) from observations in the v2 (6.3 microns) water vapor absorption band was performed. This intercomparison is one part of a coordinated effort within the GEWEX Water Vapor Project (GVaP) to assess our ability to monitor the distribution and variations of upper tropospheric moisture from space-borne sensors. A total of 23 different codes, ranging from detailed line-by-line (LBL) models, to coarser resolution narrow-band (NB) models, to highly-parameterized single-band (SB) models participated in the study. Forward calculations were performed using a carefully selected set of temperature and moisture profiles chosen to be representative of a wide range of atmospheric conditions. The LBL model calculations exhibited the greatest consistency with each other, typically agreeing to within 0.5 K in terms of the equivalent blackbody brightness temperature (T(sub b)). The majority of NB and SB models agreed to within +/- 1 K of the LBL models, although a few older models exhibited systematic T(sub b) biases in excess of 2 K. A discussion of the discrepancies between various models, their association with differences in model physics (e.g. continuum absorption), and their implications for UTH retrieval and radiance assimilation is presented.

[1] 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.

[1] A comparison of radiative transfer models for simulating radiances from the Atmospheric Infrared Sounder (AIRS), has been undertaken. Results from 14 line-by-line and fast parameterized infrared models were submitted. Several aspects of the models were compared. First, the forward model calculations for all 2378 AIRS channels for 52 diverse atmospheric profiles and one tropical Pacific profile coincident with AIRS data were performed for three local zenith viewing angles: nadir, 45, and 60 degrees. Second, for a subset of the models and only 20 AIRS channels the transmittances from each layer to space were provided. Finally, for some models the Jacobians with respect to temperature, water vapor, and ozone were also computed. For the forward model calculations, most models agree to within 0.02 K when compared to a reference line-by-line model averaged over a subset of profiles, with the exception of a few spectral regions. When compared with AIRS observations, however, the mean differences increase to 0.2 K, and for a few models even greater differences are seen. The transmittance differences highlighted regions of the spectrum where the spectroscopy of the models differs, particularly in the carbon dioxide absorption bands at 667 cm−1 and 2386 cm−1. For the Jacobians all models have some profiles/channels that do not fit the reference well, and the main problems are documented here. The model differences only increase slightly for off-nadir viewing angles for both forward and Jacobian calculations.

The Advanced Microwave Sounding Unit (AMSU), launched on May 13 1998, is a twenty-channel passive microwaveradiometer designed to provide information on atmospheric temperature and humidity structure in clear and cloudy con-ditions, complementing existing infra-red radiometers which provide information only in clear areas. AMSU has somechannels similar to those flown on existing missions and others which are new. Observed radiances from the Special Sen-sor Microwave Imager (SSM/I) and the Microwave Sounding Unit (MSU) are compared with radiances calculated fromnumerical weather prediction (NWP) model profiles, and the differences discussed. A processing method for ATOVSradiances is described, and differences from previous techniques are highlighted. An initial evaluation of AMSU data isgiven. Finally the impact of passive microwave observations on the skill of numerical weather forecasts is discussed.Key-words: Numerical weather prediction, ATOVS, AMSU, MSU, SSMII, preprocessing, idvar, radiance, assimilation