RTS is a modular program that simulates atmospheric radiative transfer. The paper describes ARTS version 1.0, which is applicable in the absence of scattering. An overview over all major parts of the model is given: calculation of absorption coefficients, the radiative transfer itself, and the calculation of Jacobians. ARTS can be freely used under a GNU general public license. Unique features of the program are its scalability and modularity, the ability to work with different sources of spectroscopic parameters, the availability of several self-consistent water continuum and line absorption models, and the analytical calculation of Jacobians.
We present a method to efficiently simulate the measurements of a broadband infrared instrument. The High Resolution Infrared Radiation Sounder (HIRS) instrument is used as example to illustrate the method. The method uses two basic ideas. Firstly, the channel radiance can be approximated by a weighted mean of the radiance at some representative frequencies, where the weights can be determined by linear regression. Secondly, a near-optimal set of representative frequencies can be found by simulated annealing.
The paper does not only describe and analyze the method, it also describes how the method was used to derive optimized frequency grids for the HIRS instruments on the satellites TIROS N, NOAA 6-19, and Metop A. The grids and weights, as well as the optimization algorithm itself are openly available under a GNU public license.
183.31 GHz observations from the Advanced Microwave Sounding Unit B (AMSUB) instruments onboard the NOAA 15, 16, and 17 satellites were used to derive a new data set of Upper Tropospheric Humidity (UTH). The data set consist of monthly median and mean data on a 1.5 degrees latitude-longitude grid between 60 degrees S and 60 degrees N, and covers the time period of January 2000 to February 2007. The data from all three instruments are very consistent, with relative difference biases of less than 4% and relative difference standard deviations of 7%. Radiometric contributions by high ice clouds and by the Earth's surface affect the measurements in certain areas. The uncertainty due to clouds is estimated to be up to approximately 10%RH in areas with deep convection. The uncertainty associated with contamination from surface emission can exceed 10%RH in midlatitude winter, where the data therefore should be regarded with caution. Otherwise the surface influence appears negligible. The paper also discusses the UTH median climatology and seasonal cycle, which are found to be broadly consistent with UTH climatologies from other sensors. Finally, the paper presents an initial validation of the new data set against IR satellite data and radiosonde data. The observed biases of up to 9%RH (wet bias relative to HIRS) were found to be broadly consistent with expectations based on earlier studies. The observed standard deviations against all other data sets were below 6%RH. The UTH data are available to the scientific community on http://www.sat.ltu.se.
A Monte Carlo method is used to study the propagation of temperature uncertainties into relative humidity with respect to ice (RH i ) calculated from specific humidity. For a flat specific humidity distribution and Gaussian temperature uncertainties the resulting RH i distribution drops exponentially at high RH i values—much slower than a Gaussian. This agrees well with the RH i distribution measured by the Microwave Limb Sounder (MLS), which means that such remotely measured RH i distributions can be explained, at least partly, by temperature uncertainties.
A comparison between the fast radiative transfer model Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-7) and the physical radiative transfer model Atmospheric Radiative Transfer Simulator ( ARTS) was carried out. Radiances were simulated for the sounding channels of the Advanced Microwave Sounding Unit B (AMSU-B) for the whole globe for a single time of a single day ( 1 January 2000, 0000 UT). Temperature, pressure, and specific humidity profiles from the reanalysis data set ERA-40 of the European Centre for Medium-Range Weather Forecasts (ECMWF) were used as input for both models; geopotential height profiles were also used but only as input for ARTS. The simulations were made for two different surface emissivities, 0.60 and 0.95. The low surface emissivity case exhibits the larger radiance differences. Although the global values of the mean difference and standard deviation are small ( for example, the global mean difference for channel 18 is 0.014 K and the standard deviation is 0.232 K), the examination of the geographical distribution of the differences shows that large positive or negative values are observed over dry regions of high northern and southern latitudes and over dry elevated regions. The origin of these differences was found to be due to errors introduced by the transmittance parametrization used in RTTOV.
Passive submillimeter-wave sensors are a way to obtain urgently needed global data on ice clouds, particularly on the so far poorly characterized 'essential climate variable' ice water path (IWP) and on ice particle size. CloudIce was a mission proposal to the European Space Agency ESA in response to the call for Earth Explorer 8 (EE8), which ran in 2009/2010. It proposed a passive submillimeter-wave sensor with channels ranging from 183 GHz to 664 GHz. The article describes the CloudIce mission proposal, with particular emphasis on describing the algorithms for the data-analysis of submillimeter-wave cloud ice data (retrieval algorithms) and demonstrating their maturity. It is shown that we have a robust understanding of the radiative properties of cloud ice in the millimeter/submillimeter spectral range, and that we have a proven toolbox of retrieval algorithms to work with these data. Although the mission was not selected for EE8, the concept will be useful as a reference for other future mission proposals.
We describe the lookup table approach that is used to store pre-calculated absorption data in the radiative transfer model ARTS. The table stores absorption cross sections as a function of frequency, pressure, temperature, and the water vapor volume mixing ratio, where the last dimension is only included for those gas species that require it. The table is used together with an extraction strategy, which uses polynomial interpolation, with recommended interpolation orders between five and seven. We also derived recommended default settings for grid spacings and interpolation orders, and verified that the approach gives very accurate results with these default settings. The tested instrument setups were for AMSU-B, HIRS, and Odin, three well-known satellite remote sensing instruments covering a wide range of frequencies and viewing geometries. Errors introduced by the lookup table were found to be always below a few millikelvin, in terms of the simulated brightness temperature.
A passive satellite radiometer operating at submillimetre wavelengths can measure cloud ice water path (IWP), ice particle size, and cloud altitude. The paper first discusses the scientific background for such measurements. Formal scientific mission requirements are derived, based on this background and earlier assessments. The paper then presents a comprehensive prototype instrument and mission concept, and demonstrates that it meets the requirements. The instrument is a conically scanning 12-channel radiometer with channels between 183 and 664 GHz, proposed to fly in tandem with one of the Metop satellites. It can measure IWP with a relative accuracy of approximately 20% and a detection threshold of approximately 2 g m-2. The median mass equivalent sphere diameter of the ice particles can be measured with an accuracy of approximately 30 µm, and the median IWP cloud altitude can be measured with an accuracy of approximately 300 m. All the above accuracies are median absolute error values; root mean square error values are approximately twice as high, due to rare outliers.
A brightness temperature (BT) transformation method can be applied to microwave data to retrieve Jacobian weighted upper tropospheric relative humidity (UTH) in a broad layer centered roughly between 6 and 8 km altitude. The UTH bias is below 4% RH, and the relative UTH bias below 20%. The UTH standard deviation is between 2 and 6.5% RH in absolute numbers, or between 10 and 27% in relative numbers. The standard deviation is dominated by the regression noise, resulting from vertical structure not accounted for by the simple transformation relation. The UTH standard deviation due to radiometric noise alone has a relative standard deviation of approximately 7% for a radiometric noise level of 1 K. The retrieval performance was shown to be of almost constant quality for all viewing angles and latitudes, except for problems at high latitudes due to surface effects. A validation of AMSU UTH against radiosonde UTH shows reasonable agreement if known systematic differences between AMSU and radiosonde are taken into account. When the method is applied to supersaturation studies, regression noise and radiometric noise could lead to an apparent supersaturation even if there were no supersaturation. For a radiometer noise level of 1 K the drop-off slope of the apparent supersaturation is 0.17% RH−1, for a noise level of 2 K the slope is 0.12% RH−1. The main conclusion from this study is that the BT transformation method is very well suited for microwave data. Its particular strength is in climatological applications where the simplicity and the a priori independence are key advantages.
A simple method of averaging measurements for different scan positions was used to quantify scan asymmetries in AMSU-B brightness temperatures for the sensors on the satellites NOAA 15, 16, and 17. The method works particularly well for the sounding channels 18 to 20. The asymmetries are small in most cases. In particular, asymmetries for Channel 18 are below 1.90, −0.53, and 0.49 K for NOAA 15, 16, and 17, respectively. On the other hand, it was found that the instrument on NOAA 15 has significant asymmetries for Channels 19 and 20, which seem to be related to the known radio frequency interference problem for this instrument. The use of the appropriate set of interference correction coefficients significantly reduces the asymmetry.
This article documents a case study comparing radiosonde humidity data to Advanced Microwave Sounding Unit (AMSU) satellite humidity data. The study had two goals: first, to develop a robust method for such a comparison, and second, to check the quality and mutual consistency of radiosonde data, radiative transfer model, and AMSU data. The radiosonde data used are Vaisala RS80 data from the station Lindenberg of the German Weather Service (DWD), which have been subject to several corrections compared to the standard data processing. The radiative transfer model is the Atmospheric Radiative Transfer Simulator ( ARTS), and the AMSU data are those of the satellites NOAA 15 and 16 for the time periods 2001 and 2002. The comparison was done in radiance space, using a radiative transfer model to simulate AMSU radiances from the radiosonde data. The overall agreement is very good, with radiance biases below 1.5 K and standard deviations below 2 K. The main source of "noise'' in the comparison is atmospheric inhomogeneity on the 10-km scale. While the radiosonde correction performed at Lindenberg significantly reduces the bias between simulated and measured AMSU radiance, there still remains a slope in the radiance difference. Possible reasons for this were investigated. Most likely, the radiosondes underestimate the relative humidity under extremely dry conditions, showing 0 % RH when the true value is 2 - 4 % RH.
The paper presents a cloud filtering method for upper tropospheric humidity (UTH) measurements at 183.31±1.00 GHz. The method uses two criteria: a viewing angle dependent threshold on the brightness temperature at 183.31±1.00 GHz, and a threshold on the brightness temperature difference between another channel and 183.31±1.00 GHz. Two different alternatives, using 183.31±3.00 GHz or 183.31±7.00 GHz as the other channel, are studied. The robustness of this cloud filtering method is demonstrated by a mid-latitudes winter case study. The paper then studies different biases on UTH climatologies. Clouds are associated with high humidity, therefore the possible dry bias introduced by cloud filtering is discussed and compared to the wet biases introduced by the clouds radiative effect if no filtering is done. This is done by means of a case study, and by means of a stochastic cloud database with representative statistics for midlatitude conditions. Both studied filter alternatives perform nearly equally well, but the alternative using 183.31±3.00 GHz as other channel is preferable, because that channel is less likely to see the Earth's surface than the one at 183.31±7.00 GHz. The consistent result of all case studies and for both filter alternatives is that both cloud wet bias and cloud filtering dry bias are modest for microwave data. The recommended strategy is to use the cloud filtered data as an estimate for the true all-sky UTH value, but retain the unfiltered data to have an estimate of the cloud induced uncertainty. The focus of the paper is on midlatitude data, since atmospheric data to test the filter for that case were readily available. The filter is expected to be applicable also to subtropical and tropical data, but should be further validated with case studies similar to the one presented here for those cases.
Upper tropospheric humidity (UTH) and cloud ice (measured as ice water content IWC or vertically integrated ice water path IWP) are parameters of the climate system on which current global climate models do not agree well. This is illustrated by intercomparing the models in the IPCC AR4 archive. It is then discussed, to what extent different satellite measurements agree on these parameters. The focus is on passive observations from different infrared (HIRS, IASI) and microwave (AMSU-B, HSB) sensors.
The simulated retrieval performance of a submillimeter wave limb sounder was compared with that of an up-looking instrument with identical observation frequency bands and comparable noise temperature. The frequency bands were 624.32-626.32 and 649.12-650.32 GHz, and the retrieval simulations focused on the key trace gas species O-3, HCl, and ClO. As expected, the limb geometry leads to a better altitude resolution and larger measurement altitude range. The same retrieval setup was applied to measured spectra, taken by the up-looking Airborne Submillimeter Radiometer ( ASUR) instrument on 4 September 2002 at 19.11 degrees E, 71.90 degrees N and on 19 September 2002 at 44.10 degrees E, 4.10 degrees S. The observed structures in the fit residual near the HCl spectral lines at 625.9 GHz lead to the conclusion that the pressure shift parameter of HCl is likely to be higher than the value in the HITRAN spectroscopic database. Depending on the assumed temperature dependence of the shift, the HCl pressure shift value consistent with the ASUR data is 0.090-0.117 MHz/hPa instead of the 0.030 MHz/hPa reported in HITRAN. This result is in good agreement with very recent independent laboratory work which suggests a value of 0.110 MHz/hPa for the shift.
High frequency resolution radiative transfer model calculations with the Atmospheric Radiative Transfer Simulator (ARTS) were used to simulate the clear-sky outgoing longwave radiative flux (OLR) at the top of the atmosphere. Compared to earlier calculations by Clough and coworkers the model used a spherical atmosphere instead of a plane parallel atmosphere, updated spectroscopic parameters from HITRAN, and updated continuum parameterizations from Mlawer and coworkers. These modifications lead to a reduction in simulated OLR by approximately 4.1%, the largest part, approximately 2.5%, being due to the absence of the plane parallel approximation. As a simple application of the new model, the sensitivity of OLR to changes in humidity, carbon dioxide concentration, and temperature were investigated for different cloud-free atmospheric scenarios. It was found that for the tropical scenario a 20% change in humidity has a larger impact than a doubling of the carbon dioxide concentration. The sensitive altitude region for temperature and humidity changes is the entire free troposphere, including the upper troposphere where humidity data quality is poor.
We compare measurements of integrated water vapour (IWV) over a subarctic site (Kiruna, Northern Sweden) from five different sensors and retrieval methods: Radiosondes, Global Positioning System (GPS), ground-based Fourier-transform infrared (FTIR) spectrometer, ground-based microwave radiometer, and satellite-based microwave radiometer (AMSU-B). Additionally, we compare also to ERA-Interim model reanalysis data. GPS-based IWV data have the highest temporal coverage and resolution and are chosen as reference data set. All datasets agree reasonably well, but the ground-based microwave instrument only if the data are cloud-filtered. We also address two issues that are general for such intercomparison studies, the impact of different lower altitude limits for the IWV integration, and the impact of representativeness error. We develop methods for correcting for the former, and estimating the random error contribution of the latter. A literature survey reveals that reported systematic differences between different techniques are study-dependent and show no overall consistent pattern. Further improving the absolute accuracy of IWV measurements and providing climate-quality time series therefore remain challenging problems.
Sub-millimetre remote sensing instruments can provide critical information on cirrus clouds and an alternative way of measuring precipitation with a much smaller antenna than those which microwave sensors currently use. Two satellite concepts CIWSIR and GOMAS were proposed as ESA Earth Explorer missions; these were not funded, however they were recommended for an aircraft demonstrator. ESA studies have been performed to identify the optimum instrument and platform to demonstrate these satellite concepts. This paper reports on one of these preparatory activities; the design of a sub-millimetre wave airborne demonstrator for both ice cloud and precipitation observations which will be able to prove the feasibility of the scientific principles of both satellite missions. The paper will describe the derivation of the demonstrator requirements, consideration of the available platform and instrument options, the design of the selected concept, performance prediction and the outline of a proof of concept flight campaign. It will present the outcome of the study which describes a demonstrator design based upon the new Met Office International Sub-Millimetre Airborne Radiometer (ISMAR).
Global observations of ice clouds are needed to improve our understanding of their impact on earth's radiation balance and the water-cycle. Passive mm/sub-mm has some advantages compared to other space-borne cloud-ice remote sensing techniques. The physics of scattering makes forward radiative transfer modelling for such instruments challenging. This paper demonstrates the ability of a recently developed RT code, ARTS-MC, to accurately simulate observations of this type for a variety of viewing geometries corresponding to operational (AMSU-B, EOS-MLS) and proposed (CIWSIR) instruments. ARTS-MC employs an adjoint Monte-Carlo method, makes proper account of polarisation, and uses 3-D spherical geometry. The actual field of view characteristics for each instrument are also accounted for. A 3-D midlatitude cirrus scenario is used, which is derived from Chilbolton cloud radar data and a stochastic method for generating 3-D ice water content fields. These demonstration simulations clearly demonstrate the beamfilling effect, significant polarisation effects for non-spherical particles, and also a beamfilling effect with regard to polarisation.
The climate models used in the IPCC AR4 show large differences in monthly mean cloud ice. The most valuable source of information that can be used to potentially constrain the models is global satellite data. For this, the data sets must be long enough to capture the inter-annual variability of Ice Water Path (IWP). PATMOS-x was used together with ISCCP for the annual cycle evaluation in Fig. 7 while ECHAM-5 was used for the correlation with other models in Table 3. A clear distinction between ice categories in satellite retrievals, as desired from a model point of view, is currently impossible. However, long-term satellite data sets may still be used to indicate the climatology of IWP spatial distribution. We evaluated satellite data sets from CloudSat, PATMOS-x, ISCCP, MODIS and MSPPS in terms of monthly mean IWP, to determine which data sets can be used to evaluate the climate models. IWP data from CloudSat cloud profiling radar provides the most advanced data set on clouds. As CloudSat data are too short to evaluate the model data directly, it was mainly used here to evaluate IWP from the other satellite data sets. ISCCP and MSPPS were shown to have comparatively low IWP values. ISCCP shows particularly low values in the tropics, while MSPPS has particularly low values outside the tropics. MODIS and PATMOS-x were in closest agreement with CloudSat in terms of magnitude and spatial distribution, with MODIS being the best of the two. As PATMOS-x extends over more than 25 years and is in fairly close agreement with CloudSat, it was chosen as the reference data set for the model evaluation. In general there are large discrepancies between the individual climate models, and all of the models show problems in reproducing the observed spatial distribution of cloud-ice. Comparisons consistently showed that ECHAM-5 is the GCM from IPCC AR4 closest to satellite observations
The climate models used in the IPCC AR4 show large differences in monthly mean ice water path (IWP). The most valuable source of information that can be used to potentially constrain the models is global satellite data. The satellite datasets also have large differences. The retrieved IWP depends on the technique used, as retrievals based on different techniques are sensitive to different parts of the cloud column. Building on the foundation of Waliser et al. (2009), this article provides a more comprehensive comparison between satellite datasets. IWP data from the CloudSat cloud profiling radar provide the most advanced dataset on clouds. For all its unmistakable value, CloudSat data are too short and too sparse to assess climatic distributions of IWP, hence the need to also use longer datasets. We evaluate satellite datasets from CloudSat, PATMOS-x, ISCCP, MODIS and MSPPS in terms of monthly mean IWP, in order to determine the differences and relate them to the sensitivity of the instrument used in the retrievals. This information is also used to evaluate the climate models, to the extent that is possible. ISCCP and MSPPS were shown to have comparatively low IWP values. ISCCP shows particularly low values in the tropics, while MSPPS has particularly low values outside the tropics. MODIS and PATMOS-x were in closest agreement with CloudSat in terms of magnitude and spatial distribution, with MODIS being the better of the two. Additionally PATMOS-x and ISCCP, which have a temporal range long enough to capture the inter-annual variability of IWP, are used in conjunction with CloudSat IWP (after removing profiles that contain precipitation) to assess the IWP variability and mean of the climate models. In general there are large discrepancies between the individual climate models, and all of the models show problems in reproducing the observed spatial distribution of cloud-ice. Comparisons consistently showed that ECHAM-5 is probably the GCM from IPCC AR4 closest to satellite observations
There remains large disagreement between IWP in observational datasets, largely because the sensors observe different parts of the ice particle size distribution. A detailed comparison of retrieved IWP from satellite observations in the Tropics ({plus minus}30{degree sign} latitude) in 2007 is made using collocated measurements. The DARDAR IWP dataset, based on combined Radar/Lidar measurements, is used as a reference as it provides arguably the best estimate of the total column IWP. For each dataset, usable IWP dynamic ranges are inferred from this comparison. IWP retrievals based on solar reflectance measurements, MODIS, and AVHRR-based CMSAF, and PATMOS-x, were found to be correlated with DARDAR over a large IWP range (~20-7000 g/m-2;). The random errors of the collocated datasets have a close to log-normal distribution, and the combined random error of MODIS and DARDAR is less than a factor of 2, which also sets the upper limit for MODIS alone. In the same way the upper limit for the random error of all considered datasets is determined. Datasets based on passive microwave measurements,MSPPS, MiRS, and CMO, are largely correlated with DARDAR for IWP values larger than approximately 700 g/m². The combined uncertainty between these datasets and DARDAR in this range is slightly less MODIS-DARDAR, but the systematic bias is nearly an order of magnitude.
This article describes one of the scattering algorithms of the three-dimensional polarized radiative transfer model ARTS (Atmospheric Radiative Transfer Simulator) which has been implemented to study for example the influence of cirrus clouds on microwave limb sounding. The model uses the DOIT (Discrete Ordinate Iterative) method to solve the vector radiative transfer equation. The implementation of a discrete ordinate method is challenging due to the spherical geometry of the model atmosphere which is required for the simulation of limb radiances. The involved numerical issues, which are grid optimization and interpolation methods, are discussed in this paper. Scattering simulations are presented for limb- and down-looking geometries, for one-dimensional and three-dimensional spherical atmospheres. They show the impact of cloud particle size, shape, and orientation on the brightness temperatures and on the polarization of microwave radiation in the atmosphere. The cloud effect is much larger for limb radiances than for nadir radiances. Particle size is a very important parameter in all simulations. The polarization signal is negligible for simulations with completely randomly oriented particles, whereas for horizontally aligned particles with random azimuthal orientation the polarization signal is significant. Moreover, the effect of particle shape is only relevant for oriented cloud particles. The simulations show that it is essential to use a three-dimensional scattering model for inhomogeneous cloud layers.
This study presents and analyses the first simulations of microwave limb radiances with clouds. They are computed using the 1D unpolarized version of the Atmospheric Radiative Transfer System (ARTS). The study is meant to set a theoretical foundation for using microwave limb measurements for cloud monitoring. Information about clouds is required for the validation of climate models.Limb spectra are generated for the frequency bands of the Millimeter wave Acquisitions for Stratosphere/Troposphere Exchange Research (MASTER) instrument. For these simulations, the radiative transfer equation is solved using the Discrete Ordinate ITerative (DOIT) method, which is briefly described. Single scattering properties for the cloud particles are calculated using the T-matrix method.The impact of various cloud parameters is investigated. Simulated brightness temperatures most strongly depend on particle size, ice mass content and cloud altitude. The impact of particle shape is much smaller, but still significant. Increasing the ice mass content has a similar effect as increasing the particle size; this complicates the prediction of the impact of clouds on microwave radiances without exact knowledge of these cloud parameters.
The second version of the atmospheric radiative transfer simulator, ARTS, is introduced. This is a general software package for long wavelength radiative transfer simulations, with a focus on passive microwave observations. The core part provides a workspace environment, in line with script languages. New for this version is an agenda mechanism that gives a high degree of modularity. The framework is intended to be as general as possible: the polarisation state can be fully described, the model atmosphere can be one- (1D), two- (2D) or three-dimensional (3D), a full description of geoid and surface is possible, observation geometries from the ground, from satellite, and from aeroplane or balloon are handled, and surface reflection can be treated in simple or complex manners. Remote sensing applications are supported by a comprehensive and efficient treatment of sensor characteristics. Jacobians can be calculated for the most important atmospheric variables in non-scattering conditions. Finally, the most prominent feature is the rigorous treatment of scattering that has been implemented in two modules: a discrete ordinate iterative approach mainly used for 1D atmospheres, and a Monte Carlo approach which is the preferred algorithm for 3D atmospheres. ARTS is freely available, and maintained as an open-source project.
The polarization, frequency and spatial responses of the sensor can be considered by calculating the Stokes vector of monochromatic pencil beam radiances for a set of frequencies and viewing directions, and weight these radiances with the instrument responses. This paper presents a highly efficient solution for this calculation procedure. The basic idea is to pre-calculate a matrix that represents the mapping from polarisation, frequency and spatial values to measured data. Sensor impacts can then be included by a simple matrix multiplication. The full sensor matrix can be obtained by determining the response matrix for the sensor parts individually. Data reduction methods can also be incorporated. A simple method for optimizing the calculation grids is further presented. The described approach for sensor modeling has been implemented in two public available softwares for atmospheric radiative transfer simulations.