Numerical Weather Prediction Model Analyses Research Articles

Assessment for operational assimilation of horizontal line of sight winds from the European Space Agency's Aeolus at the Met Office

AbstractThe European Space Agency's Aeolus satellite was launched in August 2018 and began delivering horizontal line‐of‐sight (HLOS) wind observations in early September 2018. In early 2019, the Met Office began assessing the suitability of the HLOS winds for operational assimilation into its global numerical weather prediction system. We performed a number of assimilation experiments to assess the impact of HLOS wind observations on our global forecasts. We have found that assimilating HLOS winds changes the zonal winds in the analysis fields predominantly in the Tropics and Southern Hemisphere, with the largest changes being in the upper troposphere and lower stratosphere. This has a positive impact on the accuracy of the global weather forecasts, with improvements in the root‐mean‐square error seen throughout the troposphere. Assimilation of Aeolus HLOS winds improves the standard deviation of the observation minus background (a 6 hr forecast) of almost all other observation types, suggesting that the numerical weather prediction model analysis is improved, which consequently improves the 6 hr forecast. In a set of short‐period observation denial experiments, we found that assimilating Aeolus has an impact similar in magnitude to assimilating surface winds from scatterometers. Assimilating winds from the Rayleigh channel has approximately three times the impact that assimilating HLOS winds from the Mie channel does. Both channels contribute a measureable improvement to the global forecast, and we therefore started operational assimilation of winds from the Mie channel in December 2020 and the Rayleigh channel operationally in May 2022.

A New Progressive EOFs Quality Control Method for Surface Pressure Data Based on the Barometric Height and Biweight Average Correction

When assimilating surface pressure data in synoptic-scale models, we find the utilization rate of surface pressure data in zones with complex terrains is not high. Therefore, it is particularly important and urgent to carry out quality control of surface pressure data. Numerical weather prediction model analysis and forecasting provide essential data that can be compared with surface observations. The main adverse effects on surface pressure quality control include elevation differences between the model terrain and the observation stations and continuous outliers with the same characteristics in the initiation stage of quality control. Therefore, we propose a progressive empirical orthogonal function (EOF) with simulated observation (EOFs) combining barometric height correction (BHC) and biweight average correction (BAC) methods for the quality control of surface pressure data in this study. From the quality control results of the surface pressure data in regions with complex topography in China during June–August 2013, it was found that the BHC method could effectively reduce the deviations caused by elevation differences between the model terrain and the observation stations, and the BAC method could obviously reduce systematic deviations due to physical processes and the parameterization schemes of the models. The BHC-BAC method integrated the advantages of both methods and had the best correction effect. When continuous outliers with the same characteristics occurred in the initiation stage of quality control, the progressive EOF method might unreasonably eliminate observations. However, the progressive EOFs method could effectively solve this problem and had better performance in data quality control. The progressive EOFs quality control method with the combined BHC-BAC method could obviously reject outliers. The observation increment (deviations between observations and background field) after quality control by the progressive EOFs method was the closest to normal distribution, satisfying the Gaussian distribution assumption of data assimilation.

Retrieval of cloud-cleared radiances using numerical weather prediction model-analysis and forecast fields for INSAT-3D sounder longwave window channel observations

Estimation of cloud-cleared radiances, without utilizing any supplementary sensors for INSAT-3D Sounder InfraRed window observations is presented here. This method utilizes simulated clear sky radiances by radiative transfer for TOVS (RTTOV) tool, with the help of global numerical weather prediction (NWP) models analysis and forecast fields, unlike the previously reported approaches where observations from auxiliary/collocated sensors are used. The validation of these derived cloud-cleared radiances is carried out with the already existing operational Level 2 (L2) cloud-cleared radiances of Atmospheric Infra-Red Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI), where the retrieval is carried out utilizing auxiliary Microwave Sounders. Also, a comparative study with the optimal cloud-clearing technique has been carried out here, where the retrieval is done by utilizing the clear sky radiances by means of collocated high-resolution InfraRed observations with the help of auxiliary sensors. The results indicate the scope and promising potential of this technique towards the future operational endeavor of deriving the cloud-cleared radiances without any assistance of co-existing auxiliary hardware system.

Improvement of numerical weather prediction model analysis during fog conditions through the assimilation of ground-based microwave radiometer observations: a 1D-Var study

Abstract. This paper investigates the potential benefit of ground-based microwave radiometers (MWRs) to improve the initial state (analysis) of current numerical weather prediction (NWP) systems during fog conditions. To this end, temperature, humidity and liquid water path (LWP) retrievals have been performed by directly assimilating brightness temperatures using a one-dimensional variational technique (1D-Var). This study focuses on a fog-dedicated field-experiment performed over winter 2016–2017 in France. In situ measurements from a 120 m tower and radiosoundings are used to assess the improvement brought by the 1D-Var analysis to the background. A sensitivity study demonstrates the importance of the cross-correlations between temperature and specific humidity in the background-error-covariance matrix as well as the bias correction applied on MWR raw measurements. With the optimal 1D-Var configuration, root-mean-square errors smaller than 1.5 K (respectively 0.8 K) for temperature and 1 g kg−1 (respectively 0.5 g kg−1) for humidity are obtained up to 6 km altitude (respectively within the fog layer up to 250 m). A thin radiative fog case study has shown that the assimilation of MWR observations was able to correct large temperature errors of the AROME (Application of Research to Operations at MEsoscale) model as well as vertical and temporal errors observed in the fog life cycle. A statistical evaluation through the whole period has demonstrated that the largest impact when assimilating MWR observations is obtained on the temperature and LWP fields, while it is neutral to slightly positive for the specific humidity. Most of the temperature improvement is observed during false alarms when the AROME forecasts tend to significantly overestimate the temperature cooling. During missed fog profiles, 1D-Var analyses were found to increase the atmospheric stability within the first 100 m above the surface compared to the initial background profile. Concerning the LWP, the RMSE with respect to MWR statistical regressions is decreased from 101 g m−2 in the background to 27 g m−2 in the 1D-Var analysis. These encouraging results led to the deployment of eight MWRs during the international SOFOG3D (SOuth FOGs 3D experiment for fog processes study) experiment conducted by Météo-France.

Bayesian Selection of Atmospheric Profiles from an Ensemble Data Assimilation System using Infrasonic Observations of May 2016 Mount Etna Eruptions

AbstractObserved infrasound arrivals are considered as a way to provide additional knowledge of the middle atmosphere for numerical weather prediction (NWP) models. To do so, a Bayesian approach, based on the discrepancies between predicted and observed infrasound arrival characteristics, is adopted for selecting the most likely atmospheric states, in terms of temperature and wind velocity profiles, among an ensemble of NWP model analyses. The predicted characteristics, in terms of trace velocity and the back azimuth angle, are computed using a three‐dimensional ray‐tracing model and atmospheric profiles extracted once a day from ensemble of analyses produced by the Météo‐France global NWP model. The performance of the method is demonstrated using a set of thousands of volcanic eruptions that correspond to an upsurge in volcanic activity of Mount Etna (Sicily) during May 2016. It is shown that the Bayesian approach allows the identification of the most likely members and hence provides additional knowledge on the atmosphere truly probed by the infrasound. When the atmospheric temperature and wind profiles are reasonably meeting the effective sound speed assumption requirements, the members retrieved are similar in terms of effective sound speed but not in terms of wind and temperature. For improving both temperature and wind velocity profile selection, additional infrasound arrival characteristics are then required (e.g., travel time, amplitude, etc.). The confidence on the retrieved profiles is also sensitive to the time of validity of the meteorological analyses and on the definition of the surface conditions involved in the estimation of the predicted trace velocities.

Impact of GNSS Signal Delay Assimilation on Short Range Weather Forecasts Over the Indian Region

AbstractRadio signals transmitted from a satellite constellation and gathered by a ground‐based Global Navigation Satellite System (GNSS) receiver allow computation of zenith tropospheric delay (ZTD), which is related to the atmospheric moisture. This study investigates the impact of assimilating ZTD obtained from a ground‐based GNSS network on a numerical weather prediction model analyses and subsequent forecasts quality. The numerical data assimilation experiments are performed using three‐dimensional variational data assimilation system in the Weather Research and Forecasting model, at 10‐km horizontal grid spacing for the entire month of July 2017. A comparison with European Centre for Medium‐Range Weather Forecast analyses shows a clear positive impact of ZTD assimilation on the lower to middle tropospheric moisture, upper air temperature, and middle and upper tropospheric wind; errors are reduced by as large as 4%, when compared to the model run without ZTD assimilation. The impact on the analyses and forecast quality of surface meteorological variables is mostly neutral with some indication of positive impact on surface pressure. An improvement in the rainfall forecasts is also noticed when model assimilates ZTD observations. In addition, the impact of the formulation of forward model, which calculates model equivalent of the GNSS ZTD, has been assessed on ZTD assimilation. A revised forward model has been implemented within the Weather Research and Forecasting assimilation system. The revised forward model outperforms the original model for ZTD assimilation. Overall result implies that GNSS ZTD data has a good potential for improving the weather prediction and advocates the strengthening of the ground‐based GNSS network over the Indian region, which is currently very sparse.

Assessment of a new quality control technique in the retrieval of atmospheric motion vectors

Atmospheric motion vectors (AMVs) are generally derived using three successive images of a geosyn- chronous satellite in most of the operational centres in world. This study presents a new quality control technique used during the retrieval of AMVs from the images of infrared and water vapour channels of the Indian geostationary satellite Kalpana-1. The new technique proposed here is based on wind retrieval using the previous eight images. The AMVs retrieved using a new algorithm and the old algorithm that uses the automatic quality control procedure of European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), are compared with collocated radiosonde data and numerical weather prediction model analysis. The new algorithm shows an improvement over the old one at all levels both in terms of root mean square vector difference and spatial pattern. It has also produced significantly larger numbers of valid retrievals.

The impact of cloud‐affected IR radiances on forecast accuracy of a limited‐area NWP model

AbstractThe impact of cloud‐affected satellite radiances on numerical weather prediction (NWP) accuracy is investigated. The NWP model used is the HIgh Resolution Limited Area Model (HIRLAM). Its four‐dimensional variational data assimilation (4D‐Var) system was used to assimilate cloud‐affected infrared (IR) radiances from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI). Cloud parameters are modelled internally in the observation operator and used in the radiative transfer calculations. The interaction between the cloud parameters and the model control vector variables is incorporated in the adjoint version of the observation operator, which is used to derive cloud‐affected Jacobians prior to the inner‐loop minimization of the cost function.The developed framework supports an extensive usage of satellite observations with spatial coverage extended into cloudy regions, which therefore provides additional analysis increments and supports a more accurate description of the atmospheric state. In extended assimilation and forecast experiments the total number of assimilated satellite observations could be increased by approximately 10%. This was associated with a clear indication of a positive impact of cloud‐affected radiances on the moisture and geopotential height fields of the NWP model analysis and forecast accuracy when used on top of clear‐sky radiance observations. This is revealed by reduced analysis errors of the total integrated water vapour and by reduced forecast errors in the mid and upper troposphere.

Cross-Validation of Scatterometer Measurements via Sea-Level Pressure Retrieval

A combined analysis of ocean surface wind vector measurements by the European Advanced Scatterometer (ASCAT) and the National Aeronautics and Space Administration QuikSCAT (QS) scatterometer using buoy measurements, numerical weather prediction model analyses, and spectral decomposition reveals significant statistical differences between the two data sets. While QS wind speeds agree better with buoy wind speeds than ASCAT above 15 m s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> , ASCAT wind directions agree better with buoy directions overall than QS. In contrast, it is shown that sea-level pressure (SLP) fields derived from ASCAT and QS measurements compare better with each other than the winds in a statistical sense, even though ASCAT bulk pressure gradients (BPGs) are slightly weaker than buoy pressure gradients and have slightly lower spectral energy than QS. Weaker BPGs in ASCAT are consistent with the low bias in ASCAT wind speeds. Thus, it is proposed that scatterometer-derived SLP fields can be used as a filter to improve the wind directions. This improves the QS wind directions but has less effect on the more accurate ASCAT wind directions. The unfiltered ASCAT wind vector statistics compare well with the statistics of the direction-filtered QS winds. It is suggested that this methodology might provide a basis for minimizing the discrepancies between various satellite wind measurement data sets in view of producing a long-term record of satellite-derived SLP fields and ocean surface wind vectors.

A vortex isolation and removal algorithm for numerical weather prediction model tropical cyclone applications

[1] Inserting an externally defined (i.e., synthetic) tropical cyclone (TC) vortex into numerical weather prediction (NWP) model analyses requires that an existing TC vortex first be removed. Similarly, statistical-dynamical forecasting methods require that the larger-scale environmental attributes of the flow be separated (and preserved) from those on the smaller meso- and TC vortex scales. The existing operational methods to accomplish such tasks are optimized particularly for the respective models grid spacing resolution and thus are not general when applied to finer resolution analyses. Further, the existing methods often adhere to rigid assumptions regarding the size and structure of the TC. A methodology is provided in this study to overcome these limitations. This is accomplished through analyzing the features of the NWP model analysis (e.g., the variables in the vicinity of the TC) and then systematically removing the TC through the application of both a smoothing operator and a subsequent statistical evaluation of the smoothed analysis variable. The value of our methodology is determined when analyzing the results from experiments initialized from an analysis containing TCs and those initialized from analyses without the respective TCs. This methodology is also robust for it does not require a tuning of parameters relative to varying grid-spacing resolutions and may thus benefit the statistical-dynamical TC intensity prediction schemes.

A method for including mesoscale and synoptic‐scale information in scatterometer wind retrievals

A method for improving scatterometer wind retrievals based on the mesoscale and synoptic‐scale structure of the flow field is presented and evaluated. The large‐scale structure of the flow is inferred from the sea‐level pressure (SLP) field derived from the scatterometer winds using a planetary boundary layer model. The wind vectors derived from this SLP field can be used to either (1) inform the ambiguity selection, (2) correct the direction of the scatterometer wind vectors, in all cases or above a certain threshold, or (3) replace the scatterometer winds. The methodology is demonstrated with QuikSCAT (QS) scatterometer wind vectors. The new wind vector set is evaluated statistically by comparison with buoy measurements, with numerical weather prediction model analyses, and by spectral analysis. It is found that the new wind vectors are particularly valuable at nadir and in rain‐contaminated areas, and that their spectral behavior is closer to a power law than are the uncorrected QS winds.

Evaluation of Arctic cloud products from the EUMETSAT Climate Monitoring Satellite Application Facility based on CALIPSO-CALIOP observations

Abstract. The performance of the three cloud products cloud fractional cover, cloud type and cloud top height, derived from NOAA AVHRR data and produced by the EUMETSAT Climate Monitoring Satellite Application Facility, has been evaluated in detail over the Arctic region for four months in 2007 using CALIPSO-CALIOP observations. The evaluation was based on 142 selected NOAA/Metop overpasses allowing almost 400 000 individual matchups between AVHRR pixels and CALIOP measurements distributed approximately equally over the studied months (June, July, August and December 2007). Results suggest that estimations of cloud amounts are very accurate during the polar summer season while a substantial loss of detected clouds occurs in the polar winter. Evaluation results for cloud type and cloud top products point at specific problems related to the existence of near isothermal conditions in the lower troposphere in the polar summer and the use of reference vertical temperature profiles from Numerical Weather Prediction model analyses. The latter are currently not detailed enough in describing true conditions relevant on the pixel scale. This concerns especially the description of near-surface temperature inversions which are often too weak leading to large errors in interpreted cloud top heights.

West African Monsoon observed with ground‐based GPS receivers during African Monsoon Multidisciplinary Analysis (AMMA)

A ground‐based GPS network has been established over West Africa in the framework of African Monsoon Multidisciplinary Analysis (AMMA) in tight cooperation between French and African institutes. The experimental setup is described and preliminary highlights are given for different applications using these data. Precipitable water vapor (PWV) estimates from GPS are used for evaluating numerical weather prediction (NWP) models and radiosonde humidity data. Systematic tendency errors in model forecasts are evidenced. Correlated biases in NWP model analyses and radiosonde data are evidenced also, which emphasize the importance of radiosonde humidity data in this region. PWV and precipitation are tightly correlated at seasonal and intraseasonal timescales. Almost no precipitation occurs when PWV is smaller than 30 kg m−2. This limit in PWV also coincides well with the location of the intertropical discontinuity. Five distinct phases in the monsoon season are determined from the GPS PWV, which correspond either to transition or stationary periods of the West African Monsoon system. They may serve as a basis for characterizing interannual variability. Significant oscillations in PWV are observed with 10‐ to 15‐day and 15‐ to 20‐day periods, which suggest a strong impact of atmospheric circulation on moisture and precipitation. The presence of a diurnal cycle oscillation in PWV with marked seasonal evolutions is found. This oscillation involves namely different phasing of moisture fluxes in different layers implying the low‐level jet, the return flow, and the African Easterly Jet. The broad range of timescales observed with the GPS systems shows a high potential for investigating many atmospheric processes of the West African Monsoon.

Diagnosis of an Intense Atmospheric River Impacting the Pacific Northwest: Storm Summary and Offshore Vertical Structure Observed with COSMIC Satellite Retrievals

Abstract This study uses the new satellite-based Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission to retrieve tropospheric profiles of temperature and moisture over the data-sparse eastern Pacific Ocean. The COSMIC retrievals, which employ a global positioning system radio occultation technique combined with “first-guess” information from numerical weather prediction model analyses, are evaluated through the diagnosis of an intense atmospheric river (AR; i.e., a narrow plume of strong water vapor flux) that devastated the Pacific Northwest with flooding rains in early November 2006. A detailed analysis of this AR is presented first using conventional datasets and highlights the fact that ARs are critical contributors to West Coast extreme precipitation and flooding events. Then, the COSMIC evaluation is provided. Offshore composite COSMIC soundings north of, within, and south of this AR exhibited vertical structures that are meteorologically consistent with satellite imagery and global reanalysis fields of this case and with earlier composite dropsonde results from other landfalling ARs. Also, a curtain of 12 offshore COSMIC soundings through the AR yielded cross-sectional thermodynamic and moisture structures that were similarly consistent, including details comparable to earlier aircraft-based dropsonde analyses. The results show that the new COSMIC retrievals, which are global (currently yielding ∼2000 soundings per day), provide high-resolution vertical-profile information beyond that found in the numerical model first-guess fields and can help monitor key lower-tropospheric mesoscale phenomena in data-sparse regions. Hence, COSMIC will likely support a wide array of applications, from physical process studies to data assimilation, numerical weather prediction, and climate research.

International MODIS and AIRS processing package: AIRS products and applications

The high-spectral-resolution AIRS (Atmospheric InfraRed Sounder) instrument onboard the NASA (National Aeronautics and Space Administration) Earth Observing System (EOS)-Aqua satellite represents the most advanced sounding system in space and provides unprecedented wealth of highly accurate radiance measurements. This paper describes a standalone and fast single field-of-view (FOV) algorithm to retrieve atmospheric sounding profiles (temperature, humidity, ozone) and surface parameters (surface skin temperature, surface emissivity) from AIRS Level 1B (L1B) clear only infrared radiance measurements. The retrieval algorithm is part of the International MODIS (Moderate Resolution Imaging Spectroradiometer)/AIRS Processing Package (IMAPP) software package, which provides international users with the capability of receiving and processing direct broadcast data in real-time. The IMAPP AIRS retrieval algorithm is based on principal component regression to obtain fast and accurate estimates of the atmospheric state at single FOV. This algorithm is designed specifically for real-time direct broadcast applications where sounding products can be processed efficiently at highest possible spatial resolution. Simulated radiance data is trained on a global set of profiles, representative of a wide variety of atmospheric scenes, which makes the algorithm globally applicable. The results presented and discussed in this paper demonstrate that the IMAPP AIRS retrieval product is rigorously evaluated by various product sources such as numerical weather prediction model analysis fields, retrieved parameters from the operational AIRS L2 product and data from other instruments.

Relationship between synoptic forcing and polynya formation in the Cosmonaut Sea: 1. Polynya climatology

A polynya has been observed in satellite imagery to occur irregularly between the months of July and October in the Cosmonaut Sea. To investigate the processes of formation, maintenance, and decay of these polynyas, a 13‐year climatology of Antarctic atmospheric and surface conditions is assembled from numerical weather prediction model analyses and remotely sensed satellite observations. An analytical technique used previously to identify and track atmospheric low pressure systems is modified to identify polynyas from daily analyses of sea ice area. This information is used to prepare a set of monthly climatological conditions as well as their anomalies when a polynya is present. Previous studies have suggested a link between atmospheric forcing and polynya formation. Along with the drop in sea ice area, there is a drop in sea level pressure as well as an increase in atmospheric wind divergence when the Cosmonaut Sea polynya is present. In some months, the sea ice area decrease is preceded by the passage of an atmospheric low pressure system 1–2 days earlier, but due to the continuous presence of katabatic winds and the coarseness of the ECMWF data, there is not as definitive a relationship between wind divergence and sea level pressure or ice area. Errors associated with the processing of passive microwave remotely sensed data, as well as differences in spatial scale limit that can be determined about the ocean state from the data sets, suggest the need for improved surface classification techniques, as well as regional modeling studies.

The impact of mesoscale convective‐system potential‐vorticity anomalies on numerical‐weather‐prediction forecasts

AbstractThe effect of the potential vorticity (PV) anomalies formed by mesoscale convective systems (MCSs) on the downstream flow is investigated using a numerical weather prediction (NWP) model. Four case studies of North American MCSs from June and July 1999 are selected in which the MCS PV anomalies are not present in the model analysis. Idealized MCS PV anomalies are introduced into the model analyses and their impact on the NWP model forecast ascertained.In two of the four cases the presence of the MCS PV anomalies leads to significant perturbation in the forecast evolution. Sensitivity studies show that it is the presence of the mid‐level cyclonic vortex (MCV) that has the biggest impact on the NWP forecast. This positive PV anomaly is shown to cause areas of ascent and descent, as has been postulated following other studies. This can lead to the development of a growing mesoscale disturbance if the MCV becomes situated in a strong baroclinic gradient. These disturbances are shown to have direct and indirect impacts on the depths of synoptic‐scale depressions.The presence of the MCS PV anomalies leads to some reduction in the forecast errors. It is suggested that attempting to assimilate the MCS PV anomalies into the NWP model analysis may be of some benefit to forecast accuracy. However, uncertainties in the properties of the MCV may mean that a more practical application may be in perturbing the initial states of ensemble forecasts.

An improved fast radiative transfer model for assimilation of satellite radiance observations

To assimilate atmospheric and surface radiance measurements from satellites in a numerical weather prediction model, a fast radiative transfer model is required to compute radiances from the model first guess fields at every observation point. Such a model for satellite infrared and microwave radiance measurements is used operationally for the assimilation of TIROS operational vertical sounder radiances at the European Centre for Medium-Range Weather Forecasts. An improved version of this model has been developed which requires ozone, in addition to temperature and water vapour, in the input profile and it has been generalized to compute radiances for other satellite radiometers using the same code. Instruments such as the high resolution infrared radiation sounder and the advanced microwave sounding unit on the National Oceanic and Atmospheric Administration polar orbiters, the METEOSAT water vapour imager and the Geostationary Operational Environmental Satellite infrared sounder have been simulated. It is demonstrated, by comparisons with line-by-line model computed radiances, that the fast model can reproduce the line-by-line model radiances for the TIROS operational vertical sounder stratospheric temperature sounding channels to an accuracy below the instrumental noise. The tropospheric, surface sensing, water vapour and ozone channel radiances cannot be predicted to such an accuracy, but still accurately enough for numerical weather prediction assimilation. A comparison of measured TIROS operational vertical sounder radiances with predicted values from numerical weather prediction model analyses gives larger differences than would be expected from the combination of the fast model and instrument related errors for most channels. The validity of the tangent linear approximation of the model gradient for typical radiance departures is also explored, with several examples, for the high resolution infrared radiation sounder/advanced microwave sounding unit instrument combination. The tangent-linear approximation is valid for temperature but significant departures from linearity about the first guess profile are observed for water vapour and ozone. Cloud affected infrared radiances have a highly non-linear response.

An improved fast radiative transfer model for assimilation of satellite radiance observations

AbstractTo assimilate atmospheric and surface radiance measurements from satellites in a numerical weather prediction model, a fast radiative transfer model is required to compute radiances from the model first guess fields at every observation point. Such a model for satellite infrared and microwave radiance measurements is used operationally for the assimilation of TIROS operational vertical sounder radiances at the European Centre for Medium‐Range Weather Forecasts. An improved version of this model has been developed which requires ozone, in addition to temperature and water vapour, in the input profile and it has been generalized to compute radiances for other satellite radiometers using the same code. Instruments such as the high resolution infrared radiation sounder and the advanced microwave sounding unit on the National Oceanic and Atmospheric Administration polar orbiters, the METEOSAT water vapour imager and the Geostationary Operational Environmental Satellite infrared sounder have been simulated. It is demonstrated, by comparisons with line‐by‐line model computed radiances, that the fast model can reproduce the line‐by‐line model radiances for the TIROS operational vertical sounder stratospheric temperature sounding channels to an accuracy below the instrumental noise. The tropospheric, surface sensing, water vapour and ozone channel radiances cannot be predicted to such an accuracy, but still accurately enough for numerical weather prediction assimilation. A comparison of measured TIROS operational vertical sounder radiances with predicted values from numerical weather prediction model analyses gives larger differences than would be expected from the combination of the fast model and instrument related errors for most channels. The validity of the tangent linear approximation of the model gradient for typical radiance departures is also explored, with several examples, for the high resolution infrared radiation sounder/advanced microwave sounding unit instrument combination. The tangent‐linear approximation is valid for temperature but significant departures from linearity about the first guess profile are observed for water vapour and ozone. Cloud affected infrared radiances have a highly non‐linear response.

The information content of data from satellite sounding systems: A simulation study

The potential of the TIROS Operational Vertical Sounder (TOVS) and the Advanced TOYS (ATOVS) for providing information on the vertical profiles of atmospheric temperature and humidity is investigated. the theoretical performances of these systems are examined primarily in terms of their ability to contribute information to a numerical weather prediction (NWP) system. the covariance of retrieval error expected from an optimal nonlinear inversion scheme, which retrieves simultaneously all the atmospheric and surface variables which significantly affect the measured radiances, is calculated theoretically and compared with the covariance of error assumed in the prior information which constrains the inversion. Performance is assessed for different combinations of radiometers, corresponding to present and future operational systems, and for different sets of assumed conditions: in the atmospheric profile, the cloud conditions, the surface characteristics and the assumed prior knowledge of these variables. The potential performance of ATOVS is shown to be a considerable improvement on the present TOVS in many respects. the value of retaining a combined infrared and microwave system is confirmed. It is demonstrated that, although the ability of these sounding systems to improve on an NWP model's analyses of temperature and humidity is rather limited, the potential for improving on the analysis of layer quantities is much greater. Some new ideas are presented for extending current retrieval methods to accommodate the additional problems presented by ATOVS data. Methods are proposed for treating the effects of microwave surface emissivity and cloud liquid water in a simultaneous inversion.