RTTOV-gb – adapting the fast radiative transfer model RTTOV for the
assimilation of ground-based microwave radiometer observations

Angelis, Francesco De; Cimini, Domenico; Hocking, James; Martinet, Pauline; Kneifel, Stefan

doi:10.5194/gmd-9-2721-2016

Cited by 23 publications

(32 citation statements)

References 29 publications

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“…MWR TB are simulated at the specific frequency channel and elevation angle from the AROME-France thermodynamic profiles using the fast radiative transfer model RTTOVgb (De Angelis et al, 2016). RTTOV-gb has been developed modifying the RTTOV code (version 11.2) to simulate ground-based MWR observations, as the original RTTOV (Saunders et al, 1999) was meant to simulate downwardviewing satellite observations only.…”

Section: Radiative Transfer Modelmentioning

confidence: 99%

“…The parameterization of the transmittances makes the radiative model computationally much more efficient and in principle should not add significantly to the errors generated by uncertainties in the spectroscopic data used by the LBL model on which the fast model is based (Matricardi et al, 2001). The additional uncertainty due to the use of RTTOVgb instead of a LBL model has been quantified in De Angelis et al (2016).…”

Section: Radiative Transfer Modelmentioning

confidence: 99%

“…The LBL model by Rosenkranz (1998) has been used for the water vapour and oxygen absorption to calculate the clear-sky transmittances needed in the RTTOV-gb regression coefficients computation (De Angelis et al, 2016). For the RTTOV-gb training we used 83 profiles, interpolated on 101 pressure levels and carefully chosen from a NW-PSAF (Numerical Weather Prediction Satellite Application Facility) profile dataset to represent a wide range of physically realistic atmospheric states (Matricardi, 2008).…”

Section: Radiative Transfer Modelmentioning

confidence: 99%

“…The development of the ground-based version of the fast radiative transfer model Radiative Transfer for TOVS (RTTOV), i.e. RTTOV-gb (De Angelis et al, 2016), allows the fast simulation of ground-based MWR TB, paving the way towards the operational assimilation of MWR TB into NWP models.…”

Section: Introductionmentioning

confidence: 99%

“…The occurring small correlations can be easily taken into account by observing the ambient black-body load included in the MWR hardware. Errors arising from the observation operator uncertainty in the context of radiative transfer modelling have been considered by De Angelis et al (2016) for RTTOV-gb. These errors include uncertainty due to the spectroscopy parameters (dominant and most difficult to estimate accurately) and fast model parameterizations.…”

Section: Introductionmentioning

confidence: 99%

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Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network

et al. 2017

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Abstract. Ground-based microwave radiometers (MWRs) offer the capability to provide continuous, high-temporalresolution observations of the atmospheric thermodynamic state in the planetary boundary layer (PBL) with low maintenance. This makes MWR an ideal instrument to supplement radiosonde and satellite observations when initializing numerical weather prediction (NWP) models through data assimilation. State-of-the-art data assimilation systems (e.g. variational schemes) require an accurate representation of the differences between model (background) and observations, which are then weighted by their respective errors to provide the best analysis of the true atmospheric state. In this perspective, one source of information is contained in the statistics of the differences between observations and their background counterparts (O-B). Monitoring of O-B statistics is crucial to detect and remove systematic errors coming from the measurements, the observation operator, and/or the NWP model. This work illustrates a 1-year O-B analysis for MWR observations in clear-sky conditions for an European-wide network of six MWRs. Observations include MWR brightness temperatures (TB) measured by the two most common types of MWR instruments. Background profiles are extracted from the French convective-scale model AROME-France before being converted into TB. The observation operator used to map atmospheric profiles into TB is the fast radiative transfer model RTTOV-gb. It is shown that O-B monitoring can effectively detect instrument malfunctions. O-B statistics (bias, standard deviation, and root mean square) for water vapour channels (22.24-30.0 GHz) are quite consistent for all the instrumental sites, decreasing from the 22.24 GHz line centre ( ∼ 2-2.5 K) towards the high-frequency wing (∼ 0.8-1.3 K). Statistics for zenith and lower-elevation observations show a similar trend, though values increase with increasing air mass. O-B statistics for temperature channels show different behaviour for relatively transparent (51-53 GHz) and opaque channels (54-58 GHz). Opaque channels show lower uncertainties (< 0.8-0.9 K) and little variation with elevation angle. Transparent channels show larger biases (∼ 2-3 K) with relatively low standard deviations (∼ 1-1.5 K). The observations minus analysis TB statistics are similar to the O-B statistics, suggesting a possible improvement to be expected by assimilating MWR TB into NWP models. Lastly, the O-B TB differences have been evaluated to verify the normal-distribution hypothesis underPublished by Copernicus Publications on behalf of the European Geosciences Union.

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Section: Radiative Transfer Modelmentioning

confidence: 99%

Section: Radiative Transfer Modelmentioning

confidence: 99%

Section: Radiative Transfer Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network

et al. 2017

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Assimilation of humidity and temperature observations retrieved from ground‐based microwave radiometers into a convective‐scale NWP model

Caumont

Cimini²,

Löhnert

et al. 2016

Quart J Royal Meteoro Soc

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Temperature and humidity retrievals from an international network of ground-based microwave radiometers (MWRs) have been collected to assess the potential of their assimilation into a convective-scale numerical weather prediction (NWP) system. Thirteen stations over a domain encompassing the western Mediterranean basin were considered for a time period of 41 days in autumn, when heavy precipitation events most often plague this area.Prior to their assimilation, MWR data were compared to very-short-term forecasts. Observation-minus-background statistics revealed some biases, but standard deviations were comparable to that obtained with radiosondes. The MWR data were then assimilated in a three-dimensional variational data assimilation system through the use of a rapid update cycle. A first set of four different experiments were designed to assess the impact of the assimilation of temperature and humidity profiles, both separately and jointly. This assessment was done through the use of a comprehensive dataset of upper-air and surface observations collected in the framework of the HyMeX programme.The results showed that the impact was generally very limited on all verified parameters, except for precipitation. The impact was found to be generally beneficial in terms of most verification metrics for about 18 h, especially for larger accumulations. Two additional data-denial experiments showed that even more positive impact could be obtained when MWR data were assimilated without other redundant observations. The conclusion of the study points to possible ways of enhancing the impact of the assimilation of MWR data in convective-scale NWP systems.

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Improving the representation of the atmospheric boundary layer by direct assimilation of ground‐based microwave radiometer observations

Vural,

Merker,

Löffler

et al. 2024

Quart J Royal Meteoro Soc

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In a joint effort, MeteoSwiss and Deutscher Wetterdienst (DWD) address the need for improving the initial state of the atmospheric boundary layer (ABL) by exploiting ground‐based profiling observations that aim to fill the existing observational gap in the ABL. We implemented the brightness temperature observations from ground‐based microwave radiometers (MWRs) into our data assimilation systems using a local ensemble transform Kalman filter (LETKF) with RTTOV‐gb (Radiative Transfer for TOVS, ground‐based) as a forward operator. We were able to obtain a positive impact on the brightness temperature first guess and analysis as well as a slight impact on the ABL humidity using two MWRs at MeteoSwiss. These results led to a subsequent operational implementation of the observing system at MeteoSwiss. Furthermore, we performed an extensive set of assimilation experiments at DWD to further investigate various aspects such as the vertical localisation of selected single channels. We obtained a positive impact on the 6 h‐forecast of ABL temperature and humidity by assimilating two channels employing a dynamical localisation based on the sensitivity functions of RTTOV‐gb but also with a static localisation in a single‐channel setup. Our experiments indicate the importance of the vertical localisation when using more than one channel, although reliable improvements are challenging to obtain without a larger number of observations for both assimilation and verification.This article is protected by copyright. All rights reserved.

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RTTOV-gb – adapting the fast radiative transfer model RTTOV for the assimilation of ground-based microwave radiometer observations

Cited by 23 publications

References 29 publications

Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network

Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network

Assimilation of humidity and temperature observations retrieved from ground‐based microwave radiometers into a convective‐scale NWP model

Improving the representation of the atmospheric boundary layer by direct assimilation of ground‐based microwave radiometer observations

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