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

Francesco de Angelis, Domenico Cimini, Ulrich Löhnert, Olivier Caumont, Alexander Haefele, Bernhard Pospichal, Pauline Martinet, Francisco Navas-Guzmán, Henk Klein-Baltink, Jean-Charles Dupont, et al.

► To cite this version:

Francesco de Angelis, Domenico Cimini, Ulrich Löhnert, Olivier Caumont, Alexander Haefele, et al. Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network. Atmospheric Measurement Techniques, 2017, 10, pp.3947-3961. 10.5194/amt-10-3947-2017. insu-03668336

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

Francesco De Angelis<fnref n="1" />, Domenico Cimini<fnref n="2" />,<fnref n="1" />, Ulrich Löhnert<fnref n="3" />, Olivier Caumont<fnref n="4" />, Alexander Haefele<fnref n="5" />,
Bernhard Pospichal<fnref n="3" />, Pauline Martinet<fnref n="4" />, Francisco Navas-Guzmán<fnref n="6" />, Henk Klein-Baltink<fnref n="7" />, Jean-Charles Dupont<fnref n="8" />,
and James Hocking<fnref n="9" />

*Correspondence to:* F. De Angelis (francesco.deangelis1@graduate.univaq.it)

Received: 12 April 2017 – Discussion started: 21 April 2017

Revised: 25 August 2017 – Accepted: 15 September 2017 – Published: 25 October 2017

**Abstract.** Ground-based microwave radiometers (MWRs) offer the capability to provide continuous, high-temporal-resolution 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 pro-

files 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 under-

<footnote n="1">¹CETEMPS, University of L’Aquila, L’Aquila, Italy</footnote>

<footnote n="2">²National Research Council of Italy, Institute of Methodologies for Environmental Analysis (IMAA-CNR), Potenza, Italy</footnote>

<footnote n="3">³Institute for geophysics and meteorology, University of Cologne, Cologne, Germany</footnote>

<footnote n="4">⁴CNRM UMR 3589, Météo-France/CNRS, Toulouse, France</footnote>

<footnote n="5">⁵Federal Office of Meteorology and Climatology MeteoSwiss, Payerne, Switzerland</footnote>

<footnote n="6">⁶Institute of Applied Physics (IAP), University of Bern, Bern, Switzerland</footnote>

<footnote n="7">⁷Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands</footnote>

<footnote n="8">⁸Institut Pierre-Simon Laplace (IPSL), Université Versailles Saint Quentin, Guyancourt, France</footnote>

<footnote n="9">⁹Met Office, Exeter, UK</footnote>
lying variational and ensemble Kalman filter-based DA systems. Absolute values of excess kurtosis and skewness are generally within 1 and 0.5, respectively, for all instrumental sites, demonstrating O–B normal distribution for most of the channels and elevations angles.

# 1 Introduction

The new generation of high-resolution (~1 km grid size) weather forecast models now operational over Europe promises to improve predictions of high-impact weather, ranging from flash floods to episodes of poor air quality. To realize this, a dense observing network is required, focusing especially on the lowest few kilometres of the atmosphere, so that forecast models have the most realistic state of the atmosphere for initial states and subsequent forecasts. The United States National Research Council (NRC) recently reported that continuous planetary boundary layer (PBL) thermodynamic observations provide a practical and cost-effective means to improve local high-impact weather forecasting (National Research Council, 2008, 2010). However, they stated that the structure and variability of the lower troposphere is currently not well known because vertical profiles of water vapour, temperature, and winds are not systematically observed. This lack of observations results in the PBL being the single most important under-sampled part of the atmosphere. While the thermodynamic state of the atmosphere is well measured at the surface by in situ sensors (e.g. weather stations) and in the upper troposphere by satellite sounders, there is currently an observational gap in the PBL. Ground-based microwave radiometers (MWRs) offer the capability to provide continuous temperature and humidity profiles in both clear- and cloudy-sky conditions with high temporal resolution, low-to-moderate vertical resolution, and with information mostly residing in the PBL (Cimini et al., 2006). Thus, MWR can help bridging the current observational gap in this thin layer of the troposphere. More than 30 MWR are currently installed in Europe, most of which are operating continuously, and the number is increasing. In this framework, MWR are candidates to supplement radiosonde and satellite observations to feed modern numerical weather prediction (NWP) models through assimilation of their data. This has been recently investigated in a few sporadic cases, assimilating retrieved temperature and humidity profiles into NWP models (Cimini et al., 2012, 2014; Caumont et al., 2016). Martinet et al. (2015) illustrate the attempt to assimilating the primary observable, i.e. brightness temperature (TB) instead of retrieved profiles, within a simplified 1-D framework, showing positive impact on the NWP forecasts in the PBL. 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.

The quality of the analyses produced by data assimilation (DA) systems primarily relies on the accuracy of all used information such as the observations, the model forecast (i.e. the background), and the observation operator (the latter for modern DA systems such as variational systems and ensemble Kalman filters). Hence, the best estimate of the atmospheric state is obtained only if background and observation errors are correctly described and follow Gaussian distributions with zero mean. The representation of background and observation errors is thus essential in the assimilation system (Waller et al., 2016).

For modern DA techniques, the observation error can be attributed to the radiometric noise, observation operator, representativeness errors, and calibration uncertainties. The radiometric noise of the MWR is often known (~0.1–0.2 K), well understood, and approximately uncorrelated between frequency channels (Hewison, 2006a). 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. The representativeness error corresponds to MWR fluctuations on smaller scales that cannot be represented by the NWP model. In general, the contributing error terms have similar magnitudes (Hewison, 2006a). However, it is also noticeable that channels near the water vapour line at 22 GHz are dominated by spectroscopic uncertainties, while channels most sensitive to cloud liquid water (31, 51, and 52 GHz) are dominated by their representativeness errors. Moreover, channels at 31, 51, and 52 GHz are also sensitive to the water vapour continuum as well and to the spectroscopy of the 50–60 GHz line complex. Finally, the highest-frequency channels (> 55 GHz), which are only sensitive to the temperature in the lowest few hundred metres, are dominated by radiometric noise (Hewison, 2006a).

Concerning calibration errors, Maschwitz et al. (2013) quantify the uncertainty for the tipping curve calibration in ±0.1 to ±0.2 K (22–31 GHz) and ±0.6 to ±0.7 K (51–52 GHz, only to be applied at high-altitude sites with extremely low water vapour content), while they show an uncertainty of ±0.9 to ±1.6 K (22–31 GHz), ±0.5 to ±1.0 K (51–53 GHz), and ±0.2 to ±0.3 K (54–58 GHz) for the liquid nitrogen calibration. To our knowledge, Maschwitz et al. (2013) presented the most complete uncertainty analysis of LN2 calibration available in the literature. However, there may be additional error sources (such as condensate on the radome, spurious reflections, and receiver sensitivity drifts) that could increase the total uncertainty in field conditions.

The background error covariance matrix plays an important role in data assimilation and analysis systems by spread-
ing the information contained in the observation both in space and between variables through cross correlations. A good specification of background errors is thus an essential part of any state-of-the-art data assimilation system (Ingleby, 2001), since it affects the impact of the observations on the analysis.

The accuracy of NWP analysis systems is thus strongly dependent on appropriate statistics for both observation and background errors. Unfortunately, those statistics are not exactly known and their determination remains a major challenge in assimilation systems. Background errors are often determined using ensemble assimilation systems to compute the forecast differences between each member (Brousseau et al., 2011). Differences between observations and their background counterparts (O–B) are often used to determine observation error statistics (Desroziers et al., 2005). The O–B monitoring in radiance space can reveal systematic errors coming from the measurements, the radiative transfer model, or the NWP forecast model (Hollingsworth et al., 1986; Stationer et al., 2004). This approach is widely used by the satellite data assimilation community, although it may not be always straightforward to differentiate the source of the systematic errors (Waller et al., 2016).

The bias arising from the O–B monitoring can be removed to guarantee the assumption of unbiased observations, which is inherent to optimal estimation retrieval, such as the variational DA and ensemble Kalman filter schemes.

In this context, an accurate characterization of the MWR O–B departures represents an important step towards the operational exploitation of the so far under-exploited MWR instruments.

This paper illustrates the analysis of MWR O–B TB differences in clear-sky conditions during 1 year over a network of six instrumental sites in central Europe. Section 2 describes the dataset and the methodology used for this long-term O–B monitoring. Section 3 discusses the results of this study, while Sect. 4 summarizes the findings and draws the final conclusions.

## 2 Dataset and methodology

### 2.1 Ground-based microwave radiometer observations

The microwave radiometer observations considered in this analysis consist of downwelling TB measured by six commercial ground-based MWR. The MWR is a passive remote sensing instrument that measures the radiance naturally emitted by the atmosphere at selected frequency channels in the 20–60 GHz range (Westwater et al., 2004). MWR represent a mature technique for the retrieval of atmospheric temperature and humidity profiles as well as integrated water vapour and liquid water path. MWRs provide retrievals in both clear- and cloudy-sky conditions, with high temporal resolution, low-to-moderate vertical resolution, and most of the information

content residing in the PBL. MWR channels near the 60 GHz oxygen complex are used to retrieve temperature profiles, while channels near the 22.235 GHz water vapour line provide humidity and integrated water vapour information and are also sensitive to the column integrated liquid water content. A first attempt of MWR networking in Europe was reported by Güldner et al. (2009) for a temporary network, while Cadeddu et al. (2013) describe the details of the MWR network belonging to the US Atmospheric Radiation Measurement program. In this study we consider the MWR units deployed permanently at six observing sites in central Europe (JOYCE, CESAR, LACROS, SIRTA, Payerne, and RAO – see details on Table 1). These instruments belong to different European institutions and were chosen to be representative of the MWR technology currently deployed in Europe. In addition, these six sites fall within the domain of the convective-scale model AROME-France (Seity et al., 2011), as shown in Fig. 1. These MWR are all multichannel temperature and humidity profilers; five are manufactured by RPG (HATPRO, Rose et al., 2005), while the remaining one (at RAO) is manufactured by Radiometrics (MP3000A; Ware et al., 2003). HATPRO detects radiances at 14 frequency channels (22.24, 23.04, 23.84, 25.44, 26.24, 27.84, 31.40, 51.26, 52.28, 53.86, 54.94, 56.66, 57.30, and 58.00 GHz). The first seven frequency channels are in the K-band (22–31 GHz), while the last seven are in the V-band (51–60 GHz). MP3000A detects radiances at 12 channels (5 in the K-band and 7 in V-band). Both MWR types have elevation scanning capabilities for improved temperature profiling in the boundary layer. The observations presented here are taken at six elevation angles (90.0, 42.0, 30.0, 19.2, 10.2, and 5.4°) for the HATPRO units and at two elevation angles (90.0 and 15.7°) for the MP3000A. The period considered in this study extends from 1 January to 31 December 2014. During this period, the MWR units undergo regular maintenance, including antenna radome cleaning, sanity checks, and absolute calibrations. The maintenance strategy is currently not harmonized across the network. Absolute calibration is commonly obtained via the cryogenic liquid nitrogen (LN2) calibration method. LN2 calibrations are typically performed once or twice a year to correct for instrument drifts. Note that faulty calibration may happen, manifesting as discontinuities in the time series of O–B statistics (Löhnert and Maier, 2012). Temporal matching of MWR observations and NWP model forecasts has been obtained by selecting MWR TB records closest in time to the model forecast time (only one observation without any average over several). The following O–B analysis is performed on the sample of temporal match-up observation–model couples.

### 2.2 NWP model

The NWP model used in this study is AROME (Seity et al., 2010). AROME has a nonhydrostatic dynamical core inherited from the ALADIN-NH model (Bubnová et al., 1995) and
<table><caption>**Table 1.** Sample size at all the instrumental sites before and after the quality control screening. Position, height, and reference of each instrument are reported. For HATPRO only, the generation family is also reported (G5 is currently commercialized).</caption><thead><tr><th>Location</th><th>Lat</th><th>Long</th><th>Height (m)</th><th>MWR</th><th>Pre-screening</th><th>Post-screening</th><th>Reference</th></tr></thead><tbody><tr><td>JOYCE</td><td>50.91</td><td>6.41</td><td>111</td><td>HATPRO G2</td><td>602</td><td>557</td><td>Löhnert et al. (2015)</td></tr><tr><td>LACROS</td><td>51.35</td><td>12.43</td><td>125</td><td>HATPRO G2</td><td>542</td><td>502</td><td>Bühl et al. (2013)</td></tr><tr><td>Payerne</td><td>46.82</td><td>6.95</td><td>491</td><td>HATPRO G1</td><td>1087</td><td>955</td><td>Collaud Coen et al. (2014)</td></tr><tr><td>SIRTA</td><td>48.80</td><td>2.36</td><td>156</td><td>HATPRO G2</td><td>1022</td><td>923</td><td>Haeffelin et al. (2005)</td></tr><tr><td>CESAR</td><td>51.97</td><td>4.93</td><td>-0.7</td><td>HATPRO G1</td><td>988</td><td>664</td><td>CESAR (2017)</td></tr><tr><td>RAO</td><td>52.21</td><td>14.12</td><td>125</td><td>MP3000A</td><td>709</td><td>680</td><td>Neisser et al. (2002)<br>RAO (2017)</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Figure 1.** Topography (m) and domain of AROME-France (large area delineated by solid black line). Locations of MWR sites are also shown (+ and × indicate, respectively, HATPRO and MP3000A).</figcaption></figure>

physical parameterizations taken from the research model Meso-NH (Lafore et al., 1998). In 2014, the operational configuration AROME-France covered the domain shown in Fig. 1. The model had an horizontal resolution of 2.5 km and used 60 vertical levels, following the terrain in the lowest layers and the isobars in the upper atmosphere. The lateral boundary conditions were provided by the global ARPEGE NWP system (Courtier et al., 1991). AROME-France used a three-dimensional variational (3D-VAR) data assimilation system run in a rapid forward intermittent assimilation cycle; i.e. analyses were performed every 3 h starting from 00:00 UTC by assimilating all the observations available at Météo-France in order to provide new initial states for subsequent forecasts. The background error covariance matrices were specified through the use of an ensemble method (Brousseau et al., 2011). Data assimilated by the AROME-France DA system included observations from radiosondes, wind profilers, aircrafts, ships, buoys, automatic weather stations, satellites, GPS stations, and both Doppler radar wind velocity and radar reflectivity (Brousseau et al., 2014).

Temperature, humidity, and pressure profiles are extracted from the AROME-France 3 h forecasts and analyses. AROME-France cloud liquid water profiles are not available in the dataset used for this study. The profile extracted from the central point of the 3 × 3 model grid centred on each MWR site (i.e. closest in space to the MWR location) has been used as background.

## 2.3 Radiative transfer model

MWR TB are simulated at the specific frequency channel and elevation angle from the AROME-France thermodynamic profiles using the fast radiative transfer model RTTOV-gb (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 downward-viewing satellite observations only.

Fast radiative transfer models perform simplified calculations of the atmospheric radiances by parameterizing the atmospheric transmittances. Accurate transmittances, computed with a slower line-by-line (LBL) model for a set of climatological atmospheric profiles, are used to calculate channel-specific regression coefficients in the training phase. Given these regression coefficients, the fast radiative transfer model can compute transmittances for any other input profile. 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 RTTOV-gb instead of a LBL model has been quantified in De Angelis et al. (2016).

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 physi-
cally realistic atmospheric states (Matricardi, 2008). These
101 pressure levels (ranging from 0.005 to 1050 hPa) have
been specifically selected for ground-based perspective to be
denser close to ground (34 levels below 2 km; De Angelis et
al., 2016).

In this work, the RTTOV-gb simulations consider the MWR channel bandwidth through the training performed by using LBL double sideband opacities. MWR detects radiance through narrow bandpass filters for each frequency channel. The nominal MWR channels are characterized by the mid-frequency, which is a weighted average over the bandpass filter. In the RTTOV-gb training we consider a rectangular filter shape characterized by two frequencies equally weighted at the edges of the full width at half maximum (FWHM). For the RPG-HATPRO, the filter's FWHM is 0.23 GHz, except for the opaque V-band channels (0.6-2.0 GHz; Rose et al., 2005). The FWHM is 0.30 GHz at all the channels for the Radiometrics MP3000A (Solheim et al., 1998). RTTOV-gb simulations take also into account atmospheric propagation effects due to Earth curvature and atmospheric refraction (Saunders et al., 2010). In this work, RTTOV-gb does not consider the finite antenna beamwidth as this feature is not available in the original RTTOV code. Thus, the antenna pattern, defining the region from where radiometer antennas receive their signal, is assumed as an ideal single pencil beam. This assumption becomes important only at low elevation angles, e.g. up to 1-1.5 K in K-band at 5° elevation angle (Meunier et al., 2013; Navas-Guzmán et al., 2016).

## 2.4 Quality control (QC)

Routine QC is applied by MWR instrument operators at the
individual sites, resulting in a quality flag encoded within the
data files. The complete datasets collected by each MWR
in Table 1 have been transferred to a common centralized
server. Then, MWR observations have been quality con-
trolled before entering the O–B dataset. First of all, data
flagged by the sanity/rain checks provided within the instru-
ment data stream were discarded. In addition, we applied a
cloud screening, as we intend to monitor O–B TB differences
in clear-sky only to avoid the uncertainty stemming from the
forecast and absorption of cloud liquid water. Clear-sky con-
ditions have been selected using a two-stage screening: (i) 1 h
standard deviation of the MWR TB at 30–31 GHz (σ<sub>C</sub>) and
(ii) sky infrared temperature from the 10.5 µm infrared ra-
diometer mounted within the MWR housing (T<sub>IR</sub>). Channels
at 30–31 GHz are the most sensitive to clouds as they are
in a gas absorption window, where the signal is relatively
insensitive to changes in atmospheric temperature and hu-
midity. Thus, the TB standard deviation at 30–31 GHz over
a defined time period (e.g. 1 h) can be used to indicate the
presence of liquid clouds within the MWR field of view. In
addition, the infrared radiometer (not available in Payerne,
SIRTA, and CESAR) is sensitive to cloud base temperature

and indicates the presence of thick clouds when the infrared
temperature is high (meaning no contribution from the cold
background above the cloud; Martinet et al., 2015). Thresholds for this screening procedure were determined in order
to have a good compromise between a sufficient data sample
and a high confidence of cloudy-sky rejections (Martinet et
al., 2015). Periods with σ<sub>C</sub> > 0.5 K (Turner at al., 2007) or
T<sub>IR</sub> > −30 °C (Martinet et al., 2015) were rejected. In addition,
O–B TB differences larger than 3 standard deviations
with respect to the mean difference were rejected to remove
outliers (e.g. possible obstructions or undetected cloud contamination). Table 1 reports the sample size from each instru-
mental site before and after the QC screening.

3 Results

An example of O–B monitoring is reported in Fig. 2, show-
ing 1-year time series of the O–B TB differences at JOYCE
for channels 22.24, 31.40, 52.28, and 58.00 GHz. Here, ob-
servations are TB measured by the HATPRO at zenith, while
background are TB simulated with RTTOV-gb from the 3 h
forecast profiles at the model grid column closest to JOYCE.
It is evident that O–B TB differences show different vari-
ance depending on the frequency, being largest at 22.24 GHz
and smallest at 58.0 GHz. O–B TB differences show to be
quite steady, with the exception of channel 31.40 GHz; here a
large difference (up to 10 K) is evident until 3 June 2014 (Ju-
lian day 154). This misbehaviour was later confirmed by the
instrument operator and it was attributed to a faulty calibra-
tion. In fact, 3 June 2014 corresponds to the date of the new
LN2 absolute calibration at JOYCE, after which the obser-
vation comes closer to background again. This demonstrates
that the O–B monitoring is able to detect instrument mal-
functions, and it should be implemented and performed at
each MWR site as part of its QC procedure. Similar mis-
behaviours were detected and later confirmed by instrument
operators at other sites: specifically, (i) at CESAR at all the
channels below 54 GHz between 15 June and 18 September
2014, corresponding again to a period after a faulty calibra-
tion; (ii) at CESAR at channel 22.24 GHz and elevation an-
gles below 42 degrees due to radio frequency interference
leaking into the channel bandpass filter; (iii) at Payerne at
26.24 GHz for the whole period due to an unknown malfunc-
tion possibly related to hardware components causing large
observed TB variations.

Figure 3 shows the O–B TB statistics for the six instru-
mental sites at zenith (i.e. 90° elevation angle). The re-
ported bias, standard deviation (SD), and root mean square
(RMS) are computed from the QC dataset. Note that periods
of instrument malfunctions have been removed in JOYCE
(before Julian day 154) and CESAR (Julian days between
165 and 261) by discarding the data before computing the
statistics. The 26.24 GHz channel misbehaviour in Payerne
has not been removed because the source is still unidenti-
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Time series of the O–B TB differences at JOYCE; from top to bottom: channels 22.24 (blue dots), 31.40 (red dots), 52.28 (magenta dots), and 58.00 GHz (cyan dots). The black solid line represents the date of the new calibration (3 June 2014).</figcaption></figure>

fied and it also affects the whole dataset. Accordingly, bias, SD, and RMS for this channel show a peak, reaching –3, 2, and 4 K, respectively. SD statistics for the K-band channels show very similar behaviour from site to site, decreasing from the line centre towards the high-frequency wing. This may suggest that the O–B difference is mostly due to an uncertainty in the humidity profile forecast. For example, at JOYCE the SD ranges from 1.6 K at 22.24 GHz to 0.7–0.8 K at 27.84/31.40 GHz. Note that channels close to the 22.24 GHz line centre show the highest values of TB and the highest dynamic range in clear-sky conditions. Thus, the large absolute uncertainty at these channels may correspond to similar relative accuracy when compared to the other K-band channels.

The maximum RMS and biases are located at 22.24 GHz at all the sites except RAO, where they are at 23.04 GHz (around 2.5 and 2.0 K, respectively). At JOYCE the bias and RMS range, respectively, from 0.9 and 1.8 K at 22.24 GHz to 0.1 and 0.7 K at 27.84 GHz. Albeit one may not expect the variability to increase in the window channel, we see slightly larger differences at 31.40 GHz with respect to 27.84 GHz (bias and RMS equal to 0.8 and 1.3 K, respectively), which may be attributed to few undetected cases of cloud contamination. Similar statistics are reported at all sites except LACROS. At LACROS we see similar standard deviations but larger bias and, consequently, RMS (ranging from 1.7 to 3.6 K in K-band, with maximum value at 22.24 GHz). The reason for these larger biases is still under investigation.

O–B statistics at V-band show different behaviour at lower-frequency (i.e. transparent) and higher-frequency (i.e. opaque) channels. Opaque channels (54–58 GHz) show low bias, SD, and RMS (all within 0.9 K) as the atmosphere is opaque due to oxygen and therefore water vapour and the

effect of clouds on observed TB is negligible at these channels. Transparent channels (51–53 GHz) show rather large biases (2–3 K and up to 5 K in Payerne) with relatively low SD (1.0–1.5 K). Bias values of the same order of magnitude for the 51–53 GHz range were previously reported (Hewison et al., 2006b; Löhnert and Maier, 2012; Martinet et al., 2015; Blumberg et al., 2015; Navas-Guzmán et al., 2016), employing MWR of different types and manufacturers. Large biases at lower V-band channels (50–54 GHz) are likely due to a combination of systematic uncertainties stemming from inaccurate instrument bandpass characterization, calibration and absorption model. In fact, these channels are located on a steep shoulder of the O₂ absorption complex and thus are sensitive to uncertainty in bandpass modelling. In addition, they suffer from larger calibration uncertainty due to the relative low opacity as well as larger radiative transfer model errors due to the lack of well-calibrated data usable for tuning spectroscopic parameters. However, it is important to note that the standard deviation remains below 1 K, allowing for an easy bias correction. In this study, a bias correction based on simulated TB computed from clear-sky NWP model profiles can thus be applied on the measurements.

Figure 4 shows O–B statistics for zenith MWR observations in JOYCE, before and after such a bias correction. Ideally, the bias correction should be computed using the same NWP and radiative transfer models used for the O–B, as done in operational systems. However, applying such a bias correction on this same dataset, the resulting bias would of course be zero. Thus, aiming to a qualitative demonstration of the bias correction, we use here independent bias correction values computed for JOYCE with respect to another NWP model. The correction values were computed by a previous work considering the DWD COSMO-DE model (Baldauf et al., 2011), using forecasts not older than 3 h at the closest vertical column to JOYCE. The radiative transfer models are also different, though both adopt the atmospheric absorption model of Rosenkranz (1998). All clear-sky observations between two absolute calibrations have been used to compute the V-band biases by considering simultaneous observations and forward modelled TB. This approach assumes a constant bias between two adjacent calibrations, which has been justified for a HATPRO system by Löhnert and Maier (2012). In this way, the TB biases in the V-band are decreased from 1–1.5 to 0.1–0.5 K between 51 and 53 GHz (Fig. 4). Note that the bias correction is applied to the V-band channels only, since the humidity uncertainty affecting the model and the colocation is deemed too high to provide a reliable bias correction for the K-band channels. A different approach using NWP model output to adjust microwave observations for operational applications is discussed by Güldner (2013).

Although the caveats described above make the bias correction results only qualitative, we can see a significant improvement in the O–B statistics, demonstrating that a bias correction would remove most of the systematic errors at 51–
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Statistics of the differences between observations and background TB. Observations are TB measured by ground-based MWR. Background counterparts are TB simulated with RTTOV-gb from AROME-France 3 h forecast profiles in clear-sky conditions at zenith. Panels (a), (b), (c), (d), (e), and (f) refer, respectively, to JOYCE, LACROS, Payerne, SIRTA, CESAR, and RAO. Biases are shown with black lines, standard deviations with red lines, and RMS with blue lines.</figcaption></figure>

53 GHz, on the assumption of consistent NWP and radiative transfer models. Apart from this qualitative demonstration, a consistent bias correction, computed using the same NWP and radiative transfer models used for the O–B, is highly recommended for any further use of this dataset.

Table 2 reports the O–B TB mean differences (i.e. biases) and their 95 % confidence intervals, for each instrumental site at zenith.

Observations at different elevation angles allow to check the robustness of the previous results. Figure 5 shows the statistics at JOYCE at elevation angles 90.0, 42.0, 30.0, 19.2, 10.2, and 5.4°. Results at K-band show similar tendencies at lower elevation angles (panels B to F). However, the variability increases with decreasing elevation angle because uncertainty in the AROME-France humidity profile gets amplified with increasing air mass. This happens also due to a
<table><caption>**Table 2.** Biases of O–B TB differences and their 95 % confidence intervals for all the instrumental sites at zenith. Values at RAO (MP3000A) are reported on the column of the closest HATPRO frequency channel.</caption><thead><tr><th>Chan<br>(GHz)</th><th>22.24</th><th>23.04</th><th>23.84</th><th>25.44</th><th>26.24</th><th>27.84</th><th>31.40</th></tr></thead><tbody><tr><td>JOYCE</td><td>0.791 ± 0.124</td><td>-0.411 ± 0.121</td><td>0.075 ± 0.098</td><td>-0.303 ± 0.077</td><td>-0.148 ± 0.067</td><td>0.068 ± 0.053</td><td>0.630 ± 0.114</td></tr><tr><td>LACROS</td><td>3.288 ± 0.130</td><td>2.776 ± 0.121</td><td>2.179 ± 0.105</td><td>1.698 ± 0.075</td><td>1.688 ± 0.063</td><td>1.605 ± 0.049</td><td>2.425 ± 0.093</td></tr><tr><td>Payerne</td><td>1.666 ± 0.107</td><td>1.105 ± 0.103</td><td>0.995 ± 0.087</td><td>-0.085 ± 0.061</td><td>-3.156 ± 0.162</td><td>0.029 ± 0.050</td><td>0.409 ± 0.038</td></tr><tr><td>SIRTA</td><td>2.243 ± 0.110</td><td>1.645 ± 0.106</td><td>1.810 ± 0.089</td><td>1.161 ± 0.061</td><td>1.105 ± 0.053</td><td>1.203 ± 0.043</td><td>0.943 ± 0.039</td></tr><tr><td>CESAR</td><td>1.615 ± 0.150</td><td>0.840 ± 0.124</td><td>0.293 ± 0.117</td><td>-0.190 ± 0.073</td><td>-0.061 ± 0.064</td><td>0.065 ± 0.053</td><td>0.243 ± 0.050</td></tr><tr><td>RAO</td><td>1.588 ± 0.122</td><td>2.032 ± 0.117</td><td>0.647 ± 0.086</td><td></td><td>0.821 ± 0.063</td><td></td><td>0.940 ± 0.060</td></tr></tbody><thead><tr><th>Chan<br>(GHz)</th><th>51.26</th><th>52.28</th><th>53.86</th><th>54.94</th><th>56.66</th><th>57.30</th><th>58.00</th></tr></thead><tbody><tr><td>JOYCE</td><td>0.874 ± 0.089</td><td>-1.052 ± 0.095</td><td>-1.519 ± 0.034</td><td>0.196 ± 0.027</td><td>0.337 ± 0.033</td><td>0.275 ± 0.033</td><td>0.428 ± 0.035</td></tr><tr><td>LACROS</td><td>-0.341 ± 0.047</td><td>-2.536 ± 0.039</td><td>0.026 ± 0.030</td><td>-0.179 ± 0.034</td><td>-0.009 ± 0.042</td><td>0.066 ± 0.044</td><td>-0.004 ± 0.044</td></tr><tr><td>Payerne</td><td>3.941 ± 0.051</td><td>-5.230 ± 0.039</td><td>1.978 ± 0.019</td><td>-0.781 ± 0.020</td><td>-0.017 ± 0.029</td><td>0.082 ± 0.035</td><td>-0.090 ± 0.031</td></tr><tr><td>SIRTA</td><td>-1.284 ± 0.051</td><td>-3.425 ± 0.046</td><td>-2.301 ± 0.023</td><td>-0.153 ± 0.033</td><td>-0.064 ± 0.035</td><td>0.012 ± 0.037</td><td>0.121 ± 0.037</td></tr><tr><td>CESAR</td><td>0.838 ± 0.062</td><td>-0.883 ± 0.043</td><td>1.139 ± 0.029</td><td>-0.632 ± 0.029</td><td>-0.279 ± 0.033</td><td>-0.300 ± 0.032</td><td>-0.233 ± 0.034</td></tr><tr><td>RAO</td><td>-0.549 ± 0.057</td><td>-2.706 ± 0.048</td><td>-2.429 ± 0.031</td><td>-1.262 ± 0.034</td><td>-0.905 ± 0.039</td><td>-0.932 ± 0.039</td><td>-0.831 ± 0.045</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Figure 4.** Statistics of the differences between observations and background TB, as in Fig. 3. Here, results from JOYCE at zenith are shown. Panels (a) and (b) refer, respectively, to before and after the bias correction with the COSMO-DE model. Biases are shown with black lines, standard deviations with red lines, and RMS with blue lines.</figcaption></figure>

stronger TB signal, resulting in larger absolute noise. Statistics follow a similar trend up to 19.2° elevation angle, where RMS reaches 6 K. Larger differences are found at 10 and 5° elevation angles in K-band (biases, SD, and RMS, respectively, up to 16, 8, and 18 K at 5°) probably due to (i) the current version of RTTOV-gb not being designed for elevation angles lower than 15° (De Angelis et al., 2016), as simulations at low elevation angles were not necessary in the original satellite perspective; and (ii) the violation of the homogeneity assumption, which needs to be satisfied when using low elevation angles. This may also be due to the fact that 10 and 5° are outside the elevation angle range used in the RTTOV-gb training configuration (elevation angle set between 90 and 16°; De Angelis et al., 2016). Moreover, while RTTOV-gb considers Earth curvature, bandwidth, and atmospheric refraction (as explained in Sect. 2.3), it currently does

not take into account the antenna beam width; this aspect can cause large biases between simulations and observations at very low elevation angles.

Statistics at V-band opaque channels show little variation with elevation angle. The zenith systematic O–B differences in the 52.28 and 53.86 GHz channels decrease with decreasing elevation angle due to the fact that atmosphere becomes more and more opaque. However, the systematic difference at 51.26 GHz stays between 1 and 2 K, independent of elevation angle. Here, the systematic offset at zenith (possibly due to calibration uncertainty) is probably taken over by effects of not considering the antenna beam width at low elevation angles (see Meunier et al., 2013, Fig. 14). The statistics of random uncertainty (i.e. SD) follow a similar trend with elevation angle at all the instrumental sites (figures for all sites are reported in the Supplement).
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Statistics of the differences between observations and background TB, as in Fig. 3. Here, results from JOYCE at different observing angle are shown. Panels (a), (b), (c), (d), (e), and (f) refer, respectively, to 90, 42, 30, 19.2, 10.2, and 5.4° elevation angle. Biases are shown with black lines, standard deviations with red lines, and RMS with blue lines.</figcaption></figure>

Note that both the clear-sky selections performed with the IR measurements and the 31 GHz standard deviations only refer to zenith observations. This may not be fully representative of off-zenith measurements. Ancillary data providing cloud presence at other elevation angles (such as those provided by whole-sky imagers) are not available at the considered MWR sites. In addition, the aim of this study is to present a method that can be applied to any site where a MWR instrument is operated alone. The uncertainty due to

residual off-zenith cloud contamination may contribute to enhanced O–B differences at lower elevation angles, possibly adding to both bias and standard deviation. However, cloud contamination does not significantly affect V-band opaque channels, which are those used at lower elevation for boundary layer temperature profile retrievals.

Moreover, O–B statistics are found to be consistent among the instrumental sites (in particular standard deviations) down to 10° elevation angle; this seems to suggest that no
<figure><img src="image_9.png" /><figcaption>**Figure 6.** Statistics of the differences between TB observations and model background (solid lines) and between TB observations and model analysis (dashed lines). Simulated TB are computed with RTTOV-gb from AROME-France 3 h forecast (solid lines) and AROME-France analyses (dashed lines) profiles in clear-sky conditions for Joyce at zenith. Panels (a) and (b) refer, respectively, to 90 and 19.2° elevation angle. Biases are shown with black lines, standard deviations with red lines, and RMS with blue lines.</figcaption></figure>

significant site-specific contamination is affecting the comparison.

Figure 6 shows the statistics of O–B differences as well as observation minus analysis (O–A), in which the AROME analysis is used instead of the 3 h forecast as background. The AROME analysis is the result of the blending of the AROME 3 h forecast with all the observations available at Météo-France (from satellites, radiosondes, surface networks, etc., but not from MWRs) at the same time. O–B and O–A statistics at JOYCE, elevation angles 90 and 19.2°, are shown. O–B and O–A biases are similar at both elevation angles in the K-band and in the V-band’s more transparent channels. Thus, forecast and analysis compare almost equally to MWR observations; despite the large number of observations assimilated into the analyses (but not into the 3 h forecasts), O–B and O–A statistics are almost identical. This seems to indicate that the newly assimilated data did not bring significant information to the analysis with respect to forecast in terms of MWR observables. Assuming the observations as the reference, and considering that MWR uncertainty for transparent channels is typically smaller than the RMS in Fig. 6, this may suggest that the assimilated data perhaps provide little information where it would be useful (e.g. in the boundary layer). In this perspective, the assimilation of MWR brightness temperatures into NWP may provide useful information in the boundary layer. Considering V-band opaque channels, we note smaller biases for O–A than O–B differences (up to 40% smaller at 19.2°). These channels are mostly sensitive to temperature profile in the PBL, and thus this result suggests that most of the 3 h forecast errors point toward the PBL. PBL is indeed the atmospheric layer where most of the information provided by MWRs is located, though this may be redundant with that of other assimilated observations (e.g. radiosondes). Quantification of the infor-

mation brought by MWR into NWP data assimilation will be the subject of future research.

Note that O–A SDs are slightly lower than or equal to the corresponding O–B SDs at all the frequency channels; this is consistent with the assumptions that the analysis variance is lower than or equal to the background variance, and the observation errors and the model errors (either analysis or background) are independent. These assumptions are usually made in modern DA techniques.

In addition to unbiased observations, another hypothesis common to variational and ensemble Kalman filter-based DA systems is that the observations and background errors are Gaussian, which implies that the distribution of the O–B TB differences is Gaussian. This assumption has been verified by exploiting excess kurtosis and skewness scores. Kurtosis can be formally defined as the standardized fourth population moment about the mean of a specific distribution. The normal distribution has a kurtosis of 3, and thus the “excess kurtosis” is usually used (i.e. kurtosis –3). A distribution with positive excess kurtosis has heavier tails and a higher peak than the normal distribution, whereas a distribution with negative excess kurtosis has lighter tails and is flatter. Skewness is formally the third central moment of the specific distribution, divided by the cube of its standard deviation, and it is a measure of symmetry, or more precisely, the lack thereof. A positive skewness value indicates positive (right) skew; a negative value indicates negative (left) skew. The higher the absolute value is, the greater the skew.

For both excess kurtosis and skewness, a normal distribution should return a score of 0. In general, fair approximations to normal distribution should have skewness and excess kurtosis within –1 and +1 (Bulmer, 1979). Figure 7 shows two histograms of O–B TB differences at 90° elevation angle, for JOYCE (58.00 GHz) and Payerne (52.28 GHz). The
<figure><img src="image_10.png" /><figcaption>**Figure 7.** Histograms of the O–B TB differences. Panel (a) refers to JOYCE at 58.00 GHz and 90° elevation angle, while panel (b) refers to Payerne at 52.28 GHz and 90° elevation angle.</figcaption></figure>

histogram at JOYCE shows a distribution approximately Gaussian, with excess kurtosis 0.15 and skewness -0.07. Conversely, the distribution at Payerne has heavier tails (excess kurtosis 1.86) than Gaussian and moderate asymmetry (skewness 0.72). Figures 8 and 9 show, respectively, excess kurtosis and skewness as function of frequency, at each instrumental site and for elevation angles 90.0, 42.0, 19.2 (15.7 for RAO), and 10.2°. In general, excess kurtosis is within ±1, demonstrating fair approximation to Gaussian error. Excess kurtosis above 2 is reported for Payerne at 51–53 GHz and 90–42° elevation and for CESAR at 22.24 GHz and 42° elevation. These same channels are also characterized by large O–B TB statistics, as shown in Fig. 3 and in the Supplement. Above 10° elevation, absolute values of excess kurtosis slightly exceeding 1 are reported for RAO at 23.04 GHz and 15.7° and for LACROS at 51.26 GHz and 19.2°. At 10°, kurtosis around 1.3–1.5 is reported for Payerne (31.40, 51.26, and 52.28 GHz) and for CESAR (51.26 GHz). In general, the absolute value of the skewness is within 0.5 in K-band (22–28 GHz) and in the V-band opaque channels (54–58 GHz) at each instrumental site, meaning approximately symmetric distributions. The only exception is CESAR at 22 GHz and 42°, where skewness is 1.2. Larger skewness are reported for more transparent channels (31, 51, and 52 GHz), in particular for Payerne (52 GHz, elevation angles 90 and 42°, up to 0.9) and for Joyce (51 GHz, 10° elevation angle, up to 1.1), demonstrating moderate asymmetry. In summary, of the 328 channel and elevation angle combinations that were evaluated, only 4.2% (0.6%) showed an absolute value of excess kurtosis (skewness) larger than 1. Among these are the channels that showed large O–B statistics and are thus suspect of instrumental misbehaviour.

<figure><img src="image_11.png" /><figcaption>**Figure 8.** Excess kurtosis as function of frequency for elevation angles 90.0, 42.0, 19.2 (15.7 for RAO), and 10.2°. Scores for JOYCE, LACROS, Payerne, SIRTA, CESAR, and RAO are reported, respectively, in blue, black, red, yellow, cyan, and magenta.</figcaption></figure>

## 4 Summary and conclusions

This work illustrates the first O–B analysis of ground-based TB observations from an European network of six MWRs over a 1-year period (2014). Statistics of the differences between MWR observations and their NWP model background counterparts can be used to shed light on observation and background errors. The knowledge of these errors is crucial for data assimilation because observations and short-term model forecasts are the primary sources of information used to produce analyses. Moreover, the O–B monitoring is essential to detect and possibly remove any systematic errors
<figure><img src="image_12.png" /><figcaption>**Figure 9.** Skewness as function of frequency for elevation angles 90.0, 42.0, 19.2 (15.7 for RAO), and 10.2°. Scores for JOYCE, LACROS, Payerne, SIRTA, CESAR, and RAO are reported, respectively, in blue, black, red, yellow, cyan, and magenta.</figcaption></figure>

coming from the MWR measurements, the radiative transfer model, or the NWP model forecast.

In this analysis, observations are MWR TB measured by two types of commercial MWR (RPG-HATPRO and Radiometrics' MP3000A). Background counterparts are TB simulated with the fast radiative transfer model RTTOV-gb from the AROME-France 3 h forecasts and analyses. Quality control and clear-sky selection are performed with a three-stage screening based on the 1 h standard deviation of the MWR TB at 31 GHz, the infrared radiometer TB, and the quality/rain flag provided by the manufacturer.

It is shown how O–B monitoring can be used to detect instrument malfunctions by exploiting the time series of the O–B TB differences. The results strongly suggest an operational implementation of this monitoring at all sites deploying a MWR as part of the QC procedure.

Observations minus background statistics are quite consistent between the instrumental sites. They decrease in K-band at zenith from the 22.24 GHz line centre (SD ~ 1.5–2.0 K) towards the high-frequency wing (SD~0.5–1.0 K). V-band opaque channels (54–58 GHz) show low statistics (RMS within 0.8–0.9 K) due to the saturation and the dependence to only temperature. V-band more transparent channels (51–53 GHz) show large biases (up to 5 K in Payerne) with relatively low SD (1.0–1.5 K), demonstrating that these biases can be effectively removed by applying a bias correction based on TB simulated from a NWP model (if the forecast errors are within the expected accuracy).

Bias, standard deviation, and RMS at K-band increase with decreasing elevation angle, following a similar trend. Large differences are found at low (5–10°) elevation angles

(RMS up to 20 K) due to atmospheric inhomogeneity and known RTTOV-gb limitations at elevation angles below 15°. Statistics at V-band opaque channels show only small variations with the elevation angle. The O–B mean differences decrease at 52.28 and 53.86 GHz and increase at 51.26 GHz with decreasing elevation angles.

The observations minus analysis TB statistics are similar to the O–B, except for a bias deflection (up to 40%) in the V-band opaque channels, especially at low elevation angles. This suggests the possible level of improvement that may be expected by assimilating MWR TB into NWP models, at least for boundary layer temperature profiling.

The Gaussian error assumption, typical of variational and ensemble Kalman filter-based DA systems, has been evaluated by computing excess kurtosis and skewness scores of the O–B TB distributions. Among the evaluated angle/channel combinations, excess kurtosis and skewness are typically within, respectively, 1 (95.8%) and 0.5 (99.4%). This demonstrates that O–B TB distributions are typically Gaussian with good approximation. Larger scores (excess kurtosis and skewness above 2 and 1) are reported for Payerne at 51–53 GHz and high elevation angles (90–42°) and for Cesar at 22.24 GHz and 42° elevation angle. These scores result in O–B TB distributions with moderate asymmetry and heavier tails than Gaussian, possibly due to instrument malfunction or radio frequency interference.

In conclusion, the presented O–B analysis demonstrated the typical operational performances of a prototype network of six MWR in Europe, showing

1. robust and mature technology, suitable for operational use;

2. continuous TB observations, typically stable and reliable, whose quality can be monitored remotely;

3. consistent O–B statistics throughout the network;

4. moderate O–B systematic differences that can be effectively addressed through bias correction;

5. typically Gaussian O–B distributions.

This work provides a comprehensive characterization of the MWR O–B statistics and distributions that may serve as a reference for the other MWR currently deployed in Europe and worldwide, including commercial (e.g. Attex MTP-5) and research types (e.g. TEMPERA; Navas-Guzman et al., 2016). It also represents a step towards the operational exploitation of ground-based MWRs, thus far under-exploited instruments which may play a crucial role in the accurate characterization of boundary layer thermodynamics into NWP models.

*Data availability.* The dataset used for this analysis is available from the authors upon request.
The Supplement related to this article is available online
at https://doi.org/10.5194/amt-10-3947-2017-supplement.

*Competing interests.* The authors declare that they have no conflict of interest.

*Acknowledgements.* This work has been stimulated through the COST Action ES1303 (TOPROF), supported by COST (European Cooperation in Science and Technology). Part of the work was supported by the EU H2020 project GAIA-CLIM (Ares(2014)3708963/project 640276). The JOYCE observations and data analysis have been supported through the German Science Foundation DFG under grant LO 901/7-1. RAO, LACROS, JOYCE, and CESAR observations have also been supported through the German BMBF research initiative HD(CP)² under grants FKZ01LK1209A-E and FKZ01LK1502A. Thanks to the Leibniz-Institute for Tropospheric Research (TROPOS) in Leipzig and the Richard-Aßmann-Observatorium (RAO) of the Deutscher Wetterdienst (DWD) in Lindenberg for providing the data for, respectively, LACROS and RAO stations. The authors would like to thank the technical and computer staffs of SIRTA Observatory for taking the observations and making the dataset easily accessible. Comments on early drafts by Christine Knist (DWD) were greatly appreciated. The authors would like to acknowledge all the members of the TOPROF Working Group 3 for providing fruitful discussion on the instrument performances and O–B analysis.

Edited by: Marcos Portabella

Reviewed by: Maria Cadeddu

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