A Proof of Concept for Boundary-Layer Moisture Data Assimilation Using Scanning Microwave Radiometer Observations

Abstract. Ground-based microwave radiometers (MWRs) offer a new capability to provide continuous observations of the atmospheric thermodynamic state in the planetary boundary layer. Thus, they are potential candidates to supplement radiosonde network and satellite data to improve numerical weather prediction (NWP) models through a variational assimilation of their data. However in order to assimilate MWR observations, a fast radiative transfer model is required and such a model is not currently available. This is necessary for going from the model state vector space to the observation space at every observation point. The fast radiative transfer model RTTOV is well accepted in the NWP community, though it was developed to simulate satellite observations only. In this work, the RTTOV code has been modified to allow for simulations of ground-based upward-looking microwave sensors. In addition, the tangent linear, adjoint, and K-modules of RTTOV have been adapted to provide Jacobians (i.e., the sensitivity of observations to the atmospheric thermodynamical state) for ground-based geometry. These modules are necessary for the fast minimization of the cost function in a variational assimilation scheme. The proposed ground-based version of RTTOV, called RTTOV-gb, has been validated against accurate and less time-efficient line-by-line radiative transfer models. In the frequency range commonly used for temperature and humidity profiling (22–60 GHz), root-mean-square brightness temperature differences are smaller than typical MWR uncertainties (∼ 0.5 K) at all channels used in this analysis. Brightness temperatures (TBs) computed with RTTOV-gb from radiosonde profiles have been compared with nearly simultaneous and co-located ground-based MWR observations. Differences between simulated and measured TBs are below 0.5 K for all channels except for the water vapor band, where most of the uncertainty comes from instrumental errors. The Jacobians calculated with the K-module of RTTOV-gb have been compared with those calculated with the brute force technique and those from the line-by-line model ARTS. Jacobians are found to be almost identical, except for liquid water content Jacobians for which a 10 % difference between ARTS and RTTOV-gb at transparent channels around 450 hPa is attributed to differences in liquid water absorption models. Finally, RTTOV-gb has been applied as the forward model operator within a one-dimensional variational (1D-Var) software tool in an Observing System Simulation Experiment (OSSE). For both temperature and humidity profiles, the 1D-Var with RTTOV-gb improves the retrievals with respect to the NWP model in the first few kilometers from the ground.

Time and intensity-specific very short-term forecasting or nowcasting is the biggest challenge faced by an aviation meteorologist. Ground-based microwave radiometer (MWR) has been used for nowcasting convective activity and it was established that there is a good comparison between thermodynamic parameters derived from MWR and GPS radiosonde observations, indicating that MWR observations can be used to develop techniques for nowcasting severe convective activity. In this study, efforts have been made to bring out the efficacy of MWR in nowcasting thunderstorms and fog. Firstly, the observations of MWR located at Palam, New Delhi, India have been compared with the nearest radiosonde data to ascertain the variation in respective profiles. Large differences were found in relative humidity (RH), whereas temperatures from MWR were found to be close to radiosonde observed temperature up to 3.5 km. Subsequently, the scatter plots and correlation coefficients of thermodynamic indices/parameters indicated that most of the parameters are either not correlated or have moderate correlation only for 12:00 UTC profiles. The superepoch technique of lagged composite for various thermodynamic indices/parameters to obtain a combined picture of all the thunderstorm and dense fog cases on the time series could not determine any pattern to predict thunderstorm and dense fog with lead time of 2-4 hours. MWR profile for a case of occurrence of thunderstorm was analyzed. No significant variation was observed in most of the indices (as calculated from MWR observed parameters) prior to the occurrence of thunderstorm. RH at freezing level and between 950 and 700 hPa levels were the only parameters, which increased four hours prior to the occurrence.

During the Juno extended mission, the spacecraft passed Jupiter’s Galilean moon Ganymede. The flyby of Ganymede was in June 2021, at a distance of ~1000 km, providing an opportunity to probe Ganymede’s icy subsurface at multiple microwave frequencies using Juno’s Microwave Radiometer (MWR). The observations provided several swaths across the moons at six frequencies, ranging from 600 MHz to 22 GHz.Early radar results of Jupiter’s icy moons dating back several decades identified the moons as extremely bright objects with significant radar scattering (Ostro et al., 1980). Comparisons of the radar properties of Europa & Ganymede indicated important differences in the radar signatures from each other and our Moon (Ostro et al., 1992). Possible explanations included modulations in porosity (Ostro and Shoemaker, 1990), random facets, larger than the observed wavelengths (Goldstein and Green, 1980), and the idea that the top meters of ice covering their surfaces may be crazed, fissured, and/or filled with jagged ice boulders (Goldstein and Green 1980).The Juno MWR observations represent the first resolved interrogation of Ganymede’s subsurface structure. For icy bodies such as Ganymede and Europa, the MWR observed brightness temperature, TB, is dependent on such ice shell parameters as ice purity, the thermal structure of the icy shell (providing a constraint on the global heat flux) as well as the distribution of internal microwave scattering, thus allowing MWR to provide integral constraints on these shell properties. The MWR observations of Ganymede showed TB generally increases with depth, has a significant reflected synchrotron radiation component at the lowest MWR frequency, 600 MHz, and was well correlated with terrain type. The TB was generally anticorrelated with visible reflectivity (albedo). hermal gradient from deepest channels constrains heat flux (and thickness of conductive ice shell). Analysis of the MWR results at Ganymede provided a new constraint on Ganymede’s heat flux and shell thickness (conductive and total). Using a thermal model based on modified Mixing Length Theory from Kamata et al. (2018) and a Radiative transfer model, the microwave radiation propagation through the ice shell constrains the properties of the subsurface shell including heat flux and thickness of conductive ice shell. The unprecedented MWR measurements of Ganymede, Europa and Io by Juno allow comparative studies of their surfaces and subsurface structures. The Juno MWR measurements complement previous ground-based radar and microwave radiometry observations, which provided early characterization of these surfaces. A comparison of the microwave spectra for all three satellites will be presented, as well as a detailed analysis and interpretation of the Ganymede MWR data that provide new constraints on ice subsurface properties.

<p>While fog can severely affect human activities (air, land and marine transportation), its forecast with current numerical weather prediction (NWP) models stays challenging, especially due to the lack of observations in the atmospheric boundary layer and the misrepresentation of non linear small scale processes. To improve knowledge on fog formation, evolution and dissipation, several instruments have been deployed during the SOFOG3D (SOuth west FOGs 3D experiment for processes study) experimental campaign to provide an unprecedent database of detailed 3D observations. In that context, a network of 8 ground-based microwave radiometers (MWR) provided continuous temperature profiling as well as integrated water vapor and liquid water path measurements during a 6 month period. Additionally, Martinet <em>et al</em> (2020) highlighted large temperature errors in the AROME-France (Application of Research to Operations at Mesoscale) NWP model background profiles during fog forecasts, leading to temperature differences up to 6 K when compared to tower measurements. Nevertheless, this study also demonstrate that the assimilation of MWR observations with a one dimensional variational data assimilation scheme could leads to improved initial conditions. To go further in that direction, MWR temperature profile observations from the SOFOG3D experiment have been added in the AROME-France operational data assimilation system, which uses a three dimensional variational algorithm (3D-Var) and climatological and homogeneous background error covariances (<strong>B</strong> matrix), to quantify the benefit on operational analyses and forecasts of several fog events. Then, the recently developped ensemble variational (EnVar) data assimilation system has been used to conduct new assimilation experiments. The main advantage of such method is to prescribe a fully flow dependent <strong>B</strong> matrix which is spatially and temporally coherent with the forecasted meteorological conditions. In consequences, it leads to more realistic increments. The results obtained with the different assimilation experiments will be presented. Firstly, a statistical analysis of the impact on the AROME-France analyses and short-range forecasts against conventional observations will be discussed. Secondly, specific SOFOG3D observations will be used to investigate the benefit on dedicated fog case studies.</p>

Abstract. At millimeter wavelengths, attenuation by hydrometeors, such as liquid droplets or large snowflakes, is generally not negligible. When using multifrequency ground-based radar measurements, it is common practice to use the Rayleigh targets at cloud top as a reference in order to derive attenuation-corrected reflectivities and meaningful dual-frequency ratios (DFRs). By capitalizing on this idea, this study describes a new quality-controlled approach that aims at identifying regions of cloud where particle growth is negligible. The core of the method is the identification of a “Rayleigh plateau”, i.e., a large enough region near cloud top where the vertical gradient of DFR remains small. By analyzing co-located Ka–W band radar and microwave radiometer (MWR) observations taken at two European sites under various meteorological conditions, it is shown how the resulting estimates of differential path-integrated attenuation (ΔPIA) can be used to characterize hydrometeor properties. When the ΔPIA is predominantly produced by cloud liquid droplets, this technique alone can provide accurate estimates of the liquid water path. When combined with MWR observations, this methodology paves the way towards profiling the cloud liquid water, quality-flagging the MWR retrieval for rain and drizzle contamination, and/or estimating the snow differential attenuation.

Abstract. A RPG-HATPRO ground-based microwave radiometer (MWR) was operated in a deep Alpine valley during the Passy-2015 field campaign. This experiment aims to investigate how stable boundary layers during wintertime conditions drive the accumulation of pollutants. In order to understand the atmospheric processes in the valley, MWRs continuously provide vertical profiles of temperature and humidity at a high time frequency, providing valuable information to follow the evolution of the boundary layer. A one-dimensional variational (1DVAR) retrieval technique has been implemented during the field campaign to optimally combine an MWR and 1 h forecasts from the French convective scale model AROME. Retrievals were compared to radiosonde data launched at least every 3 h during two intensive observation periods (IOPs). An analysis of the AROME forecast errors during the IOPs has shown a large underestimation of the surface cooling during the strongest stable episode. MWR brightness temperatures were monitored against simulations from the radiative transfer model ARTS2 (Atmospheric Radiative Transfer Simulator) and radiosonde launched during the field campaign. Large errors were observed for most transparent channels (i.e., 51–52 GHz) affected by absorption model and calibration uncertainties while a good agreement was found for opaque channels (i.e., 54–58 GHz). Based on this monitoring, a bias correction of raw brightness temperature measurements was applied before the 1DVAR retrievals. 1DVAR retrievals were found to significantly improve the AROME forecasts up to 3 km but mainly below 1 km and to outperform usual statistical regressions above 1 km. With the present implementation, a root-mean-square error (RMSE) of 1 K through all the atmospheric profile was obtained with values within 0.5 K below 500 m in clear-sky conditions. The use of lower elevation angles (up to 5°) in the MWR scanning and the bias correction were found to improve the retrievals below 1000 m. MWR retrievals were found to catch deep near-surface temperature inversions very well. Larger errors were observed in cloudy conditions due to the difficulty of ground-based MWRs to resolve high level inversions that are still challenging. Finally, 1DVAR retrievals were optimized for the analysis of the IOPs by using radiosondes as backgrounds in the 1DVAR algorithm instead of the AROME forecasts. A significant improvement of the retrievals in cloudy conditions and below 1000 m in clear-sky conditions was observed. From this study, we can conclude that MWRs are expected to bring valuable information into numerical weather prediction models up to 3 km in altitude both in clear-sky and cloudy-sky conditions with the maximum improvement found around 500 m. With an accuracy between 0.5 and 1 K in RMSE, our study has also proven that MWRs are capable of resolving deep near-surface temperature inversions observed in complex terrain during highly stable boundary layer conditions.

A new generation of cloud radars, with the ability to make observations close to the surface, presents the possibility of observing fog properties with better insight than was previously possible. The use of these instruments as part of an operational observation network could improve the prediction of fog events, something which is still a problem for even high-resolution Numerical Weather Prediction models. However, the retrieval of liquid water content (LWC) profiles from radar reflectivity alone is an under-determined problem, something which ground-based microwave radiometer observations can help to constrain. In fact, microwave radiometers are not only sensitive to temperature and humidity profiles but also known to be instruments of reference for the liquid water path. By providing the thermodynamic state of the atmosphere, to which the formation and evolution of fog events are highly sensitive, in addition to accurate liquid water path, which can be used to constrain the LWC retrieval from the cloud radar alone, combining microwave radiometers with cloud radars seems a natural next step to better understand and forecast fog events. To that end, a newly developed one dimensional variational (1D-Var) algorithm designed for the retrieval of temperature, specific humidity and liquid water content profiles with both cloud radar and microwave radiometer (MWR) observations is presented in this study. The algorithm was developed to evaluate the capability of cloud radar and MWR to provide accurate LWC profiles in addition to temperature and humidity in view of assimilating the retrieved profiles into a 3D/4D-Var operational assimilation system. The algorithm is firstly tested on a synthetic dataset, which allows the evaluation of the developed algorithm in idealised conditions. It is then tested with real data from the recent field campaign SOFOG-3D, carried out with the use of LWC measurements made from a tethered balloon platform. As expected, results from the synthetic dataset study were found to contain lower errors than that found from the retrievals on the dataset of real observations. It was found that retrieval of LWC can be obtained on idealised conditions with an uncertainty of less than 0.04 gm−3. With real data, as expected, retrievals with a good correlation (0.7) to in-situ measurements, but with a higher uncertainty than the synthetic dataset, of around 0.6 gm−3, was found. This was reduced to 0.5 gm−3 when an accurate droplet number concentration could be prescribed to the algorithm. A sensitivity study was conducted to discuss the impact of different settings used in the 1D-Var algorithm and the forward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational background profiles of the AROME model (Application of Research to Operations at MEsoscale) showing encouraging results for future improvement of the AROME model initial state during fog conditions.

Abstract. A new generation of cloud radars, with the ability to make observations close to the surface, presents the possibility of observing fog properties with better insight than was previously possible. The use of these instruments as part of an operational observation network could improve the prediction of fog events, something which is still a problem for even high-resolution numerical weather prediction models. However, the retrieval of liquid water content (LWC) profiles from radar reflectivity alone is an under-determined problem, something which ground-based microwave radiometer observations can help to constrain. In fact, microwave radiometers are not only sensitive to temperature and humidity profiles but are also known to be instruments of reference for the liquid water path. By providing the thermodynamic state of the atmosphere, to which the formation and evolution of fog events are highly sensitive, in addition to accurate liquid water path, which can be used to constrain the LWC retrieval from the cloud radar alone, combining microwave radiometers with cloud radars seems a natural next step to better understand and forecast fog events. To that end, a newly developed one-dimensional variational (1D-Var) algorithm designed for the retrieval of temperature, specific humidity and liquid water content profiles with both cloud radar and microwave radiometer (MWR) observations is presented in this study. The algorithm was developed to evaluate the capability of cloud radar and MWR to provide accurate LWC profiles in addition to temperature and humidity in view of assimilating the retrieved profiles into a 3D- and 4D-Var operational assimilation system. The algorithm is firstly tested on a synthetic dataset, which allows the evaluation of the developed algorithm in idealised conditions. This dataset was constructed by perturbing a high-resolution forecast dataset of fog and low-cloud cases by its expected errors. The algorithm is then tested with real data from the recent field campaign SOFOG-3D, carried out with the use of LWC measurements made from a tethered balloon platform. As expected, results from the synthetic dataset study were found to contain lower errors than those found from the retrievals on the dataset of real observations. It was found that LWC can be retrieved in idealised conditions with an uncertainty of less than 0.04 g m−3. With real data, as expected, retrievals with a good correlation (0.7) to in situ measurements were found but with a higher uncertainty than the synthetic dataset of around 0.06 g m−3 (41 %). This was reduced to 0.05 g m−3 (35 %) when an accurate droplet number concentration could be prescribed to the algorithm. A sensitivity study was conducted to discuss the impact of different settings used in the 1D-Var algorithm and the forward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational background profiles of the AROME (Application of Research to Operations at MEsoscale) model, showing encouraging results for future improvement of the AROME model initial state during fog conditions.

<p>The project “Pilotstation” at DWD employs a test bed setup to assess data availability, quality, observation impact and operational sustainability for five different ground based remote sensing instruments. The instruments in question, also referred to as “profilers”, are designed to continuously measure vertical profiles of thermodynamic and cloud/aerosol related variables.</p> <p>A ground based microwave radiometer (MWR) is one of the instruments evaluated in the project “Pilotstation”. MWR primarily measure downwelling radiation in the K-band and V-band in the form of brightness temperatures (TB). All-sky temperature and low-resolution humidity profiles as well as high-accuracy liquid water path (LWP, ΔLWP: ± 10-20 gm<sup>-2</sup>) and integrated water vapour (IWV, ΔIWV: ~ ± 0.5 kgm<sup>-2</sup>) are secondary products, which can be derived from the TB.</p> <p>The adaptation of the fast radiative transfer model RTTOV for ground based instruments enabled weather services to go forward with directly assimilating MWR TB rather than secondary products. First assimilation experiments of MWR TB at DWD were successful. Alongside other quality checks, the data assimilation (DA) relies on a cloud detection beforehand. The most frequent reason for rejecting data from DA is the suspected presence of clouds, consequently reliably identifying clouds without excessively rejecting clear-sky data is especially important for a high availability of suitable data.</p> <p>The study presented focuses on the requirements of operational DA and a stand-alone setup of an MWR. The work compares the performance of cloud detection algorithms used in scientific publications based on MWR observations. The comparisons include methods using TB, LWP and their variability. For this the CloudNet classification time series at Lindenberg and observation minus model background statistics serve as references. The presentation will also include progress made on refining the cloud detection schemes at hand in order to achieve a higher precision and to better meet the requirements of DA.</p>

Abstract. Atmospheric stability controls the evolution of wind turbine wakes and thus the yield and performance of wind parks. For estimations of wind park power output and for improving analyses of wind park wakes, crucial parameters were found to be profiles of atmospheric temperature and stability metrics. Atmospheric temperature profiles are available from numerical weather prediction (NWP) models or are measured in situ by balloon-borne sensors but can also be estimated from the ground using radiometric observations. This paper reviews the stability metrics useful for monitoring wind park performances and provides a quantitative assessment of the value of NWP model data for estimating these metrics. This paper also extends previous work, quantifying the performances of microwave radiometer (MWR) observations to estimate stability metrics from surface-based observations in three climatological conditions (marine, continental, and polar) and with different instrument types, situated either on land or in the ocean. Two NWP systems (DOWA and NEWA) have been evaluated against temperature profiles measured by offshore met masts in the 30–100 m layer from the surface. The systematic differences are ∼ 0.3–0.5 K, with no clear dependence on the stability class. Conversely, both models show larger random differences in stable compared to unstable conditions. Root-mean-square (rms) differences were within 1 K for DOWA (Dutch Offshore Wind Atlas), while they exceeded 2 K for NEWA (New European Wind Atlas) in very stable conditions. For temperature gradients in the 50–100 m vertical layer, the mean absolute error (MAE) was ∼ 3.4–4.2 K km−1, with 5.8–8.4 K km−1 rms and 0.7–0.8 correlation. From the six datasets of MWR and radiosonde observations considered here, temperature profiles mostly agree within ∼ 0.5 K near the surface, increasing to ∼ 1.5 K at 2 km. Substantial differences are found between MWR performances in retrieving temperature and potential temperature gradients (50–300 m) onshore and offshore. Onshore, potential temperature gradients agree with 2.1–3.4 K km−1 MAE and 0.7–0.9 correlation. Offshore, both MAE (0.9–1.9 K km−1) and correlation (0.3–0.4) are relatively lower, although performances tend to improve using elevation scanning retrievals. Considering all the datasets, the reported MAE values range from 0.9–3.4 K km−1 while rms values range from 1.2–5.1 K km−1. Thus, the uncertainty of MWR for temperature and potential temperature gradients in the 50–300 m vertical layer is ∼ 0.5–4.3 K km−1. The relatively lower performances offshore may be attributed to the training of the inversion method, which may under-represent the peculiar offshore conditions, and the ship movements, which can impact low-elevation observations. These considerations suggest that appropriate dedicated training and elevation scanning with ship movement compensation may be required for the MWR to better catch potential temperature gradients typical of offshore conditions.

Juno’s Microwave Radiometer (MWR) is providing an unprecedented opportunity to explore the dynamical properties and composition of Jupiter’s atmosphere well below its cloud tops. Jupiter’s atmosphere is arguably the most visibly heterogeneous and time variable in the solar system. Since its arrival on 2016 August 27, the MWR has observed variability in microwave emission at wavelengths between 1.3 and 50 cm, sensing from 0.7 bar to over 100 bars of atmospheric pressure at over 69 close approaches to the atmosphere, known as “perijoves”. There has been a concerted effort to collect contextual information from other Juno instruments, as well as ground- and space-based observations to help interpret the MWR results. The space-based observations have included those from Juno’s own visible camera (JunoCam) and its Jupiter Infrared Auroral Mapper (JIRAM), as well as the Hubble Space Telescope (HST). The ground-based observations have included images and spectra from both professional and citizen-science astronomers. We report here observations that are constrained to spatial resolutions of 2° in latitude or better, and have been subject to recent improvements in the calibration drift for all MWR’s channels with an improved relative calibration uncertainty of 0.5% or better over the entire mission. These are shown in Figure 1 for the MWR observations covering planetocentric latitudes from 40°S to 80°N. This has allowed us to evaluate zonal-mean temperatures and variability with improved confidence that these are real and not an artifact of receiver drift. The region that shows the greatest variability from a zonal mean is the North Equatorial Belt, (NEB: 12°N-16°N) with a 2% standard deviation from the mean at all levels sensed by the MWR except for the 50-cm channel that senses variability in temperature and ammonia and water composition at pressures in excess of 100 bars of pressure. Among the strongest variability associated with discrete features in the atmosphere is a major upwelling and subsequent clearing of cloud cover in the North Temperate Belt (NTB: 20°N-26°N) in August-September of 2020. In general, the microwave brightness temperature variability often but not always correlates with visible or near- to mid-infrared variability. In some regions, such as the Equatorial Zone (EZ: 3°S-6°N), substantial variability is detected not only in regions above the level of the water-condensate cloud (~10 bars) but also at great depth (>100 bars). Current work is focusing on identifying where variabilities in the zonal-mean microwave brightness are the result of zonally discrete features in the atmosphere, particularly the NEB. Future work will address observations fulfilling the same spatial resolution requirements but covering higher northern latitudes. Figure 1. Composite MWR maps of observed zonal-mean antenna temperatures in all channels that cover planetocentric latitudes 40°S to 80°N with 2° meridional resolution, interpolated to a common emission angle of 47°. Maps have been expanded by 0.1 year for visibility. White circles denote the actual date of the measurements, with the responsible perijove indicated at the bottom. Rightmost panels display the zonal-mean temperatures over all perijoves, together with the associated standard deviation. Unfilled spots in the maps correspond to spacecraft attitudes that precluded MWR observations being made at the required spatial resolution. Note that Channels1 and 2 have angular resolutions (“footprints”) twice the size of Channels 3-6; keeping a 2° latitude resolution for those channels thus restricts their coverage relative to Channels 3-6.

Uncertainties in the absolute calibration of the water vapor measurements are currently the limiting factor in the improvement of radiative transfer algorithms for clear skies. While instruments such as the microwave radiometer can provide accurate measurements of total precipitable water vapor in the column, accurate profiles of water vapor are also needed in order to calculate accurate cooling rate profiles. Raman lidar and differential absorption lidar (DIAL) are presently the most advanced techniques to measure the vertical distribution of water vapor in the atmosphere with both high temporal and vertical resolution and accuracy. Therefore, it is important to perform a dedicated experiment to assess the performance of both systems, with respect to accuracy, resolution, and available range. A secondary goal of this experiment was to better characterize the operational Raman lidar at the ARM Southern Great Plains (SGP) Cloud and Radiation Testbed (CART) site in north central Oklahoma. The water vapor DIAL from the Max-Planck Institute (MPI) for Meteorology in Hamburg, Germany was collocated with the Raman lidar from 29 September - 19 October 1999. The two systems were located about 15 m apart, and more than 100 h of coincident data was collected during both daytime and nighttime. Radiosondes, which were launched from the CART site every three hours during this experiment, and co-located microwave radiometer data are also used in this study.

The role of atmospheric forcings and WRF physical set-up on convective initiation over Córdoba, Argentina

Simulation of an extreme heavy rainfall event over Chennai, India using WRF: Sensitivity to grid resolution and boundary layer physics

<strong class="journal-contentHeaderColor">Abstract.</strong> Accurate boundary layer temperature and humidity profiles are crucial for successful forecasting of fog, and accurate retrievals of liquid water path are important for understanding the climatological significance of fog. Passive ground-based remote sensing systems such as microwave radiometers (MWRs) that have multiple channels between 22&ndash;31 GHz and 51&ndash;59 GHz, and infrared spectrometers such as the Atmospheric Emitted Radiance Interferometer (AERI) which measures spectrally resolved infrared radiation (3.3 to 19.2 <em>&mu;m</em>) can retrieve both thermodynamic profiles and liquid water path. Both instruments are capable of long-term unattended operation and have the potential to support operational forecasting. Here we compare physical retrievals of boundary layer thermodynamic profiles and liquid water path during 13 cases of supercooled radiation fog from a MWR and an AERI collocated in central Greenland. We compare both sets of retrievals to in-situ measurements from radiosondes and surface-based temperature and humidity sensors. The retrievals based on AERI observations accurately capture shallow surface-based temperature inversions (0&ndash;10 m a.g.l) with lapse rates of up to -1.2 &deg;C m^&minus;1, whereas the strength of the surface-based temperature inversions retrieved from MWR observations are uncorrelated with in-situ measurements. For all but one case study, the retrievals based on AERI observations detect fog onset (defined by a threshold in liquid water path) earlier than those based on MWR observations by up to 4 hours. We propose that due to the high sensitivity of the AERI instrument to near-surface temperature and small changes in liquid water path, the AERI (or an equivalent infrared spectrometer) could be a useful instrument for improving fog monitoring and nowcasting.