3.2.1. Microwave radiometers

The two microwave radiometers Humidity And Temperature PROfiler (HATPRO; Rose and others, Reference Rose, Crewell, Löhnert and Simmer2005) and Microwave Radar/radiometer for Arctic Clouds-Passive (MiRAC-P; Mech and others, Reference Mech, Kliesch, Anhäuser, Rose, Kollias and Crewell2019) were installed on Polarstern’s top deck (‘Peildeck’) on starboard (see Fig. 3). HATPRO measures TBs at seven channels between 22 and 31 GHz with the 22.24 GHz channel being close to the water absorption line (receiver 1) and at seven channels in the 58 GHz oxygen absorption complex (receiver 2). MiRAC-P utilizes six channels along the 183 GHz water vapor line (receiver 1) and two window channels at 243 and 340 GHz (receiver 2). Unfortunately, the receiver at 340 GHz failed at the beginning of the cruise. Both MWRs measure with 1 ${\textrm{s}}$ integration time. Internally, both radiometers contain a rotatable mirror that can change the incidence angle, and a wire grid that separates the incoming radiation in vertically and horizontally polarized fractions, so that at each frequency, one of these fractions is measured by the receivers (more about the polarization below). The instruments’ specifications are listed in Table 1. An absolute calibration with liquid nitrogen was performed twice during the cruise (7 and 30 July 2022). We estimate the uncertainty in TBs because of these calibrations to be below 1 ${\textrm{K}}$. The radiometers pointed zenith for 45 min h⁻¹ to derive temperature and humidity profiles, IWV and LWP and toward the mirrors for about 15 min h⁻¹. For the data analysis, we used only data where the flag visual_inspection_filter, indicating reduced data quality (e.g. because of a wet radome, provided with the data), was not set.

Figure 3.

(A) Sky camera, (B) IR surface camera with VIS surface camera on top, (C) MiRAC-P and (D) HATPRO on board Polarstern.

Table 1.

The HATPRO and MiRAC-P frequency, polarization for our setup (assuming 53^∘ zenith angle, either predominantly vertical (PV) or predominantly horizontal (PH)), half power beam width and footprint geometry. The footprint geometry is calculated for 53^∘ zenith angle, 22 m instrument height and without ship motion

* the 340 GHz receiver was malfunctioning.

3.2.2. Mirrors and polarization

Fixed to the stands of the MWRs, aluminum plates with a size of 0.55 ${\textrm{m}^2}$ were installed as external rotatable mirrors (see Fig. 4). This mirror setup was developed for this cruise and deployed for the first time. It redirects upwelling radiation from 53^∘ zenith angle, i.e. the typical angle of operational satellite microwave imagers, to the MWR receivers. As we are not observing in zenith direction, this corresponds to an uneven distribution of the polarized radiation to the receivers, resulting in polarization mixing of the linear polarized contributions. The plane of incidence defining vertical and horizontal polarization is the plane containing the direction of the upwelling radiation from the surface from 53^∘ zenith angle and the surface normal to the Earth’s surface. Then the contribution of vertically polarized TB, $T_{\mathrm{b,v}}$, and of horizontally polarized TB, $T_{\mathrm{b,h}}$, to the overall mixed polarization signal is given by

(3)\begin{equation} \begin{array}{rcl} T_{\mathrm{b,1}} &=& T_{\mathrm{b,h}}\cos({53}^{\circ})^2 + T_{\mathrm{b,v}}\sin({53}^{\circ})^2 \\ &\approx & 0.36\cdot T_{\mathrm{b,h}} + 0.64\cdot T_{\mathrm{b,v}} \end{array} \end{equation}

for receiver 1 (between 22 and 31 GHz and along the 183 GHz line) and

(4)\begin{equation} \begin{array}{rcl} T_{\mathrm{b,2}} &=& T_{\mathrm{b,h}}\sin({53}^{\circ})^2 +T_{\mathrm{b,v}}\cos({53}^{\circ})^2 \\ &\approx & 0.64\cdot T_{\mathrm{b,h}} + 0.36\cdot T_{\mathrm{b,v}} \end{array} \end{equation}

for receiver 2 (58 GHz oxygen absorption complex, at 243 and at 340 GHz; equations following personal communication with manufacturer RPG). We thus call the polarization of $T_{\mathrm{b,1}}$, according to the dominating polarization, predominantly vertical (PV), which corresponds to the channels between 22 and 31 GHz and along the 183 GHz water vapor line. And for $T_{\mathrm{b,2}}$ we call the polarization predominantly horizontal (PH), which corresponds to all other frequency channels. We assume that the mirrors are perfectly reflective. Ship motions resulted in a few degree deviations from this target zenith angle. We routinely manually dried the mirror surface in foggy conditions due to the lack of preventive measures like heating. However, the presence of liquid droplets creates potential uncertainty. The mirror was rotated manually five times during the cruise to scan various zenith angles. However, these scans are inconclusive due to heterogeneous ice surfaces, such as melt ponds and leads, and not presented here.

Figure 4.

Mirror setup. Left: the receiver of the radiometer is positioned at x = 0 m with an elevation angle of 40^∘. Because of the mirror alignment, this results in a zenith angle of 53^∘. The mirror has a size of 0.55 $\textrm{m}^2$. The red lines are the path of rays of the microwave radiation with the center and the outer rays obtained from the beam width (3 dB). Right: projected footprints of HATPRO (left ellipse) and MiRAC-P (right ellipse) on the surface.

3.2.3. Auxiliary measurements

A visual (VIS; GoPro HERO 10) and an IR camera (InfraTec VarioCAM HD, 7.5–14 μm) observing the surface as well as a sky camera (VIS and IR) were complementing the microwave measurements and provide context for the interpretation of the data. The surface-facing cameras were installed next to the radiometers on the deck (see Fig. 3) and took images every 5 ${\textrm{s}}$. An inertial motion unit (IMU) measuring roll and pitch angles was installed on top of the IR camera. The resulting angles measured with the IMU were used to project the IR data to the ground. Missing IMU data due to instrument or recording issues were filled using the average angle of the respective day.

Additionally, on-ice measurements of the ice and surface conditions conducted during 12 ice stations are available. Here, ice cores were taken to measure vertical profiles of density, temperature and salinity in the sea ice (Rückert and others, Reference Rückert, Walbröl, Niehaus and Spreen2023a, Reference Rückert, Walbröl and Spreen2023b). During the time of the campaign, the snow on the sea ice had already melted. The resulting surface scattering layer was described using traditional snow pit methods. The microwave radiometer footprint was sampled on 24 July, 31 July and 6 August. Results of most of the measurements are given in the cruise report (Kanzow, Reference Kanzow2023). In summary, the spatial variability of some of the surface and ice parameters, e.g. the diameters of the crystals making up the uppermost layer or the ice thickness in some cases, was high and could change significantly within a few meters.

4.1.1. Case 1: 18 July 2022

On 18 July 2022 (Fig. 5), Polarstern was moving slowly with a mean speed of 0.25 $\textrm{m}\,\textrm{s}^{-1}$ and we can see small ice floes drifting in and out of the footprint. The distance from the start (at 80 $^{\circ}49.48^{\prime}$ N, 9 $^{\circ}9.96^{\prime}$ E) to the endpoint of the measurements is about 0.2 ${\textrm{km}}$. The sea-ice temperatures are around 0 $^{{\circ}}$C and ocean temperatures are slightly colder. On this day, a warm air intrusion occurred in the measurement area and air temperatures were well above freezing with an average of 7.0 $^{{\circ}}$C during the surface observation. The atmospheric HATPRO measurements right before and after the surface observation yield IWV values of 16.4 and 16.1 ${\textrm{kg}\,\textrm{m}^{-2}}$, respectively.

Figure 5a and b show the HATPRO TBs at 22–31 GHz and at frequencies in the 58 GHz oxygen absorption complex, respectively. TBs at 22–31 GHz show lower values (about 160 ${\textrm{K}}$) over open ocean than over sea ice (about 250 ${\textrm{K}}$). In addition to the surface-driven variability of the TBs resulting in TB changes of up to 90 ${\textrm{K}}$, small periodic oscillations of the order of a few Kelvin are visible at these frequencies.

At 51.26 and 52.28 GHz, TBs over open ocean are about 220 and 240 ${\textrm{K}}$, respectively, and about 265 ${\textrm{K}}$ over sea ice. Qualitatively, their behavior is similar to the 22–31 GHz range. At frequencies between 54.94 and 58 GHz, an opposite behavior can be seen with slightly higher (about 3 ${\textrm{K}}$) TBs over the ocean than over the sea ice, with TBs ranging between 273 and 278 ${\textrm{K}}$. No changes with changing surface conditions are observed at 53.86 GHz, showing almost constant TBs of about 273 ${\textrm{K}}$ (with a standard deviation below 1 ${\textrm{K}}$).

Figure 5c and d show the TBs measured by the MiRAC-P radiometer at frequencies in the 183 GHz water vapor line and at 243 GHz, respectively. Due to instrument settings, the measurement period ended about 2 min earlier than the HATPRO measurements. TBs in the 183 GHz water vapor line change little (about 1 ${\textrm{K}}$) during the observation period and are around 277 ${\textrm{K}}$. At the 243 GHz window channel, TBs show lower values (about 264 ${\textrm{K}}$) over open ocean than over sea ice (about 269 ${\textrm{K}}$).

We can conclude that we find a clear surface signal from ice and ocean in the TBs of the frequencies for which the atmosphere is rather transparent, i.e. where $T_{\mathrm{b,atm}}$ in Eqn (1) is low (in this case 22–31 GHz, 51.26 and 52.28 GHz). Lower TBs occur over the highly reflective ocean compared to the highly emissive ice for the TBs between 22 and 31 GHz and TBs at 51.26 and 52.28 GHz. The small-scale oscillations correspond to changing zenith angles due to periodic ship motions. However, at frequencies in the oxygen absorption complex where the atmosphere is rather opaque (54.94–58 GHz), an opposite behavior can be seen with higher TBs over the ocean and lower TBs over the sea ice. For these frequencies, the reflected atmospheric signal $T_{\mathrm{b,atm}}$ originates only from the lowest few kilometers of the atmosphere. Due to the warm air intrusion, the lower atmosphere was very warm on this day. Therefore, a distinct reflected atmospheric signal can be found for the TBs over the ocean, which is more reflective than the sea ice.

We observe no apparent changes in TB with changing surface conditions at frequencies in the 183 GHz water vapor line. Here, the surface and atmospheric contributions cannot easily be disentangled. The atmospheric water vapor content is high enough to cause atmospheric downwelling TB of the same order as the surface temperatures. Therefore, the measured TBs are insensitive to changes in emissivity of different surface types (see Eqn (1)).

At the 243 GHz window channel, the surface signal is again identified: here, the contribution by $T_{\mathrm{b,atm}}$ is lower than at frequencies along the 183 GHz water vapor line and thus differences in ice and ocean emissivities can be detected. We find higher TBs (by about 5 ${\textrm{K}}$) over ice than over open ocean. Still, atmospheric emissions are an order of magnitude larger than at 22–31 GHz and thus significantly decreases the sensitivity to surface emissivity changes. We attribute the dip seen around 17:20 UTC in the TBs at 243 GHz to a fraction of open ocean within the footprint. Because HATPRO has a larger footprint, the signal of open ocean within the footprint is less pronounced here. Since the footprints do not overlap, the decrease in TBs is not observed simultaneously.

4.1.2. Case 2: 8 August 2022

During the second case on 8 August 2022, Polarstern travelled through an area with small ice floes (around 76 $^{\circ}14.11^{\prime}$ N and 15 $^{\circ}37.36^{\prime}$ W; Fig. 6), covering a distance of about 3.3 ${\textrm{km}}$ with a mean speed of 3.4 $\textrm{m}\,\textrm{s}^{-1}$. Ice and ocean surfaces had similar temperatures (below 0 $^{{\circ}}$C). On that day, the general ice conditions are different from the first case as we can also observe the formation of new ice, e.g. around 5:43 UTC, which is slightly visible in the VIS and IR images. Air temperatures were close to but below 0 ${^{\circ}C}$. Atmospheric HATPRO measurements right before and after yield IWV values of 10.7–10.9 ${\textrm{kg}\,\textrm{m}^{-2}}$.

The TBs at the frequencies between 22 and 31 GHz (Fig. 6a) and at 51.26 and 52.28 GHz (Fig. 6b) change more rapidly than in the first case. Due to the faster speed of the vessel on that day, surface conditions also changed more rapidly. Here, the TBs show a qualitatively similar behavior compared to the case on 18 July with high TBs over sea ice (for example, 240–260 ${\textrm{K}}$ for frequencies between 22 and 31 GHz and 240 and 250 ${\textrm{K}}$ for 51.26 and 52.28 GHz around 5:37:20 UTC) and lower TBs over open ocean (around 155 ${\textrm{K}}$ for frequencies between 22 and 31 GHz and 220 and 235 ${\textrm{K}}$ for 51.26 and 52.28 GHz). Around 5:42 UTC over new ice, TB values higher than over open ocean are detected. At frequencies between 53.86 and 58 GHz, no distinct changes with changing surface conditions are observed with slightly lower TBs at 53.86 GHz of 270 ${\textrm{K}}$ and TBs ranging between 271 and 275 ${\textrm{K}}$ for the other frequencies that vary little during the time period (standard deviations below 1 ${\textrm{K}}$).

The TBs at the four lower frequencies of the 183 GHz water vapor line (Fig. 6c) are around 271 ${\textrm{K}}$ and also vary little during the observation period (standard deviations below 0.5 ${\textrm{K}}$). However, the TBs at 190.81 GHz (and to some extent also at 188.31 GHz) do vary with changing surface types. Here, we observe the opposite of what has been seen in the 22 and 31 GHz channels: lower TB values over ice at 190.81 GHz (for example 261 ${\textrm{K}}$ at 5:37:20 UTC, indicated by B in Fig. 6) compared to the TBs over ocean (around 267 ${\textrm{K}}$). At the 243 GHz window channel, the same behavior can be found as well: TBs over ice (246 ${\textrm{K}}$ at 5:37:20 UTC) are about 10 ${\textrm{K}}$ lower than over ocean (around 256 ${\textrm{K}}$). Interestingly, the young ice, which was observed at several times, one example indicated by C in Fig. 6, shows another signature, where the TBs at 243 GHz are up to 263 ${\textrm{K}}$ and thus higher than the ones observed over ocean. Such higher TBs can also be observed at earlier times during the observation period, when the VIS imagery also suggests that new ice could have been present.

Again, we conclude that distinct surface signals can be observed in the TBs of the frequencies between 22 to 31 GHz and 51.26 to 52.28 GHz. Here, also different TB signatures of ice and ocean surfaces are seen at 190.81 GHz (and to a certain extent at 188.31 GHz) because of the lower atmospheric moisture content on 9 August compared to 18 July. The influence of the surface is also clearly visible at 243 GHz. TBs at 243 GHz are lower over ice than over ocean, except for the young ice, where they are higher, suggesting differences in emissivity between different ice types.

In the two cases, we observe higher and lower TBs over ice than over ocean at 243 GHz. But we also encountered cases when the TBs were the same: In Fig. A1 (in the Appendix), ice and ocean are not distinguishable at all at 243 GHz. Here, ocean and ice have similar physical temperatures (Fig. A1f). We assume that for this case, the emissivities of ice and ocean are alike at 243 GHz, which could explain why there is no signal in TB at 243 GHz of the floe around 01:19 UTC, which is identified in the TBs of the 22 and 31 GHz channels (Fig. A1a).

The different behavior of TBs at 243 GHz in the three cases is attributed to the interplay of $T_{\mathrm{b,atm}}$ (which is, for example, lower on 8 August compared to 18 July) and different ice temperatures and emissivities. For example, we do not expect surface melt on 8 August (ice surface temperatures were below freezing) in contrast to 18 July, and the presence of liquid water strongly affects the dielectric permittivity and, in turn, the emissivity of the ice surface.

4.1.3. Brightness temperature statistics

Before analyzing the surface emissivities in more detail, we examine the statistics of all measured surface TBs during the campaign. The distribution of the surface TBs of all measurements during the cruise are shown in Figs. 7–10 as density plots.

Figure 7.

All surface TB observations during ATWAICE at frequencies between 22 and 31 GHz at a 53^∘ zenith angle and predominantly vertical polarization. The total number of measurements is 500 982 (equivalent to about 139 hours). The data are shown as probability density using kernel density estimation with a Gaussian kernel.

Figure 7 shows the brightness temperature distribution for the TBs between 22 and 31 GHz. Here, we assume that the atmosphere does not dominate the signal. Indeed, we observe that the surface signal is evident. The prominent peaks around 160 ${\textrm{K}}$ correspond to open water, while the peaks around 260 ${\textrm{K}}$ correspond to observations of the ice.

At the frequencies in the 58 GHz oxygen absorption complex (Fig. 8), the absorption and downwelling TB of the atmosphere is higher than for the lower frequencies and we assume that the atmospheric contributions dominate the signal, especially closer to the center of the oxygen complex. Hence, we do not identify a distinction between ice and open ocean at these frequencies, except for 51.26 and 52.28 GHz. The relation of the oxygen absorption to the air temperature is indicated in Fig. 8b by additionally showing the distribution of measured air temperatures at 29 m height above sea level. Note that although two peaks are visible for the three highest frequencies in Fig. 8b we do not attribute them to different surface types as they do not correspond to the surface signals identified between 22 and 31 GHz.

Figure 8.

All surface TB observations during ATWAICE at frequencies (a) 51.26 and 52.28 GHz and (b) between 53.86 and 58 GHz at a 53^∘ zenith angle and predominantly horizontal polarization. The total number of measurements is 485 649 (equivalent to about 135 hours). The data are shown as probability density using kernel density estimation with a Gaussian kernel. In addition, the distribution of air temperatures (T air) measured onboard is shown (dashed black line).

As for the oxygen absorption complex, we assume that atmospheric contributions are dominating the observed TBs along the 183 GHz water vapor line. We observe a trimodal distribution (Fig. 9), similar to the distribution found for the IWV measurements from radiosonde data. Thus, we cannot identify a distinct surface signal.

Figure 9.

All surface TB observations during ATWAICE at frequencies in the 183 GHz water vapor line at a 53^∘ zenith angle and predominantly vertical polarization. The total number of measurements is 600 607 (equivalent to about 167 hours). The data are shown as probability density using kernel density estimation with a Gaussian kernel. In addition, the distribution of integrated water vapor (IWV) derived from 117 radiosonde measurements is shown (dashed black line).

At 243 GHz (Fig. 10), the atmospheric moisture content has a stronger influence on the TBs than at the 22–31 GHz channels (water vapor continuum). However, the atmosphere does not dominate the TBs as for the 183 GHz water vapor line since we do not observe a trimodal distribution here. Neither do we observe the bimodal structure as for the 22–31 GHz channels. At this frequency and polarization, we therefore find that ice and ocean appear to have less distinct emission signatures.

Figure 10.

Same as Fig. 7 but for 243 GHz, predominantly horizontal polarization. The total number of measurements is 600 607 (equivalent to about 167 hours).

In summary, these examples show that the general idea of our measurement setup is working: the mirrors allow observations of the surface as we can identify different surface types in the measurements. We can attribute differences in the TBs at different frequencies to varying sensitivities to atmospheric parameters like water vapor. In addition, surface emissivities depending on the surface type influence the TBs and thus we continue by estimating surface emissivities.

4.2.1. Estimation of $T_{\mathrm{b,atm}}$

Due to the mirror position, it was not possible to directly measure the downwelling TBs under a zenith angle of 53^∘ (see Fig. 4). When we assume specular reflections, these TBs would equal $T_{\mathrm{b,atm}}$ in Eqn (1). Therefore, we estimate them using an empirical relation for the measured TBs of the radiometers viewing in zenith direction measured directly before and after the surface observations. To derive an empirical relation, we use the radiosondes launched at least twice a day (12:00 and 00:00 UTC) as model input to the Passive and Active Microwave TRAnsfer tool (PAMTRA; Mech and others, Reference Mech2020) to calculate the downwelling TBs at zenith angles of 0^∘ ( $T_\mathrm{b,atm,{0}^{\circ}}$) and 53^∘ ( $T_{\mathrm{b,atm}}$). The scatter plots and fitted linear least-squares relations are shown in Appendix Fig. A2. $T_{\mathrm{b,atm}}(t)$ at time t is then given as $T_{\mathrm{b,atm}}(t) = a_1 \cdot T_\mathrm{b,atm,{0}^{\circ}}(t) + a_2$, with the coefficients a ₁ and a ₂ given by the regression (annotated in Fig. A2, for 243 GHz a quadratic fit is used). $T_\mathrm{b,atm,{0}^{\circ}}(t)$ is derived from linearly interpolating measured $T_\mathrm{b,atm,{0}^{\circ}}$ before and after the surface observations.

Based on the scatter plots and the measurement uncertainty of the radiometers as well as on the standard deviations of the atmospheric observations within 15 min before the surface observations (on the order of 0.15 ${\textrm{K}}$ around 22.24 GHz and 0.6 ${\textrm{K}}$ for 243 GHz), the overall uncertainty of $T_{\mathrm{b,atm}}$ is estimated to be 1 ${\textrm{K}}$ for the frequencies around 22.24 GHz and 3 ${\textrm{K}}$ for 243 GHz.

4.2.2. Estimation of $T_{\mathrm{s}}$

Using the opening angles of the IR camera and the MWRs, we estimate a footprint position and size within the IR measurements. Note that the elliptical footprints differ depending on the frequency. We have to account for uncertainties originating from a drift in the timestamps recorded by the IR camera and uncertainties of the measured zenith angle affecting the projection of the IR imagery. The timestamp was manually corrected by comparing the IR imagery to the VIS images that provide a GPS-derived timestamp. Still, to consider these uncertainties, we use a rectangular box here (larger than the calculated elliptical footprints, shown as a black outline in Figs. 5 and 6) and estimate the uncertainty of the surface temperature using the variability of $T_{\mathrm{s}}$ within that rectangle. Because the cruise was conducted in the melting season, the IR temperatures of ocean, ice and melt pond are similar. The standard deviation within our defined rectangle is usually below 1 ${\textrm{K}}$.

The thermal IR measured TBs averaged within the rectangular footprint are then converted to physical surface temperatures using an IR emissivity of 0.996 (Thielke, Reference Thielke2023). The uncertainty of the IR emissivity adds to the overall uncertainty of $T_{\mathrm{s}}$, which we estimate to be 1.5 ${\textrm{K}}$. Every 5 ${\textrm{s}}$, a thermal IR image is taken, and for the following calculation we select the coinciding TB measurements.

4.2.3. Estimation of emissivities and their uncertainties

The uncertainty of the derived emissivities $\Delta\epsilon$ can be estimated using a Gaussian propagation of uncertainty. The resulting expression is given by

(5)\begin{equation} \Delta\epsilon =\bigg | \frac{1}{T_{\mathrm{s}} -T_{\mathrm{b,atm}}}\bigg | \sqrt{(\Delta T_{\mathrm{b}})^2+ A(\Delta T_{\mathrm{s}})^2 + B(\Delta T_{\mathrm{b,atm}})^2} \end{equation}

where

\begin{equation*} \begin{array}{rcl} A &=& \frac{(T_{\mathrm{b,atm}} -T_{\mathrm{b}})^2 }{(T_{\mathrm{s}} -T_{\mathrm{b,atm}})^2} \\ B &=& \frac{(T_{b} -T_{\mathrm{s}})^2 }{(T_{\mathrm{s}} -T_{\mathrm{b,atm}})^2}. \end{array} \end{equation*}

Equation 5 contains the individual uncertainties of $T_{\mathrm{s}}$, $\Delta T_{\mathrm{s}}$, estimated to be 1.5 ${\textrm{K}}$, of $T_{\mathrm{b,atm}}$, $\Delta T_{\mathrm{b,atm}}$, which are 1 and 3 ${\textrm{K}}$ between 22–31 GHz and 243 GHz, respectively, and the uncertainty of the radiometer TB measurements, $\Delta T_{\mathrm{b}}$, estimated to be 0.5 ${\textrm{K}}$. Note that $\Delta T_{\mathrm{b}}$ would be higher when assuming a mirror that is not perfectly reflective. Also, $\Delta\epsilon$ is proportional to $(T_{\mathrm{s}}-T_{\mathrm{b,atm}})^{-1}$. That means $\Delta\epsilon$ is high when the downwelling $T_{\mathrm{b,atm}}$ is close to the surface temperature $T_{\mathrm{s}}$. This is of importance at 243 GHz, where $T_{\mathrm{b,atm}}$ and $T_{\mathrm{s}}$ are of the same order of magnitude. Therefore, we exclude calculated values of ϵ with uncertainties higher than 0.025 in the following, reducing the relative uncertainties of the individual measurements to below 5%. For the remaining data, the resulting calculated average uncertainties are 0.005 between 22–31 GHz, 0.011 and 0.016 for 51.26 and 52.28 GHz and 0.022 for 243 GHz. The difference between specular and Lambertian reflections is minimal near our observation angle (Mätzler, Reference Mätzler2005), so our assumption about specular reflections in deriving $T_{\mathrm{b,atm}}$ hardly contributes to our uncertainty estimates. To support the interpretation of the derived surface emissivities, we extracted the RGB values from the VIS camera observation closest in time and within an area containing the radiometer footprints. A statistical analysis of emissivity and RGB values provides reasonable results, despite high collocation uncertainties.

Figure 11a shows a scatter plot and histograms with kernel density estimates of the ratio of red (R) to blue (B) from the VIS camera and the emissivities at 22.24 GHz. The left, low emissivity peak of ϵ corresponds to open ocean (low R to B values) and the peak at the higher emissivities to ice surfaces (high R to B value). The calculated emissivities for the other frequencies between 22 and 31 GHz are shown in Fig. A3. The maximum of the ocean emissivity shifts with increasing frequencies (from 0.51 to 0.55), while the maximum emissivity of ice lies between 0.95 and 0.96. Here, values for 23.84 GHz are slightly lower than for the other frequencies (but still within the derived uncertainty). We cannot rule out that this is a systematic uncertainty stemming from, e.g., the instrument or the liquid nitrogen calibration.

Figure 11.

Bivariate and marginal histograms with kernel density estimates (blue line) of the red (R) to blue (B) ratio of the RGB values from the visual camera and the calculated microwave emissivities ϵ in (a) for 22.43 GHz (predominantly vertical polarization) and in (b) for 243 GHz (predominantly horizontal polarization). The shown cases have been manually selected for clear-sky situations and uncertainties in ϵ smaller than 0.025, resulting in 3730 (a) and 1100 (b) measurements. The R to B ratio has been calculated for the MWR footprint area. The dashed red lines are Gaussian functions fitted to the data.

We often observe values between the two peaks that we attribute to different surface types being present within the footprint. Figure A4 shows the derived emissivities at 51.26 and 52.28 GHz. They resemble the ones at lower frequency with an ocean emissivity around 0.57 and ice emissivities around 0.94 but with wider peaks and more intermediate emissivities occurring.

Figure A4.

Kernel density estimate of the distribution of emissivities (ϵ) calculated for the frequencies 51.26 and 52.28 GHz at a 53^∘ zenith angle for manually selected times with clear sky (N = 3730).

Figure 11b shows the derived emissivities at 243 GHz. For this frequency, the distinction between different surface types is less pronounced. Here, we attribute the second peak around 0.78 to the ocean and the higher values above 0.80 to ice and mixed surface types. These higher values correspond for example to the sea ice observed on 18 July (Fig. 5) and to the young ice observed on 8 August (Fig. 6). Note that for the young, dark-looking ice we do not expect high R to B values. In addition, we can observe a small and broad peak around 0.67. These are emissivities of ice as well, observed, e.g., on 8 August (corresponding to the low TBs in Fig. 6d). The widths of the modal peaks for the different surface types can be attributed to a variety of causes. In addition to the natural variability of emissivity for different ice types and the induced uncertainties, we might observe mixed surface types (ice and open water) in the footprint, as discussed before. Furthermore, varying zenith angles (e.g. by ship movement) contribute to a high variability of ϵ over open ocean and, likely to a lesser degree, also over ice.

Table 2 summarizes the estimated emissivities by listing the maximum values of the surface modes and their widths. The values are found by fitting bi- and trimodal distributions, given as sum of normal distributions, to the data, i.e. the values are means and standard deviations of the fitted Gaussian distributions. The estimated emissivities agree with a modelling study by Willmes and others (Reference Willmes, Nicolaus and Haas2014) who estimate emissivities above 0.94 with a monthly standard deviation of 0.04 in June at 19 GHz, 50^∘ viewing angle. Our observations also align with previously reported emissivity values, see Table A1, that show values up to 0.96 at 21 GHz vertical polarization and 50^∘ viewing angle. Converted to our PV polarization measurements, this would result in an emissivity of 0.94 according to Eqn (3). However, also values as low as 0.79 (0.73) for vertical (PV, converted by Equation 3) polarization are reported for multiyear ice in September. To our knowledge, there are no estimates of emissivities at 243 GHz off-nadir, and thus we show literature values at nadir in Table A1 where comparisons should be approached with greater caution. The values of this study for one ice type (above 0.8) fit to the literature while the lower ice emissivities of 0.67 have not been reported previously.

Table 2.

Calculated emissivities ϵ at 53^∘ zenith angle for different surface types and their variability σ_ϵ, given by the width of Gaussian functions fitted to the multimodal distribution of the data