ALBERT

Description: Water vapour is a critical component of the Earth system. Techniques to acquire and improve measurements of atmospheric water vapour and its isotopes are under active development. This work presents a detailed intercomparison of water vapour total column measurements taken between 2006 and 2014 at a Canadian High Arctic research site (Eureka, Nunavut). Instruments include radiosondes, sun photometers, a microwave radiometer, and emission and solar absorption Fourier transform infrared (FTIR) spectrometers. Close agreement is observed between all combination of datasets, with mean differences  ≤  1.0 kg m−2 and correlation coefficients  ≥  0.98. The one exception in the observed high correlation is the comparison between the microwave radiometer and a radiosonde product, which had a correlation coefficient of 0.92.A variety of biases affecting Eureka instruments are revealed and discussed. A subset of Eureka radiosonde measurements was processed by the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) for this study. Comparisons reveal a small dry bias in the standard radiosonde measurement water vapour total columns of approximately 4 %. A recently produced solar absorption FTIR spectrometer dataset resulting from the MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) retrieval technique is shown to offer accurate measurements of water vapour total columns (e.g. average agreement within −5.2 % of GRUAN and −6.5 % of a co-located emission FTIR spectrometer). However, comparisons show a small wet bias of approximately 6 % at the high-latitude Eureka site. In addition, a new dataset derived from Atmospheric Emitted Radiance Interferometer (AERI) measurements is shown to provide accurate water vapour measurements (e.g. average agreement was within 4 % of GRUAN), which usefully enables measurements to be taken during day and night (especially valuable during polar night).

Description: Polar regions are characterized by their remoteness, making measurements challenging, but an improved knowledge of clouds and radiation is necessary to understand polar climate change. Infrared radiance spectrometers can operate continuously from the surface and have low power requirements relative to active sensors. Here we explore the feasibility of retrieving cloud height with an infrared spectrometer that would be designed for use in remote locations, for single-layer, mixed-phase polar clouds, using the CO2 slicing/sorting and the Minimum Local Emissivity Variance (MLEV) methods. In the absence of imposed errors and for clouds with optical depths greater than ∼0.3, cloud height retrievals from simulated spectra using CO2 slicing/sorting and MLEV are found to have roughly equivalent, high accuracies: at an instrument resolution of 0.5 cm−1, mean biases are found to be ∼0.2 km for low clouds (bases below 2 km) and −0.2 km for medium-to-high clouds (hereafter “high clouds”). Accuracy is found to decrease with decreasing cloud signal and increasing cloud height (independent of signal). Accuracy also decreases with coarsening resolution and becomes worse overall for MLEV than for CO2 slicing/sorting; however, the two methods have differing sensitivity to different sources of error, suggesting an approach that combines them. In the presence of errors, the dependence of retrieval accuracy on resolution is weakened. Further, errors have a small effect on retrievals of low clouds but a large effect on high clouds. Expected errors in the atmospheric state indicate that at a resolution of 0.5 cm−1, instrument noise level and bias of 0.1 mW/(m2 sr cm−1) would permit a retrieval accuracy of −2 ± 2 km for high clouds and ∼0.2 ± 0.5 km for low clouds, for both methods. This study highlights the sensitivity of surface-based infrared spectrometers to low clouds prevalent in polar regions.

Description: Cloud microphysical and macrophysical properties are critical for understanding the role of clouds in climate. These properties are commonly retrieved from ground-based and satellite-based infrared remote sensing instruments. However, retrieval uncertainties are difficult to quantify without a standard for comparison. This is particularly true over the polar regions, where surface-based data for a cloud climatology are sparse, yet clouds represent a major source of uncertainty in weather and climate models. We describe a synthetic high-spectral-resolution infrared data set that is designed to facilitate validation and development of cloud retrieval algorithms for surface- and satellite-based remote sensing instruments. Since the data set is calculated using pre-defined cloudy atmospheres, the properties of the cloud and atmospheric state are known a priori. The atmospheric state used for the simulations is drawn from radiosonde measurements made at the North Slope of Alaska (NSA) Atmospheric Radiation Measurement (ARM) site at Barrow, Alaska (71.325° N, 156.615° W), a location that is generally representative of the western Arctic. The cloud properties for each simulation are selected from statistical distributions derived from past field measurements. Upwelling (at 60 km) and downwelling (at the surface) infrared spectra are simulated for 260 cloudy cases from 50 to 3000 cm−1 (3.3 to 200 µm) at monochromatic (line-by-line) resolution at a spacing of  ∼  0.01 cm−1 using the Line-by-line Radiative Transfer Model (LBLRTM) and the discrete-ordinate-method radiative transfer code (DISORT). These spectra are freely available for interested researchers from the NSF Arctic Data Center data repository (doi:10.5065/D61J97TT).

Description: Improvements to climate model results in polar regions require improved knowledge of cloud properties. Surface-based infrared (IR) radiance spectrometers have been used to retrieve cloud properties in polar regions, but measurements are sparse. Reductions in cost and power requirements to allow more widespread measurements could be aided by reducing instrument resolution. Here we explore the effects of errors and instrument resolution on cloud property retrievals from downwelling IR radiances for resolutions of 0.1 to 20 cm−1. Retrievals are tested on 336 radiance simulations characteristic of the Arctic, including mixed-phase, vertically inhomogeneous, and liquid-topped clouds and a variety of ice habits. Retrieval accuracy is found to be unaffected by resolution from 0.1 to 4 cm−1, after which it decreases slightly. When cloud heights are retrieved, errors in retrieved cloud optical depth (COD) and ice fraction are considerably smaller for clouds with bases below 2 km than for higher clouds. For example, at a resolution of 4 cm−1, with errors imposed (noise and radiation bias of 0.2 mW/(m2 sr cm−1) and biases in temperature of 0.2 K and in water vapor of −3 %), using retrieved cloud heights, root-mean-square errors decrease from 1.1 to 0.15 for COD, 0.3 to 0.18 for ice fraction (fice), and 10 to 7 µm for ice effective radius (errors remain at 2 µm for liquid effective radius). These results indicate that a moderately low-resolution, surface-based IR spectrometer could provide cloud property retrievals with accuracy comparable to existing higher-resolution instruments and that such an instrument would be particularly useful for low-level clouds.

Description: Improvements to climate model results in polar regions require improved knowledge of cloud microphysical properties. Surface-based infrared radiance spectrometers have been used to retrieve cloud microphysical properties in polar regions, but measurements are sparse. Reductions in cost and power requirements to allow more widespread measurements could be aided by reducing instrument resolution. Here we explore the effect of errors and instrument resolution on cloud microphysical property retrievals from downwelling infrared radiances for resolutions of 0.1 to 8 cm−1. Retrievals are tested on 331 radiance simulations characteristic of the Arctic, including mixed-phase, vertically inhomogeneous, and liquid-topped clouds and a variety of ice habits. Results indicate that measurement biases lead to biases in retrieved properties that are not represented by the retrieval error covariance matrix. Retrieval errors are high if mixed-phase is assumed throughout liquid-topped ice clouds. Errors due to assuming ice habit is spherical are progressively larger for solid columns, plates, and hollow bullet rosettes. Using retrieved cloud heights, particularly when errors are imposed, increases retrieval errors but decreases sensitivity to incorrect ice habits and vertical variation. Results indicate that retrieval accuracy is unaffected by resolution from 0.1 to 2 cm−1, after which it decreases only slightly. At a resolution of 4 cm−1, for typical errors expected in temperature (0.2 K) and water vapour (3 %), and assuming radiation bias and noise of 0.2 mW/(m2 sr cm−1), using retrieved cloud heights, error estimates are 0.1 ± 0.6 for optical depth, 0.0 ± 0.3 for ice fraction, 0 ±l 2 μm for effective radius of liquid, and 2 ± 2 μm for effective radius of ice. These results indicate that a moderately low resolution, portable, surface-based infrared spectrometer could provide microphysical properties to help constrain climate models.

Description: Polar regions are characterized by their remoteness, making measurements challenging, but an improved knowledge of clouds and radiation is necessary to understand polar climate change. Infrared radiance spectrometers can operate continuously from the surface and have low power requirements relative to active sensors. Here we explore the feasibility of retrieving cloud height with an infrared spectrometer that would be designed for use in remote polar locations. Using a wide variety of simulated spectra of mixed-phase polar clouds at varying instrument resolutions, retrieval accuracy is explored using the CO2 slicing/sorting and the minimum local emissivity variance (MLEV) methods. In the absence of imposed errors and for clouds with optical depths greater than  ∼  0.3, cloud-height retrievals from simulated spectra using CO2 slicing/sorting and MLEV are found to have roughly equivalent high accuracies: at an instrument resolution of 0.5 cm−1, mean biases are found to be  ∼  0.2 km for clouds with bases below 2 and −0.2 km for higher clouds. Accuracy is found to decrease with coarsening resolution and become worse overall for MLEV than for CO2 slicing/sorting; however, the two methods have differing sensitivity to different sources of error, suggesting an approach that combines them. For expected errors in the atmospheric state as well as both instrument noise and bias of 0.2 mW/(m2 sr cm−1), at a resolution of 4 cm−1, average retrieval errors are found to be less than  ∼  0.5 km for cloud bases within 1 km of the surface, increasing to  ∼  1.5 km at 4 km. This sensitivity indicates that a portable, surface-based infrared radiance spectrometer could provide an important complement in remote locations to satellite-based measurements, for which retrievals of low-level cloud are challenging.

Description: Water vapour is a critical component of the Earth system. Techniques to acquire and improve measurements of atmospheric water vapour and its isotopes are under active development. This work presents a detailed intercomparison of water vapour total column measurements taken between 2006 and 2014 at a Canadian high Arctic research site. Instruments include radiosondes, sun photometers, a microwave radiometer, and emission and solar absorption Fourier transform spectrometers (FTSs). Good agreement is observed between all combination of datasets, with correlation coefficients ≥ 0.90 showing high correlations. A variety of biases and calibration issues are revealed and discussed for all instruments. A new FTS dataset, resulting from the MUSICA (Multi-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) retrieval technique, is shown to offer accurate measurements of water vapour total columns; however, measurements show a small wet bias of approximately 6 %. A new dataset derived from Atmospheric Emitted Radiance Interferometer (AERI) measurements is also shown to provide accurate water vapour measurements, which usefully enables measurements to be taken during day and night (especially valuable during Polar Night). In addition, limited profile comparisons are conducted using radiosonde and ground-based FTS measurements. Results show MUSICA FTS profiles were within 15 % of radiosonde measurements throughout the troposphere.

Description: A methodology for retrieving high-latitude winter water vapour columns from passive microwave satellite measurements from Perro et al. (2016) is extended to use measured surface reflectance ratios under more realistic surface reflection assumptions. Pan-Arctic wintertime water vapour is retrieved from Advanced Technology Microwave Sounder (ATMS) measurements made from January 2012 through March 2015 (December to March). The water vapour retrievals are validated using two ground based instruments: the G-band Vapor Radiometer (GVR) at Barrow, Alaska, and the Extended-Range Atmospheric Emitted Radiance Interferometer (E-AERI) at Eureka, Nunavut. E-AERI was chosen as an additional point of validation compared to Perro et al. (2016) due to the different technology and frequencies employed to determine water vapour column compared to the ATMS and GVR. For water vapour columns less than 6 kg m−2, the biases are +2.6 % and +0.01 % relative to the GVR and E-AERI, respectively. A comparison with radiosonde humidity measurements shows they are dry relative to the ATMS measurements in North America and Western Europe, and moist in Asia and Eastern Europe, with an apparent dependence on radiosonde manufacturer. Reanalyses (ERA-5, ERA-Interim, ASR V2, JRA-55 and NCEP) are systematically drier than the ATMS measurements for water vapour columns less than 6 kg m−2, with relative biases ranging from −10 % to −23 %. These differences could have implications for the understanding of the Arctic water budget and climate.