Search for: All records

Abstract. Improvements to climate model results in polar regions require improvedknowledge of cloud properties. Surface-based infrared (IR) radiancespectrometers have been used to retrieve cloud properties in polar regions,but measurements are sparse. Reductions in cost and power requirements toallow more widespread measurements could be aided by reducing instrumentresolution. Here we explore the effects of errors and instrument resolutionon cloud property retrievals from downwelling IR radiances for resolutionsof 0.1 to 20 cm−1. Retrievals are tested on 336 radiance simulationscharacteristic of the Arctic, including mixed-phase, verticallyinhomogeneous, 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 areretrieved, errors in retrieved cloud optical depth (COD) and ice fractionare considerably smaller for clouds with bases below 2 km than for higherclouds. 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 intemperature of 0.2 K and in water vapor of −3 %), using retrieved cloudheights, root-mean-square errors decrease from 1.1 to 0.15 for COD, 0.3 to0.18 for ice fraction (fice), and 10 to 7 µm for iceeffective radius (errors remain at 2 µm for liquid effective radius).These results indicate that a moderately low-resolution, surface-based IRspectrometer could provide cloud property retrievals with accuracycomparable to existing higher-resolution instruments and that such aninstrument would be particularly useful for low-level clouds. 

Abstract. 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). 

Abstract. 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 0.5cm⁻¹, mean biases are found to be  ∼ 0.2km for clouds with bases below 2 and −0.2km 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 0.2mW/(m²srcm⁻¹), at a resolution of 4cm⁻¹, average retrieval errors are found to be less than  ∼ 0.5km for cloud bases within 1km of the surface, increasing to  ∼ 1.5km at 4km. 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.