Sensitivity analysis of polarimetric O₂ A-band spectra for potential cloud retrievals using OCO-2/GOSAT measurements

Clouds play a crucial role in Earth's radiative budget, yet their climate feedbacks are poorly understood. The advent of space-borne high resolution spectrometers probing the O2 A band, like GOSAT and OCO-2, could make it possible to simultaneously retrieve vertically resolved cloud parameters that play a vital role in Earth's radiative budget, thereby allowing a reduction of the corresponding uncertainty due to clouds. Such retrievals would also facilitate air mass bias reduction in corresponding measurements of CO2 columns. In this work, the hyperspectral, polarimetric response of the O2 A band to mainly three important cloud parameters, viz., optical thickness, top height and droplet size has been studied, revealing a different sensitivity to each for the varying atmospheric absorption strength within the A band. Cloud optical thickness finds greatest sensitivity in intensity measurements, the sensitivity of other Stokes parameters being limited to low cloud optical thicknesses. Cloud height had a negligible effect on intensity measurements at non-absorbing wavelengths but finds maximum sensitivity at an intermediate absorption strength, which increases with cloud height. The same is found to hold for cloud geometric thickness. The geometry-dependent sensitivity to droplet size is maximum at non-absorbing wavelengths and diminishes with increasing absorption strength. It has been shown that significantly more information on droplet size can be drawn from multi-angle measurements. We find that, in the absence of sunglint, the backscatter hemisphere (scattering angle larger than 90°) is richer in information on droplet size, especially in the glory and rainbow regions. It has been shown that I and Q generally have differing sensitivities to all cloud parameters. Thus, accurate measurements of two orthogonal components IP andIS (as in GOSAT) are expected to contain more information than measurements of only I, Ih or Iv (as in the case of OCO-2)

Differential absorption radar techniques: water vapor retrievals

Two radar pulses sent at different frequencies near the 183 GHz water vapor line can be used to determine total column water vapor and water vapor profiles (within clouds or precipitation) exploiting the differential absorption on and off the line. We assess these water vapor measurements by applying a radar instrument simulator to CloudSat pixels and then running end-to-end retrieval simulations. These end-to-end retrievals enable us to fully characterize not only the expected precision but also their potential biases, allowing us to select radar tones that maximize the water vapor signal minimizing potential errors due to spectral variations in the target extinction properties. A hypothetical CloudSat-like instrument with 500 m by  ∼  1 km vertical and horizontal resolution and a minimum detectable signal and radar precision of −30 and 0.16 dBZ, respectively, can estimate total column water vapor with an expected precision of around 0.03 cm, with potential biases smaller than 0.26 cm most of the time, even under rainy conditions. The expected precision for water vapor profiles was found to be around 89 % on average, with potential biases smaller than 77 % most of the time when the profile is being retrieved close to surface but smaller than 38 % above 3 km. By using either horizontal or vertical averaging, the precision will improve vastly, with the measurements still retaining a considerably high vertical and/or horizontal resolution

Differential absorption radar techniques: surface pressure

Two radar pulses sent at different frequencies near the 60 GHz O₂ absorption band can be used to determine surface pressure by measuring the differential absorption on and off the band. Results of inverting synthetic data assuming an airborne radar are presented. The analysis includes the effects of temperature, water vapor, and hydrometeors, as well as particle size distributions and surface backscatter uncertainties. Results show that an airborne radar (with sensitivity of −20 and 0.05 dBZ speckle and relative calibration uncertainties) can estimate surface pressure with a precision of ~ 1.0 hPa and accuracy better than 1.0 hPa for clear-sky and cloudy conditions and better than 3.5 hPa for precipitating conditions. Generally, accuracy would be around 0.5 and 2 hPa for non-precipitating and precipitating conditions, respectively

Retrieval of snowflake microphysical properties from multifrequency radar observations

We have developed an algorithm that retrieves the size, number concentration and density of falling snow from multifrequency radar observations. This work builds on previous studies that have indicated that three-frequency radars can provide information on snow density, potentially improving the accuracy of snow parameter estimates. The algorithm is based on a Bayesian framework, using lookup tables mapping the measurement space to the state space, which allows fast and robust retrieval. In the forward model, we calculate the radar reflectivities using recently published snow scattering databases. We demonstrate the algorithm using multifrequency airborne radar observations from the OLYMPEX-RADEX field campaign, comparing the retrieval results to hydrometeor identification using ground-based polarimetric radar and also to collocated in situ observations made using another aircraft. Using these data, we examine how the availability of multiple frequencies affects the retrieval accuracy, and we test the sensitivity of the algorithm to the prior assumptions. The results suggest that multifrequency radars are substantially better than single-frequency radars at retrieving snow microphysical properties. Meanwhile, triple-frequency radars can retrieve wider ranges of snow density than dual-frequency radars and better locate regions of highdensity snow such as graupel, although these benefits are relatively modest compared to the difference in retrieval performance between dual- and single-frequency radars. We also examine the sensitivity of the retrieval results to the fixed a priori assumptions in the algorithm, showing that the multi-frequency method can reliably retrieve snowflake size, while the retrieved number concentration and density are affected significantly by the assumptions.Peer reviewe