Cloud optical thickness and effective particle radius derived from transmitted solar radiation measurements : Comparison with cloud radar observations

A method is presented for determining the optical thickness and effective particle radius of stratiform clouds containing liquid water drops in the absence of drizzle from transmitted solar radiation measurements. The procedure compares measurements of the cloud transmittance from the ground at water-absorbing and nonabsorbing wavelengths with lookup tables of the transmittance precomputed for plane-parallel, vertically homogeneous clouds. The optical thickness derived from the cloud transmittance may be used to retrieve vertical profiles of cloud microphysics in combination with the radar reflectivity factor. To do this, we also present an algorithm for solving the radar equation with a constraint of the optical thickness at the visible wavelength. Observations of clouds were made in August and September 2003 at Koganei, Tokyo, Japan, using a PREDE i-skyradiometer and a 95-GHz cloud radar Super Polarimetric Ice Crystal Detection and Explication Radar (SPIDER). The optical thickness and effective radius of water clouds were derived from the i-skyradiometer. Then, the vertical profile of the effective radius was retrieved from SPIDER, using the optical thickness determined from the i-skyradiometer. We found that the effective radii derived by using these two instruments were in good agreement

Satellite and ground-based measurements of XCO2 in a remote semiarid region of Australia

In this study, we present ground-based measurements of column-averaged dry-air mole fractions (DMFs) of CO2 (or XCO2) taken in a semiarid region of Australia with an EM27/SUN portable spectrometer equipped with an automated clamshell cover. We compared these measurements to space-based XCO2 retrievals from the Greenhouse Gases Observing Satellite (GOSAT). Side-by-side measurements of EM27/SUN with the Total Carbon Column Observing Network (TCCON) instrument at the University of Wollongong were conducted in 2015-2016 to derive an XCO2 scaling factor of 0.9954 relative to TCCON. Although we found a slight drift of 0.13 % over 3 months in the calibration curve of the EM27/SUN vs. TCCON XCO2, the alignment of the EM27/SUN proved stable enough for a 2-week campaign, keeping the retrieved Xair values, another measure of stability, to within 0.5 % and the modulation efficiency to within 2 %. From the measurements in Alice Springs, we confirm a small bias of around 2 ppm in the GOSAT M-gain to H-gain XCO2 retrievals, as reported by the NIES GOSAT validation team. Based on the reported random errors from GOSAT, we estimate the required duration of a future campaign in order to better understand the estimated bias between the EM27/SUN and GOSAT. The dataset from the Alice Springs measurements is accessible at https://doi.org/10.4225/48/5b21f16ce69bc (Velazco et al., 2018)

Update on the GOSAT TANSO–FTS SWIR Level 2 retrieval algorithm

The National Institute for Environmental Studies has provided the column-averaged dry-air mole fraction of carbon dioxide and methane (XCO$_2$ and XCH$_4$) products (L2 products) obtained from the Greenhouse gases Observing SATellite (GOSAT) for more than a decade. Recently, we updated the retrieval algorithm used to produce the new L2 product, V03.00. The main changes from the previous version (V02) of the retrieval algorithm are the treatment of cirrus clouds, the degradation model of the Thermal And Near-infrared Spectrometer for carbon Observation–Fourier Transform Spectrometer (TANSO–FTS), solar irradiance spectra, and gas absorption coefficient tables. The retrieval results from the updated algorithm showed improvements in fitting accuracies in the O$_2$ A, weak CO$_2$, and CH$_4$ bands of TANSO–FTS, although the residuals increase in the strong CO$_2$ band over the ocean. The direct comparison of the new product obtained from the updated (V03) algorithm with the previous version V02.90/91 and the validations using the Total Carbon Column Observing Network revealed that the V03 algorithm increases the amount of data without diminishing the data qualities of XCO$_2$ and XCH$_4$ over land. However, the negative bias of XCO$_2$ is larger than that of the previous version over the ocean, and bias correction is still necessary. Additionally, the V03 algorithm resolves the underestimation of the XCO$_2$ growth rate compared with the in situ measurements over the ocean recently found using V02.90/91 and V02.95/96

Impact of Changes in Minimum Reflectance on Cloud Discrimination

Greenhouse Gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2018. GOSAT-2 will be equipped with two Earth-observing instruments: the Thermal and Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer 2 (TANSO-FTS-2) and TANSO-Cloud and Aerosol Imager 2 (CAI-2). CAI-2 can be used to perform cloud discrimination in each band. The cloud discrimination algorithm uses minimum reflectance (Rmin) for comparisons with observed top-of-atmosphere reflectance. The creation of cloud-free Rmin requires 10 CAI or CAI-2 data. Thus, Rmin is created from CAI L1B data for a 30-day period in GOSAT, with a revisit time of 3 days. It is necessary to change the way in which 10 observations are chosen for GOSAT-2, which has a revisit time of 6 days. Additionally, Rmin processing for GOSAT CAI data was updated to version 02.00 in December 2016. Along with this change, the resolution of Rmin changed from 1/30° to 500 m. We examined the impact of changes in Rmin on cloud discrimination results using GOSAT CAI data. In particular, we performed comparisons of: (1) Rmin calculated using different methods to choose the 10 observations and (2) Rmin calculated using different generation procedures and spatial resolutions. The results were as follows: (1) The impact of using different methods to choose the 10 observations on cloud discrimination results was small, except for a few cases, e.g., snow-covered regions and sun-glint regions; (2) Cloud discrimination results using Rmin in version 02.00 were better than results obtained using Rmin in the previous version, apart from some special situations. The main causes of this were as follows: (1) The change of used band from band 2 to band 1 for Rmin calculation; (2) The change of spatial resolution of Rmin from 1/30° to 500-m

Vertical cloud structure observed from shipborne radar and lidar : Midlatitude case study during the MR01/K02 cruise of the research vessel Mirai

We observed the vertical distribution of clouds over the Pacific Ocean near Japan in May 2001 using lidar and a 95-GHz radar on the Research Vessel Mirai. Cloud analyses derived from synergy use of radar and lidar observations showed that there were two local maxima of cirrus cloud frequency of occurrence at 7 and 10.5 km and the drizzle frequency of occurrence was about the half compared with that of clouds below 4 km. The number of layers could be also measured using these schemes. Single, double, triple, and quadruple (or more) cloud layers had a 48, 23, 7, and 2% probability of occurrence, respectively. The average number of cloud layers when clouds existed was 1.54. The vertical structure of clouds observed with the radar/lidar system was compared to clouds in the aerosol transport model SPRINTARS, which is based on the CCSR-NIES Atmospheric General Circulation Model. The cloud fraction, radar reflectivity factor, and lidar backscattering coefficient were simulated by the model and compared to those by the observations using height-time cross-sections where the radar sensitivity was taken into account. The overall pattern of cloud fraction was well reproduced, although the model underestimated (overestimated) mean cloud fraction below 8 km (above 8 km). Cloud microphysics in the model could also be validated through comparison of derived model radar and lidar signals in grid mean with observations. The model overestimated ice particle size above 10 km, and simulated particle sizes in water clouds of 10 μm were larger than observed