Cloud statistics-based estimation of land surface albedo from AVHRR data

Manninen, Terhikki; Siljamo, Niilo; Poutiainen, Jani; Vuilleumier, Laurent; Bosveld, Fred C.; Gratzki, A.

doi:10.1117/12.565133

Cited by 8 publications

(25 citation statements)

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“…For each case, we used a flat spectral albedo corresponding to the observations of the upward and downward shortwave radiation fluxes measured at the time of the studied case. In a study by Manninen et al [2004], integrated spectral albedo values measured by AVHRR were compared to albedo values calculated with the upward and downward shortwave radiation fluxes observed at the BSRN Payerne site. Two algorithms were used to derive the satellite integrated value.…”

Section: Discussionmentioning

confidence: 99%

Solar irradiance computations compared with observations at the Baseline Surface Radiation Network Payerne site

et al. 2008

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[1] Radiative transfer model calculations of solar fluxes during cloud-free periods often show considerable discrepancies with surface radiation observations. Many efforts have been undertaken to explain the differences between modeled and observed shortwave downward radiation (SDR). In this study, MODTRAN4v3r1TM (designed later simply as MODTRAN TM ) was used for model simulations and compared with high-quality radiation observations of the Baseline Surface Radiation Network (BSRN) site at Payerne, Switzerland. Results are presented for cloud-free shortwave downward radiation calculations. The median differences of modeled minus observed global SDR are small (<1%) and within the instrumental error. The differences of modeled and observed direct and diffuse SDR show larger discrepancies of À1.8% and 5.2%, respectively. The diffuse SDR is generally overestimated by the model, and more important, the model to observation linear regression slope and zero intercept differ significantly from their ideal values of 1 and 0. Possible reasons for the discrepancies are presented and discussed, and some modifications are investigated for decreasing such differences between modeled and observed diffuse SDR. However, we could not resolve all the discrepancies. The best agreement is obtained when comparing model simulations whose 550-nm aerosol optical depth input is inferred from observations using nine spectral channels and using BSRN observations performed with a new and more precise shading disk and Sun-tracking system. In this case, the median bias between model simulations and observed diffuse SDR is À0.4 W m À2 (<1%).

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Section: Discussionmentioning

confidence: 99%

Solar irradiance computations compared with observations at the Baseline Surface Radiation Network Payerne site

et al. 2008

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“…This maximum limit is defined in order to avoid cloud misclassifications due to the darker camera images that correspond to higher solar angles. Another reason for this threshold is the possible overestimation of the ground albedo estimation for high SZAs (Manninen et al, 2004). Table 1 gives a summary of the number of measurements in total and per day, the cloud fractions considered and the occurrences throughout the year per cloud class separately.…”

Section: Case Selectionmentioning

confidence: 99%

Estimation of cloud optical thickness, single scattering albedo and effective droplet radius using a shortwave radiative closure study in Payerne

Aebi¹,

Gröbner²,

Kazadzis³

et al. 2019

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Abstract. We have used a method based on ground-based solar radiation measurements and radiative transfer models (RTM) in order to estimate the following cloud optical properties: cloud optical thickness (COT), cloud single scattering albedo (SSAc) and effective droplet radius (reff). The method is based on the minimisation of the difference between modelled and measured downward shortwave radiation (DSR). The optical properties are estimated for more than 3,000 stratus-altostratus (St-As) and 206 cirrus-cirrostratus (Ci-Cs) measurements during 2013–2017, at the Baseline Surface Radiation Network (BSRN) station in Payerne, Switzerland. The RTM libRadtran is used to simulate the total DSR, as well as its direct and diffuse components. The model inputs of additional atmospheric parameters are either ground- or satellite-based measurements. The cloud cases are identified by the use of an all-sky cloud camera. For the low- to mid-level cloud class St-As, 95 % of the estimated COT values from DSR measurements (COTDSR) are between 11.9 and 91.5 with a geometric mean and standard deviation of 33.81 and 1.67, respectively. The comparison of these COTDSR values with COTBarnard values retrieved from an independent empirical equation, results in a mean difference of −1.20 ± 2.73 and is thus within the method uncertainty. However, there is a larger mean difference of around 18 between COTDSR and COT values derived from MODIS level-2 (L2), Collection 6.1 (C6.1) data (COTMODIS). The estimated reff (from liquid water path (LWP) and COTDSR) for St-As are between 2.1 and 20.4 μm. For the high-level cloud class Ci-Cs, COTDSR is derived considering the direct radiation and 95 % of the values are between 0.32 and 1.40. For Ci-Cs, 95 % of the SSAc values are estimated to be between 0.84 and 0.99 using diffuse radiation measurements. The COT values for Ci-Cs are also estimated from data from precision filter radiometers (PFR) at various wavelengths. The herein presented method could be applied and validated at other stations with direct and diffuse radiation measurements.

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“…Another approach to tackle cloudy conditions has been developed in this study. It appears that clear (or almost clear) sky and completely cloudy sky situations are much more frequent than the intermediate conditions (Manninen et al, 2004). Due to the great variation of cloud properties also the cloud albedo varies considerably.…”

Section: Introductionmentioning

confidence: 99%

Cloud probability-based estimation of black-sky surface albedo from AVHRR data

Manninen

Jääskeläinen

Siljamo

et al. 2021

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Abstract. Cloud cover constitutes a major challenge for the surface albedo estimation using Advanced Very High Resolution Radiometer AVHRR data for all possible conditions of cloud fraction and cloud type on any land cover type and solar zenith angle. Cloud masking has been the traditional way to estimate surface albedo from individual satellite images. Another approach to tackle cloudy conditions is presented in this study. Cloudy broadband albedo distributions were simulated first for theoretical cloud distributions and then using global cloud probability (CP) data of one month. A weighted mean approach based on the CP values was shown to produce very high accuracy black-sky surface albedo estimates for simulated data. The 90 % quantile for the error was 1.1 % (in absolute albedo percentage) and for the relative error it was 2.2 %. AVHRR based and in situ albedo distributions were in line with each other and also the monthly mean values were consistent. Comparison with binary cloud masking indicated that the developed method improves cloud contamination removal.

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Cloud statistics-based estimation of land surface albedo from AVHRR data

Cited by 8 publications

References 0 publications

Solar irradiance computations compared with observations at the Baseline Surface Radiation Network Payerne site

Solar irradiance computations compared with observations at the Baseline Surface Radiation Network Payerne site

Estimation of cloud optical thickness, single scattering albedo and effective droplet radius using a shortwave radiative closure study in Payerne

Cloud probability-based estimation of black-sky surface albedo from AVHRR data

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