Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

Abstract. Clouds play an important role in balancing the Earth's radiation budget. Hence, it is vital that cloud climatologies are produced that quantify cloud macro and micro physical parameters and the associated uncertainty. In this paper, we present an algorithm ORAC (Oxford-RAL retrieval of Aerosol and Cloud) which is based on fitting a physically consistent cloud model to satellite observations simultaneously from the visible to the mid-infrared, thereby ensuring that the resulting cloud properties provide both a good representation of the short-wave and long-wave radiative effects of the observed cloud. The advantages of the optimal estimation method are that it enables rigorous error propagation and the inclusion of all measurements and any a priori information and associated errors in a rigorous mathematical framework. The algorithm provides a measure of the consistency between retrieval representation of cloud and satellite radiances. The cloud parameters retrieved are the cloud top pressure, cloud optical depth, cloud effective radius, cloud fraction and cloud phase. The algorithm can be applied to most visible/infrared satellite instruments. In this paper, we demonstrate the applicability to the Along-Track Scanning Radiometers ATSR-2 and AATSR. Examples of applying the algorithm to ATSR-2 flight data are presented and the sensitivity of the retrievals assessed, in particular the algorithm is evaluated for a number of simulated single-layer and multi-layer conditions. The algorithm was found to perform well for single-layer cloud except when the cloud was very thin; i.e., less than 1 optical depths. For the multi-layer cloud, the algorithm was robust except when the upper ice cloud layer is less than five optical depths. In these cases the retrieved cloud top pressure and cloud effective radius become a weighted average of the 2 layers. The sum of optical depth of multi-layer cloud is retrieved well until the cloud becomes thick, greater than 50 optical depths, where the cloud begins to saturate. The cost proved a good indicator of multi-layer scenarios. Both the retrieval cost and the error need to be considered together in order to evaluate the quality of the retrieval. This algorithm in the configuration described here has been applied to both ATSR-2 and AATSR visible and infrared measurements in the context of the GRAPE (Global Retrieval and cloud Product Evaluation) project to produce a 14 yr consistent record for climate research.

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Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-4-2389-2011 ◽

2011 ◽

Vol 4 (2) ◽

pp. 2389-2431 ◽

Cited By ~ 13

Author(s):

C. A. Poulsen ◽

P. D. Watts ◽

G. E. Thomas ◽

A. M. Sayer ◽

R. Siddans ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Single Layer ◽

Optimal Estimation ◽

Product Evaluation ◽

Radiation Budget ◽

Climate Feedback ◽

Intergovernmental Panel ◽

Cloud Parameters ◽

Infrared Measurements

Abstract. Clouds play an important role in balancing the Earth's radiation budget. Clouds reflect sunlight which cools the Earth, and also trap infrared radiation in the same manner as greenhouse gases. Changes in cloud cover and cloud properties over time can have important consequences for climate. The Intergovernmental Panel for Climate Change (IPCC) has identified current gaps in the understanding of clouds and related climate feedback processes as a leading cause of uncertainty in forecasting climate change. In this paper we present an algorithm that uses optimal estimation to retrieve cloud parameters from satellite multi-spectral imager data, in particular the Along-Track Scanning Radiometers ATSR-2 and AATSR. The cloud parameters retrieved are the cloud top pressure, cloud optical depth, cloud effective radius, cloud fraction and cloud phase. Importantly, the technique also provides estimated errors along with the retrieved values and quantifies the consistency between retrieval representation of cloud and satellite radiances. This should enable the effective use of the products for comparison with climate models or for exploitation via data assimilation. The technique is evaluated by performing retrieval simulations for a variety of simulated single layer and multi-layer conditions. Examples of applying the algorithm to ATSR-2 flight data are presented and the sensitivity of the retrievals assessed. This algorithm has been applied to both ATSR-2 and AATSR visible and infrared measurements in the context of the GRAPE (Global Retrieval and cloud Product Evaluation) project to produce a 14 year consistent record for climate research (Sayer et al., 2010).

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Marine liquid cloud geometric thickness retrieved from OCO-2's oxygen A-band spectrometer

Atmospheric Measurement Techniques ◽

10.5194/amt-12-1717-2019 ◽

2019 ◽

Vol 12 (3) ◽

pp. 1717-1737 ◽

Cited By ~ 5

Author(s):

Mark Richardson ◽

Jussi Leinonen ◽

Heather Q. Cronk ◽

James McDuffie ◽

Matthew D. Lebsock ◽

...

Keyword(s):

Optical Depth ◽

Single Layer ◽

Cloud Droplet ◽

Satellite Observation ◽

Cloud Optical Depth ◽

First Case ◽

Phase Classification ◽

Moderate Resolution Imaging Spectroradiometer ◽

Cloud Top Pressure ◽

Geometric Thickness

Abstract. This paper introduces the OCO2CLD-LIDAR-AUX product, which uses the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar and the Orbiting Carbon Observatory-2 (OCO-2) hyperspectral A-band spectrometer. CALIPSO provides a prior cloud top pressure (Ptop) for an OCO-2-based retrieval of cloud optical depth, Ptop and cloud geometric thickness expressed in hPa. Measurements are of single-layer liquid clouds over oceans from September 2014 to December 2016 when collocated data are available. Retrieval performance is best for solar zenith angles <45∘ and when the cloud phase classification, which also uses OCO-2's weak CO2 band, is more confident. The highest quality optical depth retrievals agree with those from the Moderate Resolution Imaging Spectroradiometer (MODIS) with discrepancies smaller than the MODIS-reported uncertainty. Retrieved thicknesses are consistent with a substantially subadiabatic structure over marine stratocumulus regions, in which extinction is weighted towards the cloud top. Cloud top pressure in these clouds shows a 4 hPa bias compared with CALIPSO which we attribute mainly to the assumed vertical structure of cloud extinction after showing little sensitivity to the presence of CALIPSO-identified aerosol layers or assumed cloud droplet effective radius. This is the first case of success in obtaining internal cloud structure from hyperspectral A-band measurements and exploits otherwise unused OCO-2 data. This retrieval approach should provide additional constraints on satellite-based estimates of cloud droplet number concentration from visible imagery, which rely on parameterization of the cloud thickness.

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Is positive correlation between cloud droplet effective radius and aerosol optical depth over land due to retrieval artifacts or real physical processes?

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-8879-2019 ◽

2019 ◽

Vol 19 (13) ◽

pp. 8879-8896 ◽

Cited By ~ 4

Author(s):

Hailing Jia ◽

Xiaoyan Ma ◽

Johannes Quaas ◽

Yan Yin ◽

Tom Qiu

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Radiant Energy ◽

Single Layer ◽

Cloud Droplet ◽

Energy System ◽

Effective Radius ◽

Liquid Water Path ◽

Anthropogenic Aerosol ◽

Aerosol Cloud

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) C6 L3, Clouds and the Earth's Radiant Energy System (CERES) Edition-4 L3 products, and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis data are employed to systematically study aerosol–cloud correlations over three anthropogenic aerosol regions and their adjacent oceans, as well as explore the effect of retrieval artifacts and underlying physical mechanisms. This study is confined to warm phase and single-layer clouds without precipitation during the summertime (June, July, and August). Our analysis suggests that cloud effective radius (CER) is positively correlated with aerosol optical depth (AOD) over land (positive slopes), but negatively correlated with aerosol index (AI) over oceans (negative slopes) even with small ranges of liquid water path (quasi-constant). The changes in albedo at the top of the atmosphere (TOA) corresponding to aerosol-induced changes in CER also lend credence to the authenticity of this opposite aerosol–cloud correlation between land and ocean. It is noted that potential artifacts, such as the retrieval biases of both cloud (partially cloudy and 3-D-shaped clouds) and aerosol, can result in a serious overestimation of the slope of CER–AOD/AI. Our results show that collision–coalescence seems not to be the dominant cause for positive slope over land, but the increased CER caused by increased aerosol might further increase CER by initializing collision–coalescence, generating a positive feedback. By stratifying data according to the lower tropospheric stability and relative humidity near cloud top, it is found that the positive correlations more likely occur in the case of drier cloud top and stronger turbulence in clouds, while negative correlations occur in the case of moister cloud top and weaker turbulence in clouds, which implies entrainment mixing might be a possible physical interpretation for such a positive CER–AOD slope.

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MODIS Global Cloud-Top Pressure and Amount Estimation: Algorithm Description and Results

Journal of Applied Meteorology and Climatology ◽

10.1175/2007jamc1705.1 ◽

2008 ◽

Vol 47 (4) ◽

pp. 1175-1198 ◽

Cited By ~ 197

Author(s):

W. Paul Menzel ◽

Richard A. Frey ◽

Hong Zhang ◽

Donald P. Wylie ◽

Chris C. Moeller ◽

...

Keyword(s):

Spatial Resolution ◽

Single Layer ◽

Estimation Algorithm ◽

Spectral Coverage ◽

Earth Observing System ◽

Cloud Properties ◽

True Location ◽

Moderate Resolution Imaging Spectroradiometer ◽

Cloud Top Pressure ◽

Layer Cloud

Abstract The Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Earth Observing System (EOS) Terra and Aqua platforms provides unique measurements for deriving global and regional cloud properties. MODIS has spectral coverage combined with spatial resolution in key atmospheric bands, which is not available on previous imagers and sounders. This increased spectral coverage/spatial resolution, along with improved onboard calibration, enhances the capability for global cloud property retrievals. MODIS operational cloud products are derived globally at spatial resolutions of 5 km (referred to as level-2 products) and are aggregated to a 1° equal-angle grid (referred to as level-3 product), available for daily, 8-day, and monthly time periods. The MODIS cloud algorithm produces cloud-top pressures that are found to be within 50 hPa of lidar determinations in single-layer cloud situations. In multilayer clouds, where the upper-layer cloud is semitransparent, the MODIS cloud pressure is representative of the radiative mean between the two cloud layers. In atmospheres prone to temperature inversions, the MODIS cloud algorithm places the cloud above the inversion and hence is as much as 200 hPa off its true location. The wealth of new information available from the MODIS operational cloud products offers the promise of improved cloud climatologies. This paper 1) describes the cloud-top pressure and amount algorithm that has evolved through collection 5 as experience has been gained with in-flight data from NASA Terra and Aqua platforms; 2) compares the MODIS cloud-top pressures, converted to cloud-top heights, with similar measurements from airborne and space-based lidars; and 3) introduces global maps of MODIS and High Resolution Infrared Sounder (HIRS) cloud-top products.

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Cloud-top pressure retrieval with DSCOVR EPIC oxygen A- and B-band observations

Atmospheric Measurement Techniques ◽

10.5194/amt-13-5259-2020 ◽

2020 ◽

Vol 13 (10) ◽

pp. 5259-5275

Author(s):

Bangsheng Yin ◽

Qilong Min ◽

Emily Morgan ◽

Yuekui Yang ◽

Alexander Marshak ◽

...

Keyword(s):

Single Layer ◽

Computation Time ◽

Observation Data ◽

Atmospheric Conditions ◽

Analytic Equation ◽

Retrieval Processes ◽

The Difference ◽

High Level ◽

Cloud Top Pressure ◽

Layer Cloud

Abstract. An analytic transfer inverse model for Earth Polychromatic Imaging Camera (EPIC) observations is proposed to retrieve the cloud-top pressure (CTP) with the consideration of in-cloud photon penetration. In this model, an analytic equation was developed to represent the reflection at the top of the atmosphere from above cloud, in cloud, and below cloud. The coefficients of this analytic equation can be derived from a series of EPIC simulations under different atmospheric conditions using a nonlinear regression algorithm. With estimated cloud pressure thickness, the CTP can be retrieved from EPIC observation data by solving the analytic equation. To simulate the EPIC measurements, a program package using the double-k approach was developed. Compared to line-by-line calculation, this approach can calculate high-accuracy results with a 100-fold computation time reduction. During the retrieval processes, two kinds of retrieval results, i.e., baseline CTP and retrieved CTP, are provided. The baseline CTP is derived without considering in-cloud photon penetration, and the retrieved CTP is derived by solving the analytic equation, taking into consideration in-cloud and below-cloud interactions. The retrieved CTPs for the oxygen A and B bands are smaller than their related baseline CTP. At the same time, both baseline CTP and retrieved CTP at the oxygen B band are larger than those at the oxygen A band. Compared to the difference in baseline CTP between the B band and A band, the difference in retrieved CTP between these two bands is generally reduced. Out of around 10 000 cases, in retrieved CTP between the A and B bands we found an average bias of 93 mb with a standard deviation of 81 mb. The cloud layer top pressure from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements is used for validation. Under single-layer cloud situations, the retrieved CTPs for the oxygen A band agree well with the CTPs from CALIPSO, the mean difference of which within 5 mb in the case study. Under multiple-layer cloud situations, the CTPs derived from EPIC measurements may be larger than the CTPs of high-level thin clouds due to the effect of photon penetration.

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An Update on SURFRAD—The GCOS Surface Radiation Budget Network for the Continental United States

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech1806.1 ◽

2005 ◽

Vol 22 (10) ◽

pp. 1460-1472 ◽

Cited By ~ 115

Author(s):

John A. Augustine ◽

Gary B. Hodges ◽

Christopher R. Cornwall ◽

Joseph J. Michalsky ◽

Carlos I. Medina

Keyword(s):

United States ◽

Aerosol Optical Depth ◽

Optical Depth ◽

The United States ◽

Ultraviolet B ◽

Model Verification ◽

Surface Radiation ◽

Radiation Budget ◽

Surface Radiation Budget ◽

Infrared Measurements

Abstract The Surface Radiation budget (SURFRAD) network was developed for the United States in the middle 1990s in response to a growing need for more sophisticated in situ surface radiation measurements to support satellite system validation; numerical model verification; and modern climate, weather, and hydrology research applications. Operational data collection began in 1995 with four stations; two stations were added in 1998. Since its formal introduction to the research community in 2000, several additions and improvements have been made to the network’s products and infrastructure. To better represent the climate types of the United States, a seventh SURFRAD station was installed near Sioux Falls, South Dakota, in June 2003. In 2001, the instrument used for the diffuse solar measurement was replaced with a type of pyranometer that does not have a bias associated with infrared radiative cooling of its receiving surface. Subsequently, biased diffuse solar data from 1996 to 2001 were corrected using a generally accepted method. Other improvements include the implementation of a clear-sky diagnostic algorithm and associated products, better continuity in the ultraviolet-B (UVB) data record, a reduced potential for error in the downwelling infrared measurements, and development of an aerosol optical depth algorithm. Of these, only the aerosol optical depth product has yet to be finalized. All SURFRAD stations are members of the international Baseline Surface Radiation Network (BSRN). Data are submitted regularly in monthly segments to the BSRN archive in Zurich, Switzerland. Through this affiliation, the SURFRAD network became an official part of the Global Climate Observing System (GCOS) in April 2004.

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Liquid marine cloud geometric thickness retrieved from OCO-2's oxygen A-band spectrometer

10.5194/amt-2018-387 ◽

2018 ◽

Author(s):

Mark Richardson ◽

Jussi Leinonen ◽

Heather Q. Cronk ◽

James McDuffie ◽

Matthew D. Lebsock ◽

...

Keyword(s):

Optical Depth ◽

Zenith Angle ◽

Solar Zenith Angle ◽

Single Layer ◽

Satellite Observation ◽

Retrieval Performance ◽

Cloud Optical Depth ◽

Cloud Top Pressure ◽

Geometric Thickness

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The interdependence of continental warm cloud properties derived from unexploited solar background signals in ground-based lidar measurements

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-8389-2014 ◽

2014 ◽

Vol 14 (16) ◽

pp. 8389-8401 ◽

Cited By ~ 7

Author(s):

J. C. Chiu ◽

J. A. Holmes ◽

R. J. Hogan ◽

E. J. O'Connor

Keyword(s):

Optical Depth ◽

Effective Radius ◽

Liquid Water Path ◽

Cloud Optical Depth ◽

Cloud Properties ◽

Warm Cloud ◽

Lidar Measurements ◽

Atmospheric Radiation Measurement ◽

Ground Based Lidar ◽

Geometric Thickness

Abstract. We have extensively analysed the interdependence between cloud optical depth, droplet effective radius, liquid water path (LWP) and geometric thickness for stratiform warm clouds using ground-based observations. In particular, this analysis uses cloud optical depths retrieved from untapped solar background signals that are previously unwanted and need to be removed in most lidar applications. Combining these new optical depth retrievals with radar and microwave observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility in Oklahoma during 2005–2007, we have found that LWP and geometric thickness increase and follow a power-law relationship with cloud optical depth regardless of the presence of drizzle; LWP and geometric thickness in drizzling clouds can be generally 20–40% and at least 10% higher than those in non-drizzling clouds, respectively. In contrast, droplet effective radius shows a negative correlation with optical depth in drizzling clouds and a positive correlation in non-drizzling clouds, where, for large optical depths, it asymptotes to 10 μm. This asymptotic behaviour in non-drizzling clouds is found in both the droplet effective radius and optical depth, making it possible to use simple thresholds of optical depth, droplet size, or a combination of these two variables for drizzle delineation. This paper demonstrates a new way to enhance ground-based cloud observations and drizzle delineations using existing lidar networks.

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FAME-C: cloud property retrieval using synergistic AATSR and MERIS observations

Atmospheric Measurement Techniques ◽

10.5194/amt-7-3873-2014 ◽

2014 ◽

Vol 7 (11) ◽

pp. 3873-3890 ◽

Cited By ~ 6

Author(s):

C. K. Carbajal Henken ◽

R. Lindstrot ◽

R. Preusker ◽

J. Fischer

Keyword(s):

Optical Thickness ◽

Single Layer ◽

Cloud Water ◽

Effective Radius ◽

Cloud Property ◽

Microphysical Properties ◽

Microphysical Property ◽

Resolution Imaging ◽

Cloud Optical Thickness ◽

Cloud Water Path

Abstract. A newly developed daytime cloud property retrieval algorithm, FAME-C (Freie Universität Berlin AATSR MERIS Cloud), is presented. Synergistic observations from the Advanced Along-Track Scanning Radiometer (AATSR) and the Medium Resolution Imaging Spectrometer (MERIS), both mounted on the polar-orbiting Environmental Satellite (Envisat), are used for cloud screening. For cloudy pixels two main steps are carried out in a sequential form. First, a cloud optical and microphysical property retrieval is performed using an AATSR near-infrared and visible channel. Cloud phase, cloud optical thickness, and effective radius are retrieved, and subsequently cloud water path is computed. Second, two cloud top height products are retrieved based on independent techniques. For cloud top temperature, measurements in the AATSR infrared channels are used, while for cloud top pressure, measurements in the MERIS oxygen-A absorption channel are used. Results from the cloud optical and microphysical property retrieval serve as input for the two cloud top height retrievals. Introduced here are the AATSR and MERIS forward models and auxiliary data needed in FAME-C. Also, the optimal estimation method, which provides uncertainty estimates of the retrieved property on a pixel basis, is presented. Within the frame of the European Space Agency (ESA) Climate Change Initiative (CCI) project, the first global cloud property retrievals have been conducted for the years 2007–2009. For this time period, verification efforts are presented, comparing, for four selected regions around the globe, FAME-C cloud optical and microphysical properties to cloud optical and microphysical properties derived from measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite. The results show a reasonable agreement between the cloud optical and microphysical property retrievals. Biases are generally smallest for marine stratocumulus clouds: −0.28, 0.41 μm and −0.18 g m−2 for cloud optical thickness, effective radius and cloud water path, respectively. This is also true for the root-mean-square deviation. Furthermore, both cloud top height products are compared to cloud top heights derived from ground-based cloud radars located at several Atmospheric Radiation Measurement (ARM) sites. FAME-C mostly shows an underestimation of cloud top heights when compared to radar observations. The lowest bias of −0.3 km is found for AATSR cloud top heights for single-layer clouds, while the highest bias of −3.0 km is found for AATSR cloud top heights for multilayer clouds. Variability is low for MERIS cloud top heights for low-level clouds, and high for MERIS cloud top heights for mid-level and high-level single-layer clouds, as well as for both AATSR and MERIS cloud top heights for multilayer clouds.

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The interdependence of continental warm cloud properties derived from unexploited solar background signal in ground-based lidar measurements

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-14-8963-2014 ◽

2014 ◽

Vol 14 (7) ◽

pp. 8963-8996

Author(s):

J. C. Chiu ◽

J. A. Holmes ◽

R. J. Hogan ◽

E. J. O'Connor

Keyword(s):

Optical Depth ◽

Effective Radius ◽

Liquid Water Path ◽

Background Signal ◽

Cloud Optical Depth ◽

Cloud Properties ◽

Lidar Measurements ◽

Atmospheric Radiation Measurement ◽

Ground Based Lidar ◽

Geometric Thickness

Abstract. We have extensively analysed the interdependence between cloud optical depth, droplet effective radius, liquid water path (LWP) and geometric thickness for stratiform warm clouds using ground-based observations. In particular, this analysis uses cloud optical depths retrieved from untapped solar background signal that is previously unwanted and needs to be removed in most lidar applications. Combining these new optical depth retrievals with radar and microwave observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility in Oklahoma during 2005–2007, we have found that LWP and geometric thickness increase and follow a power-law relationship with cloud optical depth regardless of the presence of drizzle; LWP and geometric thickness in drizzling clouds can be generally 20–40% and at least 10% higher than those in non-drizzling clouds, respectively. In contrast, droplet effective radius shows a negative correlation with optical depth in drizzling clouds, while it increases with optical depth and reaches an asymptote of 10 μm in non-drizzling clouds. This asymptotic behaviour in non-drizzling clouds is found in both droplet effective radius and optical depth, making it possible to use simple thresholds of optical depth, droplet size, or a combination of these two variables for drizzle delineation. This paper demonstrates a new way to enhance ground-based cloud observations and drizzle delineations using existing lidar networks.

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