Microphysical properties of three types of snow clouds: implication for satellite snowfall retrievals

Abstract. Ground-based radar and radiometer data observed during the 2017–2018 winter season over the Pyeongchang area on the east coast of the Korean Peninsula were used to simultaneously estimate both the cloud liquid water path and snowfall rate for three types of snow clouds: near-surface, shallow, and deep. Surveying all the observed data, it is found that near-surface clouds are the most frequently observed cloud type with an area fraction of over 60 %, while deep clouds contribute the most in snowfall volume with about 50 % of the total. The probability distributions of snowfall rates are clearly different among the three types of clouds, with the vast majority hardly reaching 0.3 mm h−1 (liquid water equivalent snowfall rate) for near-surface, 0.5 mm h−1 for shallow, and 1 mm h−1 for deep clouds. However, the liquid water paths in the three types of clouds all have the substantial probability to reach 500 g m−2. There is no clear correlation found between snowfall rate and the liquid water path for any of the cloud types. Based on all observed snow profiles, brightness temperatures at Global Precipitation Measurement Microwave Imager (GPM/GMI) channels are simulated, and the ability of a Bayesian algorithm to retrieve snowfall rate is examined using half the profiles as observations and the other half as an a priori database. Under an idealized scenario, i.e., without considering the uncertainties caused by surface emissivity, ice particle size distribution, and particle shape, the study found that the correlation as expressed by R2 between the “retrieved” and “observed” snowfall rates is about 0.32, 0.41, and 0.62, respectively, for near-surface, shallow, and deep snow clouds over land surfaces; these numbers basically indicate the upper limits capped by cloud natural variability, to which the retrieval skill of a Bayesian retrieval algorithm can reach. A hypothetical retrieval for the same clouds but over ocean is also studied, and a major improvement in skills is found for near-surface clouds with R2 increasing from 0.32 to 0.52, while a smaller improvement is found for shallow and deep clouds. This study provides a general picture of the microphysical characteristics of the different types of snow clouds and points out the associated challenges in retrieving their snowfall rate from passive microwave observations.

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Microphysical Properties of Three Types of Snow Clouds: Implication to Satellite Snowfall Retrievals

10.5194/acp-2020-757 ◽

2020 ◽

Author(s):

Hwayoung Jeoung ◽

Guosheng Liu ◽

Kwonil Kim ◽

Gyuwon Lee ◽

Eun-Kyoung Seo

Keyword(s):

Liquid Water ◽

Land Surface ◽

Probability Distributions ◽

A Priori ◽

Retrieval Algorithm ◽

Clear Correlation ◽

Liquid Water Path ◽

Near Surface ◽

Water Path ◽

Microphysical Properties

Abstract. Ground-based radar and radiometer data observed during the 2017–18 winter were used to simultaneously estimate both cloud liquid water path and snowfall rate for three types of snowing clouds: near-surface, shallow and deep. Surveying all the observed data, it is found that near-surface cloud is the most frequently observed cloud type with an area fraction of over 60 %, while deep cloud contributes the most in snowfall volume with about 50 % of the total. The probability distributions of snowfall rates are clearly different among the three types of clouds, with vast majority hardly reaching to 0.3 mm h−1 (liquid water equivalent snowfall rate) for near-surface, 0.5 mm h−1 for shallow, and 1 mm h−1 for deep clouds. However, liquid water path in the three types of clouds all has substantial probability to reach 500 g m−2. There is no clear correlation found between snowfall rate and liquid water path for any of the cloud types. Based on all observed snow profiles, brightness temperatures at Global Precipitation Measurement Microwave Imager channels are simulated, and the ability of a Bayesian algorithm to retrieve snowfall rate is examined using half the profiles as observations and the other half as a priori database. Under idealized scenario, i.e., without considering the uncertainties caused by surface emissivity, ice particle size distribution and particle shape, the study found that the correlation as expressed by R2 between the “retrieved” and “observed” snowfall rates is about 0.33, 0.48 and 0.74, respectively, for near-surface, shallow and deep snowing clouds over land surface; these numbers basically indicate the upper limits capped by cloud natural variability, to which the retrieval skill of a Bayesian retrieval algorithm can reach. A hypothetical retrieval for the same clouds but over ocean is also studied, and a major improvement in skills is found for near-surface clouds with R2 increased from 0.33 to 0.54, while virtually no change in skills is found for deep clouds and only marginal improvement is found for shallow clouds. This study provides a general picture of the microphysical characteristics of the different types of snowing clouds and points out the associated challenges in retrieving their snowfall rate from passive microwave observations.

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Estimation of liquid water path below the melting layer in stratiform precipitation systems using radar measurements during MC3E

Atmospheric Measurement Techniques ◽

10.5194/amt-12-3743-2019 ◽

2019 ◽

Vol 12 (7) ◽

pp. 3743-3759 ◽

Cited By ~ 2

Author(s):

Jingjing Tian ◽

Xiquan Dong ◽

Baike Xi ◽

Christopher R. Williams ◽

Peng Wu

Keyword(s):

Liquid Water ◽

Department Of Energy ◽

Cloud Base ◽

Retrieval Algorithm ◽

Doppler Velocity ◽

Liquid Water Path ◽

Water Path ◽

Melting Layer ◽

Stratiform Precipitation ◽

Cloud Particles

Abstract. In this study, the liquid water path (LWP) below the melting layer in stratiform precipitation systems is retrieved, which is a combination of rain liquid water path (RLWP) and cloud liquid water path (CLWP). The retrieval algorithm uses measurements from the vertically pointing radars (VPRs) at 35 and 3 GHz operated by the US Department of Energy Atmospheric Radiation Measurement (ARM) and National Oceanic and Atmospheric Administration (NOAA) during the field campaign Midlatitude Continental Convective Clouds Experiment (MC3E). The measured radar reflectivity and mean Doppler velocity from both VPRs and spectrum width from the 35 GHz radar are utilized. With the aid of the cloud base detected by a ceilometer, the LWP in the liquid layer is retrieved under two different situations: (I) no cloud exists below the melting base, and (II) cloud exists below the melting base. In (I), LWP is primarily contributed from raindrops only, i.e., RLWP, which is estimated by analyzing the Doppler velocity differences between two VPRs. In (II), cloud particles and raindrops coexist below the melting base. The CLWP is estimated using a modified attenuation-based algorithm. Two stratiform precipitation cases (20 and 11 May 2011) during MC3E are illustrated for two situations, respectively. With a total of 13 h of samples during MC3E, statistical results show that the occurrence of cloud particles below the melting base is low (9 %); however, the mean CLWP value can be up to 0.56 kg m−2, which is much larger than the RLWP (0.10 kg m−2). When only raindrops exist below the melting base, the average RLWP value is larger (0.32 kg m−2) than the with-cloud situation. The overall mean LWP below the melting base is 0.34 kg m−2 for stratiform systems during MC3E.

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Joint Cloud Water Path and Rain Water Path Retrievals from ORACLES Observations

10.5194/acp-2020-849 ◽

2020 ◽

Author(s):

Andrew M. Dzambo ◽

Tristan L'Ecuyer ◽

Kenneth Sinclair ◽

Bastiaan van Diedenhoven ◽

Siddhant Gupta ◽

...

Keyword(s):

Optical Depth ◽

Liquid Water ◽

Cloud Water ◽

Retrieval Algorithm ◽

Liquid Water Path ◽

Cloud Optical Depth ◽

Water Path ◽

Surface Precipitation ◽

Uncertainty Estimates ◽

Cloud Water Path

Abstract. This study presents a new algorithm that combines W-band reflectivity measurements from the Airborne Precipitation Radar-3rd generation (APR-3), passive radiometric cloud optical depth and effective radius retrievals from the Research Scanning Polarimeter (RSP) to estimate total liquid water path in warm clouds and identify the contributions from cloud water path (CWP) and rainwater path (RWP). The resulting CWP estimates are primarily determined by the optical depth input, although reflectivity measurements contribute ~ 10–50 % of the uncertainty due to attenuation through the profile. Uncertainties in CWP estimates across all conditions are 25 % to 35 %, while RWP uncertainty estimates frequently exceed 100 %. Two thirds of all radar-detected clouds observed during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign that took place from 2016–2018 over the southeast Atlantic Ocean have CWP between 41 and 168 g m−2 and almost all CWPs (99 %) between 6 to 445 g m−2. RWP, by contrast, typically makes up a much smaller fraction of total liquid water path (LWP) with more than 70 % of raining clouds having less than 10 g m−2 of rainwater. In heavier warm rain (i.e. rain rate exceeding 40 mm h−1 or 1000 mm d−1), however, RWP is observed to exceed 2500 g m−2. CWP (RWP) is found to be approximately 30 g m−2 (7 g m−2) larger in unstable environments compared to stable environments. Surface precipitation is also more than twice as likely in unstable environments. Comparisons against in-situ cloud microphysical probe data spanning the range of thermodynamic stability and meteorological conditions encountered across the southeast Atlantic basin demonstrate that the combined APR-3 and RSP dataset enable a robust joint cloud-precipitation retrieval algorithm to support future ORACLES precipitation susceptibility and cloud–aerosol–precipitation interaction studies.

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Cloud<i>_</i>cci ATSR-2 and AATSR data set version 3: a 17-year climatology of global cloud and radiation properties

Earth System Science Data ◽

10.5194/essd-12-2121-2020 ◽

2020 ◽

Vol 12 (3) ◽

pp. 2121-2135

Author(s):

Caroline A. Poulsen ◽

Gregory R. McGarragh ◽

Gareth E. Thomas ◽

Martin Stengel ◽

Matthew W. Christensen ◽

...

Keyword(s):

Liquid Water ◽

European Space Agency ◽

Retrieval Algorithm ◽

Transfer Model ◽

Liquid Water Path ◽

Data Set ◽

Water Path ◽

Cloud Properties ◽

Wide Range ◽

Good Agreement

Abstract. We present version 3 (V3) of the Cloud_cci Along-Track Scanning Radiometer (ATSR) and Advanced ATSR (AATSR) data set. The data set was created for the European Space Agency (ESA) Cloud_cci (Climate Change Initiative) programme. The cloud properties were retrieved from the second ATSR (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) spanning 1995–2003 and the AATSR on board Envisat, which spanned 2002–2012. The data are comprised of a comprehensive set of cloud properties: cloud top height, temperature, pressure, spectral albedo, cloud effective emissivity, effective radius, and optical thickness, alongside derived liquid and ice water path. Each retrieval is provided with its associated uncertainty. The cloud property retrievals are accompanied by high-resolution top- and bottom-of-atmosphere shortwave and longwave fluxes that have been derived from the retrieved cloud properties using a radiative transfer model. The fluxes were generated for all-sky and clear-sky conditions. V3 differs from the previous version 2 (V2) through development of the retrieval algorithm and attention to the consistency between the ATSR-2 and AATSR instruments. The cloud properties show improved accuracy in validation and better consistency between the two instruments, as demonstrated by a comparison of cloud mask and cloud height with co-located CALIPSO data. The cloud masking has improved significantly, particularly in its ability to detect clear pixels. The Kuiper Skill score has increased from 0.49 to 0.66. The cloud top height accuracy is relatively unchanged. The AATSR liquid water path was compared with the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) in regions of stratocumulus cloud and shown to have very good agreement and improved consistency between ATSR-2 and AATSR instruments. The correlation with MAC-LWP increased from 0.4 to over 0.8 for these cloud regions. The flux products are compared with NASA Clouds and the Earth's Radiant Energy System (CERES) data, showing good agreement within the uncertainty. The new data set is well suited to a wide range of climate applications, such as comparison with climate models, investigation of trends in cloud properties, understanding aerosol–cloud interactions, and providing contextual information for co-located ATSR-2/AATSR surface temperature and aerosol products. The following new digital identifier has been issued for the Cloud_cci ATSR-2/AATSRv3 data set: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003 (Poulsen et al., 2019).

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Version 4 CALIPSO IIR ice and liquid water cloud microphysical properties, Part II: results over oceans

10.5194/amt-2020-388 ◽

2020 ◽

Author(s):

Anne Garnier ◽

Jacques Pelon ◽

Nicolas Pascal ◽

Mark A. Vaughan ◽

Philippe Dubuisson ◽

...

Keyword(s):

Liquid Water ◽

Effective Diameter ◽

Liquid Water Path ◽

Ice Clouds ◽

Water Path ◽

Random Uncertainty ◽

Microphysical Properties ◽

Uncertainty Estimates ◽

Data Products ◽

Level 2

Abstract. Following the release of the Version 4 Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data products from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a new version 4 (V4) of the CALIPSO Imaging Infrared Radiometer (IIR) Level 2 data products has been developed. The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter (De) and ice or liquid water path estimates. This paper (Part II) shows retrievals over ocean and describes the improvements made with respect to version (V3) as a result of the significant changes implemented in the V4 algorithms, which are presented in a companion paper (Part I). The analysis of the three-channel IIR observations (08.65 μm, 10.6 μm, and 12.05 μm) is informed by the scene classification provided in the V4 CALIOP 5-km cloud layer and aerosol layer products. Thanks to the reduction of inter-channel effective emissivity biases in semi-transparent (ST) clouds when the oceanic background radiance is derived from model computations, the number of unbiased emissivity retrievals is increased by a factor 3 in V4. In V3, these biases caused inconsistencies between the effective diameters retrieved from the 12/10 and 12/08 pairs of channels at emissivities smaller than 0.5. In V4, microphysical retrievals in ST ice clouds are possible in more than 80 % of the pixels down to effective emissivities of 0.05 (or visible optical depth ~ 0.1). For the month of January 2008 chosen to illustrate the results, median ice De and ice water path (IWP) are, respectively, 38 µm and 3 g⋅m−2 in ST clouds, with random uncertainty estimates of 50 %. The relationship between the V4 IIR 12/10 and 12/08 microphysical indices is in better agreement with the severely roughened single column ice crystal model than with the severely roughened 8-element aggregate model for 80 % of the pixels in the coldest clouds ( 230 K). Retrievals in opaque ice clouds are improved in V4, especially at night and for 12/10 pair of channels, owing to corrections of the V3 radiative temperature estimates derived from CALIOP geometric altitudes. Median ice De and IWP are 58 µm and 97 g⋅m−2 at night in opaque clouds, with again random uncertainty estimates of 50 %. Comparisons of ice retrievals with Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) in the tropics show a better agreement of IIR De with MODIS visible/3.7 µm than with MODIS visible/2.1 µm in the coldest ST clouds and the opposite for opaque clouds. In prevailingly supercooled liquid water clouds with centroid altitudes above 4 km, retrieved median De and liquid water path are 13 µm and 3.4 g.m−2 in ST clouds, with estimated random uncertainties of 45 % and 35 % respectively. In opaque liquid clouds, these values are 18 µm and 31 g.m−2 at night, with estimated uncertainties of 50 %. IIR De in opaque liquid clouds is smaller than MODIS visible/2.1 and visible/3.7 by 8 µm and 3 µm, respectively.

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Cross-comparison of cloud liquid water path derived from observations by two space-borne and one ground-based instrument in northern Europe

Atmospheric Measurement Techniques ◽

10.5194/amt-12-5927-2019 ◽

2019 ◽

Vol 12 (11) ◽

pp. 5927-5946 ◽

Cited By ~ 2

Author(s):

Vladimir S. Kostsov ◽

Anke Kniffka ◽

Martin Stengel ◽

Dmitry V. Ionov

Keyword(s):

Liquid Water ◽

Spatial Inhomogeneity ◽

Retrieval Algorithm ◽

Liquid Water Path ◽

Simple Estimate ◽

Cloud Liquid Water ◽

Water Path ◽

Geographical Regions ◽

Temporal And Spatial ◽

Cloud Liquid Water Path

Abstract. Cloud liquid water path (LWP) is one of the target atmospheric parameters retrieved remotely from ground-based and space-borne platforms using different observation methods and processing algorithms. Validation of LWP retrievals is a complicated task since a cloud cover is characterised by strong temporal and spatial variability while remote sensing methods have different temporal and spatial resolutions. An attempt has been made to compare and analyse the collocated LWP data delivered by two satellite instruments SEVIRI and AVHRR together with the data derived from microwave observations by the ground-based radiometer RPG-HATPRO. The geographical region of interest is the vicinity of St. Petersburg, Russia, where the RPG-HATPRO radiometer is operating. The study is focused on two problems. The first one is the so-called scale difference problem, which originates from dissimilar spatial resolutions of measurements. The second problem refers to the land–sea LWP gradient. The radiometric site is located 2.5 km from the coastline where the effects of the LWP gradient are pronounced. A good agreement of data obtained at the microwave radiometer location by all three instruments (HATPRO, SEVIRI, and AVHRR) during warm and cold seasons is demonstrated (the largest correlation coefficient 0.93 was detected for HATPRO and AVHRR datasets). The analysis showed no bias of the SEVIRI results with respect to HATPRO data and a large positive bias (0.013–0.017 kg m−2) of the AVHRR results for both warm and cold seasons. The analysis of LWP maps plotted on the basis of the SEVIRI and AVHRR measurements over land and water surfaces in the vicinity of St. Petersburg revealed the unexpectedly high LWP values delivered by AVHRR during the cold season over the Neva River bay and over the Saimaa Lake and the abnormal land–sea LWP gradient in these areas. For the detailed evaluation of atmospheric state and ice cover in the considered geographical regions during the periods of ground-based and satellite measurements, reanalysis data were used. It is shown that the most probable reason for the observed artefacts in the AVHRR measurements over water and ice surfaces is the coarse resolution of the land–sea and snow–ice masks used by the AVHRR retrieval algorithm. The influence of a cloud field inhomogeneity on the agreement between the satellite and the ground-based data is studied. For this purpose, the simple estimate of the LWP temporal variability is used as a measure of the spatial inhomogeneity. It has been demonstrated that both instruments are equally sensitive to the inhomogeneity of a cloud field despite the fact that they have different spatial resolutions.

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Cloud_cci ATSR-2 and AATSR dataset version 3: a 17-yearclimatology of global cloud and radiation properties

10.5194/essd-2019-217 ◽

2019 ◽

Author(s):

Caroline A. Poulsen ◽

Gregory R. Mcgarragh ◽

Gareth E. Thomas ◽

Martin Stengel ◽

Matthew W. Christiensen ◽

...

Keyword(s):

Liquid Water ◽

European Space Agency ◽

Retrieval Algorithm ◽

Transfer Model ◽

Liquid Water Path ◽

Water Path ◽

Cloud Properties ◽

Wide Range ◽

Initiative Program ◽

Good Agreement

Abstract. We present version 3 (V3) of the Cloud_cci ATSR-2/AATSR dataset. The dataset was created for the European Space Agency (ESA) Cloud_cci (Climate Change Initiative) program. The cloud properties were retrieved from the second Along- Track Scanning Radiometer (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) spanning 1995–2003 and the Advanced ATSR (AATSR) on board Envisat, which spanned 2002–2012. The data comprises a comprehensive set of cloud properties: cloud top height, temperature, pressure, spectral albedo, cloud effective emissivity, effective radius and optical thickness alongside derived liquid and ice water path. Each retrieval is provided with its associated uncertainty. The cloud property retrievals are accompanied by high-resolution top and bottom-of-atmosphere short- and long-wave fluxes that have been derived from the retrieved cloud properties using a radiative transfer model. The fluxes were generated for all-sky and clear-sky conditions. V3 differs from the previous version 2 (V2) through development of the retrieval algorithm and attention to the consistency between the ATSR-2 and AATSR instruments. The cloud properties show improved accuracy in validation and better consistency between the two instruments, as demonstrated by a comparison of cloud mask and cloud height with collocated CALIPSO data. The cloud masking has improved significantly, particularly the ability to detect clear pixels The Kuiper Skill score has increased from .49 to .66. The cloud top height accuracy is relatively unchanged. The AATSR liquid water path was compared with the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) in regions of stratocumulous cloud and shown to have very good agreement and improved consistency between ATSR-2 and AATSR instruments, the Correlation with MAC-LWP increase from .4 to over .8 for these cloud regions. The flux products are compared with NASA Clouds and the Earth’s Radiant Energy System (CERES) data, showing good agreement within the uncertainty. The new dataset is well suited to a wide range of climate applications, such as comparison with climate models, investigation of trends in cloud properties, understanding aerosol-cloud interactions, and providing contextual information for collocated ATSR-2/AATSR surface temperature and aerosol products. For the Cloud_cci ATSR-2/AATSRv3 dataset a new digital identifier has been issued: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003 Poulsen et al. (2019).

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Large eddy simulation of radiation fog: impact of dynamics on the fog life cycle

Atmospheric Chemistry and Physics ◽

10.5194/acp-17-13017-2017 ◽

2017 ◽

Vol 17 (21) ◽

pp. 13017-13035 ◽

Cited By ~ 16

Author(s):

Marie Mazoyer ◽

Christine Lac ◽

Odile Thouron ◽

Thierry Bergot ◽

Valery Masson ◽

...

Keyword(s):

Life Cycle ◽

Liquid Water ◽

Horizontal Resolution ◽

Liquid Water Path ◽

Radiation Fog ◽

Near Surface ◽

Water Path ◽

Fog Prediction ◽

Large Eddy ◽

The Impact

Abstract. Large eddy simulations (LESs) of a radiation fog event occurring during the ParisFog experiment are studied with a view to analyse the impact of the dynamics of the boundary layer on the fog life cycle. The LES, performed with the Meso-NH model at 5 m resolution horizontally and 1 m vertically, and with a 2-moment microphysical scheme, includes the drag effect of a tree barrier and the deposition of droplets on vegetation. The model shows good agreement with measurements of near-surface dynamic and thermodynamic parameters and liquid water path. The blocking effect of the trees induces elevated fog formation, as actually observed, and horizontal heterogeneities during the formation. It also limits cooling and cloud water production. Deposition is found to exert the most significant impact on fog prediction as it not only erodes the fog near the surface but also modifies the fog life cycle and induces vertical heterogeneities. A comparison with the 2 m horizontal resolution simulation reveals small differences, meaning that grid convergence is achieved. Conversely, increasing numerical diffusion through a wind advection operator of lower order leads to an increase in the liquid water path and has a very similar effect to removing the tree barrier. This study allows us to establish the major dynamical ingredients needed to accurately represent the fog life cycle at very high-resolution.

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On the Consequences of Resampling Microwave Radiometer Observations for Use in Retrieval Algorithms

Journal of Applied Meteorology and Climatology ◽

10.1175/2009jamc2155.1 ◽

2009 ◽

Vol 48 (9) ◽

pp. 1981-1993 ◽

Cited By ~ 18

Author(s):

Anita D. Rapp ◽

M. Lebsock ◽

C. Kummerow

Keyword(s):

Wind Speed ◽

Liquid Water ◽

Microwave Radiometer ◽

Retrieval Algorithm ◽

Time Averaging ◽

Liquid Water Path ◽

Cloud Liquid Water ◽

Water Path ◽

Brightness Temperatures ◽

Cloud Liquid Water Path

Abstract How to deal with the different spatial resolutions of multifrequency satellite microwave radiometer measurements is a common problem in retrievals of cloud properties and rainfall. Data convolution and deconvolution is a common approach to resampling the measurements to a single resolution. Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) measurements are resampled to the resolution of the 19-GHz field of view for use in a multifrequency optimal estimation retrieval algorithm of cloud liquid water path, total precipitable water, and wind speed. Resampling the TMI measurements is found to have a strong influence on retrievals of cloud liquid water path and a slight influence on wind speed. Beam-filling effects in the resampled brightness temperatures are shown to be responsible for the large differences between the retrievals using the TMI native resolution and resampled brightness temperatures. Synthetic retrievals are performed to test the sensitivity of the retrieved parameters to beam-filling effects in the resampling of each of the different channels. Beam-filling effects due to the convolution of the 85-GHz channels are shown to be the largest contributor to differences in retrieved cloud liquid water path. Differences in retrieved wind speeds are found to be a combination of effects from deconvolving the 10-GHz brightness temperatures and compensation effects due to the lower liquid water path being retrieved by the high-frequency channels. The influence of beam-filling effects on daily and monthly averages of cloud liquid water path is also explored. Results show that space–time averaging of cloud liquid water path cannot fully compensate for the beam-filling effects and should be considered when using cloud liquid water path data for validation or in climate studies.

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Cross-comparison of cloud liquid water path derived from observations by two space-borne and one ground-based instrument in Northern Europe

10.5194/amt-2019-225 ◽

2019 ◽

Author(s):

Vladimir S. Kostsov ◽

Anke Kniffka ◽

Martin Stengel ◽

Dmitry V. Ionov

Keyword(s):

Spatial Resolution ◽

Liquid Water ◽

Retrieval Algorithm ◽

Liquid Water Path ◽

Simple Estimate ◽

Cloud Liquid Water ◽

Water Path ◽

Geographical Regions ◽

Temporal And Spatial ◽

Cloud Liquid Water Path

Abstract. Cloud liquid water path (LWP) is one of the target atmospheric parameters retrieved remotely from ground-based and space-borne platforms using different observation methods and processing algorithms. Validation of LWP retrievals is a complicated task since a cloud cover is characterised by strong temporal and spatial variability while remote sensing methods have different temporal and spatial resolution. An attempt has been made to compare and analyse the collocated LWP data delivered by two satellite instruments SEVIRI and AVHRR together with the data derived from microwave observations by the ground-based radiometer RPG‑HATPRO. The geographical region of interest is the vicinity of St.Petersburg, Russia, where the RPG‑HATPRO radiometer is operating. The study is focused on two problems. The first one is the so-called scale difference problem which originates from dissimilar spatial resolutions of measurements. The second problem refers to the land-sea LWP gradient. The radiometric site is located 2.5 km from the coastline where the effects of the LWP gradient are pronounced. A good agreement of data obtained at the microwave radiometer location by all three instruments (HATPRO, SEVIRI and AVHRR) during warm and cold seasons is demonstrated (the largest correlation coefficient 0.93 was detected for HATPRO and AVHRR data sets). The analysis showed no bias of the SEVIRI results with respect to HATPRO data and a high bias (0.013–0.017 kg m−2) of the AVHRR results for both warm and cold seasons. The analysis of LWP maps plotted on the basis of the SEVIRI and AVHRR measurements over land and water surfaces in the vicinity of St.Petersburg revealed the unexpectedly high LWP values delivered by AVHRR during cold season over the Neva river bay and over the Saimaa Lake and the abnormal land-sea LWP gradient in these areas. For the detailed evaluation of atmospheric state and ice cover in the considered geographical regions during the periods of ground-based and satellite measurements, reanalysis data were used. It is shown that the most probable reason for the observed artifacts in the AVHRR measurements over water/ice surfaces is the coarse resolution of the land-sea and snow/ice masks used by the AVHRR retrieval algorithm. The influence of a cloud field inhomogeneity on the agreement between the satellite and the ground-based data was studied. For this purpose, the simple estimate of the LWP temporal variability was used as a measure of the spatial inhomogeneity. It has been demonstrated that both instruments are equally sensitive to the inhomogeneity of a cloud field despite the fact that they have different spatial resolution.

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