Satellite-imager-based operational cloud property
retrievals generally assume that a cloudy pixel can be treated as being
plane-parallel with horizontally homogeneous properties. This assumption can
lead to high uncertainties in cloud heights, particularly for the case of
optically thin, but geometrically thick, clouds composed of ice particles.
This study demonstrates that ice cloud emissivity uncertainties can be used
to provide a reasonable range of ice cloud layer boundaries, i.e., the
minimum to maximum heights. Here ice cloud emissivity uncertainties are
obtained for three IR channels centered at 11, 12, and 13.3
Satellite sensors provide data daily that are essential for determining global cloud properties, including cloud height–pressure–temperature, thermodynamic phase (ice or liquid water), cloud optical thickness, and effective particle size. These variables are essential for understanding the net radiation of the Earth and the impact of clouds (L'Ecuyer et al., 2019). In particular, cloud heights at the top and base levels are necessary to determine upwelling and downwelling infrared (IR) radiation (Slingo and Slingo, 1988; Baker, 1997; Harrop and Hartmann; 2012). Additionally, cloud heights are used to derive atmospheric motion vectors that are important for most global data-assimilation systems (Bouttier and Kelly, 2001), affecting the accuracy of the global model forecast (Lee and Song, 2018). However, in most operational retrievals of cloud properties, only a single cloud height is inferred for a given pixel, or field of view. The goal of this study is to develop an algorithm to infer cloud height boundaries for semitransparent ice clouds using only IR measurements for its applicability of global data regardless of solar illumination. Where this study could provide the most benefit is for the case where an ice cloud is geometrically thick but optically thin.
Although our approach will be applied to geostationary satellites in future work, the algorithm is developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for two reasons: (1) our resulting cloud temperatures can be compared to those from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO CALIOP) active lidar version 4 products for verification and (2) further comparison can be made to the MODIS Collection 6 cloud products. The approach adopted in our study for the inference of ice cloud height has a basis in the work of Inoue (1985), who developed this approach using only the split-window channels on the Advanced Very High Resolution Radiometer (AVHRR). The goal of the Inoue (1985) approach was to improve the inference of cloud temperatures for semitransparent ice clouds. Heidinger and Pavolonis (2009) further improved this approach and generated a 25-year climatology of ice cloud properties from AVHRR analysis.
For satellite-based cloud height retrievals based on passive IR measurements, the radiative emission level is regarded as the cloud top. When the emissivity is 1, the cloud is emitting as a blackbody and the cloud top is at, or close to, the actual cloud's upper boundary. As the emissivity decreases, the cloud top inferred from IR measurements will be lower than the actual cloud-top level. This is demonstrated in Holz et al. (2006), who compared the cloud tops from aircraft Scanning High-Resolution Interferometer Sounder (S-HIS) measurements to those from coincident measurements from the Cloud Physics Lidar (CPL). They found that the best match between the cloud tops based on the passive S-HIS measurements and the CPL occurs when the integrated cloud optical thickness is approximately 1. This implies that the differences of cloud-top heights by IR measurements from those by CALIOP are expected since the IR method reports the height where the integrated cloud optical thickness, beginning at cloud top and moving downwards into the cloud, is approximately 1 while CALIOP reports the actual cloud top to be where the first particles are encountered.
With regard to geometric differences of IR cloud tops from the actual cloud tops, optically thin but geometrically thick clouds show the largest bias since the level at which the integrated optical thickness reaches 1 is much lower than the height at which the first ice particles occur. In a review of 10 different satellite retrieval methods for cloud-top heights by IR measurements (Hamann et al., 2014), the heights inferred for optically thin clouds are generally below the cloud's mid-level height. When lower-level clouds are present below the cirrus in a vertical column, the inferred cloud height can be between the cloud layers, depending on the optical thickness of the uppermost layer.
There is a retrieval approach to infer optically thin cloud-top pressure
that uses multiple IR absorption bands within the 15
To complement the use of IR window channels, the addition of a single IR
absorption channel, such as one within the broad 15
Rather than inferring a single ice cloud temperature in each pixel, we infer
a range of ice cloud temperatures (minimum to maximum temperature per
ice cloud pixel) that correspond to uncertainties in the cloud spectral
emissivity. We note that the spectral cloud emissivity, which can be
obtained using measurements at 11, 12, and 13.3
The paper is organized as follows. Section 2 describes the data used in this study. Section 3 presents the methodology and the generation of the relevant look-up tables (LUTs) for the radiances and brightness temperatures used in our analyses. Section 4 provides results for the western North Pacific Ocean during August 2015 and comparisons with CALIOP. Section 5 discusses the results and Sect. 6 summarizes this paper.
The study domain is the western North Pacific Ocean (0–30
The detailed information used to generate empirical look-up tables (LUTs) of min–max(
The MODIS is a 36-channel whisk-broom scanning radiometer on the NASA Earth
Observing System Terra and Aqua platforms. The Aqua platform is in a daytime
ascending orbit at 13:30 LST. The MODIS sensor has four focal planes that
cover the spectral range 0.42–14.24
The CALIPSO satellite platform carries several instruments, among which is a near-nadir-viewing lidar called CALIOP (Winker et al., 2007, 2009). Originally, CALIPSO flew in formation with NASA's Earth Observing System Aqua platform since 2006 and was part of the A-Train suite of sensors. At the time of this writing, it is no longer part of the A-Train but flies in formation with CloudSat in a lower orbit. CALIOP takes data at 532 and 1064 nm. The CALIOP 532 nm channel also measures the linear polarization state of the lidar returns. The depolarization ratio contains information about aerosol and cloud properties. This study uses CALIPSO version 4 products that were released in November 2016. With the updated radiometric calibration at 532 and 1064 nm (Getzewich et al., 2018; Vaughan et al., 2019), cloud products such as cloud–aerosol discrimination and extinction coefficients show significant improvement relative to previous versions (Young et al., 2018; Liu et al., 2019). CALIPSO products are used to validate our retrievals, including CAL_L1D_L2_ VFM-Standard-V4, which provides cloud vertical features; CAL_LID_L2_05kmCPro-Standard-V4 and CAL_LID_L2_05kmCLay-Standard-V4, which provide cloud-top and cloud-base temperature (height); extinction coefficients; and temperature profiles (Table 1).
The Global Forecast System (GFS) model is produced by the National Centers
for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric
Administration (NOAA) (Moorthi et al., 2001). GFS provides global NWP model
outputs at 0.5
The estimated clear-sky radiance map at
The MODIS pixels identified as being clear sky are used to generate a
gridded clear-sky map, which is another ancillary product required for our
method. To simplify the generation of this map, the MODIS data with 1 km
resolution are converted to 5 km resolution. Monthly composites of clear-sky
radiances (
The conceptual model for
The basis for the retrieval algorithm is provided in Inoue (1985). Figure 2a shows the plane-parallel homogeneous cloud model with no scattering.
The ice cloud layer at a given height has a corresponding ice cloud
temperature (
Equation (1) can be rearranged to solve for the emissivity:
Inoue (1985) discusses the range of uncertainties in both
Another way to constrain these uncertainties is by using multiple IR channel
measurements, specifically the spectral emissivity differences between two
IR window channels (
For the case of two IR channels, Inoue (1985) formulated the retrieval of
the cirrus cloud temperature and effective emissivity by setting up three
equations with three unknowns (specifically referring to Inoue's equations
5, 6, and 7): two equations are the same as Eq. (2) at 11 and 12
The approach of Inoue (1985) for developing the spectral cloud emissivity
relationship improved the accuracy of the cirrus temperature retrievals.
More recent studies explored the extinction coefficient ratio between the 11
and 12
In this study, we apply a range of spectral cloud emissivity values to infer
cloud temperatures rather than an optimum value. In our approach, the cloud
is considered to be a number of plane-parallel homogeneous cloud layers. The
cloud layer temperature ranges,
The differences between this study and Inoue (1985) are summarized as follows.
Constraints in the iteration range for cloud emissivity are provided in look-up tables (LUTs) discussed in the next section, as opposed to considering the full range of possible values from 0 to 1.
Emissivity differences (
Given the range of emissivity differences (
A flowchart for estimation of
The first step in the current method (Fig. 3) is to constrain 11
The second step is to constrain cloud emissivity differences between 11 and
12
The initial assumed 11
For our method, relevant information for the western North Pacific Ocean is
stored in look-up tables (LUTs). The LUTs include the
min–max(
First, the BTD
Second, the BTD
Finally, BT
The LUTs are compiled for
Parameter ranges and discretization of parameters in the
LUTs for
The second step is to categorize the ensemble of ice cloud pixels by three
parameters, BTD
The final step is to find the possible ranges of
Look-up table values for
min–max(
Figure 4 shows examples of LUT values for
Look-up tables for min–max(
Figure 5 shows examples of LUT values of
The current algorithm analyses are performed over the study domain, the western North Pacific Ocean, in August 2015. Note that the Typhoon Goni formed on 13 August and dissipated on 30 August 2015, and affected East Asia. Case studies involving Typhoon Goni scenes are provided in Sect. 4.1. Quantitative analysis and comparison of our results with CALIOP cloud products are described in Sect. 4.2.
Data used for the tests shown in Fig. 3. Input and
auxiliary data are taken from the MODIS C6 cloud products and from CALIOP v4
cloud products. The abbreviations CTT–CBT, CTH–CBH, COT,
Figure 6 is a scene analysis for single-layered optically thin ice clouds
for a granule at 03:20 UTC on 19 August 2015. Figure 6a is a MODIS false
color image that captures Typhoon Goni. Note that the image is
rotated 90
For comparison with CALIPSO, the min–max(
Note that the max(
The second case is the single-layered optically thick ice clouds (Fig. 7) at
15:30 UTC on 19 August 2015. Here we show the BT
Joint histograms of three cloud categories:
The third case also involves a cross section of Goni, but this scene is more
complex in that there is evidence of both multilayered and less homogeneous
ice clouds on the southern boundary of the typhoon (Fig. 8a). Note that the
SD(
In this section, the max–min(
First, we qualitatively examine the max–min(
The matchup data are filtered as follows: only ice cloud phase pixels are
chosen that have the highest quality (CALIOP QC for cloud phase
Comparison of max(
Figure 9 shows the joint histogram of the max–min(
However, the scatter is higher for optically thick clouds, with corr
The comparisons of the min(
The results in Figs. 6–9 show the comparisons of the ice cloud height ranges obtained based on the ice cloud emissivity uncertainties with both MODIS C6 products and vertical cross sections of clouds from CALIOP. We investigated minimum and maximum ice cloud heights for each cloud pixel for three cloud regimes during August 2015: (1) single-layered optically thin clouds, (2) optically thick ice clouds, and (3) multilayered clouds.
Overall, the maximum values of the estimated ice cloud height ranges for
single-layered optically thin and thick ice clouds show some skill in comparison
with the cloud tops from CALIOP: corr
The minimum heights for single-layered optically thin ice clouds reach near
the base of the cloud, with corr
A frequency of biases of mean(
To better understand the potential biases of the current algorithm in
comparison with CALIOP, we compare the mean(
Figure 10 illustrates that our resulting single-layered ice cloud boundaries
are consistent with CALIOP measurements, showing slightly negative biases
except for the region near “COT
The negative biases of the mean(
Figure 10 also addresses the weaknesses of our method. In the region of
COT
A limitation of this study is that the LUTs are generated for spectral emissivity using IR sensor observations and level-2 products that still have errors and uncertainties. It would be interesting to extend this preliminary research by generating LUTs for spectral emissivity using CALIOP, not IR sensors. If we can obtain more diverse ice cloud emissivity in vertical cloud thickness, it could result in improvements in the resulting cloud temperatures and height ranges. Also, the LUTs based on CALIOP data/products could be used to reduce errors in inferring cloud temperatures for multilayered clouds.
The intent of our study is to demonstrate that ice cloud emissivity uncertainties, obtained from three IR channels generally available on various satellite-based sensors, can be used to estimate a reasonable range of ice cloud temperatures as verified through comparison with active measurements from CALIPSO. For satellite-based retrievals with heavy data volumes, the general assumption is that the cloud in any given pixel can be treated as plane parallel, which simplifies the retrieval algorithms. However, for ice clouds and particularly optically thin ice clouds known as cirrus, the plane-parallel assumption breaks down because cirrus tends to be optically thin but geometrically thick, which is different with lower-level liquid water clouds. For cirrus, the inference of a cloud-top temperature for a given measurement may not be optimal. In our approach, a range of spectral ice cloud emissivity is calculated, which is, in turn, used to infer a range of cloud temperatures. These temperatures are converted to heights and subsequently compared to active lidar measurements provided by CALIPSO CALIOP products.
This study provides a methodology to infer a range of spectral cloud
emissivity for each cloud pixel. The range in emissivity represents
uncertainty in the cloud microphysics to some degree. In our approach, we
generate two LUTs for cloud emissivity at 11
We estimate a range of ice cloud temperature corresponding to the ice cloud
uncertainty generated by three IR channels centered at 11, 12, and 13.3
This approach can be applied to the new geostationary satellites, such as Himawari-8 (launched in 2015), GOES-16/17 (launched in 2016 and 2017), and GK-2A (launched in 2018). The new features of ice cloud temperatures from base to top by geostationary IR observation could contribute to improved accuracy of weather prediction and cloud radiative effects.
In future work, we intend to improve upon this methodology by developing lookup tables for spectral cloud emissivity uncertainty with CALIOP. Above all, it is required to study for global area for applying this method to the new geostationary satellites. Also, further study is required to add more infrared channels to resolve more accurate spectral cloud emissivity uncertainties.
The current algorithm uses MODIS C6, that are available from
HSK built, tested, and validated the algorithm and wrote the paper. BB contributed to completing the algorithm and to reviewing and editing the paper carefully. YSC provided the initial idea for the algorithm and guidance on this study. All authors were actively involved in interpreting results and discussions on the paper.
The authors declare that they have no conflict of interest.
We are grateful to the MODIS and CALIOP science teams for their continuous efforts in providing high-quality measurements and products. We also appreciate Carynelisa Haspel and the two anonymous referees for deliberate reviewing and fruitful comments and suggestions.
This work was supported by the “Development of Cloud/Precipitation Algorithms” project, funded by ETRI, which is a subproject of the “Development of Geostationary Meteorological Satellite Ground Segment (NMSC-2019-01)” program funded by the National Meteorological Satellite Center (NMSC) of the Korea Meteorological Administration (KMA).
This paper was edited by Alexander Kokhanovsky and reviewed by Carynelisa Haspel and three anonymous referees.