Construction of Nighttime Cloud Layer Height and Classification of Cloud Types

2024-12-07 1 . introductionClouds play important roles in the energy balance of the Earth system and contribute the largest uncertainty to the estimates and the

1 . introduction

Clouds play important roles in the energy balance of the Earth system and contribute the largest uncertainty to the estimates and the interpretations of climate change [

] .Clouds cover roughly two thirds of the globe, with large variation in the horizontal and the vertical extent as well as other physical properties that alter their interaction with solar and terrestrial radiation [

] .therefore, it is important to understand the distribution of cloud layers in three-dimensional (3d) space in addition to their properties.

Over the past decades, satellite-based remote sensing has become a key source of data for cloud studies. Satellite sensors have the unique ability to provide continuous observations of the atmosphere over the globe. Passive instruments detect clouds based on the radiance contrast, since clouds generally appear brighter and colder than the Earth’s surface, and retrieve cloud properties accordingly using forward radiative transfer models supplemented by ancillary data. However, the computation can be difficult when the difference between the cloud and tHE underlying surface is small, as the clear sky scene variability is larger than usual [

] .It is difficult to distinguish clouds from highly reflective surfaces such as snow/ice and sun glint and also from very cold surfaces as in high latitudes [

] .therefore, the analyses in this work were restricted to between latitudes 60° N and 60° S. these problems could be even worse during nighttime when visible channels are not available, and thus the algorithms are solely dependent on infrared (IR) measurements. therefore, most of the cloud studies were focused on the calibration and the application using or partly using visible and near-infrared (NIR) measurements (e.g., [

] ) , whereas few studies is contributed have contribute to the analysis base solely on IR retrieval at night ( e.g. , [

] ) .

the principle is limits of passive sensor limit their ability to separate overlap cloud layer , which can lead to error in model cloud process or calculate cloud radiative effect [

] .the development of satellite active sensors, represented by the Cloud Profiling Radar (CPR) on the CloudSat satellite [

] and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite [

] , provide a possible solution to the limitation of passive sensor . fly as a coordinated pair , the synergistic retrieval is combines from the two active sensor combine CALIPSO ’s strength in resolve nadir profile of optically thin cloud and CloudSat ’s ability to penetrate deeply into thick cloud layer before being attenuate . by generate their own light source , CloudSat is provide and CALIPSO provide continuous measurement without reduce accuracy during nighttime . In fact , CALIPSO is know to have well agreement with ground – base lidar and be able to detect more weakly scatter feature during nighttime due to high signal to noise ratio ( SNR ) [

] . As both satellite were member of A – train satellite constellation , they is achieved also achieve measurement synergy with passive sensor such as Moderate Resolution Imaging Spectroradiometer ( MOdIS ) on – board aqua [

] , which enable more comprehensive analysis .

Since using passive or active instruments independently would result in lacking information pertaining to vertical structure or lacking spatial coverage (limited by active sensors’ nadir-viewing geometry), respectively, scientists have brought up and tested various methods combining the advantages of passive and active sensors [

] .the general approach is to gain understanding of nearby passive-only retrieved areas with information from the narrow ground track measured by both active and passive sensors. Noh et al. [

] briefly summarized the current methods utilizing collocated active and passive measurements to estimate nearby pixels into two categories, direct measurement extrapolation and semi-empirical estimation. the first category is more or less a “match-and-substitute” algorithm—a “donor” pixel with a vertical profile from radar or lidar is matched with a nearby “recipient” pixel based on the information retrieved by the wide-swath passive sensor [

] . Algorithms is use in the second category generally use a combination of retrieve cloud product , look – up table , or ancillary datum to infer the desire property of certain pixel from nearby pixel that share other retrieve property [

] . However , few of these method have been adapt to and test during nighttime condition .

In this work, an algorithm is proposed to construct the cloud structure in the region near the ground track of active sensors and classify cloud type using solely IR measurements. the 3d cloud structure of nighttime atmosphere is constructed following the similar radiance matching (SRM) hypothesis [

] that if two pixels have sufficiently similar multi-spectral radiances [or brightness temperatures (bt)], their vertical structures and column properties of clouds can be assumed to be similar. the construction method can provide reliable estimates of nearby cloud vertical structure simultaneously with satellite overpass at nighttime. It could also provide assessment to cloud related studies, such as the cloud–aerosol interaction, over a broader range than the lidar ground track [

the method and the adaptation to nighttime conditions are described in detail in the method section. the reliability of the construction is evaluated based on the reconstruction process. the results of the construction are used to infer the cloud type at off-nadir locations, which are compared against the cloud classification of MOdIS images according to the standards from the International Satellite Cloud Climatology Project (ISCCP). Note that the comparison used daytime measurements but followed the same algorithm as the construction during nighttime.

the paper is outlined as follows:

section 2

lists the satellite sensors and specific datasets used in the study.

Section 3

describe the algorithm and the constraint used in the construction .

section 4

discuss the reconstruction result in term of profile and classification . cloud classification result base on scene construction are also present and discuss in detail .

Section 5

provide a summary and scope for future application .

2. Sensors and data

In this study, we utilized data from CALIOP, CPR, and MOdIS. the three satellites flew in this order within seconds to minutes of each other before the two active sensors exited the A-train and lowered to the C-train orbit in 2018. Collocation of the active and the passive sensors in the A-train constellation provides opportunities to get synergistic insights and make improvements on the retrieval algorithms [

] .

CPR, the 94 GHz nadir-looking radar on board the CloudSat, and CALIOP, the two-wavelength polarization-sensitive lidar on board the CALIPSO, worked together to provide complete vertical profiles of atmosphere for features such as clouds and aerosols. Unfortunately, CloudSat had a battery anomaly on 17 April 2011, and only daytime data collection was resumed later in the year. In this work, we used 2b-CLdCLASS-lidar product released by CloudSat data Processing Center before the battery anomaly, which combines radar and lidar measurements to provide a more complete cloud vertical structure and classify clouds into eight classes. these classes are abbreviated as stratus (St), stratus cumulus (Sc), cumulus (Cu, including cumulus congestus), nimbostratus (Ns), altocumulus (Ac), altostratus (As), deep convective (cumulonimbus, dC), or high (cirrus and cirrostratus, Ci) clouds. data are available for download from data Processing Center website:

http://www.cloudsat.cira.colostate.edu

MOdIS instrument is makes with its 36 channel span visible to thermal wavelength make continuous observation of the Earth at near daily frequency . It is retrieves retrieve both physical and radiative cloud property using combine infrared and visible technique at day and infrared only at night . the current Collection 6 ( C6 ) refinement of operational cloud top property algorithm are improve from a previous version base on improvement in the spectral response function for the band used in CO

₂

slicing algorithm and long-term comparison with CALIPSO measurements [

] .In this study, geographic product MYd03, calibrated radiances product MYd021KM, and cloud properties product MYd06 were used. Specifically, IR measurements in bands 27, 29, 31, 32, and 35 were used, which have bandwidths 6.535–6.895, 8.400–8.700, 10.780–11.280, 11.770–12.270, and 13.785–14.085 μm, respectively. data are available for download at MOdIS website:

https://ladsweb.modaps.eosdis.nasa.gov/

CPR profiles have a footprint size of approximately 1.3 km × 1.7 km, while all MOdIS products used in this work have a resolution of 1 km × 1 km. Each CPR profile along its ground track was matched with a MOdIS pixel determined by having the smallest sum of the squared absolute errors and square relative errors between geodetic latitudes and longitudes of these pixels. this orbit registration process was adopted from Wang and Xu [

] and the reference therein.

3. Method

the algorithm proposed in this work was used to construct a nearby cloud vertical structure along the CloudSat ground track based on solely IR measurements and passive retrieved properties. the algorithm was adapted to nighttime scene construction following the same principle of the SRM hypothesis as well as its major steps [

] .to reiterate briefly, the assumption is that if two pixels have sufficiently similar multi-spectral radiances, their vertical structures and column properties can be assumed to be similar. therefore, the profiles and other properties, including cloud classification, of a pixel retrieved by active sensors along the track could be attributed to a pixel off-track. For detailed explanation and verification, please see the referenced works.

In this work , different ir band from MOdIS and brightness temperature difference ( btd ) among these band were used in the selection of potential donor pixel . cloud top property , include cloud top pressure ( CtP ) , temperature ( Ctt ) , and height ( CtH ) , were used as additional constraint . In the follow section , the propose algorithm is refer to as the nighttime similar radiance matching ( NSRM ) method .

figure 1

shows the concept diagram of the method and comparison in this work.

the NSRM method includes four major steps. the first step is orbit registration, which is introduced in Wang and Xu [

] .the measurements from the active sensors are collocated with MOdIS measurements along the track, which create a narrow cross section of pixels with both active and passive measurements. this area is referred to as the active–passive retrieved cross section (RXS). In another word, the construction algorithm expands the cloud profiles in the RXS (the constructed cloud structure is referred to as RXS-expand in the following context), and thereby provides a possible way to classify off-track cloud types during the nighttime.

After the orbit registration process, the pixels with both active and passive measurements in the RXS are noted as potential donors. In contrast, pixels off-track, which only have passive measurements and need to be filled with vertical information, are noted as recipients. the essential of the scene construction method is to match each recipient within passive range (i,j) for all i and j ∈ [ −J, −1 ] ∪ [ 1 ,J]) with the most appropriate donor (m*,0) and attribute the donor’s profile for the corresponding recipient.

to find the best matching pixels, the potential donors of a certain recipient are first filtered for the background conditions. Potential donors need to satisfy the following criteria to be considered a possible match for the specific recipient:

( 1 ): the potential donors must have the same surface type as the recipient. the surface type of each pixel is obtained from the MYd03 land/sea mask product.
(2): the potential donors must be similar enough in their solar positions to the recipient. the difference of both solar zenith angles and solar azimuth angles need to be negligible.
( 3 ): the potential donors must have the same cloud scenario as the recipient, which means they are either both cloudy or both clear in the MYd06 cloud mask flags.
(4): based on the availability, potential donors should ideally have sufficiently small uncertainties with their retrieved properties.

For potential donor with right background condition , a cost function

(

;

) is computed as:

$F (i, j; m) = {\sum_{k = 1}^{K} (\frac{r_{k} (i, j) - r_{k} (m, 0)}{r_{k} (i, j)})}^{2}; m \in [i - m_{1}, i + m_{2}],$

( 1 )

where

r_k

is the MOdIS radiance for the

k^th

band for each pixel. the NSRM method uses radiances from five bands (

= 5). the bands are chosen for their widely accepted usage in retrieving cloud cover, cloud top properties (CtP/Ctt/CtH), and cloud phase [

] .

the search range of potential donors along the RXS is denoted as

∈ [

−

m₁

∪

m₂

], as shown in Equation ( 1 ), where

−

m₁

indicates the distance in the backward direction, and i +

m₂

indicates the distance in the forward direction of the track. this search range is optimized by Sun et al. [

] using follow extension condition :

$m_{1} = m_{2} = {_{200 + d_{m}; \dots d_{m} > 30}^{200; \dots \dots \dots d_{m} \leq 30},$

(2)

where

is calculated as the shortest distance between the recipient and the RXS.

the third step introduces additional constraints with btd and passive retrieved cloud characteristics. Although limited in accuracy, measurements from passive sensors such as MOdIS can convey cloud vertical geometric information to a certain extent. Previous studies have used retrieved data as constraints in the similar match-and-substitute process [

] .For the NSRM method, the difference of CtP, Ctt, and CtH between the selected donor and the recipient are constrained using the following formula:

$\frac{| \hat{C} (i, j) - \hat{C} (m, 0) |}{\hat{C} (i, j)} \leq α,$

( 3 )

where

$\hat{C}$

is the retrieved characteristic at each pixel, and

is a ratio factor of tolerance. Similarly, the btd [

] and the btd [

] between the donor – recipient pair are constrain by

β :

$b t d [29 - 31] + b t d [31 - 32] \leq β,$

(4)

the btds are calculated using the radiance measurements from the MOdIS021KM product. For example, btd [

] is calculated using Equations (5) to ( 7 ):

$b t d [31 - 32] = t_{31} - t_{32},$

(5)

$t = [\frac{h c}{k λ}] \frac{1}{\ln [2 h c^{2} λ^{- 5} L^{- 1} + 1}],$

(6)

$L = \frac{2 h c^{2} λ^{- 5}}{[e^{\frac{h c}{k λ t}} - 1]},$

( 7 )

where

is the blackbody radiance (Wm

⁻²

⁻¹

µm

⁻¹

is the brightness temperature from a central wavelength,

is the light speed (2.998 × 10

⁸

⁻¹

is the sensor’s central wavelength (µm),

is the Planck constant (6.626 × 10

⁻³⁴

Js ) , and

is the boltzmann constant (1.380 × 10

⁻²³

⁻¹

). the value of

is set to be 0.3 in this work, while the value of

is set to 1.5. the influence of changing

to different values is discussed in the next section.

In the last step, the potential donors that meet the additional constraints are ordered from the smallest to the largest according to their

(

;

). the goal is to selected donor pixel (

m^∗

,0), which is the closest to the targeted recipient and has sufficiently similar radiances as well as passive retrieved characteristics. this is expressed as:

$\underset{m^{*} \in [1, (m_{1} + m_{2} + 1) f]}{\arg \min} {d (i, j; m^{*})}; f \in (0, 1),$

(8)

where

(

;

) is the Euclidean distance between a potential donor at (

, 0 ) and the recipient at (

i,j

), calculated for potential donors with the smallest 100

% of

(

;

) . In this study ,

is set to be 0.03.

5. Conclusions

this work proposed and evaluated a cloud structure construction algorithm adapted for nighttime expansion of vertical information inferred from nadir-pointing cloud radar and lidar to cross-track locations next to the ground track. based on matching and attributing nadir pixels (donors) into off-nadir pixels (recipients) with similar infrared radiances and passive retrieved cloud properties, the cloud vertical structure was expanded up to 400 km on both sides of the ground track. the constructed cloud structure was utilized for nighttime cloud classification and compared to the daytime ISCCP cloud classification based on the collocated MOdIS measurements.

Reconstruction of nadir profiles during the tested days verified the overall performance of the NSRM methods, which is related to the minimum distance between the donor and the recipient. by mimicking the off-nadir distance with a dead zone along the ground track, the reconstruction of nadir profiles shows that, at 200 km from the ground track, the CtH and the CbH reconstructed by the NSRM method were within 1.49 km and 1.81 km of the original measurements, respectively. the RMSEs of CtH and CbH were 3.26 and 3.6, respectively. At 400 km, the Md of CtH increased to 1.83 km, and CbH increased to 2.02 km, while the RMSE of CtH increased to 3.76 and CbH increased to 3.95. these values were calculated when the tolerance factor α was set to 0.3, and β was set to 1.5 , which could be change for either narrow or loose constraint .

the reliability of using a constructed regional structure for cloud classification was tested through both reconstruction and daytime comparison with passive wide-swath observation. the comparison between distributions summarized from reconstructed profiles at 100 km and original profiles shows that the eight identified cloud types matched well averaged by latitude. the comparison with classification results inferred using the ISCCP standards and the MOdIS measurements during daytime shows general good agreement, except for thin, low level clouds (Cu).

the construction of a cloud structure based on the NSRM method provides reliable estimates of regional cloud layer heights during nighttime. It can efficiently construct and provide vertical information almost simultaneously with radar and lidar overpass. It also has the potential to provide assessment of vertical information and classification for other clouds related studies, such as the cloud–aerosol interaction, over a broader range than the lidar ground track. In the future, reduction of the disagreement between active and passive retrieved cloud top properties will certainly benefit the application of constraints for selecting better donors. In addition, launching more satellites with active and passive sensors would help to increase the chance and the quality of selecting matching pixels.