Corrections for Geostationary Cloud Liquid Water Path Using Microwave Imagery

Kevin M. Smalley aJet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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Matthew D. Lebsock aJet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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Abstract

Geostationary observations provide measurements of the cloud liquid water path (LWP), permitting continuous observation of cloud evolution throughout the daylit portion of the diurnal cycle. Relative to LWP derived from microwave imagery, these observations have biases related to scattering geometry, which systematically varies throughout the day. Therefore, we have developed a set of bias corrections using microwave LWP for the Geostationary Operational Environmental Satellite-16 and -17 (GOES-16 and GOES-17) observations of LWP derived from retrieved cloud-optical properties. The bias corrections are defined based on scattering geometry (solar zenith, sensor zenith, and relative azimuth) and low cloud fraction. We demonstrate that over the low-cloud regions of the northeast and southeast Pacific, these bias corrections drastically improve the characteristics of the retrieved LWP, including its regional distribution, diurnal variation, and evolution along short-time-scale Lagrangian trajectories.

Significance Statement

Large uncertainty exists in cloud liquid water path derived from geostationary observations, which is caused by changes in the scattering geometry of sunlight throughout the day. This complicates the usefulness of geostationary satellites to analyze the time evolution of clouds using geostationary data. Therefore, microwave imagery observations of liquid water path, which do not depend on scattering geometry, are used to create a set of corrections for geostationary data that can be used in future studies to analyze the time evolution of clouds from space.

© 2023 American Meteorological Society. This published article is licensed under the terms of a Creative Commons Attribution 4.0 International (CC BY 4.0) License .

Corresponding author: Kevin M. Smalley, kevin.m.smalley@jpl.nasa.gov

Abstract

Geostationary observations provide measurements of the cloud liquid water path (LWP), permitting continuous observation of cloud evolution throughout the daylit portion of the diurnal cycle. Relative to LWP derived from microwave imagery, these observations have biases related to scattering geometry, which systematically varies throughout the day. Therefore, we have developed a set of bias corrections using microwave LWP for the Geostationary Operational Environmental Satellite-16 and -17 (GOES-16 and GOES-17) observations of LWP derived from retrieved cloud-optical properties. The bias corrections are defined based on scattering geometry (solar zenith, sensor zenith, and relative azimuth) and low cloud fraction. We demonstrate that over the low-cloud regions of the northeast and southeast Pacific, these bias corrections drastically improve the characteristics of the retrieved LWP, including its regional distribution, diurnal variation, and evolution along short-time-scale Lagrangian trajectories.

Significance Statement

Large uncertainty exists in cloud liquid water path derived from geostationary observations, which is caused by changes in the scattering geometry of sunlight throughout the day. This complicates the usefulness of geostationary satellites to analyze the time evolution of clouds using geostationary data. Therefore, microwave imagery observations of liquid water path, which do not depend on scattering geometry, are used to create a set of corrections for geostationary data that can be used in future studies to analyze the time evolution of clouds from space.

© 2023 American Meteorological Society. This published article is licensed under the terms of a Creative Commons Attribution 4.0 International (CC BY 4.0) License .

Corresponding author: Kevin M. Smalley, kevin.m.smalley@jpl.nasa.gov

1. Introduction

Launched in 2016, GOES-16 was the first satellite of the GOES-R series. Operationally known as GOES-East, it provides a good view of marine clouds over the eastern Pacific and western Atlantic. GOES-17 was the second satellite of the GOES series, launched in 2018 with similar characteristics to GOES-16. GOES-17 is known as GOES-West, and it extends our view of marine clouds to the central and eastern Pacific. The Advanced Baseline Imager (ABI; Schmit et al. 2017) on board both satellites provides 16 channels (compared to 5 on board prior generations of GOES) that have been used to observe cloud properties (Delgado-Bonal et al. 2022; Smalley et al. 2022). Additionally, the spatiotemporal resolution of GOES-R drastically increased relative to prior GOES generations, with both satellites completing a full-disk scan every 10 min (30 min prior) at a spatial resolution of 0.5–2 km (1–4 km prior).

Operationally, the higher spatiotemporal resolution of GOES-R has informed forecasters and models about the short-term evolution of clouds (e.g., fronts and tropical cyclones) associated with different synoptic conditions (e.g., Zhang et al. 2018; Ribeiro et al. 2019; Marion et al. 2019; Jones et al. 2020). Their usefulness further extends to the scientific exploration of the short-term evolution of clouds including the stratocumulus-to-cumulus transition (Smalley et al. 2022) and deep convection (e.g., Nussbaumer et al. 2021; Munsell et al. 2021; Machado et al. 2021).

Painemal et al. (2021) compared cloud-optical properties obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS; Platnick et al. 2017) with those from GOES-13, finding GOES-13 overestimates cloud-top effective radius (re) when the scattering angle exceeds 140°. They also found that GOES-13 and GOES-16 exhibit very similar biases, indicating that they persist in GOES-16. Biases in cloud-optical depth (τ) and re primarily occur at oblique solar and sensor angles (Walther and Straka 2020) and in heterogeneous situations (Painemal et al. 2021) that bias liquid water path (LWP) predominately near twilight. These biases primarily stem from 3D radiative effects that are not accounted for in the radiative transfer model used in the retrieval algorithms (Walther and Straka 2020).

Previous studies have utilized conically scanning passive microwave imagers to investigate LWP under various environmental conditions over oceans (Wood et al. 2002; Huang et al. 2015; Manaster et al. 2017). Passive microwave retrievals have their own set of uncertainties, including cloud emission temperature bias (O’Dell et al. 2008), a clear-sky bias dependent on wind speed and water vapor (Lebsock and Su 2014), and the partitioning of rain and cloud water (Seethala and Horváth 2010; Lebsock and Su 2014; Greenwald et al. 2018). However, these observations are not affected by solar scattering geometry. Consequently, passive microwave provides a reliable reference against which scattering-geometry-dependent biases in the ABI products can be quantified.

Furthermore, the adjustment of low-cloud LWP to aerosols remains highly uncertain. Global climate models suggest a positive adjustment linked to the second indirect effect (Michibata et al. 2016), whereas high-resolution large-eddy simulations (LES) propose the opposite (Glassmeier et al. 2019; Hoffmann et al. 2020). Observational studies utilizing MODIS yield similar results to pre-LES investigations (Toll et al. 2019; Gryspeerdt et al. 2022; Zhang et al. 2022). However, MODIS lacks the ability to capture the temporal evolution of LWP that GOES-R enables. The intent of this paper is to use the passive microwave to develop a set of bias correction factors for the ABI LWP retrievals, so that the corrected ABI data can be useful for examining the diurnal evolution of cloud LWP with a primary focus on the marine low-cloud regions and the effect of aerosol perturbations on the temporal evolution of LWP.

2. Data products

a. ABI cloud liquid water path derivation

Using liquid-only pixels (Pavolonis 2020) to define low clouds, we use ABI τ and re (Walther and Straka 2020) observations between 2019 and 2021 (downloaded from NOAA CLASS; https://www.avl.class.noaa.gov) to derive LWP using an adiabatic cloud model, as described in Grosvenor et al. (2018). We aggregate these LWP to 1° × 1° 10-min gridded means, with clear sky included in the averages. We separately store the 1° × 1° gridded low cloud fraction (f).

There are a few limitations in the ABI retrieved τ and re that must be addressed prior to the derivation of the grid-mean LWP. First, LWP is assumed to vary linearly with re; however, re has a large dependence on the near-infrared channel (operationally 2.2 μm is used) used to derive it (Baum et al. 2000). Additionally, shadows represent a potential source of bias in both clear sky and over low-cloud layers (Ackerman et al. 2004). Prior studies have also shown that biases exist in optical properties within broken cloud regions due to cloud-edge effects (Coakley et al. 2005) and 3D radiative artifacts (Zhang et al. 2012; Liang et al. 2015). Smalley et al. (2022) addressed this by removing any cloudy pixels that were not surrounded by four connected cloudy pixels (excluding corners). Here our intent is to quantify and correct the LWP biases of all of the data as they are, so we do not use this method. Second, although the NOAA product includes retrievals at night, τ and re are limited to a smaller range of values at night than during the day (Lin and Coakley 1993); Therefore, we further limit our analysis to daytime-only observations when solar zenith angle (θs) is < 82°, which represents the θs where NOAA switches to the nighttime retrieval algorithm (Walther and Straka 2020).

b. Microwave imagers

We use LWP derived from the microwave imagers listed in Table 1. We download these data from Remote Sensing Systems (RSS; https://www.remss.com). RSS uses the same algorithm (e.g., Wentz 1997; Wentz and Spencer 1998) to derive LWP from each satellite and then aggregated daily on the same 0.25° × 0.25° grid. Because the algorithm and calibration procedure is common to each sensor, these products minimize biases between satellites. The LWP is derived from brightness temperature at 37 GHz. Overall, ABI can sense a smaller LWP but saturates at smaller values than microwave (Stephens and Kummerow 2007).

Table 1.

Characteristics of the six different microwave imagers used in this study.

Table 1.

Additionally, these microwave measurements are significantly more sensitive to liquid cloud emission than to ice cloud scattering in the absence of large ice crystals associated with precipitating clouds (Liu and Curry 1993). Therefore, any microwave grid box containing a cloudy ABI pixel flagged as nonliquid is excluded along with all ABI pixels contained within it. This should ensure our corrections are limited to liquid cloud pixels, but ABI falsely classifies pixels as liquid 9%–10% of the time (Pavolonis 2020) meaning that there still may be some bias related to falsely classified liquid pixels.

Furthermore, the microwave observations also inherently measure total-liquid water path (cloud water + precipitation). Overall, Fig. 1 demonstrates that, although both ABI and microwave LWP are larger when not filtering for precipitation, the ABI − microwave LWP differences between both datasets when filtering for precipitation compared to not filtering precipitation are similar. The differences likely relate to biases in the partitioning of rain and cloud water (Seethala and Horváth 2010; Lebsock and Su 2014; Greenwald et al. 2018). Therefore, similar to ice cloud pixels, we exclude any grid boxes flagged (in the products) as precipitating and all ABI pixels contained within it from our analysis. As a result, our bias corrections are optimized for nonprecipitating conditions; however, they are likely adequate for many purposes for light precipitation as indicated by Fig. 1.

Fig. 1.
Fig. 1.

Mean GOES-16 (a) Advanced Baseline Imager (ABI) and (b) microwave liquid water path (LWP) conditioned by low cloud fraction (f) for matched 1° × 1° grid boxes filtering for (red) and not filtering for (blue) precipitation. (c) The mean ratio of ABI to microwave LWP conditioned by f for both datasets.

Citation: Journal of Atmospheric and Oceanic Technology 40, 9; 10.1175/JTECH-D-23-0030.1

Five of the satellites used are sun synchronous but have different equatorial-crossing times. We also use the Global Precipitation Measurement Microwave Imager (GMI), which has a precessing equatorial-crossing time. Together, Fig. 2 illustrates that this suite of measurements enables us to sample microwave derived LWP at different ABI viewing angles and sun angles, across the diurnal cycle throughout the analysis period.

Fig. 2.
Fig. 2.

Total counts of matched microwave and GOES-16 observations within a given local hour.

Citation: Journal of Atmospheric and Oceanic Technology 40, 9; 10.1175/JTECH-D-23-0030.1

c. Collocating ABI and microwave observations

We collocate variables on a 1° × 1° 10-min grid. The binned variables include ABI LWP, ABI liquid f, microwave LWP, θs, ABI sensor zenith (θυ), and ABI relative azimuth angle (ϕ; solar − sensor azimuth angle). Collocations are performed within each 10-min full disk scan over the time period 2019–21. This provides a large dataset across a wide range of scattering geometries.

3. ABI cloud liquid water path biases

Figure 3 shows the spatial distribution of f, matched raw ABI and microwave LWP, root-mean-squared error (RMSE), and mean bias (ABI − microwave). It shows that ABI LWP is generally 2–3 times larger than microwave within both GOES satellites domains. Moreover, RMSE tends to increase toward the edge of the Earth disk and these errors are dominated by the mean bias. These are geometries in which the viewing zenith becomes very large.

Fig. 3.
Fig. 3.

The average low cloud fractions observed by (a) GOES-16 and (b) GOES-17, (c) GOES-16 and (e) GOES-17 uncorrected Advanced Baseline Imager (ABI) liquid water path (LWP), and microwave LWP matched to (d) GOES-16 and (f) GOES-17 from 2019 to 2021. The root-mean-squared error (RMSE) between ABI and microwave LWP for both (g) GOES-16, (i) GOES-17, and the mean bias (ABI − microwave) for both (h) GOES-16 and (j) GOES-17.

Citation: Journal of Atmospheric and Oceanic Technology 40, 9; 10.1175/JTECH-D-23-0030.1

To investigate the dependence of bias on scattering geometry, Fig. 4 shows LWP conditioned by θs, θυ, and ϕ as a function of f for both sets of satellites. The comparison of ABI and microwave LWP illuminate several key points:

  • The comparisons of LWP are generally similar for ABI on GOES-16 and GOES-17.

  • ABI LWP increases strongly as either θs or θυ increases beyond about 60°. Microwave LWP does not show this dependence. The dependence on θs explains the overall high bias of ABI compared to the microwave while the dependence on θυ explains the high biases at the field of view edges.

  • ABI LWP is suspiciously high in the principal plane of scattering (e.g., ϕ = 0° and 180°). This dependence is likely an aliasing of the θs and θυ biases.

Fig. 4.
Fig. 4.

Average liquid water path (LWP) conditioned by ABI low cloud fraction as a function of (a)–(d) solar zenith angle, (e)–(h) ABI sensor zenith angle, and (i)–(l) ABI relative azimuth angle for GOES-16 and GOES-17 compared to microwave between 2019 and 2021.

Citation: Journal of Atmospheric and Oceanic Technology 40, 9; 10.1175/JTECH-D-23-0030.1

4. Corrections algorithm

Here we outline a procedure to bias correct the ABI LWP to the passive microwave LWP. The intent of this correction is to mitigate the strong biases in ABI LWP associated with scattering geometry that limit its utility to evaluate diurnal variations. We define a fractional correction factor [βLWP; Eq. (1)] in terms of the scattering geometry and the f. Scattering geometry is fully defined by the cosine of θs (μ) and θυ (μυ), ϕ, and f as a coarse measure of cloud regime. We use fractional rather than additive corrections because this both improves the precision of the corrected ABI LWP within individual grid boxes and constrains corrected LWP to physically realistic positive values:
βLWP=CWPmicrowave(μ°,μυ,ϕ,f)CWPGOES(μ°,μυ,ϕ,f).

We use 80% (12 420 689 matched GOES-16 and 14 580 396 matched GOES-17 observations) of the matched observations (randomly sampled with replacement) to create a lookup table of corrections and use the remaining 20% (3 105 172 matched GOES-16 and 3 645 099 matched GOES-17 observations) as a validation dataset. We then correct the ABI LWP observations within the validation dataset by mapping each uncorrected value to a discrete (θs, θυ, ϕ, and f) bin, and then multiply it by the correctional factor within that bin; the corrected ABI LWP is then compared those to the uncorrected values at different θs, θυ, ϕ, and f. Conditional RMSE and mean bias differences between both the uncorrected and corrected observations are then compared to evaluate the corrected observations. This process is repeated 10 000 times to assure robustness in the corrected results.

5. Evaluations of the corrected ABI cloud liquid water paths

a. Evaluation against correction variables

We begin by evaluating the corrected ABI LWP along the dimensions of the variables defining the correction (θs, θυ, ϕ, and f). This evaluation is shown in Fig. 5. Figures 5a–h show that the spread between the corrected ABI and microwave LWP is generally around 2–10 times smaller than the uncorrected ABI observations. This is particularly noticeable at θs > 65° where ABI cloud-optical properties are most problematic (Walther and Straka 2020). Unfortunately, corrected observations start to break down at the largest θs (>80°) and θυ (>65°). Above θυ of 65°, the ABI LWP are very near zero (Figs. 4e,g) and there is little to no information content in the uncorrected ABI LWP. Therefore, the application of the correction only introduces additional uncertainty. On the other hand, ABI LWP retrievals are available from the daytime algorithm up θs of 82° when the algorithm switches from day to night retrievals. Nevertheless, we choose a conservative θs threshold of 70°, because we found residual errors in the corrected ABI LWP near twilight hours when attempting to resolve the diurnal cycle when using observations with θs larger than this threshold. Therefore, we recommend excluding any observations at these angles from any future analysis.

Fig. 5.
Fig. 5.

Bootstrapped median root-mean-squared error (RMSE) and mean bias between GOES-16 (blue) and GOES-17 (red) for both uncorrected (solid) and corrected (dashed) Advanced Baseline Imager (ABI) relative to the microwave liquid water path (LWP) conditioned by (a),(b) solar zenith angle, (c),(d) sensor zenith angle, (e),(f) relative azimuth angle, and (g),(h) low cloud fraction.

Citation: Journal of Atmospheric and Oceanic Technology 40, 9; 10.1175/JTECH-D-23-0030.1

As shown in Figs. 4i–l, 4e, and 4f, we define ϕ from −180° to 180°, rather than 0° to 180°. Considering the scattering angle at ±ϕ, we use the full range of ϕ to control for cloud-state differences in the morning compared to the afternoon. Overall, the spread between the corrected ABI and microwave is smaller than the uncorrected ABI LWP.

Figures 5g and 5h show that the bias is effectively mitigated across f; however, the RMSE in the corrected observations rapidly decreases as f increases compared to the uncorrected results. This suggests that the corrected ABI LWP should be more realistic in predominantly low-cloud regimes like stratocumulus (i.e., northeast and southeast Pacific) than in scattered cumulus regimes.

b. Evaluation against independent variables

We now evaluate the corrected ABI LWP against the reference microwave data sorted by variables not included in the correction procedure. For this purpose, we use 1) geography, 2) diurnal variability, and 3) short-time-scale evolution of cloud fields.

Returning to the spatial patterns in LWP, Fig. 6 shows the same variables as Fig. 3 with the corrected instead of the uncorrected ABI observations. Across the entire hemispheric view observed by GOES-16 and GOES-17, Fig. 6 illustrates that the spatial biases in ABI LWP are largely reduced in all regions (with RMSE generally between 20 and 40 g m−2 and mean bias between −20 and +20 g m−2). A noticeable limitation of the correction occurs within the intertropical convergence zone (ITCZ) where mean bias is largest. Recall that we filtered ice clouds, so these are shallow liquid-phase convective clouds. We suspect that the limited skill of the correction within the ITCZ is due to the combination of 1) the limited number of shallow scenes from this region that enter the correction tables and 2) the characteristic differences of these shallow clouds compared to others with similar cloud fraction (i.e., they are taller, have higher LWP, and are more likely to precipitate).

Fig. 6.
Fig. 6.

As in Fig. 3, but for the corrected Advanced Baseline Imager liquid water path (LWP).

Citation: Journal of Atmospheric and Oceanic Technology 40, 9; 10.1175/JTECH-D-23-0030.1

The primary benefit of using geostationary observations is that diurnal changes in different cloud properties like LWP can be observationally constrained unlike previous analyses using polar orbiters. However, any conclusions made near sunset/sunrise are likely not robust due to the biases in ABI LWP. To demonstrate the viability of our approach, we show a preliminary analysis of the diurnal cycle in uncorrected and corrected ABI LWP relative to microwave across the full disk image (limiting θs < 70° and θυ < 65°). Figure 7 shows the following:

  1. Uncorrected ABI LWP is always larger than microwave.

  2. The uncorrected ABI shows a dramatic diurnal cycle that has a midday minimum.

  3. Mean corrected ABI LWP is similar to microwave.

  4. The corrected ABI LWP diurnal cycle is significantly muted.

Fig. 7.
Fig. 7.

The average diurnal cycle (limited to solar zenith < 70° and sensor zenith < 60°) in uncorrected Advanced Baseline Imager (ABI) (green), corrected ABI (brown), and microwave (purple) liquid water path (LWP) observed over the entire disk from (a),(c),(e) GOES-16 and (b),(d),(f) GOES-17. Bootstrapped (c),(d) median root-mean-squared error and (e),(f) mean bias between GOES-16 and GOES-17 for both uncorrected and corrected ABI LWP relative to the microwave conditioned by local hour.

Citation: Journal of Atmospheric and Oceanic Technology 40, 9; 10.1175/JTECH-D-23-0030.1

Furthermore, the spread between the corrected ABI LWP and microwave is generally small; however, it increases from 10 to 20 g m−2 in the middle of the day to approximately 70 g m−2 near twilight (sunset/sunrise). This relates to increasing θs to and from the middle of the day which results in larger overall errors (Figs. 7a,b). Sun glint, which is not flagged in the NOAA products, may also contribute to the spread between corrected ABI and microwave LWP, but note that we indirectly account for this by controlling for scattering geometry. Nevertheless, the mean bias is near zero across the diurnal cycle indicating that these errors are random and using microwave to correct ABI LWP does not artificially mute the diurnal cycle.

Figure 7 combines data from the entire disk, making it difficult to observe the distinct daily patterns of LWP in specific cloud regions. To address this, we use GOES-16 to analyze the diurnal cycle in two low-cloud regions where GOES-16 has a good view: one region centered west of Peru and another east of Barbados (Fig. 8a). We chose to analyze the LWP diurnal cycles over the southeast Pacific because it is primarily stratocumulus, whereas low clouds east of Barbados consist primarily of trade cumulus.

Fig. 8.
Fig. 8.

The average diurnal cycle and low cloud fraction conditioned (limited to solar zenith < 70° and sensor zenith < 60°) in uncorrected (green), corrected (purple) Advanced Baseline Imager, and microwave (brown) liquid water path observed from (a) GOES-16 for the regions (b),(d) west of Peru and (c),(e) east of Barbados.

Citation: Journal of Atmospheric and Oceanic Technology 40, 9; 10.1175/JTECH-D-23-0030.1

The LWP diurnal cycle in each of these regions is characteristically different. Each shows a midday minimum; however, the stratocumulus LWP has a significant peak in the early morning hours, which is consistent with physical understanding of the role of nighttime cloud radiative cooling in the deepening of these clouds (Wood 2012). After correcting GOES-16 ABI LWP over both regions, GOES-16 ABI LWP is generally within 10–20 g m−2 of the microwave observations across the daylit part of the diurnal cycle. However, GOES-16 ABI LWP decreases faster than microwave from 0500 to 1100 local time east of Barbados. This may be related to 9–36 times fewer samples in the morning than afternoon. Biases east of Barbados may also be related to generally lower cloud amounts; however, Fig. 8e shows that differences between corrected ABI and microwave LWP occur at the highest f. This is likely related to cloud regime, with deeper cumulus being more likely when f is highest in this regime, whereas most of the data points in the correction lookup table are drawn from stratocumulus regions. This finding suggests that cloud fraction does not fully distinguishing cloud regimes in a manner that can resolve all of the ABI bias and that perhaps other, more granular definitions of cloud regime could help improve the corrections at the margins. Overall, the spread between GOES-16 ABI and microwave LWP is drastically reduced and demonstrate the corrected GOES-16 ABI LWP provides an adequate representation of the diurnal cycle in different low-cloud regime, especially in stratocumulus, and can be used in future studies.

Now we show the ability of the corrected data to monitor the evolution of the LWP over a short time frame. Following Smalley et al. (2022), we created an example daytime Lagrangian trajectory west of Peru (Fig. 9a), and analyzed how the uncorrected GOES-16 ABI LWP compares to the corrected GOES-16 ABI and microwave LWP.

Fig. 9.
Fig. 9.

(a) An example trajectory west of Peru with (b) GOES-16 uncorrected Advanced Baseline Imager (ABI) (green), GOES-16 corrected ABI (purple), and microwave (brown) liquid water path (LWP) during the day and night (black) along the trajectory.

Citation: Journal of Atmospheric and Oceanic Technology 40, 9; 10.1175/JTECH-D-23-0030.1

Figure 9b once again highlights the large biases between GOES-16 ABI and microwave LWP close to twilight. However, the corrected GOES-16 ABI LWP closely follows the changes in microwave LWP, especially near twilight. Thus, corrected GOES-16 ABI LWP can be used similar to Smalley et al. (2022) in the future to expand on topics like those related to stratocumulus transitions and aerosol–cloud interactions that otherwise cannot be analyzed from polar-orbiting satellites (e.g., Zhang et al. 2022).

c. Low-cloud region only comparisons

Since only four variables are included in the correction and the correction is based on full-disk data, one might speculate that regionally specific correction might outperform the approach presented above. To test this, we repeat the same analysis but limited to the southeast (GOES-16) and northeast (GOES-17) stratocumulus regions (Klein and Hartmann 1993), and compared it to ABI LWP corrected over both regions using the full-disk corrections.

Figure 10 shows the RMSE, conditioned by θs, θυ, and ϕ, between the microwave and ABI LWP observed by both satellites are similar when using both the full-disk corrections and the regionally derived southeast Pacific corrections. However, the RMSE, conditioned by f, for the full-disk corrections outperform the regionally derived corrections when f falls below 30%.

Fig. 10.
Fig. 10.

Bootstrapped median root-mean-squared error (RMSE) between GOES-16 and GOES-17 corrected ABI liquid water path (LWP) relative to microwave conditioned by (a),(b) solar zenith angle, (c),(d) sensor zenith angle, (e),(f) relative azimuth angle, and (g),(h) low cloud fraction.

Citation: Journal of Atmospheric and Oceanic Technology 40, 9; 10.1175/JTECH-D-23-0030.1

This analysis demonstrates, even though RMSE at the different scattering angles is similar, that using the full-disk oceanic observations is preferred, because there are 3–5 times more observations over the entire disk than one region alone, which allows for the development of a more statistically robust correction lookup table. We note, however, that future work may find that regional correction factors that are based on a longer time record may eventually outperform the full-disk approach that we present, especially at small f.

6. Summary

This study creates a set of corrections for GOES-R ABI LWP conditioned by scattering angles, and low cloud fraction using microwave imagery observations. We find that the overall reliability in ABI LWP is drastically improved, especially near sunrise and sunset (when θs is largest). There are still limitations in the corrected GOES-R ABI LWP specifically: 1) at θs > 70° and θυ > 65° and 2) at small f. Regarding θs and θυ, we would recommend ABI LWP observed at these angles to not be included in any future analysis. Additionally, our corrections are optimized for nonraining conditions. Therefore, biases may exist in the application of these corrections to scenes containing significant precipitation. Given these limitations, we intend that future studies using these corrections would be focused on primarily low-cloud stratocumulus regions (e.g., northeast and southeast Pacific), therefore limiting any potential residual associated with small cloud fractions or precipitation encountered in scattered cumulus regimes.

A major advantage of using ABI observations instead of observations from a polar orbiter like MODIS is the ability to track the evolution of different cloud types and their properties over short time scales. Moreover, our preliminary trajectory analysis demonstrates corrected ABI LWP compares well to independent observations from the passive microwave sensors. However, this only represented one trajectory and needs to be verified by analyzing many trajectories and their statistics. With that said, we believe using microwave LWP to correct ABI will improve any analysis similar to Smalley et al. (2022) going forward.

Acknowledgments.

This work was performed at the Jet Propulsion Laboratory, California Institute of Technology (government sponsorship acknowledged), under a contract with the National Aeronautics and Space Administration with funding from the CloudSat mission. We also thank Mark Richardson, Mark Smalley, Mikael Witte, and Jong-Hoon Jeong for several helpful suggestions that made this paper stronger.

Data availability statement.

The GOES-R level-2 products can be downloaded from the NOAA CLASS system (https://www.avl.class.noaa.gov), while microwave liquid water path can be downloaded from Remote Sensing Systems (https://www.remss.com). The lookup tables of GOES-16 and GOES-17 correctional liquid water path factors can be downloaded from Zenodo repository: https://doi.org/10.5281/zenodo.7647786.

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  • Gryspeerdt, E., F. Glassmeier, G. Feingold, F. Hoffmann, and R. J. Murray-Watson, 2022: Observing short-timescale cloud development to constrain aerosol–cloud interactions. Atmos. Chem. Phys., 22, 11 72711 738, https://doi.org/10.5194/acp-22-11727-2022.

    • Search Google Scholar
    • Export Citation
  • Hoffmann, F., F. Glassmeier, T. Yamaguchi, and G. Feingold, 2020: Liquid water path steady states in stratocumulus: Insights from process-level emulation and mixed-layer theory. J. Atmos. Sci., 77, 22032215, https://doi.org/10.1175/JAS-D-19-0241.1.

    • Search Google Scholar
    • Export Citation
  • Huang, L., J. H. Jiang, Z. Wang, H. Su, M. Deng, and S. Massie, 2015: Climatology of cloud water content associated with different cloud types observed by A-Train satellites. J. Geophys. Res. Atmos., 120, 41964212, https://doi.org/10.1002/2014JD022779.

    • Search Google Scholar
    • Export Citation
  • Jones, T. A., and Coauthors, 2020: Assimilation of GOES-16 radiances and retrievals into the Warn-on-Forecast System. Mon. Wea. Rev., 148, 18291859, https://doi.org/10.1175/MWR-D-19-0379.1.

    • Search Google Scholar
    • Export Citation
  • Klein, S. A., and D. L. Hartmann, 1993: The seasonal cycle of low stratiform clouds. J. Climate, 6, 15871606, https://doi.org/10.1175/1520-0442(1993)006%3c1587:TSCOLS%3e2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lebsock, M., and H. Su, 2014: Application of active spaceborne remote sensing for understanding biases between passive cloud water path retrievals. J. Geophys. Res. Atmos., 119, 89628979, https://doi.org/10.1002/2014JD021568.

    • Search Google Scholar
    • Export Citation
  • Liang, L., L. Di Girolamo, and W. Sun, 2015: Bias in MODIS cloud drop effective radius for oceanic water clouds as deduced from optical thickness variability across scattering angles. J. Geophys. Res. Atmos., 120, 76617681, https://doi.org/10.1002/2015JD023256.

    • Search Google Scholar
    • Export Citation
  • Lin, X., and J. A. Coakley Jr., 1993: Retrieval of properties for semitransparent clouds from multispectral infrared imagery data. J. Geophys. Res., 98, 18 50118 514, https://doi.org/10.1029/93JD01793.

    • Search Google Scholar
    • Export Citation
  • Liu, G., and J. A. Curry, 1993: Determination of characteristic features of cloud liquid water from satellite microwave measurements. J. Geophys. Res., 98, 50695092, https://doi.org/10.1029/92JD02888.

    • Search Google Scholar
    • Export Citation
  • Machado, L. A. T., and Coauthors, 2021: How weather events modify aerosol particle size distributions in the Amazon boundary layer. Atmos. Chem. Phys., 21, 18 06518 086, https://doi.org/10.5194/acp-21-18065-2021.

    • Search Google Scholar
    • Export Citation
  • Manaster, A., C. W. O’Dell, and G. Elsaesser, 2017: Evaluation of cloud liquid water path trends using a multidecadal record of passive microwave observations. J. Climate, 30, 58715884, https://doi.org/10.1175/JCLI-D-16-0399.1.

    • Search Google Scholar
    • Export Citation
  • Marion, G. R., R. J. Trapp, and S. W. Nesbitt, 2019: Using overshooting top area to discriminate potential for large, intense tornadoes. Geophys. Res. Lett., 46, 12 52012 526, https://doi.org/10.1029/2019GL084099.

    • Search Google Scholar
    • Export Citation
  • Michibata, T., K. Suzuki, Y. Sato, and T. Takemura, 2016: The source of discrepancies in aerosol–cloud–precipitation interactions between GCM and A-Train retrievals. Atmos. Chem. Phys., 16, 15 41315 424, https://doi.org/10.5194/acp-16-15413-2016.

    • Search Google Scholar
    • Export Citation
  • Munsell, E. B., S. A. Braun, and F. Zhang, 2021: GOES-16 observations of rapidly intensifying tropical cyclones: Hurricanes Harvey (2017), Maria (2017), and Michael (2018). Mon. Wea. Rev., 149, 16951714, https://doi.org/10.1175/MWR-D-19-0298.1.

    • Search Google Scholar
    • Export Citation
  • Nussbaumer, C. M., and Coauthors, 2021: Measurement report: In situ observations of deep convection without lightning during the Tropical Cyclone Florence 2018. Atmos. Chem. Phys., 21, 79337945, https://doi.org/10.5194/acp-21-7933-2021.

    • Search Google Scholar
    • Export Citation
  • O’Dell, C. W., F. J. Wentz, and R. Bennartz, 2008: Cloud liquid water path from satellite-based passive microwave observations: A new climatology over the global oceans. J. Climate, 21, 17211739, https://doi.org/10.1175/2007JCLI1958.1.

    • Search Google Scholar
    • Export Citation
  • Painemal, D., and Coauthors, 2021: Evaluation of satellite retrievals of liquid clouds from the GOES-13 imager and MODIS over the midlatitude North Atlantic during the NAAMES campaign. Atmos. Meas. Tech., 14, 66336646, https://doi.org/10.5194/amt-14-6633-2021.

    • Search Google Scholar
    • Export Citation
  • Pavolonis, M., 2020: Enterprise algorithm theoretical basis document for cloud type and cloud phase. NOAA Tech. Rep., 113 pp., https://www.star.nesdis.noaa.gov/goesr/rework/documents/ATBDs/Enterprise/Enterprise_ATBD_Cloud_CldType_G17_Mitigation_Jun2020.pdf.

  • Platnick, S., and Coauthors, 2017: The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua. IEEE Trans. Geosci. Remote Sens., 55, 502525, https://doi.org/10.1109/TGRS.2016.2610522.

    • Search Google Scholar
    • Export Citation
  • Ribeiro, B. Z., L. A. T. Machado, J. H. Huamán Ch., T. S. Biscaro, E. D. Freitas, K. W. Mozer, and S. J. Goodman, 2019: An evaluation of the GOES-16 rapid scan for nowcasting in southeastern Brazil: Analysis of a severe hailstorm case. Wea. Forecasting, 34, 18291848, https://doi.org/10.1175/WAF-D-19-0070.1.

    • Search Google Scholar
    • Export Citation
  • Schmit, T. J., P. Griffith, M. M. Gunshor, J. M. Daniels, S. J. Goodman, and W. J. Lebair, 2017: A closer look at the ABI on the GOES-R series. Bull. Amer. Meteor. Soc., 98, 681698, https://doi.org/10.1175/BAMS-D-15-00230.1.

    • Search Google Scholar
    • Export Citation
  • Seethala, C., and Á. Horváth, 2010: Global assessment of AMSR-E and MODIS cloud liquid water path retrievals in warm oceanic clouds. J. Geophys. Res., 115, D13202, https://doi.org/10.1029/2009JD012662.

    • Search Google Scholar
    • Export Citation
  • Smalley, K. M., M. D. Lebsock, R. Eastman, M. Smalley, and M. K. Witte, 2022: A Lagrangian analysis of pockets of open cells over the southeastern Pacific. Atmos. Chem. Phys., 22, 81978219, https://doi.org/10.5194/acp-22-8197-2022.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and C. D. Kummerow, 2007: The remote sensing of clouds and precipitation from space: A review. J. Atmos. Sci., 64, 37423765, https://doi.org/10.1175/2006JAS2375.1.

    • Search Google Scholar
    • Export Citation
  • Toll, V., M. Christensen, J. Quaas, and N. Bellouin, 2019: Weak average liquid-cloud-water response to anthropogenic aerosols. Nature, 572, 5155, https://doi.org/10.1038/s41586-019-1423-9.

    • Search Google Scholar
    • Export Citation
  • Walther, A., and W. Straka, 2020: Algorithm theoretical basis document for daytime cloud optical and microphysical properties (DCOMP). NOAA Tech. Rep., 64 pp., https://www.star.nesdis.noaa.gov/goesr/documents/ATBDs/Enterprise/ATBD_Enterprise_Daytime_Cloud_Optical_and_Microphysical_Properties(DCOMP)_v1.2_2020-10-09.pdf.

  • Wentz, F. J., 1997: A well-calibrated ocean algorithm for Special Sensor Microwave/Imager. J. Geophys. Res., 102, 87038718, https://doi.org/10.1029/96JC01751.

    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., and R. W. Spencer, 1998: SSM/I rain retrievals within a unified all-weather ocean algorithm. J. Atmos. Sci., 55, 16131627, https://doi.org/10.1175/1520-0469(1998)055<1613:SIRRWA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., K. A. Hilburn, and D. K. Smith, 2012: Remote Sensing Systems DMSP SSMIS daily environmental suite on 0.25 deg grid, version 7. Remote Sensing Systems, accessed 26 October 2022, https://www.remss.com/missions/ssmi.

  • Wentz, F. J., L. Ricciardulli, C. Gentemann, T. Meissner, K. A. Hilburn, and J. Scott, 2013: Remote Sensing Systems Coriolis WindSat daily environmental suite on 0.25 deg grid, version 7.0.1. Remote Sensing Systems, accessed 26 October 2022, https://www.remss.com/missions/windsat.

  • Wentz, F. J., T. Meissner, C. Gentemann, and M. Brewer, 2014: Remote Sensing Systems GCOM-W1 AMSR2 daily environmental suite on 0.25 deg grid, version 8.2. Remote Sensing Systems, accessed 26 October 2022, https://www.remss.com/missions/amsr.

  • Wentz, F. J., T. Meissner, J. Scott, and K. A. Hilburn, 2015: Remote Sensing Systems GPM GMI daily environmental suite on 0.25 deg grid, version 8.2. Remote Sensing Systems, accessed 26 October 2022, https://www.remss.com/missions/gmi.

  • Wood, R., 2012: Stratocumulus clouds. Mon. Wea. Rev., 140, 23732423, https://doi.org/10.1175/MWR-D-11-00121.1.

  • Wood, R., C. S. Bretherton, and D. L. Hartmann, 2002: Diurnal cycle of liquid water path over the subtropical and tropical oceans. Geophys. Res. Lett., 29, 2092, https://doi.org/10.1029/2002GL015371.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., X. Zhou, T. Goren, and G. Feingold, 2022: Albedo susceptibility of northeastern Pacific stratocumulus: The role of covarying meteorological conditions. Atmos. Chem. Phys., 22, 861880, https://doi.org/10.5194/acp-22-861-2022.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., F. Zhang, and D. J. Stensrud, 2018: Assimilating all-sky infrared radiances from GOES-16 ABI using an ensemble Kalman filter for convection-allowing severe thunderstorms prediction. Mon. Wea. Rev., 146, 33633381, https://doi.org/10.1175/MWR-D-18-0062.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, Z., A. S. Ackerman, G. Feingold, S. Platnick, R. Pincus, and H. Xue, 2012: Effects of cloud horizontal inhomogeneity and drizzle on remote sensing of cloud droplet effective radius: Case studies based on large-eddy simulations. J. Geophys. Res., 117, D19208, https://doi.org/10.1029/2012JD017655.

    • Search Google Scholar
    • Export Citation
Save
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  • Gryspeerdt, E., F. Glassmeier, G. Feingold, F. Hoffmann, and R. J. Murray-Watson, 2022: Observing short-timescale cloud development to constrain aerosol–cloud interactions. Atmos. Chem. Phys., 22, 11 72711 738, https://doi.org/10.5194/acp-22-11727-2022.

    • Search Google Scholar
    • Export Citation
  • Hoffmann, F., F. Glassmeier, T. Yamaguchi, and G. Feingold, 2020: Liquid water path steady states in stratocumulus: Insights from process-level emulation and mixed-layer theory. J. Atmos. Sci., 77, 22032215, https://doi.org/10.1175/JAS-D-19-0241.1.

    • Search Google Scholar
    • Export Citation
  • Huang, L., J. H. Jiang, Z. Wang, H. Su, M. Deng, and S. Massie, 2015: Climatology of cloud water content associated with different cloud types observed by A-Train satellites. J. Geophys. Res. Atmos., 120, 41964212, https://doi.org/10.1002/2014JD022779.

    • Search Google Scholar
    • Export Citation
  • Jones, T. A., and Coauthors, 2020: Assimilation of GOES-16 radiances and retrievals into the Warn-on-Forecast System. Mon. Wea. Rev., 148, 18291859, https://doi.org/10.1175/MWR-D-19-0379.1.

    • Search Google Scholar
    • Export Citation
  • Klein, S. A., and D. L. Hartmann, 1993: The seasonal cycle of low stratiform clouds. J. Climate, 6, 15871606, https://doi.org/10.1175/1520-0442(1993)006%3c1587:TSCOLS%3e2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lebsock, M., and H. Su, 2014: Application of active spaceborne remote sensing for understanding biases between passive cloud water path retrievals. J. Geophys. Res. Atmos., 119, 89628979, https://doi.org/10.1002/2014JD021568.

    • Search Google Scholar
    • Export Citation
  • Liang, L., L. Di Girolamo, and W. Sun, 2015: Bias in MODIS cloud drop effective radius for oceanic water clouds as deduced from optical thickness variability across scattering angles. J. Geophys. Res. Atmos., 120, 76617681, https://doi.org/10.1002/2015JD023256.

    • Search Google Scholar
    • Export Citation
  • Lin, X., and J. A. Coakley Jr., 1993: Retrieval of properties for semitransparent clouds from multispectral infrared imagery data. J. Geophys. Res., 98, 18 50118 514, https://doi.org/10.1029/93JD01793.

    • Search Google Scholar
    • Export Citation
  • Liu, G., and J. A. Curry, 1993: Determination of characteristic features of cloud liquid water from satellite microwave measurements. J. Geophys. Res., 98, 50695092, https://doi.org/10.1029/92JD02888.

    • Search Google Scholar
    • Export Citation
  • Machado, L. A. T., and Coauthors, 2021: How weather events modify aerosol particle size distributions in the Amazon boundary layer. Atmos. Chem. Phys., 21, 18 06518 086, https://doi.org/10.5194/acp-21-18065-2021.

    • Search Google Scholar
    • Export Citation
  • Manaster, A., C. W. O’Dell, and G. Elsaesser, 2017: Evaluation of cloud liquid water path trends using a multidecadal record of passive microwave observations. J. Climate, 30, 58715884, https://doi.org/10.1175/JCLI-D-16-0399.1.

    • Search Google Scholar
    • Export Citation
  • Marion, G. R., R. J. Trapp, and S. W. Nesbitt, 2019: Using overshooting top area to discriminate potential for large, intense tornadoes. Geophys. Res. Lett., 46, 12 52012 526, https://doi.org/10.1029/2019GL084099.

    • Search Google Scholar
    • Export Citation
  • Michibata, T., K. Suzuki, Y. Sato, and T. Takemura, 2016: The source of discrepancies in aerosol–cloud–precipitation interactions between GCM and A-Train retrievals. Atmos. Chem. Phys., 16, 15 41315 424, https://doi.org/10.5194/acp-16-15413-2016.

    • Search Google Scholar
    • Export Citation
  • Munsell, E. B., S. A. Braun, and F. Zhang, 2021: GOES-16 observations of rapidly intensifying tropical cyclones: Hurricanes Harvey (2017), Maria (2017), and Michael (2018). Mon. Wea. Rev., 149, 16951714, https://doi.org/10.1175/MWR-D-19-0298.1.

    • Search Google Scholar
    • Export Citation
  • Nussbaumer, C. M., and Coauthors, 2021: Measurement report: In situ observations of deep convection without lightning during the Tropical Cyclone Florence 2018. Atmos. Chem. Phys., 21, 79337945, https://doi.org/10.5194/acp-21-7933-2021.

    • Search Google Scholar
    • Export Citation
  • O’Dell, C. W., F. J. Wentz, and R. Bennartz, 2008: Cloud liquid water path from satellite-based passive microwave observations: A new climatology over the global oceans. J. Climate, 21, 17211739, https://doi.org/10.1175/2007JCLI1958.1.

    • Search Google Scholar
    • Export Citation
  • Painemal, D., and Coauthors, 2021: Evaluation of satellite retrievals of liquid clouds from the GOES-13 imager and MODIS over the midlatitude North Atlantic during the NAAMES campaign. Atmos. Meas. Tech., 14, 66336646, https://doi.org/10.5194/amt-14-6633-2021.

    • Search Google Scholar
    • Export Citation
  • Pavolonis, M., 2020: Enterprise algorithm theoretical basis document for cloud type and cloud phase. NOAA Tech. Rep., 113 pp., https://www.star.nesdis.noaa.gov/goesr/rework/documents/ATBDs/Enterprise/Enterprise_ATBD_Cloud_CldType_G17_Mitigation_Jun2020.pdf.

  • Platnick, S., and Coauthors, 2017: The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua. IEEE Trans. Geosci. Remote Sens., 55, 502525, https://doi.org/10.1109/TGRS.2016.2610522.

    • Search Google Scholar
    • Export Citation
  • Ribeiro, B. Z., L. A. T. Machado, J. H. Huamán Ch., T. S. Biscaro, E. D. Freitas, K. W. Mozer, and S. J. Goodman, 2019: An evaluation of the GOES-16 rapid scan for nowcasting in southeastern Brazil: Analysis of a severe hailstorm case. Wea. Forecasting, 34, 18291848, https://doi.org/10.1175/WAF-D-19-0070.1.

    • Search Google Scholar
    • Export Citation
  • Schmit, T. J., P. Griffith, M. M. Gunshor, J. M. Daniels, S. J. Goodman, and W. J. Lebair, 2017: A closer look at the ABI on the GOES-R series. Bull. Amer. Meteor. Soc., 98, 681698, https://doi.org/10.1175/BAMS-D-15-00230.1.

    • Search Google Scholar
    • Export Citation
  • Seethala, C., and Á. Horváth, 2010: Global assessment of AMSR-E and MODIS cloud liquid water path retrievals in warm oceanic clouds. J. Geophys. Res., 115, D13202, https://doi.org/10.1029/2009JD012662.

    • Search Google Scholar
    • Export Citation
  • Smalley, K. M., M. D. Lebsock, R. Eastman, M. Smalley, and M. K. Witte, 2022: A Lagrangian analysis of pockets of open cells over the southeastern Pacific. Atmos. Chem. Phys., 22, 81978219, https://doi.org/10.5194/acp-22-8197-2022.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and C. D. Kummerow, 2007: The remote sensing of clouds and precipitation from space: A review. J. Atmos. Sci., 64, 37423765, https://doi.org/10.1175/2006JAS2375.1.

    • Search Google Scholar
    • Export Citation
  • Toll, V., M. Christensen, J. Quaas, and N. Bellouin, 2019: Weak average liquid-cloud-water response to anthropogenic aerosols. Nature, 572, 5155, https://doi.org/10.1038/s41586-019-1423-9.

    • Search Google Scholar
    • Export Citation
  • Walther, A., and W. Straka, 2020: Algorithm theoretical basis document for daytime cloud optical and microphysical properties (DCOMP). NOAA Tech. Rep., 64 pp., https://www.star.nesdis.noaa.gov/goesr/documents/ATBDs/Enterprise/ATBD_Enterprise_Daytime_Cloud_Optical_and_Microphysical_Properties(DCOMP)_v1.2_2020-10-09.pdf.

  • Wentz, F. J., 1997: A well-calibrated ocean algorithm for Special Sensor Microwave/Imager. J. Geophys. Res., 102, 87038718, https://doi.org/10.1029/96JC01751.

    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., and R. W. Spencer, 1998: SSM/I rain retrievals within a unified all-weather ocean algorithm. J. Atmos. Sci., 55, 16131627, https://doi.org/10.1175/1520-0469(1998)055<1613:SIRRWA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., K. A. Hilburn, and D. K. Smith, 2012: Remote Sensing Systems DMSP SSMIS daily environmental suite on 0.25 deg grid, version 7. Remote Sensing Systems, accessed 26 October 2022, https://www.remss.com/missions/ssmi.

  • Wentz, F. J., L. Ricciardulli, C. Gentemann, T. Meissner, K. A. Hilburn, and J. Scott, 2013: Remote Sensing Systems Coriolis WindSat daily environmental suite on 0.25 deg grid, version 7.0.1. Remote Sensing Systems, accessed 26 October 2022, https://www.remss.com/missions/windsat.

  • Wentz, F. J., T. Meissner, C. Gentemann, and M. Brewer, 2014: Remote Sensing Systems GCOM-W1 AMSR2 daily environmental suite on 0.25 deg grid, version 8.2. Remote Sensing Systems, accessed 26 October 2022, https://www.remss.com/missions/amsr.

  • Wentz, F. J., T. Meissner, J. Scott, and K. A. Hilburn, 2015: Remote Sensing Systems GPM GMI daily environmental suite on 0.25 deg grid, version 8.2. Remote Sensing Systems, accessed 26 October 2022, https://www.remss.com/missions/gmi.

  • Wood, R., 2012: Stratocumulus clouds. Mon. Wea. Rev., 140, 23732423, https://doi.org/10.1175/MWR-D-11-00121.1.

  • Wood, R., C. S. Bretherton, and D. L. Hartmann, 2002: Diurnal cycle of liquid water path over the subtropical and tropical oceans. Geophys. Res. Lett., 29, 2092, https://doi.org/10.1029/2002GL015371.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., X. Zhou, T. Goren, and G. Feingold, 2022: Albedo susceptibility of northeastern Pacific stratocumulus: The role of covarying meteorological conditions. Atmos. Chem. Phys., 22, 861880, https://doi.org/10.5194/acp-22-861-2022.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., F. Zhang, and D. J. Stensrud, 2018: Assimilating all-sky infrared radiances from GOES-16 ABI using an ensemble Kalman filter for convection-allowing severe thunderstorms prediction. Mon. Wea. Rev., 146, 33633381, https://doi.org/10.1175/MWR-D-18-0062.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, Z., A. S. Ackerman, G. Feingold, S. Platnick, R. Pincus, and H. Xue, 2012: Effects of cloud horizontal inhomogeneity and drizzle on remote sensing of cloud droplet effective radius: Case studies based on large-eddy simulations. J. Geophys. Res., 117, D19208, https://doi.org/10.1029/2012JD017655.

    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    Mean GOES-16 (a) Advanced Baseline Imager (ABI) and (b) microwave liquid water path (LWP) conditioned by low cloud fraction (f) for matched 1° × 1° grid boxes filtering for (red) and not filtering for (blue) precipitation. (c) The mean ratio of ABI to microwave LWP conditioned by f for both datasets.

  • Fig. 2.

    Total counts of matched microwave and GOES-16 observations within a given local hour.

  • Fig. 3.

    The average low cloud fractions observed by (a) GOES-16 and (b) GOES-17, (c) GOES-16 and (e) GOES-17 uncorrected Advanced Baseline Imager (ABI) liquid water path (LWP), and microwave LWP matched to (d) GOES-16 and (f) GOES-17 from 2019 to 2021. The root-mean-squared error (RMSE) between ABI and microwave LWP for both (g) GOES-16, (i) GOES-17, and the mean bias (ABI − microwave) for both (h) GOES-16 and (j) GOES-17.

  • Fig. 4.

    Average liquid water path (LWP) conditioned by ABI low cloud fraction as a function of (a)–(d) solar zenith angle, (e)–(h) ABI sensor zenith angle, and (i)–(l) ABI relative azimuth angle for GOES-16 and GOES-17 compared to microwave between 2019 and 2021.

  • Fig. 5.

    Bootstrapped median root-mean-squared error (RMSE) and mean bias between GOES-16 (blue) and GOES-17 (red) for both uncorrected (solid) and corrected (dashed) Advanced Baseline Imager (ABI) relative to the microwave liquid water path (LWP) conditioned by (a),(b) solar zenith angle, (c),(d) sensor zenith angle, (e),(f) relative azimuth angle, and (g),(h) low cloud fraction.

  • Fig. 6.

    As in Fig. 3, but for the corrected Advanced Baseline Imager liquid water path (LWP).

  • Fig. 7.

    The average diurnal cycle (limited to solar zenith < 70° and sensor zenith < 60°) in uncorrected Advanced Baseline Imager (ABI) (green), corrected ABI (brown), and microwave (purple) liquid water path (LWP) observed over the entire disk from (a),(c),(e) GOES-16 and (b),(d),(f) GOES-17. Bootstrapped (c),(d) median root-mean-squared error and (e),(f) mean bias between GOES-16 and GOES-17 for both uncorrected and corrected ABI LWP relative to the microwave conditioned by local hour.

  • Fig. 8.

    The average diurnal cycle and low cloud fraction conditioned (limited to solar zenith < 70° and sensor zenith < 60°) in uncorrected (green), corrected (purple) Advanced Baseline Imager, and microwave (brown) liquid water path observed from (a) GOES-16 for the regions (b),(d) west of Peru and (c),(e) east of Barbados.

  • Fig. 9.

    (a) An example trajectory west of Peru with (b) GOES-16 uncorrected Advanced Baseline Imager (ABI) (green), GOES-16 corrected ABI (purple), and microwave (brown) liquid water path (LWP) during the day and night (black) along the trajectory.

  • Fig. 10.

    Bootstrapped median root-mean-squared error (RMSE) between GOES-16 and GOES-17 corrected ABI liquid water path (LWP) relative to microwave conditioned by (a),(b) solar zenith angle, (c),(d) sensor zenith angle, (e),(f) relative azimuth angle, and (g),(h) low cloud fraction.