Top of the Atmosphere Reflected Shortwave Radiative Fluxes from ABI on GOES-18

1. Introduction

Our climate is controlled by the surface energy budget on a global scale and is dominated by radiative fluxes. The incoming shortwave radiation (SWR) from the sun that reaches the Earth’s surface is a dominant component of this budget. Strides in technical capabilities of satellites make them a primary source for information at large scales. Satellites, originally polar orbiters, have become an integral part of most programs to monitor climate. The limitation and impact of poor representation of the diurnal cycle from polar orbiters has been recognized and interest in geostationary satellites has increased. Yet, satellite-based estimates differ from each other and from those provided by numerical models. Major differences are related to quality of satellite observations, such as the frequent changes in satellite observing systems, degradation of sensors, restricted spectral intervals, viewing geometry of sensors, and changes in the quality of atmospheric inputs that drive the inference schemes. Achievable accuracy is also related to the lack of maturity of basic information needed in the implementation process, such as a reliable cloud screened product which is in a process of development and modifications (Loeb et al. 2005). Reducing differences among the satellite-based estimates requires, among others, updates to inference schemes so that the most recent auxiliary information can be fully utilized.

Two products, the reflected SWR at top of atmosphere and the downward SWR at surface are routinely generated at NOAA from the Advanced Baseline Imager (ABI) onboard the GOES-R series of US satellites (Laszlo et al. [1], Laszlo et al. [2]). Both products require a shortwave broadband top-of-atmosphere (TOA) albedo converted from six ABI narrowband reflectance (Table 1). Critical elements of an inference scheme for TOA radiative flux estimates from satellite observations are: 1) transformation of narrowband quantities into broadband ones; 2) transformation of bi-directional reflectance into albedo by applying Angular Distribution Models (ADMs). The narrow-to-broadband (NTB) conversion used in this study is derived from simulated broadband and narrowband reflectance as detailed in Pinker et al. [3] for the ABI on GOES-16 and 17. Observation-based broadband ADMs derived primarily from the Clouds and the Earth’s Radiant Energy System (CERES) are used to complete the derivation of the TOA radiative flux. The “ground truth”, namely, the CERES observations are also undergoing adjustments and recalibration. As such, an evolutionary process can be expected as demonstrated in this study by emphasizing changes made in our approach when dealing with GOES-18 as compared to GOES-16 and 17. New spectral characteristics of ABI onboard GOES-18 have been applied to the simulated narrowband fluxes. The procedure of matching GOES-18 observations with those from CERES have been updated. We used the radiance from the ABI product, converted it to reflectance, applied NTB conversions and ADMs before comparing to CERES fluxes. We used the radiances in six relevant ABI bands (out of sixteen) as shown in Table 1.

Each band has a different resolution. Before use we resampled them to 2 km. Subsequently, these 2 km data were remapped to the 20 km CERES data. Previously, we remapped the CERES observations to those of GOES-16 and 17. Now we remap the high-resolution observations from GOES-18 to those of CERES. In Section 2 we describe the entire process that includes a brief description of methodology, data used, matching of observations between ABI/GOES-18 and CERES Single Scanner Footprint (SSF) and averaging the ABI data to match the CERES pixel; in section 3 we present results and in section 4 we discuss and summarize the findings.

2. Approach

2.1. Methodology

We briefly describe the physical basis and the development of the NTB transformations of satellite observed radiances and the bi-directional corrections to be applied to the broadband reflectance to obtain broadband TOA albedo. A detailed description as applies to GOES-16 and 17 can be found in Pinker et al. [3]. The Advanced Baseline Imager (ABI) observations onboard of the NOAA GOES-R series of satellites provide reflectance in six narrow bands in the shortwave spectrum. The following is done: 1) calculation of TOA high-resolution spectral and broadband (total shortwave) reflectance with MODTRAN-4.3 (Berk et al. [4]) and scene dependent land use classifications from the International Geosphere-Biosphere Programme (IGBP) (Hansen et al. [5]); 2) convolution of the high-resolution spectral reflectance to simulate ABI narrowband reflectance; 3) regression of the simulated ABI reflectance against the broadband reflectance calculated in step 1. The ADMs from CERES (Loeb et al., [6]) were augmented with theoretical simulations (Niu and Pinker, [7]). This was done to extend the observational CERES database that was under sampled in certain directions (angular bins). Surface conditions were one of the primary inputs into the MODTRAN simulations. The International Geosphere-Biosphere Programme (IGBP) land classification (Hansen et al., [5]; Loveland et al., [8]) dataset is at 1/6^o resolution and includes 18 surface types. We have converted the 1/6^o (~18.5 km) resolution to the ABI 2-km grid using the nearest grid method. The method for cloudy sky uses four surface types; these are also derived from 12 IGBP types (Pinker et al. [3]). Modifications to these procedures when applied to GOES-18 will be described in sections 2.3 and 2.4.

2.2. Data Used

L1B GOES-18 radiance data files for the CONUS domain (file names OR_ABI-L1b-RadC-) were downloaded from the NOAA Comprehensive Large Array-Data Stewardship System (https://www.aev.class.noaa.gov/) and the ABI Spectral Response Function (SRF) from NOAA’s Center for Satellite Application and Research Calibration Center website (https://ncc.nesdis.noaa.gov/GOESR/ABI.php. Not all the required angular information needed for implementation of regressions was available online and had to be recomputed (such as solar geometry). Reference data for CERES observation were downloaded from (https://cmr.earthdata.nasa.gov/search/concepts/C7460991-LARC_ASDC.html. Data file name is “CERES_SSF_Terra-XTRK_Edition4A_Subset”.

2.3. Matching Observations Between ABI/GOES-18 and CERES SSF

ABI on GOES-18 is a geostationary sensor at nadir longitude of 137^o W. The Pacific U.S. (PACUS) sector coverage of the GOES-West satellite is 12^o N-60^o N, 90^o W-175^o W, as shown in Figure 1. (For details: https://www.star.nesdis.noaa.gov/goes/index.php.)

The ABI GOES-18 data are provided as images for each observation time on a fixed grid whose coordinate values are the sensor scanning angle in units of radians relative to the satellite sub-point location. The fixed grid is rectified to an ellipsoid defined by the Geodetic Reference System 1980 (GRS80) earth model. The navigation between the fixed grid coordinates (scanning angle in east/west and elevation angle in north/south) and geodetic latitude and longitude is determined by the geostationary satellite projection or fixed grid projection (FGP). Details can be found in “GOES R SERIES PRODUCT DEFINITION AND USERS’ GUIDE” (https://www.goes-r.gov/users/docs/PUG-main-vol1.pdf).

Below (Figure 2) is an example of matchup between CERES and ABI for day 327 in 2022, 21:21:17 UTC.

The CERES data are given in a time series format. Every CERES pixel’s longitude, latitude and time are provided along with the flux data. To find the matchups, we loop over each ABI image time and search in the CERES time series to find the CERES pixels that fall in the ABI domain within a certain period. For example, given an ABI image timed at 2022-12-1 17:15:00 with area coverage from 15^o N to 50^o N latitude and from 170^o W to 80^o W longitude, we find that there are 502 CERES pixels that fall within the area in a time interval of +/-5 minutes around the ABI time as shown in Figure 3a.

We illustrate with a case for November 2022. We use CERES Single Scanner Footprint (SSF) data (Terra and Aqua) from November 23 to December 1, 2022. The number of overlapping pixels ranges from 0 to 18,000 for Terra and 16,000 for Aqua. From all ABI images available from November 23 thirty-eight cases for Terra to December 1, 2022, the number of images that overlap with CERES data are counted and shown in Table 2.

If we chose only those ABI images with more than 12,000 overlap pixels, for CERES/Aqua, we have sixteen matchups between CERES and ABI at a specific (single) ABI time as listed in Appendix A. Next, we process these ABI cases to obtain TOA fluxes and do the comparison with the co-located CERES data.

2.4. Averaging ABI Data to Match the CERES Pixel.

Due to the fixed grid projection format (FGF) used to store the ABI data, one can easily transform the map coordinates between the longitude/latitude format and ABI image col/row format. This analytic way to calculate the location of the matchup pixel in ABI image saves time compared to searching through the whole ABI image to find the location of the matchups.

ABI flux data have 2-km nadir resolution. CERES Field of View (FOV) is about 16x32 km (the 20 km is a nominal spatial resolution that is roughly the same size as 16*32 km). When matching ABI with CERES, we first calculate the col/row location of the CERES pixel in the ABI image. Around that location, a 16x32 km block of ABI data is extracted and averaged to match the CERES SSF data (this is the values at nadir, however, in this study we use the same size for all CERES FOVs). An example is shown in Figure 3b.

Since ABI data are available about every 5 minutes for the CONUS domain, there might be more than one ABI image that overlaps with a CERES pixel within the 10-minute time interval. In such a situation, the ABI data from all available times within the 10-minute time interval and within the 16x32 block are averaged to match the CERES data.

2.5. Experiments with New ADMs

For GOES-16 and 17 used were ADMs as described in Loeb et al. [6]. The next-generation ADMs that were developed for Terra and Aqua using all available CERES rotating azimuth plane radiance measurements (Su et al. [9]) are not yet available to the public and therefore, not used in this study. CERES radiance measurements are stratified by scene type and by other parameters that are important for determining the anisotropy of the given scene. As reported in Su et al. [10], significant differences between the new and the older ADMs are for clear-sky scene and polar scene types. Over clear land, the ADMs are developed for every 1° latitude × 1° longitude region for every calendar month. It is claimed that compared to the Loeb at al. [6] ADMs (hereafter CERES2003 ADM), the new ADMs change the monthly mean instantaneous fluxes by up to 5 W m⁻² on a regional scale of 1° latitude × 1° longitude, but the flux changes are less than 0.5 W m⁻² on a global scale.

Match ups between ABI and CERES SSF TOA fluxes in 2022-11-23 00Z to 2022-12-01 23Z have been used. There were twenty-one cases for Aqua and forty-seven cases for Terra (Appendix A).

As seen in Figure 4, compared to the CERES TOA fluxes, the ABI fluxes are larger in the low range (CERES TOA fluxes less than about 100 W m^-2) and smaller in the high range (CERES TOA fluxes larger than about 250 W m^-2). The low and high ranges generally correspond to cloud-free scenes (and or low solar zenith angles) and cloudy (and or high solar zenith angles), respectively. In the mid-range (approximately between 100 and 250 W m^-2), there is a large scatter of data points, several of them with significant negative ABI-CERES TOA flux differences. However, most of them are clustered around the one-to-one line. As a result, the frequency distribution of the ABI-CERES TOA flux differences peaks around zero with a large negative tail. Another peak at about a positive 30 W m^-2 is present that comes from the low range of CERES fluxes as shown in the density scatterplot. We have conducted several experiments to identify the reason for this secondary peak.

2.5.1. Experiment 1

We applied the old approach (Pinker et al. [3]) to the ADMs and the secondary spike disappeared. The old approach is based on a synergy between the original CERES ADMs as described in Loeb et al. [6] and simulations, as described in Niu and Pinker [7]. The bias increased but there was some reduction in the standard deviation of biases.

Figure 5. Same granule as used in Figure 4, but using the older ADM approach.

Figure 5. Same granule as used in Figure 4, but using the older ADM approach.

2.5.2. Experiment 2

In the second experiement we have used the CERES2003 ADM for all surface types except for clear ocean. For clear ocean we replaced it with our original ADMs that combined the CERES2003 ADM models and our simulations (Niu and Pinker [7]) (hereafter Niu2011 ADM). This resulted in lower ABI fluxes in the mid range, which in turn increased the number of points below the one-to-one line and reduced the overall bias, but with practically no change in the standard deviation. (Figure 6).

2.5.3. Experiment 3

Based on the improvement seen in Experiment 2, we have redone all the cases for November 2022 using this approach (for clear ocean). The cases used are detailed in Appendix A. A summary of the results are shown in Figure 7 and detailed in Appendix B. As evident, substantial improvement has been achieved.

3. Results

3.1. Results for January, April, and July After Removing Outliers

Outliers are defined as data points with std greater than three. At least for some of the outliers, the large differences are from locations where there are only a few valid pixels available in the corresponding CERES box. In this case, the block average may not be representative of the real condition of the CERES box. The scatter plots after removing the outliers are shown below. The ADM used is the CERES2003 ADMs with ocean ADM values replaced with the old, synthesized ADMs.

Figure 8. ABI TOA flux compared to CERES TOA flux for January 2023. The number of outliers removed was 0.7 %.

Figure 8. ABI TOA flux compared to CERES TOA flux for January 2023. The number of outliers removed was 0.7 %.

Figure 9. ABI TOA flux compared to CERES TOA flux for April 2023. The number of outliers removed was 1 %.

Figure 9. ABI TOA flux compared to CERES TOA flux for April 2023. The number of outliers removed was 1 %.

Figure 10. ABI TOA flux compared to CERES TOA flux for July 2023. The number of outliers removed was 2 %.

Figure 10. ABI TOA flux compared to CERES TOA flux for July 2023. The number of outliers removed was 2 %.

3.2. Investigation of the July Case

To better understand why the results for July agree less with CERES than those for the other three months, we have investigated the cloudy conditions during the selected months. For example, we have plotted the conditions for each day for July and April 2023. We illustrate each month with one case. The notations in the following figures are:

(a)

Matchup between ABI and CERES swaths. Red pixels are CERES FOVs.

(b)

ABI flux image in 2-km resolution matched to CERES swath.

(c)

ABI flux image re-gridded to CERES resolution. Matched to CERES swath.

(d)

CERES flux image.

(e)

Density scatter plot between CERES and ABI.

(f)

Histogram and statistics.

(g)

ABI cloud fraction.

(h)

ABI 640-nm cloud optical depth.

In the following figure we illustrate the cloud conditions for 14^th of July 2023. The figures show TOA fluxes that are considered as proxy for the presence of clouds Illustrated are also the results of evaluation as specified above for (a) to (f). (g) and (h) show the ABI cloud fraction and the 640-nm cloud optical depth, respectively.

It was established, and shown in Figure 11 specifically for July 14, 2023, that most July cases have clear conditions or thin clouds over the ABI-CERES overlap region. The higher bias comes mainly from the clear pixels.

We have also investigated cloud conditions during April 2023. In April, cloudy cases are more frequent than in July. A case for April is shown in Figure 12. We see a lower bias in April than in July.

4. Discussion and Summary

Most satellite observations provide information at TOA only and observe in narrow spectral bands. To derive the total flux at the TOA as well as at the surface there is a need to transform such observations into broadband fluxes. This is accomplished primarily by simulations that require validation against broadband observations. The primary source of broadband observations at the TOA comes from CERES; however, these observations also require some empirical corrections using Angular Distribution Models (ADM). As such, there is no absolute “truth” available for evaluations. In this study we use narrow-band observations from ABI on GOES-18 that are transformed to broadband based on simulations (NTB) and adjusted to total fluxes using an ADM that is a combination of the CERES2003 ADM and the Niu2011 ADM for ocean to estimate the broadband flux. Subsequently, the GOES-18 estimates are evaluated against the Clouds and the Earth's Radiant Energy System (CERES) data, the only available source of such information. The importance of agreement at the TOA is because most methodologies to derive surface SWR fluxes start with the satellite observation at the TOA. Moreover, information on radiative fluxes at both boundaries (TOA and surface) is needed for estimating the energy absorbed by the atmosphere. The methodology described to make such comparisons has been evaluated and possible sources of error were identified.

We have completed an evaluation of shortwave (SW) radiative fluxes at the top of the atmosphere (TOA) as derived from GOES-18 against CERES for four months during 2022 representing different seasons. This is the first effort to evaluate ABI for GOES-18 for such an extensive time period. In contrast to evaluation of GOES-16 and 17, we have remapped ABI observations to CERES. Previously, the mapping was from CERES to ABI. Previous experiments have shown that mapping procedures do have an impact on the results. The results of the evaluation indicate that the accuracy achieved in estimating TOA SWR fluxes ranges between 0.55 to 17.14 for the bias and 22.21 to 31.20 for the std. It is believed that the high bias of 17.14 for July is related to the predominantly clear sky conditions when the used ADMs may have some problems.