# Optimization of Sensitivity of GOES-16 ABI Sea Surface Temperature by Matching Satellite Observations with L4 Analysis

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

_{SKIN}), which forms the thermal infrared emission of the ocean, and T

_{DEPTH}, measured at 0.2–1 m depth by drifting and moored buoys, respectively, and customarily used for training regression SST algorithms [14,15]. This calls for algorithms more specifically targeted at T

_{SKIN}versus T

_{DEPTH}retrievals.

_{SKIN}. The algorithms are evaluated with the emphasis on sensitivity—a scale, in which variations in true SST are reproduced in the retrieved SST, T

_{S}[16]. It should be noted that the µ is not a measured quantity. Rather, it is calculated by replacing observed brightness temperatures with simulated derivatives of brightness temperatures in terms of SST in the regression equation and zeroing the terms independent from brightness temperatures. The radiative transfer simulations in ACSPO, including calculations of brightness temperature derivatives, are performed using the Community Radiative Transfer Model (CRTM) [17]. The input data for the CRTM are the analysis SST, produced by the Canadian Meteorological Center (CMC) [18], and atmospheric profiles of temperature and humidity from the NCEP Global Forecast System, GFS [19]. Since the CRTM treats the input SST as T

_{SKIN}, the sensitivity characterizes response of T

_{S}specifically to T

_{SKIN}. We assume that errors of CRTM-based sensitivity calculations are much smaller than typical deficits in sensitivity (~0.1–0.5) for T

_{S}retrieved from geostationary data with conventional algorithms. Optimization of sensitivity (i.e., bringing it as close to 1 as possible) is a prerequisite for accurate DCM estimation, as well as for reproduction of spatial contrasts in T

_{S}. The importance of optimal sensitivity for the analyses of diurnal SST variations was recently stressed in, e.g., [20].

_{S}biases are reduced in the statistical sense rather than suppressed in every single image. For this reason, we eventually decided not to implement the incremental method for AHI and ABI within ACSPO in favor of global and piecewise regression algorithms enhanced by using extended sets of radiometric bands and advanced methods of training regression coefficients.

_{S}sensitivity has been the most challenging aspect of the adaptation of ACSPO to geostationary data. The processing of Himawari-8 AHI at NOAA started in April 2015 with the initial set of global regression coefficients produced, consistently with the polar-orbiting sensors, by the least-squares fit to in situ SSTs (T

_{IS}) within the dataset of matchups (MDS) of clear-sky satellite brightness temperatures with T

_{IS}from the NOAA in situ SST Quality Monitor (iQuam) system [33,34]. However, the sensitivity of the AHI global regression SSTs was found to be much lower than that for the polar-orbiting sensors (VIIRS, AVHRR, and MODIS). The difference in sensitivities between geostationary and polar-orbiting sensors was due to the peculiarities of the observed SST domains. Figure 1 shows the domains observed by the SNPP VIIRS and the GOES-16 ABI. Compared to VIIRS, ABI observes mostly low-latitude regions with relatively warm SSTs, whereas the mid-to-high latitudes with colder SSTs are underrepresented in the ABI images. Moreover, those regions are observed under less favorable conditions (larger VZA and lower spatial resolution). As a result, the MDSs for geostationary sensors are dominated by matchups from low-latitude regions with relatively warm SST, resulting in a narrower distribution of matchups in terms of SST. The global regression coefficients for geostationary satellites, derived from such MDS, are mainly optimized for low-latitude regions, and the mean sensitivity of retrieved SST, averaged over the range of view zenith angles, VZA, 0° ≤ VZA < 67°, is as low as ~0.7, compared to a typical mean sensitivity of ~0.85–0.90 for VIIRS [25].

_{IS}under a predefined value of mean sensitivity over the MDS [8]. This way, the mean sensitivity of AHI global regression SST was raised to ~0.95, which, however, came at the expense of larger regional T

_{S}biases. An alternative method for training the regression coefficients, based on matching nighttime satellite observations with CMC analysis of SST, has been explored after ABI thermal IR data became available in January 2017. Note that the CMC SST is a foundation level 4 product, derived on a daily basis on a 0.1° grid from nighttime satellite SSTs and anchored to in situ SST measurements [18]. ACSPO interpolates the gridded CMC SST to every pixel of the sensor. The advantage of the newly developed training method is that, in contrast with matchups with in situ SST, the number of clear-sky pixels, supplied with CMC SST, is much larger than the number of matchups with in situ SST, even in near-polar regions. Using the regression coefficients, calculated with the least-squares method from matchups with CMC SST, the mean sensitivity of the global regression SST was raised to ~0.9, without a noticeable increase in regional biases. The next step towards optimization of T

_{SKIN}estimates has been the development of the piecewise regression algorithm, which provides optimal and uniform sensitivity in each SST pixel. In this paper, we compare the performance of the global regression algorithms trained against in situ and CMC SSTs (GR-IS SST and GR-L4 SST, respectively), and explore the potential of further sensitivity optimization by employing a PWR algorithm, trained against the CMC (PWR-L4).

## 2. Regression SST Equation

_{S}= a +

**C**

^{T}

**R**.

**R**is a vector of regressors:

**R**

^{T}

**=**{T

_{11}, (T

_{11}− T

_{8}), (T

_{11}− T

_{10}), (T

_{11}− T

_{12}), T

_{11}S

_{θ}, (T

_{11}− T

_{8}) S

_{θ}, (T

_{11}− T

_{10}) S

_{θ}, (T

_{11}− T

_{12}) S

_{θ},

(T

_{11}− T

_{8}) T

_{S}

^{0}, (T

_{11}− T

_{10}) T

_{S}

^{0}, (T

_{11}− T

_{12}) T

_{S}

^{0}, S

_{θ}};

**C**is a vector of regression coefficients; a is offset; T

_{8}, T

_{10}, T

_{11,}and T

_{12}are brightness temperatures observed in the 8.4, 10.3, 11.2, and 12.3 μm bands, respectively; S

_{θ}= 1/cos(θ) − 1; θ is VZA; T

_{S}

^{0}is the CMC SST (in °C), interpolated from the original 0.1° grid to sensor’s pixels. Derivatives of brightness temperatures in terms of SST, D

_{8}, D

_{10}, D

_{11,}and D

_{12}, are calculated with the CRTM, and sensitivity in every SST pixel is calculated as follows:

**K**,

**C**) =

**C**

^{T}

**K**,

**K**

^{T}

**=**{D

_{11}, (D

_{11}− D

_{8}), (D

_{11}− D

_{10}), (D

_{11}− D

_{12}), D

_{11}S

_{θ}, (D

_{11}− D

_{8}) S

_{θ}, (D

_{11}− D

_{10}) S

_{θ}, (D

_{11}− D

_{12}) S

_{θ},

(D

_{11}− D

_{8}) T

_{S}

^{0}, (D

_{11}− D

_{10}) T

_{S}

^{0}, (D

_{11}− D

_{12}) T

_{S}

^{0}, 0}

_{IS}in GR-IS SST or T

_{S}

^{0}in GR-L4 SST and PWR-L4 SST) within the corresponding MDSs. The method for stable estimation of regression coefficients [8] minimizes/avoids potential instabilities, caused by correlations between the regressors within the MDS with minimum loss of the information.

## 3. Training Global Regression Algorithms

_{SKIN}retrievals is that T

_{IS}represents T

_{DEPTH}, which may significantly deviate from T

_{SKIN.}The discrepancy between T

_{SKIN}and T

_{IS}reaches the maximum during the daytime under the conditions of strong insolation and low wind speeds near the sea surface [14,15]. To minimize the effect of this discrepancy on trained GR-IS SST coefficients, the daytime matchups with V < 6 m/s were excluded from the training MDS (TMDS-IS). This has reduced the number of matchups within the TMDS-IS to N = 795,877 matchups, or by 18.6%.

_{SKIN}) and does not capture the daytime variations in T

_{SKIN}. Therefore, using daytime SST pixels for training the GR-L4 SST coefficients would result in additional errors. The TMDS-L4 was formed from 744 ABI images taken every hour from 15 December 2017 to 15 January 2018. A full ABI L2P SST image contains on average 3.2 × 10

^{6}clear-sky pixels, with approximately half of those being nighttime. The total number of nighttime pixels within the TMDS-L4 reaches 1.19 × 10

^{9}, which is 1.5 × 10

^{3}times larger than the number of matchups within the TMDS-IS.

_{S}from T

_{IS}. The GR-L4 SST was trained by minimization of the weighted standard deviation of Ts from T

_{S}

^{0}. After training, the offsets of the GR-IS and GR-L4 equations were adjusted to zero bias between retrieved SSTs and T

_{IS}averaged over matchups within the TMDS-IS from 12 am to 7 am local solar time.

## 4. The PWR-L4 SST Algorithm

**C**, is derived from TMDS-L4, as described in Section 3, and the GR-L4 sensitivity, µ

_{GR-L4}_{GR-L}

_{4}=

**C**, is calculated for all pixels (

_{GR-L4}^{T}K**K**is defined in Equation (4)). The whole TMDS-L4 is subdivided into 9 subsets in terms of µ

_{GR-L4}:

_{GR-L}

_{4}< 0.6

_{GR-L}

_{4}< 0.6 + 0.05(I − 1)

_{GR-L}

_{4}≥ 0.95

^{th}subset,

**C**, is derived with the Constrained Least-Squares Method [8], by minimization of the weighted standard deviation of T

_{1}^{i}_{S}− T

_{S}

^{0}under the constraint on the mean sensitivity: (

**C**)

_{1}^{i}^{T}

**<K>**= 1, <*> denotes averaging over the pixels belonging to a given TMDS-L4 subset. The offsets a

^{i}are defined in order to zero the bias of T

_{S}with respect to in situ SST over the subset of TMDS-IS, which includes matchups with the corresponding sensitivities, taken from 0 to 7 am of the local solar time:

_{1}

^{i}= <<T

_{IS}>> − (

**C**)

_{1}^{i}^{T}<<

**R**>>

^{th}subset, b

^{i}, are defined as

^{i}= <<T

_{IS}>> − (

**C**)

_{GR-L4}^{i}^{T}<<

**R**>>

**+ b**

^{i}_{0}

**C**, a

_{1}^{i}_{1}

^{i}, b

^{i}and mean values of the GR-L4 SST sensitivities, µ

^{i}= <µ

_{GR-L4}>, I = 1, 2, …, 9, are stored in the look-up table.

_{GR-L}

_{4}and the PWR-L4 SST equation for this pixel is modified with two sequential iterations. The second iteration of the PWR-L4 coefficients and the offsets, {a

_{2},

**C**} ensures their continuity in terms of µ

_{2}_{GR-L}

_{4}:

_{GR-L}

_{4}≤ µ

_{1}:

**C**=

_{2}**C**, a

_{1}^{1}_{2}= a

_{2}

^{1}

_{i}< µ

_{GR-L}

_{4}≤ µ

_{i +}

_{1}, I = 1, 2, …, 8:

**C**=

_{2}**C**+ [(

_{1}^{i}**C**

_{1}^{i}^{+ 1-}

**C**)/(µ

_{1}^{i}^{i+}

^{1}− µ

^{i})] (µ

_{GR-L4}− µ

^{i}), a

_{2}= a

_{2}

^{i}+ [(a

_{2}

^{i +}

^{1}− a

_{2}

^{i})/(µ

^{i +}

^{1}− µ

^{i})] (µ

_{GR-L4}− µ

^{i})

_{GR-L}

_{4}> µ

_{9}:

**C**=

_{2}**C**, a

_{1}^{9}_{2}= a

_{2}

^{9}

_{2}=

**C**, and µ

_{2}^{T}K_{GR-L4}:

**C**=

_{3}**C**+ [(

_{GR-L4}**C**−

_{2}**C**)/([µ

_{GR-L4}_{2}− µ

_{GR-L4})](1 − µ

_{GR-L4}), a

_{3}= b

^{i}+ {(a

_{2}− b

^{i})/[µ

_{2}− µ

_{GR-L4}]}(1 − µ

_{GR-L4})

_{GR-L4}=

**C**

_{GR-L4}^{T}

**K**, the sensitivity of the T

_{S}, produced with coefficients

**C**from Equation (11),

_{3}**C**

_{3}^{T}

**K**≡ 1. Finally, the PWR-L4 SST is calculated as

_{S}= a

_{3}+

**C**

_{3}^{T}

**R**

## 5. Validation Against In Situ SST

_{IS}and T

_{SKIN}to the validation statistics, biases and standard deviations of T

_{S}– T

_{IS}, as well as mean sensitivities are calculated from TMDS-IS, which, as described in Section 3, excludes daytime matchups with GFS wind speeds V < 6 m/s. In contrast, the DCM estimates are produced from the full MDS-IS, taking into account both daytime and nighttime matchups at all wind speeds. It should be noted that since the TMDS-IS was used for training the GR-IS coefficients, it can be viewed as a dependent MDS in terms of validating the GR-IS SST. However, it is independent in terms of validating the GR-L4 and the PWR-L4 SSTs. This suggests that the conditions of the comparison are more favorable for the GR-IS SST than for two other algorithms.

_{SKIN}with the explored algorithms. The statistical factor which should also be considered when comparing different training techniques is the distribution of matchups within the training MDS. Figure 3 shows the normalized histograms of matchups within TMDS-IS and TMDS-L4 as functions of STPW. The histogram for TMDS-L4 accounts for weighting the pixels during training the GR-L4 and PWR-L4 coefficients, as described in Section 3. The latter histogram shows wider maximum and increased contribution of matchups with STPW < 30 kg/m

^{2}, typical for mid- and high latitudes. Figure 4 shows mean sensitivities, biases, and standard deviations as functions of STPW. All algorithms perform best near the maxima of the corresponding matchups’ distributions at 30 < STPW < 50 kg/m

^{2}, rather than at the minimum of STPW, as one could expect from physical considerations. At smaller STPWs the biases and the standard deviations increase for all three algorithms, and the sensitivities for the GR-IS and GR-L4 SSTs reduce due to reduced relative numbers of corresponding matchups within the training MDSs. At larger STPWs, the statistics are affected by a combination of increasing atmospheric absorption and decreasing relative numbers of matchups. In Figure 4a, the GR-IS SST sensitivity is as low as 0.75 at its maximum near STPW = 30 kg/m

^{2}and reduces to 0.40 at STPW = 130 kg/m

^{2}. The sensitivity of the GR-L4 SST reaches 0.95 at STPW = 20 kg/m

^{2}but reduces to 0.56 at STPW = 130 kg/m

^{2}. The sensitivity of the PWR-L4 SST, by design, remains constant and optimal at all STPWs. In Figure 4b, the biases for all three SSTs do not exceed 0.1 K within the range 20 < STPW < 80 kg/m

^{2}. At STPW < 20 kg/m

^{2}, the large bias in the GR-IS SST is caused by the insufficient number of matchups within the TMDS-IS, whereas the biases in the GR-L4 SST and, especially, PWR-L4 SST increase to a lesser extent. In Figure 4c, the standard deviations for all three SSTs increase with STPW > 40 kg/m

^{2}, consistently with the sensitivities in Figure 4a. Thus, we conclude that the sensitivity of the GR-L4 SST is suboptimal for estimations of the DCM in T

_{SKIN}within the whole range of STPW. The DCM estimates from GR-L4 SST may be appropriate at STPW < 60 kg/m

^{2}but become suboptimal at larger STPWs because of degraded sensitivity. On the other hand, the T

_{SKIN}retrievals and DCM estimates, made from the PWR-L4 SST, may be inefficient at large STPWs due to degraded accuracy and precision. Considering the above, we restrict the range of STPW for T

_{SKIN}retrieval with STPW < 100 kg/m

^{2}. This excludes nearly 0.6% of the MDS-IS and TMDS-IS matchups from the further analysis.

_{S}with three algorithms by showing the hourly biases of T

_{S}− T

_{S}

^{0}as functions of local solar time. The latter biases were estimated from the MDS-IS, using 970,371 daytime and nighttime matchups at all wind speeds and with STPW < 100 kg/m

^{2}. Figure 5a also shows the hourly biases in T

_{IS}− T

_{S}

^{0}. The diurnal cycles are well expressed in all three retrieved SST as well as in T

_{is}, although with substantially different DCMs. The maxima and the minima in T

_{IS}− T

_{S}

^{0}take place later than in T

_{S}− T

_{S}

^{0}, consistent with the fact that the “skin” layer cools down and warms up faster than the “depth” layer, at which the in situ SST is measured. Figure 5b shows the hourly biases in T

_{S}− T

_{IS.}The biases in T

_{S}− T

_{IS}in Figure 5b are smaller than in T

_{S}− T

_{S}

^{0}in Figure 5a. One can also notice that the maxima and the minima of biases in T

_{S}− T

_{IS}shift to a later time when the mean sensitivity of an SST product increases.

## 6. Processing GOES-16 ABI Data

_{S}− T

_{S}

^{0}for the images in Figure 7 are given in Table 3. In Figure 6, the GR-IS SST has the lowest and variable sensitivity. The GR-L4 SST sensitivity is higher than for the GR-IS SST and is also variable. For both the GR-IS SST and the GR-L4 SST, the sensitivities are especially low in the low latitudes at large VZAs, due to large atmospheric absorption along the line of sight, consistent with the behavior of sensitivities at large STPWs in Figure 4a. The GR-IS sensitivity is also reduced in the near-polar zones, which are poorly represented within TMDS-IS. The PWR-L4 SST, by design, provides optimal and uniform sensitivity, µ ≡ 1, over the full SST domain.

^{2}confirms that this algorithm is inefficient at small STPWs. Other than that, the changes in DCM between the algorithms are consistent with the differences in the sensitivities. The difference between the DCMs estimated from the GR-IS SST and the GR-L4 SST is the largest at STPW < 60 kg/m

^{2}. In contrast, the difference between the DCMs for GR-L4 SST and PWR-L4 SST is small at 10 < STPW < 40 kg/m

^{2}and increases with STPW growing over 40 kg/m

^{2}due to increased difference in sensitivities. Assuming that that the maximum of the clear-sky pixels distribution takes place at 30 < STPW < 50 kg/m

^{2}, consistent with the maximum of the distribution of matchups in Figure 3, the adjustment of the DCM estimates in the PWR-L4 SST at larger STPWs may essentially exceed the mean difference between the average DCM values for GR-L4 and PWR-L4 SSTs shown in Table 4.

## 7. Training SST Algorithms Against Different L4 Analyses

_{S}retrieved with the GR-L4 and the PWR-L4 algorithms, which may be caused by training against different SST analyses.

_{S}caused by training the same algorithm against different L4 analyses (i.e., CMC vs. OSTIA or CMC vs. OISST). In all cases, the algorithms were trained using ABI and L4 SST data from 15 December 2017 to 15 January 2018. The training procedures were similar to those described in Section 3 and Section 4 with the exception that the offsets of the SST equations were selected to fit the nighttime L4 SST rather than in situ SSTs. The statistics are shown for two ABI images taken on 31 December 2017 at 9:00 and 20:00 UTC, close, respectively, to the minimum and the maximum of the diurnal cycle in the GOES-16 ABI SST domain. The difference between the daytime and nighttime biases caused by training against different L4 analyses is negligible in all cases. This suggests that the estimates of DCM, averaged over the full ABI SST domain, are practically insensitive to the selection of the L4 analysis for training. The standard deviations of variations in the GR-L4 SST, do not exceed 0.06 K. The corresponding standard deviations for the PWR-L4 SST are larger, especially in the case of switching between CMC and OISST. This suggests that the PWR-L4 SST algorithm requires more careful selection of the analysis SST for training.

## 8. Summary and Conclusions

_{SKIN}is a characteristic of an SST retrieval algorithm, which determines the reproduction of true spatial and temporal SST variations in retrieved SST. The sensitivity directly affects the magnitude of diurnal SST variations, estimated from geostationary sensors, and, therefore, it is of particular importance for the quantitative monitoring of the diurnal cycle in SST. The sensitivity may substantially vary for different SST algorithms depending on the algorithm’s design and the method of training regression coefficients. Optimization of the sensitivity at the stage of algorithm development, i.e., bringing it as close to 1 as possible, is a prerequisite for accurate estimation of the magnitude of the diurnal cycle in SST.

_{SKIN}, averaged over the full observed SST domain, as low as ~0.7. As an alternative to training against in situ SST, we have developed the method of training by matching nighttime clear-sky satellite observations with analysis L4 SST in conjunction with weighting the clear-sky pixels in the inverse proportion to their spatial density. With the newly developed training method, the average sensitivity of the global regression SST for GOES-16 ABI has been increased from ~0.7 to ~0.9 without noticeable increase of regional biases. However, even with this improved training method, the sensitivity of the global regression SST remains variable and suboptimal.

_{SKIN}retrievals from the aforementioned satellites and optimization of SST retrieval algorithms, including minimization of the diurnal variability of biases in retrieved SST.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Composite maps of nighttime sea surface temperature (SST) retrieved with Advanced Clear-Sky Processor for Oceans (ACSPO on 4 April 2018 from (

**a**) polar-orbiting Suomi National Polar-Orbiting Partnership Visible and Infrared Imager/Radiometer Suite (SNPP VIIRS) and (

**b**) GOES-16 Advanced Baseline Imager (ABI) (the latter taken at 9:00 UTC). Both images are from the NOAA SST Quality Monitor (NOAA SQUAM) [35,36].

**Figure 2.**Normalized histograms of matchups within (1) training data set of matchups of satellite observations with in situ SST (TMDS-IS) (N = 795,877) (2) TMDS-L4 (N = 1.19 × 10

^{9}), (3) same as (2) but taking into account weights of the pixels within the MDS-L4 (as explained in Section 3). All histograms are shown as functions of Canadian Meteorological Center (CMC) SST.

**Figure 3.**Normalized histograms in terms of total precipitable water vapor content along slant line of sight (STPW) of (1) matchups with in situ SSTs within TMDS-IS and (2) weighted matchups of nighttime ABI pixels with CMC L4 SSTs within the MDS-L4.

**Figure 4.**(

**a**) Sensitivity, (

**b**) bias and (

**c**) standard deviation with respect to in situ SST of the (1) GR-IS SST, (2) GR-L4 SST, and (3) PWR-L4 SST. All shown as functions of slant-path total precipitable water vapor content, STPW.

**Figure 5.**(

**a**) Diurnal cycles in T

_{S}− T

_{S}

^{0}averaged over the full ABI MDS-IS (April 2017–March 2018) for (1) GR-IS SST (2) GR-L4 SST, (3) PWR-L4 SST and (4) in situ SST. (

**b**) Diurnal mean deviations of (1) GR-IS SST (2) GR-L4 SST and (3) PWR-L4 SST from in situ SST. All shown as functions of local solar time.

**Figure 6.**Images of the sensitivity of GOES-16 ABI SST for (

**a**) GR-IS SST, (

**b**) GR-L4 SST, and (

**c**) PWR-L4 SST at 20:00 UTC 1 March 2018.

**Figure 7.**Images of the deviation of GOES-16 ABI SST from CMC for (

**a**) GR-IS SST, (

**b**) GR-L4 SST, and (

**c**) PWR-L4 SST at 20:00 UTC 1 March 2018.

**Figure 8.**Time series of the bias of T

_{S}− T

_{S}

^{0}derived from GR-IS SST, GR-L4 SST, and PWR-L4 SST for 24–30 March 2018.

**Figure 9.**Mean DCMs for (1) GR-IS SST, (2) GR-L4 SST, and (4) PWR-L4 SST, averaged from 1 to 31 March 2018, as functions of STPW.

Abbreviation | Meaning |
---|---|

ABI | Advanced Baseline Imager |

ACSPO | Advanced Clear-Sky Processor for Oceans |

AHI | Advanced Himawari Imager |

AVHRR | Advanced Very High Resolution Radiometer |

CMC SST | Canadian Meteorological Center analysis SST |

CRTM | Community Radiative Transfer Model |

DCM | Magnitude of diurnal cycle |

EUMETSAT | European Organization for the Exploitation of Meteorological Satellites |

GFS | Global Forecast System |

GOES | Geostationary Operational Environmental Satellite |

GR-IS SST | Global regression SST algorithm trained against in situ SST |

GR-L4 SST | Global regression SST algorithm trained against L4 SST |

iQuam | In situ Quality Monitor |

MDS | Data set of matchups |

MODIS | Moderate Resolution Imaging Spectroradiometer |

NLSST | Non-linear SST algorithm |

NOAA | National Oceanic and Atmospheric Administration |

OISST | Optimal Interpolation SST |

OSTIA | Operational Sea Surface Temperature and Sea Ice Analysis SST |

PWR-L4 SST | Piecewise Regression SST algorithm trained against L4 SST |

SEVIRI | Spinning Enhanced Visible and Infrared Imager |

S-NPP | Suomi National Polar-Orbiting Partnership |

SQUAM | SST Quality Monitor |

SST | Sea surface temperature |

STPW | Total precipitable water vapor content along slant line of sight |

TMDS-IS | Training data set of matchups of satellite observations with in situ SST |

TMDS-L4 | Training data set of matchups of satellite observations with L4 SST |

VIIRS | Visible and Infrared Imager/Radiometer Suite |

VZA | Satellite view zenith angle |

**Table 2.**Mean sensitivities, standard deviations of retrieved SST – in situ SST, and average magnitudes of diurnal cycle (DCMs), estimated from MDS-IS, using matchups with STPW < 100 kg/m

^{2}.

Algorithm | Mean Sensitivity | Standard Deviation | DCM |
---|---|---|---|

GR-IS SST | 0.72 | 0.32 K | 0.27 K |

GR-L4 SST | 0.90 | 0.35 K | 0.33 K |

PWR-L4 SST | 1.00 | 0.40 K | 0.37 K |

Algorithm | Mean Sensitivity | Standard Deviation | DCM |
---|---|---|---|

GR-IS SST | 0.72 | 0.32 K | 0.27 K |

GR-L4 SST | 0.90 | 0.35 K | 0.33 K |

PWR-L4 SST | 1.00 | 0.40 K | 0.37 K |

**Table 4.**Mean sensitivities and magnitudes of the diurnal cycle, averaged from 1 to 31 March 2018, for the three explored algorithms.

Algorithm | Mean Sensitivity | DCM |
---|---|---|

GR-IS SST | 0.73 | 0.34 K |

GR-L4 SST | 0.91 | 0.43 K |

PWR-L4 SST | 1.00 | 0.47 K |

Compared L4 SSTs | Bias | Standard Deviation |
---|---|---|

CMC vs OSTIA | 0.01 K | 0.22 K |

CMC vs OISST | −0.07 K | 0.36 K |

**Table 6.**Bias and standard deviation of variations in the GR-L4 SST and the PWR-L4 SSTs caused by training on different L4 analyses, averaged over the GOES-16 ABI clear-sky SST domains on 31 December 2017 at 9:00 and 20:00 UTC.

Compared L4 SSTs | Night (9:00 UTC) | Day (20:00 UTC) | ||
---|---|---|---|---|

Bias | Standard Deviation | Bias | Standard Deviation | |

GR-L4 SST | ||||

CMC vs OSTIA | −0.02 K | 0.02 K | −0.02 K | 0.02 K |

CMC vs OISST | −0.02 K | 0.06 K | −0.02 K | 0.06 K |

PWR-L4 SST | ||||

CMC vs OSTIA | 0.02 K | 0.08 K | 0.02 K | 0.07 K |

CMC vs OISST | 0.00 K | 0.13 K | 0.01 K | 0.13 K |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Petrenko, B.; Ignatov, A.; Kihai, Y.; Pennybacker, M. Optimization of Sensitivity of GOES-16 ABI Sea Surface Temperature by Matching Satellite Observations with L4 Analysis. *Remote Sens.* **2019**, *11*, 206.
https://doi.org/10.3390/rs11020206

**AMA Style**

Petrenko B, Ignatov A, Kihai Y, Pennybacker M. Optimization of Sensitivity of GOES-16 ABI Sea Surface Temperature by Matching Satellite Observations with L4 Analysis. *Remote Sensing*. 2019; 11(2):206.
https://doi.org/10.3390/rs11020206

**Chicago/Turabian Style**

Petrenko, Boris, Alexander Ignatov, Yury Kihai, and Matthew Pennybacker. 2019. "Optimization of Sensitivity of GOES-16 ABI Sea Surface Temperature by Matching Satellite Observations with L4 Analysis" *Remote Sensing* 11, no. 2: 206.
https://doi.org/10.3390/rs11020206