1) Overview

An overview of the enhanced system is presented in Fig. 1. Modifications to the baseline system are shown in blocks with orange border, and they include the following: addition of the diurnal SST model to NAVGEM, inclusion of the climatological SST perturbations in the ensemble, and modifications to the hybrid-4DVAR solver. The details of these modifications are described below.

2) SST perturbations in the ensemble system

To emulate the distribution of the mesoscale ocean features that one might expect from an ensemble of fully coupled models, we perturbed the SST in the low-resolution (T119) ensemble [see Fig. 1 and Kuhl et al. (2013) for a description of how the high-resolution control and the low-resolution ensemble interact]. These SST perturbations were generated by sampling random perturbations from a 20-yr-long archive of SST anomalies computed from the ERA-Interim reanalysis (Dee et al. 2011). The anomalies were computed by differencing the 20-yr archive from a seasonal SST mean. The random anomalies where then selected within 7 days from the month–day of the experimental date (e.g., for each date there were 14 days × 20 years = 280 possible samples to draw from). The random perturbations were then scaled to enforce the average standard deviation of 0.4 K. We chose this magnitude as an optimistic value for the average RMS error of the NCODA SST analysis. After the experiments were conducted, the actual SST analysis errors were evaluated to be closer to 0.7 K (not shown). We do not expect that this slightly lower magnitude of the enforced SST spread would lead to significantly different conclusions from this paper. We decided to implement this simple scaling algorithm instead of the original coupled ET approach of McLay et al. (2012) because coupled ET has significantly more atmospheric grid points than SST and, hence, the rotation and scaling of the SST fields in the original approach was dominated by the number of the atmospheric grid points. As a result, the original coupled ET struggled to preserve the desired SST ensemble variance.

3) Diurnal surface model

To add diurnal variation to our SST analysis, we used the diurnal SST model of (McLay et al. 2012). The diurnal SST model was applied to both the high-resolution forecast and to each individual ensemble member used in the computation of the hybrid error covariance. Our diurnal SST model (McLay et al. 2012) was based on Takaya et al. (2010), which represents the following surface layer processes: shortwave and longwave radiative flux, evaporation, molecular thermal conduction, and wind-driven turbulent diffusion determined through Monin–Obhukov similarity theory. Unlike the original model of Takaya et al. (2010), our implementation did not represent impact of Langmuir circulation directly. Instead the friction velocity was multiplied by a factor of 1.4 to obtain a slight enhancement of surface stress. Unlike in (McLay et al. 2012), the cool skin correction was active in our experiments following the original publication of Zeng and Beljaars (2005). The diurnal SST model was active between 60°N and 60°S, where we expect the magnitude of the diurnal signal to be the greatest because of the favorable sun inclination angle.

It is important to note that in the original paper, McLay et al. (2012) tested the impact of the diurnal SST model in the context of the extended-range ensemble prediction system, where an ensemble of 10-day forecasts was initialized using the ET technique centered on an external control analysis. In McLay et al. (2012), the diurnal SST model did not impact the centering analysis. In contrast, this paper exercises the diurnal SST model in a deterministic, cycling forecast system, so that diurnal SST will impact the first guess of the high-resolution (T425) forecast, and will improve the near-surface spread of the low-resolution (T119) ensemble system.

4) Changes to the hybrid-4DVAR system

The second change to Eq. (2) was to include coupling through the observation operator between infrared radiances and the ocean surface:

$y^{\mathrm{radiance}}={\mathsf{H}}^{\mathrm{crtm}-\mathrm{jacobian}}{\mathbf{x}}^{\mathrm{coupled}}=\left[{\mathsf{J}}^{\mathrm{atm}}{\mathsf{J}}^{\mathrm{sst}}\right]\left[\begin{array}{c}{\mathbf{x}}^{\mathrm{atm}}\\ {\mathbf{x}}^{\mathrm{sst}}\end{array}\right],$(4)

where $\mathsf{J}$^atm and $\mathsf{J}$^sst are the Jacobians of the radiative transfer model provided by the Community Radiative Transfer Model v 2.2.3 (CRTM; JCSDA 2018). In the baseline system (CONTROL experiment in Table 1), x^sst was not included in the state vector, which was mathematically equivalent to setting $\mathsf{J}$^sst to zero. The observation operator in Eq. (4) was only coupled for low-peaking infrared satellite channels which were assimilated over the surface of the ocean (x^sst).

Table 1.

Configuration of the experiments.

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The last change was to include coupled initial time covariance:

${\mathsf{P}}^{\mathrm{coupled}}=\left[\begin{array}{cc}{\mathsf{P}}^{\mathrm{AA}}& {\mathsf{P}}^{\mathrm{AE}}\\ {\mathsf{P}}^{\mathrm{EA}}& {\mathsf{P}}^{\mathrm{EE}}\end{array}\right]\approx {\alpha }^{\mathrm{static}}\left[\begin{array}{cc}{\mathsf{P}}_{\mathrm{static}}^{\mathrm{AA}}& 0\\ 0& {\sigma }_{\mathrm{EST}}^2\mathsf{I}\end{array}\right]+{\alpha }^{\mathrm{ens}}\left[\begin{array}{cc}{\mathsf{P}}_{\mathrm{ens}}^{\mathrm{AA}}& {\mathsf{P}}_{\mathrm{ens}}^{\mathrm{AE}}\\ {\mathsf{P}}_{\mathrm{ens}}^{\mathrm{EA}}& {\mathsf{P}}_{\mathrm{ens}}^{\mathrm{EE}}\end{array}\right],$(5)

where superscripts AA, EE, and AE denote cross covariances between atmosphere–atmosphere, Earth surface–Earth surface, and atmosphere–Earth surface states, respectively. The subscript static corresponds to the static (climatological) covariance and ens to the localized ensemble covariance. We used the hybrid coefficient of α^static = α^ens = 0.5. We did not specify cross covariances for the static and we used a trivial identity matrix ($\mathsf{I}$) to specify covariance of errors in the Earth surface. Our weak justification for the last assumption was based on the very coarse resolution of the atmospheric ensemble (approximately 100 km). We assumed that at such long scales, horizontal errors in the Earth surface temperature can be assumed largely decorrelated (e.g., our stand-alone SST analysis system assumes that decorrelation scales exceed 100 km only in the vicinity of the equator). We also expect that at these scales, the vertical processes will dominate, as evidenced by robust vertical cross correlations (${\mathsf{P}}_{\mathrm{ens}}^{\mathrm{AE}}$) (Fig. 2). We also assumed a conservative EST forecast error σ_EST of 0.4 K, which was possibly too low over land and ice.

Statistics of the Earth surface and the lower atmosphere temperatures in run DSST. (a) Average standard deviation of EST, (b) average standard deviation of 2-m atmospheric temperature, and (c) average correlations between EST and 2-m temperature. Standard deviations and correlations were first computed from 80 members of the T119 ensemble for each analysis window and then averaged over all analysis windows from 1 Jun to 1 Aug 2016.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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Statistics of the Earth surface and the lower atmosphere temperatures in run DSST. (a) Average standard deviation of EST, (b) average standard deviation of 2-m atmospheric temperature, and (c) average correlations between EST and 2-m temperature. Standard deviations and correlations were first computed from 80 members of the T119 ensemble for each analysis window and then averaged over all analysis windows from 1 Jun to 1 Aug 2016.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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Statistics of the Earth surface and the lower atmosphere temperatures in run DSST. (a) Average standard deviation of EST, (b) average standard deviation of 2-m atmospheric temperature, and (c) average correlations between EST and 2-m temperature. Standard deviations and correlations were first computed from 80 members of the T119 ensemble for each analysis window and then averaged over all analysis windows from 1 Jun to 1 Aug 2016.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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To localize the ensemble correlation between the EST and the atmosphere, we used the same vertical and horizontal correlation matrix as used in the atmospheric hybrid-4DVAR to localize the information between the lowest level of the atmosphere and the rest of the vertical column. Following Fig. 1 from Kuhl et al. (2013), the ensemble correlations between the EST and the atmospheric variables were localized to zero at the level of 500 hPa and were localized to 0.5 at approximately 800 hPa. Horizontal localization scales where about 2000 km.

1) Skill evaluation against ECMWF analysis

To evaluate the skill of the modified system, we compared the 0–5-day forecast skill against European Centre for Medium-Range Forecasts (ECMWF) real-time analysis from the TIGGE archive.

Our primary metric was improvement in the root-mean-square error (RMSE) between the CONTROL forecast and each of the experimental forecasts. We computed this metric d(r, z, τ) as follows for a set of regions (r), forecast length (τ), and height (z):

$d\left(r,z,\tau \right)={\displaystyle \frac{100}{N_t}}\hspace{0.17em}{\displaystyle \sum _t\left[{\displaystyle \frac{\sqrt{{\displaystyle \frac{1}{N_r}}{\displaystyle \sum _{\hspace{0.17em}\left\{i,j\right\}\in r}\left\{{\left[x_{\mathrm{cntrl}}\left(i,j,z,t,\tau \right)-x_{\mathrm{verif}}\left(i,j,z,t,\tau \right)\right]}^2\right\}}}-\sqrt{{\displaystyle \frac{1}{N_r}}{\displaystyle \sum _{\left\{i,j\right\}\in r}\left\{{\left[x_{\exp }\left(i,j,z,t,\tau \right)-x_{\mathrm{verif}}\left(i,j,z,t,\tau \right)\right]}^2\right\}}}}{\sqrt{{\displaystyle \frac{1}{N_r}}{\displaystyle \sum _{\left\{i,j\right\}\in r}\left\{{\left[x_{\mathrm{cntrl}}\left(i,j,t,z\right)-x_{\mathrm{verif}}\left(i,j,t,z\right)\right]}^2\right\}}}}}\right]},$(7)

where x (i, j, z, t, τ) is the field of interest (temperature, vector winds, precipitable water, or geopotential height); i, j, z, t, τ are indices of the longitude, latitude, vertical level, initial time of the forecast and the forecast lead time respectively; r are the verifying regions [NH (20°–80°N), tropics (20°S–20°N), SH (20°–80°S)]; N_r is the number of points in the verifying region; and N_t is the number of verifying forecasts. Note that d(r, z, τ) is positive whenever the forecast error measure $\sqrt{\left(1/N_r\right){\displaystyle {\sum }_{\left\{i,j\right\}\in r}\left\{{\left[x_{\exp }\left(i,j,z,t,\tau \right)-x_{\mathrm{verif}}\left(i,j,z,t,\tau \right)\right]}^2\right\}}}$ associated with the experimental setup is smaller than that associated with the baseline control experiment. Negative values of d(r, z, τ) indicate that the experimental configuration has actually degraded forecast skill. Also note that d(r, z, τ) is a “percentage-improvement” type measure so that, for example, if the experimental configuration had zero error by exactly matching the verification then d(r, z, τ) = 100. The averaged metric d(r, z, τ) is used in Fig. 3 with positive (negative) change colored in red (blue). All colored boxes in Fig. 3 are statistically significant at the 95% level. White boxes are approximately neutral and not significantly different.

Improved (degraded) RMSE skill score [Eq. (7)] for CONTROL vs COUPLED_DA run in red (blue) compared against the ECMWF analysis. Colored boxes indicate change at 95% significance level. White boxes are approximately neutral and not significantly different. Tropics are defined from 20°S to 20°N and the Northern and Southern Hemispheres between 20° and 80° latitude. The availability of vertical levels for verification was determined by the availability of verifying data on the TIGGE archive.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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Improved (degraded) RMSE skill score [Eq. (7)] for CONTROL vs COUPLED_DA run in red (blue) compared against the ECMWF analysis. Colored boxes indicate change at 95% significance level. White boxes are approximately neutral and not significantly different. Tropics are defined from 20°S to 20°N and the Northern and Southern Hemispheres between 20° and 80° latitude. The availability of vertical levels for verification was determined by the availability of verifying data on the TIGGE archive.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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Improved (degraded) RMSE skill score [Eq. (7)] for CONTROL vs COUPLED_DA run in red (blue) compared against the ECMWF analysis. Colored boxes indicate change at 95% significance level. White boxes are approximately neutral and not significantly different. Tropics are defined from 20°S to 20°N and the Northern and Southern Hemispheres between 20° and 80° latitude. The availability of vertical levels for verification was determined by the availability of verifying data on the TIGGE archive.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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2) Background fit to satellite observations

In addition to traditional long-forecast error statistics described in section 2d(1), we also implemented background fit-to-observations statistics for the 6-h short forecasts used in the DA cycle (similar to observation-minus-background statistics in Akella et al. 2017, see their Fig. 12). It has been shown that improvements in the background fit-to-observation statistics track improvements in the long-forecast skill well (A. Geer 2017, personal communication). In addition, it only requires about two weeks of cycling to establish if a proposed system change has a positive, negative, or neutral impact using the background fit-to-observation statistics. In contrast, it takes two to four month of long forecasts to establish similar improvement or degradation with the traditional long forecast metrics. Finally, unlike the external analysis, background fit-to-observation statistics compares the forecast against observations and, hence, is not sensitive to biases present in the external analysis.

In this paper, we evaluated the background fit-to-observations statistics only for radiance channels used in the assimilation (see Table A1 for the complete list of channels and categories used for verification). Because of the uncertainty in the radiance bias correction, we only computed statistics for the standard deviation of the innovations (i.e., our statistics is insensitive to changes in the bias of the innovations). Details of our background fit-to-observation calculations are presented in appendix B.

1) Long forecast error diagnostics

Comparisons of the control forecast (CONTROL) and the forecast initialized using coupled DA (COUPLED_DA) against ECMWF analysis showed that COUPLED_DA forecasts were significantly better than CONTROL forecasts for a wide range of metrics and forecast lead times (Fig. 3). The RMSE scores improved the most (greater than 3%) for the tropical geopotential above 700 hPa and for the boundary layer and 200 hPa temperatures in the tropics. Modest improvements (1%–2%) were also detected for humidity in the tropics and the SH. The impact of coupled DA was not localized to the lower atmosphere and extends throughout the atmospheric column (see e.g., temperature scores up to day 3 that were improved throughout the troposphere). Closer investigation (not shown) showed that the positive impact was also apparent in the bias score metrics.

We hypothesize that improved tropical temperatures near 200 hPa were due to improved representation of tropical convection, which connects surface perturbations with the tropopause. This hypothesis is supported by McLay et al. (2012) where introduction of the diurnal SST submodel enhanced deep convection along the intertropical convergence zone.

Some degradation of the forecast scores was also apparent (Fig. 3). Specifically, the tropical geopotential and temperature scores (between 500 and 200 hPa) were degraded beyond day 3 of forecast. This degradation is likely due to known biases in NAVGEM tendency terms. NAVGEM is overactive and tends to have a strong negative geopotential tendency (R. Langland 2019, personal communication). As a result, NAVGEM has been historically tuned to produce a biased analysis in favor of a more accurate 5-day forecast score. In other words, an improved temperature analysis in this study (as shown by Fig. 3) will lead to degraded midtroposphere temperature scores at day 5. Such mixed impacts on forecast scores are common for almost any changes to the forecast system and, in this case, they are clearly outweighed by the overwhelming positive impacts for other variables and lead times.

2) Short-forecast errors and attribution of forecast impact

Figure 4 summarizes background fit-to-observation statistics for a sequence of runs with progressively increasing level of coupling. The grand score across all channels (line 1) indicates that introduction of diurnal SST model in the high-resolution model was responsible for the majority of the positive impact; however, using our conservative metric, the introduction of the diurnal SST along was still marked as a “tie.” Introduction of the diurnal SST had the biggest positive impact on water vapor and surface channels in both infrared (IR) and microwave (MW), which is consistent with the long-forecast score statistics in Fig. 3. Diurnal SST also improved the fit to tropospheric sensitive channels in the MW.

Background fit-to-observations scores for experiments in Table 1. Scores are computed for assimilated channels only. Number of channels corresponds to a unique combination of platform, sensor, and channel identification and will not necessary replicate number of channels in Table A1. W, L, and T stand for “win,” “loss,” and “tie” as specified in appendix B. Highlighted in red is the best score in this category.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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Background fit-to-observations scores for experiments in Table 1. Scores are computed for assimilated channels only. Number of channels corresponds to a unique combination of platform, sensor, and channel identification and will not necessary replicate number of channels in Table A1. W, L, and T stand for “win,” “loss,” and “tie” as specified in appendix B. Highlighted in red is the best score in this category.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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Background fit-to-observations scores for experiments in Table 1. Scores are computed for assimilated channels only. Number of channels corresponds to a unique combination of platform, sensor, and channel identification and will not necessary replicate number of channels in Table A1. W, L, and T stand for “win,” “loss,” and “tie” as specified in appendix B. Highlighted in red is the best score in this category.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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Progressive addition of coupling characteristics [introduction of diurnal SST model in the ensemble (DSST), introduction of coupled Jacobians (CJAC), and introduction of coupled covariance (COUPLED_DA)] resulted in progressive improvements to the grand score (line 1, Fig. 4). Specifically, introduction of the diurnal SST model in the ensemble (cf. DSST_HR_ONLY and DSST) made statistically significant changes to temperature soundings in the IR and MW (lines 2 and 3) and, as a result, flipped the grand score in line 1 from a tie to a “win.” The same change however, resulted in the degradation of the IR surface scores (line 6).

Introduction of the coupled Jacobian and coupled covariance further improved the grand score (cf. columns DSST against COUPLED_DA). The biggest improvements that can be attributed to coupled DA were in surface IR (line 6), tropospheric temperatures (lines 10 and 11), and in the water vapor channels (lines 12 and 13). Improvements in the surface IR channels almost completely reversed degradation of scores due to introduction of diurnal SST model in the ensemble (cf. line 6 in columns DSST_HR_ONLY, DSST, and COUPLED_DA).

3) Comparison of analysis increments

To further understand the impact of coupled observation operators and coupled initial time covariances on the analysis, we compared average analysis fields from the CONTROL, DSST, CJAC, and COUPLED_DA experiments. On average, the analysis increment in the CONTROL experiment Fig. 5 was cooling the air above the ocean (indicating warm bias over the ocean) and warming the air above the continents (indicating cold bias over land). For specific humidity, the analysis was adding moisture over most of the ocean (dry bias) and removing moisture over land (wet bias). The pattern of warm bias in the troposphere (and the corresponding cool correction) is consistent with known NAVGEM biases as illustrated by the average first guess and analysis errors measured against radiosondes (Fig. 6).

Average increments for the lowest atmospheric level for the CONTROL experiment: (left) temperature increment and (right) specific humidity.

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Average increments for the lowest atmospheric level for the CONTROL experiment: (left) temperature increment and (right) specific humidity.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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Average increments for the lowest atmospheric level for the CONTROL experiment: (left) temperature increment and (right) specific humidity.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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The 6-h temperature forecast bias averaged from 15 Jun to 1 Aug 2016. (a) First guess bias, (b) analysis bias, (c) ratio of the experiment bias to control bias (forecast), and (d) ratio of the experiment bias to control bias (analysis). Lower (higher) than 1 magnitudes in (c) and (d) indicate improvements (degradation) in the bias of one of the experiments compared to the control.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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The 6-h temperature forecast bias averaged from 15 Jun to 1 Aug 2016. (a) First guess bias, (b) analysis bias, (c) ratio of the experiment bias to control bias (forecast), and (d) ratio of the experiment bias to control bias (analysis). Lower (higher) than 1 magnitudes in (c) and (d) indicate improvements (degradation) in the bias of one of the experiments compared to the control.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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The 6-h temperature forecast bias averaged from 15 Jun to 1 Aug 2016. (a) First guess bias, (b) analysis bias, (c) ratio of the experiment bias to control bias (forecast), and (d) ratio of the experiment bias to control bias (analysis). Lower (higher) than 1 magnitudes in (c) and (d) indicate improvements (degradation) in the bias of one of the experiments compared to the control.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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Introduction of the diurnal SST further increased the strength of the average temperature correction that cooled the warm bias over the oceans (Fig. 7a). This correction extends through a large part of the troposphere (Fig. 8a). No coherent change in the humidity increment emerged from introduction of the diurnal SST model.

Difference between the average analysis increments for the CONTROL experiment and the (a),(b) DSST experiment; (c),(d) CJAC experiment; and (e),(f) COUPLED_DA experiment. (left) Lowest level of temperature and (right) lowest level of specific humidity.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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Difference between the average analysis increments for the CONTROL experiment and the (a),(b) DSST experiment; (c),(d) CJAC experiment; and (e),(f) COUPLED_DA experiment. (left) Lowest level of temperature and (right) lowest level of specific humidity.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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Difference between the average analysis increments for the CONTROL experiment and the (a),(b) DSST experiment; (c),(d) CJAC experiment; and (e),(f) COUPLED_DA experiment. (left) Lowest level of temperature and (right) lowest level of specific humidity.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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Zonal averages of the average difference between temperature analysis increments for CONTROL and (a) DSST_HR_ONLY experiment, (b) DSST experiment, (c) CJAC experiment, and (d) COUPLED_DA experiment.

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Zonal averages of the average difference between temperature analysis increments for CONTROL and (a) DSST_HR_ONLY experiment, (b) DSST experiment, (c) CJAC experiment, and (d) COUPLED_DA experiment.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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Zonal averages of the average difference between temperature analysis increments for CONTROL and (a) DSST_HR_ONLY experiment, (b) DSST experiment, (c) CJAC experiment, and (d) COUPLED_DA experiment.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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Introduction of the coupled DA altered the structure of the average near-surface temperature correction over the oceans (Figs. 7e and 8d) by introducing a warm correction in the boundary layer while slightly strengthening the cold correction aloft. These near-surface changes can be attributed to the impact of both coupled Jacobians and coupled covariances (Figs. 8c,d).

In COUPLED_DA, the largest EST increments (in both the average magnitude and the standard deviation, Fig. 9) were over land (positive) and off the west coast of Americas (negative). This pattern of EST increments is consistent with the pattern of cold average corrections to the atmospheric temperatures in Figs. 5–8. We attribute these large signals to 1) systematic errors in our land model (the land was too cold) and 2) known warm bias in SST quality control procedure that erroneously rejects cold water pixels in the upwelling filaments in presence of low stratus clouds (C. Barron, December 2018, personal communication). The EST increments were close to zero over ice because very few atmospheric observations were available to constrain the lower atmosphere and, through cross covariances, the ice surface temperatures. EST increments were also close to zero over the Amazon, possibly because of the extensive tropical forest cover that isolated EST from the fluctuations in the tropospheric temperatures.

Statistics of EST increments for the COUPLED_DA experiment. (a) Mean EST increment and (b) standard deviation of the EST increment.

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Statistics of EST increments for the COUPLED_DA experiment. (a) Mean EST increment and (b) standard deviation of the EST increment.

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Statistics of EST increments for the COUPLED_DA experiment. (a) Mean EST increment and (b) standard deviation of the EST increment.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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The variance of the EST increments was largest over land (Fig. 9b)–indicating a strong diurnal cycle in the land surface temperatures (see discussion below). Over the ocean, the variance of the corrections was largest in the boundary current regions (Kuroshio, Gulf Stream, and the Agulhas current retroflection region) and the west coast of the Americas. Large SST increments can be expected in the boundary current regions because of large SST gradients, difficulties with the cloud clearing algorithms, and advection of the ocean mesoscale features.

Finally, examination of the average EST increments binned by the time of the day (Fig. 10) revealed strong diurnal patterns of the average EST increment. We found that the strongest corrections over land were in the afternoon, suggesting that our land surface was too cold during the sun-lit hours. In contrast, the bias over the ocean upwelling regions was positive and strongest during the night time, suggesting that the warm bias in the SST quality control procedure was greater at night. We also found that coastal upwelling regions were always too warm (cold increment on average), and the equatorial Pacific region was too warm at night (cold increment) and too cold during the daytime (warm increment).

Diurnal evolution of the average COUPLED_DA EST increment. Panels show increments centered at (a) 0000, (b) 0600, (c) 1200, and (d) 1800 UTC.

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Diurnal evolution of the average COUPLED_DA EST increment. Panels show increments centered at (a) 0000, (b) 0600, (c) 1200, and (d) 1800 UTC.

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Diurnal evolution of the average COUPLED_DA EST increment. Panels show increments centered at (a) 0000, (b) 0600, (c) 1200, and (d) 1800 UTC.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0029.1

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