Assimilation of Low-Peaking Satellite Observations Using the Coupled Interface Framework

2020 ◽  
Vol 148 (2) ◽  
pp. 637-654
Author(s):  
Sergey Frolov ◽  
William Campbell ◽  
Benjamin Ruston ◽  
Craig H. Bishop ◽  
David Kuhl ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Circulation Model ◽  
Satellite Observations ◽  
Sea Surface ◽  
Transfer Model ◽  
Western Boundary ◽  
Diurnal Variability ◽  
Near Surface ◽  
The Impact

Abstract Coupled data assimilation (DA) provides a consistent framework for assimilating satellite observations that are sensitive to several components of the Earth system. In this paper, we focus on low-peaking infrared satellite channels that are sensitive to the lower atmosphere and Earth surface temperature (EST) over both ocean and land. Our atmospheric hybrid-4DVAR system [the Navy Global Environmental Model (NAVGEM)] is extended to include the following: 1) variability in the sea surface temperature (both diurnal variability and climatological perturbations to the ensemble members), 2) the coupled Jacobians of the radiative transfer model for the infrared sensors, and 3) the coupled covariances between the EST and the atmosphere. Our coupling approach is found to improve forecast accuracy and to provide corrections to the EST that are in balance with the atmospheric analysis. The largest impact of the coupling is found on near-surface atmospheric temperature and humidity in the tropics, but the impact extends all the way to the stratosphere. The role of each coupling element on the performance of the global atmospheric circulation model is investigated. Inclusion of variability in the sea surface temperature has the strongest positive impact on the forecast quality. Additional inclusion of the coupled Jacobian and ensemble-based coupled covariances led to further improvements in scores and to modification of the corrections to the ocean boundary layer. Coupled DA had significant impact on latent and sensible heat fluxes over land, locations of western boundary currents, and along the ice edge.

scholarly journals North Indian Ocean variability during the Indian Ocean dipole

2008 ◽  
Vol 5 (2) ◽  
pp. 213-253 ◽  
Author(s):  
J. Brown ◽  
C. A. Clayson ◽  
L. Kantha ◽  
T. Rojsiraphisal
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Indian Ocean Dipole ◽  
Numerical Models ◽  
Circulation Model ◽  
North Indian Ocean ◽  
Sea Surface ◽  
Large Temperature ◽  
The Impact

Abstract. The circulation in the North Indian Ocean (NIO henceforth) is highly seasonally variable. Periodically reversing monsoon winds (southwesterly during summer and northeasterly during winter) give rise to seasonally reversing current systems off the coast of Somalia and India. In addition to this annual monsoon cycle, the NIO circulation varies semiannually because of equatorial currents reversing four times each year. These descriptions are typical, but how does the NIO circulation behave during anomalous years, during an Indian Ocean dipole (IOD) for instance? Unfortunately, in situ observational data are rather sparse and reliance has to be placed on numerical models to understand this variability. In this paper, we estimate the surface current variability from a 12-year hindcast of the NIO for 1993–2004 using a 1/2° resolution circulation model that assimilates both altimetric sea surface height anomalies and sea surface temperature. Presented in this paper is an examination of surface currents in the NIO basin during the IOD. During the non-IOD period of 2000–2004, the typical equatorial circulation of the NIO reverses four times each year and transports water across the basin preventing a large sea surface temperature difference between the western and eastern NIO. Conversely, IOD years are noted for strong easterly and westerly wind outbursts along the equator. The impact of these outbursts on the NIO circulation is to reverse the direction of the currents – when compared to non-IOD years – during the summer for negative IOD events (1996 and 1998) and during the fall for positive IOD events (1994 and 1997). This reversal of current direction leads to large temperature differences between the western and eastern NIO.

scholarly journals The impact of diurnal variability in sea surface temperature on the atlantic air-sea CO<sub>2</sub> flux

2008 ◽  
Vol 8 (4) ◽  
pp. 15825-15853 ◽  
Author(s):  
H. Kettle ◽  
C. J. Merchant ◽  
C. D. Jeffery ◽  
M. J. Filipiak ◽  
C. L. Gentemann
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Co2 Flux ◽  
Sea Surface ◽  
Temperature Fields ◽  
Gas Transfer ◽  
Transfer Velocity ◽  
Diurnal Variability ◽  
Diurnal Warming ◽  
The Impact

Abstract. The effect of diurnal variations in sea surface temperature (SST) on the air-sea flux of CO2 over the central Atlantic ocean and Mediterranean Sea is evaluated for 2005–2006. We use high resolution hourly satellite SST data to determine the diurnal warming (ΔSST). The CO2 flux is then computed using three different temperature fields – a foundation temperature (Tf, measured at a depth where there is no diurnal variation), Tf plus the hourly ΔSST and Tf plus monthly-averaged ΔSST. This is done in conjunction with a physically-based parameterisation for the gas transfer velocity (NOAA-COARE). The differences between the fluxes evaluated for these three different temperature fields quantifies the effects of both diurnal warming and diurnal covariations. We find that including diurnal warming increases the CO2 flux out of the Atlantic for 2005–2006 from 9.6 Tg C a−1 to 30.4 Tg C a−1 (hourly ΔSST) and 31.2 Tg C a−1 (monthly ΔSST). Diurnal warming, therefore, has a large impact on the annual net CO2 flux but diurnal covariations in variables are negligible implying that CO2 fluxes may be adequately computed using monthly averaged ΔSSTs along with a suitable foundation temperature.

scholarly journals An Index to Distinguish Surface- and Subsurface-Intensified Vortices from Surface Observations

2016 ◽  
Vol 46 (8) ◽  
pp. 2529-2552 ◽  
Author(s):  
C. Assassi ◽  
Y. Morel ◽  
F. Vandermeirsch ◽  
A. Chaigneau ◽  
C. Pegliasco ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Circulation Model ◽  
Satellite Observations ◽  
Sea Surface ◽  
Sea Level Anomaly ◽  
Simple Index ◽  
Sea Surface Temperature Anomaly ◽  
Surface Data ◽  
Surface Observations

AbstractIn this study, the authors first show that it is difficult to reconstruct the vertical structure of vortices using only surface observations. In particular, they show that the recent surface quasigeostrophy (SQG) and interior and surface quasigeostrophy (ISQG) methods systematically lead to surface-intensified vortices, and those subsurface-intensified vortices are thus not correctly modeled. The authors then investigate the possibility of distinguishing between surface- and subsurface-intensified eddies from surface data only, using the sea surface height and the sea surface temperature available from satellite observations. A simple index, based on the ratio of the sea surface temperature anomaly and the sea level anomaly, is proposed. While the index is expected to give perfect results for isolated vortices, the authors show that in a complex environment, errors can be expected, in particular when strong currents exist in the vicinity of the vortex. The validity of the index is then analyzed using results from a realistic regional circulation model of the Peru–Chile upwelling system, where both surface and subsurface eddies coexist. The authors find that errors are mostly associated with double-core eddies (aligned surface and subsurface cores) and that the index can be useful to determine the nature of mesoscale eddies (surface or subsurface intensified) from surface (satellite) observations. However, the errors reach 24%, and some possible improvements of the index calculations are discussed.

scholarly journals Improving WRF-Hydro Runoff Predictions of Heavy Floods Through Higher Spatio-Temporal Sea Surface Temperature Products

2021 ◽  
Author(s):  
Berina Kilicarslan ◽  
ismail yucel ◽  
Heves Pilatin ◽  
Eren Duzenli ◽  
Mustafa Yılmaz
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Wrf Model ◽  
Sea Surface ◽  
Model Parameters ◽  
Flood Events ◽  
Near Surface ◽  
Daily Streamflow ◽  
Spatio Temporal ◽  
The Impact

In this study, the impact of spatio-temporal accuracy of four different sea surface temperature (SST) datasets on the accuracy of the Weather Research and Forecasting (WRF)-Hydro system to simulate hydrological response during two catastrophic flood events over Eastern Black Sea (EBS) and Mediterranean (MED) regions of Turkey is investigated. Three time-varying and high spatial resolution external SST products (GHRSST, Medspiration, and NCEP-SST) and one coarse-resolution and invariable SST product (ERA5- and GFS-SST for EBS and MED regions, respectively) already embedded in the initial and boundary condition dataset of WRF model are used in deriving near-surface weather variables through WRF. After the proper event-based calibration performed to the WRF-Hydro using hourly and daily streamflow data of small catchments in both regions, uncoupled model simulations for independent SST events are conducted to assess the impact of SST-triggered precipitation on simulated extreme runoff. Some localized and temporal differences in the occurrence of the flood events with respect to observations depending on the SST representation are noticeable. SST products represented with higher temporal and spatial correlation revealed significant improvement in flood hydrographs for both regions. The higher spatial and temporal correlations of GHRSST dataset show RMSE reduction up to 20% and increase in correlation from 0.3 to 0.8 with respect to the invariable SST (ERA5) in simulated runoffs over the EBS region. The error reduction with GHRSST reached 35% after the calibration of hydrological model parameters compared to not calibrated model. The use of both GHRSST and Medspiration SST data characterized with high spatiotemporal correlation resulted in runoff simulations exactly matching the observed runoff peak of 300 m3/s by reducing the overestimation seen in not calibrated runs over the MED region.

scholarly journals Interannual Variability and Trends in Sea Surface Temperature, Lower and Middle Atmosphere Temperature at Different Latitudes for 1980–2019

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 454
Author(s):  
Andrew R. Jakovlev ◽  
Sergei P. Smyshlyaev ◽  
Vener Y. Galin
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Lower Stratosphere ◽  
Middle Atmosphere ◽  
Southern Oscillation ◽  
Lower Troposphere ◽  
Reanalysis Data ◽  
Sea Surface ◽  
The Arctic ◽  
The Impact

The influence of sea-surface temperature (SST) on the lower troposphere and lower stratosphere temperature in the tropical, middle, and polar latitudes is studied for 1980–2019 based on the MERRA2, ERA5, and Met Office reanalysis data, and numerical modeling with a chemistry-climate model (CCM) of the lower and middle atmosphere. The variability of SST is analyzed according to Met Office and ERA5 data, while the variability of atmospheric temperature is investigated according to MERRA2 and ERA5 data. Analysis of sea surface temperature trends based on reanalysis data revealed that a significant positive SST trend of about 0.1 degrees per decade is observed over the globe. In the middle latitudes of the Northern Hemisphere, the trend (about 0.2 degrees per decade) is 2 times higher than the global average, and 5 times higher than in the Southern Hemisphere (about 0.04 degrees per decade). At polar latitudes, opposite SST trends are observed in the Arctic (positive) and Antarctic (negative). The impact of the El Niño Southern Oscillation phenomenon on the temperature of the lower and middle atmosphere in the middle and polar latitudes of the Northern and Southern Hemispheres is discussed. To assess the relative influence of SST, CO2, and other greenhouse gases’ variability on the temperature of the lower troposphere and lower stratosphere, numerical calculations with a CCM were performed for several scenarios of accounting for the SST and carbon dioxide variability. The results of numerical experiments with a CCM demonstrated that the influence of SST prevails in the troposphere, while for the stratosphere, an increase in the CO2 content plays the most important role.

scholarly journals Deep Learning of Sea Surface Temperature Patterns to Identify Ocean Extremes

2021 ◽  
Vol 13 (4) ◽  
pp. 744
Author(s):  
J. Xavier Prochaska ◽  
Peter C. Cornillon ◽  
David M. Reiman
Keyword(s):  
Deep Learning ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Space Distribution ◽  
Western Boundary ◽  
Learning Modules ◽  
Ocean Science ◽  
The Many ◽  
Future Work

We performed an out-of-distribution (OOD) analysis of ∼12,000,000 semi-independent 128 × 128 pixel2 sea surface temperature (SST) regions, which we define as cutouts, from all nighttime granules in the MODIS R2019 Level-2 public dataset to discover the most complex or extreme phenomena at the ocean’s surface. Our algorithm (ULMO) is a probabilistic autoencoder (PAE), which combines two deep learning modules: (1) an autoencoder, trained on ∼150,000 random cutouts from 2010, to represent any input cutout with a 512-dimensional latent vector akin to a (non-linear) Empirical Orthogonal Function (EOF) analysis; and (2) a normalizing flow, which maps the autoencoder’s latent space distribution onto an isotropic Gaussian manifold. From the latter, we calculated a log-likelihood (LL) value for each cutout and defined outlier cutouts to be those in the lowest 0.1% of the distribution. These exhibit large gradients and patterns characteristic of a highly dynamic ocean surface, and many are located within larger complexes whose unique dynamics warrant future analysis. Without guidance, ULMO consistently locates the outliers where the major western boundary currents separate from the continental margin. Prompted by these results, we began the process of exploring the fundamental patterns learned by ULMO thereby identifying several compelling examples. Future work may find that algorithms such as ULMO hold significant potential/promise to learn and derive other, not-yet-identified behaviors in the ocean from the many archives of satellite-derived SST fields. We see no impediment to applying them to other large remote-sensing datasets for ocean science (e.g., SSH and ocean color).

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