scholarly journals Sensor-Specific Error Statistics for SST in the Advanced Clear-Sky Processor for Oceans

2016 ◽  
Vol 33 (2) ◽  
pp. 345-359 ◽  
Author(s):  
B. Petrenko ◽  
A. Ignatov ◽  
Y. Kihai ◽  
P. Dash
Keyword(s):  
Regression Coefficients ◽  
Local Regression ◽  
Error Statistics ◽  
Surface Warming ◽  
Piecewise Regression ◽  
Clear Sky ◽  
In Situ Data ◽  
Fitting In ◽  
Specific Error

AbstractThe formulation of the sensor-specific error statistics (SSES) has been redesigned in the latest implementation of the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) to enable efficient use of SSES for assimilation of the ACSPO baseline regression SST (BSST) into level 4 (L4) analyses. The SSES algorithm employs segmentation of the SST domain in the space of regressors and derives the segmentation parameter from the statistics of regressors within the global dataset of matchups. For each segment, local regression coefficients and standard deviations (SDs) of BSST minus in situ SST are calculated from the corresponding subset of matchups. The local regression coefficients are used to generate an auxiliary product—piecewise regression (PWR) SST—and SSES biases are estimated as differences between BSST and PWR SST. Correction of SSES biases, which transforms BSST back into PWR SST, reduces the effects of residual cloud; variations in view zenith angle; and, during the daytime, diurnal surface warming. This results in significant reduction in the global SD of fitting in situ SST, making it comparable with SD for the Canadian Meteorological Centre (CMC) L4 SST. Unlike the foundation CMC SST (which is consistent with in situ SST at night but biased cold during the daytime), the PWR SST is consistent with in situ data during both day and night and thus may be viewed as an estimate of “depth” in situ SST. The PWR SST is expected to be a useful input into L4 SST analyses, especially for foundation SST products, such as the CMC L4.

scholarly journals Long-term sea surface temperature variability in the Aegean Sea

2011 ◽  
Vol 2 (2) ◽  
pp. 125 ◽  
Author(s):  
Nikolaos Skliris ◽  
Sarantis S. Sofianos ◽  
Athanasios Gkanasos ◽  
Panagiotis Axaopoulos ◽  
Anneta Mantziafou ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Aegean Sea ◽  
Sea Surface ◽  
Surface Warming ◽  
In Situ Data ◽  
Nao Index ◽  
Decadal Scale

The inter-annual/decadal scale variability of the Aegean Sea Surface Temperature (SST) is investigated by means of long-term series of satellite-derived and in situ data. Monthly mean declouded SST maps are constructed over the 1985–2008 period, based on a re-analysis of AVHRR Oceans Pathfinder optimally interpolated data over the Aegean Sea. Basin-average SST time series are also constructed using the ICOADS in situ data over 1950–2006. Results indicate a small SST decreasing trend until the early nineties, and then a rapid surface warming consistent with the acceleration of the SST rise observed on the global ocean scale. Decadal-scale SST anomalies were found to be negatively correlated with the winter North Atlantic Oscillation (NAO) index over the last 60 years suggesting that along with global warming effects on the regional scale, a part of the long-term SST variability in the Aegean Sea is driven by large scale atmospheric natural variability patterns. In particular, the acceleration of surface warming in the Aegean Sea began nearly simultaneously with the NAO index abrupt shift in the mid-nineties from strongly positive values to weakly positive/negative values.

scholarly journals A singular evolutive extended Kalman filter to assimilate real in situ data in a 1-D marine ecosystem model

2003 ◽  
Vol 21 (1) ◽  
pp. 389-397 ◽  
Author(s):  
I. Hoteit ◽  
G. Triantafyllou ◽  
G. Petihakis ◽  
J. I. Allen
Keyword(s):  
Estimation Error ◽  
Marine Ecosystem ◽  
Ecosystem Model ◽  
Ocean Model ◽  
Empirical Orthogonal Functions ◽  
Error Statistics ◽  
Orthogonal Functions ◽  
In Situ Data ◽  
Marine Ecosystem Model

Abstract. A singular evolutive extended Kalman (SEEK) filter is used to assimilate real in situ data in a water column marine ecosystem model. The biogeochemistry of the ecosystem is described by the European Regional Sea Ecosystem Model (ERSEM), while the physical forcing is described by the Princeton Ocean Model (POM). In the SEEK filter, the error statistics are parameterized by means of a suitable basis of empirical orthogonal functions (EOFs). The purpose of this contribution is to track the possibility of using data assimilation techniques for state estimation in marine ecosystem models. In the experiments, real oxygen and nitrate data are used and the results evaluated against independent chlorophyll data. These data were collected from an offshore station at three different depths for the needs of the MFSPP project. The assimilation results show a continuous decrease in the estimation error and a clear improvement in the model behavior. Key words. Oceanography: general (ocean prediction; numerical modelling) – Oceanography: biological and chemical (ecosystems and ecology)

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

2019 ◽  
Vol 11 (2) ◽  
pp. 206 ◽  
Author(s):  
Boris Petrenko ◽  
Alexander Ignatov ◽  
Yury Kihai ◽  
Matthew Pennybacker
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Observations ◽  
Sea Surface ◽  
High Latitudes ◽  
Training Method ◽  
Piecewise Regression ◽  
Clear Sky ◽  
Regression Algorithms

Monitoring of the diurnal warming cycle in sea surface temperature (SST) is one of the key tasks of the new generation geostationary sensors, the Geostationary Operational Environmental Satellite (GOES)-16/17 Advanced Baseline Imager (ABI), and the Himawari-8/9 Advanced Himawari Imager (AHI). However, such monitoring requires modifications of the conventional SST retrieval algorithms. In order to closely reproduce temporal and spatial variations in SST, the sensitivity of retrieved SST to SSTskin should be as close to 1 as possible. Regression algorithms trained by matching satellite observations with in situ SST from drifting and moored buoys do not meet this requirement. Since the geostationary sensors observe tropical regions over larger domains and under more favorable conditions than mid-to-high latitudes, the matchups are predominantly concentrated within a narrow range of in situ SSTs >2 85 K. As a result, the algorithms trained against in situ SST provide the sensitivity to SSTskin as low as ~0.7 on average. An alternative training method, employed in the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear-Sky Processor for Oceans, matches nighttime satellite clear-sky observations with the analysis L4 SST, interpolated to the sensor’s pixels. The method takes advantage of the total number of clear-sky pixels being large even at high latitudes. The operational use of this training method for ABI and AHI has increased the mean sensitivity of the global regression SST to ~0.9 without increasing regional biases. As a further development towards improved SSTskin retrieval, the piecewise regression SST algorithm was developed, which provides optimal sensitivity in every SST pixel. The paper describes the global and the piecewise regression algorithms trained against analysis SST and illustrates their performance with SST retrievals from the GOES-16 ABI.

scholarly journals Evaluation of the MODIS (C6) Daily Albedo Products for Livingston Island, Antarctic

2021 ◽  
Vol 13 (12) ◽  
pp. 2357
Author(s):  
Alejandro Corbea-Pérez ◽  
Javier F. Calleja ◽  
Carmen Recondo ◽  
Susana Fernández
Keyword(s):  
Greenland Ice Sheet ◽  
South Shetland Islands ◽  
Entire Study ◽  
Clear Sky ◽  
In Situ Data ◽  
Livingston Island ◽  
Significant Difference ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Sky Conditions

Although extensive research of Moderate Resolution Imaging Spectroradiometer (MODIS) albedo data is available on the Greenland Ice Sheet, there is a lack of studies evaluating MODIS albedo products over Antarctica. In this paper, MOD10A1, MYD10A1, and MCD43 (C6) daily albedo products were compared with the in situ albedo data on Livingston Island, South Shetland Islands (SSI), Antarctica, from 2006 to 2015, for both all-sky and clear-sky conditions, and for the entire study period and only the southern summer months. This is the first evaluation in which MYD10A1 and MCD43 are also included, which can be used to improve the accuracy of the snow BRDF/albedo modeling. The best correlation was obtained with MOD10A1 in clear-sky conditions (r = 0.7 and RMSE = 0.042). With MCD43, only data from the backup algorithm could be used, so the correlations obtained were lower (r = 0.6). However, it was found that there was no significant difference between the values obtained for all-sky and for clear-sky data. In addition, the MODIS products were found to describe the in situ data trend, with increasing albedo values in the range between 0.04 decade−1 and 0.16 decade−1. We conclude that MODIS daily albedo products can be applied to study the albedo in the study area.

scholarly journals Metop First Generation AVHRR FRAC SST Reanalysis Version 1

2021 ◽  
Vol 13 (20) ◽  
pp. 4046
Author(s):  
Victor Pryamitsyn ◽  
Boris Petrenko ◽  
Alexander Ignatov ◽  
Yury Kihai
Keyword(s):  
First Generation ◽  
Time Windows ◽  
Residual Effects ◽  
Data Set ◽  
Piecewise Regression ◽  
Clear Sky ◽  
In Situ Data ◽  
Highly Sensitive ◽  
Global Regression ◽  
Performance Statistics

The first full-mission global AVHRR FRAC sea surface temperature (SST) dataset with a nominal 1.1km resolution at nadir was produced from three Metop First Generation (FG) satellites: Metop-A (2006-on), -B (2012-on) and -C (2018-on), using the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) SST enterprise system. Historical reprocessing (‘Reanalysis-1’, RAN1) starts at the beginning of each mission and continues into near-real time (NRT). ACSPO generates two SST products, one with global regression (GR; highly sensitive to skin SST), and another one with piecewise regression (PWR; proxy for depth SST) algorithms. Small residual effects of orbital and sensor instabilities on SST retrievals are mitigated by retraining the regression coefficients daily, using matchups with drifting and tropical moored buoys within moving time windows. In RAN, the training windows are centered at the processed day. In NRT, the same size windows are employed but delayed in time, ending four to ten days prior to the processed day. Delayed-mode RAN reprocessing follows the NRT with a two-month lag, resulting in a higher quality and a more consistent SST record. In addition to its completeness, the newly created Metop-FG RAN1 SST dataset shows very close agreement with in situ data (including the fully independent Argo floats), well within the NOAA specifications for accuracy (global mean bias; ±0.2 K) and precision (global standard deviation; 0.6 K) in a ~20% clear-sky domain (percent of clear-sky SST pixels to the total of ice-free ocean). All performance statistics are stable in time, and consistent across the three platforms. The Metop-FG RAN1 data set is archived at the NASA JPL PO.DAAC and NOAA NCEI. This paper documents the newly created dataset and evaluates its performance.

scholarly journals Improved AIRS Temperature and Moisture Soundings with Local A Priori Information for the 1DVAR Method

2017 ◽  
Vol 34 (5) ◽  
pp. 1083-1095 ◽  
Author(s):  
Hyun-Sung Jang ◽  
Byung-Ju Sohn ◽  
Hyoung-Wook Chun ◽  
Jun Li ◽  
Elisabeth Weisz
Keyword(s):  
A Priori ◽  
Training Data ◽  
Regression Coefficients ◽  
Moving Window ◽  
A Priori Information ◽  
Error Statistics ◽  
Atmospheric Infrared Sounder ◽  
One Dimensional ◽  
Clear Sky ◽  
Priori Information

AbstractA moving-window regression technique was developed for obtaining better a priori information for one-dimensional variational (1DVAR) physical retrievals. Using this technique regression coefficients were obtained for a specific geographical 10° × 10° window and for a given season. Then, regionally obtained regression retrievals over East Asia were used as a priori information for physical retrievals. To assess the effect of improved a priori information on the accuracy of the physical retrievals, error statistics of the physical retrievals from clear-sky Atmospheric Infrared Sounder (AIRS) measurements during 4 months of observation (March, June, September, and December of 2010) were compared; the results obtained using new a priori information were compared with those using a priori information from a global set of training data classified into six classes of infrared (IR) window channel brightness temperature. This comparison demonstrated that the moving-window regression method can successfully improve the accuracy of physical retrieval. For temperature, root-mean-square error (RMSE) improvements of 0.1–0.2 and 0.25–0.5 K were achieved over the 150–300- and 900–1000-hPa layers, respectively. For water vapor given as relative humidity, the RMSE was reduced by 1.5%–3.5% above the 300-hPa level and by 0.5%–1% within the 700–950-hPa layer.

STORM ICE OIL WIND WAVE WATCH SYSTEM (SIOWS): WEB GIS APPLICATION FOR MONITORING THE ARCTIC

2017 ◽  
Author(s):  
Alexander Myasoedov ◽  
Alexander Myasoedov ◽  
Sergey Azarov ◽  
Sergey Azarov ◽  
Ekaterina Balashova ◽  
...  
Keyword(s):  
Satellite Data ◽  
Wind Wave ◽  
Arctic Region ◽  
Web Gis ◽  
The Arctic ◽  
Flexible Tool ◽  
In Situ Data ◽  
Current State ◽  
Watch System

Working with satellite data, has long been an issue for users which has often prevented from a wider use of these data because of Volume, Access, Format and Data Combination. The purpose of the Storm Ice Oil Wind Wave Watch System (SIOWS) developed at Satellite Oceanography Laboratory (SOLab) is to solve the main issues encountered with satellite data and to provide users with a fast and flexible tool to select and extract data within massive archives that match exactly its needs or interest improving the efficiency of the monitoring system of geophysical conditions in the Arctic. SIOWS - is a Web GIS, designed to display various satellite, model and in situ data, it uses developed at SOLab storing, processing and visualization technologies for operational and archived data. It allows synergistic analysis of both historical data and monitoring of the current state and dynamics of the "ocean-atmosphere-cryosphere" system in the Arctic region, as well as Arctic system forecasting based on thermodynamic models with satellite data assimilation.

scholarly journals Critical Dynamics of Defects and Mechanisms of Damage-Failure Transitions in Fatigue

Materials ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2554
Author(s):  
Oleg Naimark ◽  
Vladimir Oborin ◽  
Mikhail Bannikov ◽  
Dmitry Ledon
Keyword(s):  
Fatigue Damage ◽  
Kinetic Equations ◽  
Experimental Methodology ◽  
Preliminary Deformation ◽  
Scaling Properties ◽  
In Situ Data ◽  
Number Of Cycles ◽  
Fish Eye ◽  
Very High

An experimental methodology was developed for estimating a very high cycle fatigue (VHCF) life of the aluminum alloy AMG-6 subjected to preliminary deformation. The analysis of fatigue damage staging is based on the measurement of elastic modulus decrement according to “in situ” data of nonlinear dynamics of free-end specimen vibrations at the VHCF test. The correlation of fatigue damage staging and fracture surface morphology was studied to establish the scaling properties and kinetic equations for damage localization, “fish-eye” nucleation, and transition to the Paris crack kinetics. These equations, based on empirical parameters related to the structure of the material, allows us to estimate the number of cycles for the nucleation and advance of fatigue crack.

scholarly journals Measuring and inferring the ice thickness distribution of four glaciers in the Tien Shan, Kyrgyzstan

2020 ◽  
pp. 1-18
Author(s):  
Lander Van Tricht ◽  
Philippe Huybrechts ◽  
Jonas Van Breedam ◽  
Johannes J. Fürst ◽  
Oleg Rybak ◽  
...  
Keyword(s):  
Cross Validation ◽  
Thickness Distribution ◽  
Tien Shan ◽  
Ice Thickness ◽  
Ice Volume ◽  
In Situ Data ◽  
Radio Echo Sounding ◽  
Thickness Measurements ◽  
Tien Shan Mountains

Abstract Glaciers in the Tien Shan mountains contribute considerably to the fresh water used for irrigation, households and energy supply in the dry lowland areas of Kyrgyzstan and its neighbouring countries. To date, reconstructions of the current ice volume and ice thickness distribution remain scarce, and accurate data are largely lacking at the local scale. Here, we present a detailed ice thickness distribution of Ashu-Tor, Bordu, Golubin and Kara-Batkak glaciers derived from radio-echo sounding measurements and modelling. All the ice thickness measurements are used to calibrate three individual models to estimate the ice thickness in inaccessible areas. A cross-validation between modelled and measured ice thickness for a subset of the data is performed to attribute a weight to every model and to assemble a final composite ice thickness distribution for every glacier. Results reveal the thickest ice on Ashu-Tor glacier with values up to 201 ± 12 m. The ice thickness measurements and distributions are also compared with estimates composed without the use of in situ data. These estimates approach the total ice volume well, but local ice thicknesses vary substantially.

Sign in / Sign up

Export Citation Format

Share Document