scholarly journals THREE-WAY ERROR ANALYSIS OF SEA SURFACE TEMPERATURE (SST) BETWEEN HIMAWARI-8, BUOY, AND MUR SST IN SAVU SEA

2018 ◽  
Vol 15 (1) ◽  
pp. 25 ◽  
Author(s):  
Bambang Sukresno ◽  
Rizki Hanintyo ◽  
Denny Wijaya Kusuma ◽  
Dinarika Jatisworo ◽  
Ari Murdimanto
Keyword(s):  
Error Analysis ◽  
Reference Data ◽  
Sea Surface ◽  
Time Data ◽  
Night Time ◽  
Multi Scale ◽  
Level 3 ◽  
Buoy Data ◽  
Better Than

Variance errors of Himawari-8, buoy, and Multi-scale Ultra-high Resolution (MUR) SST in Savu Sea have been investigated. This research used level 3 Himawari-8 hourly SST, in situ measurement of buoy, and daily MUR SST in the period of December 2016 to July 2017. The data were separated into day time data and night time. Skin temperature of Himawari-8 and subskin tempertaure of MUR SST were corrected with the value of 15∆Tdept">  before compared with buoy data. Hourly SST of Himawari-8 and buoy data were converted to daily format by averaging process before collocated with MUR SST data. The number of 2,264 matchup data are obtained. Differences average between Himawari-8, buoy and MUR SST were calculated to get the value of variance (Vij).  Using three-way error analysis, variance errors of each observation type can be known. From the analysis results can be seen that the variance error of Himawari-8, buoy and MUR SST are 2.5 oC, 0.28oC and 1.21oC respectively. The accuracy of buoy data was better than the other. With a small variance errors, thus buoy data can be used as a reference data for validation of SST from different observation type.

scholarly journals In-situ measurement of reactive hydrocarbons at Hohenpeissenberg with comprehensive gas chromatography (GCxGC-FID): use in estimating HO and NO<sub>3</sub>

2006 ◽  
Vol 6 (4) ◽  
pp. 8155-8188
Author(s):  
S. Bartenbach ◽  
J. Williams ◽  
C. Plass-Dülmer ◽  
H. Berresheim ◽  
J. Lelieveld
Keyword(s):  
Mean Value ◽  
Time Data ◽  
Night Time ◽  
Radical Species ◽  
Diurnal Cycles ◽  
Meteorological Observatory ◽  
Variate Analysis ◽  
The Relationship ◽  
Good Agreement

Abstract. During a field campaign at the Meteorological Observatory Hohenpeissenberg (MOHp) in July 2004, VOCs were measured using GCxGC-FID. Comparison to routinely made GC-MS measurements showed good agreement for a variety of anthropogenic and biogenic ambient VOCs ranging in concentration from below the detection limit (0.1 pmol mol−1) to 180 pmol mol−1. Pronounced diurnal cycles were found for both the biogenic and anthropogenic compounds, driven for the most part by the daily rise and fall of the boundary layer over the station. For the reactive compounds (lifetimes <2 days), a significant, non-zero dependency of the variability on lifetime was found, indicating that chemistry (as opposed to transport alone) was playing a role in determining the ambient VOC concentrations. The relationship was exploited using a single-variate analysis to derive a daytime mean value of HO (5.3±1.4×106 molecules cm−3), which compares well to that measured at the site, 3.2±2.3×106 molecules cm−3. The analysis was extended to the night time data to estimate concentrations for NO3 (1.47±0.2×108 molecules cm−3), which is not measured at the site. The feasibility of this approach for environments dominated by emissions of short-lived VOCs to estimate ambient levels of radical species is discussed.

scholarly journals Validation of Sea Surface Temperature from GCOM-C Satellite Using iQuam Datasets and MUR-SST in Indonesian Waters

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Bambang Sukresno ◽  
Dinarika Jatisworo ◽  
Rizki Hanintyo
Keyword(s):  
High Resolution ◽  
Error Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Important Variable ◽  
Sea Surface ◽  
Analysis Method ◽  
Validation Process ◽  
Indonesian Seas

Sea surface temperature (SST) is an important variable in oceanography. One of the SST data can be obtained from the Global Observation Mission-Climate (GCOM-C) satellite. Therefore, this data needs to be validated before being applied in various fields. This study aimed to validate SST data from the GCOM-C satellite in the Indonesian Seas. Validation was performed using the data of Multi-sensor Ultra-high Resolution sea surface temperature (MUR-SST) and in situ sea surface temperature Quality Monitor (iQuam). The data used are the daily GCOM-C SST dataset from January to December 2018, as well as the daily dataset from MUR-SST and iQuam in the same period. The validation process was carried out using the three-way error analysis method. The results showed that the accuracy of the GCOM-C SST was 0.37oC.

Validation and correction of sea surface salinity retrieval from SMAP

2020 ◽  
Author(s):  
Sisi Qin
Keyword(s):  
Soil Moisture ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Tropical Regions ◽  
Ocean Salinity ◽  
Forecast Models ◽  
Level 3

&lt;p&gt;In this study, Sea Surface Salinity (SSS) Level 3 (L3) daily product derived from Soil Moisture Active Passive (SMAP) during the year 2016, was validated and compared with SSS daily products derived from Soil Moisture and Ocean Salinity (SMOS) and in-situ measurements. Generally, the Root Mean Square Error (RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the Sea Surface Temperature (SST).Then, aregression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.&lt;/p&gt;

Towards the removal of model bias from ESA CCI SM by using an L-band scaling reference

2021 ◽  
Author(s):  
Rémi Madelon ◽  
Nemesio Rodriguez-Fernandez ◽  
Robin Van Der Shalie ◽  
Yann Kerr ◽  
Tracy Scalon ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Climate Models ◽  
In Situ Measurements ◽  
Time Data ◽  
Data Set ◽  
Spatial Coverage ◽  
L Band ◽  
Better Than

&lt;p&gt;Merging data from different instruments is required to construct long time data records of soil moisture (SM). This is the goal of projects such as the ESA Climate Change Initiative (CCI) for SM (Gruber et al., 2019), which uses both active and passive microwave sensors. Currently, the GLDAS v2.1 model is used as reference to re-scale active and passive time series by matching their Cumulative Density Function (CDF) to that of the model. Removing the dependency on models is important, in particular for data assimilation applications into hydrological or climate models, and it has been proposed (Van der Schalie et al., 2018) to use L-band data from one of the two instruments specifically designed to measure SM, ESA Soil Moisture and Ocean Salinity (SMOS) and NASA Soil Moisture Active Passive (SMAP) satellites, as reference to re-scale other time series.&lt;br&gt;To investigate this approach, AMSR-2 SM time series obtained from C1-, C2- and X-band observations using LPRM (Land Parameter Retrieval Model) were re-scaled by CDF-matching (Brocca et al., 2011) using different SMAP and SMOS official (SMAP L2 V005, SMOS L3 V300, SMOS NRT V100&amp;V200) and research (SMOS IC V103) SM products as well as the SMAP and SMOS LPRM v6 SM data used by the ESA CCI. The time series re-scaled using L-band remote sensing data were compared to those re-scaled using GLDAS and were evaluated against in situ measurements at several hundred sites retrieved from the International Soil Moisture Network (Dorigo et al., 2011). The results were analyzed as a function of the land cover class and the Koppen-Geiger climate classification.&lt;br&gt;Overall, AMSR-2 time series re-scaled using SMAP L2, SMAP LPRM and SMOS IC data sets as reference gave the best correlations with respect to in situ measurements, similar to those obtained by the time series re-scaled using GLDAS and slightly better than those of the original AMSR-2 time series. These results imply that different SMAP and SMOS products could actually be used to replace GLDAS as reference for the re-scaling of other sensors time series within the ESA CCI. However, one must bear in mind that this study is limited to the re-scaling of AMSR-2 data at a few hundred sites.&lt;br&gt;For a more detailed assessment of the L-band data set to be used for a global re-scaling, it is necessary to investigate other effects such as the spatial coverage or the time series length. SMAP spatial coverage is better than that of SMOS in regions affected by radio frequency interference. In contrast, the length of SMAP time series can be too short to capture the long term SM variability for climate applications in some regions. The CDF of SMOS time series computed from the date of SMAP launch is significantly different to those of the full length SMOS time series in some regions of the Globe. Possible ways of using a coherent SMAP/SMOS L-band data set will be discussed.&lt;/p&gt;

scholarly journals Retrieval of ozone profiles from GOMOS limb scattered measurements

2010 ◽  
Vol 3 (5) ◽  
pp. 4355-4382 ◽  
Author(s):  
S. Tukiainen ◽  
E. Kyrölä ◽  
P. T. Verronen ◽  
D. Fussen ◽  
L. Blanot ◽  
...  
Keyword(s):  
Imaging System ◽  
Signal To Noise Ratio ◽  
Stratospheric Ozone ◽  
Stray Light ◽  
Time Data ◽  
Night Time ◽  
Retrieval Method ◽  
Stellar Occultation ◽  
Infra Red ◽  
Better Than

Abstract. The GOMOS (Global Ozone Monitoring by Occultation of Stars) instrument on board the Envisat satellite measures the vertical composition of the atmosphere using the stellar occultation technique. While the night-time data of GOMOS are proved to be of good quality, the daytime observations are more challenging due to poorer signal-to-noise ratio. In this paper we present an alternative technique, which uses GOMOS limb scattered radiances instead of the stellar signal, to retrieve stratospheric ozone profiles. Like for many other limb-viewing instruments, GOMOS observations contain stray light at high altitudes. We introduce a method for removing the stray light and demonstrate its feasibility by comparing the corrected radiances against those from the OSIRIS (Optical Spectrograph &amp; Infra Red Imaging System) instrument. For the retrieval of ozone profiles, an onion peeling method is used. The first validation results suggest that the retrieval of stratospheric ozone is possible with a typical accuracy better than 10% at 22–50 km. GOMOS has measured about 350 000 daytime profiles since 2002. The new retrieval method presented here makes this large amount of data finally available for scientific use.

scholarly journals Three-Way Error Analysis between AATSR, AMSR-E, and In Situ Sea Surface Temperature Observations

2008 ◽  
Vol 25 (7) ◽  
pp. 1197-1207 ◽  
Author(s):  
Anne G. O’Carroll ◽  
John R. Eyre ◽  
Roger W. Saunders
Keyword(s):  
Standard Deviation ◽  
Error Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Data Sources ◽  
Independent Data ◽  
Earth Observing System ◽  
Along Track

Abstract Using collocations of three different observation types of sea surface temperatures (SSTs) gives enough information to enable the standard deviation of error on each observation type to be derived. SSTs derived from the Advanced Along-Track Scanning Radiometer (AATSR) and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) instruments are used, along with SST observations from buoys. Various assumptions are made within the error theory, including that the errors are not correlated, which should be the case for three independent data sources. An attempt is made to show that this assumption is valid and that the covariances between the different observations because of representativity error are negligible. Overall, the spatially averaged nighttime AATSR dual-view three-channel bulk SST observations for 2003 are shown to have a very small standard deviation of error of 0.16 K, whereas the buoy SSTs have an error of 0.23 K and the AMSR-E SST observations have an error of 0.42 K.

scholarly journals In-situ measurement of reactive hydrocarbons at Hohenpeissenberg with comprehensive two-dimensional gas chromatography (GC×GC-FID): use in estimating HO and NO<sub>3</sub>

2007 ◽  
Vol 7 (1) ◽  
pp. 1-14 ◽  
Author(s):  
S. Bartenbach ◽  
J. Williams ◽  
C. Plass-Dülmer ◽  
H. Berresheim ◽  
J. Lelieveld
Keyword(s):  
Gas Chromatography ◽  
Mean Value ◽  
Gas Chromatography Mass Spectrometry ◽  
Two Dimensional ◽  
Time Data ◽  
Night Time ◽  
Diurnal Cycles ◽  
Meteorological Observatory ◽  
The Relationship

Abstract. During a field campaign at the Meteorological Observatory Hohenpeissenberg (MOHp) in July 2004, volatile organic compounds (VOCs) were measured using comprehensive two-dimensional gas chromatography (GC×GC). Comparison to routinely made gas chromatography mass spectrometry (GC-MS) measurements showed good agreement for a variety of anthropogenic and biogenic ambient VOCs ranging in concentration from below the detection limit (0.1 pmol mol−1) to 180 pmol mol−1. Pronounced diurnal cycles were found for both the biogenic and anthropogenic compounds, driven for the most part by the daily rise and fall of the boundary layer over the station. For the reactive compounds (lifetimes <2 days), a significant, non-zero dependency of the variability on lifetime was found, indicating that chemistry (as opposed to transport alone) was playing a role in determining the ambient VOC concentrations. The relationship was exploited using a single-variate analysis to derive a daytime mean value of HO (5.3±1.4×106molecules cm−3), which compares well to that measured at the site, 3.2±2.3×106molecules cm−3. The analysis was extended to the night time data to estimate concentrations for NO3 (1.47±0.2×108molecules cm−3), which is not measured at the site. The feasibility of this approach for environments dominated by emissions of short-lived VOCs to estimate ambient levels of radical species is discussed.

scholarly journals VARIABILITY AND VALIDATION OF SEA SURFACE TEMPERATURE ESTIMATED BY PATHFINDER ALGORITHM OF NOAA-AVHRR SATELLITE IN THE NORTH PAPUA WATERS

2013 ◽  
Vol 8 (1) ◽  
Author(s):  
Bisman Nababan ◽  
Bidawi Hasyim ◽  
Hilda I.N. Bada
Keyword(s):  
Satellite Data ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Night Time ◽  
In Situ Data ◽  
The North ◽  
Satellite Sst ◽  
Noaa Avhrr ◽  
Valid Estimate

Variability and validation of sea surface temperatures (SST) in north Papua waters were conducted using SST estimated by Pathfinder algorithm of NOAA AVHRR satellite and SST measurements from TAO buoy in 2001-2009. Satellite data (SST Pathfinder) were daily, weekly, and monthly composite with 4x4 km2 resolution and downloaded from http://poet.jpl.nasa.gov. In situ data (SST measurement from buoy TAO) were measured at a depth of 1.5 m and recorded every hour (http://www.pmel.noaa.gov/tao_deliv). The in situ data then converted into daily, weekly, and monthly average data. In general, the SST values of both satellite and in situ SST in the north Papua waters ranged between 27.10 - 31.90 °C. During the east season (June-September), SST values (27.90-31.90 °C) were generally higher than the SST values ( 27.10-30.13 °C) during the west season (December-February). In general, the SST values both day-time and night-time from in situ and the satellite measurements showed no significant differences except in waters close to the shore. The results also showed that the coefficient of determination values (R2) between the satellite and the in situ SST measurements were relatively low (65%) and up to 5% of RMSE. The relatively low correlation between in situ dan satellite SST measurements may be due to high cloud coverage (90-96%) in the north Papua waters so that SST satellite data become less representative of the in situ data. These results also indicated that the Pathfinder algorithm can not be used as a valid estimate of SST NOAA AVHRR satellite for the north Papua waters. Keywords: SST Pathfinder, NOAA AVHRR, Validation, TAO buoy, North Papua Waters

Validation and Error Analysis of OSCAR Sea Surface Currents

2007 ◽  
Vol 24 (4) ◽  
pp. 688-701 ◽  
Author(s):  
Eric S. Johnson ◽  
Fabrice Bonjean ◽  
Gary S. E. Lagerloef ◽  
John T. Gunn ◽  
Gary T. Mitchum
Keyword(s):  
Error Analysis ◽  
Equatorial Region ◽  
Sea Surface ◽  
Time Variability ◽  
Surface Currents ◽  
In Situ Data ◽  
Current Correlation ◽  
Zonal Velocity ◽  
Moored Current Meters

Abstract Comparisons of OSCAR satellite-derived sea surface currents with in situ data from moored current meters, drifters, and shipboard current profilers indicate that OSCAR presently provides accurate time means of zonal and meridional currents, and in the near-equatorial region reasonably accurate time variability (correlation = 0.5–0.8) of zonal currents at periods as short as 40 days and meridional wavelengths as short as 8°. At latitudes higher than 10° the zonal current correlation remains respectable, but OSCAR amplitudes diminish unrealistically. Variability of meridional currents is poorly reproduced, with severely diminished amplitudes and reduced correlations relative to those for zonal velocity on the equator. OSCAR’s RMS differences from drifter velocities are very similar to those experienced by the ECCO (Estimating the Circulation and Climate of the Ocean) data-assimilating models, but OSCAR generally provides a larger ocean-correlated signal, which enhances its ratio of estimated signal over noise. Several opportunities exist for modest improvements in OSCAR fidelity even with presently available datasets.

scholarly journals Validation of Drifting Buoy Data for Ocean Wave Observation

2021 ◽  
Vol 9 (7) ◽  
pp. 729
Author(s):  
Yukiharu Hisaki
Keyword(s):  
Reference Data ◽  
Wave Data ◽  
Wave Parameters ◽  
Drifting Buoy ◽  
Wave Observation ◽  
Gps Buoy ◽  
Wave Heights ◽  
Buoy Data ◽  
Drifting Buoys ◽  
Better Than

Drifting buoys collect wave data in the open ocean far from land and in areas with strong currents. However, the validation of the drifting buoy wave data is limited. Here, we compared the drifting buoy wave data, ERA5 wave data, and moored GPS buoy wave data. Data from 2009 to 2018 near the coast of Japan were used. The agreement of the drifting buoy-observed wave parameters with the moored GPS buoy-observed wave parameters is better than that of ERA5 wave parameters, which is statistically significant. In particular, the accuracy of the ERA5 wave heights tends to be lower where the ocean currents are fast. On the other hand, the agreement between the drifting buoy-observed wave heights and the moored GPS buoy-observed wave heights was good even in the areas with strong currents. It is confirmed that the drifting buoy wave data can be used as reference data for wave modeling study.

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