scholarly journals Impact of ERS-2 Scatterometer wind data on Global Analysis-Forecast system

MAUSAM ◽  
2022 ◽  
Vol 53 (2) ◽  
pp. 153-164
Author(s):  
M. DAS GUPTA ◽  
S. R. H. RIZVI ◽  
A. K. MITRA

The near surface scatterometer wind data from the European remote sensing satellite ERS-2 of European space agency(ESA) became available at NCMRWF on real time basis since February 1997. An attempt has been made to assimilate this data in the global data assimilation system(GDAS) operational at NCMRWF after proper quality control to study its impact on the analysis as well as on medium range weather forecast over the tropics. For this purpose the GDAS was run for 15 days (27 May to 10 June 1998). The impact has been examined through circulation characteristics and various objective scores. The study revealed that with proper quality control the scatterometer wind data can be assimilated in real time basis, resulting in an overall improvement in performance of the analysis-forecast system.

2009 ◽  
Vol 26 (2) ◽  
pp. 395-402 ◽  
Author(s):  
Stephanie Guinehut ◽  
Christine Coatanoan ◽  
Anne-Lise Dhomps ◽  
Pierre-Yves Le Traon ◽  
Gilles Larnicol

Abstract Satellite altimeter measurements are used to check the quality of the Argo profiling floats time series. The method compares collocated sea level anomalies from altimeter measurements and dynamic height anomalies calculated from Argo temperature and salinity profiles for each Argo float time series. Different kinds of anomalies (sensor drift, bias, spikes, etc.) have been identified on some real-time but also delayed-mode Argo floats. About 4% of the floats should probably not be used until they are carefully checked and reprocessed by the principal investigators (PIs). The method appears to be very complementary to the existing quality control checks performed in real time or delayed mode. It could also be used to quantify the impact of the adjustments made in delayed mode on the pressure, temperature, and salinity fields.


2020 ◽  
Author(s):  
Daniel Aberer ◽  
Irene Himmelbauer ◽  
Lukas Schremmer ◽  
Ivana Petrakovic ◽  
Wouter Dorigo ◽  
...  

<p>The International Soil Moisture Network (ISMN, https://ismn.geo.tuwien.ac.at/) is an international cooperation to establish and maintain a unique centralized global data hosting facility, making in situ soil moisture data easily and freely accessible. This database is an essential means for validating and improving global satellite soil moisture products, land surface -, climate- , and hydrological models. </p><p>In situ measurements are crucial to calibrate and validate satellite soil moisture products. For a meaningful comparison with remotely sensed data and reliable validation results, the quality of the reference data is essential. The various independent local and regional in situ networks often do not follow standardized measurement techniques or protocols, collecting their data in different units, at different depths and at various sampling rates. Besides, quality control is rarely applied and accessing the data is often not easy or feasible.</p><p>The ISMN has been created to address the above-mentioned issues and is building a stable base to assist EO products, services and models. Within the ISMN, in situ soil moisture measurements (surface and sub-surface) are collected, harmonized in terms of units and sampling rates, advanced quality control is applied and the data is then stored in a database and made available online, where users can download it for free.</p><p>Founded in 2009, the ISMN has grown to a widely used in situ data source including 61 networks with more than 2600 stations distributed on a global scale and a steadily growing user community > 3200 registered users strong. Time series with hourly timestamps from 1952 – up to near real time are stored in the database and are available through the ISMN web portal, including daily near-real time updates from 6 networks (> 900 stations). With continuous financial support through the European Space Agency (formerly SMOS and IDEAS+ programs, currently QA4EO program), the ISMN evolved into a platform of benchmark data for several operational services such as ESA CCI Soil Moisture, the Copernicus Climate Change (C3S), the Copernicus Global Land Service (CGLS) and the online validation service Quality Assurance for Soil Moisture (QA4SM). In general, ISMN data is widely used in a variety of scientific fields (e.g. climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc.).</p><p>About 10’000 datasets are available through the web portal. However, the spatial coverage of in situ observations still needs to be improved. For example, in Africa and South America only sparse data are available. Innovative ideas, such as the inclusion of soil moisture data from low cost sensors (eventually) collected by citizen scientists, holds the potential of closing this gap, thus providing new information and knowledge.</p><p>In this session, we give an overview of the ISMN, its unique features and its benefits for validating satellite soil moisture products.</p>


2007 ◽  
Vol 135 (6) ◽  
pp. 2355-2364 ◽  
Author(s):  
Stéphane Laroche ◽  
Pierre Gauthier ◽  
Monique Tanguay ◽  
Simon Pellerin ◽  
Josée Morneau

Abstract A four-dimensional variational data assimilation (4DVAR) scheme has recently been implemented in the medium-range weather forecast system of the Meteorological Service of Canada (MSC). The new scheme is now composed of several additional and improved features as compared with the three-dimensional variational data assimilation (3DVAR): the first guess at the appropriate time from the full-resolution model trajectory is used to calculate the misfit to the observations; the tangent linear of the forecast model and its adjoint are employed to propagate the analysis increment and the gradient of the cost function over the 6-h assimilation window; a comprehensive set of simplified physical parameterizations is used during the final minimization process; and the number of frequently reported data, in particular satellite data, has substantially increased. The impact of these 4DVAR components on the forecast skill is reported in this article. This is achieved by comparing data assimilation configurations that range in complexity from the former 3DVAR with the implemented 4DVAR over a 1-month period. It is shown that the implementation of the tangent-linear model and its adjoint as well as the increased number of observations are the two features of the new 4DVAR that contribute the most to the forecast improvement. All the other components provide marginal though positive impact. 4DVAR does not improve the medium-range forecast of tropical storms in general and tends to amplify the existing, too early extratropical transition often observed in the MSC global forecast system with 3DVAR. It is shown that this recurrent problem is, however, more sensitive to the forecast model than the data assimilation scheme employed in this system. Finally, the impact of using a shorter cutoff time for the reception of observations, as the one used in the operational context for the 0000 and 1200 UTC forecasts, is more detrimental with 4DVAR. This result indicates that 4DVAR is more sensitive to observations at the end of the assimilation window than 3DVAR.


2012 ◽  
Vol 9 (2) ◽  
pp. 687-744 ◽  
Author(s):  
D. A. Ford ◽  
K. P. Edwards ◽  
D. Lea ◽  
R. M. Barciela ◽  
M. J. Martin ◽  
...  

Abstract. As part of the GlobColour project, daily chlorophyll-a observations, derived using remotely sensed ocean colour data from the MERIS, MODIS and SeaWiFS sensors, are produced. The ability of these products to be assimilated into a pre-operational global coupled physical-biogeochemical model has been tested, on both a hindcast and near-real-time basis, and the impact on the system assessed. The assimilation was found to immediately and significantly improve the bias, root mean square error and correlation of modelled surface chlorophyll concentration compared to the GlobColour observations, an improvement which was sustained throughout the year and in every ocean basin. Errors against independent in situ chlorophyll observations were also reduced, both at and beneath the ocean surface. However the model fit to in situ observations was not consistently better than that of climatology, due to errors in the underlying model. The assimilation scheme used is multivariate, updating all biogeochemical model state variables at all depths. Consistent changes were found in the other model variables, with reduced errors against in situ observations of nitrate and pCO2, and evidence of improved representation of zooplankton concentration. Annual mean surface fields of nutrients, alkalinity and carbon variables remained of similar quality compared to climatology. The near-real-time GlobColour products were found to be sufficiently reliable for operational purposes, and of benefit to both operational-style systems and reanalyses.


2008 ◽  
Vol 23 (1) ◽  
pp. 62-79 ◽  
Author(s):  
Zhaoxia Pu ◽  
Xuanli Li ◽  
Christopher S. Velden ◽  
Sim D. Aberson ◽  
W. Timothy Liu

Abstract Dropwindsonde, Geostationary Operational Environmental Satellite-11 (GOES-11) rapid-scan atmospheric motion vectors, and NASA Quick Scatterometer (QuikSCAT) near-surface wind data collected during NASA’s Tropical Cloud Systems and Processes (TCSP) field experiment in July 2005 were assimilated into an advanced research version of the Weather Research and Forecasting (WRF) model using its three-dimensional variational data assimilation (3DVAR) system. The impacts of the mesoscale data assimilation on WRF numerical simulation of Tropical Storms Cindy and Gert (2005) near landfall are examined. Sensitivity of the forecasts to the assimilation of each single data type is investigated. Specifically, different 3DVAR strategies with different analysis update cycles and resolutions are compared in order to identify the better methodology for assimilating the data from research aircraft and satellite for tropical cyclone study. The results presented herein indicate the following. 1) Assimilation of dropwindsonde and satellite wind data into the WRF model improves the forecasts of the two tropical storms up to the landfall time. The QuikSCAT wind information is very important for improving the storm track forecast, whereas the dropwindsonde and GOES-11 wind data are also necessary for improved forecasts of intensity and precipitation. 2) Data assimilation also improves the quantitative precipitation forecasts (QPFs) near landfall of the tropical storms. 3) A 1-h rapid-update analysis cycle at high resolution (9 km) provides more accurate tropical cyclone forecasts than a regular 6-h analysis cycle at coarse (27 km) resolution. The high-resolution rapidly updated 3DVAR analysis cycle might be a practical way to assimilate the data collected from tropical cyclone field experiments.


2013 ◽  
Vol 30 (10) ◽  
pp. 2434-2451 ◽  
Author(s):  
Joseph A. Grim ◽  
Jason C. Knievel ◽  
Erik T. Crosman

Abstract This study describes a stepwise methodology used to provide daily high-spatial-resolution water surface temperatures from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data for use nearly in real time for the Great Salt Lake (GSL). Land surface temperature (LST) is obtained each day and then goes through the following series of steps: land masking, quality control based on other concurrent datasets, bias correction, quality control based on LSTs from recent overpasses, temporal compositing, spatial hole filling, and spatial smoothing. Although the techniques described herein were calibrated for use on the GSL, they can also be applied to any other inland body of water large enough to be resolved by MODIS, as long as several months of in situ water temperature observations are available for calibration. For each of the buoy verification datasets, these techniques resulted in mean absolute errors for the final MODIS product that were at least 62% more accurate than those from the operational Real-Time Global analysis. The MODIS product provides realistic cross-lake temperature gradients that are representative of those directly observed from individual MODIS overpasses and is also able to replicate the temporal oscillations seen in the buoy datasets over periods of a few days or more. The increased accuracy, representative temperature gradients, and ability to resolve temperature changes over periods down to a few days can be vital for providing proper surface boundary conditions for input into numerical weather models.


Ocean Science ◽  
2012 ◽  
Vol 8 (5) ◽  
pp. 751-771 ◽  
Author(s):  
D. A. Ford ◽  
K. P. Edwards ◽  
D. Lea ◽  
R. M. Barciela ◽  
M. J. Martin ◽  
...  

Abstract. As part of the GlobColour project, daily chlorophyll a observations, derived using remotely sensed ocean colour data from the MERIS, MODIS and SeaWiFS sensors, are produced. The ability of these products to be assimilated into a pre-operational global coupled physical-biogeochemical model has been tested, on both a hindcast and near-real-time basis, and the impact on the system assessed. The assimilation was found to immediately and considerably improve the bias, root mean square error and correlation of modelled surface chlorophyll concentration compared to the GlobColour observations, an improvement which was sustained throughout the year and in every ocean basin. Errors against independent in situ chlorophyll observations were also reduced, both at and beneath the ocean surface. However, the model fit to in situ observations was not consistently better than that of climatology, due to errors in the underlying model. The assimilation scheme used is multivariate, updating all biogeochemical model state variables at all depths. The other variables were not degraded by the assimilation, with annual mean surface fields of nutrients, alkalinity and carbon variables remaining of similar quality compared to climatology. There was evidence of improved representation of zooplankton concentration, and reduced errors were seen against in situ observations of nitrate and pCO2, but too few observations were available to conclude about global model skill. The near-real-time GlobColour products were found to be sufficiently reliable for operational purposes, and of benefit to both operational-style systems and reanalyses.


2007 ◽  
Vol 135 (3) ◽  
pp. 1090-1109 ◽  
Author(s):  
Jordan C. Alpert ◽  
V. Krishna Kumar

Abstract The spatial and temporal densities of Weather Surveillance Radar-1988 Doppler (WSR-88D) raw radar radial wind represent a rich source of high-resolution observations for initializing numerical weather prediction models. A characteristic of these observations is the presence of a significant degree of redundant information imposing a burden on an operational assimilation system. Potential improvement in data assimilation efficiency can be achieved by constructing averages, called super-obs. In the past, transmission of the radar radial wind from each radar site to a central site was confined to data feeds that filter the resolution and degrade the precision. At the central site, super-obs were constructed from this data feed and called level-3 super-obs. However, the precision and information content of the radial wind can be improved if data at each radar site are directly utilized at the highest resolution and precision found at the WSR-88D radar and then transmitted to a central site for processing in assimilation systems. In addition, with data compression from using super-obs, the volume of data is reduced, allowing quality control information to be included in the data transmission. The super-ob product from each WSR-88D radar site is called level-2.5 super-obs. Parallel, operational runs and case studies of the impact of the level-2.5 radar radial wind super-ob on the NCEP operational 12-km Eta Data Assimilation System (EDAS) and forecast system are compared with Next-Generation Weather Radar level-3 radial wind super-obs, which are spatially filtered and delivered at reduced precision. From the cases studied, it is shown that the level-3 super-obs make little or no impact on the Eta data analysis and subsequent forecasts. The assimilation of the level-2.5 super-ob product in the EDAS and forecast system shows improved precipitation threat scores as well as reduction in RMS and bias height errors, particularly in the upper troposphere. In the few cases studied, the predicted mesoscale precipitation patterns benefit from the level-2.5 super-obs, and more so when greater weight is given to these high-resolution/precision observations. Direct transmission of raw (designated as level 2) radar data to a central site and its use are now imminent, but this study shows that the level-2.5 super-ob product can be used as an operational benchmark to compare with new quality control and assimilation schemes.


2021 ◽  
Author(s):  
Maria-Elissavet Koukouli ◽  
Konstantinos Michailidis ◽  
Pascal Hedelt ◽  
Isabelle A. Taylor ◽  
Antje Inness ◽  
...  

Abstract. Volcanic eruptions eject large amounts of ash and trace gases such as sulphur dioxide (SO2) into the atmosphere. A significant difficulty in mitigating the impact of volcanic SO2 clouds on air traffic safety is that these gas emissions can be rapidly transported over long distances. The use of space-borne instruments enables the global monitoring of volcanic SO2 emissions in an economical and risk-free manner. Within the European Space Agency (ESA) Sentinel-5p+ Innovation project, the S5P SO2 Layer Height (S5P+I: SO2 LH) activities led to the improvements on the retrieval algorithm and generation of the corresponding near-real-time S5P SO2 LH products. These are currently operationally provided, in near-real-time, by the German Aerospace Center (DLR) in the framework of the Innovative Products for Analyses of Atmospheric Composition, INPULS, project. The main aim of this paper is to present its extensive verification, accomplished within the S5P+I: SO2 LH project, over major recent volcanic eruptions, against collocated space-born measurements from the IASI/Metop and CALIOP/CALIPSO instruments, as well as assess its impact on the forecasts provided by the Copernicus Atmospheric Monitoring Service, CAMS. The mean difference between S5P and IASI observations for the Raikoke 2019, the Nishinoshima 2020 and the La Soufrière-St Vincent, 2021 eruptive periods is ~0.5 ± 3 km, while for the Taal 2020 eruption, a larger difference was found, between 3 and 4 ± 3 km. The comparison of the daily mean SO2 layer heights further demonstrates the capabilities of this near-real-time product, with slopes between 0.8 and 1 and correlations ranging between 0.6 and 0.8. Comparisons between the S5P+I: SO2 LH and the CALIOP/CALIPSO ash plume height are also satisfactory at −2.5 ± 2 km, considering that the injected SO2 and ash plumes’ locations do not always coincide over an eruption. Furthermore, the CAMS assimilation of the S5P+I: SO2 LH product led to much improved model output against the non-assimilated IASI layer heights, with a mean difference of 1.5 ± 2 km compared to the original CAMS analysis, and improved the geographical spread of the Raikoke volcanic plume following the eruptive days.


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