scholarly journals Techniques for Using MODIS Data to Remotely Sense Lake Water Surface Temperatures

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.

2021 ◽  
Author(s):  
Elke Rustemeier ◽  
Udo Schneider ◽  
Markus Ziese ◽  
Peter Finger ◽  
Andreas Becker

<p><span>Since its founding in 1989, the Global Precipitation Climatology Centre (GPCC) has been producing global precipitation analyses based on land surface in-situ measurements. </span><span>In the now over 30 years the underlying database has been continuously expanded and includes a high station density and large temporal coverage. Due to the semi-automatic quality control routinely performed on the incoming station data, the GPCC database has a very high quality.</span> <span>Today, the GPCC holds data from </span><span>123,000 stations, about three quarters of them having long time series.</span></p><p><span>The core of the analyses is formed by data from the global meteorological and hydrological services, which provided their records to the GPCC, as well as global and regional data collections.  </span><span>In addition, the GPCC receives SYNOP and CLIMAT reports via the WMO-GTS. These form a supplement for the high quality precipitation analyses and the basis for the near real-time evaluations.</span></p><p><span>Quality control activities include cross-referencing stations from different sources, flagging of data errors, and correcting temporally or spatially offset data. This data then forms the basis for the following interpolation and product generation.</span></p><p><span>In near real time, the 'First Guess Monthly', 'First Guess Daily', 'Monitoring Product', ‘Provisional Daily Precipitation Analysis’ and the 'GPCC Drought Index' are generated. These are based on WMO-GTS data and monthly data generated by the CPC (NOAA). </span></p><p><span>With a 2-3 year update cycle, the high quality data products are generated with intensive quality control and built on the entire GPCC data base. These non-real time products consist of the 'Full Data Monthly', 'Full Data Daily', 'Climatology', and 'HOMPRA-Europe' and are now available in the 2020 version. </span></p><p><span>A</span><span>ll gridded datasets presented in this paper are freely available in netcdf format on the GPCC website https://gpcc.dwd.de and referenced by a digital object identifier (DOI). The site also provides an overview of all datasets, as well as a detailed description and further references for each dataset.</span></p>


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>


2019 ◽  
Vol 41 (7) ◽  
pp. 2739-2758
Author(s):  
Hua Wang ◽  
Biao Yang ◽  
Jianping Jiang ◽  
Jianhong Zhou

2016 ◽  
Vol 33 (5) ◽  
pp. 953-976 ◽  
Author(s):  
Anne Ru Cheng ◽  
Tim Hau Lee ◽  
Hsin I. Ku ◽  
Yi Wen Chen

AbstractThis paper introduces a quality control (QC) program for the real-time hourly land surface temperature observation developed by the Central Weather Bureau in Taiwan. There are three strategies involved. The first strategy is a range check scheme that inspects whether the observation falls inside the climatological limits of the station to screen out the obvious outliers. Limits are adjusted according to the station’s elevation. The second strategy is a spatial check scheme that scrutinizes whether the observation falls inside the derived confidence interval, according to the data from the reference stations and the correlations among the stations, to judge the reliability of the data. The scheme is specialized, as it employs the theorems of unbiased and minimum error estimators to determine the weights. The performance evaluation results show that the new method is in theory superior to the spatial regression test (You et al.). The third strategy is a temporal check scheme that examines whether the temperature difference of two successive observations exceeds the temperature variation threshold for judging the rationality of the data. Different thresholds are applied for the data observed in different times under different rainfall conditions. Procedurally, the observation must pass the range check first and then go through the spatial or the temporal check. The temporal check is applied only when the spatial check is unavailable. Post-examinations of the data from 2014 show that the QC program is able to filter out most of the significant errors.


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.


2019 ◽  
Vol 2 (5) ◽  
Author(s):  
Tong Wang

The compaction quality of the subgrade is directly related to the service life of the road. Effective control of the subgrade construction process is the key to ensuring the compaction quality of the subgrade. Therefore, real-time, comprehensive, rapid and accurate prediction of construction compaction quality through informatization detection method is an important guarantee for speeding up construction progress and ensuring subgrade compaction quality. Based on the function of the system, this paper puts forward the principle of system development and the development mode used in system development, and displays the development system in real-time to achieve the whole process control of subgrade construction quality.


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