Land Surface Phenology: Climate Data Record and Real-Time Monitoring

Passive microwave measurements have been available on satellites back to the 1970s, first flown on research satellites developed by the National Aeronautics and Space Administration (NASA). Since then, several other sensors have been flown to retrieve hydrological products for both operational weather applications (e.g., the Special Sensor Microwave/Imager—SSM/I; the Advanced Microwave Sounding Unit—AMSU) and climate applications (e.g., the Advanced Microwave Scanning Radiometer—AMSR; the Tropical Rainfall Measurement Mission Microwave Imager—TMI; the Global Precipitation Mission Microwave Imager—GMI). Here, the focus is on measurements from the AMSU-A, AMSU-B, and Microwave Humidity Sounder (MHS). These sensors have been in operation since 1998, with the launch of NOAA-15, and are also on board NOAA-16, -17, -18, -19, and the MetOp-A and -B satellites. A data set called the “Hydrological Bundle” is a climate data record (CDR) that utilizes brightness temperatures from fundamental CDRs (FCDRs) to generate thematic CDRs (TCDRs). The TCDRs include total precipitable water (TPW), cloud liquid water (CLW), sea-ice concentration (SIC), land surface temperature (LST), land surface emissivity (LSE) for 23, 31, 50 GHz, rain rate (RR), snow cover (SC), ice water path (IWP), and snow water equivalent (SWE). The TCDRs are shown to be in general good agreement with similar products from other sources, such as the Global Precipitation Climatology Project (GPCP) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2). Due to the careful intercalibration of the FCDRs, little bias is found among the different TCDRs produced from individual NOAA and MetOp satellites, except for normal diurnal cycle differences.

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A 30+ Year AVHRR Land Surface Reflectance Climate Data Record and Its Application to Wheat Yield Monitoring

Remote Sensing ◽

10.3390/rs9030296 ◽

2017 ◽

Vol 9 (3) ◽

pp. 296 ◽

Cited By ~ 25

Author(s):

Belen Franch ◽

Eric Vermote ◽

Jean-Claude Roger ◽

Emilie Murphy ◽

Inbal Becker-Reshef ◽

...

Keyword(s):

Land Surface ◽

Wheat Yield ◽

Surface Reflectance ◽

Climate Data ◽

Data Record ◽

Climate Data Record ◽

Yield Monitoring ◽

Land Surface Reflectance

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Real-time monitoring and short-term forecasting of land surface phenology

Remote Sensing of Environment ◽

10.1016/j.rse.2006.04.014 ◽

2006 ◽

Vol 104 (1) ◽

pp. 43-49 ◽

Cited By ~ 134

Author(s):

Michael A. White ◽

Ramakrishna R. Nemani

Keyword(s):

Real Time ◽

Land Surface ◽

Real Time Monitoring ◽

Short Term ◽

Land Surface Phenology ◽

Short Term Forecasting

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Sustained Production of Multidecadal Climate Records: Lessons from the NOAA Climate Data Record Program

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-15-00015.1 ◽

2016 ◽

Vol 97 (9) ◽

pp. 1573-1581 ◽

Cited By ~ 8

Author(s):

John J. Bates ◽

Jeffrey L. Privette ◽

Edward J. Kearns ◽

Walter Glance ◽

Xuepeng Zhao

Keyword(s):

Data Management ◽

Land Surface ◽

Time Series Data ◽

Series Data ◽

Team Approach ◽

Climate Data ◽

Scientific Application ◽

Data Record ◽

Community Data ◽

Climate Data Record

Abstract The key objective of the NOAA Climate Data Record (CDR) program is the sustained production of high-quality, multidecadal time series data describing the global atmosphere, oceans, and land surface that can be used for informed decision-making. The challenges of a long-term program of sustaining CDRs, as contrasted with short-term efforts of traditional 3-yr research programs, are substantial. The sustained production of CDRs requires collaboration between experts in the climate community, data management, and software development and maintenance. It is also informed by scientific application and associated user feedback on the accessibility and usability of the produced CDRs. The CDR program has developed a metric for assessing the maturity of CDRs with respect to data management, software, and user application and applied it to over 30 CDRs. The main lesson learned over the past 7 years is that a rigorous team approach to data management, employing subject matter experts at every step, is critical to open and transparent production. This approach also makes it much easier to support the needs of users who want near-real-time production of CDRs for monitoring and users who want to use CDRs for tailored, derived information, such as a drought index.

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Land Surface Albedo Derived on a Ten Daily Basis from Meteosat Second Generation Observations: The NRT and Climate Data Record Collections from the EUMETSAT LSA SAF

Remote Sensing ◽

10.3390/rs10081262 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1262 ◽

Cited By ~ 7

Author(s):

Dominique Carrer ◽

Suman Moparthy ◽

Gabriel Lellouch ◽

Xavier Ceamanos ◽

Florian Pinault ◽

...

Keyword(s):

Land Surface ◽

Surface Albedo ◽

Second Generation ◽

Weather Prediction ◽

Retrieval Algorithm ◽

Climate Data ◽

European Organization ◽

Geostationary Satellites ◽

Data Record ◽

Climate Data Record

Land surface albedo determines the splitting of downwelling solar radiation into components which are either reflected back to the atmosphere or absorbed by the surface. Land surface albedo is an important variable for the climate community, and therefore was defined by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV). Within the scope of the Satellite Application Facility for Land Surface Analysis (LSA SAF) of EUMETSAT (European Organization for the Exploitation of Meteorological Satellites), a near-real time (NRT) daily albedo product was developed in the last decade from observations provided by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the geostationary satellites of the Meteosat Second Generation (MSG) series. In this study we present a new collection of albedo satellite products based on the same satellite data. The MSG Ten-day Albedo (MTAL) product incorporates MSG observations over 31 days with a frequency of NRT production of 10 days. The MTAL collection is more dedicated to climate analysis studies compared to the daily albedo that was initially designed for the weather prediction community. For this reason, a homogeneous reprocessing of MTAL was done in 2018 to generate a climate data record (CDR). The resulting product is called MTAL-R and has been made available to the community in addition to the NRT version of the MTAL product which has been available for several years. The retrieval algorithm behind the MTAL products comprises three distinct modules: One for atmospheric correction, one for daily inversion of a semi-empirical model of the bidirectional reflectance distribution function, and one for monthly composition, that also determines surface albedo values. In this study the MTAL-R CDR is compared to ground surface measurements and concomitant albedo products collected by sensors on-board polar-orbiting satellites (SPOT-VGT and MODIS). We show that MTAL-R meets the quality requirements if MODIS or SPOT-VGT are considered as reference. This work leads to 14 years of production of geostationary land surface albedo products with a guaranteed continuity in the LSA SAF for the future years with the forthcoming third generation of European geostationary satellites.

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The AMSU-based Hydrological Bundle Climate Data Record – Description and Comparison with Other Data Sets

10.20944/preprints201807.0600.v1 ◽

2018 ◽

Author(s):

Ralph Ferraro ◽

Brian Nelson ◽

Tom Smith ◽

Olivier Prat

Keyword(s):

Land Surface ◽

Snow Water Equivalent ◽

Precipitable Water ◽

Climate Data ◽

Sea Ice Concentration ◽

Data Set ◽

Data Record ◽

Microwave Imager ◽

Global Precipitation ◽

Climate Data Record

Passive microwave measurements have been available on satellites dating back to the 1970s on research satellites flown by the National Aeronautics and Space Administration (NASA). Since then, several other sensors have been flown to retrieve hydrological products for both operational weather applications (e.g., the Special Sensor Microwave/Imager–SSM/I; the Advanced Microwave Sounding Unit–AMSU) and climate applications (e.g., the Advanced Microwave Scanning Radiometer–AMSR; the Tropical Rainfall Measurement Mission Microwave Imager–TMI; the Global Precipitation Mission Microwave Imager–GMI). Here the focus is on measurements from the AMSU-A, AMSU-B and Microwave Humidity Sounder (MHS). These sensors have been in operation since 1998 with the launch of NOAA-15, and are also on board NOAA-16, -17, -18, -19 and the MetOp-A and -B satellites. A data set called the “Hydrological Bundle” is a Climate Data Record (CDR) that utilizes brightness temperatures from Fundamental CDRs to generate Thematic CDRs (TCDR). The TCDR’s include: Total Precipitable Water (TPW), Cloud Liquid Water (CLW), Sea-Ice concentration (SIC), Land surface temperature (LST), Land surface emissivity (LSE) for 23, 31, 50 GHz, rain rate (RR), snow cover (SC), ice water path (IWP), and snow water equivalent (SWE). The TCDR’s are shown to be in general good agreement with similar products from other sources such as the Global Precipitation Climatology Project (GPCP) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2). Because of the careful intercalibration of the FCDR’s, little bias is found among the different TCDR’s produced from individual NOAA and MetOp satellites, except for normal diurnal cycle differences.

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Climate data record of atmospheric motion vectors at EUMETSAT for usage in reanalysis

10.5194/ems2021-445 ◽

2021 ◽

Author(s):

Marie Doutriaux-Boucher ◽

Roger Huckle ◽

Alessio Lattanzio ◽

Olivier Sus ◽

Jaap Onderwaater ◽

...

Keyword(s):

Real Time ◽

Area Coverage ◽

Geostationary Orbit ◽

Climate Data ◽

Motion Vectors ◽

Geostationary Satellites ◽

Data Record ◽

Atmospheric Motion ◽

Atmospheric Motion Vectors ◽

Climate Data Record

This presentation provides an overview of the different upper-air wind data records available at EUMETSAT for usage in global and regional reanalysis. The assimilation of Atmospheric Motion Vectors (AMV) is recognised to be important to reduce the forecast errors in NWP model runs. In support of the Copernicus Climate Change Service (C3S), EUMETSAT produced several AMV Climate Data Records (CDR) from geostationary and low-earth orbit satellites for assimilation into ECMWF&#8217;s next global reanalysis ERA6.Since the launch of its first generation of geostationary satellites, EUMETSAT has developed its own unique algorithms to derive atmospheric motion vectors (AMVs). These algorithms are used to provide real time AMVs using images acquired from instruments on-board both polar and geostationary satellites. These AMVs are routinely assimilated into weather forecast models. EUMETSAT archived all image data from its instruments (MVIRI and SEVIRI) in geostationary orbit and the global record of Advanced Very High Resolution Radiometer (AVHRR) data back to the late 1970s providing a suitable data source for climate research allowing the production of consistent AMV CDRs over the entire period.Two long AMV data records are available now from the geostationary sensors on Meteosat-2 to Meteosat-10 covering 1981-2017 over Africa and Europe and from AVHRR Global Area Coverage (GAC) data from 16 AVHRR instruments starting with the TIROS-N satellite and covering polar AMVs over the Northern and Southern hemisphere from 1978-2019. In addition, full resolution AVHRR images (Local Area Coverage (LAC)) from the AVHRR aboard the polar orbiting Metop-A and -B satellites were used to generate a CDR containing polar AMVs from single satellite retrievals and global AMVs from the combined Metop-A/B dual satellite retrieval starting in 2007 and 2013, respectively.For all data records, the EUMETSAT AMV algorithm adapted for climate purposes was used and extensive validation of the data records were performed. It shows that the CDR are homogeneous and very stable over the period. They are suitable for usage in model reanalysis and climate analysis. The CDR are in agreement with ground based radiosonde and model data. For the polar AMVs, a remarkable agreement with MODIS AMVs has been found.To better serve closer to real time needs for reanalysis, EUMETSAT is experimenting with the continuous production of an Interim Climate Data Record (ICDR) with a timeliness close to real-time. With a still not completely operational low-cost approach, a timeliness of 83% within 18 hours at similar quality was achieved.In addition to the existing data records the presentation provides the plan for future improvements and new CDR releases for AMV data records in the coming years. In particular, the use of better information on multi-layer cloud objects in AMV retrievals is a central part for the improvements of the AMVs from geostationary orbit.&#160;&#160;

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Meteosat Land Surface Temperature Climate Data Record: Achievable Accuracy and Potential Uncertainties

Remote Sensing ◽

10.3390/rs71013139 ◽

2015 ◽

Vol 7 (10) ◽

pp. 13139-13156 ◽

Cited By ~ 24

Author(s):

Anke Duguay-Tetzlaff ◽

Virgílio Bento ◽

Frank Göttsche ◽

Reto Stöckli ◽

João Martins ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Climate Data ◽

Data Record ◽

Temperature Climate ◽

Climate Data Record ◽

Achievable Accuracy

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Investigations into the Development of a Satellite-Based Aerosol Climate Data Record using ATSR-2, AATSR and AVHRR data

10.5194/amt-2019-26 ◽

2019 ◽

Author(s):

Yahui Che ◽

Jie Guang ◽

Gerrit de Leeuw ◽

Yong Xue ◽

Ling Sun ◽

...

Keyword(s):

Land Surface ◽

Reference Data ◽

Mountainous Area ◽

Data Sets ◽

Climate Data ◽

Data Set ◽

North East ◽

Data Record ◽

Single Sensor ◽

Climate Data Record

Abstract. Satellites provide information on the temporal and spatial distributions of aerosols on regional and global scales. With the same method applied to a single sensor all over the world, a consistent data set is to be expected. However, the application of different retrieval algorithms to the same sensor, and the use of a series of different sensors may lead to substantial differences and no single sensor or algorithm is better than any others everywhere and at any time. For the production of long-term climate data records, the use of multiple sensors cannot be avoided. The Along Track Scanning Radiometer (ATSR-2) and the advanced ATSR (AATSR) Aerosol Optical Depth (AOD) data sets have been used to provide a global AOD data record over land and ocean of 17-years (1995–2012), which is planned to be extended with AOD retrieved from a similar sensor, i.e. the Sea and Land Surface Temperature Radiometer (SLSTR) which flies on Sentinel-3A launched in early 2016. However, this leaves a gap of about 4 years between the end of the AATSR and the start of the SLSTR data records. To fill this gap, and to investigate the possibility to extend the ATSR data record to earlier years, the use of an AOD data set from the Advanced Very High Resolution Radiometer (AVHRR) is investigated. AOD data sets used in this study were retrieved from the ATSR sensors using the ATSR Dual View algorithm ADV v2.31 developed by Finnish Meteorological Institute (FMI), and from the AVHRR sensors using the ADL algorithm developed by RADI/CAR. Together these data sets cover a multi-decadal period (1983–2014). The study area includes two contrasting areas, both as regards aerosol content and composition and surface properties, i.e. a region over North-East (NE) China encompassing a highly populated urban/industrialized area (Beijing–Tianjin–Hebei) and a sparsely populated mountainous area. Ground-based AOD observations available from ground-based sunphotometer AOD data in AERONET and CARSNET are used as reference, together with radiation-derived AOD data at Beijing to cover the time before sunphotometer observations became available in the early 2000s. In addition, MODIS-Terra C6.1 AOD data are used as reference data set over the wide area where no ground-based data are available. All satellite data over the study area were validated versus the reference data, showing the qualification of MODIS for comparison with ATSR and AVHRR. The comparison with MODIS shows that AVHRR performs better that ATSR in the north of the study area (40° N), whereas further south ATSR provides better results. The validation versus sunphotometer AOD shows that both AVHRR and ATSR underestimate the AOD, with ATSR failing to provide reliable results in the winter time. This is likely due to the highly reflecting surface in the dry season, when AVHRR-retrieved AOD traces both MODIS and reference AOD data well. However, AVHRR does not provide AOD larger than about 0.6 and hence is not reliable in the summer season when high AOD values have been observed over the last decade. In these cases, ATSR performs much better, for AOD up to about 1.3. AVHRR-retrieved AOD compares favourably with radiance-derived AOD, except for AOD higher than about 0.6. These comparisons lead to the conclusion that AVHRR and ATSR AOD data records each have their strengths and weaknesses which need to be accounted for when combining them in a single multi-decadal climate data record.

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