scholarly journals A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010

2018 ◽  
Vol 22 (1) ◽  
pp. 241-263 ◽  
Author(s):  
Yu Zhang ◽  
Ming Pan ◽  
Justin Sheffield ◽  
Amanda L. Siemann ◽  
Colby K. Fisher ◽  
...  
Keyword(s):  
Water Budget ◽  
Water Cycle ◽  
Data Sources ◽  
Climate Data ◽  
Data Set ◽  
Data Record ◽  
Terrestrial Water ◽  
In Situ Observations ◽  
Climate Data Record

Abstract. Closing the terrestrial water budget is necessary to provide consistent estimates of budget components for understanding water resources and changes over time. Given the lack of in situ observations of budget components at anything but local scale, merging information from multiple data sources (e.g., in situ observation, satellite remote sensing, land surface model, and reanalysis) through data assimilation techniques that optimize the estimation of fluxes is a promising approach. Conditioned on the current limited data availability, a systematic method is developed to optimally combine multiple available data sources for precipitation (P), evapotranspiration (ET), runoff (R), and the total water storage change (TWSC) at 0.5∘ spatial resolution globally and to obtain water budget closure (i.e., to enforce P-ET-R-TWSC= 0) through a constrained Kalman filter (CKF) data assimilation technique under the assumption that the deviation from the ensemble mean of all data sources for the same budget variable is used as a proxy of the uncertainty in individual water budget variables. The resulting long-term (1984–2010), monthly 0.5∘ resolution global terrestrial water cycle Climate Data Record (CDR) data set is developed under the auspices of the National Aeronautics and Space Administration (NASA) Earth System Data Records (ESDRs) program. This data set serves to bridge the gap between sparsely gauged regions and the regions with sufficient in situ observations in investigating the temporal and spatial variability in the terrestrial hydrology at multiple scales. The CDR created in this study is validated against in situ measurements like river discharge from the Global Runoff Data Centre (GRDC) and the United States Geological Survey (USGS), and ET from FLUXNET. The data set is shown to be reliable and can serve the scientific community in understanding historical climate variability in water cycle fluxes and stores, benchmarking the current climate, and validating models.

scholarly journals A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010

2017 ◽  
Author(s):  
Yu Zhang ◽  
Ming Pan ◽  
Justin Sheffield ◽  
Amanda Siemann ◽  
Colby Fisher ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Water Budget ◽  
Water Cycle ◽  
Data Sources ◽  
Climate Data ◽  
Data Record ◽  
Terrestrial Water ◽  
In Situ Observations ◽  
Climate Data Record

Abstract. Closing the terrestrial water budget is necessary to providing consistent estimates of budget components for understanding water resources and changes over time. Given the lack of in-situ observations of budget components at anything but local scale, merging information from multiple data sources (e.g. in-situ observation, satellite remote sensing, land surface model and reanalysis) through data assimilation techniques that optimize the estimation of fluxes is a promising approach. In this study, a systematic method is developed to optimally combine multiple available data sources for precipitation (P), evapotranspiration (ET), runoff (R) and the total water storage change (TWSC) at 0.5° spatial resolution globally and to obtain water budget closure (i.e. to enforce P − ET − R − TWSC = 0) through a Constrained Kalman Filter (CKF) data assimilation technique. The resulting long-term (1984–2010), monthly, 0.5° resolution global terrestrial water cycle Climate Data Record (CDR) dataset is developed under the auspices of the National Aeronautics and Space Administration (NASA) Earth System Data Records (ESDRs) program. This dataset serves to bridge the gap between sparsely gauged regions and the regions with sufficient in-situ observations in investigating the temporal and spatial variability in the terrestrial hydrology at multiple scales. The CDR created in this study is validated against in-situ measurements like river discharge from the Global Runoff Data Centre (GRDC) and the United States Geological Survey (USGS) and ET from FLUXNET. The dataset is shown to be reliable and can serve the scientific community in understanding historical climate variability in water cycle fluxes and stores, benchmarking the current climate, and validating models.

scholarly journals SARAH-3 - a new satellite-based Cimate Data Record for surface radiation parameters from the CM SAF

2021 ◽  
Author(s):  
Uwe Pfeifroth ◽  
Jaqueline Drücke ◽  
Jörg Trentmann ◽  
Rainer Hollmann
Keyword(s):  
Water Cycle ◽  
Sunshine Duration ◽  
Global Radiation ◽  
Surface Radiation ◽  
Climate Data ◽  
Surface Solar Radiation ◽  
Data Set ◽  
Time Period ◽  
Data Record ◽  
Climate Data Record

<p>The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates and distributes high quality long-term climate data records (CDR) of energy and water cycle parameters, which are freely available.</p><p>In fall 2021, a new version of the “Surface Solar Radiation data set – Heliosat” will be released: SARAH-3. As the previous editions, the SARAH-3 climate data record is based on satellite observations from the first and second METEOSAT generations and provides various surface radiation parameters, including global radiation, direct radiation, sunshine duration, photosynthetic active radiation and others. SARAH-3 covers the time period 1983 to 2020 and offers 30-minute instantaneous data as well as daily and monthly means on a regular 0.05° x 0.05° lon/lat grid.</p><p>In this presentation, an overview of the SARAH climate data record and their applications will be provided. A focus will be on the SARAH-3 developments and improvements (i.e. improved consideration of snow-covered surfaces). First validation results of the new Climate Data Record using surface reference observations will be presented. Further, SARAH-3 will be used for the analysis of the climate variability in Europe during the last decades.</p><p>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .</p>

scholarly journals Sea Surface Temperature Climate Data Record for the North Sea and Baltic Sea

2016 ◽  
Vol 29 (7) ◽  
pp. 2529-2541 ◽  
Author(s):  
Jacob L. Høyer ◽  
Ioanna Karagali
Keyword(s):  
Baltic Sea ◽  
North Sea ◽  
Climate Data ◽  
The North ◽  
Data Record ◽  
The North Sea ◽  
In Situ Observations ◽  
The Baltic ◽  
Climate Data Record

Abstract A 30-yr climate data record (CDR) of sea surface temperature (SST) has been produced with daily gap-free analysis fields for the North Sea and the Baltic Sea region from 1982 to 2012 by combining the Pathfinder AVHRR satellite data record with the Along-Track Scanning Radiometer (ATSR) Reprocessing for Climate (ARC) dataset and with in situ observations. A dynamical bias correction scheme adjusts the Pathfinder observations toward the ARC and in situ observations. Largest Pathfinder–ARC differences are found in the summer months, when the Pathfinder observations are up to 0.4°C colder than the ARC observations on average. Validation against independent in situ observations shows a very stable performance of the data record, with a mean difference of −0.06°C compared to moored buoys and a 0.46°C standard deviation of the differences. The mean annual biases of the SST CDR are small for all years, with a negligible temporal trend when compared against drifting and moored buoys. Analysis of the SST CDR reveals that the monthly anomalies for the North Sea, the Danish straits, and the central Baltic Sea regions show a high degree of correlation for interannual and decadal time scales, whereas the monthly variability differs from one region to another. The linear trends of the 1982–2012 SST anomaly time series range from 0.037°C yr−1 for the North Sea to 0.041°C yr−1 for the Baltic Sea.

scholarly journals The AMSU-Based Hydrological Bundle Climate Data Record—Description and Comparison with Other Data Sets

2018 ◽  
Vol 10 (10) ◽  
pp. 1640 ◽  
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 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.

scholarly journals Adjusting for Desert-Dust-Related Biases in a Climate Data Record of Sea Surface Temperature

2020 ◽  
Vol 12 (16) ◽  
pp. 2554
Author(s):  
Christopher J. Merchant ◽  
Owen Embury
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Dust Aerosol ◽  
Sea Surface ◽  
Climate Data ◽  
Desert Dust ◽  
Data Record ◽  
Climate Data Record

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol.

scholarly journals Satellite-based time-series of sea-surface temperature since 1981 for climate applications

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Christopher J. Merchant ◽  
Owen Embury ◽  
Claire E. Bulgin ◽  
Thomas Block ◽  
Gary K. Corlett ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ocean Model ◽  
Sea Surface ◽  
Climate Data ◽  
Satellite Measurements ◽  
Data Record ◽  
The Mean ◽  
Climate Data Record

Abstract A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 1012 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km2 and 45 km2. The mean density of good-quality observations is 13 km−2 yr−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

scholarly journals Satellite-derived sunshine duration data in the CM SAF SARAH-3 climate data record 

2021 ◽  
Author(s):  
Jaqueline Drücke ◽  
Uwe Pfeifroth ◽  
Jörg Trentmann ◽  
Rainer Hollmann
Keyword(s):  
Sunshine Duration ◽  
Climate Data ◽  
Surface Solar Radiation ◽  
Data Set ◽  
Climate Assessment ◽  
Climate Monitoring ◽  
Direct Normal Irradiance ◽  
Data Record ◽  
Definition Of ◽  
Climate Data Record

<p>Sunshine Duration (SDU) is an important parameter in climate monitoring (e.g., due to the availability of long term measurements) and weather application. The exceptional sunny years in Europe since 2018 have raised also the attention of the general public towards this parameter.</p><p>The definition of SDU by WMO via the threshold of 120 W/m<sup>2</sup> for the Direct Normal Irradiance (DNI) allows the estimation of sunshine duration from satellite-derived surface irradiance data. Sunshine duration is part of the climate data record (CDR) “Surface Solar Radiation data set – Heliosat” (SARAH-2.1, doi: 10.5676/EUM_SAF_CM/SARAH/V002_01) by EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), which is based on observations from the series of Meteosat satellites. The provided temporal resolutions are daily and monthly sums with a grid space of 0.05°; the data are available from 1983 to 2017 at www.cmsaf.eu. This climate data record is temporally extended by the so-called SARAH-ICDR (Interim Climate Data record) with an average timeliness of 3 days to allow climate monitoring. An updated, improved, and extended version of the SARAH-2.1 CDR is currently being developed and will be made available in early 2022. The SARAH-3 CDR of sunshine duration, covering 1983 to 2020, will be improved compared to the current version, in particular during situations with snow-covered surfaces.</p><p>Here, the algorithm, improvements compared to SARAH-2.1 and a first validation will be presented for sunshine duration, especially for Germany and Europe. The validation is based on station data from Climate Data Center (CDC) for Germany and European Climate Assessment & Dataset (ECA&D) for Europe.</p>

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