scholarly journals Best Practices in Crafting the Calibrated, Enhanced-Resolution Passive-Microwave EASE-Grid 2.0 Brightness Temperature Earth System Data Record

2018 ◽  
Vol 10 (11) ◽  
pp. 1793 ◽  
Author(s):  
Mary Brodzik ◽  
David Long ◽  
Molly Hardman
Keyword(s):  
Best Practices ◽  
Brightness Temperature ◽  
Passive Microwave ◽  
Earth System ◽  
Climate Data ◽  
Enhanced Resolution ◽  
Data Record ◽  
System Data ◽  
Land Surfaces ◽  
Climate Data Record

Since the late 1970s, satellite passive-microwave brightness temperatures have been a mainstay in remote sensing of the cryosphere. Polar snow and ice-covered ocean and land surfaces are especially sensitive to climate change and are observed to fluctuate on interannual to decadal timescales. In regions of limited sunlight and cloudy conditions, microwave measurements are particularly valuable for monitoring snow- and ice-covered ocean and land surfaces, due to microwave sensitivity to phase changes of water. Historically available at relatively low resolutions (25 km) compared to optical techniques (less than 1 km), passive-microwave sensors have provided short-timescale, large-area spatial coverage, and high temporal repeat observations for monitoring hemispheric-wide changes. However, historically available gridded passive microwave products have fallen short of modern requirements for climate data records, notably by using inconsistently-calibrated input data, including only limited periods of sensor overlaps, employing image-reconstruction methods that tuned for reduced noise rather than enhanced resolution, and using projection and grid definitions that were not easily interpreted by geolocation software. Using a recently completed Fundamental Climate Data Record of the swath format passive-microwave record that incorporated new, cross-sensor calibrations, we have produced an improved, gridded data record. Defined on the EASE-Grid 2.0 map projections and derived with numerically efficient image-reconstruction techniques, the Calibrated, Enhanced-Resolution Brightness Temperature (CETB) Earth System Data Record (ESDR) increases spatial resolution up to 3.125 km for the highest frequency channels, and satisfies modern Climate Data Record (CDR) requirements as defined by the National Research Council. We describe the best practices and development approaches that we used to ensure algorithmic integrity and to define and satisfy metadata, content and structural requirements for this high-quality, reliable, consistently gridded microwave radiometer climate data record.

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

scholarly journals An Uncertainty Quantified Fundamental Climate Data Record for Microwave Humidity Sounders

2019 ◽  
Vol 11 (5) ◽  
pp. 548 ◽  
Author(s):  
Imke Hans ◽  
Martin Burgdorf ◽  
Stefan Buehler ◽  
Marc Prange ◽  
Theresa Lang ◽  
...  
Keyword(s):  
Brightness Temperature ◽  
Uncertainty Propagation ◽  
Uncertainty Budget ◽  
Intermediate Step ◽  
Climate Data ◽  
Data Record ◽  
Upper Tropospheric Humidity ◽  
Uncertainty Information ◽  
Climate Data Record

To date, there is no long-term, stable, and uncertainty-quantified dataset of upper tropospheric humidity (UTH) that can be used for climate research. As intermediate step towards the overall goal of constructing such a climate data record (CDR) of UTH, we produced a new fundamental climate data record (FCDR) on the level of brightness temperature for microwave humidity sounders that will serve as basis for the CDR of UTH. Based on metrological principles, we constructed and implemented the measurement equation and the uncertainty propagation in the processing chain for the microwave humidity sounders. We reprocessed the level 1b data to obtain newly calibrated uncertainty quantified level 1c data in brightness temperature. Three aspects set apart this FCDR from previous attempts: (1) the data come in a ready-to-use NetCDF format; (2) the dataset provides extensive uncertainty information taking into account the different correlation behaviour of the underlying errors; and (3) inter-satellite biases have been understood and reduced by an improved calibration. Providing a detailed uncertainty budget on these data, this new FCDR provides valuable information for a climate scientist and also for the construction of the CDR.

scholarly journals Detailed characterisation of AVHRR global cloud detection performance of the CM SAF CLARA-A2 climate data record based on CALIPSO-CALIOP cloud information

2017 ◽  
Author(s):  
Karl-Göran Karlsson ◽  
Nina Håkansson
Keyword(s):  
Optical Thickness ◽  
Detection Performance ◽  
Polar Regions ◽  
Cloud Detection ◽  
Climate Data ◽  
Data Record ◽  
Cloud Optical Thickness ◽  
Land Surfaces ◽  
Cloud Mask ◽  
Climate Data Record

Abstract. The cloud detection performance of the cloud mask being used in the CM SAF cloud, albedo and surface radiation dataset from AVHRR data (CLARA-A2) cloud climate data record (CDR) has been evaluated in detail using cloud information from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the CALIPSO satellite. Validation results, including their global distribution, have been calculated from collocations of AVHRR and CALIOP measurements over a ten-year period (2006–2015). The sensitivity of the results to the cloud optical thicknesses of CALIOP-observed clouds were studied leading to the conclusion that the global cloud detection sensitivity (defined as the minimum cloud optical thickness for which 50 % of clouds could be detected) was estimated to 0.225. After applying this optical thickness threshold to the CALIOP cloud mask, results were found to be basically unbiased over most of the globe except over the polar regions where a considerably underestimation of cloudiness could be seen during the polar winter. The probability of detecting clouds in the polar winter could be as low as 50 % over the highest and coldest portions of Greenland and Antarctica, showing that also a large fraction of optically thick clouds remains undetected here. The study included an in-depth analysis of the probability of detecting a cloud as a function of the vertically integrated cloud optical thickness as well as of the cloud’s geographical position. Best results were achieved over oceanic surfaces at mid-to-high latitudes were at least 50 % of all clouds with an optical thickness down to a value of 0.075 were detected. Corresponding cloud detection sensitivities over land surfaces outside of the polar regions were generally larger than 0.2 with maximum values of approximately 0.5 over Sahara and the Arabian Peninsula. For polar land surfaces the values were close to 1 or higher with maximum values of 4.5 over the geographically highest parts of Greenland and Antarctica. The validation method is suggested to be applied also to other satellite-based CDRs and validation results are proposed to be used in Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulation Package (COSP) simulators for cloud detection characterisation of various cloud CDRs from passive imagery.

scholarly journals A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring

2013 ◽  
Vol 6 (1) ◽  
pp. 95-117
Author(s):  
G. Peng ◽  
W. N. Meier ◽  
D. J. Scott ◽  
M. H. Savoie
Keyword(s):  
Sea Ice ◽  
Passive Microwave ◽  
Climate Data ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Data Record ◽  
Climate Data Record ◽  
Climate Studies

Abstract. A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 × 25 km grid cells in both the Southern and Northern Hemisphere Polar Regions from 9 July 1987 to 31 December 2007 with an update through 2011 underway. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Oceanic and Atmospheric Administration (NOAA)'s National Climatic Data Center (NCDC) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The dataset along with detailed data processing steps and error source information can be found at: doi:10.7265/N5B56GN3.

Construction of a climate data record of sea surface temperature from passive microwave measurements

2020 ◽  
Vol 236 ◽  
pp. 111485 ◽  
Author(s):  
Emy Alerskans ◽  
Jacob L. Høyer ◽  
Chelle L. Gentemann ◽  
Leif Toudal Pedersen ◽  
Pia Nielsen-Englyst ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Passive Microwave ◽  
Sea Surface ◽  
Microwave Measurements ◽  
Climate Data ◽  
Data Record ◽  
Climate Data Record

Development of a microwave-based precipitation climate data record for the Copernicus Climate Change Service

2020 ◽  
Author(s):  
Giulia Panegrossi ◽  
Paolo Sanò ◽  
Leonardo Bagaglini ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
...  
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Satellite Observations ◽  
Passive Microwave ◽  
Monthly Precipitation ◽  
Climate Data ◽  
Data Record ◽  
Level 3 ◽  
Climate Data Record ◽  
Level 2

<p>Within the Copernicus Climate Change Service (C3S), the Climate Data Store (CDS) built by ECMWF will provide open and free access to global and regional products of Essential Climate Variables (ECV) based on satellite observations spanning several decades, amongst other things. Given its significance in the Earth system and particularly for human life, the ECV precipitation will be of major interest for users of the CDS.</p><p>C3S strives to include as many established, high-quality data sets as possible in the CDS. However, it also intends to offer new products dedicated for first-hand publication in the CDS. One of these products is a climate data record based on merging satellite observations of daily and monthly precipitation by both passive microwave (MW) sounders (AMSU-B/MHS) and imagers (SSMI/SSMIS) on a 1°x1° spatial grid in order to improve spatiotemporal satellite coverage of the globe.</p><p>The MW sounder observations will be obtained using, as input data, the FIDUCEO Fundamental Climate data Record (FCDR) for AMSU-B/MHS in a new global algorithm developed specifically for the project based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR; Sanò et al., 2015), adapted for climate applications (PNPR-CLIM). The algorithm consists of two Artificial Neural Network-based modules, one for precipitation detection, and one for precipitation rate estimate, trained on a global observational database built from Global Precipitation Measurement-Core Observatory (GPM-CO) measurements. The MW imager observations by SSM/I and SSMIS will be adopted from the Hamburg Ocean Atmosphere Fluxes and Parameters from Satellite data (HOAPS; Andersson et al., 2017), based on the CM SAF SSM/I and SSMIS FCDR (Fennig et al., 2017). The Level 2 precipitation rate estimates from MW sounders and imagers are combined through a newly developed merging module to obtain Level 3 daily and monthly precipitation and generate the 18-year precipitation CDR (2000-2017).</p><p>Here, we present the status of the Level 2 product’s development. We carry out a Level-2 comparison and present first results of the merged Level-3 precipitation fields. Based on this, we assess the product’s expected plausibility, coverage, and the added value of merging the MW sounder and imager observations.</p><p><strong>References</strong></p><p>Anderssonet al., 2017, DOI:10.5676/EUM_SAF_CM/HOAPS/V002</p><p>Fennig, et al., 2017, DOI:10.5676/EUM_SAF_CM/FCDR_MWI/V003</p><p>Sanò, P., et al., 2015, DOI: 10.5194/amt-8-837-2015</p>

scholarly journals A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring

2013 ◽  
Vol 5 (2) ◽  
pp. 311-318 ◽  
Author(s):  
G. Peng ◽  
W. N. Meier ◽  
D. J. Scott ◽  
M. H. Savoie
Keyword(s):  
Sea Ice ◽  
Passive Microwave ◽  
Climate Data ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Data Record ◽  
Climate Data Record ◽  
Climate Studies

Abstract. A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 km × 25 km grid cells in both the Southern and Northern Hemisphere polar regions from 9 July 1987 to 31 December 2007. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The data set, along with detailed data processing steps and error source information, can be found at http://dx.doi.org/10.7265/N55M63M1.

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