scholarly journals Evaluation of the 15-year ROM SAF monthly mean GPS radio occultation climate data record

2019 ◽  
Author(s):  
Hans Gleisner ◽  
Kent B. Lauritsen ◽  
Johannes K. Nielsen ◽  
Stig Syndergaard
Keyword(s):  
Radio Occultation ◽  
Climate Data ◽  
Sampling Errors ◽  
Gps Radio Occultation ◽  
Data Record ◽  
Satellite Missions ◽  
Climate Data Record ◽  
Global Mean ◽  
Better Than ◽  
Height Interval

Abstract. We here present results from an evaluation of the ROM SAF gridded monthly-mean climate data record (CDR v1.0), based on GPS radio occultation (RO) data from the CHAMP, GRACE, COSMIC, and Metop satellite missions. Systematic differences between RO missions, as well as differences of RO data relative to ERA-Interim reanalysis data, are quantified. The methods used to generate gridded monthly mean data are described, and the correction of monthly-mean RO climatologies for sampling errors, which is essential for combining data from RO missions with different sampling characteristics, is evaluated. We find a good overall agreement between the ROM SAF gridded monthly-mean CDR and the ERA-Interim reanalysis, particularly in the 8–30 km height interval. Here, the differences largely reflect time-varying biases in ERA-Interim, suggesting that the RO data record has a better long-term stability than ERA-Interim. Above 30–40 km altitude, the differences are larger, particularly for the pre-COSMIC era. In the 8–30 km altitude region, the observational data record exhibits a high degree of internal consistency between the RO satellite missions, allowing us to combine data into multi-mission records. For global mean bending angle the consistency is better than 0.04 %, for refractivity 0.05 %, and for global mean dry temperature the consistency is better than 0.15 K in this height interval. At altitudes up to 40 km, these numbers increase to 0.08 %, 0.11 %, and 0.50 K, respectively. The numbers can be up to a factor of 2 larger for certain latitude bands compared to global means. Below about 6–8 km the RO mission differences are larger, reducing the possibilities to generate multi-mission data records. We also find that the residual sampling errors are about one third of the original and that they include a component most likely related to diurnal or semi-diurnal cycles.

scholarly journals Evaluation of the 15-year ROM SAF monthly mean GPS radio occultation climate data record

2020 ◽  
Vol 13 (6) ◽  
pp. 3081-3098 ◽  
Author(s):  
Hans Gleisner ◽  
Kent B. Lauritsen ◽  
Johannes K. Nielsen ◽  
Stig Syndergaard
Keyword(s):  
Radio Occultation ◽  
Climate Data ◽  
Sampling Errors ◽  
Gps Radio Occultation ◽  
Data Record ◽  
Satellite Missions ◽  
Climate Data Record ◽  
Global Mean ◽  
Better Than ◽  
Height Interval

Abstract. We here present results from an evaluation of the Radio Occultation Meteorology Satellite Application Facility (ROM SAF) gridded monthly mean climate data record (CDR v1.0), based on Global Positioning System (GPS) radio occultation (RO) data from the CHAMP (CHAllenging Minisatellite Payload), GRACE (Gravity Recovery and Climate Experiment), COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate), and Metop satellite missions. Systematic differences between RO missions, as well as differences of RO data relative to ERA-Interim reanalysis data, are quantified. The methods used to generate gridded monthly mean data are described, and the correction of monthly mean RO climatologies for sampling errors, which is essential for combining data from RO missions with different sampling characteristics, is evaluated. We find good overall agreement between the ROM SAF gridded monthly mean CDR and the ERA-Interim reanalysis, particularly in the 8–30 km height interval. Here, the differences largely reflect time-varying biases in ERA-Interim, suggesting that the RO data record has a better long-term stability than ERA-Interim. Above 30–40 km altitude, the differences are larger, particularly for the pre-COSMIC era. In the 8–30 km altitude region, the observational data record exhibits a high degree of internal consistency between the RO satellite missions, allowing us to combine data into multi-mission records. For global mean bending angle, the consistency is better than 0.04 %, for refractivity it is better than 0.05 %, and for global mean dry temperature the consistency is better than 0.15 K in this height interval. At altitudes up to 40 km, these numbers increase to 0.08 %, 0.11 %, and 0.50 K, respectively. The numbers can be up to a factor of 2 larger for certain latitude bands compared to global means. Below about 8 km, the RO mission differences are larger, reducing the possibilities to generate multi-mission data records. We also find that the residual sampling errors are about one-third of the original and that they include a component most likely related to diurnal or semi-diurnal cycles.

scholarly journals Investigations into the development of a satellite-based aerosol climate data record using ATSR-2, AATSR and AVHRR data over north-eastern China from 1987 to 2012

2019 ◽  
Vol 12 (7) ◽  
pp. 4091-4112 ◽  
Author(s):  
Yahui Che ◽  
Jie Guang ◽  
Gerrit de Leeuw ◽  
Yong Xue ◽  
Ling Sun ◽  
...  
Keyword(s):  
Eastern China ◽  
Data Sets ◽  
Climate Data ◽  
Data Set ◽  
North Eastern ◽  
Data Record ◽  
Single Sensor ◽  
Sun Photometer ◽  
Climate Data Record ◽  
Better Than

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 other everywhere and at all times. 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. To investigate the possibility of extending 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 version 2.31, developed by Finnish Meteorological Institute (FMI), and from the AVHRR sensors using the aerosol optical depth over land (ADL) algorithm developed by RADI/CAS. Together, these data sets cover a multi-decadal period (1987–2012). The study area includes two contrasting areas, both in regards to aerosol content and composition and surface properties, i.e. a region over north-eastern 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 sun photometer AOD data in AERONET and CARSNET are used as a reference, together with broadband extinction method (BEM) data at Beijing to cover the time before sun photometer observations became available in the early 2000s. In addition, MODIS-Terra C6.1 AOD data are used as a reference data set over the wide area where no ground-based data are available. All satellite data over the study area were validated against the reference data, showing the qualification of MODIS for comparison with ATSR and AVHRR. The comparison with MODIS shows that AVHRR performs better than ATSR in the north of the study area (40∘ N), whereas further south ATSR provides better results. The validation against sun photometer AOD shows that both AVHRR and ATSR underestimate the AOD, with ATSR failing to provide reliable results in the wintertime. 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 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 BEM 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 that need to be accounted for when combining them in a single multi-decadal climate data record.

The 17-year ROM SAF radio occultation climate data record

2020 ◽  
Author(s):  
Kent B. Lauritsen ◽  
Hans Gleisner ◽  
Johannes K. Nielsen ◽  
Stig Syndergaard
Keyword(s):  
Radio Occultation ◽  
A Priori ◽  
Reanalysis Data ◽  
Radio Waves ◽  
Climate Data ◽  
Near Surface ◽  
Amplitude Data ◽  
Data Record ◽  
High Consistency ◽  
Climate Data Record

<p>The Radio Occultation (RO) technique is based on measurements of phase shifts of GNSS radio waves by an instrument onboard a low-Earth orbiting satellite. The processing of the measurements yields the refractive index of the Earth’s atmosphere, from which the temperature, pressure, and humidity fields can be retrieved. It is a limb-sounding technique, with a high vertical resolution, and with observational information retrieved from near-surface to the upper stratosphere. Numerous studies have demonstrated the accuracy of GNSS Radio Occultation (RO) data, and their usefulness as a stable climate reference. Homogeneity of the data records are obtained by reprocessing of the data using uniform processing software and a priori data throughout the length of the climate record. We here present results from a validation of the 17-year ROM SAF RO Climate Data Record (CDR), based on a new reprocessing of Metop, CHAMP, GRACE, and COSMIC data using excess-phase and amplitude data from EUMETSAT (the Metop mission) and UCAR/CDAAC (the CHAMP, GRACE, COSMIC, and Metop missions).</p><p>A central issue for the generation of RO-based CDRs is whether data from different satellite missions can be combined to form long time series of multi-mission data. This presentation explores the consistency of gridded monthly-mean data from different RO missions through comparison with ERA-Interim reanalysis data, and through a study of mission differences during mission overlap periods. It is shown that within a core region from the upper troposphere to the middle stratosphere, roughly 8 to 35-40 kilometers (depending on latitude and geophysical variable), there is a high consistency amongst the RO missions, allowing for the construction of long-term stable data sets for use in climate studies and climate monitoring.</p>

scholarly journals Jason continuity of services: continuing the Jason altimeter data records as Copernicus Sentinel-6

Ocean Science ◽  
2016 ◽  
Vol 12 (2) ◽  
pp. 471-479 ◽  
Author(s):  
Remko Scharroo ◽  
Hans Bonekamp ◽  
Christelle Ponsard ◽  
François Parisot ◽  
Axel von Engeln ◽  
...  
Keyword(s):  
Radio Occultation ◽  
Sea Surface ◽  
Altimeter Data ◽  
Climate Data ◽  
Satellite Altimeter ◽  
Data Services ◽  
Backward Compatibility ◽  
Data Record ◽  
Climate Data Record ◽  
Satellite Altimeter Data

Abstract. The Sentinel-6 mission is proposed as a multi-partner programme to continue the Jason satellite altimeter data services beyond the Jason-2 and Jason-3 missions. The Sentinel-6 mission programme consists of two identical satellites flying in sequence to prolong the climate data record of sea level accumulated by the TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3 missions from 2020 to beyond 2030. The Sentinel-6 mission intends to maintain these services in a fully operational manner. A key feature is the simultaneous pulse-limited and synthetic aperture radar processing allowing direct and continuous comparisons of the sea surface height measurements based on these processing methods and providing backward compatibility. The Sentinel-6 mission will also include radio occultation user services.

scholarly journals Jason continuity of services: continuing the Jason altimeter data records as Copernicus Sentinel-6

2015 ◽  
Vol 12 (6) ◽  
pp. 2931-2953 ◽  
Author(s):  
R. Scharroo ◽  
H. Bonekamp ◽  
C. Ponsard ◽  
F. Parisot ◽  
A. von Engeln ◽  
...  
Keyword(s):  
Radio Occultation ◽  
Sea Surface ◽  
Altimeter Data ◽  
Climate Data ◽  
Satellite Altimeter ◽  
Data Services ◽  
Backward Compatibility ◽  
Data Record ◽  
Climate Data Record ◽  
Satellite Altimeter Data

Abstract. The Sentinel-6 mission is proposed as a multi-partner programme to continue the Jason satellite altimeter data services beyond the Jason-2 and Jason-3 missions. The Sentinel-6 mission programme consists of two identical satellites flying in sequence to prolong the climate data record of sea level accumulated by the TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3 missions from 2020 to beyond 2030. The Sentinel-6 mission intends to maintain these services in a fully operational manner. A key feature is the simultaneous pulse-limited and synthetic aperture radar processing allowing direct and continuous comparisons of the sea surface height measurements based on these processing methods and providing backward compatibility. The Sentinel-6 mission will also include Radio Occultation user services.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

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 CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data

2017 ◽  
Vol 17 (9) ◽  
pp. 5809-5828 ◽  
Author(s):  
Karl-Göran Karlsson ◽  
Kati Anttila ◽  
Jörg Trentmann ◽  
Martin Stengel ◽  
Jan Fokke Meirink ◽  
...  
Keyword(s):  
Surface Albedo ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Climate Data ◽  
Ground Segment ◽  
Surface Radiation Budget ◽  
Avhrr Data ◽  
Data Record ◽  
Development And Validation ◽  
Climate Data Record

Abstract. The second edition of the satellite-derived climate data record CLARA (The CM SAF Cloud, Albedo And Surface Radiation dataset from AVHRR data – second edition denoted as CLARA-A2) is described. The data record covers the 34-year period from 1982 until 2015 and consists of cloud, surface albedo and surface radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting, operational meteorological satellites. The data record is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project as part of the operational ground segment. Its upgraded content and methodology improvements since edition 1 are described in detail, as are some major validation results. Some of the main improvements to the data record come from a major effort in cleaning and homogenizing the basic AVHRR level-1 radiance record and a systematic use of CALIPSO-CALIOP cloud information for development and validation purposes. Examples of applications studying decadal changes in Arctic summer surface albedo and cloud conditions are provided.

Towards a continuous NASA cloud climate data record from MODIS to VIIRS

2021 ◽  
Author(s):  
Kerry Meyer ◽  
Steven Platnick ◽  
Robert Holz ◽  
Steven Ackerman ◽  
Andrew Heidinger ◽  
...  
Keyword(s):  
Observation Time ◽  
Radiometric Calibration ◽  
Cloud Detection ◽  
Climate Data ◽  
Cloud Data ◽  
Microphysical Properties ◽  
Data Record ◽  
Climate Data Record ◽  
Relative Radiometric Calibration

<p>The Suomi NPP and JPSS series VIIRS imagers provide an opportunity to extend the NASA EOS Terra (20+ year) and Aqua (18+ year) MODIS cloud climate data record into the new generation NOAA operational weather satellite era. However, while building a consistent, long-term cloud data record has proven challenging for the two MODIS sensors alone, the transition to VIIRS presents additional challenges due to its lack of key water vapor and CO<sub>2</sub> absorbing channels available on MODIS that are used for high cloud detection and cloud-top property retrievals, and a mismatch in the spectral location of the 2.2µm shortwave infrared channels on MODIS and VIIRS that has important implications on inter-sensor consistency of cloud optical/microphysical property retrievals and cloud thermodynamic phase. Moreover, sampling differences between MODIS and VIIRS, including spatial resolution and local observation time, and inter-sensor relative radiometric calibration pose additional challenges. To create a continuous, long-term cloud climate data record that merges the observational records of MODIS and VIIRS while mitigating the impacts of these sensor differences, a common algorithm approach was pursued that utilizes a subset of spectral channels available on each imager. The resulting NASA CLDMSK (cloud mask) and CLDPROP (cloud-top and optical/microphysical properties) products were publicly released for Aqua MODIS and SNPP VIIRS in early 2020, with NOAA-20 (JPSS-1) VIIRS following in early 2021. Here, we present an overview of the MODIS-VIIRS CLDMSK and CLDPROP common algorithm approach, discuss efforts to monitor and address relative radiometric calibration differences, and highlight early analysis of inter-sensor cloud product dataset continuity.</p>

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